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https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/message_history.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "8165bd4c",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"keywords: [memory]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "f47033eb",
"metadata": {},
"source": [
"# How to add message history\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Configuring chain parameters at runtime](/docs/how_to/configure)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat Messages](/docs/concepts/#message-types)\n",
"\n",
":::\n",
"\n",
"Passing conversation state into and out a chain is vital when building a chatbot. The [`RunnableWithMessageHistory`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html#langchain_core.runnables.history.RunnableWithMessageHistory) class lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it. Specifically, it loads previous messages in the conversation BEFORE passing it to the Runnable, and it saves the generated response as a message AFTER calling the runnable. This class also enables multiple conversations by saving each conversation with a `session_id` - it then expects a `session_id` to be passed in the config when calling the runnable, and uses that to look up the relevant conversation history.\n",
"\n",
"\n",
"\n",
"In practice this looks something like:\n",
"\n",
"```python\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"\n",
"\n",
"with_message_history = RunnableWithMessageHistory(\n",
" # The underlying runnable\n",
" runnable, \n",
" # A function that takes in a session id and returns a memory object\n",
" get_session_history, \n",
" # Other parameters that may be needed to align the inputs/outputs\n",
" # of the Runnable with the memory object\n",
" ... \n",
")\n",
"\n",
"with_message_history.invoke(\n",
" # The same input as before\n",
" {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
" # Configuration specifying the `session_id`,\n",
" # which controls which conversation to load\n",
" config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
")\n",
"```\n",
"\n",
"\n",
"In order to properly set this up there are two main things to consider:\n",
"\n",
"1. How to store and load messages? (this is `get_session_history` in the example above)\n",
"2. What is the underlying Runnable you are wrapping and what are its inputs/outputs? (this is `runnable` in the example above, as well any additional parameters you pass to `RunnableWithMessageHistory` to align the inputs/outputs)\n",
"\n",
"Let's walk through these pieces (and more) below."
]
},
{
"cell_type": "markdown",
"id": "734123cb",
"metadata": {},
"source": [
"## How to store and load messages\n",
"\n",
"A key part of this is storing and loading messages.\n",
"When constructing `RunnableWithMessageHistory` you need to pass in a `get_session_history` function.\n",
"This function should take in a `session_id` and return a `BaseChatMessageHistory` object.\n",
"\n",
"**What is `session_id`?** \n",
"\n",
"`session_id` is an identifier for the session (conversation) thread that these input messages correspond to. This allows you to maintain several conversations/threads with the same chain at the same time.\n",
"\n",
"**What is `BaseChatMessageHistory`?** \n",
"\n",
"`BaseChatMessageHistory` is a class that can load and save message objects. It will be called by `RunnableWithMessageHistory` to do exactly that. These classes are usually initialized with a session id.\n",
"\n",
"Let's create a `get_session_history` object to use for this example. To keep things simple, we will use a simple SQLiteMessage"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e8210560",
"metadata": {},
"outputs": [],
"source": [
"! rm memory.db"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "27f36241",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.chat_message_histories import SQLChatMessageHistory\n",
"\n",
"\n",
"def get_session_history(session_id):\n",
" return SQLChatMessageHistory(session_id, \"sqlite:///memory.db\")"
]
},
{
"cell_type": "markdown",
"id": "c200cb3a",
"metadata": {},
"source": [
"Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using other providers (Redis, Postgres, etc)."
]
},
{
"cell_type": "markdown",
"id": "a531da5e",
"metadata": {},
"source": [
"## What is the runnable you are trying to wrap?\n",
"\n",
"`RunnableWithMessageHistory` can only wrap certain types of Runnables. Specifically, it can be used for any Runnable that takes as input one of:\n",
"\n",
"* a sequence of [`BaseMessages`](/docs/concepts/#message-types)\n",
"* a dict with a key that takes a sequence of `BaseMessages`\n",
"* a dict with a key that takes the latest message(s) as a string or sequence of `BaseMessages`, and a separate key that takes historical messages\n",
"\n",
"And returns as output one of\n",
"\n",
"* a string that can be treated as the contents of an `AIMessage`\n",
"* a sequence of `BaseMessage`\n",
"* a dict with a key that contains a sequence of `BaseMessage`\n",
"\n",
"Let's take a look at some examples to see how it works. "
]
},
{
"cell_type": "markdown",
"id": "6a4becbd-238e-4c1d-a02d-08e61fbc3763",
"metadata": {},
"source": [
"### Setup\n",
"\n",
"First we construct a runnable (which here accepts a dict as input and returns a message as output):\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs\n",
" customVarName=\"llm\"\n",
"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6489f585",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"# %pip install -qU langchain langchain_anthropic\n",
"\n",
"# import os\n",
"# from getpass import getpass\n",
"\n",
"# os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"model = ChatAnthropic(model=\"claude-3-haiku-20240307\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2ed413b4-33a1-48ee-89b0-2d4917ec101a",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.messages import HumanMessage\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory"
]
},
{
"cell_type": "markdown",
"id": "e8816b01",
"metadata": {},
"source": [
"### Messages input, message(s) output\n",
"\n",
"The simplest form is just adding memory to a ChatModel.\n",
"ChatModels accept a list of messages as input and output a message.\n",
"This makes it very easy to use `RunnableWithMessageHistory` - no additional configuration is needed!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0521d551",
"metadata": {},
"outputs": [],
"source": [
"runnable_with_history = RunnableWithMessageHistory(\n",
" model,\n",
" get_session_history,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d5142e1a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I help you today?\", response_metadata={'id': 'msg_01UHCCMiZz9yNYjt41xUJrtk', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-55f6a451-606b-4e04-9e39-e03b81035c1f-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44})"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" [HumanMessage(content=\"hi - im bob!\")],\n",
" config={\"configurable\": {\"session_id\": \"1\"}},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "768e0c12",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='I\\'m afraid I don\\'t actually know your name - you introduced yourself as Bob, but I don\\'t have any other information about your identity. As an AI assistant, I don\\'t have a way to independently verify people\\'s names or identities. I\\'m happy to continue our conversation, but I\\'ll just refer to you as \"Bob\" since that\\'s the name you provided.', response_metadata={'id': 'msg_018L96tAxiexMKsHBQz22CcE', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 80}}, id='run-7399ddb5-bb06-444b-bfb2-2f65674105dd-0', usage_metadata={'input_tokens': 52, 'output_tokens': 80, 'total_tokens': 132})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" [HumanMessage(content=\"whats my name?\")],\n",
" config={\"configurable\": {\"session_id\": \"1\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "9d942227",
"metadata": {},
"source": [
":::info\n",
"\n",
"Note that in this case the context is preserved via the chat history for the provided `session_id`, so the model knows the users name.\n",
"\n",
":::\n",
"\n",
"We can now try this with a new session id and see that it does not remember."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "addddd03",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"I'm afraid I don't actually know your name. As an AI assistant, I don't have personal information about you unless you provide it to me directly.\", response_metadata={'id': 'msg_01LhbWu7mSKTvKAx7iQpMPzd', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 35}}, id='run-cf86cad2-21f2-4525-afc8-09bfd1e8af70-0', usage_metadata={'input_tokens': 12, 'output_tokens': 35, 'total_tokens': 47})"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" [HumanMessage(content=\"whats my name?\")],\n",
" config={\"configurable\": {\"session_id\": \"1a\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8b26a0c0",
"metadata": {},
"source": [
":::info \n",
"\n",
"When we pass a different `session_id`, we start a new chat history, so the model does not know what the user's name is. \n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "e5bb5c7c",
"metadata": {},
"source": [
"### Dictionary input, message(s) output\n",
"\n",
"Besides just wrapping a raw model, the next step up is wrapping a prompt + LLM. This now changes the input to be a **dictionary** (because the input to a prompt is a dictionary). This adds two bits of complication.\n",
"\n",
"First: a dictionary can have multiple keys, but we only want to save ONE as input. In order to do this, we now now need to specify a key to save as the input.\n",
"\n",
"Second: once we load the messages, we need to know how to save them to the dictionary. That equates to know which key in the dictionary to save them in. Therefore, we need to specify a key to save the loaded messages in.\n",
"\n",
"Putting it all together, that ends up looking something like:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "34edd990",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"You're an assistant who speaks in {language}. Respond in 20 words or fewer\",\n",
" ),\n",
" MessagesPlaceholder(variable_name=\"history\"),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"runnable = prompt | model\n",
"\n",
"runnable_with_history = RunnableWithMessageHistory(\n",
" runnable,\n",
" get_session_history,\n",
" input_messages_key=\"input\",\n",
" history_messages_key=\"history\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c0baa075",
"metadata": {},
"source": [
":::info\n",
"\n",
"Note that we've specified `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to).\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5877544f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Ciao Bob! È un piacere conoscerti. Come stai oggi?', response_metadata={'id': 'msg_0121ADUEe4G1hMC6zbqFWofr', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 23}}, id='run-246a70df-aad6-43d6-a7e8-166d96e0d67e-0', usage_metadata={'input_tokens': 29, 'output_tokens': 23, 'total_tokens': 52})"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" {\"language\": \"italian\", \"input\": \"hi im bob!\"},\n",
" config={\"configurable\": {\"session_id\": \"2\"}},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "8605c2b1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Bob, il tuo nome è Bob.', response_metadata={'id': 'msg_01EDUZG6nRLGeti9KhFN5cek', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 60, 'output_tokens': 12}}, id='run-294b4a72-81bc-4c43-b199-3aafdff87cb3-0', usage_metadata={'input_tokens': 60, 'output_tokens': 12, 'total_tokens': 72})"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" {\"language\": \"italian\", \"input\": \"whats my name?\"},\n",
" config={\"configurable\": {\"session_id\": \"2\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "3ab7c09f",
"metadata": {},
"source": [
":::info\n",
"\n",
"Note that in this case the context is preserved via the chat history for the provided `session_id`, so the model knows the users name.\n",
"\n",
":::\n",
"\n",
"We can now try this with a new session id and see that it does not remember."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c7ddad6b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Mi dispiace, non so il tuo nome. Come posso aiutarti?', response_metadata={'id': 'msg_01Lyd9FAGQJTxxAZoFi3sQpQ', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 30, 'output_tokens': 23}}, id='run-19a82197-3b1c-4b5f-a68d-f91f4a2ba523-0', usage_metadata={'input_tokens': 30, 'output_tokens': 23, 'total_tokens': 53})"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" {\"language\": \"italian\", \"input\": \"whats my name?\"},\n",
" config={\"configurable\": {\"session_id\": \"2a\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "a05e6c12",
"metadata": {},
"source": [
":::info \n",
"\n",
"When we pass a different `session_id`, we start a new chat history, so the model does not know what the user's name is. \n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "717440a9",
"metadata": {},
"source": [
"### Messages input, dict output\n",
"\n",
"This format is useful when you are using a model to generate one key in a dictionary."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "80b8efb0",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.messages import HumanMessage\n",
"from langchain_core.runnables import RunnableParallel\n",
"\n",
"chain = RunnableParallel({\"output_message\": model})\n",
"\n",
"\n",
"runnable_with_history = RunnableWithMessageHistory(\n",
" chain,\n",
" get_session_history,\n",
" output_messages_key=\"output_message\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "9040c535",
"metadata": {},
"source": [
":::info\n",
"\n",
"Note that we've specified `output_messages_key` (the key to be treated as the output to save).\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "8b26a209",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_message': AIMessage(content=\"It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I help you today?\", response_metadata={'id': 'msg_01WWJSyUyGGKuBqTs3h18ZMM', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-0f50cb43-a734-447c-b535-07c615a0984c-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44})}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" [HumanMessage(content=\"hi - im bob!\")],\n",
" config={\"configurable\": {\"session_id\": \"3\"}},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "743edcf8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_message': AIMessage(content='I\\'m afraid I don\\'t actually know your name - you introduced yourself as Bob, but I don\\'t have any other information about your identity. As an AI assistant, I don\\'t have a way to independently verify people\\'s names or identities. I\\'m happy to continue our conversation, but I\\'ll just refer to you as \"Bob\" since that\\'s the name you provided.', response_metadata={'id': 'msg_01TEGrhfLXTwo36rC7svdTy4', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 80}}, id='run-178e8f3f-da21-430d-9edc-ef07797a5e2d-0', usage_metadata={'input_tokens': 52, 'output_tokens': 80, 'total_tokens': 132})}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" [HumanMessage(content=\"whats my name?\")],\n",
" config={\"configurable\": {\"session_id\": \"3\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "81efb7f1",
"metadata": {},
"source": [
":::info\n",
"\n",
"Note that in this case the context is preserved via the chat history for the provided `session_id`, so the model knows the users name.\n",
"\n",
":::\n",
"\n",
"We can now try this with a new session id and see that it does not remember."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b8b04907",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_message': AIMessage(content=\"I'm afraid I don't actually know your name. As an AI assistant, I don't have personal information about you unless you provide it to me directly.\", response_metadata={'id': 'msg_0118ZBudDXAC9P6smf91NhCX', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 35}}, id='run-deb14a3a-0336-42b4-8ace-ad1e52ca5910-0', usage_metadata={'input_tokens': 12, 'output_tokens': 35, 'total_tokens': 47})}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" [HumanMessage(content=\"whats my name?\")],\n",
" config={\"configurable\": {\"session_id\": \"3a\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "6716a068",
"metadata": {},
"source": [
":::info \n",
"\n",
"When we pass a different `session_id`, we start a new chat history, so the model does not know what the user's name is. \n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "ec4187d0",
"metadata": {},
"source": [
"### Dict with single key for all messages input, messages output\n",
"\n",
"This is a specific case of \"Dictionary input, message(s) output\". In this situation, because there is only a single key we don't need to specify as much - we only need to specify the `input_messages_key`."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "7530c4ed",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"\n",
"runnable_with_history = RunnableWithMessageHistory(\n",
" itemgetter(\"input_messages\") | model,\n",
" get_session_history,\n",
" input_messages_key=\"input_messages\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "def75152",
"metadata": {},
"source": [
":::info\n",
"\n",
"Note that we've specified `input_messages_key` (the key to be treated as the latest input message).\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "659bc1bf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"It's nice to meet you, Bob! I'm Claude, an AI assistant created by Anthropic. How can I help you today?\", response_metadata={'id': 'msg_01UdD5wz1J5xwoz5D94onaQC', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 32}}, id='run-91bee6eb-0814-4557-ad71-fef9b0270358-0', usage_metadata={'input_tokens': 12, 'output_tokens': 32, 'total_tokens': 44})"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" {\"input_messages\": [HumanMessage(content=\"hi - im bob!\")]},\n",
" config={\"configurable\": {\"session_id\": \"4\"}},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "6da2835e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='I\\'m afraid I don\\'t actually know your name - you introduced yourself as Bob, but I don\\'t have any other information about your identity. As an AI assistant, I don\\'t have a way to independently verify people\\'s names or identities. I\\'m happy to continue our conversation, but I\\'ll just refer to you as \"Bob\" since that\\'s the name you provided.', response_metadata={'id': 'msg_012WUygxBKXcVJPeTW14LNrc', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 52, 'output_tokens': 80}}, id='run-fcbaaa1a-8c33-4eec-b0b0-5b800a47bddd-0', usage_metadata={'input_tokens': 52, 'output_tokens': 80, 'total_tokens': 132})"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" {\"input_messages\": [HumanMessage(content=\"whats my name?\")]},\n",
" config={\"configurable\": {\"session_id\": \"4\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d4c7a6f2",
"metadata": {},
"source": [
":::info\n",
"\n",
"Note that in this case the context is preserved via the chat history for the provided `session_id`, so the model knows the users name.\n",
"\n",
":::\n",
"\n",
"We can now try this with a new session id and see that it does not remember."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "6cf6abd6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"I'm afraid I don't actually know your name. As an AI assistant, I don't have personal information about you unless you provide it to me directly.\", response_metadata={'id': 'msg_017xW3Ki5y4UBYzCU9Mf1pgM', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 12, 'output_tokens': 35}}, id='run-d2f372f7-3679-4a5c-9331-a55b820ec03e-0', usage_metadata={'input_tokens': 12, 'output_tokens': 35, 'total_tokens': 47})"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runnable_with_history.invoke(\n",
" {\"input_messages\": [HumanMessage(content=\"whats my name?\")]},\n",
" config={\"configurable\": {\"session_id\": \"4a\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "9839a6d1",
"metadata": {},
"source": [
":::info \n",
"\n",
"When we pass a different `session_id`, we start a new chat history, so the model does not know what the user's name is. \n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "a6710e65",
"metadata": {},
"source": [
"## Customization"
]
},
{
"cell_type": "markdown",
"id": "d29497be-3366-408d-bbb9-d4a8bf4ef37c",
"metadata": {},
"source": [
"The configuration parameters by which we track message histories can be customized by passing in a list of ``ConfigurableFieldSpec`` objects to the ``history_factory_config`` parameter. Below, we use two parameters: a `user_id` and `conversation_id`."
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "1c89daee-deff-4fdf-86a3-178f7d8ef536",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Ciao Bob! È un piacere conoscerti. Come stai oggi?', response_metadata={'id': 'msg_016RJebCoiAgWaNcbv9wrMNW', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 29, 'output_tokens': 23}}, id='run-40425414-8f72-47d4-bf1d-a84175d8b3f8-0', usage_metadata={'input_tokens': 29, 'output_tokens': 23, 'total_tokens': 52})"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.runnables import ConfigurableFieldSpec\n",
"\n",
"\n",
"def get_session_history(user_id: str, conversation_id: str):\n",
" return SQLChatMessageHistory(f\"{user_id}--{conversation_id}\", \"sqlite:///memory.db\")\n",
"\n",
"\n",
"with_message_history = RunnableWithMessageHistory(\n",
" runnable,\n",
" get_session_history,\n",
" input_messages_key=\"input\",\n",
" history_messages_key=\"history\",\n",
" history_factory_config=[\n",
" ConfigurableFieldSpec(\n",
" id=\"user_id\",\n",
" annotation=str,\n",
" name=\"User ID\",\n",
" description=\"Unique identifier for the user.\",\n",
" default=\"\",\n",
" is_shared=True,\n",
" ),\n",
" ConfigurableFieldSpec(\n",
" id=\"conversation_id\",\n",
" annotation=str,\n",
" name=\"Conversation ID\",\n",
" description=\"Unique identifier for the conversation.\",\n",
" default=\"\",\n",
" is_shared=True,\n",
" ),\n",
" ],\n",
")\n",
"\n",
"with_message_history.invoke(\n",
" {\"language\": \"italian\", \"input\": \"hi im bob!\"},\n",
" config={\"configurable\": {\"user_id\": \"123\", \"conversation_id\": \"1\"}},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "4f282883",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Bob, il tuo nome è Bob.', response_metadata={'id': 'msg_01Kktiy3auFDKESY54KtTWPX', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 60, 'output_tokens': 12}}, id='run-c7768420-3f30-43f5-8834-74b1979630dd-0', usage_metadata={'input_tokens': 60, 'output_tokens': 12, 'total_tokens': 72})"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# remembers\n",
"with_message_history.invoke(\n",
" {\"language\": \"italian\", \"input\": \"whats my name?\"},\n",
" config={\"configurable\": {\"user_id\": \"123\", \"conversation_id\": \"1\"}},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "fc122c18",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Mi dispiace, non so il tuo nome. Come posso aiutarti?', response_metadata={'id': 'msg_0178FpbpPNioB7kqvyHk7rjD', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 30, 'output_tokens': 23}}, id='run-df1f1768-aab6-4aec-8bba-e33fc9e90b8d-0', usage_metadata={'input_tokens': 30, 'output_tokens': 23, 'total_tokens': 53})"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# New user_id --> does not remember\n",
"with_message_history.invoke(\n",
" {\"language\": \"italian\", \"input\": \"whats my name?\"},\n",
" config={\"configurable\": {\"user_id\": \"456\", \"conversation_id\": \"1\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "3ce37565",
"metadata": {},
"source": [
"Note that in this case the context was preserved for the same `user_id`, but once we changed it, the new chat history was started, even though the `conversation_id` was the same."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/migrate_agent.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "457cdc67-1893-4653-8b0c-b185a5947e74",
"metadata": {},
"source": [
"# How to migrate from legacy LangChain agents to LangGraph\n",
"\n",
"Here we focus on how to move from legacy LangChain agents to LangGraph agents.\n",
"LangChain agents (the [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
"In this notebook we will show how those parameters map to the LangGraph [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent).\n",
"\n",
"#### Prerequisites\n",
"\n",
"This how-to guide uses OpenAI as the LLM. Install the dependencies to run."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "662fac50",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U langgraph langchain langchain-openai"
]
},
{
"cell_type": "markdown",
"id": "8e50635c-1671-46e6-be65-ce95f8167c2f",
"metadata": {},
"source": [
"## Basic Usage\n",
"\n",
"For basic creation and usage of a tool-calling ReAct-style agent, the functionality is the same. First, let's define a model and tool(s), then we'll use those to create an agent."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1e425fea-2796-4b99-bee6-9a6ffe73f756",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\")\n",
"\n",
"\n",
"@tool\n",
"def magic_function(input: int) -> int:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" return input + 2\n",
"\n",
"\n",
"tools = [magic_function]\n",
"\n",
"\n",
"query = \"what is the value of magic_function(3)?\""
]
},
{
"cell_type": "markdown",
"id": "af002033-fe51-4d14-b47c-3e9b483c8395",
"metadata": {},
"source": [
"For the LangChain [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor), we define a prompt with a placeholder for the agent's scratchpad. The agent can be invoked as follows:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "03ea357c-9c36-4464-b2cc-27bd150e1554",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': 'what is the value of magic_function(3)?',\n",
" 'output': 'The value of `magic_function(3)` is 5.'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant\"),\n",
" (\"human\", \"{input}\"),\n",
" # Placeholders fill up a **list** of messages\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt)\n",
"agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
"\n",
"agent_executor.invoke({\"input\": query})"
]
},
{
"cell_type": "markdown",
"id": "94205f3b-fd2b-4fd7-af69-0a3fc313dc88",
"metadata": {},
"source": [
"LangGraph's [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) manages a state that is defined by a list of messages. It will continue to process the list until there are no tool calls in the agent's output. To kick it off, we input a list of messages. The output will contain the entire state of the graph-- in this case, the conversation history.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "53a3737a-d167-4255-89bf-20ac37f89a3e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': 'what is the value of magic_function(3)?',\n",
" 'output': 'The value of `magic_function(3)` is 5.'}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"app = create_react_agent(model, tools)\n",
"\n",
"\n",
"messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
"{\n",
" \"input\": query,\n",
" \"output\": messages[\"messages\"][-1].content,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "74ecebe3-512e-409c-a661-bdd5b0a2b782",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': 'Pardon?',\n",
" 'output': 'The result of applying `magic_function` to the input 3 is 5.'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message_history = messages[\"messages\"]\n",
"\n",
"new_query = \"Pardon?\"\n",
"\n",
"messages = app.invoke({\"messages\": message_history + [(\"human\", new_query)]})\n",
"{\n",
" \"input\": new_query,\n",
" \"output\": messages[\"messages\"][-1].content,\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "f4466a4d-e55e-4ece-bee8-2269a0b5677b",
"metadata": {},
"source": [
"## Prompt Templates\n",
"\n",
"With legacy LangChain agents you have to pass in a prompt template. You can use this to control the agent.\n",
"\n",
"With LangGraph [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent), by default there is no prompt. You can achieve similar control over the agent in a few ways:\n",
"\n",
"1. Pass in a system message as input\n",
"2. Initialize the agent with a system message\n",
"3. Initialize the agent with a function to transform messages before passing to the model.\n",
"\n",
"Let's take a look at all of these below. We will pass in custom instructions to get the agent to respond in Spanish.\n",
"\n",
"First up, using AgentExecutor:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a9a11ccd-75e2-4c11-844d-a34870b0ff91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': 'what is the value of magic_function(3)?',\n",
" 'output': 'El valor de `magic_function(3)` es 5.'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant. Respond only in Spanish.\"),\n",
" (\"human\", \"{input}\"),\n",
" # Placeholders fill up a **list** of messages\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt)\n",
"agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
"\n",
"agent_executor.invoke({\"input\": query})"
]
},
{
"cell_type": "markdown",
"id": "bd5f5500-5ae4-4000-a9fd-8c5a2cc6404d",
"metadata": {},
"source": [
"Now, let's pass a custom system message to [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent). This can either be a string or a LangChain SystemMessage."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a9486805-676a-4d19-a5c4-08b41b172989",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.messages import SystemMessage\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"system_message = \"You are a helpful assistant. Respond only in Spanish.\"\n",
"# This could also be a SystemMessage object\n",
"# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
"\n",
"app = create_react_agent(model, tools, messages_modifier=system_message)\n",
"\n",
"\n",
"messages = app.invoke({\"messages\": [(\"user\", query)]})"
]
},
{
"cell_type": "markdown",
"id": "fc6059fd-0df7-4b6f-a84c-b5874e983638",
"metadata": {},
"source": [
"We can also pass in an arbitrary function. This function should take in a list of messages and output a list of messages.\n",
"We can do all types of arbitrary formatting of messages here. In this cases, let's just add a SystemMessage to the start of the list of messages."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d369ab45-0c82-45f4-9d3e-8efb8dd47e2c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'input': 'what is the value of magic_function(3)?', 'output': 'El valor de magic_function(3) es 5. ¡Pandamonium!'}\n"
]
}
],
"source": [
"from langchain_core.messages import AnyMessage\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant. Respond only in Spanish.\"),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"def _modify_messages(messages: list[AnyMessage]):\n",
" return prompt.invoke({\"messages\": messages}).to_messages() + [\n",
" (\"user\", \"Also say 'Pandamonium!' after the answer.\")\n",
" ]\n",
"\n",
"\n",
"app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
"\n",
"\n",
"messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
"print(\n",
" {\n",
" \"input\": query,\n",
" \"output\": messages[\"messages\"][-1].content,\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "68df3a09",
"metadata": {},
"source": [
"## Memory\n",
"\n",
"With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could add chat [Memory](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.memory) so it can engage in a multi-turn conversation."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1fb52a2c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hi Polly! The output of the magic function for the input 3 is 5.\n",
"---\n",
"Yes, I remember your name, Polly! How can I assist you further?\n",
"---\n",
"The output of the magic function for the input 3 is 5.\n"
]
}
],
"source": [
"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
"from langchain_community.chat_message_histories import ChatMessageHistory\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"from langchain_core.tools import tool\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\")\n",
"memory = ChatMessageHistory(session_id=\"test-session\")\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" # First put the history\n",
" (\"placeholder\", \"{chat_history}\"),\n",
" # Then the new input\n",
" (\"human\", \"{input}\"),\n",
" # Finally the scratchpad\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"@tool\n",
"def magic_function(input: int) -> int:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" return input + 2\n",
"\n",
"\n",
"tools = [magic_function]\n",
"\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt)\n",
"agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
"\n",
"agent_with_chat_history = RunnableWithMessageHistory(\n",
" agent_executor,\n",
" # This is needed because in most real world scenarios, a session id is needed\n",
" # It isn't really used here because we are using a simple in memory ChatMessageHistory\n",
" lambda session_id: memory,\n",
" input_messages_key=\"input\",\n",
" history_messages_key=\"chat_history\",\n",
")\n",
"\n",
"config = {\"configurable\": {\"session_id\": \"test-session\"}}\n",
"print(\n",
" agent_with_chat_history.invoke(\n",
" {\"input\": \"Hi, I'm polly! What's the output of magic_function of 3?\"}, config\n",
" )[\"output\"]\n",
")\n",
"print(\"---\")\n",
"print(agent_with_chat_history.invoke({\"input\": \"Remember my name?\"}, config)[\"output\"])\n",
"print(\"---\")\n",
"print(\n",
" agent_with_chat_history.invoke({\"input\": \"what was that output again?\"}, config)[\n",
" \"output\"\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c2a5a32f",
"metadata": {},
"source": [
"#### In LangGraph\n",
"\n",
"Memory is just [persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence/), aka [checkpointing](https://langchain-ai.github.io/langgraph/reference/checkpoints/).\n",
"\n",
"Add a `checkpointer` to the agent and you get chat memory for free."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "035e1253",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hi Polly! The output of the magic_function for the input 3 is 5.\n",
"---\n",
"Yes, your name is Polly!\n",
"---\n",
"The output of the magic_function for the input 3 was 5.\n"
]
}
],
"source": [
"from langchain_core.messages import SystemMessage\n",
"from langgraph.checkpoint import MemorySaver # an in-memory checkpointer\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"system_message = \"You are a helpful assistant.\"\n",
"# This could also be a SystemMessage object\n",
"# system_message = SystemMessage(content=\"You are a helpful assistant. Respond only in Spanish.\")\n",
"\n",
"memory = MemorySaver()\n",
"app = create_react_agent(\n",
" model, tools, messages_modifier=system_message, checkpointer=memory\n",
")\n",
"\n",
"config = {\"configurable\": {\"thread_id\": \"test-thread\"}}\n",
"print(\n",
" app.invoke(\n",
" {\n",
" \"messages\": [\n",
" (\"user\", \"Hi, I'm polly! What's the output of magic_function of 3?\")\n",
" ]\n",
" },\n",
" config,\n",
" )[\"messages\"][-1].content\n",
")\n",
"print(\"---\")\n",
"print(\n",
" app.invoke({\"messages\": [(\"user\", \"Remember my name?\")]}, config)[\"messages\"][\n",
" -1\n",
" ].content\n",
")\n",
"print(\"---\")\n",
"print(\n",
" app.invoke({\"messages\": [(\"user\", \"what was that output again?\")]}, config)[\n",
" \"messages\"\n",
" ][-1].content\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d7cf24a8",
"metadata": {},
"source": [
"## Iterating through steps\n",
"\n",
"With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could iterate over the steps using the [stream](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) (or async `astream`) methods or the [iter](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter) method. LangGraph supports stepwise iteration using [stream](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d640feb3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])]}\n",
"{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-c68fd76f-a3c3-4c3c-bfd7-748c171ed4b8', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_q9MgGFjqJbV2xSUX93WqxmOt', 'index': 0}])], tool_call_id='call_q9MgGFjqJbV2xSUX93WqxmOt'), observation=5)], 'messages': [FunctionMessage(content='5', name='magic_function')]}\n",
"{'output': 'The value of `magic_function(3)` is 5.', 'messages': [AIMessage(content='The value of `magic_function(3)` is 5.')]}\n"
]
}
],
"source": [
"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.tools import tool\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\")\n",
"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" (\"human\", \"{input}\"),\n",
" # Placeholders fill up a **list** of messages\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"@tool\n",
"def magic_function(input: int) -> int:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" return input + 2\n",
"\n",
"\n",
"tools = [magic_function]\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt=prompt)\n",
"agent_executor = AgentExecutor(agent=agent, tools=tools)\n",
"\n",
"for step in agent_executor.stream({\"input\": query}):\n",
" print(step)"
]
},
{
"cell_type": "markdown",
"id": "46ccbcbf",
"metadata": {},
"source": [
"#### In LangGraph\n",
"\n",
"In LangGraph, things are handled natively using [stream](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.graph.CompiledGraph.stream) or the asynchronous `astream` method."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "86abbe07",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_yTjXXibj76tyFyPRa1soLo0S', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 70, 'total_tokens': 84}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-b275f314-c42e-4e77-9dec-5c23f7dbd53b-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_yTjXXibj76tyFyPRa1soLo0S'}])]}}\n",
"{'tools': {'messages': [ToolMessage(content='5', name='magic_function', id='41c5f227-528d-4483-a313-b03b23b1d327', tool_call_id='call_yTjXXibj76tyFyPRa1soLo0S')]}}\n",
"{'agent': {'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 93, 'total_tokens': 107}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-0ef12b6e-415d-4758-9b62-5e5e1b350072-0')]}}\n"
]
}
],
"source": [
"from langchain_core.messages import AnyMessage\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"def _modify_messages(messages: list[AnyMessage]):\n",
" return prompt.invoke({\"messages\": messages}).to_messages()\n",
"\n",
"\n",
"app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
"\n",
"\n",
"for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
" print(step)"
]
},
{
"cell_type": "markdown",
"id": "6898ccbc-42b1-4373-954a-2c7b3849fbb0",
"metadata": {},
"source": [
"## `return_intermediate_steps`\n",
"\n",
"Setting this parameter on AgentExecutor allows users to access intermediate_steps, which pairs agent actions (e.g., tool invocations) with their outcomes.\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4eff44bc-a620-4c8a-97b1-268692a842bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log=\"\\nInvoking: `magic_function` with `{'input': 3}`\\n\\n\\n\", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls'}, id='run-837e794f-cfd8-40e0-8abc-4d98ced11b75', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{\"input\":3}', 'id': 'call_ABI4hftfEdnVgKyfF6OzZbca', 'index': 0}])], tool_call_id='call_ABI4hftfEdnVgKyfF6OzZbca'), 5)]\n"
]
}
],
"source": [
"agent_executor = AgentExecutor(agent=agent, tools=tools, return_intermediate_steps=True)\n",
"result = agent_executor.invoke({\"input\": query})\n",
"print(result[\"intermediate_steps\"])"
]
},
{
"cell_type": "markdown",
"id": "594f7567-302f-4fa8-85bb-025ac8322162",
"metadata": {},
"source": [
"By default the [react agent executor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) in LangGraph appends all messages to the central state. Therefore, it is easy to see any intermediate steps by just looking at the full state."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4f4364ea-dffe-4d25-bdce-ef7d0020b880",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'messages': [HumanMessage(content='what is the value of magic_function(3)?', id='0f63e437-c4d8-4da9-b6f5-b293ebfe4a64'),\n",
" AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_S96v28LlI6hNkQrNnIio0JPh', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ffef7898-14b1-4537-ad90-7c000a8a5d25-0', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_S96v28LlI6hNkQrNnIio0JPh'}]),\n",
" ToolMessage(content='5', name='magic_function', id='fbd9df4e-1dda-4d3e-9044-b001f7875476', tool_call_id='call_S96v28LlI6hNkQrNnIio0JPh'),\n",
" AIMessage(content='The value of `magic_function(3)` is 5.', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 87, 'total_tokens': 101}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None}, id='run-e5d94c54-d9f4-45cd-be8e-a9101a8d88d6-0')]}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"app = create_react_agent(model, tools=tools)\n",
"\n",
"messages = app.invoke({\"messages\": [(\"human\", query)]})\n",
"\n",
"messages"
]
},
{
"cell_type": "markdown",
"id": "45b528e5-57e1-450e-8d91-513eab53b543",
"metadata": {},
"source": [
"## `max_iterations`\n",
"\n",
"`AgentExecutor` implements a `max_iterations` parameter, whereas this is controlled via `recursion_limit` in LangGraph.\n",
"\n",
"Note that in AgentExecutor, an \"iteration\" includes a full turn of tool invocation and execution. In LangGraph, each step contributes to the recursion limit, so we will need to multiply by two (and add one) to get equivalent results.\n",
"\n",
"If the recursion limit is reached, LangGraph raises a specific exception type, that we can catch and manage similarly to AgentExecutor."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "16f189a7-fc78-4cb5-aa16-a94ca06401a6",
"metadata": {},
"outputs": [],
"source": [
"@tool\n",
"def magic_function(input: str) -> str:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" return \"Sorry, there was an error. Please try again.\"\n",
"\n",
"\n",
"tools = [magic_function]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c96aefd7-6f6e-4670-aca6-1ac3d4e7871f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `magic_function` with `{'input': '3'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `magic_function` with `{'input': '3'}`\n",
"responded: Parece que hubo un error al intentar obtener el valor de `magic_function(3)`. Permíteme intentarlo de nuevo.\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3mAún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'input': 'what is the value of magic_function(3)?',\n",
" 'output': 'Aún no puedo obtener el valor de `magic_function(3)`. ¿Hay algo más en lo que pueda ayudarte?'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant. Respond only in Spanish.\"),\n",
" (\"human\", \"{input}\"),\n",
" # Placeholders fill up a **list** of messages\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt)\n",
"agent_executor = AgentExecutor(\n",
" agent=agent,\n",
" tools=tools,\n",
" verbose=True,\n",
" max_iterations=3,\n",
")\n",
"\n",
"agent_executor.invoke({\"input\": query})"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b974a91f-6ae8-4644-83d9-73666258a6db",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('human', 'what is the value of magic_function(3)?')\n",
"content='' additional_kwargs={'tool_calls': [{'id': 'call_pFdKcCu5taDTtOOfX14vEDRp', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-25836468-ba7e-43be-a7cf-76bba06a2a08-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_pFdKcCu5taDTtOOfX14vEDRp'}]\n",
"content='Sorry, there was an error. Please try again.' name='magic_function' id='1a08b883-9c7b-4969-9e9b-67ce64cdcb5f' tool_call_id='call_pFdKcCu5taDTtOOfX14vEDRp'\n",
"content='It seems there was an error when trying to apply the magic function. Let me try again.' additional_kwargs={'tool_calls': [{'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 34, 'prompt_tokens': 97, 'total_tokens': 131}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-d571b774-0ea3-4e35-8b7d-f32932c3f3cc-0' tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_DA0lpDIkBFg2GHy4WsEcZG4K'}]\n",
"content='Sorry, there was an error. Please try again.' name='magic_function' id='0b45787b-c82a-487f-9a5a-de129c30460f' tool_call_id='call_DA0lpDIkBFg2GHy4WsEcZG4K'\n",
"content='It appears that there is a consistent issue when trying to apply the magic function to the input \"3.\" This could be due to various reasons, such as the input not being in the correct format or an internal error.\\n\\nIf you have any other questions or if there\\'s something else you\\'d like to try, please let me know!' response_metadata={'token_usage': {'completion_tokens': 66, 'prompt_tokens': 153, 'total_tokens': 219}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'stop', 'logprobs': None} id='run-50a962e6-21b7-4327-8dea-8e2304062627-0'\n"
]
}
],
"source": [
"from langgraph.errors import GraphRecursionError\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"RECURSION_LIMIT = 2 * 3 + 1\n",
"\n",
"app = create_react_agent(model, tools=tools)\n",
"\n",
"try:\n",
" for chunk in app.stream(\n",
" {\"messages\": [(\"human\", query)]},\n",
" {\"recursion_limit\": RECURSION_LIMIT},\n",
" stream_mode=\"values\",\n",
" ):\n",
" print(chunk[\"messages\"][-1])\n",
"except GraphRecursionError:\n",
" print({\"input\": query, \"output\": \"Agent stopped due to max iterations.\"})"
]
},
{
"cell_type": "markdown",
"id": "3a527158-ada5-4774-a98b-8272c6b6b2c0",
"metadata": {},
"source": [
"## `max_execution_time`\n",
"\n",
"`AgentExecutor` implements a `max_execution_time` parameter, allowing users to abort a run that exceeds a total time limit."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4b8498fc-a7af-4164-a401-d8714f082306",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `magic_function` with `{'input': '3'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3mSorry, there was an error. Please try again.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'input': 'what is the value of magic_function(3)?',\n",
" 'output': 'Agent stopped due to max iterations.'}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import time\n",
"\n",
"\n",
"@tool\n",
"def magic_function(input: str) -> str:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" time.sleep(2.5)\n",
" return \"Sorry, there was an error. Please try again.\"\n",
"\n",
"\n",
"tools = [magic_function]\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt)\n",
"agent_executor = AgentExecutor(\n",
" agent=agent,\n",
" tools=tools,\n",
" max_execution_time=2,\n",
" verbose=True,\n",
")\n",
"\n",
"agent_executor.invoke({\"input\": query})"
]
},
{
"cell_type": "markdown",
"id": "d02eb025",
"metadata": {},
"source": [
"With LangGraph's react agent, you can control timeouts on two levels. \n",
"\n",
"You can set a `step_timeout` to bound each **step**:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a2b29113-e6be-4f91-aa4c-5c63dea3e423",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_HaQkeCwD5QskzJzFixCBacZ4', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-596c9200-771f-436d-8576-72fcb81620f1-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_HaQkeCwD5QskzJzFixCBacZ4'}])]}}\n",
"------\n",
"{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
]
}
],
"source": [
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"app = create_react_agent(model, tools=tools)\n",
"# Set the max timeout for each step here\n",
"app.step_timeout = 2\n",
"\n",
"try:\n",
" for chunk in app.stream({\"messages\": [(\"human\", query)]}):\n",
" print(chunk)\n",
" print(\"------\")\n",
"except TimeoutError:\n",
" print({\"input\": query, \"output\": \"Agent stopped due to max iterations.\"})"
]
},
{
"cell_type": "markdown",
"id": "32a9db70",
"metadata": {},
"source": [
"The other way to set a single max timeout for an entire run is to directly use the python stdlib [asyncio](https://docs.python.org/3/library/asyncio.html) library."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "e9eb55f4-a321-4bac-b52d-9e43b411cf92",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv', 'function': {'arguments': '{\"input\":\"3\"}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a1c77db7-405f-43d9-8d57-751f2ca1a58c-0', tool_calls=[{'name': 'magic_function', 'args': {'input': '3'}, 'id': 'call_4agJXUHtmHrOOMogjF6ZuzAv'}])]}}\n",
"------\n",
"Task Cancelled.\n"
]
}
],
"source": [
"import asyncio\n",
"\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"app = create_react_agent(model, tools=tools)\n",
"\n",
"\n",
"async def stream(app, inputs):\n",
" async for chunk in app.astream({\"messages\": [(\"human\", query)]}):\n",
" print(chunk)\n",
" print(\"------\")\n",
"\n",
"\n",
"try:\n",
" task = asyncio.create_task(stream(app, {\"messages\": [(\"human\", query)]}))\n",
" await asyncio.wait_for(task, timeout=3)\n",
"except TimeoutError:\n",
" print(\"Task Cancelled.\")"
]
},
{
"cell_type": "markdown",
"id": "4884ac87",
"metadata": {},
"source": [
"## `early_stopping_method`\n",
"\n",
"With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could configure an [early_stopping_method](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.early_stopping_method) to either return a string saying \"Agent stopped due to iteration limit or time limit.\" (`\"force\"`) or prompt the LLM a final time to respond (`\"generate\"`)."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3f6e2cf2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output with early_stopping_method='force':\n",
"Agent stopped due to max iterations.\n"
]
}
],
"source": [
"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.tools import tool\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\")\n",
"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" (\"human\", \"{input}\"),\n",
" # Placeholders fill up a **list** of messages\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"@tool\n",
"def magic_function(input: int) -> int:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" return \"Sorry there was an error, please try again.\"\n",
"\n",
"\n",
"tools = [magic_function]\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt=prompt)\n",
"agent_executor = AgentExecutor(\n",
" agent=agent, tools=tools, early_stopping_method=\"force\", max_iterations=1\n",
")\n",
"\n",
"result = agent_executor.invoke({\"input\": query})\n",
"print(\"Output with early_stopping_method='force':\")\n",
"print(result[\"output\"])"
]
},
{
"cell_type": "markdown",
"id": "706e05c4",
"metadata": {},
"source": [
"#### In LangGraph\n",
"\n",
"In LangGraph, you can explicitly handle the response behavior outside the agent, since the full state can be accessed."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "73cabbc4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('human', 'what is the value of magic_function(3)?')\n",
"content='' additional_kwargs={'tool_calls': [{'id': 'call_bTURmOn9C8zslmn0kMFeykIn', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 64, 'total_tokens': 78}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-0844a504-7e6b-4ea6-a069-7017e38121ee-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_bTURmOn9C8zslmn0kMFeykIn'}]\n",
"content='Sorry there was an error, please try again.' name='magic_function' id='00d5386f-eb23-4628-9a29-d9ce6a7098cc' tool_call_id='call_bTURmOn9C8zslmn0kMFeykIn'\n",
"content='' additional_kwargs={'tool_calls': [{'id': 'call_JYqvvvWmXow2u012DuPoDHFV', 'function': {'arguments': '{\"input\":3}', 'name': 'magic_function'}, 'type': 'function'}]} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 96, 'total_tokens': 110}, 'model_name': 'gpt-4o', 'system_fingerprint': 'fp_729ea513f7', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-b73b1b1c-c829-4348-98cd-60b315c85448-0' tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_JYqvvvWmXow2u012DuPoDHFV'}]\n",
"{'input': 'what is the value of magic_function(3)?', 'output': 'Agent stopped due to max iterations.'}\n"
]
}
],
"source": [
"from langgraph.errors import GraphRecursionError\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"RECURSION_LIMIT = 2 * 1 + 1\n",
"\n",
"app = create_react_agent(model, tools=tools)\n",
"\n",
"try:\n",
" for chunk in app.stream(\n",
" {\"messages\": [(\"human\", query)]},\n",
" {\"recursion_limit\": RECURSION_LIMIT},\n",
" stream_mode=\"values\",\n",
" ):\n",
" print(chunk[\"messages\"][-1])\n",
"except GraphRecursionError:\n",
" print({\"input\": query, \"output\": \"Agent stopped due to max iterations.\"})"
]
},
{
"cell_type": "markdown",
"id": "017fe20e",
"metadata": {},
"source": [
"## `trim_intermediate_steps`\n",
"\n",
"With LangChain's [AgentExecutor](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor), you could trim the intermediate steps of long-running agents using [trim_intermediate_steps](https://api.python.langchain.com/en/latest/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.trim_intermediate_steps), which is either an integer (indicating the agent should keep the last N steps) or a custom function.\n",
"\n",
"For instance, we could trim the value so the agent only sees the most recent intermediate step."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b94bb169",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Call number: 1\n",
"Call number: 2\n",
"Call number: 3\n",
"Call number: 4\n",
"Call number: 5\n",
"Call number: 6\n",
"Call number: 7\n",
"Call number: 8\n",
"Call number: 9\n",
"Call number: 10\n",
"Call number: 11\n",
"Call number: 12\n",
"Call number: 13\n",
"Call number: 14\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Stopping agent prematurely due to triggering stop condition\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Call number: 15\n"
]
}
],
"source": [
"from langchain.agents import AgentExecutor, create_tool_calling_agent\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.tools import tool\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\")\n",
"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" (\"human\", \"{input}\"),\n",
" # Placeholders fill up a **list** of messages\n",
" (\"placeholder\", \"{agent_scratchpad}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"magic_step_num = 1\n",
"\n",
"\n",
"@tool\n",
"def magic_function(input: int) -> int:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" global magic_step_num\n",
" print(f\"Call number: {magic_step_num}\")\n",
" magic_step_num += 1\n",
" return input + magic_step_num\n",
"\n",
"\n",
"tools = [magic_function]\n",
"\n",
"agent = create_tool_calling_agent(model, tools, prompt=prompt)\n",
"\n",
"\n",
"def trim_steps(steps: list):\n",
" # Let's give the agent amnesia\n",
" return []\n",
"\n",
"\n",
"agent_executor = AgentExecutor(\n",
" agent=agent, tools=tools, trim_intermediate_steps=trim_steps\n",
")\n",
"\n",
"\n",
"query = \"Call the magic function 4 times in sequence with the value 3. You cannot call it multiple times at once.\"\n",
"\n",
"for step in agent_executor.stream({\"input\": query}):\n",
" pass"
]
},
{
"cell_type": "markdown",
"id": "3d450c5a",
"metadata": {},
"source": [
"#### In LangGraph\n",
"\n",
"We can use the [`messages_modifier`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) just as before when passing in [prompt templates](#prompt-templates)."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b309ba9a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Call number: 1\n",
"Call number: 2\n",
"Call number: 3\n",
"Call number: 4\n",
"Call number: 5\n",
"Call number: 6\n",
"Call number: 7\n",
"Call number: 8\n",
"Call number: 9\n",
"Call number: 10\n",
"Call number: 11\n",
"Call number: 12\n",
"Stopping agent prematurely due to triggering stop condition\n"
]
}
],
"source": [
"from langchain_core.messages import AnyMessage\n",
"from langgraph.errors import GraphRecursionError\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"magic_step_num = 1\n",
"\n",
"\n",
"@tool\n",
"def magic_function(input: int) -> int:\n",
" \"\"\"Applies a magic function to an input.\"\"\"\n",
" global magic_step_num\n",
" print(f\"Call number: {magic_step_num}\")\n",
" magic_step_num += 1\n",
" return input + magic_step_num\n",
"\n",
"\n",
"tools = [magic_function]\n",
"\n",
"\n",
"def _modify_messages(messages: list[AnyMessage]):\n",
" # Give the agent amnesia, only keeping the original user query\n",
" return [(\"system\", \"You are a helpful assistant\"), messages[0]]\n",
"\n",
"\n",
"app = create_react_agent(model, tools, messages_modifier=_modify_messages)\n",
"\n",
"try:\n",
" for step in app.stream({\"messages\": [(\"human\", query)]}, stream_mode=\"updates\"):\n",
" pass\n",
"except GraphRecursionError as e:\n",
" print(\"Stopping agent prematurely due to triggering stop condition\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/multi_vector.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "d9172545",
"metadata": {},
"source": [
"# How to retrieve using multiple vectors per document\n",
"\n",
"It can often be useful to store multiple vectors per document. There are multiple use cases where this is beneficial. For example, we can embed multiple chunks of a document and associate those embeddings with the parent document, allowing retriever hits on the chunks to return the larger document.\n",
"\n",
"LangChain implements a base [MultiVectorRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_vector.MultiVectorRetriever.html), which simplifies this process. Much of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`.\n",
"\n",
"The methods to create multiple vectors per document include:\n",
"\n",
"- Smaller chunks: split a document into smaller chunks, and embed those (this is [ParentDocumentRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html)).\n",
"- Summary: create a summary for each document, embed that along with (or instead of) the document.\n",
"- Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document.\n",
"\n",
"Note that this also enables another method of adding embeddings - manually. This is useful because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control.\n",
"\n",
"Below we walk through an example. First we instantiate some documents. We will index them in an (in-memory) [Chroma](/docs/integrations/providers/chroma/) vector store using [OpenAI](https://python.langchain.com/v0.2/docs/integrations/text_embedding/openai/) embeddings, but any LangChain vector store or embeddings model will suffice."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09cecd95-3499-465a-895a-944627ffb77f",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-chroma langchain langchain-openai > /dev/null"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "18c1421a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.storage import InMemoryByteStore\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.document_loaders import TextLoader\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"loaders = [\n",
" TextLoader(\"paul_graham_essay.txt\"),\n",
" TextLoader(\"state_of_the_union.txt\"),\n",
"]\n",
"docs = []\n",
"for loader in loaders:\n",
" docs.extend(loader.load())\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)\n",
"docs = text_splitter.split_documents(docs)\n",
"\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
" collection_name=\"full_documents\", embedding_function=OpenAIEmbeddings()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "fa17beda",
"metadata": {},
"source": [
"## Smaller chunks\n",
"\n",
"Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks. This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. Note that this is what the [ParentDocumentRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html) does. Here we show what is going on under the hood.\n",
"\n",
"We will make a distinction between the vector store, which indexes embeddings of the (sub) documents, and the document store, which houses the \"parent\" documents and associates them with an identifier."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0e7b6b45",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"\n",
"# The storage layer for the parent documents\n",
"store = InMemoryByteStore()\n",
"id_key = \"doc_id\"\n",
"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" byte_store=store,\n",
" id_key=id_key,\n",
")\n",
"\n",
"doc_ids = [str(uuid.uuid4()) for _ in docs]"
]
},
{
"cell_type": "markdown",
"id": "d4feded4-856a-4282-91c3-53aabc62e6ff",
"metadata": {},
"source": [
"We next generate the \"sub\" documents by splitting the original documents. Note that we store the document identifier in the `metadata` of the corresponding [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) object."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5d23247d",
"metadata": {},
"outputs": [],
"source": [
"# The splitter to use to create smaller chunks\n",
"child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
"\n",
"sub_docs = []\n",
"for i, doc in enumerate(docs):\n",
" _id = doc_ids[i]\n",
" _sub_docs = child_text_splitter.split_documents([doc])\n",
" for _doc in _sub_docs:\n",
" _doc.metadata[id_key] = _id\n",
" sub_docs.extend(_sub_docs)"
]
},
{
"cell_type": "markdown",
"id": "8e0634f8-90d5-4250-981a-5257c8a6d455",
"metadata": {},
"source": [
"Finally, we index the documents in our vector store and document store:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "92ed5861",
"metadata": {},
"outputs": [],
"source": [
"retriever.vectorstore.add_documents(sub_docs)\n",
"retriever.docstore.mset(list(zip(doc_ids, docs)))"
]
},
{
"cell_type": "markdown",
"id": "14c48c6d-850c-4317-9b6e-1ade92f2f710",
"metadata": {},
"source": [
"The vector store alone will retrieve small chunks:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8afed60c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content='Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.', metadata={'doc_id': '064eca46-a4c4-4789-8e3b-583f9597e54f', 'source': 'state_of_the_union.txt'})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.vectorstore.similarity_search(\"justice breyer\")[0]"
]
},
{
"cell_type": "markdown",
"id": "717097c7-61d9-4306-8625-ef8f1940c127",
"metadata": {},
"source": [
"Whereas the retriever will return the larger parent document:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3c9017f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9875"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(retriever.invoke(\"justice breyer\")[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "cdef8339-f9fa-4b3b-955f-ad9dbdf2734f",
"metadata": {},
"source": [
"The default search type the retriever performs on the vector database is a similarity search. LangChain vector stores also support searching via [Max Marginal Relevance](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search). This can be controlled via the `search_type` parameter of the retriever:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "36739460-a737-4a8e-b70f-50bf8c8eaae7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9875"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.retrievers.multi_vector import SearchType\n",
"\n",
"retriever.search_type = SearchType.mmr\n",
"\n",
"len(retriever.invoke(\"justice breyer\")[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "d6a7ae0d",
"metadata": {},
"source": [
"## Associating summaries with a document for retrieval\n",
"\n",
"A summary may be able to distill more accurately what a chunk is about, leading to better retrieval. Here we show how to create summaries, and then embed those.\n",
"\n",
"We construct a simple [chain](/docs/how_to/sequence) that will receive an input [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) object and generate a summary using a LLM.\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6589291f-55bb-4e9a-b4ff-08f2506ed641",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1433dff4",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"from langchain_core.documents import Document\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"chain = (\n",
" {\"doc\": lambda x: x.page_content}\n",
" | ChatPromptTemplate.from_template(\"Summarize the following document:\\n\\n{doc}\")\n",
" | llm\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "3faa9fde-1b09-4849-a815-8b2e89c30a02",
"metadata": {},
"source": [
"Note that we can [batch](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) the chain accross documents:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "41a2a738",
"metadata": {},
"outputs": [],
"source": [
"summaries = chain.batch(docs, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "markdown",
"id": "73ef599e-140b-4905-8b62-6c52cdde1852",
"metadata": {},
"source": [
"We can then initialize a `MultiVectorRetriever` as before, indexing the summaries in our vector store, and retaining the original documents in our document store:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "7ac5e4b1",
"metadata": {},
"outputs": [],
"source": [
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
"# The storage layer for the parent documents\n",
"store = InMemoryByteStore()\n",
"id_key = \"doc_id\"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" byte_store=store,\n",
" id_key=id_key,\n",
")\n",
"doc_ids = [str(uuid.uuid4()) for _ in docs]\n",
"\n",
"summary_docs = [\n",
" Document(page_content=s, metadata={id_key: doc_ids[i]})\n",
" for i, s in enumerate(summaries)\n",
"]\n",
"\n",
"retriever.vectorstore.add_documents(summary_docs)\n",
"retriever.docstore.mset(list(zip(doc_ids, docs)))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "862ae920",
"metadata": {},
"outputs": [],
"source": [
"# # We can also add the original chunks to the vectorstore if we so want\n",
"# for i, doc in enumerate(docs):\n",
"# doc.metadata[id_key] = doc_ids[i]\n",
"# retriever.vectorstore.add_documents(docs)"
]
},
{
"cell_type": "markdown",
"id": "f0274892-29c1-4616-9040-d23f9d537526",
"metadata": {},
"source": [
"Querying the vector store will return summaries:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "299232d6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content=\"President Biden recently nominated Judge Ketanji Brown Jackson to serve on the United States Supreme Court, emphasizing her qualifications and broad support. The President also outlined a plan to secure the border, fix the immigration system, protect women's rights, support LGBTQ+ Americans, and advance mental health services. He highlighted the importance of bipartisan unity in passing legislation, such as the Violence Against Women Act. The President also addressed supporting veterans, particularly those impacted by exposure to burn pits, and announced plans to expand benefits for veterans with respiratory cancers. Additionally, he proposed a plan to end cancer as we know it through the Cancer Moonshot initiative. President Biden expressed optimism about the future of America and emphasized the strength of the American people in overcoming challenges.\", metadata={'doc_id': '84015b1b-980e-400a-94d8-cf95d7e079bd'})"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub_docs = retriever.vectorstore.similarity_search(\"justice breyer\")\n",
"\n",
"sub_docs[0]"
]
},
{
"cell_type": "markdown",
"id": "e4f77ac5-2926-4f60-aad5-b2067900dff9",
"metadata": {},
"source": [
"Whereas the retriever will return the larger source document:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e4cce5c2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9194"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retrieved_docs = retriever.invoke(\"justice breyer\")\n",
"\n",
"len(retrieved_docs[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "097a5396",
"metadata": {},
"source": [
"## Hypothetical Queries\n",
"\n",
"An LLM can also be used to generate a list of hypothetical questions that could be asked of a particular document, which might bear close semantic similarity to relevant queries in a [RAG](/docs/tutorials/rag) application. These questions can then be embedded and associated with the documents to improve retrieval.\n",
"\n",
"Below, we use the [with_structured_output](/docs/how_to/structured_output/) method to structure the LLM output into a list of strings."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "03d85234-c33a-4a43-861d-47328e1ec2ea",
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class HypotheticalQuestions(BaseModel):\n",
" \"\"\"Generate hypothetical questions.\"\"\"\n",
"\n",
" questions: List[str] = Field(..., description=\"List of questions\")\n",
"\n",
"\n",
"chain = (\n",
" {\"doc\": lambda x: x.page_content}\n",
" # Only asking for 3 hypothetical questions, but this could be adjusted\n",
" | ChatPromptTemplate.from_template(\n",
" \"Generate a list of exactly 3 hypothetical questions that the below document could be used to answer:\\n\\n{doc}\"\n",
" )\n",
" | ChatOpenAI(max_retries=0, model=\"gpt-4o\").with_structured_output(\n",
" HypotheticalQuestions\n",
" )\n",
" | (lambda x: x.questions)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "6dddc40f-62af-413c-b944-f94a5e1f2f4e",
"metadata": {},
"source": [
"Invoking the chain on a single document demonstrates that it outputs a list of questions:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "11d30554",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[\"What impact did the IBM 1401 have on the author's early programming experiences?\",\n",
" \"How did the transition from using the IBM 1401 to microcomputers influence the author's programming journey?\",\n",
" \"What role did Lisp play in shaping the author's understanding and approach to AI?\"]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(docs[0])"
]
},
{
"cell_type": "markdown",
"id": "dcffc572-7b20-4b77-857a-90ec360a8f7e",
"metadata": {},
"source": [
"We can batch then batch the chain over all documents and assemble our vector store and document store as before:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b2cd6e75",
"metadata": {},
"outputs": [],
"source": [
"# Batch chain over documents to generate hypothetical questions\n",
"hypothetical_questions = chain.batch(docs, {\"max_concurrency\": 5})\n",
"\n",
"\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
" collection_name=\"hypo-questions\", embedding_function=OpenAIEmbeddings()\n",
")\n",
"# The storage layer for the parent documents\n",
"store = InMemoryByteStore()\n",
"id_key = \"doc_id\"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" byte_store=store,\n",
" id_key=id_key,\n",
")\n",
"doc_ids = [str(uuid.uuid4()) for _ in docs]\n",
"\n",
"\n",
"# Generate Document objects from hypothetical questions\n",
"question_docs = []\n",
"for i, question_list in enumerate(hypothetical_questions):\n",
" question_docs.extend(\n",
" [Document(page_content=s, metadata={id_key: doc_ids[i]}) for s in question_list]\n",
" )\n",
"\n",
"\n",
"retriever.vectorstore.add_documents(question_docs)\n",
"retriever.docstore.mset(list(zip(doc_ids, docs)))"
]
},
{
"cell_type": "markdown",
"id": "75cba8ab-a06f-4545-85fc-cf49d0204b5e",
"metadata": {},
"source": [
"Note that querying the underlying vector store will retrieve hypothetical questions that are semantically similar to the input query:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7b442b90",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='What might be the potential benefits of nominating Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court?', metadata={'doc_id': '43292b74-d1b8-4200-8a8b-ea0cb57fbcdb'}),\n",
" Document(page_content='How might the Bipartisan Infrastructure Law impact the economic competition between the U.S. and China?', metadata={'doc_id': '66174780-d00c-4166-9791-f0069846e734'}),\n",
" Document(page_content='What factors led to the creation of Y Combinator?', metadata={'doc_id': '72003c4e-4cc9-4f09-a787-0b541a65b38c'}),\n",
" Document(page_content='How did the ability to publish essays online change the landscape for writers and thinkers?', metadata={'doc_id': 'e8d2c648-f245-4bcc-b8d3-14e64a164b64'})]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub_docs = retriever.vectorstore.similarity_search(\"justice breyer\")\n",
"\n",
"sub_docs"
]
},
{
"cell_type": "markdown",
"id": "63c32e43-5f4a-463b-a0c2-2101986f70e6",
"metadata": {},
"source": [
"And invoking the retriever will return the corresponding document:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7594b24e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9194"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retrieved_docs = retriever.invoke(\"justice breyer\")\n",
"len(retrieved_docs[0].page_content)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/multimodal_inputs.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4facdf7f-680e-4d28-908b-2b8408e2a741",
"metadata": {},
"source": [
"# How to pass multimodal data directly to models\n",
"\n",
"Here we demonstrate how to pass multimodal input directly to models. \n",
"We currently expect all input to be passed in the same format as [OpenAI expects](https://platform.openai.com/docs/guides/vision).\n",
"For other model providers that support multimodal input, we have added logic inside the class to convert to the expected format.\n",
"\n",
"In this example we will ask a model to describe an image."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0d9fd81a-b7f0-445a-8e3d-cfc2d31fdd59",
"metadata": {},
"outputs": [],
"source": [
"image_url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fb896ce9",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.messages import HumanMessage\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\")"
]
},
{
"cell_type": "markdown",
"id": "4fca4da7",
"metadata": {},
"source": [
"The most commonly supported way to pass in images is to pass it in as a byte string.\n",
"This should work for most model integrations."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ca1040c",
"metadata": {},
"outputs": [],
"source": [
"import base64\n",
"\n",
"import httpx\n",
"\n",
"image_data = base64.b64encode(httpx.get(image_url).content).decode(\"utf-8\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ec680b6b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The weather in the image appears to be clear and pleasant. The sky is mostly blue with scattered, light clouds, suggesting a sunny day with minimal cloud cover. There is no indication of rain or strong winds, and the overall scene looks bright and calm. The lush green grass and clear visibility further indicate good weather conditions.\n"
]
}
],
"source": [
"message = HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": \"describe the weather in this image\"},\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": f\"data:image/jpeg;base64,{image_data}\"},\n",
" },\n",
" ],\n",
")\n",
"response = model.invoke([message])\n",
"print(response.content)"
]
},
{
"cell_type": "markdown",
"id": "8656018e-c56d-47d2-b2be-71e87827f90a",
"metadata": {},
"source": [
"We can feed the image URL directly in a content block of type \"image_url\". Note that only some model providers support this."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a8819cf3-5ddc-44f0-889a-19ca7b7fe77e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered clouds, suggesting good visibility and a likely pleasant temperature. The bright sunlight is casting distinct shadows on the grass and vegetation, indicating it is likely daytime, possibly late morning or early afternoon. The overall ambiance suggests a warm and inviting day, suitable for outdoor activities.\n"
]
}
],
"source": [
"message = HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": \"describe the weather in this image\"},\n",
" {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
" ],\n",
")\n",
"response = model.invoke([message])\n",
"print(response.content)"
]
},
{
"cell_type": "markdown",
"id": "1c470309",
"metadata": {},
"source": [
"We can also pass in multiple images."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "325fb4ca",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Yes, the two images are the same. They both depict a wooden boardwalk extending through a grassy field under a blue sky with light clouds. The scenery, lighting, and composition are identical.\n"
]
}
],
"source": [
"message = HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": \"are these two images the same?\"},\n",
" {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
" {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
" ],\n",
")\n",
"response = model.invoke([message])\n",
"print(response.content)"
]
},
{
"cell_type": "markdown",
"id": "71bd28cf-d76c-44e2-a55e-c5f265db986e",
"metadata": {},
"source": [
"## Tool calls\n",
"\n",
"Some multimodal models support [tool calling](/docs/concepts/#functiontool-calling) features as well. To call tools using such models, simply bind tools to them in the [usual way](/docs/how_to/tool_calling), and invoke the model using content blocks of the desired type (e.g., containing image data)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cd22ea82-2f93-46f9-9f7a-6aaf479fcaa9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'name': 'weather_tool', 'args': {'weather': 'sunny'}, 'id': 'call_BSX4oq4SKnLlp2WlzDhToHBr'}]\n"
]
}
],
"source": [
"from typing import Literal\n",
"\n",
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def weather_tool(weather: Literal[\"sunny\", \"cloudy\", \"rainy\"]) -> None:\n",
" \"\"\"Describe the weather\"\"\"\n",
" pass\n",
"\n",
"\n",
"model_with_tools = model.bind_tools([weather_tool])\n",
"\n",
"message = HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": \"describe the weather in this image\"},\n",
" {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n",
" ],\n",
")\n",
"response = model_with_tools.invoke([message])\n",
"print(response.tool_calls)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/multimodal_prompts.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4facdf7f-680e-4d28-908b-2b8408e2a741",
"metadata": {},
"source": [
"# How to use multimodal prompts\n",
"\n",
"Here we demonstrate how to use prompt templates to format multimodal inputs to models. \n",
"\n",
"In this example we will ask a model to describe an image."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0d9fd81a-b7f0-445a-8e3d-cfc2d31fdd59",
"metadata": {},
"outputs": [],
"source": [
"import base64\n",
"\n",
"import httpx\n",
"\n",
"image_url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n",
"image_data = base64.b64encode(httpx.get(image_url).content).decode(\"utf-8\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2671f995",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4ee35e4f",
"metadata": {},
"outputs": [],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"Describe the image provided\"),\n",
" (\n",
" \"user\",\n",
" [\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data}\"},\n",
" }\n",
" ],\n",
" ),\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "089f75c2",
"metadata": {},
"outputs": [],
"source": [
"chain = prompt | model"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "02744b06",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The image depicts a sunny day with a beautiful blue sky filled with scattered white clouds. The sky has varying shades of blue, ranging from a deeper hue near the horizon to a lighter, almost pale blue higher up. The white clouds are fluffy and scattered across the expanse of the sky, creating a peaceful and serene atmosphere. The lighting and cloud patterns suggest pleasant weather conditions, likely during the daytime hours on a mild, sunny day in an outdoor natural setting.\n"
]
}
],
"source": [
"response = chain.invoke({\"image_data\": image_data})\n",
"print(response.content)"
]
},
{
"cell_type": "markdown",
"id": "e9b9ebf6",
"metadata": {},
"source": [
"We can also pass in multiple images."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "02190ee3",
"metadata": {},
"outputs": [],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"compare the two pictures provided\"),\n",
" (\n",
" \"user\",\n",
" [\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data1}\"},\n",
" },\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": \"data:image/jpeg;base64,{image_data2}\"},\n",
" },\n",
" ],\n",
" ),\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "42af057b",
"metadata": {},
"outputs": [],
"source": [
"chain = prompt | model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "513abe00",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The two images provided are identical. Both images feature a wooden boardwalk path extending through a lush green field under a bright blue sky with some clouds. The perspective, colors, and elements in both images are exactly the same.\n"
]
}
],
"source": [
"response = chain.invoke({\"image_data1\": image_data, \"image_data2\": image_data})\n",
"print(response.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea8152c3",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/output_parser_custom.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "80f15d95-00d8-4c38-a291-07ff2233b4fd",
"metadata": {},
"source": [
"# How to create a custom Output Parser\n",
"\n",
"In some situations you may want to implement a custom parser to structure the model output into a custom format.\n",
"\n",
"There are two ways to implement a custom parser:\n",
"\n",
"1. Using `RunnableLambda` or `RunnableGenerator` in LCEL -- we strongly recommend this for most use cases\n",
"2. By inherting from one of the base classes for out parsing -- this is the hard way of doing things\n",
"\n",
"The difference between the two approaches are mostly superficial and are mainly in terms of which callbacks are triggered (e.g., `on_chain_start` vs. `on_parser_start`), and how a runnable lambda vs. a parser might be visualized in a tracing platform like LangSmith."
]
},
{
"cell_type": "markdown",
"id": "c651cc26-28cb-45d1-9969-d88deff8b819",
"metadata": {},
"source": [
"## Runnable Lambdas and Generators\n",
"\n",
"The recommended way to parse is using **runnable lambdas** and **runnable generators**!\n",
"\n",
"Here, we will make a simple parse that inverts the case of the output from the model.\n",
"\n",
"For example, if the model outputs: \"Meow\", the parser will produce \"mEOW\"."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6cd7cc21-ec51-4e22-82d0-32c4401f5adc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'hELLO!'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from typing import Iterable\n",
"\n",
"from langchain_anthropic.chat_models import ChatAnthropic\n",
"from langchain_core.messages import AIMessage, AIMessageChunk\n",
"\n",
"model = ChatAnthropic(model_name=\"claude-2.1\")\n",
"\n",
"\n",
"def parse(ai_message: AIMessage) -> str:\n",
" \"\"\"Parse the AI message.\"\"\"\n",
" return ai_message.content.swapcase()\n",
"\n",
"\n",
"chain = model | parse\n",
"chain.invoke(\"hello\")"
]
},
{
"cell_type": "markdown",
"id": "eed8baf2-f4c2-44c1-b47d-e9f560af6202",
"metadata": {},
"source": [
":::{.callout-tip}\n",
"\n",
"LCEL automatically upgrades the function `parse` to `RunnableLambda(parse)` when composed using a `|` syntax.\n",
"\n",
"If you don't like that you can manually import `RunnableLambda` and then run`parse = RunnableLambda(parse)`.\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "896f52ce-91e2-4c7c-bd62-1f901002ade2",
"metadata": {},
"source": [
"Does streaming work?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4e35389a-caa5-4c0d-9d95-48648d0b8d4f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"i'M cLAUDE, AN ai ASSISTANT CREATED BY aNTHROPIC TO BE HELPFUL, HARMLESS, AND HONEST.|"
]
}
],
"source": [
"for chunk in chain.stream(\"tell me about yourself in one sentence\"):\n",
" print(chunk, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "11c486bb-b2d4-461b-8fd8-19b9e0472129",
"metadata": {},
"source": [
"No, it doesn't because the parser aggregates the input before parsing the output.\n",
"\n",
"If we want to implement a streaming parser, we can have the parser accept an iterable over the input instead and yield\n",
"the results as they're available."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "930aa59e-82d0-447c-b711-b416d92a08b7",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnableGenerator\n",
"\n",
"\n",
"def streaming_parse(chunks: Iterable[AIMessageChunk]) -> Iterable[str]:\n",
" for chunk in chunks:\n",
" yield chunk.content.swapcase()\n",
"\n",
"\n",
"streaming_parse = RunnableGenerator(streaming_parse)"
]
},
{
"cell_type": "markdown",
"id": "62192808-c7e1-4b3a-85f4-b7901de7c0b8",
"metadata": {},
"source": [
":::{.callout-important}\n",
"\n",
"Please wrap the streaming parser in `RunnableGenerator` as we may stop automatically upgrading it with the `|` syntax.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c054d4da-66f3-4f11-8137-0734bb3de06c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'hELLO!'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = model | streaming_parse\n",
"chain.invoke(\"hello\")"
]
},
{
"cell_type": "markdown",
"id": "1d344ff2-5c93-49a9-af00-03856d2cbfdb",
"metadata": {},
"source": [
"Let's confirm that streaming works!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "26d746ae-9c5a-4cda-a535-33f555e2e04a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"i|'M| cLAUDE|,| AN| ai| ASSISTANT| CREATED| BY| aN|THROP|IC| TO| BE| HELPFUL|,| HARMLESS|,| AND| HONEST|.|"
]
}
],
"source": [
"for chunk in chain.stream(\"tell me about yourself in one sentence\"):\n",
" print(chunk, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "24067447-8a5a-4d6b-86a3-4b9cc4b4369b",
"metadata": {},
"source": [
"## Inherting from Parsing Base Classes"
]
},
{
"cell_type": "markdown",
"id": "9713f547-b2e4-48eb-807f-a0f6f6d0e7e0",
"metadata": {},
"source": [
"Another approach to implement a parser is by inherting from `BaseOutputParser`, `BaseGenerationOutputParser` or another one of the base parsers depending on what you need to do.\n",
"\n",
"In general, we **do not** recommend this approach for most use cases as it results in more code to write without significant benefits.\n",
"\n",
"The simplest kind of output parser extends the `BaseOutputParser` class and must implement the following methods:\n",
"\n",
"* `parse`: takes the string output from the model and parses it\n",
"* (optional) `_type`: identifies the name of the parser.\n",
"\n",
"When the output from the chat model or LLM is malformed, the can throw an `OutputParserException` to indicate that parsing fails because of bad input. Using this exception allows code that utilizes the parser to handle the exceptions in a consistent manner.\n",
"\n",
":::{.callout-tip} Parsers are Runnables! 🏃\n",
"\n",
"Because `BaseOutputParser` implements the `Runnable` interface, any custom parser you will create this way will become valid LangChain Runnables and will benefit from automatic async support, batch interface, logging support etc.\n",
":::\n"
]
},
{
"cell_type": "markdown",
"id": "1e0f9c59-b5bd-4ed0-a187-ae514c203e80",
"metadata": {},
"source": [
"### Simple Parser"
]
},
{
"cell_type": "markdown",
"id": "3a96a846-1296-4d92-8e76-e29e583dee22",
"metadata": {},
"source": [
"Here's a simple parser that can parse a **string** representation of a booealn (e.g., `YES` or `NO`) and convert it into the corresponding `boolean` type."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "733a0c4f-471a-4161-ad3e-804f63053e6f",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.exceptions import OutputParserException\n",
"from langchain_core.output_parsers import BaseOutputParser\n",
"\n",
"\n",
"# The [bool] desribes a parameterization of a generic.\n",
"# It's basically indicating what the return type of parse is\n",
"# in this case the return type is either True or False\n",
"class BooleanOutputParser(BaseOutputParser[bool]):\n",
" \"\"\"Custom boolean parser.\"\"\"\n",
"\n",
" true_val: str = \"YES\"\n",
" false_val: str = \"NO\"\n",
"\n",
" def parse(self, text: str) -> bool:\n",
" cleaned_text = text.strip().upper()\n",
" if cleaned_text not in (self.true_val.upper(), self.false_val.upper()):\n",
" raise OutputParserException(\n",
" f\"BooleanOutputParser expected output value to either be \"\n",
" f\"{self.true_val} or {self.false_val} (case-insensitive). \"\n",
" f\"Received {cleaned_text}.\"\n",
" )\n",
" return cleaned_text == self.true_val.upper()\n",
"\n",
" @property\n",
" def _type(self) -> str:\n",
" return \"boolean_output_parser\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "101e54f0-12f1-4734-a80d-98e6f62644b2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser = BooleanOutputParser()\n",
"parser.invoke(\"YES\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "39ed9d84-16a1-4612-a1f7-13269b9f48e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Triggered an exception of type: <class 'langchain_core.exceptions.OutputParserException'>\n"
]
}
],
"source": [
"try:\n",
" parser.invoke(\"MEOW\")\n",
"except Exception as e:\n",
" print(f\"Triggered an exception of type: {type(e)}\")"
]
},
{
"cell_type": "markdown",
"id": "c27da11a-2c64-4108-9a8a-38008d6041fc",
"metadata": {},
"source": [
"Let's test changing the parameterization"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2e94c0f4-f6c1-401b-8cee-2572a80846cb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser = BooleanOutputParser(true_val=\"OKAY\")\n",
"parser.invoke(\"OKAY\")"
]
},
{
"cell_type": "markdown",
"id": "dac313d5-20c8-44a9-bfe9-c2b5020172e2",
"metadata": {},
"source": [
"Let's confirm that other LCEL methods are present"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "97fb540f-83b2-46fd-a741-b200235f8f9e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[True, False]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser.batch([\"OKAY\", \"NO\"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "60cbdb2f-5538-4e74-ba03-53bc1bc4bb2f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[True, False]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await parser.abatch([\"OKAY\", \"NO\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6520dff0-259c-48e4-be69-829fb3275ac2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='OKAY')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_anthropic.chat_models import ChatAnthropic\n",
"\n",
"anthropic = ChatAnthropic(model_name=\"claude-2.1\")\n",
"anthropic.invoke(\"say OKAY or NO\")"
]
},
{
"cell_type": "markdown",
"id": "12dc079e-c451-496c-953c-cba55ef26de8",
"metadata": {},
"source": [
"Let's test that our parser works!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bb177c14-b1f5-474f-a1c8-5b32ae242259",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = anthropic | parser\n",
"chain.invoke(\"say OKAY or NO\")"
]
},
{
"cell_type": "markdown",
"id": "18f83192-37e8-43f5-ab29-9568b1279f1b",
"metadata": {},
"source": [
":::{.callout-note}\n",
"The parser will work with either the output from an LLM (a string) or the output from a chat model (an `AIMessage`)!\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "9ed063d3-3159-4f5b-8362-710956fc50bd",
"metadata": {},
"source": [
"### Parsing Raw Model Outputs\n",
"\n",
"Sometimes there is additional metadata on the model output that is important besides the raw text. One example of this is tool calling, where arguments intended to be passed to called functions are returned in a separate property. If you need this finer-grained control, you can instead subclass the `BaseGenerationOutputParser` class. \n",
"\n",
"This class requires a single method `parse_result`. This method takes raw model output (e.g., list of `Generation` or `ChatGeneration`) and returns the parsed output.\n",
"\n",
"Supporting both `Generation` and `ChatGeneration` allows the parser to work with both regular LLMs as well as with Chat Models."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "0fd1f936-e77d-4602-921c-52a37e589e90",
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from langchain_core.exceptions import OutputParserException\n",
"from langchain_core.messages import AIMessage\n",
"from langchain_core.output_parsers import BaseGenerationOutputParser\n",
"from langchain_core.outputs import ChatGeneration, Generation\n",
"\n",
"\n",
"class StrInvertCase(BaseGenerationOutputParser[str]):\n",
" \"\"\"An example parser that inverts the case of the characters in the message.\n",
"\n",
" This is an example parse shown just for demonstration purposes and to keep\n",
" the example as simple as possible.\n",
" \"\"\"\n",
"\n",
" def parse_result(self, result: List[Generation], *, partial: bool = False) -> str:\n",
" \"\"\"Parse a list of model Generations into a specific format.\n",
"\n",
" Args:\n",
" result: A list of Generations to be parsed. The Generations are assumed\n",
" to be different candidate outputs for a single model input.\n",
" Many parsers assume that only a single generation is passed it in.\n",
" We will assert for that\n",
" partial: Whether to allow partial results. This is used for parsers\n",
" that support streaming\n",
" \"\"\"\n",
" if len(result) != 1:\n",
" raise NotImplementedError(\n",
" \"This output parser can only be used with a single generation.\"\n",
" )\n",
" generation = result[0]\n",
" if not isinstance(generation, ChatGeneration):\n",
" # Say that this one only works with chat generations\n",
" raise OutputParserException(\n",
" \"This output parser can only be used with a chat generation.\"\n",
" )\n",
" return generation.message.content.swapcase()\n",
"\n",
"\n",
"chain = anthropic | StrInvertCase()"
]
},
{
"cell_type": "markdown",
"id": "accab8a3-6b0e-4ad0-89e6-1824ca20c726",
"metadata": {},
"source": [
"Let's the new parser! It should be inverting the output from the model."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "568fae19-b09c-484f-8775-1c9a60aabdf4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'hELLO! mY NAME IS cLAUDE.'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Tell me a short sentence about yourself\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/output_parser_fixing.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "0fee7096",
"metadata": {},
"source": [
"# How to use the output-fixing parser\n",
"\n",
"This output parser wraps another output parser, and in the event that the first one fails it calls out to another LLM to fix any errors.\n",
"\n",
"But we can do other things besides throw errors. Specifically, we can pass the misformatted output, along with the formatted instructions, to the model and ask it to fix it.\n",
"\n",
"For this example, we'll use the above Pydantic output parser. Here's what happens if we pass it a result that does not comply with the schema:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9bad594d",
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from langchain_core.output_parsers import PydanticOutputParser\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "15283e0b",
"metadata": {},
"outputs": [],
"source": [
"class Actor(BaseModel):\n",
" name: str = Field(description=\"name of an actor\")\n",
" film_names: List[str] = Field(description=\"list of names of films they starred in\")\n",
"\n",
"\n",
"actor_query = \"Generate the filmography for a random actor.\"\n",
"\n",
"parser = PydanticOutputParser(pydantic_object=Actor)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "072d2d4c",
"metadata": {},
"outputs": [],
"source": [
"misformatted = \"{'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4cbb35b3",
"metadata": {},
"outputs": [
{
"ename": "OutputParserException",
"evalue": "Failed to parse Actor from completion {'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}. Got: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/workplace/langchain/libs/langchain/langchain/output_parsers/pydantic.py:29\u001b[0m, in \u001b[0;36mPydanticOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 28\u001b[0m json_str \u001b[38;5;241m=\u001b[39m match\u001b[38;5;241m.\u001b[39mgroup()\n\u001b[0;32m---> 29\u001b[0m json_object \u001b[38;5;241m=\u001b[39m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpydantic_object\u001b[38;5;241m.\u001b[39mparse_obj(json_object)\n",
"File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/json/__init__.py:359\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 358\u001b[0m kw[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparse_constant\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m parse_constant\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;124;03mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \n\u001b[1;32m 336\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n",
"File \u001b[0;32m~/.pyenv/versions/3.10.1/lib/python3.10/json/decoder.py:353\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 353\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
"\u001b[0;31mJSONDecodeError\u001b[0m: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mOutputParserException\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmisformatted\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/workplace/langchain/libs/langchain/langchain/output_parsers/pydantic.py:35\u001b[0m, in \u001b[0;36mPydanticOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 33\u001b[0m name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpydantic_object\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m 34\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to parse \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from completion \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m OutputParserException(msg, llm_output\u001b[38;5;241m=\u001b[39mtext)\n",
"\u001b[0;31mOutputParserException\u001b[0m: Failed to parse Actor from completion {'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}. Got: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)"
]
}
],
"source": [
"parser.parse(misformatted)"
]
},
{
"cell_type": "markdown",
"id": "723c559d",
"metadata": {},
"source": [
"Now we can construct and use a `OutputFixingParser`. This output parser takes as an argument another output parser but also an LLM with which to try to correct any formatting mistakes."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4aaccbf1",
"metadata": {},
"outputs": [],
"source": [
"from langchain.output_parsers import OutputFixingParser\n",
"\n",
"new_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8031c22d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Actor(name='Tom Hanks', film_names=['Forrest Gump'])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_parser.parse(misformatted)"
]
},
{
"cell_type": "markdown",
"id": "84498e02",
"metadata": {},
"source": [
"Find out api documentation for [OutputFixingParser](https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc7af2a0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/output_parser_json.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "72b1b316",
"metadata": {},
"source": [
"# How to parse JSON output\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Structured output](/docs/how_to/structured_output)\n",
"- [Chaining runnables together](/docs/how_to/sequence/)\n",
"\n",
":::\n",
"\n",
"While some model providers support [built-in ways to return structured output](/docs/how_to/structured_output), not all do. We can use an output parser to help users to specify an arbitrary JSON schema via the prompt, query a model for outputs that conform to that schema, and finally parse that schema as JSON.\n",
"\n",
":::{.callout-note}\n",
"Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed JSON.\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "ae909b7a",
"metadata": {},
"source": [
"The [`JsonOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html) is one built-in option for prompting for and then parsing JSON output. While it is similar in functionality to the [`PydanticOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html), it also supports streaming back partial JSON objects.\n",
"\n",
"Here's an example of how it can be used alongside [Pydantic](https://docs.pydantic.dev/) to conveniently declare the expected schema:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd9d9110",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain langchain-openai\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4ccf45a3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'setup': \"Why couldn't the bicycle stand up by itself?\",\n",
" 'punchline': 'Because it was two tired!'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.output_parsers import JsonOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(temperature=0)\n",
"\n",
"\n",
"# Define your desired data structure.\n",
"class Joke(BaseModel):\n",
" setup: str = Field(description=\"question to set up a joke\")\n",
" punchline: str = Field(description=\"answer to resolve the joke\")\n",
"\n",
"\n",
"# And a query intented to prompt a language model to populate the data structure.\n",
"joke_query = \"Tell me a joke.\"\n",
"\n",
"# Set up a parser + inject instructions into the prompt template.\n",
"parser = JsonOutputParser(pydantic_object=Joke)\n",
"\n",
"prompt = PromptTemplate(\n",
" template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
")\n",
"\n",
"chain = prompt | model | parser\n",
"\n",
"chain.invoke({\"query\": joke_query})"
]
},
{
"cell_type": "markdown",
"id": "51ffa2e3",
"metadata": {},
"source": [
"Note that we are passing `format_instructions` from the parser directly into the prompt. You can and should experiment with adding your own formatting hints in the other parts of your prompt to either augment or replace the default instructions:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "72de9c82",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The output should be formatted as a JSON instance that conforms to the JSON schema below.\\n\\nAs an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\\nthe object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\\n\\nHere is the output schema:\\n```\\n{\"properties\": {\"setup\": {\"title\": \"Setup\", \"description\": \"question to set up a joke\", \"type\": \"string\"}, \"punchline\": {\"title\": \"Punchline\", \"description\": \"answer to resolve the joke\", \"type\": \"string\"}}, \"required\": [\"setup\", \"punchline\"]}\\n```'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser.get_format_instructions()"
]
},
{
"cell_type": "markdown",
"id": "37d801be",
"metadata": {},
"source": [
"## Streaming\n",
"\n",
"As mentioned above, a key difference between the `JsonOutputParser` and the `PydanticOutputParser` is that the `JsonOutputParser` output parser supports streaming partial chunks. Here's what that looks like:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0309256d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{}\n",
"{'setup': ''}\n",
"{'setup': 'Why'}\n",
"{'setup': 'Why couldn'}\n",
"{'setup': \"Why couldn't\"}\n",
"{'setup': \"Why couldn't the\"}\n",
"{'setup': \"Why couldn't the bicycle\"}\n",
"{'setup': \"Why couldn't the bicycle stand\"}\n",
"{'setup': \"Why couldn't the bicycle stand up\"}\n",
"{'setup': \"Why couldn't the bicycle stand up by\"}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself\"}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\"}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\", 'punchline': ''}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\", 'punchline': 'Because'}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\", 'punchline': 'Because it'}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\", 'punchline': 'Because it was'}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\", 'punchline': 'Because it was two'}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\", 'punchline': 'Because it was two tired'}\n",
"{'setup': \"Why couldn't the bicycle stand up by itself?\", 'punchline': 'Because it was two tired!'}\n"
]
}
],
"source": [
"for s in chain.stream({\"query\": joke_query}):\n",
" print(s)"
]
},
{
"cell_type": "markdown",
"id": "344bd968",
"metadata": {},
"source": [
"## Without Pydantic\n",
"\n",
"You can also use the `JsonOutputParser` without Pydantic. This will prompt the model to return JSON, but doesn't provide specifics about what the schema should be."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dd3806d1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'response': \"Sure! Here's a joke for you: Why couldn't the bicycle stand up by itself? Because it was two tired!\"}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"joke_query = \"Tell me a joke.\"\n",
"\n",
"parser = JsonOutputParser()\n",
"\n",
"prompt = PromptTemplate(\n",
" template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
")\n",
"\n",
"chain = prompt | model | parser\n",
"\n",
"chain.invoke({\"query\": joke_query})"
]
},
{
"cell_type": "markdown",
"id": "1eefe12b",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You've now learned one way to prompt a model to return structured JSON. Next, check out the [broader guide on obtaining structured output](/docs/how_to/structured_output) for other techniques."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4d12261",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/output_parser_retry.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4d6c0c86",
"metadata": {},
"source": [
"# How to retry when a parsing error occurs\n",
"\n",
"While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it isn't. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f28526bd",
"metadata": {},
"outputs": [],
"source": [
"from langchain.output_parsers import OutputFixingParser\n",
"from langchain_core.output_parsers import PydanticOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_openai import ChatOpenAI, OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "67c5e1ac",
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Based on the user question, provide an Action and Action Input for what step should be taken.\n",
"{format_instructions}\n",
"Question: {query}\n",
"Response:\"\"\"\n",
"\n",
"\n",
"class Action(BaseModel):\n",
" action: str = Field(description=\"action to take\")\n",
" action_input: str = Field(description=\"input to the action\")\n",
"\n",
"\n",
"parser = PydanticOutputParser(pydantic_object=Action)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "007aa87f",
"metadata": {},
"outputs": [],
"source": [
"prompt = PromptTemplate(\n",
" template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "10d207ff",
"metadata": {},
"outputs": [],
"source": [
"prompt_value = prompt.format_prompt(query=\"who is leo di caprios gf?\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "68622837",
"metadata": {},
"outputs": [],
"source": [
"bad_response = '{\"action\": \"search\"}'"
]
},
{
"cell_type": "markdown",
"id": "25631465",
"metadata": {},
"source": [
"If we try to parse this response as is, we will get an error:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "894967c1",
"metadata": {},
"outputs": [
{
"ename": "OutputParserException",
"evalue": "Failed to parse Action from completion {\"action\": \"search\"}. Got: 1 validation error for Action\naction_input\n field required (type=value_error.missing)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/workplace/langchain/libs/langchain/langchain/output_parsers/pydantic.py:30\u001b[0m, in \u001b[0;36mPydanticOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 29\u001b[0m json_object \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(json_str, strict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpydantic_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_object\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (json\u001b[38;5;241m.\u001b[39mJSONDecodeError, ValidationError) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/.pyenv/versions/3.10.1/envs/langchain/lib/python3.10/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
"File \u001b[0;32m~/.pyenv/versions/3.10.1/envs/langchain/lib/python3.10/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for Action\naction_input\n field required (type=value_error.missing)",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mOutputParserException\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbad_response\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/workplace/langchain/libs/langchain/langchain/output_parsers/pydantic.py:35\u001b[0m, in \u001b[0;36mPydanticOutputParser.parse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 33\u001b[0m name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpydantic_object\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m 34\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to parse \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m from completion \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m OutputParserException(msg, llm_output\u001b[38;5;241m=\u001b[39mtext)\n",
"\u001b[0;31mOutputParserException\u001b[0m: Failed to parse Action from completion {\"action\": \"search\"}. Got: 1 validation error for Action\naction_input\n field required (type=value_error.missing)"
]
}
],
"source": [
"parser.parse(bad_response)"
]
},
{
"cell_type": "markdown",
"id": "f6b64696",
"metadata": {},
"source": [
"If we try to use the `OutputFixingParser` to fix this error, it will be confused - namely, it doesn't know what to actually put for action input."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "78b2b40d",
"metadata": {},
"outputs": [],
"source": [
"fix_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4fe1301d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Action(action='search', action_input='input')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fix_parser.parse(bad_response)"
]
},
{
"cell_type": "markdown",
"id": "9bd9ea7d",
"metadata": {},
"source": [
"Instead, we can use the RetryOutputParser, which passes in the prompt (as well as the original output) to try again to get a better response."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7e8a8a28",
"metadata": {},
"outputs": [],
"source": [
"from langchain.output_parsers import RetryOutputParser"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5c86e141",
"metadata": {},
"outputs": [],
"source": [
"retry_parser = RetryOutputParser.from_llm(parser=parser, llm=OpenAI(temperature=0))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9c04f731",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Action(action='search', action_input='leo di caprio girlfriend')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retry_parser.parse_with_prompt(bad_response, prompt_value)"
]
},
{
"cell_type": "markdown",
"id": "16827256-5801-4388-b6fa-608991e29961",
"metadata": {},
"source": [
"We can also add the RetryOutputParser easily with a custom chain which transform the raw LLM/ChatModel output into a more workable format."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7eaff2fb-56d3-481c-99a1-a968a49d0654",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Action(action='search', action_input='leo di caprio girlfriend')\n"
]
}
],
"source": [
"from langchain_core.runnables import RunnableLambda, RunnableParallel\n",
"\n",
"completion_chain = prompt | OpenAI(temperature=0)\n",
"\n",
"main_chain = RunnableParallel(\n",
" completion=completion_chain, prompt_value=prompt\n",
") | RunnableLambda(lambda x: retry_parser.parse_with_prompt(**x))\n",
"\n",
"\n",
"main_chain.invoke({\"query\": \"who is leo di caprios gf?\"})"
]
},
{
"cell_type": "markdown",
"id": "e3a2513a",
"metadata": {},
"source": [
"Find out api documentation for [RetryOutputParser](https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html#langchain.output_parsers.retry.RetryOutputParser)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2f94fd8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/output_parser_structured.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "38831021-76ed-48b3-9f62-d1241a68b6ad",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 3\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "a745f98b-c495-44f6-a882-757c38992d76",
"metadata": {},
"source": [
"# How to use output parsers to parse an LLM response into structured format\n",
"\n",
"Language models output text. But there are times where you want to get more structured information than just text back. While some model providers support [built-in ways to return structured output](/docs/how_to/structured_output), not all do.\n",
"\n",
"Output parsers are classes that help structure language model responses. There are two main methods an output parser must implement:\n",
"\n",
"- \"Get format instructions\": A method which returns a string containing instructions for how the output of a language model should be formatted.\n",
"- \"Parse\": A method which takes in a string (assumed to be the response from a language model) and parses it into some structure.\n",
"\n",
"And then one optional one:\n",
"\n",
"- \"Parse with prompt\": A method which takes in a string (assumed to be the response from a language model) and a prompt (assumed to be the prompt that generated such a response) and parses it into some structure. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.\n",
"\n",
"## Get started\n",
"\n",
"Below we go over the main type of output parser, the `PydanticOutputParser`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1594b2bf-2a6f-47bb-9a81-38930f8e606b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.output_parsers import PydanticOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
"from langchain_openai import OpenAI\n",
"\n",
"model = OpenAI(model_name=\"gpt-3.5-turbo-instruct\", temperature=0.0)\n",
"\n",
"\n",
"# Define your desired data structure.\n",
"class Joke(BaseModel):\n",
" setup: str = Field(description=\"question to set up a joke\")\n",
" punchline: str = Field(description=\"answer to resolve the joke\")\n",
"\n",
" # You can add custom validation logic easily with Pydantic.\n",
" @validator(\"setup\")\n",
" def question_ends_with_question_mark(cls, field):\n",
" if field[-1] != \"?\":\n",
" raise ValueError(\"Badly formed question!\")\n",
" return field\n",
"\n",
"\n",
"# Set up a parser + inject instructions into the prompt template.\n",
"parser = PydanticOutputParser(pydantic_object=Joke)\n",
"\n",
"prompt = PromptTemplate(\n",
" template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
")\n",
"\n",
"# And a query intended to prompt a language model to populate the data structure.\n",
"prompt_and_model = prompt | model\n",
"output = prompt_and_model.invoke({\"query\": \"Tell me a joke.\"})\n",
"parser.invoke(output)"
]
},
{
"cell_type": "markdown",
"id": "75976cd6-78e2-458b-821f-3ddf3683466b",
"metadata": {},
"source": [
"## LCEL\n",
"\n",
"Output parsers implement the [Runnable interface](/docs/concepts#interface), the basic building block of the [LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language-lcel). This means they support `invoke`, `ainvoke`, `stream`, `astream`, `batch`, `abatch`, `astream_log` calls.\n",
"\n",
"Output parsers accept a string or `BaseMessage` as input and can return an arbitrary type."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "34f7ff0c-8443-4eb9-8704-b4f821811d93",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser.invoke(output)"
]
},
{
"cell_type": "markdown",
"id": "bdebf4a5-57a8-4632-bd17-56723d431cf1",
"metadata": {},
"source": [
"Instead of manually invoking the parser, we also could've just added it to our `Runnable` sequence:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "51f7fff5-e9bd-49a1-b5ab-b9ff281b93cb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = prompt | model | parser\n",
"chain.invoke({\"query\": \"Tell me a joke.\"})"
]
},
{
"cell_type": "markdown",
"id": "d88590a0-f36b-4ad5-8a56-d300971a6440",
"metadata": {},
"source": [
"While all parsers support the streaming interface, only certain parsers can stream through partially parsed objects, since this is highly dependent on the output type. Parsers which cannot construct partial objects will simply yield the fully parsed output.\n",
"\n",
"The `SimpleJsonOutputParser` for example can stream through partial outputs:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d7ecfe4d-dae8-4452-98ea-e48bdc498788",
"metadata": {},
"outputs": [],
"source": [
"from langchain.output_parsers.json import SimpleJsonOutputParser\n",
"\n",
"json_prompt = PromptTemplate.from_template(\n",
" \"Return a JSON object with an `answer` key that answers the following question: {question}\"\n",
")\n",
"json_parser = SimpleJsonOutputParser()\n",
"json_chain = json_prompt | model | json_parser"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "cc2b999e-47aa-41f4-ba6a-13b20a204576",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{},\n",
" {'answer': ''},\n",
" {'answer': 'Ant'},\n",
" {'answer': 'Anton'},\n",
" {'answer': 'Antonie'},\n",
" {'answer': 'Antonie van'},\n",
" {'answer': 'Antonie van Lee'},\n",
" {'answer': 'Antonie van Leeu'},\n",
" {'answer': 'Antonie van Leeuwen'},\n",
" {'answer': 'Antonie van Leeuwenho'},\n",
" {'answer': 'Antonie van Leeuwenhoek'}]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(json_chain.stream({\"question\": \"Who invented the microscope?\"}))"
]
},
{
"cell_type": "markdown",
"id": "3ca23082-c602-4ee8-af8c-a185b1f42bd1",
"metadata": {},
"source": [
"While the PydanticOutputParser cannot:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "07420e8f-e144-42aa-93ac-de890b6222f5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(chain.stream({\"query\": \"Tell me a joke.\"}))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/output_parser_xml.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "181b5b6d",
"metadata": {},
"source": [
"# How to parse XML output\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Structured output](/docs/how_to/structured_output)\n",
"- [Chaining runnables together](/docs/how_to/sequence/)\n",
"\n",
":::\n",
"\n",
"LLMs from different providers often have different strengths depending on the specific data they are trianed on. This also means that some may be \"better\" and more reliable at generating output in formats other than JSON.\n",
"\n",
"This guide shows you how to use the [`XMLOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html) to prompt models for XML output, then and parse that output into a usable format.\n",
"\n",
":::{.callout-note}\n",
"Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed XML.\n",
":::\n",
"\n",
"In the following examples, we use Anthropic's Claude-2 model (https://docs.anthropic.com/claude/docs), which is one such model that is optimized for XML tags."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aee0c52e",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain langchain-anthropic\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
]
},
{
"cell_type": "markdown",
"id": "da312f86-0d2a-4aef-a09d-1e72bd0ea9b1",
"metadata": {},
"source": [
"Let's start with a simple request to the model."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b03785af-69fc-40a1-a1be-c04ed6fade70",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here is the shortened filmography for Tom Hanks, with movies enclosed in XML tags:\n",
"\n",
"<movie>Splash</movie>\n",
"<movie>Big</movie>\n",
"<movie>A League of Their Own</movie>\n",
"<movie>Sleepless in Seattle</movie>\n",
"<movie>Forrest Gump</movie>\n",
"<movie>Toy Story</movie>\n",
"<movie>Apollo 13</movie>\n",
"<movie>Saving Private Ryan</movie>\n",
"<movie>Cast Away</movie>\n",
"<movie>The Da Vinci Code</movie>\n"
]
}
],
"source": [
"from langchain_anthropic import ChatAnthropic\n",
"from langchain_core.output_parsers import XMLOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"model = ChatAnthropic(model=\"claude-2.1\", max_tokens_to_sample=512, temperature=0.1)\n",
"\n",
"actor_query = \"Generate the shortened filmography for Tom Hanks.\"\n",
"\n",
"output = model.invoke(\n",
" f\"\"\"{actor_query}\n",
"Please enclose the movies in <movie></movie> tags\"\"\"\n",
")\n",
"\n",
"print(output.content)"
]
},
{
"cell_type": "markdown",
"id": "4db65781-3d54-4ba6-ae26-5b4ead47a4c8",
"metadata": {},
"source": [
"This actually worked pretty well! But it would be nice to parse that XML into a more easily usable format. We can use the `XMLOutputParser` to both add default format instructions to the prompt and parse outputted XML into a dict:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6917e057",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The output should be formatted as a XML file.\\n1. Output should conform to the tags below. \\n2. If tags are not given, make them on your own.\\n3. Remember to always open and close all the tags.\\n\\nAs an example, for the tags [\"foo\", \"bar\", \"baz\"]:\\n1. String \"<foo>\\n <bar>\\n <baz></baz>\\n </bar>\\n</foo>\" is a well-formatted instance of the schema. \\n2. String \"<foo>\\n <bar>\\n </foo>\" is a badly-formatted instance.\\n3. String \"<foo>\\n <tag>\\n </tag>\\n</foo>\" is a badly-formatted instance.\\n\\nHere are the output tags:\\n```\\nNone\\n```'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser = XMLOutputParser()\n",
"\n",
"# We will add these instructions to the prompt below\n",
"parser.get_format_instructions()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "87ba8d11",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'filmography': [{'movie': [{'title': 'Big'}, {'year': '1988'}]}, {'movie': [{'title': 'Forrest Gump'}, {'year': '1994'}]}, {'movie': [{'title': 'Toy Story'}, {'year': '1995'}]}, {'movie': [{'title': 'Saving Private Ryan'}, {'year': '1998'}]}, {'movie': [{'title': 'Cast Away'}, {'year': '2000'}]}]}\n"
]
}
],
"source": [
"prompt = PromptTemplate(\n",
" template=\"\"\"{query}\\n{format_instructions}\"\"\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
")\n",
"\n",
"chain = prompt | model | parser\n",
"\n",
"output = chain.invoke({\"query\": actor_query})\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"id": "327f5479-77e0-4549-8393-2cd7a286d491",
"metadata": {},
"source": [
"We can also add some tags to tailor the output to our needs. You can and should experiment with adding your own formatting hints in the other parts of your prompt to either augment or replace the default instructions:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4af50494",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The output should be formatted as a XML file.\\n1. Output should conform to the tags below. \\n2. If tags are not given, make them on your own.\\n3. Remember to always open and close all the tags.\\n\\nAs an example, for the tags [\"foo\", \"bar\", \"baz\"]:\\n1. String \"<foo>\\n <bar>\\n <baz></baz>\\n </bar>\\n</foo>\" is a well-formatted instance of the schema. \\n2. String \"<foo>\\n <bar>\\n </foo>\" is a badly-formatted instance.\\n3. String \"<foo>\\n <tag>\\n </tag>\\n</foo>\" is a badly-formatted instance.\\n\\nHere are the output tags:\\n```\\n[\\'movies\\', \\'actor\\', \\'film\\', \\'name\\', \\'genre\\']\\n```'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser = XMLOutputParser(tags=[\"movies\", \"actor\", \"film\", \"name\", \"genre\"])\n",
"\n",
"# We will add these instructions to the prompt below\n",
"parser.get_format_instructions()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b722a235",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'movies': [{'actor': [{'name': 'Tom Hanks'}, {'film': [{'name': 'Forrest Gump'}, {'genre': 'Drama'}]}, {'film': [{'name': 'Cast Away'}, {'genre': 'Adventure'}]}, {'film': [{'name': 'Saving Private Ryan'}, {'genre': 'War'}]}]}]}\n"
]
}
],
"source": [
"prompt = PromptTemplate(\n",
" template=\"\"\"{query}\\n{format_instructions}\"\"\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
")\n",
"\n",
"\n",
"chain = prompt | model | parser\n",
"\n",
"output = chain.invoke({\"query\": actor_query})\n",
"\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"id": "61ab269a",
"metadata": {},
"source": [
"This output parser also supports streaming of partial chunks. Here's an example:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "808a5df5-b11e-42a0-bd7a-6b95ca0c3eba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'movies': [{'actor': [{'name': 'Tom Hanks'}]}]}\n",
"{'movies': [{'actor': [{'film': [{'name': 'Forrest Gump'}]}]}]}\n",
"{'movies': [{'actor': [{'film': [{'genre': 'Drama'}]}]}]}\n",
"{'movies': [{'actor': [{'film': [{'name': 'Cast Away'}]}]}]}\n",
"{'movies': [{'actor': [{'film': [{'genre': 'Adventure'}]}]}]}\n",
"{'movies': [{'actor': [{'film': [{'name': 'Saving Private Ryan'}]}]}]}\n",
"{'movies': [{'actor': [{'film': [{'genre': 'War'}]}]}]}\n"
]
}
],
"source": [
"for s in chain.stream({\"query\": actor_query}):\n",
" print(s)"
]
},
{
"cell_type": "markdown",
"id": "6902fe6f",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You've now learned how to prompt a model to return XML. Next, check out the [broader guide on obtaining structured output](/docs/how_to/structured_output) for other related techniques."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/output_parser_yaml.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "72b1b316",
"metadata": {},
"source": [
"# How to parse YAML output\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Structured output](/docs/how_to/structured_output)\n",
"- [Chaining runnables together](/docs/how_to/sequence/)\n",
"\n",
":::\n",
"\n",
"LLMs from different providers often have different strengths depending on the specific data they are trianed on. This also means that some may be \"better\" and more reliable at generating output in formats other than JSON.\n",
"\n",
"This output parser allows users to specify an arbitrary schema and query LLMs for outputs that conform to that schema, using YAML to format their response.\n",
"\n",
":::{.callout-note}\n",
"Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed YAML.\n",
":::\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f142c8ca",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain langchain-openai\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()"
]
},
{
"cell_type": "markdown",
"id": "cc479f3a",
"metadata": {},
"source": [
"We use [Pydantic](https://docs.pydantic.dev) with the [`YamlOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser) to declare our data model and give the model more context as to what type of YAML it should generate:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4ccf45a3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Joke(setup=\"Why couldn't the bicycle find its way home?\", punchline='Because it lost its bearings!')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.output_parsers import YamlOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"\n",
"# Define your desired data structure.\n",
"class Joke(BaseModel):\n",
" setup: str = Field(description=\"question to set up a joke\")\n",
" punchline: str = Field(description=\"answer to resolve the joke\")\n",
"\n",
"\n",
"model = ChatOpenAI(temperature=0)\n",
"\n",
"# And a query intented to prompt a language model to populate the data structure.\n",
"joke_query = \"Tell me a joke.\"\n",
"\n",
"# Set up a parser + inject instructions into the prompt template.\n",
"parser = YamlOutputParser(pydantic_object=Joke)\n",
"\n",
"prompt = PromptTemplate(\n",
" template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
")\n",
"\n",
"chain = prompt | model | parser\n",
"\n",
"chain.invoke({\"query\": joke_query})"
]
},
{
"cell_type": "markdown",
"id": "25e2254a",
"metadata": {},
"source": [
"The parser will automatically parse the output YAML and create a Pydantic model with the data. We can see the parser's `format_instructions`, which get added to the prompt:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a4d12261",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The output should be formatted as a YAML instance that conforms to the given JSON schema below.\\n\\n# Examples\\n## Schema\\n```\\n{\"title\": \"Players\", \"description\": \"A list of players\", \"type\": \"array\", \"items\": {\"$ref\": \"#/definitions/Player\"}, \"definitions\": {\"Player\": {\"title\": \"Player\", \"type\": \"object\", \"properties\": {\"name\": {\"title\": \"Name\", \"description\": \"Player name\", \"type\": \"string\"}, \"avg\": {\"title\": \"Avg\", \"description\": \"Batting average\", \"type\": \"number\"}}, \"required\": [\"name\", \"avg\"]}}}\\n```\\n## Well formatted instance\\n```\\n- name: John Doe\\n avg: 0.3\\n- name: Jane Maxfield\\n avg: 1.4\\n```\\n\\n## Schema\\n```\\n{\"properties\": {\"habit\": { \"description\": \"A common daily habit\", \"type\": \"string\" }, \"sustainable_alternative\": { \"description\": \"An environmentally friendly alternative to the habit\", \"type\": \"string\"}}, \"required\": [\"habit\", \"sustainable_alternative\"]}\\n```\\n## Well formatted instance\\n```\\nhabit: Using disposable water bottles for daily hydration.\\nsustainable_alternative: Switch to a reusable water bottle to reduce plastic waste and decrease your environmental footprint.\\n``` \\n\\nPlease follow the standard YAML formatting conventions with an indent of 2 spaces and make sure that the data types adhere strictly to the following JSON schema: \\n```\\n{\"properties\": {\"setup\": {\"title\": \"Setup\", \"description\": \"question to set up a joke\", \"type\": \"string\"}, \"punchline\": {\"title\": \"Punchline\", \"description\": \"answer to resolve the joke\", \"type\": \"string\"}}, \"required\": [\"setup\", \"punchline\"]}\\n```\\n\\nMake sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parser.get_format_instructions()"
]
},
{
"cell_type": "markdown",
"id": "b31ac9ce",
"metadata": {},
"source": [
"You can and should experiment with adding your own formatting hints in the other parts of your prompt to either augment or replace the default instructions.\n",
"\n",
"## Next steps\n",
"\n",
"You've now learned how to prompt a model to return XML. Next, check out the [broader guide on obtaining structured output](/docs/how_to/structured_output) for other related techniques."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "666ba894",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/parallel.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "e2596041-9b76-4e74-836f-e6235086bbf0",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 1\n",
"keywords: [RunnableParallel, RunnableMap, LCEL]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "b022ab74-794d-4c54-ad47-ff9549ddb9d2",
"metadata": {},
"source": [
"# How to invoke runnables in parallel\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [Chaining runnables](/docs/how_to/sequence)\n",
"\n",
":::\n",
"\n",
"The [`RunnableParallel`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableParallel.html) primitive is essentially a dict whose values are runnables (or things that can be coerced to runnables, like functions). It runs all of its values in parallel, and each value is called with the overall input of the `RunnableParallel`. The final return value is a dict with the results of each value under its appropriate key.\n",
"\n",
"## Formatting with `RunnableParallels`\n",
"\n",
"`RunnableParallels` are useful for parallelizing operations, but can also be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence. You can use them to split or fork the chain so that multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:\n",
"\n",
"```text\n",
" Input\n",
" / \\\n",
" / \\\n",
" Branch1 Branch2\n",
" \\ /\n",
" \\ /\n",
" Combine\n",
"```\n",
"\n",
"Below, the input to prompt is expected to be a map with keys `\"context\"` and `\"question\"`. The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the `\"question\"` key.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2627ffd7",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"%pip install -qU langchain langchain_openai\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Harrison worked at Kensho.'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"\n",
"vectorstore = FAISS.from_texts(\n",
" [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
")\n",
"retriever = vectorstore.as_retriever()\n",
"template = \"\"\"Answer the question based only on the following context:\n",
"{context}\n",
"\n",
"Question: {question}\n",
"\"\"\"\n",
"\n",
"# The prompt expects input with keys for \"context\" and \"question\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"model = ChatOpenAI()\n",
"\n",
"retrieval_chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")\n",
"\n",
"retrieval_chain.invoke(\"where did harrison work?\")"
]
},
{
"cell_type": "markdown",
"id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
"metadata": {},
"source": [
"::: {.callout-tip}\n",
"Note that when composing a RunnableParallel with another Runnable we don't even need to wrap our dictionary in the RunnableParallel class — the type conversion is handled for us. In the context of a chain, these are equivalent:\n",
":::\n",
"\n",
"```\n",
"{\"context\": retriever, \"question\": RunnablePassthrough()}\n",
"```\n",
"\n",
"```\n",
"RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
"```\n",
"\n",
"```\n",
"RunnableParallel(context=retriever, question=RunnablePassthrough())\n",
"```\n",
"\n",
"See the section on [coercion for more](/docs/how_to/sequence/#coercion)."
]
},
{
"cell_type": "markdown",
"id": "7c1b8baa-3a80-44f0-bb79-d22f79815d3d",
"metadata": {},
"source": [
"## Using itemgetter as shorthand\n",
"\n",
"Note that you can use Python's `itemgetter` as shorthand to extract data from the map when combining with `RunnableParallel`. You can find more information about itemgetter in the [Python Documentation](https://docs.python.org/3/library/operator.html#operator.itemgetter). \n",
"\n",
"In the example below, we use itemgetter to extract specific keys from the map:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "84fc49e1-2daf-4700-ae33-a0a6ed47d5f6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Harrison ha lavorato a Kensho.'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from operator import itemgetter\n",
"\n",
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"\n",
"vectorstore = FAISS.from_texts(\n",
" [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
")\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"template = \"\"\"Answer the question based only on the following context:\n",
"{context}\n",
"\n",
"Question: {question}\n",
"\n",
"Answer in the following language: {language}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"chain = (\n",
" {\n",
" \"context\": itemgetter(\"question\") | retriever,\n",
" \"question\": itemgetter(\"question\"),\n",
" \"language\": itemgetter(\"language\"),\n",
" }\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")\n",
"\n",
"chain.invoke({\"question\": \"where did harrison work\", \"language\": \"italian\"})"
]
},
{
"cell_type": "markdown",
"id": "bc2f9847-39aa-4fe4-9049-3a8969bc4bce",
"metadata": {},
"source": [
"## Parallelize steps\n",
"\n",
"RunnableParallels make it easy to execute multiple Runnables in parallel, and to return the output of these Runnables as a map."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "31f18442-f837-463f-bef4-8729368f5f8b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'joke': AIMessage(content=\"Why don't bears like fast food? Because they can't catch it!\", response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 13, 'total_tokens': 28}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'stop', 'logprobs': None}, id='run-fe024170-c251-4b7a-bfd4-64a3737c67f2-0'),\n",
" 'poem': AIMessage(content='In the quiet of the forest, the bear roams free\\nMajestic and wild, a sight to see.', response_metadata={'token_usage': {'completion_tokens': 24, 'prompt_tokens': 15, 'total_tokens': 39}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-2707913e-a743-4101-b6ec-840df4568a76-0')}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnableParallel\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI()\n",
"joke_chain = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
"poem_chain = (\n",
" ChatPromptTemplate.from_template(\"write a 2-line poem about {topic}\") | model\n",
")\n",
"\n",
"map_chain = RunnableParallel(joke=joke_chain, poem=poem_chain)\n",
"\n",
"map_chain.invoke({\"topic\": \"bear\"})"
]
},
{
"cell_type": "markdown",
"id": "833da249-c0d4-4e5b-b3f8-cab549f0f7e1",
"metadata": {},
"source": [
"## Parallelism\n",
"\n",
"RunnableParallel are also useful for running independent processes in parallel, since each Runnable in the map is executed in parallel. For example, we can see our earlier `joke_chain`, `poem_chain` and `map_chain` all have about the same runtime, even though `map_chain` executes both of the other two."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "38e47834-45af-4281-991f-86f150001510",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"610 ms ± 64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%%timeit\n",
"\n",
"joke_chain.invoke({\"topic\": \"bear\"})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d0cd40de-b37e-41fa-a2f6-8aaa49f368d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"599 ms ± 73.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%%timeit\n",
"\n",
"poem_chain.invoke({\"topic\": \"bear\"})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "799894e1-8e18-4a73-b466-f6aea6af3920",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"643 ms ± 77.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%%timeit\n",
"\n",
"map_chain.invoke({\"topic\": \"bear\"})"
]
},
{
"cell_type": "markdown",
"id": "7d4492e1",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You now know some ways to format and parallelize chain steps with `RunnableParallel`.\n",
"\n",
"To learn more, see the other how-to guides on runnables in this section."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4af8bebd",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/parent_document_retriever.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "34883374",
"metadata": {},
"source": [
"# How to use the Parent Document Retriever\n",
"\n",
"When splitting documents for retrieval, there are often conflicting desires:\n",
"\n",
"1. You may want to have small documents, so that their embeddings can most\n",
" accurately reflect their meaning. If too long, then the embeddings can\n",
" lose meaning.\n",
"2. You want to have long enough documents that the context of each chunk is\n",
" retained.\n",
"\n",
"The `ParentDocumentRetriever` strikes that balance by splitting and storing\n",
"small chunks of data. During retrieval, it first fetches the small chunks\n",
"but then looks up the parent ids for those chunks and returns those larger\n",
"documents.\n",
"\n",
"Note that \"parent document\" refers to the document that a small chunk\n",
"originated from. This can either be the whole raw document OR a larger\n",
"chunk."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8b6e74b2",
"metadata": {},
"outputs": [],
"source": [
"from langchain.retrievers import ParentDocumentRetriever"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1d17af96",
"metadata": {},
"outputs": [],
"source": [
"from langchain.storage import InMemoryStore\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.document_loaders import TextLoader\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "604ff981",
"metadata": {},
"outputs": [],
"source": [
"loaders = [\n",
" TextLoader(\"paul_graham_essay.txt\"),\n",
" TextLoader(\"state_of_the_union.txt\"),\n",
"]\n",
"docs = []\n",
"for loader in loaders:\n",
" docs.extend(loader.load())"
]
},
{
"cell_type": "markdown",
"id": "d3943f72",
"metadata": {},
"source": [
"## Retrieving full documents\n",
"\n",
"In this mode, we want to retrieve the full documents. Therefore, we only specify a child splitter."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1a8b2e5f",
"metadata": {},
"outputs": [],
"source": [
"# This text splitter is used to create the child documents\n",
"child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
" collection_name=\"full_documents\", embedding_function=OpenAIEmbeddings()\n",
")\n",
"# The storage layer for the parent documents\n",
"store = InMemoryStore()\n",
"retriever = ParentDocumentRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" child_splitter=child_splitter,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2b107935",
"metadata": {},
"outputs": [],
"source": [
"retriever.add_documents(docs, ids=None)"
]
},
{
"cell_type": "markdown",
"id": "d05b97b7",
"metadata": {},
"source": [
"This should yield two keys, because we added two documents."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "30e3812b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['9a63376c-58cc-42c9-b0f7-61f0e1a3a688',\n",
" '40091598-e918-4a18-9be0-f46413a95ae4']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(store.yield_keys())"
]
},
{
"cell_type": "markdown",
"id": "f895d62b",
"metadata": {},
"source": [
"Let's now call the vector store search functionality - we should see that it returns small chunks (since we're storing the small chunks)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b261c02c",
"metadata": {},
"outputs": [],
"source": [
"sub_docs = vectorstore.similarity_search(\"justice breyer\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5108222f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
"\n",
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.\n"
]
}
],
"source": [
"print(sub_docs[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "bda8ed5a",
"metadata": {},
"source": [
"Let's now retrieve from the overall retriever. This should return large documents - since it returns the documents where the smaller chunks are located."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "419a91c4",
"metadata": {},
"outputs": [],
"source": [
"retrieved_docs = retriever.invoke(\"justice breyer\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cf10d250",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38540"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(retrieved_docs[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "14f813a5",
"metadata": {},
"source": [
"## Retrieving larger chunks\n",
"\n",
"Sometimes, the full documents can be too big to want to retrieve them as is. In that case, what we really want to do is to first split the raw documents into larger chunks, and then split it into smaller chunks. We then index the smaller chunks, but on retrieval we retrieve the larger chunks (but still not the full documents)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b6f9a4f0",
"metadata": {},
"outputs": [],
"source": [
"# This text splitter is used to create the parent documents\n",
"parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)\n",
"# This text splitter is used to create the child documents\n",
"# It should create documents smaller than the parent\n",
"child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
" collection_name=\"split_parents\", embedding_function=OpenAIEmbeddings()\n",
")\n",
"# The storage layer for the parent documents\n",
"store = InMemoryStore()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "19478ff3",
"metadata": {},
"outputs": [],
"source": [
"retriever = ParentDocumentRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" child_splitter=child_splitter,\n",
" parent_splitter=parent_splitter,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "fe16e620",
"metadata": {},
"outputs": [],
"source": [
"retriever.add_documents(docs)"
]
},
{
"cell_type": "markdown",
"id": "64ad3c8c",
"metadata": {},
"source": [
"We can see that there are much more than two documents now - these are the larger chunks."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "24d81886",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"66"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(list(store.yield_keys()))"
]
},
{
"cell_type": "markdown",
"id": "baaef673",
"metadata": {},
"source": [
"Let's make sure the underlying vector store still retrieves the small chunks."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b1c859de",
"metadata": {},
"outputs": [],
"source": [
"sub_docs = vectorstore.similarity_search(\"justice breyer\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6fffa2eb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
"\n",
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.\n"
]
}
],
"source": [
"print(sub_docs[0].page_content)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3a3202df",
"metadata": {},
"outputs": [],
"source": [
"retrieved_docs = retriever.invoke(\"justice breyer\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "684fdb2c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1849"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(retrieved_docs[0].page_content)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "9f17f662",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n",
"\n",
"We cannot let this happen. \n",
"\n",
"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
"\n",
"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
"\n",
"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
"\n",
"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n",
"\n",
"A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
"\n",
"And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
"\n",
"We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n",
"\n",
"We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n",
"\n",
"We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
"\n",
"We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n"
]
}
],
"source": [
"print(retrieved_docs[0].page_content)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/passthrough.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "d35de667-0352-4bfb-a890-cebe7f676fe7",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 5\n",
"keywords: [RunnablePassthrough, LCEL]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "b022ab74-794d-4c54-ad47-ff9549ddb9d2",
"metadata": {},
"source": [
"# How to pass through arguments from one step to the next\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Calling runnables in parallel](/docs/how_to/parallel/)\n",
"- [Custom functions](/docs/how_to/functions/)\n",
"\n",
":::\n",
"\n",
"\n",
"When composing chains with several steps, sometimes you will want to pass data from previous steps unchanged for use as input to a later step. The [`RunnablePassthrough`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html) class allows you to do just this, and is typically is used in conjuction with a [RunnableParallel](/docs/how_to/parallel/) to pass data through to a later step in your constructed chains.\n",
"\n",
"See the example below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e169b952",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain langchain-openai\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "03988b8d-d54c-4492-8707-1594372cf093",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'passed': {'num': 1}, 'modified': 2}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
"\n",
"runnable = RunnableParallel(\n",
" passed=RunnablePassthrough(),\n",
" modified=lambda x: x[\"num\"] + 1,\n",
")\n",
"\n",
"runnable.invoke({\"num\": 1})"
]
},
{
"cell_type": "markdown",
"id": "702c7acc-cd31-4037-9489-647df192fd7c",
"metadata": {},
"source": [
"As seen above, `passed` key was called with `RunnablePassthrough()` and so it simply passed on `{'num': 1}`. \n",
"\n",
"We also set a second key in the map with `modified`. This uses a lambda to set a single value adding 1 to the num, which resulted in `modified` key with the value of `2`."
]
},
{
"cell_type": "markdown",
"id": "15187a3b-d666-4b9b-a258-672fc51fe0e2",
"metadata": {},
"source": [
"## Retrieval Example\n",
"\n",
"In the example below, we see a more real-world use case where we use `RunnablePassthrough` along with `RunnableParallel` in a chain to properly format inputs to a prompt:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Harrison worked at Kensho.'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"\n",
"vectorstore = FAISS.from_texts(\n",
" [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
")\n",
"retriever = vectorstore.as_retriever()\n",
"template = \"\"\"Answer the question based only on the following context:\n",
"{context}\n",
"\n",
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"model = ChatOpenAI()\n",
"\n",
"retrieval_chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")\n",
"\n",
"retrieval_chain.invoke(\"where did harrison work?\")"
]
},
{
"cell_type": "markdown",
"id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
"metadata": {},
"source": [
"Here the input to prompt is expected to be a map with keys \"context\" and \"question\". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the \"question\" key. The `RunnablePassthrough` allows us to pass on the user's question to the prompt and model. \n",
"\n",
"## Next steps\n",
"\n",
"Now you've learned how to pass data through your chains to help to help format the data flowing through your chains.\n",
"\n",
"To learn more, see the other how-to guides on runnables in this section."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/prompts_composition.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "02a1c8fb",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 5\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "4de4e022",
"metadata": {},
"source": [
"# How to compose prompts together\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"\n",
":::\n",
"\n",
"LangChain provides a user friendly interface for composing different parts of prompts together. You can do this with either string prompts or chat prompts. Constructing prompts this way allows for easy reuse of components."
]
},
{
"cell_type": "markdown",
"id": "c3190650",
"metadata": {},
"source": [
"## String prompt composition\n",
"\n",
"When working with string prompts, each template is joined together. You can work with either prompts directly or strings (the first element in the list needs to be a prompt)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "69b17f05",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PromptTemplate(input_variables=['language', 'topic'], template='Tell me a joke about {topic}, make it funny\\n\\nand in {language}')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"prompt = (\n",
" PromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
" + \", make it funny\"\n",
" + \"\\n\\nand in {language}\"\n",
")\n",
"\n",
"prompt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dbba24ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Tell me a joke about sports, make it funny\\n\\nand in spanish'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt.format(topic=\"sports\", language=\"spanish\")"
]
},
{
"cell_type": "markdown",
"id": "4e4f6a8a",
"metadata": {},
"source": [
"## Chat prompt composition"
]
},
{
"cell_type": "markdown",
"id": "8554bae5",
"metadata": {},
"source": [
"A chat prompt is made up a of a list of messages. Similarly to the above example, we can concatenate chat prompt templates. Each new element is a new message in the final prompt.\n",
"\n",
"First, let's initialize the a [`ChatPromptTemplate`](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html) with a [`SystemMessage`](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.system.SystemMessage.html)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cab8dd65",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.messages import AIMessage, HumanMessage, SystemMessage\n",
"\n",
"prompt = SystemMessage(content=\"You are a nice pirate\")"
]
},
{
"cell_type": "markdown",
"id": "30656ef8",
"metadata": {},
"source": [
"You can then easily create a pipeline combining it with other messages *or* message templates.\n",
"Use a `Message` when there is no variables to be formatted, use a `MessageTemplate` when there are variables to be formatted. You can also use just a string (note: this will automatically get inferred as a [`HumanMessagePromptTemplate`](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.HumanMessagePromptTemplate.html).)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a2ddd0a1",
"metadata": {},
"outputs": [],
"source": [
"new_prompt = (\n",
" prompt + HumanMessage(content=\"hi\") + AIMessage(content=\"what?\") + \"{input}\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "72294e1b",
"metadata": {},
"source": [
"Under the hood, this creates an instance of the ChatPromptTemplate class, so you can use it just as you did before!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "297932de",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[SystemMessage(content='You are a nice pirate'),\n",
" HumanMessage(content='hi'),\n",
" AIMessage(content='what?'),\n",
" HumanMessage(content='i said hi')]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_prompt.format_messages(input=\"i said hi\")"
]
},
{
"cell_type": "markdown",
"id": "0e1d47e3-b05a-4aef-a58c-3057fa628c1c",
"metadata": {},
"source": [
"## Using PipelinePrompt"
]
},
{
"cell_type": "markdown",
"id": "0a5892f9-e4d8-4b7c-b6a5-4651539b9734",
"metadata": {},
"source": [
"LangChain includes a class called [`PipelinePromptTemplate`](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.pipeline.PipelinePromptTemplate.html), which can be useful when you want to reuse parts of prompts. A PipelinePrompt consists of two main parts:\n",
"\n",
"- Final prompt: The final prompt that is returned\n",
"- Pipeline prompts: A list of tuples, consisting of a string name and a prompt template. Each prompt template will be formatted and then passed to future prompt templates as a variable with the same name."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4face631-74d7-49ca-93b1-1e6e66fa58e2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['person', 'example_a', 'example_q', 'input']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.prompts import PipelinePromptTemplate, PromptTemplate\n",
"\n",
"full_template = \"\"\"{introduction}\n",
"\n",
"{example}\n",
"\n",
"{start}\"\"\"\n",
"full_prompt = PromptTemplate.from_template(full_template)\n",
"\n",
"introduction_template = \"\"\"You are impersonating {person}.\"\"\"\n",
"introduction_prompt = PromptTemplate.from_template(introduction_template)\n",
"\n",
"example_template = \"\"\"Here's an example of an interaction:\n",
"\n",
"Q: {example_q}\n",
"A: {example_a}\"\"\"\n",
"example_prompt = PromptTemplate.from_template(example_template)\n",
"\n",
"start_template = \"\"\"Now, do this for real!\n",
"\n",
"Q: {input}\n",
"A:\"\"\"\n",
"start_prompt = PromptTemplate.from_template(start_template)\n",
"\n",
"input_prompts = [\n",
" (\"introduction\", introduction_prompt),\n",
" (\"example\", example_prompt),\n",
" (\"start\", start_prompt),\n",
"]\n",
"pipeline_prompt = PipelinePromptTemplate(\n",
" final_prompt=full_prompt, pipeline_prompts=input_prompts\n",
")\n",
"\n",
"pipeline_prompt.input_variables"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c6cabb16-ea30-4de0-8548-dcce84df8421",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are impersonating Elon Musk.\n",
"\n",
"Here's an example of an interaction:\n",
"\n",
"Q: What's your favorite car?\n",
"A: Tesla\n",
"\n",
"Now, do this for real!\n",
"\n",
"Q: What's your favorite social media site?\n",
"A:\n"
]
}
],
"source": [
"print(\n",
" pipeline_prompt.format(\n",
" person=\"Elon Musk\",\n",
" example_q=\"What's your favorite car?\",\n",
" example_a=\"Tesla\",\n",
" input=\"What's your favorite social media site?\",\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "96922030",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You've now learned how to compose prompts together.\n",
"\n",
"Next, check out the other how-to guides on prompt templates in this section, like [adding few-shot examples to your prompt templates](/docs/how_to/few_shot_examples_chat)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/prompts_partial.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "45e0127d",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 4\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "d8ca736e",
"metadata": {},
"source": [
"# How to partially format prompt templates\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"\n",
":::\n",
"\n",
"Like partially binding arguments to a function, it can make sense to \"partial\" a prompt template - e.g. pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values.\n",
"\n",
"LangChain supports this in two ways:\n",
"\n",
"1. Partial formatting with string values.\n",
"2. Partial formatting with functions that return string values.\n",
"\n",
"In the examples below, we go over the motivations for both use cases as well as how to do it in LangChain.\n",
"\n",
"## Partial with strings\n",
"\n",
"One common use case for wanting to partial a prompt template is if you get access to some of the variables in a prompt before others. For example, suppose you have a prompt template that requires two variables, `foo` and `baz`. If you get the `foo` value early on in your chain, but the `baz` value later, it can be inconvenient to pass both variables all the way through the chain. Instead, you can partial the prompt template with the `foo` value, and then pass the partialed prompt template along and just use that. Below is an example of doing this:\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5f1942bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"foobaz\n"
]
}
],
"source": [
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"prompt = PromptTemplate.from_template(\"{foo}{bar}\")\n",
"partial_prompt = prompt.partial(foo=\"foo\")\n",
"print(partial_prompt.format(bar=\"baz\"))"
]
},
{
"cell_type": "markdown",
"id": "79af4cea",
"metadata": {},
"source": [
"You can also just initialize the prompt with the partialed variables.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "572fa26f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"foobaz\n"
]
}
],
"source": [
"prompt = PromptTemplate(\n",
" template=\"{foo}{bar}\", input_variables=[\"bar\"], partial_variables={\"foo\": \"foo\"}\n",
")\n",
"print(prompt.format(bar=\"baz\"))"
]
},
{
"cell_type": "markdown",
"id": "ab12d50d",
"metadata": {},
"source": [
"## Partial with functions\n",
"\n",
"The other common use is to partial with a function. The use case for this is when you have a variable you know that you always want to fetch in a common way. A prime example of this is with date or time. Imagine you have a prompt which you always want to have the current date. You can't hard code it in the prompt, and passing it along with the other input variables is inconvenient. In this case, it's handy to be able to partial the prompt with a function that always returns the current date.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c538703a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tell me a funny joke about the day 04/21/2024, 19:43:57\n"
]
}
],
"source": [
"from datetime import datetime\n",
"\n",
"\n",
"def _get_datetime():\n",
" now = datetime.now()\n",
" return now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n",
"\n",
"\n",
"prompt = PromptTemplate(\n",
" template=\"Tell me a {adjective} joke about the day {date}\",\n",
" input_variables=[\"adjective\", \"date\"],\n",
")\n",
"partial_prompt = prompt.partial(date=_get_datetime)\n",
"print(partial_prompt.format(adjective=\"funny\"))"
]
},
{
"cell_type": "markdown",
"id": "da80290e",
"metadata": {},
"source": [
"You can also just initialize the prompt with the partialed variables, which often makes more sense in this workflow.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f86fce6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tell me a funny joke about the day 04/21/2024, 19:43:57\n"
]
}
],
"source": [
"prompt = PromptTemplate(\n",
" template=\"Tell me a {adjective} joke about the day {date}\",\n",
" input_variables=[\"adjective\"],\n",
" partial_variables={\"date\": _get_datetime},\n",
")\n",
"print(prompt.format(adjective=\"funny\"))"
]
},
{
"cell_type": "markdown",
"id": "3895b210",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You've now learned how to partially apply variables to your prompt templates.\n",
"\n",
"Next, check out the other how-to guides on prompt templates in this section, like [adding few-shot examples to your prompt templates](/docs/how_to/few_shot_examples_chat)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/pydantic_compatibility.md | # How to use LangChain with different Pydantic versions
- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/)
- Pydantic v2 and v1 are under the same package name, so both versions cannot be installed at the same time
## LangChain Pydantic migration plan
As of `langchain>=0.0.267`, LangChain will allow users to install either Pydantic V1 or V2.
* Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
* During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
User can either pin to pydantic v1, and upgrade their code in one go once LangChain has migrated to v2 internally, or they can start a partial migration to v2, but must avoid mixing v1 and v2 code for LangChain.
Below are two examples of showing how to avoid mixing pydantic v1 and v2 code in
the case of inheritance and in the case of passing objects to LangChain.
**Example 1: Extending via inheritance**
**YES**
```python
from pydantic.v1 import root_validator, validator
from langchain_core.tools import BaseTool
class CustomTool(BaseTool): # BaseTool is v1 code
x: int = Field(default=1)
def _run(*args, **kwargs):
return "hello"
@validator('x') # v1 code
@classmethod
def validate_x(cls, x: int) -> int:
return 1
CustomTool(
name='custom_tool',
description="hello",
x=1,
)
```
Mixing Pydantic v2 primitives with Pydantic v1 primitives can raise cryptic errors
**NO**
```python
from pydantic import Field, field_validator # pydantic v2
from langchain_core.tools import BaseTool
class CustomTool(BaseTool): # BaseTool is v1 code
x: int = Field(default=1)
def _run(*args, **kwargs):
return "hello"
@field_validator('x') # v2 code
@classmethod
def validate_x(cls, x: int) -> int:
return 1
CustomTool(
name='custom_tool',
description="hello",
x=1,
)
```
**Example 2: Passing objects to LangChain**
**YES**
```python
from langchain_core.tools import Tool
from pydantic.v1 import BaseModel, Field # <-- Uses v1 namespace
class CalculatorInput(BaseModel):
question: str = Field()
Tool.from_function( # <-- tool uses v1 namespace
func=lambda question: 'hello',
name="Calculator",
description="useful for when you need to answer questions about math",
args_schema=CalculatorInput
)
```
**NO**
```python
from langchain_core.tools import Tool
from pydantic import BaseModel, Field # <-- Uses v2 namespace
class CalculatorInput(BaseModel):
question: str = Field()
Tool.from_function( # <-- tool uses v1 namespace
func=lambda question: 'hello',
name="Calculator",
description="useful for when you need to answer questions about math",
args_schema=CalculatorInput
)
```
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/qa_chat_history_how_to.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "86fc5bb2-017f-434e-8cd6-53ab214a5604",
"metadata": {},
"source": [
"# How to add chat history\n",
"\n",
"In many Q&A applications we want to allow the user to have a back-and-forth conversation, meaning the application needs some sort of \"memory\" of past questions and answers, and some logic for incorporating those into its current thinking.\n",
"\n",
"In this guide we focus on **adding logic for incorporating historical messages.**\n",
"\n",
"This is largely a condensed version of the [Conversational RAG tutorial](/docs/tutorials/qa_chat_history).\n",
"\n",
"We will cover two approaches:\n",
"1. [Chains](/docs/how_to/qa_chat_history_how_to#chains), in which we always execute a retrieval step;\n",
"2. [Agents](/docs/how_to/qa_chat_history_how_to#agents), in which we give an LLM discretion over whether and how to execute a retrieval step (or multiple steps).\n",
"\n",
"For the external knowledge source, we will use the same [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng from the [RAG tutorial](/docs/tutorials/rag)."
]
},
{
"cell_type": "markdown",
"id": "487d8d79-5ee9-4aa4-9fdf-cd5f4303e099",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"### Dependencies\n",
"\n",
"We'll use OpenAI embeddings and a Chroma vector store in this walkthrough, but everything shown here works with any [Embeddings](/docs/concepts#embedding-models), and [VectorStore](/docs/concepts#vectorstores) or [Retriever](/docs/concepts#retrievers). \n",
"\n",
"We'll use the following packages:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ede7fdc0-ef31-483d-bd67-32e4b5c5d527",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install --upgrade --quiet langchain langchain-community langchain-chroma bs4"
]
},
{
"cell_type": "markdown",
"id": "51ef48de-70b6-4f43-8e0b-ab9b84c9c02a",
"metadata": {},
"source": [
"We need to set environment variable `OPENAI_API_KEY`, which can be done directly or loaded from a `.env` file like so:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "143787ca-d8e6-4dc9-8281-4374f4d71720",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"if not os.environ.get(\"OPENAI_API_KEY\"):\n",
" os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# import dotenv\n",
"\n",
"# dotenv.load_dotenv()"
]
},
{
"cell_type": "markdown",
"id": "1665e740-ce01-4f09-b9ed-516db0bd326f",
"metadata": {},
"source": [
"### LangSmith\n",
"\n",
"Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with [LangSmith](https://smith.langchain.com).\n",
"\n",
"Note that LangSmith is not needed, but it is helpful. If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "07411adb-3722-4f65-ab7f-8f6f57663d11",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"if not os.environ.get(\"LANGCHAIN_API_KEY\"):\n",
" os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "fa6ba684-26cf-4860-904e-a4d51380c134",
"metadata": {},
"source": [
"## Chains {#chains}\n",
"\n",
"In a conversational RAG application, queries issued to the retriever should be informed by the context of the conversation. LangChain provides a [create_history_aware_retriever](https://api.python.langchain.com/en/latest/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) constructor to simplify this. It constructs a chain that accepts keys `input` and `chat_history` as input, and has the same output schema as a retriever. `create_history_aware_retriever` requires as inputs: \n",
"\n",
"1. LLM;\n",
"2. Retriever;\n",
"3. Prompt.\n",
"\n",
"First we obtain these objects:\n",
"\n",
"### LLM\n",
"\n",
"We can use any supported chat model:"
]
},
{
"cell_type": "markdown",
"id": "646840fb-5212-48ea-8bc7-ec7be5ec727e",
"metadata": {},
"source": [
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cb58f273-2111-4a9b-8932-9b64c95030c8",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "6bb76a36-15b1-4589-8a3d-18c6f5fdb7e0",
"metadata": {},
"source": [
"### Retriever"
]
},
{
"cell_type": "markdown",
"id": "15f8ad59-19de-42e3-85a8-3ba95ee0bd43",
"metadata": {},
"source": [
"For the retriever, we will use [WebBaseLoader](https://api.python.langchain.com/en/latest/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `Chroma` vectorstore and then use its [.as_retriever](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/#langchain-expression-language) chains."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "820244ae-74b4-4593-b392-822979dd91b8",
"metadata": {},
"outputs": [],
"source": [
"import bs4\n",
"from langchain.chains import create_retrieval_chain\n",
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"loader = WebBaseLoader(\n",
" web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n",
" bs_kwargs=dict(\n",
" parse_only=bs4.SoupStrainer(\n",
" class_=(\"post-content\", \"post-title\", \"post-header\")\n",
" )\n",
" ),\n",
")\n",
"docs = loader.load()\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
"splits = text_splitter.split_documents(docs)\n",
"vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
"retriever = vectorstore.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "776ae958-cbdc-4471-8669-c6087436f0b5",
"metadata": {},
"source": [
"### Prompt\n",
"\n",
"We'll use a prompt that includes a `MessagesPlaceholder` variable under the name \"chat_history\". This allows us to pass in a list of Messages to the prompt using the \"chat_history\" input key, and these messages will be inserted after the system message and before the human message containing the latest question."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2b685428-8b82-4af1-be4f-7232c5d55b73",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import create_history_aware_retriever\n",
"from langchain_core.prompts import MessagesPlaceholder\n",
"\n",
"contextualize_q_system_prompt = (\n",
" \"Given a chat history and the latest user question \"\n",
" \"which might reference context in the chat history, \"\n",
" \"formulate a standalone question which can be understood \"\n",
" \"without the chat history. Do NOT answer the question, \"\n",
" \"just reformulate it if needed and otherwise return it as is.\"\n",
")\n",
"\n",
"contextualize_q_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", contextualize_q_system_prompt),\n",
" MessagesPlaceholder(\"chat_history\"),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "9d2a692e-a019-4515-9625-5b0530c3c9af",
"metadata": {},
"source": [
"### Assembling the chain\n",
"\n",
"We can then instantiate the history-aware retriever:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4c4b1695-6217-4ee8-abaf-7cc26366d988",
"metadata": {},
"outputs": [],
"source": [
"history_aware_retriever = create_history_aware_retriever(\n",
" llm, retriever, contextualize_q_prompt\n",
")"
]
},
{
"cell_type": "markdown",
"id": "42a47168-4a1f-4e39-bd2d-d5b03609a243",
"metadata": {},
"source": [
"This chain prepends a rephrasing of the input query to our retriever, so that the retrieval incorporates the context of the conversation.\n",
"\n",
"Now we can build our full QA chain.\n",
"\n",
"As in the [RAG tutorial](/docs/tutorials/rag), we will use [create_stuff_documents_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) to generate a `question_answer_chain`, with input keys `context`, `chat_history`, and `input`-- it accepts the retrieved context alongside the conversation history and query to generate an answer.\n",
"\n",
"We build our final `rag_chain` with [create_retrieval_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html). This chain applies the `history_aware_retriever` and `question_answer_chain` in sequence, retaining intermediate outputs such as the retrieved context for convenience. It has input keys `input` and `chat_history`, and includes `input`, `chat_history`, `context`, and `answer` in its output."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "afef4385-f571-4874-8f52-3d475642f579",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = (\n",
" \"You are an assistant for question-answering tasks. \"\n",
" \"Use the following pieces of retrieved context to answer \"\n",
" \"the question. If you don't know the answer, say that you \"\n",
" \"don't know. Use three sentences maximum and keep the \"\n",
" \"answer concise.\"\n",
" \"\\n\\n\"\n",
" \"{context}\"\n",
")\n",
"qa_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" MessagesPlaceholder(\"chat_history\"),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
"\n",
"rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)"
]
},
{
"cell_type": "markdown",
"id": "53a662c2-f38b-45f9-95c4-66de15637614",
"metadata": {},
"source": [
"### Adding chat history\n",
"\n",
"To manage the chat history, we will need:\n",
"\n",
"1. An object for storing the chat history;\n",
"2. An object that wraps our chain and manages updates to the chat history.\n",
"\n",
"For these we will use [BaseChatMessageHistory](https://api.python.langchain.com/en/latest/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html) and [RunnableWithMessageHistory](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html). The latter is a wrapper for an LCEL chain and a `BaseChatMessageHistory` that handles injecting chat history into inputs and updating it after each invocation.\n",
"\n",
"For a detailed walkthrough of how to use these classes together to create a stateful conversational chain, head to the [How to add message history (memory)](/docs/how_to/message_history/) LCEL how-to guide.\n",
"\n",
"Below, we implement a simple example of the second option, in which chat histories are stored in a simple dict. LangChain manages memory integrations with [Redis](/docs/integrations/memory/redis_chat_message_history/) and other technologies to provide for more robust persistence.\n",
"\n",
"Instances of `RunnableWithMessageHistory` manage the chat history for you. They accept a config with a key (`\"session_id\"` by default) that specifies what conversation history to fetch and prepend to the input, and append the output to the same conversation history. Below is an example:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9c3fb176-8d6a-4dc7-8408-6a22c5f7cc72",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.chat_message_histories import ChatMessageHistory\n",
"from langchain_core.chat_history import BaseChatMessageHistory\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"\n",
"store = {}\n",
"\n",
"\n",
"def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
" if session_id not in store:\n",
" store[session_id] = ChatMessageHistory()\n",
" return store[session_id]\n",
"\n",
"\n",
"conversational_rag_chain = RunnableWithMessageHistory(\n",
" rag_chain,\n",
" get_session_history,\n",
" input_messages_key=\"input\",\n",
" history_messages_key=\"chat_history\",\n",
" output_messages_key=\"answer\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1046c92f-21b3-4214-907d-92878d8cba23",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Task decomposition involves breaking down a complex task into smaller and simpler steps to make it more manageable and easier to accomplish. This process can be done using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Task decomposition can be facilitated by providing simple prompts to a language model, task-specific instructions, or human inputs.'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conversational_rag_chain.invoke(\n",
" {\"input\": \"What is Task Decomposition?\"},\n",
" config={\n",
" \"configurable\": {\"session_id\": \"abc123\"}\n",
" }, # constructs a key \"abc123\" in `store`.\n",
")[\"answer\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0e89c75f-7ad7-4331-a2fe-57579eb8f840",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Task decomposition can be achieved through various methods, including using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Common ways of task decomposition include providing simple prompts to a language model, task-specific instructions, or human inputs to break down complex tasks into smaller and more manageable steps. Additionally, task decomposition can involve utilizing resources like internet access for information gathering, long-term memory management, and GPT-3.5 powered agents for delegation of simple tasks.'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conversational_rag_chain.invoke(\n",
" {\"input\": \"What are common ways of doing it?\"},\n",
" config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
")[\"answer\"]"
]
},
{
"cell_type": "markdown",
"id": "3ab59258-84bc-4904-880e-2ebfebbca563",
"metadata": {},
"source": [
"The conversation history can be inspected in the `store` dict:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7686b874-3a85-499f-82b5-28a85c4c768c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User: What is Task Decomposition?\n",
"\n",
"AI: Task decomposition involves breaking down a complex task into smaller and simpler steps to make it more manageable and easier to accomplish. This process can be done using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Task decomposition can be facilitated by providing simple prompts to a language model, task-specific instructions, or human inputs.\n",
"\n",
"User: What are common ways of doing it?\n",
"\n",
"AI: Task decomposition can be achieved through various methods, including using techniques like Chain of Thought (CoT) or Tree of Thoughts to guide the model in breaking down tasks effectively. Common ways of task decomposition include providing simple prompts to a language model, task-specific instructions, or human inputs to break down complex tasks into smaller and more manageable steps. Additionally, task decomposition can involve utilizing resources like internet access for information gathering, long-term memory management, and GPT-3.5 powered agents for delegation of simple tasks.\n",
"\n"
]
}
],
"source": [
"from langchain_core.messages import AIMessage\n",
"\n",
"for message in store[\"abc123\"].messages:\n",
" if isinstance(message, AIMessage):\n",
" prefix = \"AI\"\n",
" else:\n",
" prefix = \"User\"\n",
"\n",
" print(f\"{prefix}: {message.content}\\n\")"
]
},
{
"cell_type": "markdown",
"id": "0ab1ded4-76d9-453f-9b9b-db9a4560c737",
"metadata": {},
"source": [
"### Tying it together"
]
},
{
"cell_type": "markdown",
"id": "8a08a5ea-df5b-4547-93c6-2a3940dd5c3e",
"metadata": {},
"source": [
"\n",
"\n",
"For convenience, we tie together all of the necessary steps in a single code cell:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "71c32048-1a41-465f-a9e2-c4affc332fd9",
"metadata": {},
"outputs": [],
"source": [
"import bs4\n",
"from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.chat_message_histories import ChatMessageHistory\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_core.chat_history import BaseChatMessageHistory\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"### Construct retriever ###\n",
"loader = WebBaseLoader(\n",
" web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n",
" bs_kwargs=dict(\n",
" parse_only=bs4.SoupStrainer(\n",
" class_=(\"post-content\", \"post-title\", \"post-header\")\n",
" )\n",
" ),\n",
")\n",
"docs = loader.load()\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
"splits = text_splitter.split_documents(docs)\n",
"vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"\n",
"### Contextualize question ###\n",
"contextualize_q_system_prompt = (\n",
" \"Given a chat history and the latest user question \"\n",
" \"which might reference context in the chat history, \"\n",
" \"formulate a standalone question which can be understood \"\n",
" \"without the chat history. Do NOT answer the question, \"\n",
" \"just reformulate it if needed and otherwise return it as is.\"\n",
")\n",
"contextualize_q_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", contextualize_q_system_prompt),\n",
" MessagesPlaceholder(\"chat_history\"),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"history_aware_retriever = create_history_aware_retriever(\n",
" llm, retriever, contextualize_q_prompt\n",
")\n",
"\n",
"\n",
"### Answer question ###\n",
"system_prompt = (\n",
" \"You are an assistant for question-answering tasks. \"\n",
" \"Use the following pieces of retrieved context to answer \"\n",
" \"the question. If you don't know the answer, say that you \"\n",
" \"don't know. Use three sentences maximum and keep the \"\n",
" \"answer concise.\"\n",
" \"\\n\\n\"\n",
" \"{context}\"\n",
")\n",
"qa_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" MessagesPlaceholder(\"chat_history\"),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
"\n",
"rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)\n",
"\n",
"\n",
"### Statefully manage chat history ###\n",
"store = {}\n",
"\n",
"\n",
"def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
" if session_id not in store:\n",
" store[session_id] = ChatMessageHistory()\n",
" return store[session_id]\n",
"\n",
"\n",
"conversational_rag_chain = RunnableWithMessageHistory(\n",
" rag_chain,\n",
" get_session_history,\n",
" input_messages_key=\"input\",\n",
" history_messages_key=\"chat_history\",\n",
" output_messages_key=\"answer\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6d0a7a73-d151-47d9-9e99-b4f3291c0322",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Task decomposition involves breaking down a complex task into smaller and simpler steps to make it more manageable. Techniques like Chain of Thought (CoT) and Tree of Thoughts help in decomposing hard tasks into multiple manageable tasks by instructing models to think step by step and explore multiple reasoning possibilities at each step. Task decomposition can be achieved through various methods such as using prompting techniques, task-specific instructions, or human inputs.'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conversational_rag_chain.invoke(\n",
" {\"input\": \"What is Task Decomposition?\"},\n",
" config={\n",
" \"configurable\": {\"session_id\": \"abc123\"}\n",
" }, # constructs a key \"abc123\" in `store`.\n",
")[\"answer\"]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "17021822-896a-4513-a17d-1d20b1c5381c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Task decomposition can be done in common ways such as using prompting techniques like Chain of Thought (CoT) or Tree of Thoughts, which instruct models to think step by step and explore multiple reasoning possibilities at each step. Another way is to provide task-specific instructions, such as asking to \"Write a story outline\" for writing a novel, to guide the decomposition process. Additionally, task decomposition can also involve human inputs to break down complex tasks into smaller and simpler steps.'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conversational_rag_chain.invoke(\n",
" {\"input\": \"What are common ways of doing it?\"},\n",
" config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
")[\"answer\"]"
]
},
{
"cell_type": "markdown",
"id": "861da8ed-d890-4fdc-a3bf-30433db61e0d",
"metadata": {},
"source": [
"## Agents {#agents}\n",
"\n",
"Agents leverage the reasoning capabilities of LLMs to make decisions during execution. Using agents allow you to offload some discretion over the retrieval process. Although their behavior is less predictable than chains, they offer some advantages in this context:\n",
"- Agents generate the input to the retriever directly, without necessarily needing us to explicitly build in contextualization, as we did above;\n",
"- Agents can execute multiple retrieval steps in service of a query, or refrain from executing a retrieval step altogether (e.g., in response to a generic greeting from a user).\n",
"\n",
"### Retrieval tool\n",
"\n",
"Agents can access \"tools\" and manage their execution. In this case, we will convert our retriever into a LangChain tool to be wielded by the agent:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "809cc747-2135-40a2-8e73-e4556343ee64",
"metadata": {},
"outputs": [],
"source": [
"from langchain.tools.retriever import create_retriever_tool\n",
"\n",
"tool = create_retriever_tool(\n",
" retriever,\n",
" \"blog_post_retriever\",\n",
" \"Searches and returns excerpts from the Autonomous Agents blog post.\",\n",
")\n",
"tools = [tool]"
]
},
{
"cell_type": "markdown",
"id": "f77e0217-28be-4b8b-b4c4-9cc4ed5ec201",
"metadata": {},
"source": [
"### Agent constructor\n",
"\n",
"Now that we have defined the tools and the LLM, we can create the agent. We will be using [LangGraph](/docs/concepts/#langgraph) to construct the agent. \n",
"Currently we are using a high level interface to construct the agent, but the nice thing about LangGraph is that this high-level interface is backed by a low-level, highly controllable API in case you want to modify the agent logic."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1726d151-4653-4c72-a187-a14840add526",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"agent_executor = create_react_agent(llm, tools)"
]
},
{
"cell_type": "markdown",
"id": "6d5152ca-1c3b-4f58-bb28-f31c0be7ba66",
"metadata": {},
"source": [
"We can now try it out. Note that so far it is not stateful (we still need to add in memory)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "52ae46d9-43f7-481b-96d5-df750be3ad65",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error in LangChainTracer.on_tool_end callback: TracerException(\"Found chain run at ID 5cd28d13-88dd-4eac-a465-3770ac27eff6, but expected {'tool'} run.\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_TbhPPPN05GKi36HLeaN4QM90', 'function': {'arguments': '{\"query\":\"Task Decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 68, 'total_tokens': 87}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2e60d910-879a-4a2a-b1e9-6a6c5c7d7ebc-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Task Decomposition'}, 'id': 'call_TbhPPPN05GKi36HLeaN4QM90'}])]}}\n",
"----\n",
"{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', name='blog_post_retriever', tool_call_id='call_TbhPPPN05GKi36HLeaN4QM90')]}}\n",
"----\n",
"{'agent': {'messages': [AIMessage(content='Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. This approach helps in transforming big tasks into multiple manageable tasks, making it easier for autonomous agents to handle and interpret the thinking process. One common method for task decomposition is the Chain of Thought (CoT) technique, where models are instructed to \"think step by step\" to decompose hard tasks. Another extension of CoT is the Tree of Thoughts, which explores multiple reasoning possibilities at each step by creating a tree structure of multiple thoughts per step. Task decomposition can be facilitated through various methods such as using simple prompts, task-specific instructions, or human inputs.', response_metadata={'token_usage': {'completion_tokens': 130, 'prompt_tokens': 636, 'total_tokens': 766}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-3ef17638-65df-4030-a7fe-795e6da91c69-0')]}}\n",
"----\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"\n",
"query = \"What is Task Decomposition?\"\n",
"\n",
"for s in agent_executor.stream(\n",
" {\"messages\": [HumanMessage(content=query)]},\n",
"):\n",
" print(s)\n",
" print(\"----\")"
]
},
{
"cell_type": "markdown",
"id": "1df703b1-aad6-48fb-b6fa-703e32ea88b9",
"metadata": {},
"source": [
"LangGraph comes with built in persistence, so we don't need to use ChatMessageHistory! Rather, we can pass in a checkpointer to our LangGraph agent directly.\n",
"\n",
"Distinct conversations are managed by specifying a key for a conversation thread in the config dict, as shown below."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "837a401e-9757-4d0e-a0da-24fa097d887e",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
"\n",
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
"\n",
"agent_executor = create_react_agent(llm, tools, checkpointer=memory)"
]
},
{
"cell_type": "markdown",
"id": "02026f78-338e-4d18-9f05-131e1dd59197",
"metadata": {},
"source": [
"This is all we need to construct a conversational RAG agent.\n",
"\n",
"Let's observe its behavior. Note that if we input a query that does not require a retrieval step, the agent does not execute one:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "d6d70833-b958-4cd7-9e27-29c1c08bb1b8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'agent': {'messages': [AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 67, 'total_tokens': 78}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-1cd17562-18aa-4839-b41b-403b17a0fc20-0')]}}\n",
"----\n"
]
}
],
"source": [
"config = {\"configurable\": {\"thread_id\": \"abc123\"}}\n",
"\n",
"for s in agent_executor.stream(\n",
" {\"messages\": [HumanMessage(content=\"Hi! I'm bob\")]}, config=config\n",
"):\n",
" print(s)\n",
" print(\"----\")"
]
},
{
"cell_type": "markdown",
"id": "a7928865-3dd6-4d36-abc6-2a30de770d09",
"metadata": {},
"source": [
"Further, if we input a query that does require a retrieval step, the agent generates the input to the tool:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "e2c570ae-dd91-402c-8693-ae746de63b16",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error in LangChainTracer.on_tool_end callback: TracerException(\"Found chain run at ID c54381c0-c5d9-495a-91a0-aca4ae755663, but expected {'tool'} run.\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_rg7zKTE5e0ICxVSslJ1u9LMg', 'function': {'arguments': '{\"query\":\"Task Decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 91, 'total_tokens': 110}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-122bf097-7ff1-49aa-b430-e362b51354ad-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Task Decomposition'}, 'id': 'call_rg7zKTE5e0ICxVSslJ1u9LMg'}])]}}\n",
"----\n",
"{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', name='blog_post_retriever', tool_call_id='call_rg7zKTE5e0ICxVSslJ1u9LMg')]}}\n",
"----\n",
"{'agent': {'messages': [AIMessage(content='Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. This approach helps in managing and solving intricate problems by dividing them into more manageable components. By decomposing tasks, agents or models can better understand the steps involved and plan their actions accordingly. Techniques like Chain of Thought (CoT) and Tree of Thoughts are examples of methods that enhance model performance on complex tasks by breaking them down into smaller steps.', response_metadata={'token_usage': {'completion_tokens': 87, 'prompt_tokens': 659, 'total_tokens': 746}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-b9166386-83e5-4b82-9a4b-590e5fa76671-0')]}}\n",
"----\n"
]
}
],
"source": [
"query = \"What is Task Decomposition?\"\n",
"\n",
"for s in agent_executor.stream(\n",
" {\"messages\": [HumanMessage(content=query)]}, config=config\n",
"):\n",
" print(s)\n",
" print(\"----\")"
]
},
{
"cell_type": "markdown",
"id": "26eaae33-3c4e-49fc-9fc6-db8967e25579",
"metadata": {},
"source": [
"Above, instead of inserting our query verbatim into the tool, the agent stripped unnecessary words like \"what\" and \"is\".\n",
"\n",
"This same principle allows the agent to use the context of the conversation when necessary:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "570d8c68-136e-4ba5-969a-03ba195f6118",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6kbxTU5CDWLmF9mrvR7bWSkI', 'function': {'arguments': '{\"query\":\"Common ways of task decomposition\"}', 'name': 'blog_post_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 769, 'total_tokens': 790}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2d2c8327-35cd-484a-b8fd-52436657c2d8-0', tool_calls=[{'name': 'blog_post_retriever', 'args': {'query': 'Common ways of task decomposition'}, 'id': 'call_6kbxTU5CDWLmF9mrvR7bWSkI'}])]}}\n",
"----\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error in LangChainTracer.on_tool_end callback: TracerException(\"Found chain run at ID 29553415-e0f4-41a9-8921-ba489e377f68, but expected {'tool'} run.\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'tools': {'messages': [ToolMessage(content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nFig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.\\n\\nTree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', name='blog_post_retriever', tool_call_id='call_6kbxTU5CDWLmF9mrvR7bWSkI')]}}\n",
"----\n",
"{'agent': {'messages': [AIMessage(content='Common ways of task decomposition include:\\n1. Using LLM with simple prompting like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\"\\n2. Using task-specific instructions, for example, \"Write a story outline\" for writing a novel.\\n3. Involving human inputs in the task decomposition process.', response_metadata={'token_usage': {'completion_tokens': 67, 'prompt_tokens': 1339, 'total_tokens': 1406}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9ad14cde-ca75-4238-a868-f865e0fc50dd-0')]}}\n",
"----\n"
]
}
],
"source": [
"query = \"What according to the blog post are common ways of doing it? redo the search\"\n",
"\n",
"for s in agent_executor.stream(\n",
" {\"messages\": [HumanMessage(content=query)]}, config=config\n",
"):\n",
" print(s)\n",
" print(\"----\")"
]
},
{
"cell_type": "markdown",
"id": "f2724616-c106-4e15-a61a-3077c535f692",
"metadata": {},
"source": [
"Note that the agent was able to infer that \"it\" in our query refers to \"task decomposition\", and generated a reasonable search query as a result-- in this case, \"common ways of task decomposition\"."
]
},
{
"cell_type": "markdown",
"id": "1cf87847-23bb-4672-b41c-12ad9cf81ed4",
"metadata": {},
"source": [
"### Tying it together\n",
"\n",
"For convenience, we tie together all of the necessary steps in a single code cell:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b1d2b4d4-e604-497d-873d-d345b808578e",
"metadata": {},
"outputs": [],
"source": [
"import bs4\n",
"from langchain.tools.retriever import create_retriever_tool\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
"\n",
"memory = SqliteSaver.from_conn_string(\":memory:\")\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"### Construct retriever ###\n",
"loader = WebBaseLoader(\n",
" web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n",
" bs_kwargs=dict(\n",
" parse_only=bs4.SoupStrainer(\n",
" class_=(\"post-content\", \"post-title\", \"post-header\")\n",
" )\n",
" ),\n",
")\n",
"docs = loader.load()\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
"splits = text_splitter.split_documents(docs)\n",
"vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"\n",
"### Build retriever tool ###\n",
"tool = create_retriever_tool(\n",
" retriever,\n",
" \"blog_post_retriever\",\n",
" \"Searches and returns excerpts from the Autonomous Agents blog post.\",\n",
")\n",
"tools = [tool]\n",
"\n",
"\n",
"agent_executor = create_react_agent(llm, tools, checkpointer=memory)"
]
},
{
"cell_type": "markdown",
"id": "cd6bf4f4-74f4-419d-9e26-f0ed83cf05fa",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"We've covered the steps to build a basic conversational Q&A application:\n",
"\n",
"- We used chains to build a predictable application that generates search queries for each user input;\n",
"- We used agents to build an application that \"decides\" when and how to generate search queries.\n",
"\n",
"To explore different types of retrievers and retrieval strategies, visit the [retrievers](/docs/how_to#retrievers) section of the how-to guides.\n",
"\n",
"For a detailed walkthrough of LangChain's conversation memory abstractions, visit the [How to add message history (memory)](/docs/how_to/message_history) LCEL page.\n",
"\n",
"To learn more about agents, head to the [Agents Modules](/docs/tutorials/agents)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/qa_citations.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "1b79ff35-50a3-40cd-86d9-703f1f8cd2c5",
"metadata": {},
"source": [
"# How to get a RAG application to add citations\n",
"\n",
"This guide reviews methods to get a model to cite which parts of the source documents it referenced in generating its response.\n",
"\n",
"We will cover five methods:\n",
"\n",
"1. Using tool-calling to cite document IDs;\n",
"2. Using tool-calling to cite documents IDs and provide text snippets;\n",
"3. Direct prompting;\n",
"4. Retrieval post-processing (i.e., compressing the retrieved context to make it more relevant);\n",
"5. Generation post-processing (i.e., issuing a second LLM call to annotate a generated answer with citations).\n",
"\n",
"We generally suggest using the first item of the list that works for your use-case. That is, if your model supports tool-calling, try methods 1 or 2; otherwise, or if those fail, advance down the list.\n",
"\n",
"Let's first create a simple RAG chain. To start we'll just retrieve from Wikipedia using the [WikipediaRetriever](https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html)."
]
},
{
"cell_type": "markdown",
"id": "8a70c423-f61f-4230-b70a-d3605b31afab",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"First we'll need to install some dependencies and set environment vars for the models we'll be using."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f1d26ded-e8d5-4f80-86b9-26d464869175",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain langchain-openai langchain-anthropic langchain-community wikipedia"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8732a85a-dd1a-483c-8da7-a81251276aa1",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()\n",
"\n",
"# Uncomment if you want to log to LangSmith\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "30a4401f-7feb-4bd9-9409-77c3859c4292",
"metadata": {},
"source": [
"Let's first select a LLM:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dd00165d-0b32-466d-8f75-ec26326a9e36",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4e17c3f6-8ce6-4767-b615-50a57c84c7b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m System Message \u001b[0m================================\n",
"\n",
"You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question. If none of the articles answer the question, just say you don't know.\n",
"\n",
"Here are the Wikipedia articles: \u001b[33;1m\u001b[1;3m{context}\u001b[0m\n",
"\n",
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n"
]
}
],
"source": [
"from langchain_community.retrievers import WikipediaRetriever\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"system_prompt = (\n",
" \"You're a helpful AI assistant. Given a user question \"\n",
" \"and some Wikipedia article snippets, answer the user \"\n",
" \"question. If none of the articles answer the question, \"\n",
" \"just say you don't know.\"\n",
" \"\\n\\nHere are the Wikipedia articles: \"\n",
" \"{context}\"\n",
")\n",
"\n",
"retriever = WikipediaRetriever(top_k_results=6, doc_content_chars_max=2000)\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"prompt.pretty_print()"
]
},
{
"cell_type": "markdown",
"id": "c89e2045-9244-43e6-bf3f-59af22658529",
"metadata": {},
"source": [
"Now that we've got a model, retriver and prompt, let's chain them all together. We'll need to add some logic for formatting our retrieved Documents to a string that can be passed to our prompt. Following the how-to guide on [adding citations](/docs/how_to/qa_citations) to a RAG application, we'll make it so our chain returns both the answer and the retrieved Documents."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4cd55e1c-a6b7-44b7-9dde-5f42abe714ea",
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from langchain_core.documents import Document\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"\n",
"def format_docs(docs: List[Document]):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain_from_docs = (\n",
" RunnablePassthrough.assign(context=(lambda x: format_docs(x[\"context\"])))\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"retrieve_docs = (lambda x: x[\"input\"]) | retriever\n",
"\n",
"chain = RunnablePassthrough.assign(context=retrieve_docs).assign(\n",
" answer=rag_chain_from_docs\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "42b28717-d34c-42de-b923-155ac60529a2",
"metadata": {},
"outputs": [],
"source": [
"result = chain.invoke({\"input\": \"How fast are cheetahs?\"})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8b20cf8e-dccd-45d1-aef0-25f1ad1aca6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['input', 'context', 'answer'])\n"
]
}
],
"source": [
"print(result.keys())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ae5ed9a7-c72a-480d-80c6-0a6bd38b9941",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"page_content='The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.\\nThe cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.\\nThe cheetah lives in three main social groups: females and their cubs, male \"coalitions\", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson\\'s gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned a' metadata={'title': 'Cheetah', 'summary': 'The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.\\nThe cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.\\nThe cheetah lives in three main social groups: females and their cubs, male \"coalitions\", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson\\'s gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned at around four months and are independent by around 20 months of age.\\nThe cheetah is threatened by habitat loss, conflict with humans, poaching and high susceptibility to diseases. In 2016, the global cheetah population was estimated at 7,100 individuals in the wild; it is listed as Vulnerable on the IUCN Red List. It has been widely depicted in art, literature, advertising, and animation. It was tamed in ancient Egypt and trained for hunting ungulates in the Arabian Peninsula and India. It has been kept in zoos since the early 19th century.', 'source': 'https://en.wikipedia.org/wiki/Cheetah'}\n"
]
}
],
"source": [
"print(result[\"context\"][0])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "31f20897-0a7a-44e8-aeac-75d54f6e3789",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cheetahs are capable of running at speeds of 93 to 104 km/h (58 to 65 mph). They have evolved specialized adaptations for speed, including a light build, long thin legs, and a long tail.\n"
]
}
],
"source": [
"print(result[\"answer\"])"
]
},
{
"cell_type": "markdown",
"id": "0f1f9a49-8f3f-44dd-98df-0218b5fb93a6",
"metadata": {},
"source": [
"LangSmith trace: https://smith.langchain.com/public/0472c5d1-49dc-4c1c-8100-61910067d7ed/r"
]
},
{
"cell_type": "markdown",
"id": "a7619ba1-33bd-48bf-8637-be409c94037f",
"metadata": {},
"source": [
"## Function-calling\n",
"\n",
"If your LLM of choice implements a [tool-calling](/docs/concepts#functiontool-calling) feature, you can use it to make the model specify which of the provided documents it's referencing when generating its answer. LangChain tool-calling models implement a `.with_structured_output` method which will force generation adhering to a desired schema (see for example [here](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.with_structured_output)).\n",
"\n",
"### Cite documents\n",
"\n",
"To cite documents using an identifier, we format the identifiers into the prompt, then use `.with_structured_output` to coerce the LLM to reference these identifiers in its output.\n",
"\n",
"First we define a schema for the output. The `.with_structured_output` supports multiple formats, including JSON schema and Pydantic. Here we will use Pydantic:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0af2c3a1-870c-428e-95da-0c2fd04d5616",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class CitedAnswer(BaseModel):\n",
" \"\"\"Answer the user question based only on the given sources, and cite the sources used.\"\"\"\n",
"\n",
" answer: str = Field(\n",
" ...,\n",
" description=\"The answer to the user question, which is based only on the given sources.\",\n",
" )\n",
" citations: List[int] = Field(\n",
" ...,\n",
" description=\"The integer IDs of the SPECIFIC sources which justify the answer.\",\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "68b95186-faf5-46f1-8715-ebbc38207d5d",
"metadata": {},
"source": [
"Let's see what the model output is like when we pass in our functions and a user input:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2e2b7a87-3642-4ed8-9445-684daa93b0d7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CitedAnswer(answer='Brian\\'s height is 5\\'11\".', citations=[1, 3])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"structured_llm = llm.with_structured_output(CitedAnswer)\n",
"\n",
"example_q = \"\"\"What Brian's height?\n",
"\n",
"Source: 1\n",
"Information: Suzy is 6'2\"\n",
"\n",
"Source: 2\n",
"Information: Jeremiah is blonde\n",
"\n",
"Source: 3\n",
"Information: Brian is 3 inches shorter than Suzy\"\"\"\n",
"result = structured_llm.invoke(example_q)\n",
"\n",
"result"
]
},
{
"cell_type": "markdown",
"id": "7b847b53-987e-4d3a-9621-77e613d49cfd",
"metadata": {},
"source": [
"Or as a dict:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3ee49bbd-567f-41cc-8798-d5aad0fe1cea",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'answer': 'Brian\\'s height is 5\\'11\".', 'citations': [1, 3]}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.dict()"
]
},
{
"cell_type": "markdown",
"id": "bb8bbbb5-2afc-401f-a140-648c3d2c4522",
"metadata": {},
"source": [
"Now we structure the source identifiers into the prompt to replicate with our chain. We will make three changes:\n",
"\n",
"1. Update the prompt to include source identifiers;\n",
"2. Use the `structured_llm` (i.e., `llm.with_structured_output(CitedAnswer));\n",
"3. Remove the `StrOutputParser`, to retain the Pydantic object in the output."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3cb835f3-3cf5-4144-bf6b-24558b9faf31",
"metadata": {},
"outputs": [],
"source": [
"def format_docs_with_id(docs: List[Document]) -> str:\n",
" formatted = [\n",
" f\"Source ID: {i}\\nArticle Title: {doc.metadata['title']}\\nArticle Snippet: {doc.page_content}\"\n",
" for i, doc in enumerate(docs)\n",
" ]\n",
" return \"\\n\\n\" + \"\\n\\n\".join(formatted)\n",
"\n",
"\n",
"rag_chain_from_docs = (\n",
" RunnablePassthrough.assign(context=(lambda x: format_docs_with_id(x[\"context\"])))\n",
" | prompt\n",
" | structured_llm\n",
")\n",
"\n",
"retrieve_docs = (lambda x: x[\"input\"]) | retriever\n",
"\n",
"chain = RunnablePassthrough.assign(context=retrieve_docs).assign(\n",
" answer=rag_chain_from_docs\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3e259b2f-5147-4c3c-9c26-b4eb8143e5f0",
"metadata": {},
"outputs": [],
"source": [
"result = chain.invoke({\"input\": \"How fast are cheetahs?\"})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2d8d2a01-608d-479f-85f1-eb8d14b11bc2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"answer='Cheetahs can run at speeds of 93 to 104 km/h (58 to 65 mph). They are known as the fastest land animals.' citations=[0]\n"
]
}
],
"source": [
"print(result[\"answer\"])"
]
},
{
"cell_type": "markdown",
"id": "da8341f5-a48a-4c07-8445-a313e20c36a2",
"metadata": {},
"source": [
"We can inspect the document at index 0, which the model cited:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "02d19f2b-2e15-492f-b44b-577990d15a86",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"page_content='The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.\\nThe cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.\\nThe cheetah lives in three main social groups: females and their cubs, male \"coalitions\", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson\\'s gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned a' metadata={'title': 'Cheetah', 'summary': 'The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.\\nThe cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.\\nThe cheetah lives in three main social groups: females and their cubs, male \"coalitions\", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson\\'s gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned at around four months and are independent by around 20 months of age.\\nThe cheetah is threatened by habitat loss, conflict with humans, poaching and high susceptibility to diseases. In 2016, the global cheetah population was estimated at 7,100 individuals in the wild; it is listed as Vulnerable on the IUCN Red List. It has been widely depicted in art, literature, advertising, and animation. It was tamed in ancient Egypt and trained for hunting ungulates in the Arabian Peninsula and India. It has been kept in zoos since the early 19th century.', 'source': 'https://en.wikipedia.org/wiki/Cheetah'}\n"
]
}
],
"source": [
"print(result[\"context\"][0])"
]
},
{
"cell_type": "markdown",
"id": "94f2898a-ef4d-423a-b002-910fef7a65c9",
"metadata": {},
"source": [
"LangSmith trace: https://smith.langchain.com/public/aff39dc7-3e09-4d64-8083-87026d975534/r"
]
},
{
"cell_type": "markdown",
"id": "fdbd1407-8a5b-4c35-aa2b-9d26424edb93",
"metadata": {},
"source": [
"### Cite snippets\n",
"\n",
"To return text spans (perhaps in addition to source identifiers), we can use the same approach. The only change will be to build a more complex output schema, here using Pydantic, that includes a \"quote\" alongside a source identifier.\n",
"\n",
"*Aside: Note that if we break up our documents so that we have many documents with only a sentence or two instead of a few long documents, citing documents becomes roughly equivalent to citing snippets, and may be easier for the model because the model just needs to return an identifier for each snippet instead of the actual text. Probably worth trying both approaches and evaluating.*"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "fbf708aa-e8ac-4dea-bb57-82229597e2e0",
"metadata": {},
"outputs": [],
"source": [
"class Citation(BaseModel):\n",
" source_id: int = Field(\n",
" ...,\n",
" description=\"The integer ID of a SPECIFIC source which justifies the answer.\",\n",
" )\n",
" quote: str = Field(\n",
" ...,\n",
" description=\"The VERBATIM quote from the specified source that justifies the answer.\",\n",
" )\n",
"\n",
"\n",
"class QuotedAnswer(BaseModel):\n",
" \"\"\"Answer the user question based only on the given sources, and cite the sources used.\"\"\"\n",
"\n",
" answer: str = Field(\n",
" ...,\n",
" description=\"The answer to the user question, which is based only on the given sources.\",\n",
" )\n",
" citations: List[Citation] = Field(\n",
" ..., description=\"Citations from the given sources that justify the answer.\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "beabab7b-7b6b-4eef-b874-e92d1ed8707c",
"metadata": {},
"outputs": [],
"source": [
"rag_chain_from_docs = (\n",
" RunnablePassthrough.assign(context=(lambda x: format_docs_with_id(x[\"context\"])))\n",
" | prompt\n",
" | llm.with_structured_output(QuotedAnswer)\n",
")\n",
"\n",
"retrieve_docs = (lambda x: x[\"input\"]) | retriever\n",
"\n",
"chain = RunnablePassthrough.assign(context=retrieve_docs).assign(\n",
" answer=rag_chain_from_docs\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "9709ee6d-416f-4bd3-89c6-23667b9f3cca",
"metadata": {},
"outputs": [],
"source": [
"result = chain.invoke({\"input\": \"How fast are cheetahs?\"})"
]
},
{
"cell_type": "markdown",
"id": "b42ba8c6-4214-49f5-b920-f0e028f301c2",
"metadata": {},
"source": [
"Here we see that the model has extracted a relevant snippet of text from source 0:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "56b01963-8680-4782-9c3f-384c197f0c2d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"QuotedAnswer(answer='Cheetahs can run at speeds of 93 to 104 km/h (58 to 65 mph).', citations=[Citation(source_id=0, quote='The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.')])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[\"answer\"]"
]
},
{
"cell_type": "markdown",
"id": "28676cf1-4a2e-44d2-8b2f-36303a12a371",
"metadata": {},
"source": [
"LangSmith trace: https://smith.langchain.com/public/0f638cc9-8409-4a53-9010-86ac28144129/r"
]
},
{
"cell_type": "markdown",
"id": "fb2d90a4-0370-4598-9f4b-e8e9a554346e",
"metadata": {},
"source": [
"## Direct prompting\n",
"\n",
"Many models don't support function-calling. We can achieve similar results with direct prompting. Let's try instructing a model to generate structured XML for its output:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "4e95bd8a-2f15-4e20-a1d9-225974b8d598",
"metadata": {},
"outputs": [],
"source": [
"xml_system = \"\"\"You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, \\\n",
"answer the user question and provide citations. If none of the articles answer the question, just say you don't know.\n",
"\n",
"Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that \\\n",
"justifies the answer and the ID of the quote article. Return a citation for every quote across all articles \\\n",
"that justify the answer. Use the following format for your final output:\n",
"\n",
"<cited_answer>\n",
" <answer></answer>\n",
" <citations>\n",
" <citation><source_id></source_id><quote></quote></citation>\n",
" <citation><source_id></source_id><quote></quote></citation>\n",
" ...\n",
" </citations>\n",
"</cited_answer>\n",
"\n",
"Here are the Wikipedia articles:{context}\"\"\"\n",
"xml_prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", xml_system), (\"human\", \"{input}\")]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "2d3bd0f7-e249-4bc6-bd46-6fb74ebf0118",
"metadata": {},
"source": [
"We now make similar small updates to our chain:\n",
"\n",
"1. We update the formatting function to wrap the retrieved context in XML tags;\n",
"2. We do not use `.with_structured_output` (e.g., because it does not exist for a model);\n",
"3. We use [XMLOutputParser](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html) in place of `StrOutputParser` to parse the answer into a dict."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "5861ca8c-63b7-4918-bdc6-fe4e53fe03ca",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import XMLOutputParser\n",
"\n",
"\n",
"def format_docs_xml(docs: List[Document]) -> str:\n",
" formatted = []\n",
" for i, doc in enumerate(docs):\n",
" doc_str = f\"\"\"\\\n",
" <source id=\\\"{i}\\\">\n",
" <title>{doc.metadata['title']}</title>\n",
" <article_snippet>{doc.page_content}</article_snippet>\n",
" </source>\"\"\"\n",
" formatted.append(doc_str)\n",
" return \"\\n\\n<sources>\" + \"\\n\".join(formatted) + \"</sources>\"\n",
"\n",
"\n",
"rag_chain_from_docs = (\n",
" RunnablePassthrough.assign(context=(lambda x: format_docs_xml(x[\"context\"])))\n",
" | xml_prompt\n",
" | llm\n",
" | XMLOutputParser()\n",
")\n",
"\n",
"retrieve_docs = (lambda x: x[\"input\"]) | retriever\n",
"\n",
"chain = RunnablePassthrough.assign(context=retrieve_docs).assign(\n",
" answer=rag_chain_from_docs\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "f1edb401-6027-4112-82ec-25736e8ebabd",
"metadata": {},
"outputs": [],
"source": [
"result = chain.invoke({\"input\": \"How fast are cheetahs?\"})"
]
},
{
"cell_type": "markdown",
"id": "e5264571-48c2-492d-a750-640f9fff3e71",
"metadata": {},
"source": [
"Note that citations are again structured into the answer:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "a2b4bdc9-92dd-434c-b61c-11ec44c92905",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'cited_answer': [{'answer': 'Cheetahs are capable of running at 93 to 104 km/h (58 to 65 mph).'},\n",
" {'citations': [{'citation': [{'source_id': '0'},\n",
" {'quote': 'The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.'}]}]}]}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[\"answer\"]"
]
},
{
"cell_type": "markdown",
"id": "940db8d5-8f43-44dd-9738-04fc7464baac",
"metadata": {},
"source": [
"LangSmith trace: https://smith.langchain.com/public/a3636c70-39c6-4c8f-bc83-1c7a174c237e/r"
]
},
{
"cell_type": "markdown",
"id": "9d4180b0-5d29-4bfa-85be-2a6161a872c4",
"metadata": {},
"source": [
"## Retrieval post-processing\n",
"\n",
"Another approach is to post-process our retrieved documents to compress the content, so that the source content is already minimal enough that we don't need the model to cite specific sources or spans. For example, we could break up each document into a sentence or two, embed those and keep only the most relevant ones. LangChain has some built-in components for this. Here we'll use a [RecursiveCharacterTextSplitter](https://api.python.langchain.com/en/latest/text_splitter/langchain_text_splitters.RecursiveCharacterTextSplitter.html#langchain_text_splitters.RecursiveCharacterTextSplitter), which creates chunks of a sepacified size by splitting on separator substrings, and an [EmbeddingsFilter](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html#langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter), which keeps only the texts with the most relevant embeddings.\n",
"\n",
"This approach effectively swaps our original retriever with an updated one that compresses the documents. To start, we build the retriever:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "9b14f817-4454-47b2-9eb0-2b8783a8c252",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail\n",
"\n",
"\n",
"\n",
"The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in)\n",
"\n",
"\n",
"\n",
"2 mph), or 171 body lengths per second. The cheetah, the fastest land mammal, scores at only 16 body lengths per second, while Anna's hummingbird has the highest known length-specific velocity attained by any vertebrate\n",
"\n",
"\n",
"\n",
"It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson's gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year\n",
"\n",
"\n",
"\n",
"The cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran\n",
"\n",
"\n",
"\n",
"The cheetah lives in three main social groups: females and their cubs, male \"coalitions\", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk\n",
"\n",
"\n",
"\n",
"The Southeast African cheetah (Acinonyx jubatus jubatus) is the nominate cheetah subspecies native to East and Southern Africa. The Southern African cheetah lives mainly in the lowland areas and deserts of the Kalahari, the savannahs of Okavango Delta, and the grasslands of the Transvaal region in South Africa. In Namibia, cheetahs are mostly found in farmlands\n",
"\n",
"\n",
"\n",
"Subpopulations have been called \"South African cheetah\" and \"Namibian cheetah.\"\n",
"\n",
"\n",
"\n",
"In India, four cheetahs of the subspecies are living in Kuno National Park in Madhya Pradesh after having been introduced there\n",
"\n",
"\n",
"\n",
"Acinonyx jubatus velox proposed in 1913 by Edmund Heller on basis of a cheetah that was shot by Kermit Roosevelt in June 1909 in the Kenyan highlands.\n",
"Acinonyx rex proposed in 1927 by Reginald Innes Pocock on basis of a specimen from the Umvukwe Range in Rhodesia.\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"from langchain.retrievers.document_compressors import EmbeddingsFilter\n",
"from langchain_core.runnables import RunnableParallel\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(\n",
" chunk_size=400,\n",
" chunk_overlap=0,\n",
" separators=[\"\\n\\n\", \"\\n\", \".\", \" \"],\n",
" keep_separator=False,\n",
")\n",
"compressor = EmbeddingsFilter(embeddings=OpenAIEmbeddings(), k=10)\n",
"\n",
"\n",
"def split_and_filter(input) -> List[Document]:\n",
" docs = input[\"docs\"]\n",
" question = input[\"question\"]\n",
" split_docs = splitter.split_documents(docs)\n",
" stateful_docs = compressor.compress_documents(split_docs, question)\n",
" return [stateful_doc for stateful_doc in stateful_docs]\n",
"\n",
"\n",
"new_retriever = (\n",
" RunnableParallel(question=RunnablePassthrough(), docs=retriever) | split_and_filter\n",
")\n",
"docs = new_retriever.invoke(\"How fast are cheetahs?\")\n",
"for doc in docs:\n",
" print(doc.page_content)\n",
" print(\"\\n\\n\")"
]
},
{
"cell_type": "markdown",
"id": "984bc1e1-76fb-4d84-baa9-5fa5abca9da4",
"metadata": {},
"source": [
"Next, we assemble it into our chain as before:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "fa2adb01-5d8f-484c-8216-bae35717db0d",
"metadata": {},
"outputs": [],
"source": [
"rag_chain_from_docs = (\n",
" RunnablePassthrough.assign(context=(lambda x: format_docs(x[\"context\"])))\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"chain = RunnablePassthrough.assign(\n",
" context=(lambda x: x[\"input\"]) | new_retriever\n",
").assign(answer=rag_chain_from_docs)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "1a5b72f8-135b-4604-8777-59f2ef682323",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cheetahs are capable of running at speeds between 93 to 104 km/h (58 to 65 mph), making them the fastest land animals.\n"
]
}
],
"source": [
"result = chain.invoke({\"input\": \"How fast are cheetahs?\"})\n",
"\n",
"print(result[\"answer\"])"
]
},
{
"cell_type": "markdown",
"id": "d9ac43ab-db4f-458a-9b5a-fd3e116229bd",
"metadata": {},
"source": [
"Note that the document content is now compressed, although the document objects retain the original content in a \"summary\" key in their metadata. These summaries are not passed to the model; only the condensed content is."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "80625506-8764-4adf-a467-33f465d0f51f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[\"context\"][0].page_content # passed to model"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "672c5691-5d54-4271-9d97-93571eebda91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The cheetah (Acinonyx jubatus) is a large cat and the fastest land animal. It has a tawny to creamy white or pale buff fur that is marked with evenly spaced, solid black spots. The head is small and rounded, with a short snout and black tear-like facial streaks. It reaches 67–94 cm (26–37 in) at the shoulder, and the head-and-body length is between 1.1 and 1.5 m (3 ft 7 in and 4 ft 11 in). Adults weigh between 21 and 72 kg (46 and 159 lb). The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.\\nThe cheetah was first described in the late 18th century. Four subspecies are recognised today that are native to Africa and central Iran. An African subspecies was introduced to India in 2022. It is now distributed mainly in small, fragmented populations in northwestern, eastern and southern Africa and central Iran. It lives in a variety of habitats such as savannahs in the Serengeti, arid mountain ranges in the Sahara, and hilly desert terrain.\\nThe cheetah lives in three main social groups: females and their cubs, male \"coalitions\", and solitary males. While females lead a nomadic life searching for prey in large home ranges, males are more sedentary and instead establish much smaller territories in areas with plentiful prey and access to females. The cheetah is active during the day, with peaks during dawn and dusk. It feeds on small- to medium-sized prey, mostly weighing under 40 kg (88 lb), and prefers medium-sized ungulates such as impala, springbok and Thomson\\'s gazelles. The cheetah typically stalks its prey within 60–100 m (200–330 ft) before charging towards it, trips it during the chase and bites its throat to suffocate it to death. It breeds throughout the year. After a gestation of nearly three months, females give birth to a litter of three or four cubs. Cheetah cubs are highly vulnerable to predation by other large carnivores. They are weaned at around four months and are independent by around 20 months of age.\\nThe cheetah is threatened by habitat loss, conflict with humans, poaching and high susceptibility to diseases. In 2016, the global cheetah population was estimated at 7,100 individuals in the wild; it is listed as Vulnerable on the IUCN Red List. It has been widely depicted in art, literature, advertising, and animation. It was tamed in ancient Egypt and trained for hunting ungulates in the Arabian Peninsula and India. It has been kept in zoos since the early 19th century.'"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[\"context\"][0].metadata[\"summary\"] # original document"
]
},
{
"cell_type": "markdown",
"id": "88ab8fd6-f6b4-4ba5-b022-f10cca983490",
"metadata": {},
"source": [
"LangSmith trace: https://smith.langchain.com/public/a61304fa-e5a5-4c64-a268-b0aef1130d53/r"
]
},
{
"cell_type": "markdown",
"id": "445722dc-2ecb-45a4-9d4d-c172d0a2fa7d",
"metadata": {},
"source": [
"## Generation post-processing\n",
"\n",
"Another approach is to post-process our model generation. In this example we'll first generate just an answer, and then we'll ask the model to annotate it's own answer with citations. The downside of this approach is of course that it is slower and more expensive, because two model calls need to be made.\n",
"\n",
"Let's apply this to our initial chain."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "daff5cb9-7639-4d30-b6e7-d795736a2b58",
"metadata": {},
"outputs": [],
"source": [
"class Citation(BaseModel):\n",
" source_id: int = Field(\n",
" ...,\n",
" description=\"The integer ID of a SPECIFIC source which justifies the answer.\",\n",
" )\n",
" quote: str = Field(\n",
" ...,\n",
" description=\"The VERBATIM quote from the specified source that justifies the answer.\",\n",
" )\n",
"\n",
"\n",
"class AnnotatedAnswer(BaseModel):\n",
" \"\"\"Annotate the answer to the user question with quote citations that justify the answer.\"\"\"\n",
"\n",
" citations: List[Citation] = Field(\n",
" ..., description=\"Citations from the given sources that justify the answer.\"\n",
" )\n",
"\n",
"\n",
"structured_llm = llm.with_structured_output(AnnotatedAnswer)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "6f505eb9-db02-4c49-add3-1e469844d7ca",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import MessagesPlaceholder\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" (\"human\", \"{question}\"),\n",
" MessagesPlaceholder(\"chat_history\", optional=True),\n",
" ]\n",
")\n",
"answer = prompt | llm\n",
"annotation_chain = prompt | structured_llm\n",
"\n",
"chain = (\n",
" RunnableParallel(\n",
" question=RunnablePassthrough(), docs=(lambda x: x[\"input\"]) | retriever\n",
" )\n",
" .assign(context=format)\n",
" .assign(ai_message=answer)\n",
" .assign(\n",
" chat_history=(lambda x: [x[\"ai_message\"]]),\n",
" answer=(lambda x: x[\"ai_message\"].content),\n",
" )\n",
" .assign(annotations=annotation_chain)\n",
" .pick([\"answer\", \"docs\", \"annotations\"])\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "eb11c422-09b3-4d5a-87eb-3bad2e73cf6c",
"metadata": {},
"outputs": [],
"source": [
"result = chain.invoke({\"input\": \"How fast are cheetahs?\"})"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "5b8bbc02-f753-4abc-87ec-211aac3dc3d0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cheetahs are capable of running at speeds between 93 to 104 km/h (58 to 65 mph). Their specialized adaptations for speed, such as a light build, long thin legs, and a long tail, allow them to be the fastest land animals.\n"
]
}
],
"source": [
"print(result[\"answer\"])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "c7882b76-db21-40ee-bb31-ff438880adf6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AnnotatedAnswer(citations=[Citation(source_id=0, quote='The cheetah is capable of running at 93 to 104 km/h (58 to 65 mph); it has evolved specialized adaptations for speed, including a light build, long thin legs and a long tail.')])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[\"annotations\"]"
]
},
{
"cell_type": "markdown",
"id": "803c6155-48af-40db-b4b0-1ecc5328e99b",
"metadata": {},
"source": [
"LangSmith trace: https://smith.langchain.com/public/bf5e8856-193b-4ff2-af8d-c0f4fbd1d9cb/r"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/qa_per_user.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "14d3fd06",
"metadata": {},
"source": [
"# How to do per-user retrieval\n",
"\n",
"This guide demonstrates how to configure runtime properties of a retrieval chain. An example application is to limit the documents available to a retriever based on the user.\n",
"\n",
"When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see eachother's data. This means that you need to be able to configure your retrieval chain to only retrieve certain information. This generally involves two steps.\n",
"\n",
"**Step 1: Make sure the retriever you are using supports multiple users**\n",
"\n",
"At the moment, there is no unified flag or filter for this in LangChain. Rather, each vectorstore and retriever may have their own, and may be called different things (namespaces, multi-tenancy, etc). For vectorstores, this is generally exposed as a keyword argument that is passed in during `similarity_search`. By reading the documentation or source code, figure out whether the retriever you are using supports multiple users, and, if so, how to use it.\n",
"\n",
"Note: adding documentation and/or support for multiple users for retrievers that do not support it (or document it) is a GREAT way to contribute to LangChain\n",
"\n",
"**Step 2: Add that parameter as a configurable field for the chain**\n",
"\n",
"This will let you easily call the chain and configure any relevant flags at runtime. See [this documentation](/docs/how_to/configure) for more information on configuration.\n",
"\n",
"Now, at runtime you can call this chain with configurable field.\n",
"\n",
"## Code Example\n",
"\n",
"Let's see a concrete example of what this looks like in code. We will use Pinecone for this example.\n",
"\n",
"To configure Pinecone, set the following environment variable:\n",
"\n",
"- `PINECONE_API_KEY`: Your Pinecone API key"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7345de3c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ce15571e-4e2f-44c9-98df-7e83f6f63095']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"vectorstore = PineconeVectorStore(index_name=\"test-example\", embedding=embeddings)\n",
"\n",
"vectorstore.add_texts([\"i worked at kensho\"], namespace=\"harrison\")\n",
"vectorstore.add_texts([\"i worked at facebook\"], namespace=\"ankush\")"
]
},
{
"cell_type": "markdown",
"id": "39c11920",
"metadata": {},
"source": [
"The pinecone kwarg for `namespace` can be used to separate documents"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3c2a39fa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='i worked at facebook')]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This will only get documents for Ankush\n",
"vectorstore.as_retriever(search_kwargs={\"namespace\": \"ankush\"}).get_relevant_documents(\n",
" \"where did i work?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "56393baa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='i worked at kensho')]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This will only get documents for Harrison\n",
"vectorstore.as_retriever(\n",
" search_kwargs={\"namespace\": \"harrison\"}\n",
").get_relevant_documents(\"where did i work?\")"
]
},
{
"cell_type": "markdown",
"id": "88ae97ed",
"metadata": {},
"source": [
"We can now create the chain that we will use to do question-answering over.\n",
"\n",
"Let's first select a LLM.\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "68162d05",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "markdown",
"id": "b6778ffa",
"metadata": {},
"source": [
"This is basic question-answering chain set up."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "44a865f6",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import (\n",
" ConfigurableField,\n",
" RunnablePassthrough,\n",
")\n",
"\n",
"template = \"\"\"Answer the question based only on the following context:\n",
"{context}\n",
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"retriever = vectorstore.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "72125166",
"metadata": {},
"source": [
"Here we mark the retriever as having a configurable field. All vectorstore retrievers have `search_kwargs` as a field. This is just a dictionary, with vectorstore specific fields.\n",
"\n",
"This will let us pass in a value for `search_kwargs` when invoking the chain."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "babbadff",
"metadata": {},
"outputs": [],
"source": [
"configurable_retriever = retriever.configurable_fields(\n",
" search_kwargs=ConfigurableField(\n",
" id=\"search_kwargs\",\n",
" name=\"Search Kwargs\",\n",
" description=\"The search kwargs to use\",\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "2d481b70",
"metadata": {},
"source": [
"We can now create the chain using our configurable retriever"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "210b0446",
"metadata": {},
"outputs": [],
"source": [
"chain = (\n",
" {\"context\": configurable_retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7f6458c3",
"metadata": {},
"source": [
"We can now invoke the chain with configurable options. `search_kwargs` is the id of the configurable field. The value is the search kwargs to use for Pinecone"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a38037b2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The user worked at Kensho.'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\n",
" \"where did the user work?\",\n",
" config={\"configurable\": {\"search_kwargs\": {\"namespace\": \"harrison\"}}},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0ff4f5f2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The user worked at Facebook.'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\n",
" \"where did the user work?\",\n",
" config={\"configurable\": {\"search_kwargs\": {\"namespace\": \"ankush\"}}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7fb27b941602401d91542211134fc71a",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"For more vectorstore implementations for multi-user, please refer to specific pages, such as [Milvus](/docs/integrations/vectorstores/milvus)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/qa_sources.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4ef893cf-eac1-45e6-9eb6-72e9ca043200",
"metadata": {},
"source": [
"# How to get your RAG application to return sources\n",
"\n",
"Often in Q&A applications it's important to show users the sources that were used to generate the answer. The simplest way to do this is for the chain to return the Documents that were retrieved in each generation.\n",
"\n",
"We'll work off of the Q&A app we built over the [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng in the [RAG tutorial](/docs/tutorials/rag).\n",
"\n",
"We will cover two approaches:\n",
"\n",
"1. Using the built-in [create_retrieval_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html), which returns sources by default;\n",
"2. Using a simple [LCEL](/docs/concepts#langchain-expression-language-lcel) implementation, to show the operating principle."
]
},
{
"cell_type": "markdown",
"id": "487d8d79-5ee9-4aa4-9fdf-cd5f4303e099",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"### Dependencies\n",
"\n",
"We'll use OpenAI embeddings and a Chroma vector store in this walkthrough, but everything shown here works with any [Embeddings](/docs/concepts#embedding-models), [VectorStore](/docs/concepts#vectorstores) or [Retriever](/docs/concepts#retrievers). \n",
"\n",
"We'll use the following packages:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "28d272cd-4e31-40aa-bbb4-0be0a1f49a14",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai langchain-chroma bs4"
]
},
{
"cell_type": "markdown",
"id": "51ef48de-70b6-4f43-8e0b-ab9b84c9c02a",
"metadata": {},
"source": [
"We need to set environment variable `OPENAI_API_KEY`, which can be done directly or loaded from a `.env` file like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "143787ca-d8e6-4dc9-8281-4374f4d71720",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# import dotenv\n",
"\n",
"# dotenv.load_dotenv()"
]
},
{
"cell_type": "markdown",
"id": "1665e740-ce01-4f09-b9ed-516db0bd326f",
"metadata": {},
"source": [
"### LangSmith\n",
"\n",
"Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with [LangSmith](https://smith.langchain.com).\n",
"\n",
"Note that LangSmith is not needed, but it is helpful. If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07411adb-3722-4f65-ab7f-8f6f57663d11",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "fa6ba684-26cf-4860-904e-a4d51380c134",
"metadata": {},
"source": [
"## Using `create_retrieval_chain`\n",
"\n",
"Let's first select a LLM:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5e7513b0-81e5-4477-8007-101e523f271c",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "markdown",
"id": "6b1bdfd7-8acf-4655-834d-ba7463a80fef",
"metadata": {},
"source": [
"Here is Q&A app with sources we built over the [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng in the [RAG tutorial](/docs/tutorials/rag):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "820244ae-74b4-4593-b392-822979dd91b8",
"metadata": {},
"outputs": [],
"source": [
"import bs4\n",
"from langchain.chains import create_retrieval_chain\n",
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"# 1. Load, chunk and index the contents of the blog to create a retriever.\n",
"loader = WebBaseLoader(\n",
" web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n",
" bs_kwargs=dict(\n",
" parse_only=bs4.SoupStrainer(\n",
" class_=(\"post-content\", \"post-title\", \"post-header\")\n",
" )\n",
" ),\n",
")\n",
"docs = loader.load()\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
"splits = text_splitter.split_documents(docs)\n",
"vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"\n",
"# 2. Incorporate the retriever into a question-answering chain.\n",
"system_prompt = (\n",
" \"You are an assistant for question-answering tasks. \"\n",
" \"Use the following pieces of retrieved context to answer \"\n",
" \"the question. If you don't know the answer, say that you \"\n",
" \"don't know. Use three sentences maximum and keep the \"\n",
" \"answer concise.\"\n",
" \"\\n\\n\"\n",
" \"{context}\"\n",
")\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"question_answer_chain = create_stuff_documents_chain(llm, prompt)\n",
"rag_chain = create_retrieval_chain(retriever, question_answer_chain)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0d3b0f36-7b56-49c0-8e40-a1aa9ebcbf24",
"metadata": {},
"outputs": [],
"source": [
"result = rag_chain.invoke({\"input\": \"What is Task Decomposition?\"})"
]
},
{
"cell_type": "markdown",
"id": "a8d9ac25-38bb-4ce7-ade9-b02a05ce3b27",
"metadata": {},
"source": [
"Note that `result` is a dict with keys `\"input\"`, `\"context\"`, and `\"answer\"`:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "29462727-01bc-42e7-82ed-9a0dc04b5774",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': 'What is Task Decomposition?',\n",
" 'context': [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
" Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
" Document(page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
" Document(page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\", metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})],\n",
" 'answer': 'Task decomposition involves breaking down a complex task into smaller and simpler steps. This process helps agents or models handle challenging tasks by dividing them into more manageable subtasks. Techniques like Chain of Thought and Tree of Thoughts are used to decompose tasks into multiple steps for better problem-solving.'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "markdown",
"id": "00b19e47-3e70-4a79-b458-bef55adb7517",
"metadata": {},
"source": [
"Here, `\"context\"` contains the sources that the LLM used in generating the response in `\"answer\"`."
]
},
{
"cell_type": "markdown",
"id": "1c2f99b5-80b4-4178-bf30-c1c0a152638f",
"metadata": {},
"source": [
"## Custom LCEL implementation\n",
"\n",
"Below we construct a chain similar to those built by `create_retrieval_chain`. It works by building up a dict: \n",
"\n",
"1. Starting with a dict with the input query, add the retrieved docs in the `\"context\"` key;\n",
"2. Feed both the query and context into a RAG chain and add the result to the dict."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "22ea137c-1a7a-44dd-ac73-281213979957",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': 'What is Task Decomposition',\n",
" 'context': [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
" Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
" Document(page_content='The AI assistant can parse user input to several tasks: [{\"task\": task, \"id\", task_id, \"dep\": dependency_task_ids, \"args\": {\"text\": text, \"image\": URL, \"audio\": URL, \"video\": URL}}]. The \"dep\" field denotes the id of the previous task which generates a new resource that the current task relies on. A special tag \"-task_id\" refers to the generated text image, audio and video in the dependency task with id as task_id. The task MUST be selected from the following options: {{ Available Task List }}. There is a logical relationship between tasks, please note their order. If the user input can\\'t be parsed, you need to reply empty JSON. Here are several cases for your reference: {{ Demonstrations }}. The chat history is recorded as {{ Chat History }}. From this chat history, you can find the path of the user-mentioned resources for your task planning.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),\n",
" Document(page_content='Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\\nThe system comprises of 4 stages:\\n(1) Task planning: LLM works as the brain and parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\\nInstruction:', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})],\n",
" 'answer': 'Task decomposition involves breaking down complex tasks into smaller and simpler steps to make them more manageable for autonomous agents or models. This process can be achieved by techniques like Chain of Thought (CoT) or Tree of Thoughts, which guide the model to think step by step or explore multiple reasoning possibilities at each step. Task decomposition can be done through simple prompting with language models, task-specific instructions, or human inputs.'}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"rag_chain_from_docs = (\n",
" RunnablePassthrough.assign(context=(lambda x: format_docs(x[\"context\"])))\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"\n",
"retrieve_docs = (lambda x: x[\"input\"]) | retriever\n",
"\n",
"chain = RunnablePassthrough.assign(context=retrieve_docs).assign(\n",
" answer=rag_chain_from_docs\n",
")\n",
"\n",
"chain.invoke({\"input\": \"What is Task Decomposition\"})"
]
},
{
"cell_type": "markdown",
"id": "b437da5d-ca09-4d15-9be2-c35e5a1ace77",
"metadata": {},
"source": [
":::{.callout-tip}\n",
"\n",
"Check out the [LangSmith trace](https://smith.langchain.com/public/0cb42685-e29e-4280-a503-bef2014d7ba2/r)\n",
"\n",
":::"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/qa_streaming.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4ef893cf-eac1-45e6-9eb6-72e9ca043200",
"metadata": {},
"source": [
"# How to stream results from your RAG application\n",
"\n",
"This guide explains how to stream results from a RAG application. It covers streaming tokens from the final output as well as intermediate steps of a chain (e.g., from query re-writing).\n",
"\n",
"We'll work off of the Q&A app with sources we built over the [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng in the [RAG tutorial](/docs/tutorials/rag)."
]
},
{
"cell_type": "markdown",
"id": "487d8d79-5ee9-4aa4-9fdf-cd5f4303e099",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"### Dependencies\n",
"\n",
"We'll use OpenAI embeddings and a Chroma vector store in this walkthrough, but everything shown here works with any [Embeddings](/docs/concepts#embedding-models), [VectorStore](/docs/concepts#vectorstores) or [Retriever](/docs/concepts#retrievers). \n",
"\n",
"We'll use the following packages:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "28d272cd-4e31-40aa-bbb4-0be0a1f49a14",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai langchain-chroma bs4"
]
},
{
"cell_type": "markdown",
"id": "51ef48de-70b6-4f43-8e0b-ab9b84c9c02a",
"metadata": {},
"source": [
"We need to set environment variable `OPENAI_API_KEY`, which can be done directly or loaded from a `.env` file like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "143787ca-d8e6-4dc9-8281-4374f4d71720",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# import dotenv\n",
"\n",
"# dotenv.load_dotenv()"
]
},
{
"cell_type": "markdown",
"id": "1665e740-ce01-4f09-b9ed-516db0bd326f",
"metadata": {},
"source": [
"### LangSmith\n",
"\n",
"Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with [LangSmith](https://smith.langchain.com).\n",
"\n",
"Note that LangSmith is not needed, but it is helpful. If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07411adb-3722-4f65-ab7f-8f6f57663d11",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "e2a72ca8-f8c8-4c0e-929a-223946c63f12",
"metadata": {},
"source": [
"## RAG chain\n",
"\n",
"Let's first select a LLM:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "accc4c35-e17c-4bf0-8a11-cd9e53436a3d",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "markdown",
"id": "fa6ba684-26cf-4860-904e-a4d51380c134",
"metadata": {},
"source": [
"Here is Q&A app with sources we built over the [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng in the [RAG tutorial](/docs/tutorials/rag):"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "820244ae-74b4-4593-b392-822979dd91b8",
"metadata": {},
"outputs": [],
"source": [
"import bs4\n",
"from langchain.chains import create_retrieval_chain\n",
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
"from langchain_chroma import Chroma\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"# 1. Load, chunk and index the contents of the blog to create a retriever.\n",
"loader = WebBaseLoader(\n",
" web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n",
" bs_kwargs=dict(\n",
" parse_only=bs4.SoupStrainer(\n",
" class_=(\"post-content\", \"post-title\", \"post-header\")\n",
" )\n",
" ),\n",
")\n",
"docs = loader.load()\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
"splits = text_splitter.split_documents(docs)\n",
"vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"\n",
"# 2. Incorporate the retriever into a question-answering chain.\n",
"system_prompt = (\n",
" \"You are an assistant for question-answering tasks. \"\n",
" \"Use the following pieces of retrieved context to answer \"\n",
" \"the question. If you don't know the answer, say that you \"\n",
" \"don't know. Use three sentences maximum and keep the \"\n",
" \"answer concise.\"\n",
" \"\\n\\n\"\n",
" \"{context}\"\n",
")\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"question_answer_chain = create_stuff_documents_chain(llm, prompt)\n",
"rag_chain = create_retrieval_chain(retriever, question_answer_chain)"
]
},
{
"cell_type": "markdown",
"id": "1c2f99b5-80b4-4178-bf30-c1c0a152638f",
"metadata": {},
"source": [
"## Streaming final outputs\n",
"\n",
"The chain constructed by `create_retrieval_chain` returns a dict with keys `\"input\"`, `\"context\"`, and `\"answer\"`. The `.stream` method will by default stream each key in a sequence.\n",
"\n",
"Note that here only the `\"answer\"` key is streamed token-by-token, as the other components-- such as retrieval-- do not support token-level streaming."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ded41680-b749-4e2a-9daa-b1165d74783b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'input': 'What is Task Decomposition?'}\n",
"{'context': [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content=\"(3) Task execution: Expert models execute on the specific tasks and log results.\\nInstruction:\\n\\nWith the input and the inference results, the AI assistant needs to describe the process and results. The previous stages can be formed as - User Input: {{ User Input }}, Task Planning: {{ Tasks }}, Model Selection: {{ Model Assignment }}, Task Execution: {{ Predictions }}. You must first answer the user's request in a straightforward manner. Then describe the task process and show your analysis and model inference results to the user in the first person. If inference results contain a file path, must tell the user the complete file path.\", metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})]}\n",
"{'answer': ''}\n",
"{'answer': 'Task'}\n",
"{'answer': ' decomposition'}\n",
"{'answer': ' involves'}\n",
"{'answer': ' breaking'}\n",
"{'answer': ' down'}\n",
"{'answer': ' complex'}\n",
"{'answer': ' tasks'}\n",
"{'answer': ' into'}\n",
"{'answer': ' smaller'}\n",
"{'answer': ' and'}\n",
"{'answer': ' simpler'}\n",
"{'answer': ' steps'}\n",
"{'answer': ' to'}\n",
"{'answer': ' make'}\n",
"{'answer': ' them'}\n",
"{'answer': ' more'}\n",
"{'answer': ' manageable'}\n",
"{'answer': '.'}\n",
"{'answer': ' This'}\n",
"{'answer': ' process'}\n",
"{'answer': ' can'}\n",
"{'answer': ' be'}\n",
"{'answer': ' facilitated'}\n",
"{'answer': ' by'}\n",
"{'answer': ' techniques'}\n",
"{'answer': ' like'}\n",
"{'answer': ' Chain'}\n",
"{'answer': ' of'}\n",
"{'answer': ' Thought'}\n",
"{'answer': ' ('}\n",
"{'answer': 'Co'}\n",
"{'answer': 'T'}\n",
"{'answer': ')'}\n",
"{'answer': ' and'}\n",
"{'answer': ' Tree'}\n",
"{'answer': ' of'}\n",
"{'answer': ' Thoughts'}\n",
"{'answer': ','}\n",
"{'answer': ' which'}\n",
"{'answer': ' help'}\n",
"{'answer': ' agents'}\n",
"{'answer': ' plan'}\n",
"{'answer': ' and'}\n",
"{'answer': ' execute'}\n",
"{'answer': ' tasks'}\n",
"{'answer': ' effectively'}\n",
"{'answer': ' by'}\n",
"{'answer': ' dividing'}\n",
"{'answer': ' them'}\n",
"{'answer': ' into'}\n",
"{'answer': ' sub'}\n",
"{'answer': 'goals'}\n",
"{'answer': ' or'}\n",
"{'answer': ' multiple'}\n",
"{'answer': ' reasoning'}\n",
"{'answer': ' possibilities'}\n",
"{'answer': '.'}\n",
"{'answer': ' Task'}\n",
"{'answer': ' decomposition'}\n",
"{'answer': ' can'}\n",
"{'answer': ' be'}\n",
"{'answer': ' initiated'}\n",
"{'answer': ' through'}\n",
"{'answer': ' simple'}\n",
"{'answer': ' prompts'}\n",
"{'answer': ','}\n",
"{'answer': ' task'}\n",
"{'answer': '-specific'}\n",
"{'answer': ' instructions'}\n",
"{'answer': ','}\n",
"{'answer': ' or'}\n",
"{'answer': ' human'}\n",
"{'answer': ' inputs'}\n",
"{'answer': ' to'}\n",
"{'answer': ' guide'}\n",
"{'answer': ' the'}\n",
"{'answer': ' agent'}\n",
"{'answer': ' in'}\n",
"{'answer': ' achieving'}\n",
"{'answer': ' its'}\n",
"{'answer': ' goals'}\n",
"{'answer': ' efficiently'}\n",
"{'answer': '.'}\n",
"{'answer': ''}\n"
]
}
],
"source": [
"for chunk in rag_chain.stream({\"input\": \"What is Task Decomposition?\"}):\n",
" print(chunk)"
]
},
{
"cell_type": "markdown",
"id": "72380afa-965d-4715-aac4-6049cce56313",
"metadata": {},
"source": [
"We are free to process chunks as they are streamed out. If we just want to stream the answer tokens, for example, we can select chunks with the corresponding key:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "738eb33e-6ccd-4b26-b563-beef216fb113",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Task| decomposition| is| a| technique| used| to| break| down| complex| tasks| into| smaller| and| more| manageable| steps|.| This| process| helps| agents| or| models| handle| intricate| tasks| by| dividing| them| into| simpler| sub|tasks|.| By| decom|posing| tasks|,| the| model| can| effectively| plan| and| execute| each| step| towards| achieving| the| overall| goal|.|"
]
}
],
"source": [
"for chunk in rag_chain.stream({\"input\": \"What is Task Decomposition?\"}):\n",
" if answer_chunk := chunk.get(\"answer\"):\n",
" print(f\"{answer_chunk}|\", end=\"\")"
]
},
{
"cell_type": "markdown",
"id": "8b2d224d-2a82-418b-b562-01ea210b86ef",
"metadata": {},
"source": [
"More simply, we can use the [.pick](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.pick) method to select only the desired key:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "16c20971-a6fd-4b57-83cd-7b2b453f97c9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|Task| decomposition| involves| breaking| down| complex| tasks| into| smaller| and| simpler| steps| to| make| them| more| manageable| for| an| agent| or| model| to| handle|.| This| process| helps| in| planning| and| executing| tasks| efficiently| by| dividing| them| into| a| series| of| sub|goals| or| actions|.| Task| decomposition| can| be| achieved| through| techniques| like| Chain| of| Thought| (|Co|T|)| or| Tree| of| Thoughts|,| which| enhance| model| performance| on| intricate| tasks| by| guiding| them| through| step|-by|-step| thinking| processes|.||"
]
}
],
"source": [
"chain = rag_chain.pick(\"answer\")\n",
"\n",
"for chunk in chain.stream({\"input\": \"What is Task Decomposition?\"}):\n",
" print(f\"{chunk}|\", end=\"\")"
]
},
{
"cell_type": "markdown",
"id": "fdee7ae6-4a81-46ab-8efd-d2310b596f8c",
"metadata": {},
"source": [
"## Streaming intermediate steps\n",
"\n",
"Suppose we want to stream not only the final outputs of the chain, but also some intermediate steps. As an example let's take our [Conversational RAG](/docs/tutorials/qa_chat_history) chain. Here we reformulate the user question before passing it to the retriever. This reformulated question is not returned as part of the final output. We could modify our chain to return the new question, but for demonstration purposes we'll leave it as is."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f4d7714e-bdca-419d-a6c6-7c1a70a69297",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import create_history_aware_retriever\n",
"from langchain_core.prompts import MessagesPlaceholder\n",
"\n",
"### Contextualize question ###\n",
"contextualize_q_system_prompt = (\n",
" \"Given a chat history and the latest user question \"\n",
" \"which might reference context in the chat history, \"\n",
" \"formulate a standalone question which can be understood \"\n",
" \"without the chat history. Do NOT answer the question, \"\n",
" \"just reformulate it if needed and otherwise return it as is.\"\n",
")\n",
"contextualize_q_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", contextualize_q_system_prompt),\n",
" MessagesPlaceholder(\"chat_history\"),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"contextualize_q_llm = llm.with_config(tags=[\"contextualize_q_llm\"])\n",
"history_aware_retriever = create_history_aware_retriever(\n",
" contextualize_q_llm, retriever, contextualize_q_prompt\n",
")\n",
"\n",
"\n",
"### Answer question ###\n",
"system_prompt = (\n",
" \"You are an assistant for question-answering tasks. \"\n",
" \"Use the following pieces of retrieved context to answer \"\n",
" \"the question. If you don't know the answer, say that you \"\n",
" \"don't know. Use three sentences maximum and keep the \"\n",
" \"answer concise.\"\n",
" \"\\n\\n\"\n",
" \"{context}\"\n",
")\n",
"qa_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" MessagesPlaceholder(\"chat_history\"),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)\n",
"\n",
"rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)"
]
},
{
"cell_type": "markdown",
"id": "ad306179-b6f0-4ade-9ec5-06e04fbb8d69",
"metadata": {},
"source": [
"Note that above we use `.with_config` to assign a tag to the LLM that is used for the question re-phrasing step. This is not necessary but will make it more convenient to stream output from that specific step.\n",
"\n",
"To demonstrate, we will pass in an artificial message history:\n",
"```\n",
"Human: What is task decomposition?\n",
"\n",
"AI: Task decomposition involves breaking up a complex task into smaller and simpler steps.\n",
"```\n",
"We then ask a follow up question: \"What are some common ways of doing it?\" Leading into the retrieval step, our `history_aware_retriever` will rephrase this question using the conversation's context to ensure that the retrieval is meaningful.\n",
"\n",
"To stream intermediate output, we recommend use of the async `.astream_events` method. This method will stream output from all \"events\" in the chain, and can be quite verbose. We can filter using tags, event types, and other criteria, as we do here.\n",
"\n",
"Below we show a typical `.astream_events` loop, where we pass in the chain input and emit desired results. See the [API reference](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) and [streaming guide](/docs/how_to/streaming) for more detail."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3ef2af40-e6ce-42a3-ad6a-ee405ad7f8ad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|What| are| some| typical| methods| used| for| task| decomposition|?||"
]
}
],
"source": [
"first_question = \"What is task decomposition?\"\n",
"first_answer = (\n",
" \"Task decomposition involves breaking up \"\n",
" \"a complex task into smaller and simpler \"\n",
" \"steps.\"\n",
")\n",
"follow_up_question = \"What are some common ways of doing it?\"\n",
"\n",
"chat_history = [\n",
" (\"human\", first_question),\n",
" (\"ai\", first_answer),\n",
"]\n",
"\n",
"\n",
"async for event in rag_chain.astream_events(\n",
" {\n",
" \"input\": follow_up_question,\n",
" \"chat_history\": chat_history,\n",
" },\n",
" version=\"v1\",\n",
"):\n",
" if (\n",
" event[\"event\"] == \"on_chat_model_stream\"\n",
" and \"contextualize_q_llm\" in event[\"tags\"]\n",
" ):\n",
" ai_message_chunk = event[\"data\"][\"chunk\"]\n",
" print(f\"{ai_message_chunk.content}|\", end=\"\")"
]
},
{
"cell_type": "markdown",
"id": "7da5dd1b-634c-4dd7-8235-69adec21d195",
"metadata": {},
"source": [
"Here we recover, token-by-token, the query that is passed into the retriever given our question \"What are some common ways of doing it?\"\n",
"\n",
"If we wanted to get our retrieved docs, we could filter on name \"Retriever\":"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "987ef6be-8c4e-4257-828a-a3b4fb4ccc99",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_retriever_start', 'name': 'Retriever', 'run_id': '6834097c-07fe-42f5-a566-a4780af4d1d0', 'tags': ['seq:step:4', 'Chroma', 'OpenAIEmbeddings'], 'metadata': {}, 'data': {'input': {'query': 'What are some typical methods used for task decomposition?'}}}\n",
"\n",
"{'event': 'on_retriever_end', 'name': 'Retriever', 'run_id': '6834097c-07fe-42f5-a566-a4780af4d1d0', 'tags': ['seq:step:4', 'Chroma', 'OpenAIEmbeddings'], 'metadata': {}, 'data': {'input': {'query': 'What are some typical methods used for task decomposition?'}, 'output': {'documents': [Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\\nTask decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\\nComponent One: Planning#\\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\\nTask Decomposition#\\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Resources:\\n1. Internet access for searches and information gathering.\\n2. Long Term memory management.\\n3. GPT-3.5 powered Agents for delegation of simple tasks.\\n4. File output.\\n\\nPerformance Evaluation:\\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\\n2. Constructively self-criticize your big-picture behavior constantly.\\n3. Reflect on past decisions and strategies to refine your approach.\\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Fig. 9. Comparison of MIPS algorithms, measured in recall@10. (Image source: Google Blog, 2020)\\nCheck more MIPS algorithms and performance comparison in ann-benchmarks.com.\\nComponent Three: Tool Use#\\nTool use is a remarkable and distinguishing characteristic of human beings. We create, modify and utilize external objects to do things that go beyond our physical and cognitive limits. Equipping LLMs with external tools can significantly extend the model capabilities.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})]}}}\n",
"\n"
]
}
],
"source": [
"async for event in rag_chain.astream_events(\n",
" {\n",
" \"input\": follow_up_question,\n",
" \"chat_history\": chat_history,\n",
" },\n",
" version=\"v1\",\n",
"):\n",
" if event[\"name\"] == \"Retriever\":\n",
" print(event)\n",
" print()"
]
},
{
"cell_type": "markdown",
"id": "c5470a79-258a-4108-8ceb-dfe8180160ca",
"metadata": {},
"source": [
"For more on how to stream intermediate steps check out the [streaming guide](/docs/how_to/streaming)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/query_constructing_filters.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 6\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
"metadata": {},
"source": [
"# How to construct filters for query analysis\n",
"\n",
"We may want to do query analysis to extract filters to pass into retrievers. One way we ask the LLM to represent these filters is as a Pydantic model. There is then the issue of converting that Pydantic model into a filter that can be passed into a retriever. \n",
"\n",
"This can be done manually, but LangChain also provides some \"Translators\" that are able to translate from a common syntax into filters specific to each retriever. Here, we will cover how to use those translators."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8ca446a0",
"metadata": {},
"outputs": [],
"source": [
"from typing import Optional\n",
"\n",
"from langchain.chains.query_constructor.ir import (\n",
" Comparator,\n",
" Comparison,\n",
" Operation,\n",
" Operator,\n",
" StructuredQuery,\n",
")\n",
"from langchain.retrievers.self_query.chroma import ChromaTranslator\n",
"from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator\n",
"from langchain_core.pydantic_v1 import BaseModel"
]
},
{
"cell_type": "markdown",
"id": "bc1302ff",
"metadata": {},
"source": [
"In this example, `year` and `author` are both attributes to filter on."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "64055006",
"metadata": {},
"outputs": [],
"source": [
"class Search(BaseModel):\n",
" query: str\n",
" start_year: Optional[int]\n",
" author: Optional[str]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "44eb6d98",
"metadata": {},
"outputs": [],
"source": [
"search_query = Search(query=\"RAG\", start_year=2022, author=\"LangChain\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e8ba6705",
"metadata": {},
"outputs": [],
"source": [
"def construct_comparisons(query: Search):\n",
" comparisons = []\n",
" if query.start_year is not None:\n",
" comparisons.append(\n",
" Comparison(\n",
" comparator=Comparator.GT,\n",
" attribute=\"start_year\",\n",
" value=query.start_year,\n",
" )\n",
" )\n",
" if query.author is not None:\n",
" comparisons.append(\n",
" Comparison(\n",
" comparator=Comparator.EQ,\n",
" attribute=\"author\",\n",
" value=query.author,\n",
" )\n",
" )\n",
" return comparisons"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6a79c9da",
"metadata": {},
"outputs": [],
"source": [
"comparisons = construct_comparisons(search_query)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "2d0e9689",
"metadata": {},
"outputs": [],
"source": [
"_filter = Operation(operator=Operator.AND, arguments=comparisons)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "e4c0b2ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'bool': {'must': [{'range': {'metadata.start_year': {'gt': 2022}}},\n",
" {'term': {'metadata.author.keyword': 'LangChain'}}]}}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ElasticsearchTranslator().visit_operation(_filter)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d75455ae",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'$and': [{'start_year': {'$gt': 2022}}, {'author': {'$eq': 'LangChain'}}]}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ChromaTranslator().visit_operation(_filter)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/query_few_shot.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 2\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
"metadata": {},
"source": [
"# How to add examples to the prompt for query analysis\n",
"\n",
"As our query analysis becomes more complex, the LLM may struggle to understand how exactly it should respond in certain scenarios. In order to improve performance here, we can add examples to the prompt to guide the LLM.\n",
"\n",
"Let's take a look at how we can add examples for the LangChain YouTube video query analyzer we built in the [Quickstart](/docs/tutorials/query_analysis)."
]
},
{
"cell_type": "markdown",
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
"metadata": {},
"source": [
"## Setup\n",
"#### Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
"metadata": {},
"outputs": [],
"source": [
"# %pip install -qU langchain-core langchain-openai"
]
},
{
"cell_type": "markdown",
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
"metadata": {},
"source": [
"#### Set environment variables\n",
"\n",
"We'll use OpenAI in this example:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "40e2979e-a818-4b96-ac25-039336f94319",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
"metadata": {},
"source": [
"## Query schema\n",
"\n",
"We'll define a query schema that we want our model to output. To make our query analysis a bit more interesting, we'll add a `sub_queries` field that contains more narrow questions derived from the top level question."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
"metadata": {},
"outputs": [],
"source": [
"from typing import List, Optional\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"sub_queries_description = \"\"\"\\\n",
"If the original question contains multiple distinct sub-questions, \\\n",
"or if there are more generic questions that would be helpful to answer in \\\n",
"order to answer the original question, write a list of all relevant sub-questions. \\\n",
"Make sure this list is comprehensive and covers all parts of the original question. \\\n",
"It's ok if there's redundancy in the sub-questions. \\\n",
"Make sure the sub-questions are as narrowly focused as possible.\"\"\"\n",
"\n",
"\n",
"class Search(BaseModel):\n",
" \"\"\"Search over a database of tutorial videos about a software library.\"\"\"\n",
"\n",
" query: str = Field(\n",
" ...,\n",
" description=\"Primary similarity search query applied to video transcripts.\",\n",
" )\n",
" sub_queries: List[str] = Field(\n",
" default_factory=list, description=sub_queries_description\n",
" )\n",
" publish_year: Optional[int] = Field(None, description=\"Year video was published\")"
]
},
{
"cell_type": "markdown",
"id": "f8b08c52-1ce9-4d8b-a779-cbe8efde51d1",
"metadata": {},
"source": [
"## Query generation"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"system = \"\"\"You are an expert at converting user questions into database queries. \\\n",
"You have access to a database of tutorial videos about a software library for building LLM-powered applications. \\\n",
"Given a question, return a list of database queries optimized to retrieve the most relevant results.\n",
"\n",
"If there are acronyms or words you are not familiar with, do not try to rephrase them.\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" MessagesPlaceholder(\"examples\", optional=True),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"structured_llm = llm.with_structured_output(Search)\n",
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
]
},
{
"cell_type": "markdown",
"id": "f403517a-b8e3-44ac-b0a6-02f8305635a2",
"metadata": {},
"source": [
"Let's try out our query analyzer without any examples in the prompt:"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "0bcfce06-6f0c-4f9d-a1fc-dc29342d2aae",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='web voyager vs reflection agents', sub_queries=['difference between web voyager and reflection agents', 'do web voyager and reflection agents use langgraph'], publish_year=None)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\n",
" \"what's the difference between web voyager and reflection agents? do both use langgraph?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "00962b08-899c-465c-9a41-6459b207e0f2",
"metadata": {},
"source": [
"## Adding examples and tuning the prompt\n",
"\n",
"This works pretty well, but we probably want it to decompose the question even further to separate the queries about Web Voyager and Reflection Agents.\n",
"\n",
"To tune our query generation results, we can add some examples of inputs questions and gold standard output queries to our prompt."
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "15b4923d-a08e-452d-8889-9a09a57d1095",
"metadata": {},
"outputs": [],
"source": [
"examples = []"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "da5330e6-827a-40e5-982b-b23b6286b758",
"metadata": {},
"outputs": [],
"source": [
"question = \"What's chat langchain, is it a langchain template?\"\n",
"query = Search(\n",
" query=\"What is chat langchain and is it a langchain template?\",\n",
" sub_queries=[\"What is chat langchain\", \"What is a langchain template\"],\n",
")\n",
"examples.append({\"input\": question, \"tool_calls\": [query]})"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "580e857a-27df-4ecf-a19c-458dc9244ec8",
"metadata": {},
"outputs": [],
"source": [
"question = \"How to build multi-agent system and stream intermediate steps from it\"\n",
"query = Search(\n",
" query=\"How to build multi-agent system and stream intermediate steps from it\",\n",
" sub_queries=[\n",
" \"How to build multi-agent system\",\n",
" \"How to stream intermediate steps from multi-agent system\",\n",
" \"How to stream intermediate steps\",\n",
" ],\n",
")\n",
"\n",
"examples.append({\"input\": question, \"tool_calls\": [query]})"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "fa63310d-69e3-4701-825c-fbb01f8a5a16",
"metadata": {},
"outputs": [],
"source": [
"question = \"LangChain agents vs LangGraph?\"\n",
"query = Search(\n",
" query=\"What's the difference between LangChain agents and LangGraph? How do you deploy them?\",\n",
" sub_queries=[\n",
" \"What are LangChain agents\",\n",
" \"What is LangGraph\",\n",
" \"How do you deploy LangChain agents\",\n",
" \"How do you deploy LangGraph\",\n",
" ],\n",
")\n",
"examples.append({\"input\": question, \"tool_calls\": [query]})"
]
},
{
"cell_type": "markdown",
"id": "bd21389c-f862-44e6-9d51-92db10979525",
"metadata": {},
"source": [
"Now we need to update our prompt template and chain so that the examples are included in each prompt. Since we're working with OpenAI function-calling, we'll need to do a bit of extra structuring to send example inputs and outputs to the model. We'll create a `tool_example_to_messages` helper function to handle this for us:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "68b03709-9a60-4acf-b96c-cafe1056c6f3",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"from typing import Dict\n",
"\n",
"from langchain_core.messages import (\n",
" AIMessage,\n",
" BaseMessage,\n",
" HumanMessage,\n",
" SystemMessage,\n",
" ToolMessage,\n",
")\n",
"\n",
"\n",
"def tool_example_to_messages(example: Dict) -> List[BaseMessage]:\n",
" messages: List[BaseMessage] = [HumanMessage(content=example[\"input\"])]\n",
" openai_tool_calls = []\n",
" for tool_call in example[\"tool_calls\"]:\n",
" openai_tool_calls.append(\n",
" {\n",
" \"id\": str(uuid.uuid4()),\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": tool_call.__class__.__name__,\n",
" \"arguments\": tool_call.json(),\n",
" },\n",
" }\n",
" )\n",
" messages.append(\n",
" AIMessage(content=\"\", additional_kwargs={\"tool_calls\": openai_tool_calls})\n",
" )\n",
" tool_outputs = example.get(\"tool_outputs\") or [\n",
" \"You have correctly called this tool.\"\n",
" ] * len(openai_tool_calls)\n",
" for output, tool_call in zip(tool_outputs, openai_tool_calls):\n",
" messages.append(ToolMessage(content=output, tool_call_id=tool_call[\"id\"]))\n",
" return messages\n",
"\n",
"\n",
"example_msgs = [msg for ex in examples for msg in tool_example_to_messages(ex)]"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "d9bf9f87-3e6b-4fc2-957b-949b077fab54",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import MessagesPlaceholder\n",
"\n",
"query_analyzer_with_examples = (\n",
" {\"question\": RunnablePassthrough()}\n",
" | prompt.partial(examples=example_msgs)\n",
" | structured_llm\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "e565ccb0-3530-4782-b56b-d1f6d0a8e559",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='Difference between web voyager and reflection agents, do they both use LangGraph?', sub_queries=['What is Web Voyager', 'What are Reflection agents', 'Do Web Voyager and Reflection agents use LangGraph'], publish_year=None)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer_with_examples.invoke(\n",
" \"what's the difference between web voyager and reflection agents? do both use langgraph?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "e5ea49ff-be53-4072-8c25-08682bb31a19",
"metadata": {},
"source": [
"Thanks to our examples we get a slightly more decomposed search query. With some more prompt engineering and tuning of our examples we could improve query generation even more.\n",
"\n",
"You can see that the examples are passed to the model as messages in the [LangSmith trace](https://smith.langchain.com/public/aeaaafce-d2b1-4943-9a61-bc954e8fc6f2/r)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/query_high_cardinality.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 7\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
"metadata": {},
"source": [
"# How deal with high cardinality categoricals when doing query analysis\n",
"\n",
"You may want to do query analysis to create a filter on a categorical column. One of the difficulties here is that you usually need to specify the EXACT categorical value. The issue is you need to make sure the LLM generates that categorical value exactly. This can be done relatively easy with prompting when there are only a few values that are valid. When there are a high number of valid values then it becomes more difficult, as those values may not fit in the LLM context, or (if they do) there may be too many for the LLM to properly attend to.\n",
"\n",
"In this notebook we take a look at how to approach this."
]
},
{
"cell_type": "markdown",
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
"metadata": {},
"source": [
"## Setup\n",
"#### Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
"metadata": {},
"outputs": [],
"source": [
"# %pip install -qU langchain langchain-community langchain-openai faker langchain-chroma"
]
},
{
"cell_type": "markdown",
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
"metadata": {},
"source": [
"#### Set environment variables\n",
"\n",
"We'll use OpenAI in this example:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "40e2979e-a818-4b96-ac25-039336f94319",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "d8d47f4b",
"metadata": {},
"source": [
"#### Set up data\n",
"\n",
"We will generate a bunch of fake names"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e5ba65c2",
"metadata": {},
"outputs": [],
"source": [
"from faker import Faker\n",
"\n",
"fake = Faker()\n",
"\n",
"names = [fake.name() for _ in range(10000)]"
]
},
{
"cell_type": "markdown",
"id": "41133694",
"metadata": {},
"source": [
"Let's look at some of the names"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c901ea97",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hayley Gonzalez'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names[0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b0d42ae2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Jesse Knight'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names[567]"
]
},
{
"cell_type": "markdown",
"id": "1725883d",
"metadata": {},
"source": [
"## Query Analysis\n",
"\n",
"We can now set up a baseline query analysis"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0ae69afc",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.pydantic_v1 import BaseModel, Field"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6c9485ce",
"metadata": {},
"outputs": [],
"source": [
"class Search(BaseModel):\n",
" query: str\n",
" author: str"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aebd704a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
" warn_beta(\n"
]
}
],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"system = \"\"\"Generate a relevant search query for a library system\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"structured_llm = llm.with_structured_output(Search)\n",
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
]
},
{
"cell_type": "markdown",
"id": "41709a2e",
"metadata": {},
"source": [
"We can see that if we spell the name exactly correctly, it knows how to handle it"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "cc0d344b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='books about aliens', author='Jesse Knight')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"what are books about aliens by Jesse Knight\")"
]
},
{
"cell_type": "markdown",
"id": "a1b57eab",
"metadata": {},
"source": [
"The issue is that the values you want to filter on may NOT be spelled exactly correctly"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "82b6b2ad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='books about aliens', author='Jess Knight')"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"what are books about aliens by jess knight\")"
]
},
{
"cell_type": "markdown",
"id": "0b60b7c2",
"metadata": {},
"source": [
"### Add in all values\n",
"\n",
"One way around this is to add ALL possible values to the prompt. That will generally guide the query in the right direction"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "98788a94",
"metadata": {},
"outputs": [],
"source": [
"system = \"\"\"Generate a relevant search query for a library system.\n",
"\n",
"`author` attribute MUST be one of:\n",
"\n",
"{authors}\n",
"\n",
"Do NOT hallucinate author name!\"\"\"\n",
"base_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"prompt = base_prompt.partial(authors=\", \".join(names))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "e65412f5",
"metadata": {},
"outputs": [],
"source": [
"query_analyzer_all = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
]
},
{
"cell_type": "markdown",
"id": "e639285a",
"metadata": {},
"source": [
"However... if the list of categoricals is long enough, it may error!"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "696b000f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Error code: 400 - {'error': {'message': \"This model's maximum context length is 16385 tokens. However, your messages resulted in 33885 tokens (33855 in the messages, 30 in the functions). Please reduce the length of the messages or functions.\", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}\n"
]
}
],
"source": [
"try:\n",
" res = query_analyzer_all.invoke(\"what are books about aliens by jess knight\")\n",
"except Exception as e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"id": "1d5d7891",
"metadata": {},
"source": [
"We can try to use a longer context window... but with so much information in there, it is not garunteed to pick it up reliably"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "0f0d0757",
"metadata": {},
"outputs": [],
"source": [
"llm_long = ChatOpenAI(model=\"gpt-4-turbo-preview\", temperature=0)\n",
"structured_llm_long = llm_long.with_structured_output(Search)\n",
"query_analyzer_all = {\"question\": RunnablePassthrough()} | prompt | structured_llm_long"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "03e5b7b2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='aliens', author='Kevin Knight')"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer_all.invoke(\"what are books about aliens by jess knight\")"
]
},
{
"cell_type": "markdown",
"id": "73ecf52b",
"metadata": {},
"source": [
"### Find and all relevant values\n",
"\n",
"Instead, what we can do is create an index over the relevant values and then query that for the N most relevant values,"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "32b19e07",
"metadata": {},
"outputs": [],
"source": [
"from langchain_chroma import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
"vectorstore = Chroma.from_texts(names, embeddings, collection_name=\"author_names\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "774cb7b0",
"metadata": {},
"outputs": [],
"source": [
"def select_names(question):\n",
" _docs = vectorstore.similarity_search(question, k=10)\n",
" _names = [d.page_content for d in _docs]\n",
" return \", \".join(_names)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "1173159c",
"metadata": {},
"outputs": [],
"source": [
"create_prompt = {\n",
" \"question\": RunnablePassthrough(),\n",
" \"authors\": select_names,\n",
"} | base_prompt"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "0a892607",
"metadata": {},
"outputs": [],
"source": [
"query_analyzer_select = create_prompt | structured_llm"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "8195d7cd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ChatPromptValue(messages=[SystemMessage(content='Generate a relevant search query for a library system.\\n\\n`author` attribute MUST be one of:\\n\\nJesse Knight, Kelly Knight, Scott Knight, Richard Knight, Andrew Knight, Katherine Knight, Erica Knight, Ashley Knight, Becky Knight, Kevin Knight\\n\\nDo NOT hallucinate author name!'), HumanMessage(content='what are books by jess knight')])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"create_prompt.invoke(\"what are books by jess knight\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "d3228b4e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='books about aliens', author='Jesse Knight')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer_select.invoke(\"what are books about aliens by jess knight\")"
]
},
{
"cell_type": "markdown",
"id": "46ef88bb",
"metadata": {},
"source": [
"### Replace after selection\n",
"\n",
"Another method is to let the LLM fill in whatever value, but then convert that value to a valid value.\n",
"This can actually be done with the Pydantic class itself!"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "a2e8b434",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.pydantic_v1 import validator\n",
"\n",
"\n",
"class Search(BaseModel):\n",
" query: str\n",
" author: str\n",
"\n",
" @validator(\"author\")\n",
" def double(cls, v: str) -> str:\n",
" return vectorstore.similarity_search(v, k=1)[0].page_content"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "919c0601",
"metadata": {},
"outputs": [],
"source": [
"system = \"\"\"Generate a relevant search query for a library system\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"corrective_structure_llm = llm.with_structured_output(Search)\n",
"corrective_query_analyzer = (\n",
" {\"question\": RunnablePassthrough()} | prompt | corrective_structure_llm\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "6c4f3e9a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='books about aliens', author='Jesse Knight')"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corrective_query_analyzer.invoke(\"what are books about aliens by jes knight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a309cb11",
"metadata": {},
"outputs": [],
"source": [
"# TODO: show trigram similarity"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/query_multiple_queries.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 4\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
"metadata": {},
"source": [
"# How to handle multiple queries when doing query analysis\n",
"\n",
"Sometimes, a query analysis technique may allow for multiple queries to be generated. In these cases, we need to remember to run all queries and then to combine the results. We will show a simple example (using mock data) of how to do that."
]
},
{
"cell_type": "markdown",
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
"metadata": {},
"source": [
"## Setup\n",
"#### Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
"metadata": {},
"outputs": [],
"source": [
"# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
]
},
{
"cell_type": "markdown",
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
"metadata": {},
"source": [
"#### Set environment variables\n",
"\n",
"We'll use OpenAI in this example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40e2979e-a818-4b96-ac25-039336f94319",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "c20b48b8-16d7-4089-bc17-f2d240b3935a",
"metadata": {},
"source": [
"### Create Index\n",
"\n",
"We will create a vectorstore over fake information."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1f621694",
"metadata": {},
"outputs": [],
"source": [
"from langchain_chroma import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"texts = [\"Harrison worked at Kensho\", \"Ankush worked at Facebook\"]\n",
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
"vectorstore = Chroma.from_texts(\n",
" texts,\n",
" embeddings,\n",
")\n",
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 1})"
]
},
{
"cell_type": "markdown",
"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
"metadata": {},
"source": [
"## Query analysis\n",
"\n",
"We will use function calling to structure the output. We will let it return multiple queries."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
"metadata": {},
"outputs": [],
"source": [
"from typing import List, Optional\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class Search(BaseModel):\n",
" \"\"\"Search over a database of job records.\"\"\"\n",
"\n",
" queries: List[str] = Field(\n",
" ...,\n",
" description=\"Distinct queries to search for\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
" warn_beta(\n"
]
}
],
"source": [
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"output_parser = PydanticToolsParser(tools=[Search])\n",
"\n",
"system = \"\"\"You have the ability to issue search queries to get information to help answer user information.\n",
"\n",
"If you need to look up two distinct pieces of information, you are allowed to do that!\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"structured_llm = llm.with_structured_output(Search)\n",
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
]
},
{
"cell_type": "markdown",
"id": "b9564078",
"metadata": {},
"source": [
"We can see that this allows for creating multiple queries"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bc1d3863",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(queries=['Harrison work location'])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"where did Harrison Work\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(queries=['Harrison work place', 'Ankush work place'])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"where did Harrison and ankush Work\")"
]
},
{
"cell_type": "markdown",
"id": "c7c65b2f-7881-45fc-a47b-a4eaaf48245f",
"metadata": {},
"source": [
"## Retrieval with query analysis\n",
"\n",
"So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asyncronously - this will let us loop over the queries and not get blocked on the response time."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1e047d87",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import chain"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "8dac7866",
"metadata": {},
"outputs": [],
"source": [
"@chain\n",
"async def custom_chain(question):\n",
" response = await query_analyzer.ainvoke(question)\n",
" docs = []\n",
" for query in response.queries:\n",
" new_docs = await retriever.ainvoke(query)\n",
" docs.extend(new_docs)\n",
" # You probably want to think about reranking or deduplicating documents here\n",
" # But that is a separate topic\n",
" return docs"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "232ad8a7-7990-4066-9228-d35a555f7293",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Harrison worked at Kensho')]"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await custom_chain.ainvoke(\"where did Harrison Work\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "28e14ba5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Harrison worked at Kensho'),\n",
" Document(page_content='Ankush worked at Facebook')]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"await custom_chain.ainvoke(\"where did Harrison and ankush Work\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88de5a36",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/query_multiple_retrievers.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 5\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
"metadata": {},
"source": [
"# How to handle multiple retrievers when doing query analysis\n",
"\n",
"Sometimes, a query analysis technique may allow for selection of which retriever to use. To use this, you will need to add some logic to select the retriever to do. We will show a simple example (using mock data) of how to do that."
]
},
{
"cell_type": "markdown",
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
"metadata": {},
"source": [
"## Setup\n",
"#### Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
"metadata": {},
"outputs": [],
"source": [
"# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
]
},
{
"cell_type": "markdown",
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
"metadata": {},
"source": [
"#### Set environment variables\n",
"\n",
"We'll use OpenAI in this example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40e2979e-a818-4b96-ac25-039336f94319",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "c20b48b8-16d7-4089-bc17-f2d240b3935a",
"metadata": {},
"source": [
"### Create Index\n",
"\n",
"We will create a vectorstore over fake information."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1f621694",
"metadata": {},
"outputs": [],
"source": [
"from langchain_chroma import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"texts = [\"Harrison worked at Kensho\"]\n",
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
"vectorstore = Chroma.from_texts(texts, embeddings, collection_name=\"harrison\")\n",
"retriever_harrison = vectorstore.as_retriever(search_kwargs={\"k\": 1})\n",
"\n",
"texts = [\"Ankush worked at Facebook\"]\n",
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
"vectorstore = Chroma.from_texts(texts, embeddings, collection_name=\"ankush\")\n",
"retriever_ankush = vectorstore.as_retriever(search_kwargs={\"k\": 1})"
]
},
{
"cell_type": "markdown",
"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
"metadata": {},
"source": [
"## Query analysis\n",
"\n",
"We will use function calling to structure the output. We will let it return multiple queries."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
"metadata": {},
"outputs": [],
"source": [
"from typing import List, Optional\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class Search(BaseModel):\n",
" \"\"\"Search for information about a person.\"\"\"\n",
"\n",
" query: str = Field(\n",
" ...,\n",
" description=\"Query to look up\",\n",
" )\n",
" person: str = Field(\n",
" ...,\n",
" description=\"Person to look things up for. Should be `HARRISON` or `ANKUSH`.\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"output_parser = PydanticToolsParser(tools=[Search])\n",
"\n",
"system = \"\"\"You have the ability to issue search queries to get information to help answer user information.\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"structured_llm = llm.with_structured_output(Search)\n",
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
]
},
{
"cell_type": "markdown",
"id": "b9564078",
"metadata": {},
"source": [
"We can see that this allows for routing between retrievers"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "bc1d3863",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='workplace', person='HARRISON')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"where did Harrison Work\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Search(query='workplace', person='ANKUSH')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"where did ankush Work\")"
]
},
{
"cell_type": "markdown",
"id": "c7c65b2f-7881-45fc-a47b-a4eaaf48245f",
"metadata": {},
"source": [
"## Retrieval with query analysis\n",
"\n",
"So how would we include this in a chain? We just need some simple logic to select the retriever and pass in the search query"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "1e047d87",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import chain"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "4ec0c7fe",
"metadata": {},
"outputs": [],
"source": [
"retrievers = {\n",
" \"HARRISON\": retriever_harrison,\n",
" \"ANKUSH\": retriever_ankush,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "8dac7866",
"metadata": {},
"outputs": [],
"source": [
"@chain\n",
"def custom_chain(question):\n",
" response = query_analyzer.invoke(question)\n",
" retriever = retrievers[response.person]\n",
" return retriever.invoke(response.query)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "232ad8a7-7990-4066-9228-d35a555f7293",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Harrison worked at Kensho')]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_chain.invoke(\"where did Harrison Work\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "28e14ba5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Ankush worked at Facebook')]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_chain.invoke(\"where did ankush Work\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33338d4f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/query_no_queries.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 3\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
"metadata": {},
"source": [
"# How to handle cases where no queries are generated\n",
"\n",
"Sometimes, a query analysis technique may allow for any number of queries to be generated - including no queries! In this case, our overall chain will need to inspect the result of the query analysis before deciding whether to call the retriever or not.\n",
"\n",
"We will use mock data for this example."
]
},
{
"cell_type": "markdown",
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
"metadata": {},
"source": [
"## Setup\n",
"#### Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
"metadata": {},
"outputs": [],
"source": [
"# %pip install -qU langchain langchain-community langchain-openai langchain-chroma"
]
},
{
"cell_type": "markdown",
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
"metadata": {},
"source": [
"#### Set environment variables\n",
"\n",
"We'll use OpenAI in this example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40e2979e-a818-4b96-ac25-039336f94319",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "c20b48b8-16d7-4089-bc17-f2d240b3935a",
"metadata": {},
"source": [
"### Create Index\n",
"\n",
"We will create a vectorstore over fake information."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1f621694",
"metadata": {},
"outputs": [],
"source": [
"from langchain_chroma import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"texts = [\"Harrison worked at Kensho\"]\n",
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
"vectorstore = Chroma.from_texts(\n",
" texts,\n",
" embeddings,\n",
")\n",
"retriever = vectorstore.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
"metadata": {},
"source": [
"## Query analysis\n",
"\n",
"We will use function calling to structure the output. However, we will configure the LLM such that is doesn't NEED to call the function representing a search query (should it decide not to). We will also then use a prompt to do query analysis that explicitly lays when it should and shouldn't make a search."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
"metadata": {},
"outputs": [],
"source": [
"from typing import Optional\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class Search(BaseModel):\n",
" \"\"\"Search over a database of job records.\"\"\"\n",
"\n",
" query: str = Field(\n",
" ...,\n",
" description=\"Similarity search query applied to job record.\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"system = \"\"\"You have the ability to issue search queries to get information to help answer user information.\n",
"\n",
"You do not NEED to look things up. If you don't need to, then just respond normally.\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"structured_llm = llm.bind_tools([Search])\n",
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
]
},
{
"cell_type": "markdown",
"id": "b9564078",
"metadata": {},
"source": [
"We can see that by invoking this we get an message that sometimes - but not always - returns a tool call."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bc1d3863",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_ZnoVX4j9Mn8wgChaORyd1cvq', 'function': {'arguments': '{\"query\":\"Harrison\"}', 'name': 'Search'}, 'type': 'function'}]})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"where did Harrison Work\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hello! How can I assist you today?')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_analyzer.invoke(\"hi!\")"
]
},
{
"cell_type": "markdown",
"id": "c7c65b2f-7881-45fc-a47b-a4eaaf48245f",
"metadata": {},
"source": [
"## Retrieval with query analysis\n",
"\n",
"So how would we include this in a chain? Let's look at an example below."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1e047d87",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
"from langchain_core.runnables import chain\n",
"\n",
"output_parser = PydanticToolsParser(tools=[Search])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8dac7866",
"metadata": {},
"outputs": [],
"source": [
"@chain\n",
"def custom_chain(question):\n",
" response = query_analyzer.invoke(question)\n",
" if \"tool_calls\" in response.additional_kwargs:\n",
" query = output_parser.invoke(response)\n",
" docs = retriever.invoke(query[0].query)\n",
" # Could add more logic - like another LLM call - here\n",
" return docs\n",
" else:\n",
" return response"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "232ad8a7-7990-4066-9228-d35a555f7293",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
]
},
{
"data": {
"text/plain": [
"[Document(page_content='Harrison worked at Kensho')]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_chain.invoke(\"where did Harrison Work\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "28e14ba5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hello! How can I assist you today?')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_chain.invoke(\"hi!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33338d4f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/recursive_json_splitter.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "a678d550",
"metadata": {},
"source": [
"# How to split JSON data\n",
"\n",
"This json splitter splits json data while allowing control over chunk sizes. It traverses json data depth first and builds smaller json chunks. It attempts to keep nested json objects whole but will split them if needed to keep chunks between a min_chunk_size and the max_chunk_size.\n",
"\n",
"If the value is not a nested json, but rather a very large string the string will not be split. If you need a hard cap on the chunk size consider composing this with a Recursive Text splitter on those chunks. There is an optional pre-processing step to split lists, by first converting them to json (dict) and then splitting them as such.\n",
"\n",
"1. How the text is split: json value.\n",
"2. How the chunk size is measured: by number of characters."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f335e05-e5ae-44cc-899d-749aa9031a58",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain-text-splitters"
]
},
{
"cell_type": "markdown",
"id": "a2b3fe87-d230-4cbd-b3ae-01559c5351a3",
"metadata": {},
"source": [
"First we load some json data:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3390ae1d",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"import requests\n",
"\n",
"# This is a large nested json object and will be loaded as a python dict\n",
"json_data = requests.get(\"https://api.smith.langchain.com/openapi.json\").json()"
]
},
{
"cell_type": "markdown",
"id": "3cdc725d-f4b8-4725-9084-cb395d8ef48b",
"metadata": {},
"source": [
"## Basic usage\n",
"\n",
"Specify `max_chunk_size` to constrain chunk sizes:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7bfe2c1e",
"metadata": {},
"outputs": [],
"source": [
"from langchain_text_splitters import RecursiveJsonSplitter\n",
"\n",
"splitter = RecursiveJsonSplitter(max_chunk_size=300)"
]
},
{
"cell_type": "markdown",
"id": "e03b79fb-b1c6-4324-a409-86cd3e40cb92",
"metadata": {},
"source": [
"To obtain json chunks, use the `.split_json` method:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "69250bc6-c0f5-40d0-b8ba-7a349236bfd2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'openapi': '3.1.0', 'info': {'title': 'LangSmith', 'version': '0.1.0'}, 'servers': [{'url': 'https://api.smith.langchain.com', 'description': 'LangSmith API endpoint.'}]}\n",
"{'paths': {'/api/v1/sessions/{session_id}': {'get': {'tags': ['tracer-sessions'], 'summary': 'Read Tracer Session', 'description': 'Get a specific session.', 'operationId': 'read_tracer_session_api_v1_sessions__session_id__get'}}}}\n",
"{'paths': {'/api/v1/sessions/{session_id}': {'get': {'security': [{'API Key': []}, {'Tenant ID': []}, {'Bearer Auth': []}]}}}}\n"
]
}
],
"source": [
"# Recursively split json data - If you need to access/manipulate the smaller json chunks\n",
"json_chunks = splitter.split_json(json_data=json_data)\n",
"\n",
"for chunk in json_chunks[:3]:\n",
" print(chunk)"
]
},
{
"cell_type": "markdown",
"id": "3f05bc21-227e-4d2c-af51-16d69ad3cd7b",
"metadata": {},
"source": [
"To obtain LangChain [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects, use the `.create_documents` method:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0839f4f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"page_content='{\"openapi\": \"3.1.0\", \"info\": {\"title\": \"LangSmith\", \"version\": \"0.1.0\"}, \"servers\": [{\"url\": \"https://api.smith.langchain.com\", \"description\": \"LangSmith API endpoint.\"}]}'\n",
"page_content='{\"paths\": {\"/api/v1/sessions/{session_id}\": {\"get\": {\"tags\": [\"tracer-sessions\"], \"summary\": \"Read Tracer Session\", \"description\": \"Get a specific session.\", \"operationId\": \"read_tracer_session_api_v1_sessions__session_id__get\"}}}}'\n",
"page_content='{\"paths\": {\"/api/v1/sessions/{session_id}\": {\"get\": {\"security\": [{\"API Key\": []}, {\"Tenant ID\": []}, {\"Bearer Auth\": []}]}}}}'\n"
]
}
],
"source": [
"# The splitter can also output documents\n",
"docs = splitter.create_documents(texts=[json_data])\n",
"\n",
"for doc in docs[:3]:\n",
" print(doc)"
]
},
{
"cell_type": "markdown",
"id": "677c3dd0-afc7-488a-a58d-b7943814f85d",
"metadata": {},
"source": [
"Or use `.split_text` to obtain string content directly:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "fa0a4d66-b470-404e-918b-6728df3b88b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"openapi\": \"3.1.0\", \"info\": {\"title\": \"LangSmith\", \"version\": \"0.1.0\"}, \"servers\": [{\"url\": \"https://api.smith.langchain.com\", \"description\": \"LangSmith API endpoint.\"}]}\n",
"{\"paths\": {\"/api/v1/sessions/{session_id}\": {\"get\": {\"tags\": [\"tracer-sessions\"], \"summary\": \"Read Tracer Session\", \"description\": \"Get a specific session.\", \"operationId\": \"read_tracer_session_api_v1_sessions__session_id__get\"}}}}\n"
]
}
],
"source": [
"texts = splitter.split_text(json_data=json_data)\n",
"\n",
"print(texts[0])\n",
"print(texts[1])"
]
},
{
"cell_type": "markdown",
"id": "7070bf45-b885-4949-b8e0-7d1ea5205d2a",
"metadata": {},
"source": [
"## How to manage chunk sizes from list content\n",
"\n",
"Note that one of the chunks in this example is larger than the specified `max_chunk_size` of 300. Reviewing one of these chunks that was bigger we see there is a list object there:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "86ef3195-375b-4db2-9804-f3fa5a249417",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[171, 231, 126, 469, 210, 213, 237, 271, 191, 232]\n",
"\n",
"{\"paths\": {\"/api/v1/sessions/{session_id}\": {\"get\": {\"parameters\": [{\"name\": \"session_id\", \"in\": \"path\", \"required\": true, \"schema\": {\"type\": \"string\", \"format\": \"uuid\", \"title\": \"Session Id\"}}, {\"name\": \"include_stats\", \"in\": \"query\", \"required\": false, \"schema\": {\"type\": \"boolean\", \"default\": false, \"title\": \"Include Stats\"}}, {\"name\": \"accept\", \"in\": \"header\", \"required\": false, \"schema\": {\"anyOf\": [{\"type\": \"string\"}, {\"type\": \"null\"}], \"title\": \"Accept\"}}]}}}}\n"
]
}
],
"source": [
"print([len(text) for text in texts][:10])\n",
"print()\n",
"print(texts[3])"
]
},
{
"cell_type": "markdown",
"id": "ddc98a1d-05df-48ab-8d17-6e4ee0d9d0cb",
"metadata": {},
"source": [
"The json splitter by default does not split lists.\n",
"\n",
"Specify `convert_lists=True` to preprocess the json, converting list content to dicts with `index:item` as `key:val` pairs:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "992477c2",
"metadata": {},
"outputs": [],
"source": [
"texts = splitter.split_text(json_data=json_data, convert_lists=True)"
]
},
{
"cell_type": "markdown",
"id": "912c20c2-8d05-47a6-bc03-f5c866761dff",
"metadata": {},
"source": [
"Let's look at the size of the chunks. Now they are all under the max"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7abd43f6-78ab-4a73-853a-a777ab268efc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[176, 236, 141, 203, 212, 221, 210, 213, 242, 291]\n"
]
}
],
"source": [
"print([len(text) for text in texts][:10])"
]
},
{
"cell_type": "markdown",
"id": "3e5753bf-cede-4751-a1c0-c42aca56b88a",
"metadata": {},
"source": [
"The list has been converted to a dict, but retains all the needed contextual information even if split into many chunks:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d2c2773e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"paths\": {\"/api/v1/sessions/{session_id}\": {\"get\": {\"tags\": {\"0\": \"tracer-sessions\"}, \"summary\": \"Read Tracer Session\", \"description\": \"Get a specific session.\", \"operationId\": \"read_tracer_session_api_v1_sessions__session_id__get\"}}}}\n"
]
}
],
"source": [
"print(texts[1])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8963b01a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(page_content='{\"paths\": {\"/api/v1/sessions/{session_id}\": {\"get\": {\"tags\": [\"tracer-sessions\"], \"summary\": \"Read Tracer Session\", \"description\": \"Get a specific session.\", \"operationId\": \"read_tracer_session_api_v1_sessions__session_id__get\"}}}}')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can also look at the documents\n",
"docs[1]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/recursive_text_splitter.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "52976910",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"keywords: [recursivecharactertextsplitter]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "a678d550",
"metadata": {},
"source": [
"# How to recursively split text by characters\n",
"\n",
"This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `[\"\\n\\n\", \"\\n\", \" \", \"\"]`. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.\n",
"\n",
"1. How the text is split: by list of characters.\n",
"2. How the chunk size is measured: by number of characters.\n",
"\n",
"Below we show example usage.\n",
"\n",
"To obtain the string content directly, use `.split_text`.\n",
"\n",
"To create LangChain [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects (e.g., for use in downstream tasks), use `.create_documents`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c16167c-1e56-4e11-9b8b-60f93044498e",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain-text-splitters"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3390ae1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and'\n",
"page_content='of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.'\n"
]
}
],
"source": [
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"# Load example document\n",
"with open(\"state_of_the_union.txt\") as f:\n",
" state_of_the_union = f.read()\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(\n",
" # Set a really small chunk size, just to show.\n",
" chunk_size=100,\n",
" chunk_overlap=20,\n",
" length_function=len,\n",
" is_separator_regex=False,\n",
")\n",
"texts = text_splitter.create_documents([state_of_the_union])\n",
"print(texts[0])\n",
"print(texts[1])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0839f4f0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and',\n",
" 'of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_splitter.split_text(state_of_the_union)[:2]"
]
},
{
"cell_type": "markdown",
"id": "60336622-b9d0-4172-816a-6cd1bb9ec481",
"metadata": {},
"source": [
"Let's go through the parameters set above for `RecursiveCharacterTextSplitter`:\n",
"- `chunk_size`: The maximum size of a chunk, where size is determined by the `length_function`.\n",
"- `chunk_overlap`: Target overlap between chunks. Overlapping chunks helps to mitigate loss of information when context is divided between chunks.\n",
"- `length_function`: Function determining the chunk size.\n",
"- `is_separator_regex`: Whether the separator list (defaulting to `[\"\\n\\n\", \"\\n\", \" \", \"\"]`) should be interpreted as regex."
]
},
{
"cell_type": "markdown",
"id": "2b74939c",
"metadata": {},
"source": [
"## Splitting text from languages without word boundaries\n",
"\n",
"Some writing systems do not have [word boundaries](https://en.wikipedia.org/wiki/Category:Writing_systems_without_word_boundaries), for example Chinese, Japanese, and Thai. Splitting text with the default separator list of `[\"\\n\\n\", \"\\n\", \" \", \"\"]` can cause words to be split between chunks. To keep words together, you can override the list of separators to include additional punctuation:\n",
"\n",
"* Add ASCII full-stop \"`.`\", [Unicode fullwidth](https://en.wikipedia.org/wiki/Halfwidth_and_Fullwidth_Forms_(Unicode_block)) full stop \"`．`\" (used in Chinese text), and [ideographic full stop](https://en.wikipedia.org/wiki/CJK_Symbols_and_Punctuation) \"`。`\" (used in Japanese and Chinese)\n",
"* Add [Zero-width space](https://en.wikipedia.org/wiki/Zero-width_space) used in Thai, Myanmar, Kmer, and Japanese.\n",
"* Add ASCII comma \"`,`\", Unicode fullwidth comma \"`，`\", and Unicode ideographic comma \"`、`\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6d48a8ef",
"metadata": {},
"outputs": [],
"source": [
"text_splitter = RecursiveCharacterTextSplitter(\n",
" separators=[\n",
" \"\\n\\n\",\n",
" \"\\n\",\n",
" \" \",\n",
" \".\",\n",
" \",\",\n",
" \"\\u200b\", # Zero-width space\n",
" \"\\uff0c\", # Fullwidth comma\n",
" \"\\u3001\", # Ideographic comma\n",
" \"\\uff0e\", # Fullwidth full stop\n",
" \"\\u3002\", # Ideographic full stop\n",
" \"\",\n",
" ],\n",
" # Existing args\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/response_metadata.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "6bd1219b-f31c-41b0-95e6-3204ad894ac7",
"metadata": {},
"source": [
"# Response metadata\n",
"\n",
"Many model providers include some metadata in their chat generation responses. This metadata can be accessed via the `AIMessage.response_metadata: Dict` attribute. Depending on the model provider and model configuration, this can contain information like [token counts](/docs/how_to/chat_token_usage_tracking), [logprobs](/docs/how_to/logprobs), and more.\n",
"\n",
"Here's what the response metadata looks like for a few different providers:\n",
"\n",
"## OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "161f5898-9976-4a75-943d-03eda1a40a60",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'token_usage': {'completion_tokens': 164,\n",
" 'prompt_tokens': 17,\n",
" 'total_tokens': 181},\n",
" 'model_name': 'gpt-4-turbo',\n",
" 'system_fingerprint': 'fp_76f018034d',\n",
" 'finish_reason': 'stop',\n",
" 'logprobs': None}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4-turbo\")\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
},
{
"cell_type": "markdown",
"id": "98eab683-df03-44a1-a034-ebbe7c6851b6",
"metadata": {},
"source": [
"## Anthropic"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "61c43496-83b5-4d71-bd60-3e6d46c62a5e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 'msg_01CzQyD7BX8nkhDNfT1QqvEp',\n",
" 'model': 'claude-3-sonnet-20240229',\n",
" 'stop_reason': 'end_turn',\n",
" 'stop_sequence': None,\n",
" 'usage': {'input_tokens': 17, 'output_tokens': 296}}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\")\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
},
{
"cell_type": "markdown",
"id": "c1f24f69-18f6-43c1-8b26-3f88ec515259",
"metadata": {},
"source": [
"## Google VertexAI"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "39549336-25f5-4839-9846-f687cd77e59b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'is_blocked': False,\n",
" 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH',\n",
" 'probability_label': 'NEGLIGIBLE',\n",
" 'blocked': False},\n",
" {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT',\n",
" 'probability_label': 'NEGLIGIBLE',\n",
" 'blocked': False},\n",
" {'category': 'HARM_CATEGORY_HARASSMENT',\n",
" 'probability_label': 'NEGLIGIBLE',\n",
" 'blocked': False},\n",
" {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT',\n",
" 'probability_label': 'NEGLIGIBLE',\n",
" 'blocked': False}],\n",
" 'citation_metadata': None,\n",
" 'usage_metadata': {'prompt_token_count': 10,\n",
" 'candidates_token_count': 30,\n",
" 'total_token_count': 40}}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_google_vertexai import ChatVertexAI\n",
"\n",
"llm = ChatVertexAI(model=\"gemini-pro\")\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
},
{
"cell_type": "markdown",
"id": "bc4ef8bb-eee3-4266-b530-0af9b3b79fe9",
"metadata": {},
"source": [
"## Bedrock (Anthropic)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1e4ac668-4c6a-48ad-9a6f-7b291477b45d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'model_id': 'anthropic.claude-v2',\n",
" 'usage': {'prompt_tokens': 19, 'completion_tokens': 371, 'total_tokens': 390}}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_aws import ChatBedrock\n",
"\n",
"llm = ChatBedrock(model_id=\"anthropic.claude-v2\")\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
},
{
"cell_type": "markdown",
"id": "ee040d15-5575-4309-a9e9-aed5a09c78e3",
"metadata": {},
"source": [
"## MistralAI"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "deb41321-52d0-4795-a40c-4a811a13d7b0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'token_usage': {'prompt_tokens': 19,\n",
" 'total_tokens': 141,\n",
" 'completion_tokens': 122},\n",
" 'model': 'mistral-small',\n",
" 'finish_reason': 'stop'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_mistralai import ChatMistralAI\n",
"\n",
"llm = ChatMistralAI()\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
},
{
"cell_type": "markdown",
"id": "297c7be4-9505-48ac-96c0-4dc2047cfe7f",
"metadata": {},
"source": [
"## Groq"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "744e14ec-ff50-4642-9893-ff7bdf8927ff",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'token_usage': {'completion_time': 0.243,\n",
" 'completion_tokens': 132,\n",
" 'prompt_time': 0.022,\n",
" 'prompt_tokens': 22,\n",
" 'queue_time': None,\n",
" 'total_time': 0.265,\n",
" 'total_tokens': 154},\n",
" 'model_name': 'mixtral-8x7b-32768',\n",
" 'system_fingerprint': 'fp_7b44c65f25',\n",
" 'finish_reason': 'stop',\n",
" 'logprobs': None}"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_groq import ChatGroq\n",
"\n",
"llm = ChatGroq()\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
},
{
"cell_type": "markdown",
"id": "7cdeec00-8a8f-422a-8819-47c646578b65",
"metadata": {},
"source": [
"## TogetherAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a984118e-a731-4864-bcea-7dc6c6b3d139",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'token_usage': {'completion_tokens': 208,\n",
" 'prompt_tokens': 20,\n",
" 'total_tokens': 228},\n",
" 'model_name': 'mistralai/Mixtral-8x7B-Instruct-v0.1',\n",
" 'system_fingerprint': None,\n",
" 'finish_reason': 'eos',\n",
" 'logprobs': None}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(\n",
" base_url=\"https://api.together.xyz/v1\",\n",
" api_key=os.environ[\"TOGETHER_API_KEY\"],\n",
" model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
")\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
},
{
"cell_type": "markdown",
"id": "3d5e0614-8dc2-4948-a0b5-dc76c7837a5a",
"metadata": {},
"source": [
"## FireworksAI"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "6ae32a93-26db-41bb-95c2-38ddd5085fbe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'token_usage': {'prompt_tokens': 19,\n",
" 'total_tokens': 219,\n",
" 'completion_tokens': 200},\n",
" 'model_name': 'accounts/fireworks/models/mixtral-8x7b-instruct',\n",
" 'system_fingerprint': '',\n",
" 'finish_reason': 'length',\n",
" 'logprobs': None}"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_fireworks import ChatFireworks\n",
"\n",
"llm = ChatFireworks(model=\"accounts/fireworks/models/mixtral-8x7b-instruct\")\n",
"msg = llm.invoke([(\"human\", \"What's the oldest known example of cuneiform\")])\n",
"msg.response_metadata"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv-2",
"language": "python",
"name": "poetry-venv-2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/routing.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "9e45e81c-e16e-4c6c-b6a3-2362e5193827",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 3\n",
"keywords: [RunnableBranch, LCEL]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "4b47436a",
"metadata": {},
"source": [
"# How to route between sub-chains\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Configuring chain parameters at runtime](/docs/how_to/configure)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat Messages](/docs/concepts/#message-types)\n",
"\n",
":::\n",
"\n",
"Routing allows you to create non-deterministic chains where the output of a previous step defines the next step. Routing can help provide structure and consistency around interactions with models by allowing you to define states and use information related to those states as context to model calls.\n",
"\n",
"There are two ways to perform routing:\n",
"\n",
"1. Conditionally return runnables from a [`RunnableLambda`](/docs/how_to/functions) (recommended)\n",
"2. Using a `RunnableBranch` (legacy)\n",
"\n",
"We'll illustrate both methods using a two step sequence where the first step classifies an input question as being about `LangChain`, `Anthropic`, or `Other`, then routes to a corresponding prompt chain."
]
},
{
"cell_type": "markdown",
"id": "c1c6edac",
"metadata": {},
"source": [
"## Example Setup\n",
"First, let's create a chain that will identify incoming questions as being about `LangChain`, `Anthropic`, or `Other`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8a8a1967",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Anthropic'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_anthropic import ChatAnthropic\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"chain = (\n",
" PromptTemplate.from_template(\n",
" \"\"\"Given the user question below, classify it as either being about `LangChain`, `Anthropic`, or `Other`.\n",
"\n",
"Do not respond with more than one word.\n",
"\n",
"<question>\n",
"{question}\n",
"</question>\n",
"\n",
"Classification:\"\"\"\n",
" )\n",
" | ChatAnthropic(model_name=\"claude-3-haiku-20240307\")\n",
" | StrOutputParser()\n",
")\n",
"\n",
"chain.invoke({\"question\": \"how do I call Anthropic?\"})"
]
},
{
"cell_type": "markdown",
"id": "7655555f",
"metadata": {},
"source": [
"Now, let's create three sub chains:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "89d7722d",
"metadata": {},
"outputs": [],
"source": [
"langchain_chain = PromptTemplate.from_template(\n",
" \"\"\"You are an expert in langchain. \\\n",
"Always answer questions starting with \"As Harrison Chase told me\". \\\n",
"Respond to the following question:\n",
"\n",
"Question: {question}\n",
"Answer:\"\"\"\n",
") | ChatAnthropic(model_name=\"claude-3-haiku-20240307\")\n",
"anthropic_chain = PromptTemplate.from_template(\n",
" \"\"\"You are an expert in anthropic. \\\n",
"Always answer questions starting with \"As Dario Amodei told me\". \\\n",
"Respond to the following question:\n",
"\n",
"Question: {question}\n",
"Answer:\"\"\"\n",
") | ChatAnthropic(model_name=\"claude-3-haiku-20240307\")\n",
"general_chain = PromptTemplate.from_template(\n",
" \"\"\"Respond to the following question:\n",
"\n",
"Question: {question}\n",
"Answer:\"\"\"\n",
") | ChatAnthropic(model_name=\"claude-3-haiku-20240307\")"
]
},
{
"cell_type": "markdown",
"id": "6d8d042c",
"metadata": {},
"source": [
"## Using a custom function (Recommended)\n",
"\n",
"You can also use a custom function to route between different outputs. Here's an example:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "687492da",
"metadata": {},
"outputs": [],
"source": [
"def route(info):\n",
" if \"anthropic\" in info[\"topic\"].lower():\n",
" return anthropic_chain\n",
" elif \"langchain\" in info[\"topic\"].lower():\n",
" return langchain_chain\n",
" else:\n",
" return general_chain"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "02a33c86",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnableLambda\n",
"\n",
"full_chain = {\"topic\": chain, \"question\": lambda x: x[\"question\"]} | RunnableLambda(\n",
" route\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c2e977a4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"As Dario Amodei told me, to use Anthropic, you can start by exploring the company's website and learning about their mission, values, and the different services and products they offer. Anthropic is focused on developing safe and ethical AI systems, so they have a strong emphasis on transparency and responsible AI development. \\n\\nDepending on your specific needs, you can look into Anthropic's AI research and development services, which cover areas like natural language processing, computer vision, and reinforcement learning. They also offer consulting and advisory services to help organizations navigate the challenges and opportunities of AI integration.\\n\\nAdditionally, Anthropic has released some open-source AI models and tools that you can explore and experiment with. These can be a great way to get hands-on experience with Anthropic's approach to AI development.\\n\\nOverall, Anthropic aims to be a reliable and trustworthy partner in the AI space, so I'd encourage you to reach out to them directly to discuss how they can best support your specific requirements.\", response_metadata={'id': 'msg_01CtLFgFSwvTaJomrihE87Ra', 'content': [ContentBlock(text=\"As Dario Amodei told me, to use Anthropic, you can start by exploring the company's website and learning about their mission, values, and the different services and products they offer. Anthropic is focused on developing safe and ethical AI systems, so they have a strong emphasis on transparency and responsible AI development. \\n\\nDepending on your specific needs, you can look into Anthropic's AI research and development services, which cover areas like natural language processing, computer vision, and reinforcement learning. They also offer consulting and advisory services to help organizations navigate the challenges and opportunities of AI integration.\\n\\nAdditionally, Anthropic has released some open-source AI models and tools that you can explore and experiment with. These can be a great way to get hands-on experience with Anthropic's approach to AI development.\\n\\nOverall, Anthropic aims to be a reliable and trustworthy partner in the AI space, so I'd encourage you to reach out to them directly to discuss how they can best support your specific requirements.\", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=53, output_tokens=219)})"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"how do I use Anthropic?\"})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "48913dc6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"As Harrison Chase told me, using LangChain involves a few key steps:\\n\\n1. **Set up your environment**: Install the necessary Python packages, including the LangChain library itself, as well as any other dependencies your application might require, such as language models or other integrations.\\n\\n2. **Understand the core concepts**: LangChain revolves around a few core concepts, like Agents, Chains, and Tools. Familiarize yourself with these concepts and how they work together to build powerful language-based applications.\\n\\n3. **Identify your use case**: Determine what kind of task or application you want to build using LangChain, such as a chatbot, a question-answering system, or a document summarization tool.\\n\\n4. **Choose the appropriate components**: Based on your use case, select the right LangChain components, such as agents, chains, and tools, to build your application.\\n\\n5. **Integrate with language models**: LangChain is designed to work seamlessly with various language models, such as OpenAI's GPT-3 or Anthropic's models. Connect your chosen language model to your LangChain application.\\n\\n6. **Implement your application logic**: Use LangChain's building blocks to implement the specific functionality of your application, such as prompting the language model, processing the response, and integrating with other services or data sources.\\n\\n7. **Test and iterate**: Thoroughly test your application, gather feedback, and iterate on your design and implementation to improve its performance and user experience.\\n\\nAs Harrison Chase emphasized, LangChain provides a flexible and powerful framework for building language-based applications, making it easier to leverage the capabilities of modern language models. By following these steps, you can get started with LangChain and create innovative solutions tailored to your specific needs.\", response_metadata={'id': 'msg_01H3UXAAHG4TwxJLpxwuuVU7', 'content': [ContentBlock(text=\"As Harrison Chase told me, using LangChain involves a few key steps:\\n\\n1. **Set up your environment**: Install the necessary Python packages, including the LangChain library itself, as well as any other dependencies your application might require, such as language models or other integrations.\\n\\n2. **Understand the core concepts**: LangChain revolves around a few core concepts, like Agents, Chains, and Tools. Familiarize yourself with these concepts and how they work together to build powerful language-based applications.\\n\\n3. **Identify your use case**: Determine what kind of task or application you want to build using LangChain, such as a chatbot, a question-answering system, or a document summarization tool.\\n\\n4. **Choose the appropriate components**: Based on your use case, select the right LangChain components, such as agents, chains, and tools, to build your application.\\n\\n5. **Integrate with language models**: LangChain is designed to work seamlessly with various language models, such as OpenAI's GPT-3 or Anthropic's models. Connect your chosen language model to your LangChain application.\\n\\n6. **Implement your application logic**: Use LangChain's building blocks to implement the specific functionality of your application, such as prompting the language model, processing the response, and integrating with other services or data sources.\\n\\n7. **Test and iterate**: Thoroughly test your application, gather feedback, and iterate on your design and implementation to improve its performance and user experience.\\n\\nAs Harrison Chase emphasized, LangChain provides a flexible and powerful framework for building language-based applications, making it easier to leverage the capabilities of modern language models. By following these steps, you can get started with LangChain and create innovative solutions tailored to your specific needs.\", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=50, output_tokens=400)})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"how do I use LangChain?\"})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a14d0dca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='4', response_metadata={'id': 'msg_01UAKP81jTZu9fyiyFYhsbHc', 'content': [ContentBlock(text='4', type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=28, output_tokens=5)})"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"whats 2 + 2\"})"
]
},
{
"cell_type": "markdown",
"id": "5147b827",
"metadata": {},
"source": [
"## Using a RunnableBranch\n",
"\n",
"A `RunnableBranch` is a special type of runnable that allows you to define a set of conditions and runnables to execute based on the input. It does **not** offer anything that you can't achieve in a custom function as described above, so we recommend using a custom function instead.\n",
"\n",
"A `RunnableBranch` is initialized with a list of (condition, runnable) pairs and a default runnable. It selects which branch by passing each condition the input it's invoked with. It selects the first condition to evaluate to True, and runs the corresponding runnable to that condition with the input. \n",
"\n",
"If no provided conditions match, it runs the default runnable.\n",
"\n",
"Here's an example of what it looks like in action:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2a101418",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"As Dario Amodei told me, to use Anthropic, you should first familiarize yourself with our mission and principles. Anthropic is committed to developing safe and beneficial artificial intelligence that can help solve important problems facing humanity. \\n\\nTo get started, I recommend exploring the resources on our website, which cover our research, products, and approach to AI development. You can also reach out to our team to learn more about how Anthropic's technology and services can support your specific needs.\\n\\nThe key is to engage with us in a way that aligns with our values of transparency, ethical AI, and a commitment to the wellbeing of humanity. We're here to collaborate and help you harness the power of advanced AI responsibly.\", response_metadata={'id': 'msg_0187BVnpniPDJnVvwf3M1LdY', 'content': [ContentBlock(text=\"As Dario Amodei told me, to use Anthropic, you should first familiarize yourself with our mission and principles. Anthropic is committed to developing safe and beneficial artificial intelligence that can help solve important problems facing humanity. \\n\\nTo get started, I recommend exploring the resources on our website, which cover our research, products, and approach to AI development. You can also reach out to our team to learn more about how Anthropic's technology and services can support your specific needs.\\n\\nThe key is to engage with us in a way that aligns with our values of transparency, ethical AI, and a commitment to the wellbeing of humanity. We're here to collaborate and help you harness the power of advanced AI responsibly.\", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=53, output_tokens=160)})"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.runnables import RunnableBranch\n",
"\n",
"branch = RunnableBranch(\n",
" (lambda x: \"anthropic\" in x[\"topic\"].lower(), anthropic_chain),\n",
" (lambda x: \"langchain\" in x[\"topic\"].lower(), langchain_chain),\n",
" general_chain,\n",
")\n",
"full_chain = {\"topic\": chain, \"question\": lambda x: x[\"question\"]} | branch\n",
"full_chain.invoke({\"question\": \"how do I use Anthropic?\"})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "8d8caf9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"As Harrison Chase told me, using LangChain involves several key steps. First, you'll need to install the LangChain library and import the necessary modules. Then, you'll want to define your language model, any data sources you plan to use, and the specific tasks you want to accomplish, such as question answering, text generation, or agent-based reasoning. \\n\\nLangChain provides a flexible framework for building applications that leverage large language models. It includes abstractions for things like retrievers, prompts, and chains, which allow you to compose different components together to create powerful workflows. \\n\\nThe documentation on the LangChain website is excellent and covers many common use cases in detail. I'd recommend starting there to get a solid understanding of the core concepts and how to apply them to your specific needs. And of course, feel free to reach out if you have any other questions - I'm always happy to share more insights from my conversations with Harrison.\", response_metadata={'id': 'msg_01T1naS99wGPkEAP4LME8iAv', 'content': [ContentBlock(text=\"As Harrison Chase told me, using LangChain involves several key steps. First, you'll need to install the LangChain library and import the necessary modules. Then, you'll want to define your language model, any data sources you plan to use, and the specific tasks you want to accomplish, such as question answering, text generation, or agent-based reasoning. \\n\\nLangChain provides a flexible framework for building applications that leverage large language models. It includes abstractions for things like retrievers, prompts, and chains, which allow you to compose different components together to create powerful workflows. \\n\\nThe documentation on the LangChain website is excellent and covers many common use cases in detail. I'd recommend starting there to get a solid understanding of the core concepts and how to apply them to your specific needs. And of course, feel free to reach out if you have any other questions - I'm always happy to share more insights from my conversations with Harrison.\", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=50, output_tokens=205)})"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"how do I use LangChain?\"})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "26159af7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='4', response_metadata={'id': 'msg_01T6T3TS6hRCtU8JayN93QEi', 'content': [ContentBlock(text='4', type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=28, output_tokens=5)})"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_chain.invoke({\"question\": \"whats 2 + 2\"})"
]
},
{
"cell_type": "markdown",
"id": "fa0f589d",
"metadata": {},
"source": [
"## Routing by semantic similarity\n",
"\n",
"One especially useful technique is to use embeddings to route a query to the most relevant prompt. Here's an example."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a23457d7",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.utils.math import cosine_similarity\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import PromptTemplate\n",
"from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"physics_template = \"\"\"You are a very smart physics professor. \\\n",
"You are great at answering questions about physics in a concise and easy to understand manner. \\\n",
"When you don't know the answer to a question you admit that you don't know.\n",
"\n",
"Here is a question:\n",
"{query}\"\"\"\n",
"\n",
"math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\\n",
"You are so good because you are able to break down hard problems into their component parts, \\\n",
"answer the component parts, and then put them together to answer the broader question.\n",
"\n",
"Here is a question:\n",
"{query}\"\"\"\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"prompt_templates = [physics_template, math_template]\n",
"prompt_embeddings = embeddings.embed_documents(prompt_templates)\n",
"\n",
"\n",
"def prompt_router(input):\n",
" query_embedding = embeddings.embed_query(input[\"query\"])\n",
" similarity = cosine_similarity([query_embedding], prompt_embeddings)[0]\n",
" most_similar = prompt_templates[similarity.argmax()]\n",
" print(\"Using MATH\" if most_similar == math_template else \"Using PHYSICS\")\n",
" return PromptTemplate.from_template(most_similar)\n",
"\n",
"\n",
"chain = (\n",
" {\"query\": RunnablePassthrough()}\n",
" | RunnableLambda(prompt_router)\n",
" | ChatAnthropic(model=\"claude-3-haiku-20240307\")\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "664bb851",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using PHYSICS\n",
"As a physics professor, I would be happy to provide a concise and easy-to-understand explanation of what a black hole is.\n",
"\n",
"A black hole is an incredibly dense region of space-time where the gravitational pull is so strong that nothing, not even light, can escape from it. This means that if you were to get too close to a black hole, you would be pulled in and crushed by the intense gravitational forces.\n",
"\n",
"The formation of a black hole occurs when a massive star, much larger than our Sun, reaches the end of its life and collapses in on itself. This collapse causes the matter to become extremely dense, and the gravitational force becomes so strong that it creates a point of no return, known as the event horizon.\n",
"\n",
"Beyond the event horizon, the laws of physics as we know them break down, and the intense gravitational forces create a singularity, which is a point of infinite density and curvature in space-time.\n",
"\n",
"Black holes are fascinating and mysterious objects, and there is still much to be learned about their properties and behavior. If I were unsure about any specific details or aspects of black holes, I would readily admit that I do not have a complete understanding and would encourage further research and investigation.\n"
]
}
],
"source": [
"print(chain.invoke(\"What's a black hole\"))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "df34e469",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using MATH\n",
"A path integral is a powerful mathematical concept in physics, particularly in the field of quantum mechanics. It was developed by the renowned physicist Richard Feynman as an alternative formulation of quantum mechanics.\n",
"\n",
"In a path integral, instead of considering a single, definite path that a particle might take from one point to another, as in classical mechanics, the particle is considered to take all possible paths simultaneously. Each path is assigned a complex-valued weight, and the total probability amplitude for the particle to go from one point to another is calculated by summing (integrating) over all possible paths.\n",
"\n",
"The key ideas behind the path integral formulation are:\n",
"\n",
"1. Superposition principle: In quantum mechanics, particles can exist in a superposition of multiple states or paths simultaneously.\n",
"\n",
"2. Probability amplitude: The probability amplitude for a particle to go from one point to another is calculated by summing the complex-valued weights of all possible paths.\n",
"\n",
"3. Weighting of paths: Each path is assigned a weight based on the action (the time integral of the Lagrangian) along that path. Paths with lower action have a greater weight.\n",
"\n",
"4. Feynman's approach: Feynman developed the path integral formulation as an alternative to the traditional wave function approach in quantum mechanics, providing a more intuitive and conceptual understanding of quantum phenomena.\n",
"\n",
"The path integral approach is particularly useful in quantum field theory, where it provides a powerful framework for calculating transition probabilities and understanding the behavior of quantum systems. It has also found applications in various areas of physics, such as condensed matter, statistical mechanics, and even in finance (the path integral approach to option pricing).\n",
"\n",
"The mathematical construction of the path integral involves the use of advanced concepts from functional analysis and measure theory, making it a powerful and sophisticated tool in the physicist's arsenal.\n"
]
}
],
"source": [
"print(chain.invoke(\"What's a path integral\"))"
]
},
{
"cell_type": "markdown",
"id": "ff40bcb3",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"You've now learned how to add routing to your composed LCEL chains.\n",
"\n",
"Next, check out the other how-to guides on runnables in this section."
]
},
{
"cell_type": "markdown",
"id": "927b7498",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/self_query.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "c0bc3390-4bed-49d3-96ce-072badb4110b",
"metadata": {},
"source": [
"# How to do \"self-querying\" retrieval\n",
"\n",
":::info\n",
"\n",
"Head to [Integrations](/docs/integrations/retrievers/self_query) for documentation on vector stores with built-in support for self-querying.\n",
"\n",
":::\n",
"\n",
"A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to its underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documents but to also extract filters from the user query on the metadata of stored documents and to execute those filters.\n",
"\n",
"\n",
"\n",
"## Get started\n",
"For demonstration purposes we'll use a `Chroma` vector store. We've created a small demo set of documents that contain summaries of movies.\n",
"\n",
"**Note:** The self-query retriever requires you to have `lark` package installed."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e1486ca4-9785-4107-90bd-923505542167",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet lark langchain-chroma"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "beec3e35-3750-408c-9f2a-d92cf0a9a321",
"metadata": {},
"outputs": [],
"source": [
"from langchain_chroma import Chroma\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"docs = [\n",
" Document(\n",
" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
" metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n",
" ),\n",
" Document(\n",
" page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
" metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
" ),\n",
" Document(\n",
" page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
" metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
" ),\n",
" Document(\n",
" page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
" metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
" ),\n",
" Document(\n",
" page_content=\"Toys come alive and have a blast doing so\",\n",
" metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
" ),\n",
" Document(\n",
" page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
" metadata={\n",
" \"year\": 1979,\n",
" \"director\": \"Andrei Tarkovsky\",\n",
" \"genre\": \"thriller\",\n",
" \"rating\": 9.9,\n",
" },\n",
" ),\n",
"]\n",
"vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())"
]
},
{
"cell_type": "markdown",
"id": "99771131-1efb-42e2-95f8-2aaa95f37677",
"metadata": {},
"source": [
"### Creating our self-querying retriever\n",
"\n",
"Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7832ca43-cc17-4375-bf4e-679b99584568",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.query_constructor.base import AttributeInfo\n",
"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"metadata_field_info = [\n",
" AttributeInfo(\n",
" name=\"genre\",\n",
" description=\"The genre of the movie. One of ['science fiction', 'comedy', 'drama', 'thriller', 'romance', 'action', 'animated']\",\n",
" type=\"string\",\n",
" ),\n",
" AttributeInfo(\n",
" name=\"year\",\n",
" description=\"The year the movie was released\",\n",
" type=\"integer\",\n",
" ),\n",
" AttributeInfo(\n",
" name=\"director\",\n",
" description=\"The name of the movie director\",\n",
" type=\"string\",\n",
" ),\n",
" AttributeInfo(\n",
" name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
" ),\n",
"]\n",
"document_content_description = \"Brief summary of a movie\"\n",
"llm = ChatOpenAI(temperature=0)\n",
"retriever = SelfQueryRetriever.from_llm(\n",
" llm,\n",
" vectorstore,\n",
" document_content_description,\n",
" metadata_field_info,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "9c66f4c8-3682-46ac-8f17-0839194888a3",
"metadata": {},
"source": [
"### Testing it out\n",
"\n",
"And now we can actually try using our retriever!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "21c5df28-ea78-4f4e-99d6-489c864d1a04",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': 'thriller', 'rating': 9.9, 'year': 1979}),\n",
" Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006})]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example only specifies a filter\n",
"retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "228e5d70-d4cf-43bb-bc8e-3d6f11e784f2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'director': 'Greta Gerwig', 'rating': 8.3, 'year': 2019})]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example specifies a query and a filter\n",
"retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8244591e-97b5-4aba-b1e5-fe5e1996cb99",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2006}),\n",
" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'director': 'Andrei Tarkovsky', 'genre': 'thriller', 'rating': 9.9, 'year': 1979})]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example specifies a composite filter\n",
"retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "420a6906-66fb-449f-8626-2e399ae5e6a8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995})]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This example specifies a query and composite filter\n",
"retriever.invoke(\n",
" \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "4f25a751-f1d2-405e-84d6-fe9e4f60ce95",
"metadata": {},
"source": [
"### Filter k\n",
"\n",
"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
"\n",
"We can do this by passing `enable_limit=True` to the constructor."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ab56595f-0fb4-4b7f-8fc1-e85eff13255a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'genre': 'science fiction', 'rating': 7.7, 'year': 1993}),\n",
" Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995})]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever = SelfQueryRetriever.from_llm(\n",
" llm,\n",
" vectorstore,\n",
" document_content_description,\n",
" metadata_field_info,\n",
" enable_limit=True,\n",
")\n",
"\n",
"# This example only specifies a relevant query\n",
"retriever.invoke(\"What are two movies about dinosaurs\")"
]
},
{
"cell_type": "markdown",
"id": "51e144c4-cbf4-4540-92e7-9a68e05f2480",
"metadata": {},
"source": [
"## Constructing from scratch with LCEL\n",
"\n",
"To see what's going on under the hood, and to have more custom control, we can reconstruct our retriever from scratch.\n",
"\n",
"First, we need to create a query-construction chain. This chain will take a user query and generated a `StructuredQuery` object which captures the filters specified by the user. We provide some helper functions for creating a prompt and output parser. These have a number of tunable params that we'll ignore here for simplicity."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c5f501ac-46c1-4a54-9d23-c0530e8c88f0",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.query_constructor.base import (\n",
" StructuredQueryOutputParser,\n",
" get_query_constructor_prompt,\n",
")\n",
"\n",
"prompt = get_query_constructor_prompt(\n",
" document_content_description,\n",
" metadata_field_info,\n",
")\n",
"output_parser = StructuredQueryOutputParser.from_components()\n",
"query_constructor = prompt | llm | output_parser"
]
},
{
"cell_type": "markdown",
"id": "8deb5d44-632f-4f41-b139-fc811979e6e8",
"metadata": {},
"source": [
"Let's look at our prompt:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "eed553cb-8575-486b-8349-0806b7817a8c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your goal is to structure the user's query to match the request schema provided below.\n",
"\n",
"<< Structured Request Schema >>\n",
"When responding use a markdown code snippet with a JSON object formatted in the following schema:\n",
"\n",
"```json\n",
"{\n",
" \"query\": string \\ text string to compare to document contents\n",
" \"filter\": string \\ logical condition statement for filtering documents\n",
"}\n",
"```\n",
"\n",
"The query string should contain only text that is expected to match the contents of documents. Any conditions in the filter should not be mentioned in the query as well.\n",
"\n",
"A logical condition statement is composed of one or more comparison and logical operation statements.\n",
"\n",
"A comparison statement takes the form: `comp(attr, val)`:\n",
"- `comp` (eq | ne | gt | gte | lt | lte | contain | like | in | nin): comparator\n",
"- `attr` (string): name of attribute to apply the comparison to\n",
"- `val` (string): is the comparison value\n",
"\n",
"A logical operation statement takes the form `op(statement1, statement2, ...)`:\n",
"- `op` (and | or | not): logical operator\n",
"- `statement1`, `statement2`, ... (comparison statements or logical operation statements): one or more statements to apply the operation to\n",
"\n",
"Make sure that you only use the comparators and logical operators listed above and no others.\n",
"Make sure that filters only refer to attributes that exist in the data source.\n",
"Make sure that filters only use the attributed names with its function names if there are functions applied on them.\n",
"Make sure that filters only use format `YYYY-MM-DD` when handling date data typed values.\n",
"Make sure that filters take into account the descriptions of attributes and only make comparisons that are feasible given the type of data being stored.\n",
"Make sure that filters are only used as needed. If there are no filters that should be applied return \"NO_FILTER\" for the filter value.\n",
"\n",
"<< Example 1. >>\n",
"Data Source:\n",
"```json\n",
"{\n",
" \"content\": \"Lyrics of a song\",\n",
" \"attributes\": {\n",
" \"artist\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Name of the song artist\"\n",
" },\n",
" \"length\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"Length of the song in seconds\"\n",
" },\n",
" \"genre\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The song genre, one of \"pop\", \"rock\" or \"rap\"\"\n",
" }\n",
" }\n",
"}\n",
"```\n",
"\n",
"User Query:\n",
"What are songs by Taylor Swift or Katy Perry about teenage romance under 3 minutes long in the dance pop genre\n",
"\n",
"Structured Request:\n",
"```json\n",
"{\n",
" \"query\": \"teenager love\",\n",
" \"filter\": \"and(or(eq(\\\"artist\\\", \\\"Taylor Swift\\\"), eq(\\\"artist\\\", \\\"Katy Perry\\\")), lt(\\\"length\\\", 180), eq(\\\"genre\\\", \\\"pop\\\"))\"\n",
"}\n",
"```\n",
"\n",
"\n",
"<< Example 2. >>\n",
"Data Source:\n",
"```json\n",
"{\n",
" \"content\": \"Lyrics of a song\",\n",
" \"attributes\": {\n",
" \"artist\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Name of the song artist\"\n",
" },\n",
" \"length\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"Length of the song in seconds\"\n",
" },\n",
" \"genre\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The song genre, one of \"pop\", \"rock\" or \"rap\"\"\n",
" }\n",
" }\n",
"}\n",
"```\n",
"\n",
"User Query:\n",
"What are songs that were not published on Spotify\n",
"\n",
"Structured Request:\n",
"```json\n",
"{\n",
" \"query\": \"\",\n",
" \"filter\": \"NO_FILTER\"\n",
"}\n",
"```\n",
"\n",
"\n",
"<< Example 3. >>\n",
"Data Source:\n",
"```json\n",
"{\n",
" \"content\": \"Brief summary of a movie\",\n",
" \"attributes\": {\n",
" \"genre\": {\n",
" \"description\": \"The genre of the movie. One of ['science fiction', 'comedy', 'drama', 'thriller', 'romance', 'action', 'animated']\",\n",
" \"type\": \"string\"\n",
" },\n",
" \"year\": {\n",
" \"description\": \"The year the movie was released\",\n",
" \"type\": \"integer\"\n",
" },\n",
" \"director\": {\n",
" \"description\": \"The name of the movie director\",\n",
" \"type\": \"string\"\n",
" },\n",
" \"rating\": {\n",
" \"description\": \"A 1-10 rating for the movie\",\n",
" \"type\": \"float\"\n",
" }\n",
"}\n",
"}\n",
"```\n",
"\n",
"User Query:\n",
"dummy question\n",
"\n",
"Structured Request:\n",
"\n"
]
}
],
"source": [
"print(prompt.format(query=\"dummy question\"))"
]
},
{
"cell_type": "markdown",
"id": "00420512-c395-4661-8d07-c7f6f1b45793",
"metadata": {},
"source": [
"And what our full chain produces:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "139cce01-ca75-452b-8de2-033ceec27158",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StructuredQuery(query='taxi driver', filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2000)]), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Luc Besson')]), limit=None)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_constructor.invoke(\n",
" {\n",
" \"query\": \"What are some sci-fi movies from the 90's directed by Luc Besson about taxi drivers\"\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "9582a5fa-ffed-4d50-ad74-9b12d7d94b2a",
"metadata": {},
"source": [
"The query constructor is the key element of the self-query retriever. To make a great retrieval system you'll need to make sure your query constructor works well. Often this requires adjusting the prompt, the examples in the prompt, the attribute descriptions, etc. For an example that walks through refining a query constructor on some hotel inventory data, [check out this cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/self_query_hotel_search.ipynb).\n",
"\n",
"The next key element is the structured query translator. This is the object responsible for translating the generic `StructuredQuery` object into a metadata filter in the syntax of the vector store you're using. LangChain comes with a number of built-in translators. To see them all head to the [Integrations section](/docs/integrations/retrievers/self_query)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "05f07ead-9aac-4079-9dde-784cb7aa1a8a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.retrievers.self_query.chroma import ChromaTranslator\n",
"\n",
"retriever = SelfQueryRetriever(\n",
" query_constructor=query_constructor,\n",
" vectorstore=vectorstore,\n",
" structured_query_translator=ChromaTranslator(),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0ee155c9-7b02-4fe9-8de3-e37385c465af",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995})]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.invoke(\n",
" \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/semantic-chunker.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "c3ee8d00",
"metadata": {},
"source": [
"# How to split text based on semantic similarity\n",
"\n",
"Taken from Greg Kamradt's wonderful notebook:\n",
"[5_Levels_Of_Text_Splitting](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb)\n",
"\n",
"All credit to him.\n",
"\n",
"This guide covers how to split chunks based on their semantic similarity. If embeddings are sufficiently far apart, chunks are split.\n",
"\n",
"At a high level, this splits into sentences, then groups into groups of 3\n",
"sentences, and then merges one that are similar in the embedding space."
]
},
{
"cell_type": "markdown",
"id": "542f4427",
"metadata": {},
"source": [
"## Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d8c58769",
"metadata": {},
"outputs": [],
"source": [
"!pip install --quiet langchain_experimental langchain_openai"
]
},
{
"cell_type": "markdown",
"id": "c20cdf54",
"metadata": {},
"source": [
"## Load Example Data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "313fb032",
"metadata": {},
"outputs": [],
"source": [
"# This is a long document we can split up.\n",
"with open(\"state_of_the_union.txt\") as f:\n",
" state_of_the_union = f.read()"
]
},
{
"cell_type": "markdown",
"id": "f7436e15",
"metadata": {},
"source": [
"## Create Text Splitter"
]
},
{
"cell_type": "markdown",
"id": "774a5199-c2ff-43bc-bf07-87573e0b8db4",
"metadata": {},
"source": [
"To instantiate a [SemanticChunker](https://api.python.langchain.com/en/latest/text_splitter/langchain_experimental.text_splitter.SemanticChunker.html), we must specify an embedding model. Below we will use [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openai.OpenAIEmbeddings.html). "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a88ff70c",
"metadata": {},
"outputs": [],
"source": [
"from langchain_experimental.text_splitter import SemanticChunker\n",
"from langchain_openai.embeddings import OpenAIEmbeddings\n",
"\n",
"text_splitter = SemanticChunker(OpenAIEmbeddings())"
]
},
{
"cell_type": "markdown",
"id": "91b14834",
"metadata": {},
"source": [
"## Split Text\n",
"\n",
"We split text in the usual way, e.g., by invoking `.create_documents` to create LangChain [Document](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) objects:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "295ec095",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. Last year COVID-19 kept us apart. This year we are finally together again. Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. With a duty to one another to the American people to the Constitution. And with an unwavering resolve that freedom will always triumph over tyranny. Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. He met the Ukrainian people. From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. They keep moving.\n"
]
}
],
"source": [
"docs = text_splitter.create_documents([state_of_the_union])\n",
"print(docs[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "9aed73b2",
"metadata": {},
"source": [
"## Breakpoints\n",
"\n",
"This chunker works by determining when to \"break\" apart sentences. This is done by looking for differences in embeddings between any two sentences. When that difference is past some threshold, then they are split.\n",
"\n",
"There are a few ways to determine what that threshold is, which are controlled by the `breakpoint_threshold_type` kwarg.\n",
"\n",
"### Percentile\n",
"\n",
"The default way to split is based on percentile. In this method, all differences between sentences are calculated, and then any difference greater than the X percentile is split."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a9a3b9cd",
"metadata": {},
"outputs": [],
"source": [
"text_splitter = SemanticChunker(\n",
" OpenAIEmbeddings(), breakpoint_threshold_type=\"percentile\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f311e67e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. Last year COVID-19 kept us apart. This year we are finally together again. Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. With a duty to one another to the American people to the Constitution. And with an unwavering resolve that freedom will always triumph over tyranny. Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. He met the Ukrainian people. From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. They keep moving.\n"
]
}
],
"source": [
"docs = text_splitter.create_documents([state_of_the_union])\n",
"print(docs[0].page_content)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5f5930de",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"26\n"
]
}
],
"source": [
"print(len(docs))"
]
},
{
"cell_type": "markdown",
"id": "b6b51104",
"metadata": {},
"source": [
"### Standard Deviation\n",
"\n",
"In this method, any difference greater than X standard deviations is split."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ff5e005c",
"metadata": {},
"outputs": [],
"source": [
"text_splitter = SemanticChunker(\n",
" OpenAIEmbeddings(), breakpoint_threshold_type=\"standard_deviation\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "01b8ffc0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. Last year COVID-19 kept us apart. This year we are finally together again. Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. With a duty to one another to the American people to the Constitution. And with an unwavering resolve that freedom will always triumph over tyranny. Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. He met the Ukrainian people. From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. They keep moving. And the costs and the threats to America and the world keep rising. That’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. The United States is a member along with 29 other nations. It matters. American diplomacy matters. American resolve matters. Putin’s latest attack on Ukraine was premeditated and unprovoked. He rejected repeated efforts at diplomacy. He thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready. Here is what we did. We prepared extensively and carefully. We spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. I spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression. We countered Russia’s lies with truth. And now that he has acted the free world is holding him accountable. Along with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland. We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. Together with our allies –we are right now enforcing powerful economic sanctions. We are cutting off Russia’s largest banks from the international financial system. Preventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless. We are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come. Tonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. The U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs. We are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains. And tonight I am announcing that we will join our allies in closing off American air space to all Russian flights – further isolating Russia – and adding an additional squeeze –on their economy. The Ruble has lost 30% of its value. The Russian stock market has lost 40% of its value and trading remains suspended. Russia’s economy is reeling and Putin alone is to blame. Together with our allies we are providing support to the Ukrainians in their fight for freedom. Military assistance. Economic assistance. Humanitarian assistance. We are giving more than $1 Billion in direct assistance to Ukraine. And we will continue to aid the Ukrainian people as they defend their country and to help ease their suffering. Let me be clear, our forces are not engaged and will not engage in conflict with Russian forces in Ukraine. Our forces are not going to Europe to fight in Ukraine, but to defend our NATO Allies – in the event that Putin decides to keep moving west. For that purpose we’ve mobilized American ground forces, air squadrons, and ship deployments to protect NATO countries including Poland, Romania, Latvia, Lithuania, and Estonia. As I have made crystal clear the United States and our Allies will defend every inch of territory of NATO countries with the full force of our collective power. And we remain clear-eyed. The Ukrainians are fighting back with pure courage. But the next few days weeks, months, will be hard on them. Putin has unleashed violence and chaos. But while he may make gains on the battlefield – he will pay a continuing high price over the long run. And a proud Ukrainian people, who have known 30 years of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards. To all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. And I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world. America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies. These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming.\n"
]
}
],
"source": [
"docs = text_splitter.create_documents([state_of_the_union])\n",
"print(docs[0].page_content)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "8938a5e3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"print(len(docs))"
]
},
{
"cell_type": "markdown",
"id": "6897261f",
"metadata": {},
"source": [
"### Interquartile\n",
"\n",
"In this method, the interquartile distance is used to split chunks."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "8977355b",
"metadata": {},
"outputs": [],
"source": [
"text_splitter = SemanticChunker(\n",
" OpenAIEmbeddings(), breakpoint_threshold_type=\"interquartile\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "59a40364",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. Last year COVID-19 kept us apart. This year we are finally together again. Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. With a duty to one another to the American people to the Constitution. And with an unwavering resolve that freedom will always triumph over tyranny. Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. He thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. He met the Ukrainian people. From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. Please rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. Throughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. They keep moving.\n"
]
}
],
"source": [
"docs = text_splitter.create_documents([state_of_the_union])\n",
"print(docs[0].page_content)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3a0db107",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"25\n"
]
}
],
"source": [
"print(len(docs))"
]
},
{
"cell_type": "markdown",
"source": [
"### Gradient\n",
"\n",
"In this method, the gradient of distance is used to split chunks along with the percentile method.\n",
"This method is useful when chunks are highly correlated with each other or specific to a domain e.g. legal or medical. The idea is to apply anomaly detection on gradient array so that the distribution become wider and easy to identify boundaries in highly semantic data."
],
"metadata": {
"collapsed": false
},
"id": "423c6e099e94ca69"
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1f65472",
"metadata": {},
"outputs": [],
"source": [
"text_splitter = SemanticChunker(\n",
" OpenAIEmbeddings(), breakpoint_threshold_type=\"gradient\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.\n"
]
}
],
"source": [
"docs = text_splitter.create_documents([state_of_the_union])\n",
"print(docs[0].page_content)"
],
"metadata": {},
"id": "e9f393d316ce1f6c"
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"26\n"
]
}
],
"source": [
"print(len(docs))"
],
"metadata": {},
"id": "a407cd57f02a0db4"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/sequence.ipynb | {
"cells": [
{
"cell_type": "raw",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"keywords: [Runnable, Runnables, RunnableSequence, LCEL, chain, chains, chaining]\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to chain runnables\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parser](/docs/concepts/#output-parsers)\n",
"\n",
":::\n",
"\n",
"One point about [LangChain Expression Language](/docs/concepts/#langchain-expression-language) is that any two runnables can be \"chained\" together into sequences. The output of the previous runnable's `.invoke()` call is passed as input to the next runnable. This can be done using the pipe operator (`|`), or the more explicit `.pipe()` method, which does the same thing.\n",
"\n",
"The resulting [`RunnableSequence`](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableSequence.html) is itself a runnable, which means it can be invoked, streamed, or further chained just like any other runnable. Advantages of chaining runnables in this way are efficient streaming (the sequence will stream output as soon as it is available), and debugging and tracing with tools like [LangSmith](/docs/how_to/debugging).\n",
"\n",
"## The pipe operator: `|`\n",
"\n",
"To show off how this works, let's go through an example. We'll walk through a common pattern in LangChain: using a [prompt template](/docs/how_to#prompt-templates) to format input into a [chat model](/docs/how_to#chat-models), and finally converting the chat message output into a string with an [output parser](/docs/how_to#output-parsers).\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs\n",
" customVarName=\"model\"\n",
"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"%pip install -qU langchain langchain_anthropic\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
"\n",
"model = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"prompt = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\")\n",
"\n",
"chain = prompt | model | StrOutputParser()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prompts and models are both runnable, and the output type from the prompt call is the same as the input type of the chat model, so we can chain them together. We can then invoke the resulting sequence like any other runnable:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Here's a bear joke for you:\\n\\nWhy did the bear dissolve in water?\\nBecause it was a polar bear!\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke({\"topic\": \"bears\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Coercion\n",
"\n",
"We can even combine this chain with more runnables to create another chain. This may involve some input/output formatting using other types of runnables, depending on the required inputs and outputs of the chain components.\n",
"\n",
"For example, let's say we wanted to compose the joke generating chain with another chain that evaluates whether or not the generated joke was funny.\n",
"\n",
"We would need to be careful with how we format the input into the next chain. In the below example, the dict in the chain is automatically parsed and converted into a [`RunnableParallel`](/docs/how_to/parallel), which runs all of its values in parallel and returns a dict with the results.\n",
"\n",
"This happens to be the same format the next prompt template expects. Here it is in action:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Haha, that\\'s a clever play on words! Using \"polar\" to imply the bear dissolved or became polar/polarized when put in water. Not the most hilarious joke ever, but it has a cute, groan-worthy pun that makes it mildly amusing. I appreciate a good pun or wordplay joke.'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"analysis_prompt = ChatPromptTemplate.from_template(\"is this a funny joke? {joke}\")\n",
"\n",
"composed_chain = {\"joke\": chain} | analysis_prompt | model | StrOutputParser()\n",
"\n",
"composed_chain.invoke({\"topic\": \"bears\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions will also be coerced into runnables, so you can add custom logic to your chains too. The below chain results in the same logical flow as before:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Haha, that's a cute and punny joke! I like how it plays on the idea of beets blushing or turning red like someone blushing. Food puns can be quite amusing. While not a total knee-slapper, it's a light-hearted, groan-worthy dad joke that would make me chuckle and shake my head. Simple vegetable humor!\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"composed_chain_with_lambda = (\n",
" chain\n",
" | (lambda input: {\"joke\": input})\n",
" | analysis_prompt\n",
" | model\n",
" | StrOutputParser()\n",
")\n",
"\n",
"composed_chain_with_lambda.invoke({\"topic\": \"beets\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, keep in mind that using functions like this may interfere with operations like streaming. See [this section](/docs/how_to/functions) for more information."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `.pipe()` method\n",
"\n",
"We could also compose the same sequence using the `.pipe()` method. Here's what that looks like:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"I cannot reproduce any copyrighted material verbatim, but I can try to analyze the humor in the joke you provided without quoting it directly.\\n\\nThe joke plays on the idea that the Cylon raiders, who are the antagonists in the Battlestar Galactica universe, failed to locate the human survivors after attacking their home planets (the Twelve Colonies) due to using an outdated and poorly performing operating system (Windows Vista) for their targeting systems.\\n\\nThe humor stems from the juxtaposition of a futuristic science fiction setting with a relatable real-world frustration – the use of buggy, slow, or unreliable software or technology. It pokes fun at the perceived inadequacies of Windows Vista, which was widely criticized for its performance issues and other problems when it was released.\\n\\nBy attributing the Cylons' failure to locate the humans to their use of Vista, the joke creates an amusing and unexpected connection between a fictional advanced race of robots and a familiar technological annoyance experienced by many people in the real world.\\n\\nOverall, the joke relies on incongruity and relatability to generate humor, but without reproducing any copyrighted material directly.\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.runnables import RunnableParallel\n",
"\n",
"composed_chain_with_pipe = (\n",
" RunnableParallel({\"joke\": chain})\n",
" .pipe(analysis_prompt)\n",
" .pipe(model)\n",
" .pipe(StrOutputParser())\n",
")\n",
"\n",
"composed_chain_with_pipe.invoke({\"topic\": \"battlestar galactica\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or the abbreviated:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"composed_chain_with_pipe = RunnableParallel({\"joke\": chain}).pipe(\n",
" analysis_prompt, model, StrOutputParser()\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Related\n",
"\n",
"- [Streaming](/docs/how_to/streaming/): Check out the streaming guide to understand the streaming behavior of a chain\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/serialization.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "ab3dc782-321e-4503-96ee-ac88a15e4b5e",
"metadata": {},
"source": [
"# How to save and load LangChain objects\n",
"\n",
"LangChain classes implement standard methods for serialization. Serializing LangChain objects using these methods confer some advantages:\n",
"\n",
"- Secrets, such as API keys, are separated from other parameters and can be loaded back to the object on de-serialization;\n",
"- De-serialization is kept compatible across package versions, so objects that were serialized with one version of LangChain can be properly de-serialized with another.\n",
"\n",
"To save and load LangChain objects using this system, use the `dumpd`, `dumps`, `load`, and `loads` functions in the [load module](https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.load) of `langchain-core`. These functions support JSON and JSON-serializable objects.\n",
"\n",
"All LangChain objects that inherit from [Serializable](https://api.python.langchain.com/en/latest/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.messages), [document objects](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/#retrievers)), and most [Runnables](/docs/concepts/#langchain-expression-language-lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
"\n",
"Below we walk through an example with a simple [LLM chain](/docs/tutorials/llm_chain).\n",
"\n",
":::{.callout-caution}\n",
"\n",
"De-serialization using `load` and `loads` can instantiate any serializable LangChain object. Only use this feature with trusted inputs!\n",
"\n",
"De-serialization is a beta feature and is subject to change.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f85d9e51-2a36-4f69-83b1-c716cd43f790",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.load import dumpd, dumps, load, loads\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"Translate the following into {language}:\"),\n",
" (\"user\", \"{text}\"),\n",
" ],\n",
")\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", api_key=\"llm-api-key\")\n",
"\n",
"chain = prompt | llm"
]
},
{
"cell_type": "markdown",
"id": "356ea99f-5cb5-4433-9a6c-2443d2be9ed3",
"metadata": {},
"source": [
"## Saving objects\n",
"\n",
"### To json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "26516764-d46b-4357-a6c6-bd8315bfa530",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"lc\": 1,\n",
" \"type\": \"constructor\",\n",
" \"id\": [\n",
" \"langchain\",\n",
" \"schema\",\n",
" \"runnable\",\n",
" \"RunnableSequence\"\n",
" ],\n",
" \"kwargs\": {\n",
" \"first\": {\n",
" \"lc\": 1,\n",
" \"type\": \"constructor\",\n",
" \"id\": [\n",
" \"langchain\",\n",
" \"prompts\",\n",
" \"chat\",\n",
" \"ChatPromptTemplate\"\n",
" ],\n",
" \"kwargs\": {\n",
" \"input_variables\": [\n",
" \"language\",\n",
" \"text\"\n",
" ],\n",
" \"messages\": [\n",
" {\n",
" \"lc\": 1,\n",
" \"type\": \"constructor\",\n",
" \n"
]
}
],
"source": [
"string_representation = dumps(chain, pretty=True)\n",
"print(string_representation[:500])"
]
},
{
"cell_type": "markdown",
"id": "bd425716-545d-466b-a4e5-dc9952cfd72a",
"metadata": {},
"source": [
"### To a json-serializable Python dict"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6561a968-1741-4419-8c29-e705b9d0ef39",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'dict'>\n"
]
}
],
"source": [
"dict_representation = dumpd(chain)\n",
"\n",
"print(type(dict_representation))"
]
},
{
"cell_type": "markdown",
"id": "711e986e-dd24-4839-9e38-c57903378a5f",
"metadata": {},
"source": [
"### To disk"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f818378b-f4d6-43a7-895b-76cf7359b157",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"with open(\"/tmp/chain.json\", \"w\") as fp:\n",
" json.dump(string_representation, fp)"
]
},
{
"cell_type": "markdown",
"id": "1e621a32-ff5f-4627-ad59-88cacba73c6b",
"metadata": {},
"source": [
"Note that the API key is withheld from the serialized representations. Parameters that are considered secret are specified by the `.lc_secrets` attribute of the LangChain object:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8225e150-000a-4fbc-9f3d-09568f4b560b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'openai_api_key': 'OPENAI_API_KEY'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.last.lc_secrets"
]
},
{
"cell_type": "markdown",
"id": "6d090177-eb1c-4bfb-8c13-29286afe17d9",
"metadata": {},
"source": [
"## Loading objects\n",
"\n",
"Specifying `secrets_map` in `load` and `loads` will load the corresponding secrets onto the de-serialized LangChain object.\n",
"\n",
"### From string"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "54a66267-5f3a-40a2-bfcc-8b44bb24c154",
"metadata": {},
"outputs": [],
"source": [
"chain = loads(string_representation, secrets_map={\"OPENAI_API_KEY\": \"llm-api-key\"})"
]
},
{
"cell_type": "markdown",
"id": "5ed9aff1-92cc-44ba-b2ec-4d12f924fa03",
"metadata": {},
"source": [
"### From dict"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "76979932-13de-4427-9f88-040fb05a6778",
"metadata": {},
"outputs": [],
"source": [
"chain = load(dict_representation, secrets_map={\"OPENAI_API_KEY\": \"llm-api-key\"})"
]
},
{
"cell_type": "markdown",
"id": "7dd81a2a-5163-414d-ab42-f1c35e30471b",
"metadata": {},
"source": [
"### From disk"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "033f62a7-3377-472a-be58-718baa6ab445",
"metadata": {},
"outputs": [],
"source": [
"with open(\"/tmp/chain.json\", \"r\") as fp:\n",
" chain = loads(json.load(fp), secrets_map={\"OPENAI_API_KEY\": \"llm-api-key\"})"
]
},
{
"cell_type": "markdown",
"id": "dc520fdb-035a-468f-a8a8-c3ffe8ed98eb",
"metadata": {},
"source": [
"Note that we recover the API key specified at the start of the guide:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "566b2475-d9b4-432b-8c3b-27c2f183624e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'llm-api-key'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.last.openai_api_key.get_secret_value()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b4cba53-e1d5-4979-927e-b5794a02afc3",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/split_by_token.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "a05c860c",
"metadata": {},
"source": [
"# How to split text by tokens \n",
"\n",
"Language models have a token limit. You should not exceed the token limit. When you split your text into chunks it is therefore a good idea to count the number of tokens. There are many tokenizers. When you count tokens in your text you should use the same tokenizer as used in the language model. "
]
},
{
"cell_type": "markdown",
"id": "7683b36a",
"metadata": {},
"source": [
"## tiktoken\n",
"\n",
":::{.callout-note}\n",
"[tiktoken](https://github.com/openai/tiktoken) is a fast `BPE` tokenizer created by `OpenAI`.\n",
":::\n",
"\n",
"\n",
"We can use `tiktoken` to estimate tokens used. It will probably be more accurate for the OpenAI models.\n",
"\n",
"1. How the text is split: by character passed in.\n",
"2. How the chunk size is measured: by `tiktoken` tokenizer.\n",
"\n",
"[CharacterTextSplitter](https://api.python.langchain.com/en/latest/character/langchain_text_splitters.character.CharacterTextSplitter.html), [RecursiveCharacterTextSplitter](https://api.python.langchain.com/en/latest/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html), and [TokenTextSplitter](https://api.python.langchain.com/en/latest/base/langchain_text_splitters.base.TokenTextSplitter.html) can be used with `tiktoken` directly."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c4ef83e-f43a-4658-ad1a-3952e0a5bbe7",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-text-splitters tiktoken"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1ad2d0f2",
"metadata": {},
"outputs": [],
"source": [
"from langchain_text_splitters import CharacterTextSplitter\n",
"\n",
"# This is a long document we can split up.\n",
"with open(\"state_of_the_union.txt\") as f:\n",
" state_of_the_union = f.read()"
]
},
{
"cell_type": "markdown",
"id": "a3ba1d8a",
"metadata": {},
"source": [
"To split with a [CharacterTextSplitter](https://api.python.langchain.com/en/latest/character/langchain_text_splitters.character.CharacterTextSplitter.html) and then merge chunks with `tiktoken`, use its `.from_tiktoken_encoder()` method. Note that splits from this method can be larger than the chunk size measured by the `tiktoken` tokenizer.\n",
"\n",
"The `.from_tiktoken_encoder()` method takes either `encoding_name` as an argument (e.g. `cl100k_base`), or the `model_name` (e.g. `gpt-4`). All additional arguments like `chunk_size`, `chunk_overlap`, and `separators` are used to instantiate `CharacterTextSplitter`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "825f7c0a",
"metadata": {},
"outputs": [],
"source": [
"text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n",
" encoding_name=\"cl100k_base\", chunk_size=100, chunk_overlap=0\n",
")\n",
"texts = text_splitter.split_text(state_of_the_union)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ae35d165",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n",
"\n",
"Last year COVID-19 kept us apart. This year we are finally together again. \n",
"\n",
"Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
"\n",
"With a duty to one another to the American people to the Constitution.\n"
]
}
],
"source": [
"print(texts[0])"
]
},
{
"cell_type": "markdown",
"id": "de5b6a6e",
"metadata": {},
"source": [
"To implement a hard constraint on the chunk size, we can use `RecursiveCharacterTextSplitter.from_tiktoken_encoder`, where each split will be recursively split if it has a larger size:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0262a991",
"metadata": {},
"outputs": [],
"source": [
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
" model_name=\"gpt-4\",\n",
" chunk_size=100,\n",
" chunk_overlap=0,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "04457e3a",
"metadata": {},
"source": [
"We can also load a `TokenTextSplitter` splitter, which works with `tiktoken` directly and will ensure each split is smaller than chunk size."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4454c70e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our\n"
]
}
],
"source": [
"from langchain_text_splitters import TokenTextSplitter\n",
"\n",
"text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0)\n",
"\n",
"texts = text_splitter.split_text(state_of_the_union)\n",
"print(texts[0])"
]
},
{
"cell_type": "markdown",
"id": "3bc155d0",
"metadata": {},
"source": [
"Some written languages (e.g. Chinese and Japanese) have characters which encode to 2 or more tokens. Using the `TokenTextSplitter` directly can split the tokens for a character between two chunks causing malformed Unicode characters. Use `RecursiveCharacterTextSplitter.from_tiktoken_encoder` or `CharacterTextSplitter.from_tiktoken_encoder` to ensure chunks contain valid Unicode strings."
]
},
{
"cell_type": "markdown",
"id": "55f95f06",
"metadata": {},
"source": [
"## spaCy\n",
"\n",
":::{.callout-note}\n",
"[spaCy](https://spacy.io/) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.\n",
":::\n",
"\n",
"LangChain implements splitters based on the [spaCy tokenizer](https://spacy.io/api/tokenizer).\n",
"\n",
"1. How the text is split: by `spaCy` tokenizer.\n",
"2. How the chunk size is measured: by number of characters."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0b9242f-690c-4819-b35a-bb68187281ed",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet spacy"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f1de7767",
"metadata": {},
"outputs": [],
"source": [
"# This is a long document we can split up.\n",
"with open(\"state_of_the_union.txt\") as f:\n",
" state_of_the_union = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cef2b29e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.\n",
"\n",
"Members of Congress and the Cabinet.\n",
"\n",
"Justices of the Supreme Court.\n",
"\n",
"My fellow Americans. \n",
"\n",
"\n",
"\n",
"Last year COVID-19 kept us apart.\n",
"\n",
"This year we are finally together again. \n",
"\n",
"\n",
"\n",
"Tonight, we meet as Democrats Republicans and Independents.\n",
"\n",
"But most importantly as Americans. \n",
"\n",
"\n",
"\n",
"With a duty to one another to the American people to the Constitution. \n",
"\n",
"\n",
"\n",
"And with an unwavering resolve that freedom will always triumph over tyranny. \n",
"\n",
"\n",
"\n",
"Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.\n",
"\n",
"But he badly miscalculated. \n",
"\n",
"\n",
"\n",
"He thought he could roll into Ukraine and the world would roll over.\n",
"\n",
"Instead he met a wall of strength he never imagined. \n",
"\n",
"\n",
"\n",
"He met the Ukrainian people. \n",
"\n",
"\n",
"\n",
"From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.\n"
]
}
],
"source": [
"from langchain_text_splitters import SpacyTextSplitter\n",
"\n",
"text_splitter = SpacyTextSplitter(chunk_size=1000)\n",
"\n",
"texts = text_splitter.split_text(state_of_the_union)\n",
"print(texts[0])"
]
},
{
"cell_type": "markdown",
"id": "73dbcdb9",
"metadata": {},
"source": [
"## SentenceTransformers\n",
"\n",
"The [SentenceTransformersTokenTextSplitter](https://api.python.langchain.com/en/latest/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html) is a specialized text splitter for use with the sentence-transformer models. The default behaviour is to split the text into chunks that fit the token window of the sentence transformer model that you would like to use.\n",
"\n",
"To split text and constrain token counts according to the sentence-transformers tokenizer, instantiate a `SentenceTransformersTokenTextSplitter`. You can optionally specify:\n",
"\n",
"- `chunk_overlap`: integer count of token overlap;\n",
"- `model_name`: sentence-transformer model name, defaulting to `\"sentence-transformers/all-mpnet-base-v2\"`;\n",
"- `tokens_per_chunk`: desired token count per chunk."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9dd5419e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n"
]
}
],
"source": [
"from langchain_text_splitters import SentenceTransformersTokenTextSplitter\n",
"\n",
"splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0)\n",
"text = \"Lorem \"\n",
"\n",
"count_start_and_stop_tokens = 2\n",
"text_token_count = splitter.count_tokens(text=text) - count_start_and_stop_tokens\n",
"print(text_token_count)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d7ad2213",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tokens in text to split: 514\n"
]
}
],
"source": [
"token_multiplier = splitter.maximum_tokens_per_chunk // text_token_count + 1\n",
"\n",
"# `text_to_split` does not fit in a single chunk\n",
"text_to_split = text * token_multiplier\n",
"\n",
"print(f\"tokens in text to split: {splitter.count_tokens(text=text_to_split)}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "818aea04",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lorem\n"
]
}
],
"source": [
"text_chunks = splitter.split_text(text=text_to_split)\n",
"\n",
"print(text_chunks[1])"
]
},
{
"cell_type": "markdown",
"id": "ea2973ac",
"metadata": {},
"source": [
"## NLTK\n",
"\n",
":::{.callout-note}\n",
"[The Natural Language Toolkit](https://en.wikipedia.org/wiki/Natural_Language_Toolkit), or more commonly [NLTK](https://www.nltk.org/), is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language.\n",
":::\n",
"\n",
"\n",
"Rather than just splitting on \"\\n\\n\", we can use `NLTK` to split based on [NLTK tokenizers](https://www.nltk.org/api/nltk.tokenize.html).\n",
"\n",
"1. How the text is split: by `NLTK` tokenizer.\n",
"2. How the chunk size is measured: by number of characters."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6af9886-7d53-4aab-84f6-303c4cce7882",
"metadata": {},
"outputs": [],
"source": [
"# pip install nltk"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "aed17ddf",
"metadata": {},
"outputs": [],
"source": [
"# This is a long document we can split up.\n",
"with open(\"state_of_the_union.txt\") as f:\n",
" state_of_the_union = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "20fa9c23",
"metadata": {},
"outputs": [],
"source": [
"from langchain_text_splitters import NLTKTextSplitter\n",
"\n",
"text_splitter = NLTKTextSplitter(chunk_size=1000)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5ea10835",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.\n",
"\n",
"Members of Congress and the Cabinet.\n",
"\n",
"Justices of the Supreme Court.\n",
"\n",
"My fellow Americans.\n",
"\n",
"Last year COVID-19 kept us apart.\n",
"\n",
"This year we are finally together again.\n",
"\n",
"Tonight, we meet as Democrats Republicans and Independents.\n",
"\n",
"But most importantly as Americans.\n",
"\n",
"With a duty to one another to the American people to the Constitution.\n",
"\n",
"And with an unwavering resolve that freedom will always triumph over tyranny.\n",
"\n",
"Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.\n",
"\n",
"But he badly miscalculated.\n",
"\n",
"He thought he could roll into Ukraine and the world would roll over.\n",
"\n",
"Instead he met a wall of strength he never imagined.\n",
"\n",
"He met the Ukrainian people.\n",
"\n",
"From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.\n",
"\n",
"Groups of citizens blocking tanks with their bodies.\n"
]
}
],
"source": [
"texts = text_splitter.split_text(state_of_the_union)\n",
"print(texts[0])"
]
},
{
"cell_type": "markdown",
"id": "98a3f975",
"metadata": {},
"source": [
"## KoNLPY\n",
"\n",
":::{.callout-note}\n",
"[KoNLPy: Korean NLP in Python](https://konlpy.org/en/latest/) is is a Python package for natural language processing (NLP) of the Korean language.\n",
":::\n",
"\n",
"Token splitting involves the segmentation of text into smaller, more manageable units called tokens. These tokens are often words, phrases, symbols, or other meaningful elements crucial for further processing and analysis. In languages like English, token splitting typically involves separating words by spaces and punctuation marks. The effectiveness of token splitting largely depends on the tokenizer's understanding of the language structure, ensuring the generation of meaningful tokens. Since tokenizers designed for the English language are not equipped to understand the unique semantic structures of other languages, such as Korean, they cannot be effectively used for Korean language processing.\n",
"\n",
"### Token splitting for Korean with KoNLPy's Kkma Analyzer\n",
"In case of Korean text, KoNLPY includes at morphological analyzer called `Kkma` (Korean Knowledge Morpheme Analyzer). `Kkma` provides detailed morphological analysis of Korean text. It breaks down sentences into words and words into their respective morphemes, identifying parts of speech for each token. It can segment a block of text into individual sentences, which is particularly useful for processing long texts.\n",
"\n",
"### Usage Considerations\n",
"While `Kkma` is renowned for its detailed analysis, it is important to note that this precision may impact processing speed. Thus, `Kkma` is best suited for applications where analytical depth is prioritized over rapid text processing."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "88ec8f2f",
"metadata": {},
"outputs": [],
"source": [
"# pip install konlpy"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ddfba6cf",
"metadata": {},
"outputs": [],
"source": [
"# This is a long Korean document that we want to split up into its component sentences.\n",
"with open(\"./your_korean_doc.txt\") as f:\n",
" korean_document = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "225dfc5c",
"metadata": {},
"outputs": [],
"source": [
"from langchain_text_splitters import KonlpyTextSplitter\n",
"\n",
"text_splitter = KonlpyTextSplitter()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "cf156711",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"춘향전 옛날에 남원에 이 도령이라는 벼슬아치 아들이 있었다.\n",
"\n",
"그의 외모는 빛나는 달처럼 잘생겼고, 그의 학식과 기예는 남보다 뛰어났다.\n",
"\n",
"한편, 이 마을에는 춘향이라는 절세 가인이 살고 있었다.\n",
"\n",
"춘 향의 아름다움은 꽃과 같아 마을 사람들 로부터 많은 사랑을 받았다.\n",
"\n",
"어느 봄날, 도령은 친구들과 놀러 나갔다가 춘 향을 만 나 첫 눈에 반하고 말았다.\n",
"\n",
"두 사람은 서로 사랑하게 되었고, 이내 비밀스러운 사랑의 맹세를 나누었다.\n",
"\n",
"하지만 좋은 날들은 오래가지 않았다.\n",
"\n",
"도령의 아버지가 다른 곳으로 전근을 가게 되어 도령도 떠나 야만 했다.\n",
"\n",
"이별의 아픔 속에서도, 두 사람은 재회를 기약하며 서로를 믿고 기다리기로 했다.\n",
"\n",
"그러나 새로 부임한 관아의 사또가 춘 향의 아름다움에 욕심을 내 어 그녀에게 강요를 시작했다.\n",
"\n",
"춘 향 은 도령에 대한 자신의 사랑을 지키기 위해, 사또의 요구를 단호히 거절했다.\n",
"\n",
"이에 분노한 사또는 춘 향을 감옥에 가두고 혹독한 형벌을 내렸다.\n",
"\n",
"이야기는 이 도령이 고위 관직에 오른 후, 춘 향을 구해 내는 것으로 끝난다.\n",
"\n",
"두 사람은 오랜 시련 끝에 다시 만나게 되고, 그들의 사랑은 온 세상에 전해 지며 후세에까지 이어진다.\n",
"\n",
"- 춘향전 (The Tale of Chunhyang)\n"
]
}
],
"source": [
"texts = text_splitter.split_text(korean_document)\n",
"# The sentences are split with \"\\n\\n\" characters.\n",
"print(texts[0])"
]
},
{
"cell_type": "markdown",
"id": "13dc0983",
"metadata": {},
"source": [
"## Hugging Face tokenizer\n",
"\n",
"[Hugging Face](https://huggingface.co/docs/tokenizers/index) has many tokenizers.\n",
"\n",
"We use Hugging Face tokenizer, the [GPT2TokenizerFast](https://huggingface.co/Ransaka/gpt2-tokenizer-fast) to count the text length in tokens.\n",
"\n",
"1. How the text is split: by character passed in.\n",
"2. How the chunk size is measured: by number of tokens calculated by the `Hugging Face` tokenizer."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a8ce51d5",
"metadata": {},
"outputs": [],
"source": [
"from transformers import GPT2TokenizerFast\n",
"\n",
"tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "388369ed",
"metadata": {},
"outputs": [],
"source": [
"# This is a long document we can split up.\n",
"with open(\"state_of_the_union.txt\") as f:\n",
" state_of_the_union = f.read()\n",
"from langchain_text_splitters import CharacterTextSplitter"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ca5e72c0",
"metadata": {},
"outputs": [],
"source": [
"text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(\n",
" tokenizer, chunk_size=100, chunk_overlap=0\n",
")\n",
"texts = text_splitter.split_text(state_of_the_union)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "37cdfbeb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n",
"\n",
"Last year COVID-19 kept us apart. This year we are finally together again. \n",
"\n",
"Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n",
"\n",
"With a duty to one another to the American people to the Constitution.\n"
]
}
],
"source": [
"print(texts[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a43b0fa6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
},
"vscode": {
"interpreter": {
"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/sql_csv.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "674a0d41-e3e3-4423-a995-25d40128c518",
"metadata": {},
"source": [
"# How to do question answering over CSVs\n",
"\n",
"LLMs are great for building question-answering systems over various types of data sources. In this section we'll go over how to build Q&A systems over data stored in a CSV file(s). Like working with SQL databases, the key to working with CSV files is to give an LLM access to tools for querying and interacting with the data. The two main ways to do this are to either:\n",
"\n",
"* **RECOMMENDED**: Load the CSV(s) into a SQL database, and use the approaches outlined in the [SQL tutorial](/docs/tutorials/sql_qa).\n",
"* Give the LLM access to a Python environment where it can use libraries like Pandas to interact with the data.\n",
"\n",
"We will cover both approaches in this guide.\n",
"\n",
"## ⚠️ Security note ⚠️\n",
"\n",
"Both approaches mentioned above carry significant risks. Using SQL requires executing model-generated SQL queries. Using a library like Pandas requires letting the model execute Python code. Since it is easier to tightly scope SQL connection permissions and sanitize SQL queries than it is to sandbox Python environments, **we HIGHLY recommend interacting with CSV data via SQL.** For more on general security best practices, [see here](/docs/security)."
]
},
{
"cell_type": "markdown",
"id": "d20c20d7-71e1-4808-9012-48278f3a9b94",
"metadata": {},
"source": [
"## Setup\n",
"Dependencies for this guide:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3fcf245-b0aa-4aee-8f0a-9c9cf94b065e",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain langchain-openai langchain-community langchain-experimental pandas"
]
},
{
"cell_type": "markdown",
"id": "7f2e34a3-0978-4856-8844-d8dfc6d5ac51",
"metadata": {},
"source": [
"Set required environment variables:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "53913d79-4a11-4bc6-bb49-dea2cc8c453b",
"metadata": {},
"outputs": [],
"source": [
"# Using LangSmith is recommended but not required. Uncomment below lines to use.\n",
"# import os\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "c23b4232-2f6a-4eb5-b0cb-1d48a9e02fcc",
"metadata": {},
"source": [
"Download the [Titanic dataset](https://www.kaggle.com/datasets/yasserh/titanic-dataset) if you don't already have it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c9099c7-5247-4edb-ba5d-10c3c4c60db4",
"metadata": {},
"outputs": [],
"source": [
"!wget https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv -O titanic.csv"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ad029641-6d6c-44cc-b16f-2d5472672adf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(887, 8)\n",
"['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'Siblings/Spouses Aboard', 'Parents/Children Aboard', 'Fare']\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"titanic.csv\")\n",
"print(df.shape)\n",
"print(df.columns.tolist())"
]
},
{
"cell_type": "markdown",
"id": "1779ab07-b715-49e5-ab2a-2e6be7d02927",
"metadata": {},
"source": [
"## SQL\n",
"\n",
"Using SQL to interact with CSV data is the recommended approach because it is easier to limit permissions and sanitize queries than with arbitrary Python.\n",
"\n",
"Most SQL databases make it easy to load a CSV file in as a table ([DuckDB](https://duckdb.org/docs/data/csv/overview.html), [SQLite](https://www.sqlite.org/csv.html), etc.). Once you've done this you can use all of the chain and agent-creating techniques outlined in the [SQL tutorial](/docs/tutorials/sql_qa). Here's a quick example of how we might do this with SQLite:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f61e9886-4713-4c88-87d4-dab439687f43",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"887"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_community.utilities import SQLDatabase\n",
"from sqlalchemy import create_engine\n",
"\n",
"engine = create_engine(\"sqlite:///titanic.db\")\n",
"df.to_sql(\"titanic\", engine, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3275fc91-3777-4f78-8edf-d148001684b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sqlite\n",
"['titanic']\n",
"[(1, 2, 'Master. Alden Gates Caldwell', 'male', 0.83, 0, 2, 29.0), (0, 3, 'Master. Eino Viljami Panula', 'male', 1.0, 4, 1, 39.6875), (1, 3, 'Miss. Eleanor Ileen Johnson', 'female', 1.0, 1, 1, 11.1333), (1, 2, 'Master. Richard F Becker', 'male', 1.0, 2, 1, 39.0), (1, 1, 'Master. Hudson Trevor Allison', 'male', 0.92, 1, 2, 151.55), (1, 3, 'Miss. Maria Nakid', 'female', 1.0, 0, 2, 15.7417), (0, 3, 'Master. Sidney Leonard Goodwin', 'male', 1.0, 5, 2, 46.9), (1, 3, 'Miss. Helene Barbara Baclini', 'female', 0.75, 2, 1, 19.2583), (1, 3, 'Miss. Eugenie Baclini', 'female', 0.75, 2, 1, 19.2583), (1, 2, 'Master. Viljo Hamalainen', 'male', 0.67, 1, 1, 14.5), (1, 3, 'Master. Bertram Vere Dean', 'male', 1.0, 1, 2, 20.575), (1, 3, 'Master. Assad Alexander Thomas', 'male', 0.42, 0, 1, 8.5167), (1, 2, 'Master. Andre Mallet', 'male', 1.0, 0, 2, 37.0042), (1, 2, 'Master. George Sibley Richards', 'male', 0.83, 1, 1, 18.75)]\n"
]
}
],
"source": [
"db = SQLDatabase(engine=engine)\n",
"print(db.dialect)\n",
"print(db.get_usable_table_names())\n",
"print(db.run(\"SELECT * FROM titanic WHERE Age < 2;\"))"
]
},
{
"cell_type": "markdown",
"id": "42f5a3c3-707c-4331-9f5f-0cb4919763dd",
"metadata": {},
"source": [
"And create a [SQL agent](/docs/tutorials/sql_qa) to interact with it:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e868a586-4f4e-4b1d-ab11-fae1271dd551",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "edd92649-b178-47bd-b2b7-d5d4e14b3512",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.agent_toolkits import create_sql_agent\n",
"\n",
"agent_executor = create_sql_agent(llm, db=db, agent_type=\"openai-tools\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7aefe929-5e39-4ed1-b135-aaf88edce2eb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SQL Agent Executor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `sql_db_list_tables` with `{}`\n",
"\n",
"\n",
"\u001b[0m\u001b[38;5;200m\u001b[1;3mtitanic\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `sql_db_schema` with `{'table_names': 'titanic'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m\n",
"CREATE TABLE titanic (\n",
"\t\"Survived\" BIGINT, \n",
"\t\"Pclass\" BIGINT, \n",
"\t\"Name\" TEXT, \n",
"\t\"Sex\" TEXT, \n",
"\t\"Age\" FLOAT, \n",
"\t\"Siblings/Spouses Aboard\" BIGINT, \n",
"\t\"Parents/Children Aboard\" BIGINT, \n",
"\t\"Fare\" FLOAT\n",
")\n",
"\n",
"/*\n",
"3 rows from titanic table:\n",
"Survived\tPclass\tName\tSex\tAge\tSiblings/Spouses Aboard\tParents/Children Aboard\tFare\n",
"0\t3\tMr. Owen Harris Braund\tmale\t22.0\t1\t0\t7.25\n",
"1\t1\tMrs. John Bradley (Florence Briggs Thayer) Cumings\tfemale\t38.0\t1\t0\t71.2833\n",
"1\t3\tMiss. Laina Heikkinen\tfemale\t26.0\t0\t0\t7.925\n",
"*/\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `sql_db_query` with `{'query': 'SELECT AVG(Age) AS Average_Age FROM titanic WHERE Survived = 1'}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m[(28.408391812865496,)]\u001b[0m\u001b[32;1m\u001b[1;3mThe average age of survivors in the Titanic dataset is approximately 28.41 years.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'input': \"what's the average age of survivors\",\n",
" 'output': 'The average age of survivors in the Titanic dataset is approximately 28.41 years.'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.invoke({\"input\": \"what's the average age of survivors\"})"
]
},
{
"cell_type": "markdown",
"id": "4d1eb128-842b-4018-87ab-bb269147f6ec",
"metadata": {},
"source": [
"This approach easily generalizes to multiple CSVs, since we can just load each of them into our database as its own table. See the [Multiple CSVs](/docs/how_to/sql_csv#multiple-csvs) section below."
]
},
{
"cell_type": "markdown",
"id": "fe7f2d91-2377-49dd-97a3-19d48a750715",
"metadata": {},
"source": [
"## Pandas\n",
"\n",
"Instead of SQL we can also use data analysis libraries like pandas and the code generating abilities of LLMs to interact with CSV data. Again, **this approach is not fit for production use cases unless you have extensive safeguards in place**. For this reason, our code-execution utilities and constructors live in the `langchain-experimental` package.\n",
"\n",
"### Chain\n",
"\n",
"Most LLMs have been trained on enough pandas Python code that they can generate it just by being asked to:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "27c84b27-9367-4c58-8a88-ade1fbf6683c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"```python\n",
"correlation = df['Age'].corr(df['Fare'])\n",
"correlation\n",
"```\n"
]
}
],
"source": [
"ai_msg = llm.invoke(\n",
" \"I have a pandas DataFrame 'df' with columns 'Age' and 'Fare'. Write code to compute the correlation between the two columns. Return Markdown for a Python code snippet and nothing else.\"\n",
")\n",
"print(ai_msg.content)"
]
},
{
"cell_type": "markdown",
"id": "f5e84003-5c39-496b-afa7-eaa50a01b7bb",
"metadata": {},
"source": [
"We can combine this ability with a Python-executing tool to create a simple data analysis chain. We'll first want to load our CSV table as a dataframe, and give the tool access to this dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "16abe312-b1a3-413f-bb9a-0e613d1e550b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32.30542018038331"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_experimental.tools import PythonAstREPLTool\n",
"\n",
"df = pd.read_csv(\"titanic.csv\")\n",
"tool = PythonAstREPLTool(locals={\"df\": df})\n",
"tool.invoke(\"df['Fare'].mean()\")"
]
},
{
"cell_type": "markdown",
"id": "ab1b2e7c-6ea8-4674-98eb-a43c69f5c19d",
"metadata": {},
"source": [
"To help enforce proper use of our Python tool, we'll using [tool calling](/docs/how_to/tool_calling):"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c6a9c8ec-1d06-4870-a584-b8d7b6c6ddfe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_SBrK246yUbdnJemXFC8Iod05', 'function': {'arguments': '{\"query\":\"df.corr()[\\'Age\\'][\\'Fare\\']\"}', 'name': 'python_repl_ast'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 125, 'total_tokens': 138}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'stop', 'logprobs': None}, id='run-1fd332ba-fa72-4351-8182-d464e7368311-0', tool_calls=[{'name': 'python_repl_ast', 'args': {'query': \"df.corr()['Age']['Fare']\"}, 'id': 'call_SBrK246yUbdnJemXFC8Iod05'}])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)\n",
"response = llm_with_tools.invoke(\n",
" \"I have a dataframe 'df' and want to know the correlation between the 'Age' and 'Fare' columns\"\n",
")\n",
"response"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b0e4015c-236d-42d7-ba8f-16052fa4f405",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'python_repl_ast',\n",
" 'args': {'query': \"df.corr()['Age']['Fare']\"},\n",
" 'id': 'call_SBrK246yUbdnJemXFC8Iod05'}]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response.tool_calls"
]
},
{
"cell_type": "markdown",
"id": "bdec46fb-7296-443c-9e97-cfa9045ff21d",
"metadata": {},
"source": [
"We'll add a tools output parser to extract the function call as a dict:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "476128f2-aa61-47f5-a371-dcff7b391d19",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'query': \"df[['Age', 'Fare']].corr()\"}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsParser\n",
"\n",
"parser = JsonOutputKeyToolsParser(key_name=tool.name, first_tool_only=True)\n",
"(llm_with_tools | parser).invoke(\n",
" \"I have a dataframe 'df' and want to know the correlation between the 'Age' and 'Fare' columns\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "59362ea0-cc5a-4841-b87c-51d6a87d5810",
"metadata": {},
"source": [
"And combine with a prompt so that we can just specify a question without needing to specify the dataframe info every invocation:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9e87a820-e4ce-417e-b580-043fb2d5c8f2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'query': \"df[['Age', 'Fare']].corr()\"}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"system = f\"\"\"You have access to a pandas dataframe `df`. \\\n",
"Here is the output of `df.head().to_markdown()`:\n",
"\n",
"```\n",
"{df.head().to_markdown()}\n",
"```\n",
"\n",
"Given a user question, write the Python code to answer it. \\\n",
"Return ONLY the valid Python code and nothing else. \\\n",
"Don't assume you have access to any libraries other than built-in Python ones and pandas.\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{question}\")])\n",
"code_chain = prompt | llm_with_tools | parser\n",
"code_chain.invoke({\"question\": \"What's the correlation between age and fare\"})"
]
},
{
"cell_type": "markdown",
"id": "63989e47-c0af-409e-9766-83c3fe6d69bb",
"metadata": {},
"source": [
"And lastly we'll add our Python tool so that the generated code is actually executed:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "2e56a891-4c3f-4e5a-a5ee-3973112ffeb9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.11232863699941621"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = prompt | llm_with_tools | parser | tool\n",
"chain.invoke({\"question\": \"What's the correlation between age and fare\"})"
]
},
{
"cell_type": "markdown",
"id": "fbb12764-4a90-4e84-88b4-a25949084ea2",
"metadata": {},
"source": [
"And just like that we have a simple data analysis chain. We can take a peak at the intermediate steps by looking at the LangSmith trace: https://smith.langchain.com/public/b1309290-7212-49b7-bde2-75b39a32b49a/r\n",
"\n",
"We could add an additional LLM call at the end to generate a conversational response, so that we're not just responding with the tool output. For this we'll want to add a chat history `MessagesPlaceholder` to our prompt:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "3fe3818d-0657-4729-ac46-ab5d4860d8f6",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"\n",
"from langchain_core.messages import ToolMessage\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import MessagesPlaceholder\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"system = f\"\"\"You have access to a pandas dataframe `df`. \\\n",
"Here is the output of `df.head().to_markdown()`:\n",
"\n",
"```\n",
"{df.head().to_markdown()}\n",
"```\n",
"\n",
"Given a user question, write the Python code to answer it. \\\n",
"Don't assume you have access to any libraries other than built-in Python ones and pandas.\n",
"Respond directly to the question once you have enough information to answer it.\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" system,\n",
" ),\n",
" (\"human\", \"{question}\"),\n",
" # This MessagesPlaceholder allows us to optionally append an arbitrary number of messages\n",
" # at the end of the prompt using the 'chat_history' arg.\n",
" MessagesPlaceholder(\"chat_history\", optional=True),\n",
" ]\n",
")\n",
"\n",
"\n",
"def _get_chat_history(x: dict) -> list:\n",
" \"\"\"Parse the chain output up to this point into a list of chat history messages to insert in the prompt.\"\"\"\n",
" ai_msg = x[\"ai_msg\"]\n",
" tool_call_id = x[\"ai_msg\"].additional_kwargs[\"tool_calls\"][0][\"id\"]\n",
" tool_msg = ToolMessage(tool_call_id=tool_call_id, content=str(x[\"tool_output\"]))\n",
" return [ai_msg, tool_msg]\n",
"\n",
"\n",
"chain = (\n",
" RunnablePassthrough.assign(ai_msg=prompt | llm_with_tools)\n",
" .assign(tool_output=itemgetter(\"ai_msg\") | parser | tool)\n",
" .assign(chat_history=_get_chat_history)\n",
" .assign(response=prompt | llm | StrOutputParser())\n",
" .pick([\"tool_output\", \"response\"])\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ff6e98ec-52f1-4ffd-9ea8-bacedfa29f28",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'tool_output': 0.11232863699941616,\n",
" 'response': 'The correlation between age and fare is approximately 0.1123.'}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke({\"question\": \"What's the correlation between age and fare\"})"
]
},
{
"cell_type": "markdown",
"id": "245a5a91-c6d2-4a40-9b9f-eb38f78c9d22",
"metadata": {},
"source": [
"Here's the LangSmith trace for this run: https://smith.langchain.com/public/14e38d70-45b1-4b81-8477-9fd2b7c07ea6/r"
]
},
{
"cell_type": "markdown",
"id": "6c24b4f4-abbf-4891-b200-814eb9c35bec",
"metadata": {},
"source": [
"### Agent\n",
"\n",
"For complex questions it can be helpful for an LLM to be able to iteratively execute code while maintaining the inputs and outputs of its previous executions. This is where Agents come into play. They allow an LLM to decide how many times a tool needs to be invoked and keep track of the executions it's made so far. The [create_pandas_dataframe_agent](https://api.python.langchain.com/en/latest/agents/langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent.html) is a built-in agent that makes it easy to work with dataframes:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "35ea904e-795f-411b-bef8-6484dbb6e35c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `python_repl_ast` with `{'query': \"df[['Age', 'Fare']].corr().iloc[0,1]\"}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m0.11232863699941621\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `python_repl_ast` with `{'query': \"df[['Fare', 'Survived']].corr().iloc[0,1]\"}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m0.2561785496289603\u001b[0m\u001b[32;1m\u001b[1;3mThe correlation between Age and Fare is approximately 0.112, and the correlation between Fare and Survival is approximately 0.256.\n",
"\n",
"Therefore, the correlation between Fare and Survival (0.256) is greater than the correlation between Age and Fare (0.112).\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'input': \"What's the correlation between age and fare? is that greater than the correlation between fare and survival?\",\n",
" 'output': 'The correlation between Age and Fare is approximately 0.112, and the correlation between Fare and Survival is approximately 0.256.\\n\\nTherefore, the correlation between Fare and Survival (0.256) is greater than the correlation between Age and Fare (0.112).'}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_experimental.agents import create_pandas_dataframe_agent\n",
"\n",
"agent = create_pandas_dataframe_agent(llm, df, agent_type=\"openai-tools\", verbose=True)\n",
"agent.invoke(\n",
" {\n",
" \"input\": \"What's the correlation between age and fare? is that greater than the correlation between fare and survival?\"\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "a65322f3-b13c-4949-82b2-4517b9a0859d",
"metadata": {},
"source": [
"Here's the LangSmith trace for this run: https://smith.langchain.com/public/6a86aee2-4f22-474a-9264-bd4c7283e665/r"
]
},
{
"cell_type": "markdown",
"id": "68492261-faef-47e7-8009-e20ef1420d5a",
"metadata": {},
"source": [
"### Multiple CSVs {#multiple-csvs}\n",
"\n",
"To handle multiple CSVs (or dataframes) we just need to pass multiple dataframes to our Python tool. Our `create_pandas_dataframe_agent` constructor can do this out of the box, we can pass in a list of dataframes instead of just one. If we're constructing a chain ourselves, we can do something like:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "77a70e1b-d3ee-4fa6-a4a0-d2e5005e6c8a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.14384991262954416"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_1 = df[[\"Age\", \"Fare\"]]\n",
"df_2 = df[[\"Fare\", \"Survived\"]]\n",
"\n",
"tool = PythonAstREPLTool(locals={\"df_1\": df_1, \"df_2\": df_2})\n",
"llm_with_tool = llm.bind_tools(tools=[tool], tool_choice=tool.name)\n",
"df_template = \"\"\"```python\n",
"{df_name}.head().to_markdown()\n",
">>> {df_head}\n",
"```\"\"\"\n",
"df_context = \"\\n\\n\".join(\n",
" df_template.format(df_head=_df.head().to_markdown(), df_name=df_name)\n",
" for _df, df_name in [(df_1, \"df_1\"), (df_2, \"df_2\")]\n",
")\n",
"\n",
"system = f\"\"\"You have access to a number of pandas dataframes. \\\n",
"Here is a sample of rows from each dataframe and the python code that was used to generate the sample:\n",
"\n",
"{df_context}\n",
"\n",
"Given a user question about the dataframes, write the Python code to answer it. \\\n",
"Don't assume you have access to any libraries other than built-in Python ones and pandas. \\\n",
"Make sure to refer only to the variables mentioned above.\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{question}\")])\n",
"\n",
"chain = prompt | llm_with_tool | parser | tool\n",
"chain.invoke(\n",
" {\n",
" \"question\": \"return the difference in the correlation between age and fare and the correlation between fare and survival\"\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7043363f-4ab1-41de-9318-c556e4ae66bc",
"metadata": {},
"source": [
"Here's the LangSmith trace for this run: https://smith.langchain.com/public/cc2a7d7f-7c5a-4e77-a10c-7b5420fcd07f/r"
]
},
{
"cell_type": "markdown",
"id": "a2256d09-23c2-4e52-bfc6-c84eba538586",
"metadata": {},
"source": [
"### Sandboxed code execution\n",
"\n",
"There are a number of tools like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly) that provide sandboxed environments for Python code execution, to allow for safer code-executing chains and agents."
]
},
{
"cell_type": "markdown",
"id": "1728e791-f114-41e6-aa12-0436fdeeedae",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"For more advanced data analysis applications we recommend checking out:\n",
"\n",
"* [SQL tutorial](/docs/tutorials/sql_qa): Many of the challenges of working with SQL db's and CSV's are generic to any structured data type, so it's useful to read the SQL techniques even if you're using Pandas for CSV data analysis.\n",
"* [Tool use](/docs/how_to/tool_calling): Guides on general best practices when working with chains and agents that invoke tools\n",
"* [Agents](/docs/tutorials/agents): Understand the fundamentals of building LLM agents.\n",
"* Integrations: Sandboxed envs like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly), utilities like [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), related agents like [Spark DataFrame agent](/docs/integrations/toolkits/spark)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/sql_large_db.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "6751831d-9b08-434f-829b-d0052a3b119f",
"metadata": {},
"source": [
"# How to deal with large databases when doing SQL question-answering\n",
"\n",
"In order to write valid queries against a database, we need to feed the model the table names, table schemas, and feature values for it to query over. When there are many tables, columns, and/or high-cardinality columns, it becomes impossible for us to dump the full information about our database in every prompt. Instead, we must find ways to dynamically insert into the prompt only the most relevant information.\n",
"\n",
"In this guide we demonstrate methods for identifying such relevant information, and feeding this into a query-generation step. We will cover:\n",
"\n",
"1. Identifying a relevant subset of tables;\n",
"2. Identifying a relevant subset of column values.\n",
"\n",
"\n",
"## Setup\n",
"\n",
"First, get required packages and set environment variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afd5c20e-c705-4ef4-b33b-71fa819215ce",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain langchain-community langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "592e0c93-5396-44ec-92f0-1635ddd59a42",
"metadata": {},
"outputs": [],
"source": [
"# Uncomment the below to use LangSmith. Not required.\n",
"# import os\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\""
]
},
{
"cell_type": "markdown",
"id": "590ee096-db88-42af-90d4-99b8149df753",
"metadata": {},
"source": [
"The below example will use a SQLite connection with Chinook database. Follow [these installation steps](https://database.guide/2-sample-databases-sqlite/) to create `Chinook.db` in the same directory as this notebook:\n",
"\n",
"* Save [this file](https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql) as `Chinook_Sqlite.sql`\n",
"* Run `sqlite3 Chinook.db`\n",
"* Run `.read Chinook_Sqlite.sql`\n",
"* Test `SELECT * FROM Artist LIMIT 10;`\n",
"\n",
"Now, `Chinhook.db` is in our directory and we can interface with it using the SQLAlchemy-driven [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) class:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cebd3915-f58f-4e73-8459-265630ae8cd4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sqlite\n",
"['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n",
"[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham')]\n"
]
}
],
"source": [
"from langchain_community.utilities import SQLDatabase\n",
"\n",
"db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n",
"print(db.dialect)\n",
"print(db.get_usable_table_names())\n",
"print(db.run(\"SELECT * FROM Artist LIMIT 10;\"))"
]
},
{
"cell_type": "markdown",
"id": "2e572e1f-99b5-46a2-9023-76d1e6256c0a",
"metadata": {},
"source": [
"## Many tables\n",
"\n",
"One of the main pieces of information we need to include in our prompt is the schemas of the relevant tables. When we have very many tables, we can't fit all of the schemas in a single prompt. What we can do in such cases is first extract the names of the tables related to the user input, and then include only their schemas.\n",
"\n",
"One easy and reliable way to do this is using [tool-calling](/docs/how_to/tool_calling). Below, we show how we can use this feature to obtain output conforming to a desired format (in this case, a list of table names). We use the chat model's `.bind_tools` method to bind a tool in Pydantic format, and feed this into an output parser to reconstruct the object from the model's response.\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d278de7e-9228-4265-abf2-7a5e214a7dd7",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dbfc94bb-1c64-4f77-9e65-fb2468f55a58",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Table(name='Genre')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class Table(BaseModel):\n",
" \"\"\"Table in SQL database.\"\"\"\n",
"\n",
" name: str = Field(description=\"Name of table in SQL database.\")\n",
"\n",
"\n",
"table_names = \"\\n\".join(db.get_usable_table_names())\n",
"system = f\"\"\"Return the names of ALL the SQL tables that MIGHT be relevant to the user question. \\\n",
"The tables are:\n",
"\n",
"{table_names}\n",
"\n",
"Remember to include ALL POTENTIALLY RELEVANT tables, even if you're not sure that they're needed.\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"llm_with_tools = llm.bind_tools([Table])\n",
"output_parser = PydanticToolsParser(tools=[Table])\n",
"\n",
"table_chain = prompt | llm_with_tools | output_parser\n",
"\n",
"table_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
]
},
{
"cell_type": "markdown",
"id": "1641dbba-d359-4cb2-ac52-82dfae99f392",
"metadata": {},
"source": [
"This works pretty well! Except, as we'll see below, we actually need a few other tables as well. This would be pretty difficult for the model to know based just on the user question. In this case, we might think to simplify our model's job by grouping the tables together. We'll just ask the model to choose between categories \"Music\" and \"Business\", and then take care of selecting all the relevant tables from there:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0ccb0bf5-c580-428f-9cde-a58772ae784e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Table(name='Music'), Table(name='Business')]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"system = \"\"\"Return the names of any SQL tables that are relevant to the user question.\n",
"The tables are:\n",
"\n",
"Music\n",
"Business\n",
"\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"category_chain = prompt | llm_with_tools | output_parser\n",
"category_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "883eda7a-7d0f-4012-9658-6a1010c7cda9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Album',\n",
" 'Artist',\n",
" 'Genre',\n",
" 'MediaType',\n",
" 'Playlist',\n",
" 'PlaylistTrack',\n",
" 'Track',\n",
" 'Customer',\n",
" 'Employee',\n",
" 'Invoice',\n",
" 'InvoiceLine']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from typing import List\n",
"\n",
"\n",
"def get_tables(categories: List[Table]) -> List[str]:\n",
" tables = []\n",
" for category in categories:\n",
" if category.name == \"Music\":\n",
" tables.extend(\n",
" [\n",
" \"Album\",\n",
" \"Artist\",\n",
" \"Genre\",\n",
" \"MediaType\",\n",
" \"Playlist\",\n",
" \"PlaylistTrack\",\n",
" \"Track\",\n",
" ]\n",
" )\n",
" elif category.name == \"Business\":\n",
" tables.extend([\"Customer\", \"Employee\", \"Invoice\", \"InvoiceLine\"])\n",
" return tables\n",
"\n",
"\n",
"table_chain = category_chain | get_tables\n",
"table_chain.invoke({\"input\": \"What are all the genres of Alanis Morisette songs\"})"
]
},
{
"cell_type": "markdown",
"id": "04d52d01-1ccf-4753-b34a-0dcbc4921f78",
"metadata": {},
"source": [
"Now that we've got a chain that can output the relevant tables for any query we can combine this with our [create_sql_query_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.create_sql_query_chain.html), which can accept a list of `table_names_to_use` to determine which table schemas are included in the prompt:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "79f2a5a2-eb99-47e3-9c2b-e5751a800174",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"\n",
"from langchain.chains import create_sql_query_chain\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"query_chain = create_sql_query_chain(llm, db)\n",
"# Convert \"question\" key to the \"input\" key expected by current table_chain.\n",
"table_chain = {\"input\": itemgetter(\"question\")} | table_chain\n",
"# Set table_names_to_use using table_chain.\n",
"full_chain = RunnablePassthrough.assign(table_names_to_use=table_chain) | query_chain"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3c74b418-aa9a-4eb5-89dd-6e1a99a21344",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SELECT DISTINCT \"g\".\"Name\"\n",
"FROM \"Genre\" g\n",
"JOIN \"Track\" t ON \"g\".\"GenreId\" = \"t\".\"GenreId\"\n",
"JOIN \"Album\" a ON \"t\".\"AlbumId\" = \"a\".\"AlbumId\"\n",
"JOIN \"Artist\" ar ON \"a\".\"ArtistId\" = \"ar\".\"ArtistId\"\n",
"WHERE \"ar\".\"Name\" = 'Alanis Morissette'\n",
"LIMIT 5;\n"
]
}
],
"source": [
"query = full_chain.invoke(\n",
" {\"question\": \"What are all the genres of Alanis Morisette songs\"}\n",
")\n",
"print(query)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ace7bdbf-728e-4f7b-a361-b44dae404481",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"[('Rock',)]\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.run(query)"
]
},
{
"cell_type": "markdown",
"id": "7a717020-84c2-40f3-ba84-6624138d8e0c",
"metadata": {},
"source": [
"We can see the LangSmith trace for this run [here](https://smith.langchain.com/public/4fbad408-3554-4f33-ab47-1e510a1b52a3/r).\n",
"\n",
"We've seen how to dynamically include a subset of table schemas in a prompt within a chain. Another possible approach to this problem is to let an Agent decide for itself when to look up tables by giving it a Tool to do so. You can see an example of this in the [SQL: Agents](/docs/tutorials/agents) guide."
]
},
{
"cell_type": "markdown",
"id": "cb9e54fd-64ca-4ed5-847c-afc635aae4f5",
"metadata": {},
"source": [
"## High-cardinality columns\n",
"\n",
"In order to filter columns that contain proper nouns such as addresses, song names or artists, we first need to double-check the spelling in order to filter the data correctly. \n",
"\n",
"One naive strategy it to create a vector store with all the distinct proper nouns that exist in the database. We can then query that vector store each user input and inject the most relevant proper nouns into the prompt.\n",
"\n",
"First we need the unique values for each entity we want, for which we define a function that parses the result into a list of elements:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "dc97e6ba-6055-4677-b0cc-c5425d5d4c81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['AC/DC', 'Accept', 'Aerosmith', 'Alanis Morissette', 'Alice In Chains']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import ast\n",
"import re\n",
"\n",
"\n",
"def query_as_list(db, query):\n",
" res = db.run(query)\n",
" res = [el for sub in ast.literal_eval(res) for el in sub if el]\n",
" res = [re.sub(r\"\\b\\d+\\b\", \"\", string).strip() for string in res]\n",
" return res\n",
"\n",
"\n",
"proper_nouns = query_as_list(db, \"SELECT Name FROM Artist\")\n",
"proper_nouns += query_as_list(db, \"SELECT Title FROM Album\")\n",
"proper_nouns += query_as_list(db, \"SELECT Name FROM Genre\")\n",
"len(proper_nouns)\n",
"proper_nouns[:5]"
]
},
{
"cell_type": "markdown",
"id": "22efa968-1879-4d7a-858f-7899dfa57454",
"metadata": {},
"source": [
"Now we can embed and store all of our values in a vector database:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ea50abce-545a-4dc3-8795-8d364f7d142a",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"vector_db = FAISS.from_texts(proper_nouns, OpenAIEmbeddings())\n",
"retriever = vector_db.as_retriever(search_kwargs={\"k\": 15})"
]
},
{
"cell_type": "markdown",
"id": "a5d1d5c0-0928-40a4-b961-f1afe03cd5d3",
"metadata": {},
"source": [
"And put together a query construction chain that first retrieves values from the database and inserts them into the prompt:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "006b2955-4c06-4597-9c1d-442f77cd0261",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"system = \"\"\"You are a SQLite expert. Given an input question, create a syntactically\n",
"correct SQLite query to run. Unless otherwise specificed, do not return more than\n",
"{top_k} rows.\n",
"\n",
"Only return the SQL query with no markup or explanation.\n",
"\n",
"Here is the relevant table info: {table_info}\n",
"\n",
"Here is a non-exhaustive list of possible feature values. If filtering on a feature\n",
"value make sure to check its spelling against this list first:\n",
"\n",
"{proper_nouns}\n",
"\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\")])\n",
"\n",
"query_chain = create_sql_query_chain(llm, db, prompt=prompt)\n",
"retriever_chain = (\n",
" itemgetter(\"question\")\n",
" | retriever\n",
" | (lambda docs: \"\\n\".join(doc.page_content for doc in docs))\n",
")\n",
"chain = RunnablePassthrough.assign(proper_nouns=retriever_chain) | query_chain"
]
},
{
"cell_type": "markdown",
"id": "12b0ed60-2536-4f82-85df-e096a272072a",
"metadata": {},
"source": [
"To try out our chain, let's see what happens when we try filtering on \"elenis moriset\", a misspelling of Alanis Morissette, without and with retrieval:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5ba81336-0853-43da-8b07-5a256b8ba0b6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SELECT DISTINCT g.Name \n",
"FROM Track t\n",
"JOIN Album a ON t.AlbumId = a.AlbumId\n",
"JOIN Artist ar ON a.ArtistId = ar.ArtistId\n",
"JOIN Genre g ON t.GenreId = g.GenreId\n",
"WHERE ar.Name = 'Elenis Moriset';\n"
]
},
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Without retrieval\n",
"query = query_chain.invoke(\n",
" {\"question\": \"What are all the genres of elenis moriset songs\", \"proper_nouns\": \"\"}\n",
")\n",
"print(query)\n",
"db.run(query)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "fcdd8432-07a4-4609-8214-b1591dd94950",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SELECT DISTINCT Genre.Name\n",
"FROM Genre\n",
"JOIN Track ON Genre.GenreId = Track.GenreId\n",
"JOIN Album ON Track.AlbumId = Album.AlbumId\n",
"JOIN Artist ON Album.ArtistId = Artist.ArtistId\n",
"WHERE Artist.Name = 'Elenis Moriset'\n"
]
},
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Without retrieval\n",
"query = query_chain.invoke(\n",
" {\"question\": \"What are all the genres of elenis moriset songs\", \"proper_nouns\": \"\"}\n",
")\n",
"print(query)\n",
"db.run(query)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "cbbff7cc-c616-41eb-bbf5-08bd42c6808e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SELECT DISTINCT g.Name\n",
"FROM Genre g\n",
"JOIN Track t ON g.GenreId = t.GenreId\n",
"JOIN Album a ON t.AlbumId = a.AlbumId\n",
"JOIN Artist ar ON a.ArtistId = ar.ArtistId\n",
"WHERE ar.Name = 'Alanis Morissette';\n"
]
},
{
"data": {
"text/plain": [
"\"[('Rock',)]\""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# With retrieval\n",
"query = chain.invoke({\"question\": \"What are all the genres of elenis moriset songs\"})\n",
"print(query)\n",
"db.run(query)"
]
},
{
"cell_type": "markdown",
"id": "7f99181b-a75c-4ff3-b37b-33f99a506581",
"metadata": {},
"source": [
"We can see that with retrieval we're able to correct the spelling from \"Elenis Moriset\" to \"Alanis Morissette\" and get back a valid result.\n",
"\n",
"Another possible approach to this problem is to let an Agent decide for itself when to look up proper nouns. You can see an example of this in the [SQL: Agents](/docs/tutorials/agents) guide."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/sql_prompting.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to better prompt when doing SQL question-answering\n",
"\n",
"In this guide we'll go over prompting strategies to improve SQL query generation using [create_sql_query_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.create_sql_query_chain.html). We'll largely focus on methods for getting relevant database-specific information in your prompt.\n",
"\n",
"We will cover: \n",
"\n",
"- How the dialect of the LangChain [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) impacts the prompt of the chain;\n",
"- How to format schema information into the prompt using `SQLDatabase.get_context`;\n",
"- How to build and select few-shot examples to assist the model.\n",
"\n",
"## Setup\n",
"\n",
"First, get required packages and set environment variables:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain langchain-community langchain-experimental langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment the below to use LangSmith. Not required.\n",
"# import os\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The below example will use a SQLite connection with Chinook database. Follow [these installation steps](https://database.guide/2-sample-databases-sqlite/) to create `Chinook.db` in the same directory as this notebook:\n",
"\n",
"* Save [this file](https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql) as `Chinook_Sqlite.sql`\n",
"* Run `sqlite3 Chinook.db`\n",
"* Run `.read Chinook_Sqlite.sql`\n",
"* Test `SELECT * FROM Artist LIMIT 10;`\n",
"\n",
"Now, `Chinhook.db` is in our directory and we can interface with it using the SQLAlchemy-driven `SQLDatabase` class:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sqlite\n",
"['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n",
"[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham')]\n"
]
}
],
"source": [
"from langchain_community.utilities import SQLDatabase\n",
"\n",
"db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\", sample_rows_in_table_info=3)\n",
"print(db.dialect)\n",
"print(db.get_usable_table_names())\n",
"print(db.run(\"SELECT * FROM Artist LIMIT 10;\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dialect-specific prompting\n",
"\n",
"One of the simplest things we can do is make our prompt specific to the SQL dialect we're using. When using the built-in [create_sql_query_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.create_sql_query_chain.html) and [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html), this is handled for you for any of the following dialects:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['crate',\n",
" 'duckdb',\n",
" 'googlesql',\n",
" 'mssql',\n",
" 'mysql',\n",
" 'mariadb',\n",
" 'oracle',\n",
" 'postgresql',\n",
" 'sqlite',\n",
" 'clickhouse',\n",
" 'prestodb']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains.sql_database.prompt import SQL_PROMPTS\n",
"\n",
"list(SQL_PROMPTS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, using our current DB we can see that we'll get a SQLite-specific prompt.\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.\n",
"Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.\n",
"Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\n",
"Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",
"Pay attention to use date('now') function to get the current date, if the question involves \"today\".\n",
"\n",
"Use the following format:\n",
"\n",
"Question: Question here\n",
"SQLQuery: SQL Query to run\n",
"SQLResult: Result of the SQLQuery\n",
"Answer: Final answer here\n",
"\n",
"Only use the following tables:\n",
"\u001b[33;1m\u001b[1;3m{table_info}\u001b[0m\n",
"\n",
"Question: \u001b[33;1m\u001b[1;3m{input}\u001b[0m\n"
]
}
],
"source": [
"from langchain.chains import create_sql_query_chain\n",
"\n",
"chain = create_sql_query_chain(llm, db)\n",
"chain.get_prompts()[0].pretty_print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table definitions and example rows\n",
"\n",
"In most SQL chains, we'll need to feed the model at least part of the database schema. Without this it won't be able to write valid queries. Our database comes with some convenience methods to give us the relevant context. Specifically, we can get the table names, their schemas, and a sample of rows from each table.\n",
"\n",
"Here we will use `SQLDatabase.get_context`, which provides available tables and their schemas:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['table_info', 'table_names']\n",
"\n",
"CREATE TABLE \"Album\" (\n",
"\t\"AlbumId\" INTEGER NOT NULL, \n",
"\t\"Title\" NVARCHAR(160) NOT NULL, \n",
"\t\"ArtistId\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"AlbumId\"), \n",
"\tFOREIGN KEY(\"ArtistId\") REFERENCES \"Artist\" (\"ArtistId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Album table:\n",
"AlbumId\tTitle\tArtistId\n",
"1\tFor Those About To Rock We Salute You\t1\n",
"2\tBalls to the Wall\t2\n",
"3\tRestless and Wild\t2\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Artist\" (\n",
"\t\"ArtistId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120), \n",
"\tPRIMARY KEY (\"ArtistId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Artist table:\n",
"ArtistId\tName\n",
"1\tAC/DC\n",
"2\tAccept\n",
"3\tAerosmith\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Customer\" (\n",
"\t\"CustomerId\" INTEGER NOT NULL, \n",
"\t\"FirstName\" NVARCHAR(40) NOT NULL, \n",
"\t\"LastName\" NVARCHAR(20) NOT NULL, \n",
"\t\"Company\" NVARCHAR(80), \n",
"\t\"Address\" NVARCHAR(70), \n",
"\t\"City\" NVARCHAR(40), \n",
"\t\"State\" NVARCHAR(40), \n",
"\t\"Country\" NVARCHAR(40), \n",
"\t\"PostalCode\" NVARCHAR(10), \n",
"\t\"Phone\" NVARCHAR(24), \n",
"\t\"Fax\" NVARCHAR(24), \n",
"\t\"Email\" NVARCHAR(60) NOT NULL, \n",
"\t\"SupportRepId\" INTEGER, \n",
"\tPRIMARY KEY (\"CustomerId\"), \n",
"\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Customer table:\n",
"CustomerId\tFirstName\tLastName\tCompany\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\tSupportRepId\n",
"1\tLuís\tGonçalves\tEmbraer - Empresa Brasileira de Aeronáutica S.A.\tAv. Brigadeiro Faria Lima, 2170\tSão José dos Campos\tSP\tBrazil\t12227-000\t+55 (12) 3923-5555\t+55 (12) 3923-5566\tluisg@embraer.com.br\t3\n",
"2\tLeonie\tKöhler\tNone\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t+49 0711 2842222\tNone\tleonekohler@surfeu.de\t5\n",
"3\tFrançois\tTremblay\tNone\t1498 rue Bélanger\tMontréal\tQC\tCanada\tH2G 1A7\t+1 (514) 721-4711\tNone\tftremblay@gmail.com\t3\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Employee\" (\n",
"\t\"EmployeeId\" INTEGER NOT NULL, \n",
"\t\"LastName\" NVARCHAR(20) NOT NULL, \n",
"\t\"FirstName\" NVARCHAR(20) NOT NULL, \n",
"\t\"Title\" NVARCHAR(30), \n",
"\t\"ReportsTo\" INTEGER, \n",
"\t\"BirthDate\" DATETIME, \n",
"\t\"HireDate\" DATETIME, \n",
"\t\"Address\" NVARCHAR(70), \n",
"\t\"City\" NVARCHAR(40), \n",
"\t\"State\" NVARCHAR(40), \n",
"\t\"Country\" NVARCHAR(40), \n",
"\t\"PostalCode\" NVARCHAR(10), \n",
"\t\"Phone\" NVARCHAR(24), \n",
"\t\"Fax\" NVARCHAR(24), \n",
"\t\"Email\" NVARCHAR(60), \n",
"\tPRIMARY KEY (\"EmployeeId\"), \n",
"\tFOREIGN KEY(\"ReportsTo\") REFERENCES \"Employee\" (\"EmployeeId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Employee table:\n",
"EmployeeId\tLastName\tFirstName\tTitle\tReportsTo\tBirthDate\tHireDate\tAddress\tCity\tState\tCountry\tPostalCode\tPhone\tFax\tEmail\n",
"1\tAdams\tAndrew\tGeneral Manager\tNone\t1962-02-18 00:00:00\t2002-08-14 00:00:00\t11120 Jasper Ave NW\tEdmonton\tAB\tCanada\tT5K 2N1\t+1 (780) 428-9482\t+1 (780) 428-3457\tandrew@chinookcorp.com\n",
"2\tEdwards\tNancy\tSales Manager\t1\t1958-12-08 00:00:00\t2002-05-01 00:00:00\t825 8 Ave SW\tCalgary\tAB\tCanada\tT2P 2T3\t+1 (403) 262-3443\t+1 (403) 262-3322\tnancy@chinookcorp.com\n",
"3\tPeacock\tJane\tSales Support Agent\t2\t1973-08-29 00:00:00\t2002-04-01 00:00:00\t1111 6 Ave SW\tCalgary\tAB\tCanada\tT2P 5M5\t+1 (403) 262-3443\t+1 (403) 262-6712\tjane@chinookcorp.com\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Genre\" (\n",
"\t\"GenreId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120), \n",
"\tPRIMARY KEY (\"GenreId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Genre table:\n",
"GenreId\tName\n",
"1\tRock\n",
"2\tJazz\n",
"3\tMetal\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Invoice\" (\n",
"\t\"InvoiceId\" INTEGER NOT NULL, \n",
"\t\"CustomerId\" INTEGER NOT NULL, \n",
"\t\"InvoiceDate\" DATETIME NOT NULL, \n",
"\t\"BillingAddress\" NVARCHAR(70), \n",
"\t\"BillingCity\" NVARCHAR(40), \n",
"\t\"BillingState\" NVARCHAR(40), \n",
"\t\"BillingCountry\" NVARCHAR(40), \n",
"\t\"BillingPostalCode\" NVARCHAR(10), \n",
"\t\"Total\" NUMERIC(10, 2) NOT NULL, \n",
"\tPRIMARY KEY (\"InvoiceId\"), \n",
"\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Invoice table:\n",
"InvoiceId\tCustomerId\tInvoiceDate\tBillingAddress\tBillingCity\tBillingState\tBillingCountry\tBillingPostalCode\tTotal\n",
"1\t2\t2021-01-01 00:00:00\tTheodor-Heuss-Straße 34\tStuttgart\tNone\tGermany\t70174\t1.98\n",
"2\t4\t2021-01-02 00:00:00\tUllevålsveien 14\tOslo\tNone\tNorway\t0171\t3.96\n",
"3\t8\t2021-01-03 00:00:00\tGrétrystraat 63\tBrussels\tNone\tBelgium\t1000\t5.94\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"InvoiceLine\" (\n",
"\t\"InvoiceLineId\" INTEGER NOT NULL, \n",
"\t\"InvoiceId\" INTEGER NOT NULL, \n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
"\t\"Quantity\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"InvoiceLineId\"), \n",
"\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
"\tFOREIGN KEY(\"InvoiceId\") REFERENCES \"Invoice\" (\"InvoiceId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from InvoiceLine table:\n",
"InvoiceLineId\tInvoiceId\tTrackId\tUnitPrice\tQuantity\n",
"1\t1\t2\t0.99\t1\n",
"2\t1\t4\t0.99\t1\n",
"3\t2\t6\t0.99\t1\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"MediaType\" (\n",
"\t\"MediaTypeId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120), \n",
"\tPRIMARY KEY (\"MediaTypeId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from MediaType table:\n",
"MediaTypeId\tName\n",
"1\tMPEG audio file\n",
"2\tProtected AAC audio file\n",
"3\tProtected MPEG-4 video file\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Playlist\" (\n",
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120), \n",
"\tPRIMARY KEY (\"PlaylistId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Playlist table:\n",
"PlaylistId\tName\n",
"1\tMusic\n",
"2\tMovies\n",
"3\tTV Shows\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"PlaylistTrack\" (\n",
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
"\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
"\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from PlaylistTrack table:\n",
"PlaylistId\tTrackId\n",
"1\t3402\n",
"1\t3389\n",
"1\t3390\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Track\" (\n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(200) NOT NULL, \n",
"\t\"AlbumId\" INTEGER, \n",
"\t\"MediaTypeId\" INTEGER NOT NULL, \n",
"\t\"GenreId\" INTEGER, \n",
"\t\"Composer\" NVARCHAR(220), \n",
"\t\"Milliseconds\" INTEGER NOT NULL, \n",
"\t\"Bytes\" INTEGER, \n",
"\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
"\tPRIMARY KEY (\"TrackId\"), \n",
"\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n",
"\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
"\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Track table:\n",
"TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n",
"1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n",
"2\tBalls to the Wall\t2\t2\t1\tU. Dirkschneider, W. Hoffmann, H. Frank, P. Baltes, S. Kaufmann, G. Hoffmann\t342562\t5510424\t0.99\n",
"3\tFast As a Shark\t3\t2\t1\tF. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\t230619\t3990994\t0.99\n",
"*/\n"
]
}
],
"source": [
"context = db.get_context()\n",
"print(list(context))\n",
"print(context[\"table_info\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we don't have too many, or too wide of, tables, we can just insert the entirety of this information in our prompt:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.\n",
"Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.\n",
"Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\n",
"Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",
"Pay attention to use date('now') function to get the current date, if the question involves \"today\".\n",
"\n",
"Use the following format:\n",
"\n",
"Question: Question here\n",
"SQLQuery: SQL Query to run\n",
"SQLResult: Result of the SQLQuery\n",
"Answer: Final answer here\n",
"\n",
"Only use the following tables:\n",
"\n",
"CREATE TABLE \"Album\" (\n",
"\t\"AlbumId\" INTEGER NOT NULL, \n",
"\t\"Title\" NVARCHAR(160) NOT NULL, \n",
"\t\"ArtistId\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"AlbumId\"), \n",
"\tFOREIGN KEY(\"ArtistId\") REFERENCES \"Artist\" (\"ArtistId\")\n",
")\n",
"\n",
"/*\n",
"3 rows from Album table:\n",
"AlbumId\tTitle\tArtistId\n",
"1\tFor Those About To Rock We Salute You\t1\n",
"2\tBalls to the Wall\t2\n",
"3\tRestless and Wild\t2\n",
"*/\n",
"\n",
"\n",
"CREATE TABLE \"Artist\" (\n",
"\t\"ArtistId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120)\n"
]
}
],
"source": [
"prompt_with_context = chain.get_prompts()[0].partial(table_info=context[\"table_info\"])\n",
"print(prompt_with_context.pretty_repr()[:1500])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we do have database schemas that are too large to fit into our model's context window, we'll need to come up with ways of inserting only the relevant table definitions into the prompt based on the user input. For more on this head to the [Many tables, wide tables, high-cardinality feature](/docs/how_to/sql_large_db) guide."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Few-shot examples\n",
"\n",
"Including examples of natural language questions being converted to valid SQL queries against our database in the prompt will often improve model performance, especially for complex queries.\n",
"\n",
"Let's say we have the following examples:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"examples = [\n",
" {\"input\": \"List all artists.\", \"query\": \"SELECT * FROM Artist;\"},\n",
" {\n",
" \"input\": \"Find all albums for the artist 'AC/DC'.\",\n",
" \"query\": \"SELECT * FROM Album WHERE ArtistId = (SELECT ArtistId FROM Artist WHERE Name = 'AC/DC');\",\n",
" },\n",
" {\n",
" \"input\": \"List all tracks in the 'Rock' genre.\",\n",
" \"query\": \"SELECT * FROM Track WHERE GenreId = (SELECT GenreId FROM Genre WHERE Name = 'Rock');\",\n",
" },\n",
" {\n",
" \"input\": \"Find the total duration of all tracks.\",\n",
" \"query\": \"SELECT SUM(Milliseconds) FROM Track;\",\n",
" },\n",
" {\n",
" \"input\": \"List all customers from Canada.\",\n",
" \"query\": \"SELECT * FROM Customer WHERE Country = 'Canada';\",\n",
" },\n",
" {\n",
" \"input\": \"How many tracks are there in the album with ID 5?\",\n",
" \"query\": \"SELECT COUNT(*) FROM Track WHERE AlbumId = 5;\",\n",
" },\n",
" {\n",
" \"input\": \"Find the total number of invoices.\",\n",
" \"query\": \"SELECT COUNT(*) FROM Invoice;\",\n",
" },\n",
" {\n",
" \"input\": \"List all tracks that are longer than 5 minutes.\",\n",
" \"query\": \"SELECT * FROM Track WHERE Milliseconds > 300000;\",\n",
" },\n",
" {\n",
" \"input\": \"Who are the top 5 customers by total purchase?\",\n",
" \"query\": \"SELECT CustomerId, SUM(Total) AS TotalPurchase FROM Invoice GROUP BY CustomerId ORDER BY TotalPurchase DESC LIMIT 5;\",\n",
" },\n",
" {\n",
" \"input\": \"Which albums are from the year 2000?\",\n",
" \"query\": \"SELECT * FROM Album WHERE strftime('%Y', ReleaseDate) = '2000';\",\n",
" },\n",
" {\n",
" \"input\": \"How many employees are there\",\n",
" \"query\": 'SELECT COUNT(*) FROM \"Employee\"',\n",
" },\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create a few-shot prompt with them like so:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
"\n",
"example_prompt = PromptTemplate.from_template(\"User input: {input}\\nSQL query: {query}\")\n",
"prompt = FewShotPromptTemplate(\n",
" examples=examples[:5],\n",
" example_prompt=example_prompt,\n",
" prefix=\"You are a SQLite expert. Given an input question, create a syntactically correct SQLite query to run. Unless otherwise specificed, do not return more than {top_k} rows.\\n\\nHere is the relevant table info: {table_info}\\n\\nBelow are a number of examples of questions and their corresponding SQL queries.\",\n",
" suffix=\"User input: {input}\\nSQL query: \",\n",
" input_variables=[\"input\", \"top_k\", \"table_info\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are a SQLite expert. Given an input question, create a syntactically correct SQLite query to run. Unless otherwise specificed, do not return more than 3 rows.\n",
"\n",
"Here is the relevant table info: foo\n",
"\n",
"Below are a number of examples of questions and their corresponding SQL queries.\n",
"\n",
"User input: List all artists.\n",
"SQL query: SELECT * FROM Artist;\n",
"\n",
"User input: Find all albums for the artist 'AC/DC'.\n",
"SQL query: SELECT * FROM Album WHERE ArtistId = (SELECT ArtistId FROM Artist WHERE Name = 'AC/DC');\n",
"\n",
"User input: List all tracks in the 'Rock' genre.\n",
"SQL query: SELECT * FROM Track WHERE GenreId = (SELECT GenreId FROM Genre WHERE Name = 'Rock');\n",
"\n",
"User input: Find the total duration of all tracks.\n",
"SQL query: SELECT SUM(Milliseconds) FROM Track;\n",
"\n",
"User input: List all customers from Canada.\n",
"SQL query: SELECT * FROM Customer WHERE Country = 'Canada';\n",
"\n",
"User input: How many artists are there?\n",
"SQL query: \n"
]
}
],
"source": [
"print(prompt.format(input=\"How many artists are there?\", top_k=3, table_info=\"foo\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dynamic few-shot examples\n",
"\n",
"If we have enough examples, we may want to only include the most relevant ones in the prompt, either because they don't fit in the model's context window or because the long tail of examples distracts the model. And specifically, given any input we want to include the examples most relevant to that input.\n",
"\n",
"We can do just this using an ExampleSelector. In this case we'll use a [SemanticSimilarityExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html), which will store the examples in the vector database of our choosing. At runtime it will perform a similarity search between the input and our examples, and return the most semantically similar ones.\n",
"\n",
"We default to OpenAI embeddings here, but you can swap them out for the model provider of your choice."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.example_selectors import SemanticSimilarityExampleSelector\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"example_selector = SemanticSimilarityExampleSelector.from_examples(\n",
" examples,\n",
" OpenAIEmbeddings(),\n",
" FAISS,\n",
" k=5,\n",
" input_keys=[\"input\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'input': 'List all artists.', 'query': 'SELECT * FROM Artist;'},\n",
" {'input': 'How many employees are there',\n",
" 'query': 'SELECT COUNT(*) FROM \"Employee\"'},\n",
" {'input': 'How many tracks are there in the album with ID 5?',\n",
" 'query': 'SELECT COUNT(*) FROM Track WHERE AlbumId = 5;'},\n",
" {'input': 'Which albums are from the year 2000?',\n",
" 'query': \"SELECT * FROM Album WHERE strftime('%Y', ReleaseDate) = '2000';\"},\n",
" {'input': \"List all tracks in the 'Rock' genre.\",\n",
" 'query': \"SELECT * FROM Track WHERE GenreId = (SELECT GenreId FROM Genre WHERE Name = 'Rock');\"}]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example_selector.select_examples({\"input\": \"how many artists are there?\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use it, we can pass the ExampleSelector directly in to our FewShotPromptTemplate:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"prompt = FewShotPromptTemplate(\n",
" example_selector=example_selector,\n",
" example_prompt=example_prompt,\n",
" prefix=\"You are a SQLite expert. Given an input question, create a syntactically correct SQLite query to run. Unless otherwise specificed, do not return more than {top_k} rows.\\n\\nHere is the relevant table info: {table_info}\\n\\nBelow are a number of examples of questions and their corresponding SQL queries.\",\n",
" suffix=\"User input: {input}\\nSQL query: \",\n",
" input_variables=[\"input\", \"top_k\", \"table_info\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are a SQLite expert. Given an input question, create a syntactically correct SQLite query to run. Unless otherwise specificed, do not return more than 3 rows.\n",
"\n",
"Here is the relevant table info: foo\n",
"\n",
"Below are a number of examples of questions and their corresponding SQL queries.\n",
"\n",
"User input: List all artists.\n",
"SQL query: SELECT * FROM Artist;\n",
"\n",
"User input: How many employees are there\n",
"SQL query: SELECT COUNT(*) FROM \"Employee\"\n",
"\n",
"User input: How many tracks are there in the album with ID 5?\n",
"SQL query: SELECT COUNT(*) FROM Track WHERE AlbumId = 5;\n",
"\n",
"User input: Which albums are from the year 2000?\n",
"SQL query: SELECT * FROM Album WHERE strftime('%Y', ReleaseDate) = '2000';\n",
"\n",
"User input: List all tracks in the 'Rock' genre.\n",
"SQL query: SELECT * FROM Track WHERE GenreId = (SELECT GenreId FROM Genre WHERE Name = 'Rock');\n",
"\n",
"User input: how many artists are there?\n",
"SQL query: \n"
]
}
],
"source": [
"print(prompt.format(input=\"how many artists are there?\", top_k=3, table_info=\"foo\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trying it out, we see that the model identifies the relevant table:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'SELECT COUNT(*) FROM Artist;'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = create_sql_query_chain(llm, db, prompt)\n",
"chain.invoke({\"question\": \"how many artists are there?\"})"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/sql_query_checking.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4da7ae91-4973-4e97-a570-fa24024ec65d",
"metadata": {},
"source": [
"# How to do query validation as part of SQL question-answering\n",
"\n",
"Perhaps the most error-prone part of any SQL chain or agent is writing valid and safe SQL queries. In this guide we'll go over some strategies for validating our queries and handling invalid queries.\n",
"\n",
"We will cover: \n",
"\n",
"1. Appending a \"query validator\" step to the query generation;\n",
"2. Prompt engineering to reduce the incidence of errors.\n",
"\n",
"## Setup\n",
"\n",
"First, get required packages and set environment variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d40d5bc-3647-4b5d-808a-db470d40fe7a",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain langchain-community langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71f46270-e1c6-45b4-b36e-ea2e9f860eba",
"metadata": {},
"outputs": [],
"source": [
"# Uncomment the below to use LangSmith. Not required.\n",
"# import os\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\""
]
},
{
"cell_type": "markdown",
"id": "a0a2151b-cecf-4559-92a1-ca48824fed18",
"metadata": {},
"source": [
"The below example will use a SQLite connection with Chinook database. Follow [these installation steps](https://database.guide/2-sample-databases-sqlite/) to create `Chinook.db` in the same directory as this notebook:\n",
"\n",
"* Save [this file](https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql) as `Chinook_Sqlite.sql`\n",
"* Run `sqlite3 Chinook.db`\n",
"* Run `.read Chinook_Sqlite.sql`\n",
"* Test `SELECT * FROM Artist LIMIT 10;`\n",
"\n",
"Now, `Chinhook.db` is in our directory and we can interface with it using the SQLAlchemy-driven `SQLDatabase` class:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8cedc936-5268-4bfa-b838-bdcc1ee9573c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sqlite\n",
"['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']\n",
"[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham')]\n"
]
}
],
"source": [
"from langchain_community.utilities import SQLDatabase\n",
"\n",
"db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n",
"print(db.dialect)\n",
"print(db.get_usable_table_names())\n",
"print(db.run(\"SELECT * FROM Artist LIMIT 10;\"))"
]
},
{
"cell_type": "markdown",
"id": "2d203315-fab7-4621-80da-41e9bf82d803",
"metadata": {},
"source": [
"## Query checker\n",
"\n",
"Perhaps the simplest strategy is to ask the model itself to check the original query for common mistakes. Suppose we have the following SQL query chain:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" />\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d81ebf69-75ad-4c92-baa9-fd152b8e622a",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ec66bb76-b1ad-48ad-a7d4-b518e9421b86",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import create_sql_query_chain\n",
"\n",
"chain = create_sql_query_chain(llm, db)"
]
},
{
"cell_type": "markdown",
"id": "da01023d-cc05-43e3-a38d-ed9d56d3ad15",
"metadata": {},
"source": [
"And we want to validate its outputs. We can do so by extending the chain with a second prompt and model call:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "16686750-d8ee-4c60-8d67-b28281cb6164",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"system = \"\"\"Double check the user's {dialect} query for common mistakes, including:\n",
"- Using NOT IN with NULL values\n",
"- Using UNION when UNION ALL should have been used\n",
"- Using BETWEEN for exclusive ranges\n",
"- Data type mismatch in predicates\n",
"- Properly quoting identifiers\n",
"- Using the correct number of arguments for functions\n",
"- Casting to the correct data type\n",
"- Using the proper columns for joins\n",
"\n",
"If there are any of the above mistakes, rewrite the query.\n",
"If there are no mistakes, just reproduce the original query with no further commentary.\n",
"\n",
"Output the final SQL query only.\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system), (\"human\", \"{query}\")]\n",
").partial(dialect=db.dialect)\n",
"validation_chain = prompt | llm | StrOutputParser()\n",
"\n",
"full_chain = {\"query\": chain} | validation_chain"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "28ef9c6e-21fa-4b62-8aa4-8cd398ce4c4d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SELECT AVG(i.Total) AS AverageInvoice\n",
"FROM Invoice i\n",
"JOIN Customer c ON i.CustomerId = c.CustomerId\n",
"WHERE c.Country = 'USA'\n",
"AND c.Fax IS NULL\n",
"AND i.InvoiceDate >= '2003-01-01' \n",
"AND i.InvoiceDate < '2010-01-01'\n"
]
}
],
"source": [
"query = full_chain.invoke(\n",
" {\n",
" \"question\": \"What's the average Invoice from an American customer whose Fax is missing since 2003 but before 2010\"\n",
" }\n",
")\n",
"print(query)"
]
},
{
"cell_type": "markdown",
"id": "228a1b87-4e44-4d86-bed7-fd2d7a91fb23",
"metadata": {},
"source": [
"Note how we can see both steps of the chain in the [Langsmith trace](https://smith.langchain.com/public/8a743295-a57c-4e4c-8625-bc7e36af9d74/r)."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "d01d78b5-89a0-4c12-b743-707ebe64ba86",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'[(6.632999999999998,)]'"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.run(query)"
]
},
{
"cell_type": "markdown",
"id": "6e133526-26bd-49da-9cfa-7adc0e59fd72",
"metadata": {},
"source": [
"The obvious downside of this approach is that we need to make two model calls instead of one to generate our query. To get around this we can try to perform the query generation and query check in a single model invocation:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7af0030a-549e-4e69-9298-3d0a038c2fdd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m System Message \u001b[0m================================\n",
"\n",
"You are a \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m expert. Given an input question, create a syntactically correct \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m query to run.\n",
"Unless the user specifies in the question a specific number of examples to obtain, query for at most \u001b[33;1m\u001b[1;3m{top_k}\u001b[0m results using the LIMIT clause as per \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m. You can order the results to return the most informative data in the database.\n",
"Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\n",
"Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",
"Pay attention to use date('now') function to get the current date, if the question involves \"today\".\n",
"\n",
"Only use the following tables:\n",
"\u001b[33;1m\u001b[1;3m{table_info}\u001b[0m\n",
"\n",
"Write an initial draft of the query. Then double check the \u001b[33;1m\u001b[1;3m{dialect}\u001b[0m query for common mistakes, including:\n",
"- Using NOT IN with NULL values\n",
"- Using UNION when UNION ALL should have been used\n",
"- Using BETWEEN for exclusive ranges\n",
"- Data type mismatch in predicates\n",
"- Properly quoting identifiers\n",
"- Using the correct number of arguments for functions\n",
"- Casting to the correct data type\n",
"- Using the proper columns for joins\n",
"\n",
"Use format:\n",
"\n",
"First draft: <<FIRST_DRAFT_QUERY>>\n",
"Final answer: <<FINAL_ANSWER_QUERY>>\n",
"\n",
"\n",
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n"
]
}
],
"source": [
"system = \"\"\"You are a {dialect} expert. Given an input question, create a syntactically correct {dialect} query to run.\n",
"Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per {dialect}. You can order the results to return the most informative data in the database.\n",
"Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\n",
"Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",
"Pay attention to use date('now') function to get the current date, if the question involves \"today\".\n",
"\n",
"Only use the following tables:\n",
"{table_info}\n",
"\n",
"Write an initial draft of the query. Then double check the {dialect} query for common mistakes, including:\n",
"- Using NOT IN with NULL values\n",
"- Using UNION when UNION ALL should have been used\n",
"- Using BETWEEN for exclusive ranges\n",
"- Data type mismatch in predicates\n",
"- Properly quoting identifiers\n",
"- Using the correct number of arguments for functions\n",
"- Casting to the correct data type\n",
"- Using the proper columns for joins\n",
"\n",
"Use format:\n",
"\n",
"First draft: <<FIRST_DRAFT_QUERY>>\n",
"Final answer: <<FINAL_ANSWER_QUERY>>\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system), (\"human\", \"{input}\")]\n",
").partial(dialect=db.dialect)\n",
"\n",
"\n",
"def parse_final_answer(output: str) -> str:\n",
" return output.split(\"Final answer: \")[1]\n",
"\n",
"\n",
"chain = create_sql_query_chain(llm, db, prompt=prompt) | parse_final_answer\n",
"prompt.pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "806e27a2-e511-45ea-a4ed-8ce8fa6e1d58",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"SELECT AVG(i.\"Total\") AS \"AverageInvoice\"\n",
"FROM \"Invoice\" i\n",
"JOIN \"Customer\" c ON i.\"CustomerId\" = c.\"CustomerId\"\n",
"WHERE c.\"Country\" = 'USA'\n",
"AND c.\"Fax\" IS NULL\n",
"AND i.\"InvoiceDate\" BETWEEN '2003-01-01' AND '2010-01-01';\n"
]
}
],
"source": [
"query = chain.invoke(\n",
" {\n",
" \"question\": \"What's the average Invoice from an American customer whose Fax is missing since 2003 but before 2010\"\n",
" }\n",
")\n",
"print(query)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "70fff2fa-1f86-4f83-9fd2-e87a5234d329",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'[(6.632999999999998,)]'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db.run(query)"
]
},
{
"cell_type": "markdown",
"id": "fc8af115-7c23-421a-8fd7-29bf1b6687a4",
"metadata": {},
"source": [
"## Human-in-the-loop\n",
"\n",
"In some cases our data is sensitive enough that we never want to execute a SQL query without a human approving it first. Head to the [Tool use: Human-in-the-loop](/docs/how_to/tools_human) page to learn how to add a human-in-the-loop to any tool, chain or agent.\n",
"\n",
"## Error handling\n",
"\n",
"At some point, the model will make a mistake and craft an invalid SQL query. Or an issue will arise with our database. Or the model API will go down. We'll want to add some error handling behavior to our chains and agents so that we fail gracefully in these situations, and perhaps even automatically recover. To learn about error handling with tools, head to the [Tool use: Error handling](/docs/how_to/tools_error) page."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/streaming.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "0bdb3b97-4989-4237-b43b-5943dbbd8302",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"keywords: [stream]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "bb7d49db-04d3-4399-bfe1-09f82bbe6015",
"metadata": {},
"source": [
"# How to stream runnables\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LangChain Expression Language](/docs/concepts/#langchain-expression-language)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"\n",
":::\n",
"\n",
"Streaming is critical in making applications based on LLMs feel responsive to end-users.\n",
"\n",
"Important LangChain primitives like [chat models](/docs/concepts/#chat-models), [output parsers](/docs/concepts/#output-parsers), [prompts](/docs/concepts/#prompt-templates), [retrievers](/docs/concepts/#retrievers), and [agents](/docs/concepts/#agents) implement the LangChain [Runnable Interface](/docs/concepts#interface).\n",
"\n",
"This interface provides two general approaches to stream content:\n",
"\n",
"1. sync `stream` and async `astream`: a **default implementation** of streaming that streams the **final output** from the chain.\n",
"2. async `astream_events` and async `astream_log`: these provide a way to stream both **intermediate steps** and **final output** from the chain.\n",
"\n",
"Let's take a look at both approaches, and try to understand how to use them.\n",
"\n",
":::info\n",
"For a higher-level overview of streaming techniques in LangChain, see [this section of the conceptual guide](/docs/concepts/#streaming).\n",
":::\n",
"\n",
"## Using Stream\n",
"\n",
"All `Runnable` objects implement a sync method called `stream` and an async variant called `astream`. \n",
"\n",
"These methods are designed to stream the final output in chunks, yielding each chunk as soon as it is available.\n",
"\n",
"Streaming is only possible if all steps in the program know how to process an **input stream**; i.e., process an input chunk one at a time, and yield a corresponding output chunk.\n",
"\n",
"The complexity of this processing can vary, from straightforward tasks like emitting tokens produced by an LLM, to more challenging ones like streaming parts of JSON results before the entire JSON is complete.\n",
"\n",
"The best place to start exploring streaming is with the single most important components in LLMs apps-- the LLMs themselves!\n",
"\n",
"### LLMs and Chat Models\n",
"\n",
"Large language models and their chat variants are the primary bottleneck in LLM based apps.\n",
"\n",
"Large language models can take **several seconds** to generate a complete response to a query. This is far slower than the **~200-300 ms** threshold at which an application feels responsive to an end user.\n",
"\n",
"The key strategy to make the application feel more responsive is to show intermediate progress; viz., to stream the output from the model **token by token**.\n",
"\n",
"We will show examples of streaming using a chat model. Choose one from the options below:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs\n",
" customVarName=\"model\"\n",
"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f123bdcb-8c8b-440c-9bbd-aa5ed4e9cd17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"%pip install -qU langchain langchain_anthropic\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"keys = [\n",
" \"ANTHROPIC_API_KEY\",\n",
" \"OPENAI_API_KEY\",\n",
"]\n",
"\n",
"for key in keys:\n",
" if key not in os.environ:\n",
" os.environ[key] = getpass(f\"Enter API Key for {key}=?\")\n",
"\n",
"\n",
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"model = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "a2464c57-0e89-4159-b21f-5859a21be658",
"metadata": {},
"source": [
"Let's start with the sync `stream` API:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8b44dfb2-0749-487a-8918-f8b6b8233093",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The| sky| appears| blue| during| the| day|.|"
]
}
],
"source": [
"chunks = []\n",
"for chunk in model.stream(\"what color is the sky?\"):\n",
" chunks.append(chunk)\n",
" print(chunk.content, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "8d835b5c-cbb7-41ab-8905-bdc24d515d29",
"metadata": {},
"source": [
"Alternatively, if you're working in an async environment, you may consider using the async `astream` API:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f180b6a0-0027-4bd8-8bab-fde76e282609",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The| sky| appears| blue| during| the| day|.|"
]
}
],
"source": [
"chunks = []\n",
"async for chunk in model.astream(\"what color is the sky?\"):\n",
" chunks.append(chunk)\n",
" print(chunk.content, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "66730a87-77d5-40d6-a68f-315121989bd1",
"metadata": {},
"source": [
"Let's inspect one of the chunks"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dade3000-1ac4-4f5c-b5c6-a0217f9f8a6b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessageChunk(content='The', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chunks[0]"
]
},
{
"cell_type": "markdown",
"id": "a3a47193-2bd1-46bc-9c7e-ea0f6b08c4a5",
"metadata": {},
"source": [
"We got back something called an `AIMessageChunk`. This chunk represents a part of an `AIMessage`.\n",
"\n",
"Message chunks are additive by design -- one can simply add them up to get the state of the response so far!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d3cf5f38-249c-4da0-94e6-5e5203fad52e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessageChunk(content='The sky appears blue during', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chunks[0] + chunks[1] + chunks[2] + chunks[3] + chunks[4]"
]
},
{
"cell_type": "markdown",
"id": "59ffbd9a-3b79-44b6-8883-1371f9460c77",
"metadata": {},
"source": [
"### Chains\n",
"\n",
"Virtually all LLM applications involve more steps than just a call to a language model.\n",
"\n",
"Let's build a simple chain using `LangChain Expression Language` (`LCEL`) that combines a prompt, model and a parser and verify that streaming works.\n",
"\n",
"We will use [`StrOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) to parse the output from the model. This is a simple parser that extracts the `content` field from an `AIMessageChunk`, giving us the `token` returned by the model.\n",
"\n",
":::{.callout-tip}\n",
"LCEL is a *declarative* way to specify a \"program\" by chainining together different LangChain primitives. Chains created using LCEL benefit from an automatic implementation of `stream` and `astream` allowing streaming of the final output. In fact, chains created with LCEL implement the entire standard Runnable interface.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a8562ae2-3fd1-4829-9801-a5a732b1798d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here|'s| a| joke| about| a| par|rot|:|\n",
"\n",
"A man| goes| to| a| pet| shop| to| buy| a| par|rot|.| The| shop| owner| shows| him| two| stunning| pa|rr|ots| with| beautiful| pl|um|age|.|\n",
"\n",
"\"|There|'s| a| talking| par|rot| an|d a| non|-|talking| par|rot|,\"| the| owner| says|.| \"|The| talking| par|rot| costs| $|100|,| an|d the| non|-|talking| par|rot| is| $|20|.\"|\n",
"\n",
"The| man| says|,| \"|I|'ll| take| the| non|-|talking| par|rot| at| $|20|.\"|\n",
"\n",
"He| pays| an|d leaves| with| the| par|rot|.| As| he|'s| walking| down| the| street|,| the| par|rot| looks| up| at| him| an|d says|,| \"|You| know|,| you| really| are| a| stupi|d man|!\"|\n",
"\n",
"The| man| is| stun|ne|d an|d looks| at| the| par|rot| in| dis|bel|ief|.| The| par|rot| continues|,| \"|Yes|,| you| got| r|ippe|d off| big| time|!| I| can| talk| just| as| well| as| that| other| par|rot|,| an|d you| only| pai|d $|20| |for| me|!\"|"
]
}
],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"prompt = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\")\n",
"parser = StrOutputParser()\n",
"chain = prompt | model | parser\n",
"\n",
"async for chunk in chain.astream({\"topic\": \"parrot\"}):\n",
" print(chunk, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "868bc412",
"metadata": {},
"source": [
"Note that we're getting streaming output even though we're using `parser` at the end of the chain above. The `parser` operates on each streaming chunk individidually. Many of the [LCEL primitives](/docs/how_to#langchain-expression-language-lcel) also support this kind of transform-style passthrough streaming, which can be very convenient when constructing apps. \n",
"\n",
"Custom functions can be [designed to return generators](/docs/how_to/functions#streaming), which are able to operate on streams.\n",
"\n",
"Certain runnables, like [prompt templates](/docs/how_to#prompt-templates) and [chat models](/docs/how_to#chat-models), cannot process individual chunks and instead aggregate all previous steps. Such runnables can interrupt the streaming process."
]
},
{
"cell_type": "markdown",
"id": "1b399fb4-5e3c-4581-9570-6df9b42b623d",
"metadata": {},
"source": [
":::{.callout-note}\n",
"The LangChain Expression language allows you to separate the construction of a chain from the mode in which it is used (e.g., sync/async, batch/streaming etc.). If this is not relevant to what you're building, you can also rely on a standard **imperative** programming approach by\n",
"caling `invoke`, `batch` or `stream` on each component individually, assigning the results to variables and then using them downstream as you see fit.\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "dfff2701-8887-486f-8b3b-eb26383d4bb6",
"metadata": {},
"source": [
"### Working with Input Streams\n",
"\n",
"What if you wanted to stream JSON from the output as it was being generated?\n",
"\n",
"If you were to rely on `json.loads` to parse the partial json, the parsing would fail as the partial json wouldn't be valid json.\n",
"\n",
"You'd likely be at a complete loss of what to do and claim that it wasn't possible to stream JSON.\n",
"\n",
"Well, turns out there is a way to do it -- the parser needs to operate on the **input stream**, and attempt to \"auto-complete\" the partial json into a valid state.\n",
"\n",
"Let's see such a parser in action to understand what this means."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5ff63cce-715a-4561-951f-9321c82e8d81",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{}\n",
"{'countries': []}\n",
"{'countries': [{}]}\n",
"{'countries': [{'name': ''}]}\n",
"{'countries': [{'name': 'France'}]}\n",
"{'countries': [{'name': 'France', 'population': 67}]}\n",
"{'countries': [{'name': 'France', 'population': 67413}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': ''}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan'}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125584}]}\n",
"{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125584000}]}\n"
]
}
],
"source": [
"from langchain_core.output_parsers import JsonOutputParser\n",
"\n",
"chain = (\n",
" model | JsonOutputParser()\n",
") # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n",
"async for text in chain.astream(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\"\n",
"):\n",
" print(text, flush=True)"
]
},
{
"cell_type": "markdown",
"id": "151d4323-a6cf-49be-8779-e8797c5e3b00",
"metadata": {},
"source": [
"Now, let's **break** streaming. We'll use the previous example and append an extraction function at the end that extracts the country names from the finalized JSON.\n",
"\n",
":::{.callout-warning}\n",
"Any steps in the chain that operate on **finalized inputs** rather than on **input streams** can break streaming functionality via `stream` or `astream`.\n",
":::\n",
"\n",
":::{.callout-tip}\n",
"Later, we will discuss the `astream_events` API which streams results from intermediate steps. This API will stream results from intermediate steps even if the chain contains steps that only operate on **finalized inputs**.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d9c90117-9faa-4a01-b484-0db071808d1f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['France', 'Spain', 'Japan']|"
]
}
],
"source": [
"from langchain_core.output_parsers import (\n",
" JsonOutputParser,\n",
")\n",
"\n",
"\n",
"# A function that operates on finalized inputs\n",
"# rather than on an input_stream\n",
"def _extract_country_names(inputs):\n",
" \"\"\"A function that does not operates on input streams and breaks streaming.\"\"\"\n",
" if not isinstance(inputs, dict):\n",
" return \"\"\n",
"\n",
" if \"countries\" not in inputs:\n",
" return \"\"\n",
"\n",
" countries = inputs[\"countries\"]\n",
"\n",
" if not isinstance(countries, list):\n",
" return \"\"\n",
"\n",
" country_names = [\n",
" country.get(\"name\") for country in countries if isinstance(country, dict)\n",
" ]\n",
" return country_names\n",
"\n",
"\n",
"chain = model | JsonOutputParser() | _extract_country_names\n",
"\n",
"async for text in chain.astream(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\"\n",
"):\n",
" print(text, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "cab6dca2-2027-414d-a196-2db6e3ebb8a5",
"metadata": {},
"source": [
"#### Generator Functions\n",
"\n",
"Le'ts fix the streaming using a generator function that can operate on the **input stream**.\n",
"\n",
":::{.callout-tip}\n",
"A generator function (a function that uses `yield`) allows writing code that operates on **input streams**\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "15984b2b-315a-4119-945b-2a3dabea3082",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"France|Spain|Japan|"
]
}
],
"source": [
"from langchain_core.output_parsers import JsonOutputParser\n",
"\n",
"\n",
"async def _extract_country_names_streaming(input_stream):\n",
" \"\"\"A function that operates on input streams.\"\"\"\n",
" country_names_so_far = set()\n",
"\n",
" async for input in input_stream:\n",
" if not isinstance(input, dict):\n",
" continue\n",
"\n",
" if \"countries\" not in input:\n",
" continue\n",
"\n",
" countries = input[\"countries\"]\n",
"\n",
" if not isinstance(countries, list):\n",
" continue\n",
"\n",
" for country in countries:\n",
" name = country.get(\"name\")\n",
" if not name:\n",
" continue\n",
" if name not in country_names_so_far:\n",
" yield name\n",
" country_names_so_far.add(name)\n",
"\n",
"\n",
"chain = model | JsonOutputParser() | _extract_country_names_streaming\n",
"\n",
"async for text in chain.astream(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\",\n",
"):\n",
" print(text, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "d59823f5-9b9a-43c5-a213-34644e2f1d3d",
"metadata": {},
"source": [
":::{.callout-note}\n",
"Because the code above is relying on JSON auto-completion, you may see partial names of countries (e.g., `Sp` and `Spain`), which is not what one would want for an extraction result!\n",
"\n",
"We're focusing on streaming concepts, not necessarily the results of the chains.\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "6adf65b7-aa47-4321-98c7-a0abe43b833a",
"metadata": {},
"source": [
"### Non-streaming components\n",
"\n",
"Some built-in components like Retrievers do not offer any `streaming`. What happens if we try to `stream` them? 🤨"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b9b1c00d-8b44-40d0-9e2b-8a70d238f82b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[Document(page_content='harrison worked at kensho'),\n",
" Document(page_content='harrison likes spicy food')]]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"template = \"\"\"Answer the question based only on the following context:\n",
"{context}\n",
"\n",
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"vectorstore = FAISS.from_texts(\n",
" [\"harrison worked at kensho\", \"harrison likes spicy food\"],\n",
" embedding=OpenAIEmbeddings(),\n",
")\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"chunks = [chunk for chunk in retriever.stream(\"where did harrison work?\")]\n",
"chunks"
]
},
{
"cell_type": "markdown",
"id": "6fd3e71b-439e-418f-8a8a-5232fba3d9fd",
"metadata": {},
"source": [
"Stream just yielded the final result from that component.\n",
"\n",
"This is OK 🥹! Not all components have to implement streaming -- in some cases streaming is either unnecessary, difficult or just doesn't make sense.\n",
"\n",
":::{.callout-tip}\n",
"An LCEL chain constructed using non-streaming components, will still be able to stream in a lot of cases, with streaming of partial output starting after the last non-streaming step in the chain.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "957447e6-1e60-41ef-8c10-2654bd9e738d",
"metadata": {},
"outputs": [],
"source": [
"retrieval_chain = (\n",
" {\n",
" \"context\": retriever.with_config(run_name=\"Docs\"),\n",
" \"question\": RunnablePassthrough(),\n",
" }\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "94e50b5d-bf51-4eee-9da0-ee40dd9ce42b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Base|d on| the| given| context|,| Harrison| worke|d at| K|ens|ho|.|\n",
"\n",
"Here| are| |3| |made| up| sentences| about| this| place|:|\n",
"\n",
"1|.| K|ens|ho| was| a| cutting|-|edge| technology| company| known| for| its| innovative| solutions| in| artificial| intelligence| an|d data| analytics|.|\n",
"\n",
"2|.| The| modern| office| space| at| K|ens|ho| feature|d open| floor| plans|,| collaborative| work|sp|aces|,| an|d a| vib|rant| atmosphere| that| fos|tere|d creativity| an|d team|work|.|\n",
"\n",
"3|.| With| its| prime| location| in| the| heart| of| the| city|,| K|ens|ho| attracte|d top| talent| from| aroun|d the| worl|d,| creating| a| diverse| an|d dynamic| work| environment|.|"
]
}
],
"source": [
"for chunk in retrieval_chain.stream(\n",
" \"Where did harrison work? \" \"Write 3 made up sentences about this place.\"\n",
"):\n",
" print(chunk, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "8657aa4e-3469-4b5b-a09c-60b53a23b1e7",
"metadata": {},
"source": [
"Now that we've seen how `stream` and `astream` work, let's venture into the world of streaming events. 🏞️"
]
},
{
"cell_type": "markdown",
"id": "baceb5c0-d4a4-4b98-8733-80ae4407b62d",
"metadata": {},
"source": [
"## Using Stream Events\n",
"\n",
"Event Streaming is a **beta** API. This API may change a bit based on feedback.\n",
"\n",
":::{.callout-note}\n",
"\n",
"This guide demonstrates the `V2` API and requires langchain-core >= 0.2. For the `V1` API compatible with older versions of LangChain, see [here](https://python.langchain.com/v0.1/docs/expression_language/streaming/#using-stream-events).\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61348df9-ec58-401e-be89-68a70042f88e",
"metadata": {},
"outputs": [],
"source": [
"import langchain_core\n",
"\n",
"langchain_core.__version__"
]
},
{
"cell_type": "markdown",
"id": "52e9e983-bbde-4906-9eca-4ccc06eabd91",
"metadata": {},
"source": [
"For the `astream_events` API to work properly:\n",
"\n",
"* Use `async` throughout the code to the extent possible (e.g., async tools etc)\n",
"* Propagate callbacks if defining custom functions / runnables\n",
"* Whenever using runnables without LCEL, make sure to call `.astream()` on LLMs rather than `.ainvoke` to force the LLM to stream tokens.\n",
"* Let us know if anything doesn't work as expected! :)\n",
"\n",
"### Event Reference\n",
"\n",
"Below is a reference table that shows some events that might be emitted by the various Runnable objects.\n",
"\n",
"\n",
":::{.callout-note}\n",
"When streaming is implemented properly, the inputs to a runnable will not be known until after the input stream has been entirely consumed. This means that `inputs` will often be included only for `end` events and rather than for `start` events.\n",
":::\n",
"\n",
"| event | name | chunk | input | output |\n",
"|----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------|\n",
"| on_chat_model_start | [model name] | | {\"messages\": [[SystemMessage, HumanMessage]]} | |\n",
"| on_chat_model_stream | [model name] | AIMessageChunk(content=\"hello\") | | |\n",
"| on_chat_model_end | [model name] | | {\"messages\": [[SystemMessage, HumanMessage]]} | AIMessageChunk(content=\"hello world\") |\n",
"| on_llm_start | [model name] | | {'input': 'hello'} | |\n",
"| on_llm_stream | [model name] | 'Hello' | | |\n",
"| on_llm_end | [model name] | | 'Hello human!' | |\n",
"| on_chain_start | format_docs | | | |\n",
"| on_chain_stream | format_docs | \"hello world!, goodbye world!\" | | |\n",
"| on_chain_end | format_docs | | [Document(...)] | \"hello world!, goodbye world!\" |\n",
"| on_tool_start | some_tool | | {\"x\": 1, \"y\": \"2\"} | |\n",
"| on_tool_end | some_tool | | | {\"x\": 1, \"y\": \"2\"} |\n",
"| on_retriever_start | [retriever name] | | {\"query\": \"hello\"} | |\n",
"| on_retriever_end | [retriever name] | | {\"query\": \"hello\"} | [Document(...), ..] |\n",
"| on_prompt_start | [template_name] | | {\"question\": \"hello\"} | |\n",
"| on_prompt_end | [template_name] | | {\"question\": \"hello\"} | ChatPromptValue(messages: [SystemMessage, ...]) |"
]
},
{
"cell_type": "markdown",
"id": "1f6ec135-3348-4041-8f55-bf3e59b3b2d0",
"metadata": {},
"source": [
"### Chat Model\n",
"\n",
"Let's start off by looking at the events produced by a chat model."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c00df46e-7f6b-4e06-8abf-801898c8d57f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.\n",
" warn_beta(\n"
]
}
],
"source": [
"events = []\n",
"async for event in model.astream_events(\"hello\", version=\"v2\"):\n",
" events.append(event)"
]
},
{
"cell_type": "markdown",
"id": "32972939-2995-4b2e-84db-045adb044fad",
"metadata": {},
"source": [
":::{.callout-note}\n",
"\n",
"Hey what's that funny version=\"v2\" parameter in the API?! 😾\n",
"\n",
"This is a **beta API**, and we're almost certainly going to make some changes to it (in fact, we already have!)\n",
"\n",
"This version parameter will allow us to minimize such breaking changes to your code. \n",
"\n",
"In short, we are annoying you now, so we don't have to annoy you later.\n",
"\n",
"`v2` is only available for langchain-core>=0.2.0.\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "ad2b8f47-da78-4569-a49a-53a8efaa26bc",
"metadata": {},
"source": [
"Let's take a look at the few of the start event and a few of the end events."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ce31b525-f47d-4828-85a7-912ce9f2e79b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'event': 'on_chat_model_start',\n",
" 'data': {'input': 'hello'},\n",
" 'name': 'ChatAnthropic',\n",
" 'tags': [],\n",
" 'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
" 'metadata': {}},\n",
" {'event': 'on_chat_model_stream',\n",
" 'data': {'chunk': AIMessageChunk(content='Hello', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
" 'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
" 'name': 'ChatAnthropic',\n",
" 'tags': [],\n",
" 'metadata': {}},\n",
" {'event': 'on_chat_model_stream',\n",
" 'data': {'chunk': AIMessageChunk(content='!', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
" 'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
" 'name': 'ChatAnthropic',\n",
" 'tags': [],\n",
" 'metadata': {}}]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events[:3]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "76cfe826-ee63-4310-ad48-55a95eb3b9d6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'event': 'on_chat_model_stream',\n",
" 'data': {'chunk': AIMessageChunk(content='?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
" 'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
" 'name': 'ChatAnthropic',\n",
" 'tags': [],\n",
" 'metadata': {}},\n",
" {'event': 'on_chat_model_end',\n",
" 'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},\n",
" 'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',\n",
" 'name': 'ChatAnthropic',\n",
" 'tags': [],\n",
" 'metadata': {}}]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events[-2:]"
]
},
{
"cell_type": "markdown",
"id": "98c8f173-e9c7-4c27-81a5-b7c85c12714d",
"metadata": {},
"source": [
"### Chain\n",
"\n",
"Let's revisit the example chain that parsed streaming JSON to explore the streaming events API."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4328c56c-a303-427b-b1f2-f354e9af555c",
"metadata": {},
"outputs": [],
"source": [
"chain = (\n",
" model | JsonOutputParser()\n",
") # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n",
"\n",
"events = [\n",
" event\n",
" async for event in chain.astream_events(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\",\n",
" version=\"v2\",\n",
" )\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "4cc00b99-a961-4221-a3c7-9d807114bbfb",
"metadata": {},
"source": [
"If you examine at the first few events, you'll notice that there are **3** different start events rather than **2** start events.\n",
"\n",
"The three start events correspond to:\n",
"\n",
"1. The chain (model + parser)\n",
"2. The model\n",
"3. The parser"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "8e66ea3d-a450-436a-aaac-d9478abc6c28",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'event': 'on_chain_start',\n",
" 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'},\n",
" 'name': 'RunnableSequence',\n",
" 'tags': [],\n",
" 'run_id': '4765006b-16e2-4b1d-a523-edd9fd64cb92',\n",
" 'metadata': {}},\n",
" {'event': 'on_chat_model_start',\n",
" 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}},\n",
" 'name': 'ChatAnthropic',\n",
" 'tags': ['seq:step:1'],\n",
" 'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',\n",
" 'metadata': {}},\n",
" {'event': 'on_chat_model_stream',\n",
" 'data': {'chunk': AIMessageChunk(content='{', id='run-0320c234-7b52-4a14-ae4e-5f100949e589')},\n",
" 'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',\n",
" 'name': 'ChatAnthropic',\n",
" 'tags': ['seq:step:1'],\n",
" 'metadata': {}}]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events[:3]"
]
},
{
"cell_type": "markdown",
"id": "c8512238-d035-4acd-9248-a8570da064c9",
"metadata": {},
"source": [
"What do you think you'd see if you looked at the last 3 events? what about the middle?"
]
},
{
"cell_type": "markdown",
"id": "c742cfa4-9b03-4a5b-96d9-5fe56e95e3b4",
"metadata": {},
"source": [
"Let's use this API to take output the stream events from the model and the parser. We're ignoring start events, end events and events from the chain."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "630c71d6-8d94-4ce0-a78a-f20e90f628df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chat model chunk: '{'\n",
"Parser chunk: {}\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '\"'\n",
"Chat model chunk: 'countries'\n",
"Chat model chunk: '\":'\n",
"Chat model chunk: ' ['\n",
"Parser chunk: {'countries': []}\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '{'\n",
"Parser chunk: {'countries': [{}]}\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '\"'\n",
"Chat model chunk: 'name'\n",
"Chat model chunk: '\":'\n",
"Chat model chunk: ' \"'\n",
"Parser chunk: {'countries': [{'name': ''}]}\n",
"Chat model chunk: 'France'\n",
"Parser chunk: {'countries': [{'name': 'France'}]}\n",
"Chat model chunk: '\",'\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '\"'\n",
"Chat model chunk: 'population'\n",
"...\n"
]
}
],
"source": [
"num_events = 0\n",
"\n",
"async for event in chain.astream_events(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\",\n",
" version=\"v2\",\n",
"):\n",
" kind = event[\"event\"]\n",
" if kind == \"on_chat_model_stream\":\n",
" print(\n",
" f\"Chat model chunk: {repr(event['data']['chunk'].content)}\",\n",
" flush=True,\n",
" )\n",
" if kind == \"on_parser_stream\":\n",
" print(f\"Parser chunk: {event['data']['chunk']}\", flush=True)\n",
" num_events += 1\n",
" if num_events > 30:\n",
" # Truncate the output\n",
" print(\"...\")\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "798ea891-997c-454c-bf60-43124f40ee1b",
"metadata": {},
"source": [
"Because both the model and the parser support streaming, we see streaming events from both components in real time! Kind of cool isn't it? 🦜"
]
},
{
"cell_type": "markdown",
"id": "5084148b-bcdc-4373-9caa-6568f03e7b23",
"metadata": {},
"source": [
"### Filtering Events\n",
"\n",
"Because this API produces so many events, it is useful to be able to filter on events.\n",
"\n",
"You can filter by either component `name`, component `tags` or component `type`.\n",
"\n",
"#### By Name"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "4f0b581b-be63-4663-baba-c6d2b625cdf9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': []}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}\n",
"...\n"
]
}
],
"source": [
"chain = model.with_config({\"run_name\": \"model\"}) | JsonOutputParser().with_config(\n",
" {\"run_name\": \"my_parser\"}\n",
")\n",
"\n",
"max_events = 0\n",
"async for event in chain.astream_events(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\",\n",
" version=\"v2\",\n",
" include_names=[\"my_parser\"],\n",
"):\n",
" print(event)\n",
" max_events += 1\n",
" if max_events > 10:\n",
" # Truncate output\n",
" print(\"...\")\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "c59d5626-7dba-4eb3-ad81-76c1092c5146",
"metadata": {},
"source": [
"#### By Type"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "096cd904-72f0-4ebe-a8b7-d0e730faea7f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}\n",
"...\n"
]
}
],
"source": [
"chain = model.with_config({\"run_name\": \"model\"}) | JsonOutputParser().with_config(\n",
" {\"run_name\": \"my_parser\"}\n",
")\n",
"\n",
"max_events = 0\n",
"async for event in chain.astream_events(\n",
" 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
" version=\"v2\",\n",
" include_types=[\"chat_model\"],\n",
"):\n",
" print(event)\n",
" max_events += 1\n",
" if max_events > 10:\n",
" # Truncate output\n",
" print(\"...\")\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "f1ec8dd4-9b5b-4000-b63f-5845bfc5a065",
"metadata": {},
"source": [
"#### By Tags\n",
"\n",
":::{.callout-caution}\n",
"\n",
"Tags are inherited by child components of a given runnable. \n",
"\n",
"If you're using tags to filter, make sure that this is what you want.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "26bac0d2-76d9-446e-b346-82790236b88d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'metadata': {}}\n",
"{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
"{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'metadata': {}}\n",
"{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}}\n",
"{'event': 'on_chain_stream', 'data': {'chunk': {}}, 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\\n ', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\"', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\":', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
"{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}\n",
"...\n"
]
}
],
"source": [
"chain = (model | JsonOutputParser()).with_config({\"tags\": [\"my_chain\"]})\n",
"\n",
"max_events = 0\n",
"async for event in chain.astream_events(\n",
" 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`',\n",
" version=\"v2\",\n",
" include_tags=[\"my_chain\"],\n",
"):\n",
" print(event)\n",
" max_events += 1\n",
" if max_events > 10:\n",
" # Truncate output\n",
" print(\"...\")\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "e05e54c4-61a2-4f6c-aa68-d2b09b5e1d4f",
"metadata": {},
"source": [
"### Non-streaming components\n",
"\n",
"Remember how some components don't stream well because they don't operate on **input streams**?\n",
"\n",
"While such components can break streaming of the final output when using `astream`, `astream_events` will still yield streaming events from intermediate steps that support streaming!"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0e6451d3-3b11-4a71-ae19-998f4c10180f",
"metadata": {},
"outputs": [],
"source": [
"# Function that does not support streaming.\n",
"# It operates on the finalizes inputs rather than\n",
"# operating on the input stream.\n",
"def _extract_country_names(inputs):\n",
" \"\"\"A function that does not operates on input streams and breaks streaming.\"\"\"\n",
" if not isinstance(inputs, dict):\n",
" return \"\"\n",
"\n",
" if \"countries\" not in inputs:\n",
" return \"\"\n",
"\n",
" countries = inputs[\"countries\"]\n",
"\n",
" if not isinstance(countries, list):\n",
" return \"\"\n",
"\n",
" country_names = [\n",
" country.get(\"name\") for country in countries if isinstance(country, dict)\n",
" ]\n",
" return country_names\n",
"\n",
"\n",
"chain = (\n",
" model | JsonOutputParser() | _extract_country_names\n",
") # This parser only works with OpenAI right now"
]
},
{
"cell_type": "markdown",
"id": "a972e1a6-80cd-4d59-90a0-73563f1503d4",
"metadata": {},
"source": [
"As expected, the `astream` API doesn't work correctly because `_extract_country_names` doesn't operate on streams."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "f9a8fe35-faab-4970-b8c0-5c780845d98a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['France', 'Spain', 'Japan']\n"
]
}
],
"source": [
"async for chunk in chain.astream(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\",\n",
"):\n",
" print(chunk, flush=True)"
]
},
{
"cell_type": "markdown",
"id": "b279ea33-54f1-400a-acb1-b8445ccbf1fa",
"metadata": {},
"source": [
"Now, let's confirm that with astream_events we're still seeing streaming output from the model and the parser."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "b08215cd-bffa-4e76-aaf3-c52ee34f152c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chat model chunk: '{'\n",
"Parser chunk: {}\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '\"'\n",
"Chat model chunk: 'countries'\n",
"Chat model chunk: '\":'\n",
"Chat model chunk: ' ['\n",
"Parser chunk: {'countries': []}\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '{'\n",
"Parser chunk: {'countries': [{}]}\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '\"'\n",
"Chat model chunk: 'name'\n",
"Chat model chunk: '\":'\n",
"Chat model chunk: ' \"'\n",
"Parser chunk: {'countries': [{'name': ''}]}\n",
"Chat model chunk: 'France'\n",
"Parser chunk: {'countries': [{'name': 'France'}]}\n",
"Chat model chunk: '\",'\n",
"Chat model chunk: '\\n '\n",
"Chat model chunk: '\"'\n",
"Chat model chunk: 'population'\n",
"Chat model chunk: '\":'\n",
"Chat model chunk: ' '\n",
"Chat model chunk: '67'\n",
"Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}\n",
"...\n"
]
}
],
"source": [
"num_events = 0\n",
"\n",
"async for event in chain.astream_events(\n",
" \"output a list of the countries france, spain and japan and their populations in JSON format. \"\n",
" 'Use a dict with an outer key of \"countries\" which contains a list of countries. '\n",
" \"Each country should have the key `name` and `population`\",\n",
" version=\"v2\",\n",
"):\n",
" kind = event[\"event\"]\n",
" if kind == \"on_chat_model_stream\":\n",
" print(\n",
" f\"Chat model chunk: {repr(event['data']['chunk'].content)}\",\n",
" flush=True,\n",
" )\n",
" if kind == \"on_parser_stream\":\n",
" print(f\"Parser chunk: {event['data']['chunk']}\", flush=True)\n",
" num_events += 1\n",
" if num_events > 30:\n",
" # Truncate the output\n",
" print(\"...\")\n",
" break"
]
},
{
"cell_type": "markdown",
"id": "6e91bdd3-f4a3-4b3c-b21a-26365c6c1566",
"metadata": {},
"source": [
"### Propagating Callbacks\n",
"\n",
":::{.callout-caution}\n",
"If you're using invoking runnables inside your tools, you need to propagate callbacks to the runnable; otherwise, no stream events will be generated.\n",
":::\n",
"\n",
":::{.callout-note}\n",
"When using `RunnableLambdas` or `@chain` decorator, callbacks are propagated automatically behind the scenes.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "1854206d-b3a5-4f91-9e00-bccbaebac61f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'bad_tool', 'tags': [], 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'metadata': {}}\n",
"{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'metadata': {}}\n",
"{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
"{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'name': 'bad_tool', 'tags': [], 'metadata': {}}\n"
]
}
],
"source": [
"from langchain_core.runnables import RunnableLambda\n",
"from langchain_core.tools import tool\n",
"\n",
"\n",
"def reverse_word(word: str):\n",
" return word[::-1]\n",
"\n",
"\n",
"reverse_word = RunnableLambda(reverse_word)\n",
"\n",
"\n",
"@tool\n",
"def bad_tool(word: str):\n",
" \"\"\"Custom tool that doesn't propagate callbacks.\"\"\"\n",
" return reverse_word.invoke(word)\n",
"\n",
"\n",
"async for event in bad_tool.astream_events(\"hello\", version=\"v2\"):\n",
" print(event)"
]
},
{
"cell_type": "markdown",
"id": "23e68a99-7886-465b-8575-116022857469",
"metadata": {},
"source": [
"Here's a re-implementation that does propagate callbacks correctly. You'll notice that now we're getting events from the `reverse_word` runnable as well."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "a20a6cb3-bb43-465c-8cfc-0a7349d70968",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'correct_tool', 'tags': [], 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'metadata': {}}\n",
"{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'metadata': {}}\n",
"{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
"{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'name': 'correct_tool', 'tags': [], 'metadata': {}}\n"
]
}
],
"source": [
"@tool\n",
"def correct_tool(word: str, callbacks):\n",
" \"\"\"A tool that correctly propagates callbacks.\"\"\"\n",
" return reverse_word.invoke(word, {\"callbacks\": callbacks})\n",
"\n",
"\n",
"async for event in correct_tool.astream_events(\"hello\", version=\"v2\"):\n",
" print(event)"
]
},
{
"cell_type": "markdown",
"id": "640daa94-e4fe-4997-ab6e-45120f18b9ee",
"metadata": {},
"source": [
"If you're invoking runnables from within Runnable Lambdas or `@chains`, then callbacks will be passed automatically on your behalf."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "0ac0a3c1-f3a4-4157-b053-4fec8d2e698c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'metadata': {}}\n",
"{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'metadata': {}}\n",
"{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
"{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n",
"{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n"
]
}
],
"source": [
"from langchain_core.runnables import RunnableLambda\n",
"\n",
"\n",
"async def reverse_and_double(word: str):\n",
" return await reverse_word.ainvoke(word) * 2\n",
"\n",
"\n",
"reverse_and_double = RunnableLambda(reverse_and_double)\n",
"\n",
"await reverse_and_double.ainvoke(\"1234\")\n",
"\n",
"async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
" print(event)"
]
},
{
"cell_type": "markdown",
"id": "35a34268-9b3d-4857-b4ed-65d95f4a1293",
"metadata": {},
"source": [
"And with the `@chain` decorator:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "c896bb94-9d10-41ff-8fe2-d6b05b1ed74b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'metadata': {}}\n",
"{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'metadata': {}}\n",
"{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'name': 'reverse_word', 'tags': [], 'metadata': {}}\n",
"{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n",
"{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}\n"
]
}
],
"source": [
"from langchain_core.runnables import chain\n",
"\n",
"\n",
"@chain\n",
"async def reverse_and_double(word: str):\n",
" return await reverse_word.ainvoke(word) * 2\n",
"\n",
"\n",
"await reverse_and_double.ainvoke(\"1234\")\n",
"\n",
"async for event in reverse_and_double.astream_events(\"1234\", version=\"v2\"):\n",
" print(event)"
]
},
{
"cell_type": "markdown",
"id": "2a3efcd9",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"Now you've learned some ways to stream both final outputs and internal steps with LangChain.\n",
"\n",
"To learn more, check out the other how-to guides in this section, or the [conceptual guide on Langchain Expression Language](/docs/concepts/#langchain-expression-language/)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/streaming_llm.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "fc37c39a-7406-4c13-a754-b8e95fd970a0",
"metadata": {},
"source": [
"# How to stream responses from an LLM\n",
"\n",
"All `LLM`s implement the [Runnable interface](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable), which comes with **default** implementations of standard runnable methods (i.e. `ainvoke`, `batch`, `abatch`, `stream`, `astream`, `astream_events`).\n",
"\n",
"The **default** streaming implementations provide an`Iterator` (or `AsyncIterator` for asynchronous streaming) that yields a single value: the final output from the underlying chat model provider.\n",
"\n",
"The ability to stream the output token-by-token depends on whether the provider has implemented proper streaming support.\n",
"\n",
"See which [integrations support token-by-token streaming here](/docs/integrations/llms/).\n",
"\n",
"\n",
"\n",
":::{.callout-note}\n",
"\n",
"The **default** implementation does **not** provide support for token-by-token streaming, but it ensures that the model can be swapped in for any other model as it supports the same standard interface.\n",
"\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "2f13124a-7f9d-404f-b7ac-70d8ea49ef8e",
"metadata": {},
"source": [
"## Sync stream\n",
"\n",
"Below we use a `|` to help visualize the delimiter between tokens."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9baa0527-b97d-41d3-babd-472ec5e59e3e",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"|Spark|ling| water|,| oh| so clear|\n",
"|Bubbles dancing|,| without| fear|\n",
"|Refreshing| taste|,| a| pure| delight|\n",
"|Spark|ling| water|,| my| thirst|'s| delight||"
]
}
],
"source": [
"from langchain_openai import OpenAI\n",
"\n",
"llm = OpenAI(model=\"gpt-3.5-turbo-instruct\", temperature=0, max_tokens=512)\n",
"for chunk in llm.stream(\"Write me a 1 verse song about sparkling water.\"):\n",
" print(chunk, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "596e477b-a41d-4ff5-9b9a-a7bfb53c3680",
"metadata": {},
"source": [
"## Async streaming\n",
"\n",
"Let's see how to stream in an async setting using `astream`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d81140f2-384b-4470-bf93-957013c6620b",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"|Spark|ling| water|,| oh| so clear|\n",
"|Bubbles dancing|,| without| fear|\n",
"|Refreshing| taste|,| a| pure| delight|\n",
"|Spark|ling| water|,| my| thirst|'s| delight||"
]
}
],
"source": [
"from langchain_openai import OpenAI\n",
"\n",
"llm = OpenAI(model=\"gpt-3.5-turbo-instruct\", temperature=0, max_tokens=512)\n",
"async for chunk in llm.astream(\"Write me a 1 verse song about sparkling water.\"):\n",
" print(chunk, end=\"|\", flush=True)"
]
},
{
"cell_type": "markdown",
"id": "9ab11306-b0db-4459-a9de-ecefb821c9b1",
"metadata": {
"tags": []
},
"source": [
"## Async event streaming\n",
"\n",
"\n",
"LLMs also support the standard [astream events](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) method.\n",
"\n",
":::{.callout-tip}\n",
"\n",
"`astream_events` is most useful when implementing streaming in a larger LLM application that contains multiple steps (e.g., an application that involves an `agent`).\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "399d74c7-4438-4093-ae05-47fed0255626",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_openai import OpenAI\n",
"\n",
"llm = OpenAI(model=\"gpt-3.5-turbo-instruct\", temperature=0, max_tokens=512)\n",
"\n",
"idx = 0\n",
"\n",
"async for event in llm.astream_events(\n",
" \"Write me a 1 verse song about goldfish on the moon\", version=\"v1\"\n",
"):\n",
" idx += 1\n",
" if idx >= 5: # Truncate the output\n",
" print(\"...Truncated\")\n",
" break\n",
" print(event)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/structured_output.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "27598444",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"---\n",
"sidebar_position: 3\n",
"keywords: [structured output, json, information extraction, with_structured_output]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "6e3f0f72",
"metadata": {},
"source": [
"# How to return structured data from a model\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
":::\n",
"\n",
"It is often useful to have a model return output that matches a specific schema. One common use-case is extracting data from text to insert into a database or use with some other downstream system. This guide covers a few strategies for getting structured outputs from a model.\n",
"\n",
"## The `.with_structured_output()` method\n",
"\n",
"<span data-heading-keywords=\"with_structured_output\"></span>\n",
"\n",
":::info Supported models\n",
"\n",
"You can find a [list of models that support this method here](/docs/integrations/chat/).\n",
"\n",
":::\n",
"\n",
"This is the easiest and most reliable way to get structured outputs. `with_structured_output()` is implemented for models that provide native APIs for structuring outputs, like tool/function calling or JSON mode, and makes use of these capabilities under the hood.\n",
"\n",
"This method takes a schema as input which specifies the names, types, and descriptions of the desired output attributes. The method returns a model-like Runnable, except that instead of outputting strings or Messages it outputs objects corresponding to the given schema. The schema can be specified as a [JSON Schema](https://json-schema.org/) or a Pydantic class. If JSON Schema is used then a dictionary will be returned by the Runnable, and if a Pydantic class is used then Pydantic objects will be returned.\n",
"\n",
"As an example, let's get a model to generate a joke and separate the setup from the punchline:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs\n",
" customVarName=\"llm\"\n",
"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6d55008f",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4-0125-preview\", temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "a808a401-be1f-49f9-ad13-58dd68f7db5f",
"metadata": {},
"source": [
"If we want the model to return a Pydantic object, we just need to pass in the desired Pydantic class:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "070bf702",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=None)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from typing import Optional\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class Joke(BaseModel):\n",
" \"\"\"Joke to tell user.\"\"\"\n",
"\n",
" setup: str = Field(description=\"The setup of the joke\")\n",
" punchline: str = Field(description=\"The punchline to the joke\")\n",
" rating: Optional[int] = Field(description=\"How funny the joke is, from 1 to 10\")\n",
"\n",
"\n",
"structured_llm = llm.with_structured_output(Joke)\n",
"\n",
"structured_llm.invoke(\"Tell me a joke about cats\")"
]
},
{
"cell_type": "markdown",
"id": "00890a47-3cdf-4805-b8f1-6d110f0633d3",
"metadata": {},
"source": [
":::tip\n",
"Beyond just the structure of the Pydantic class, the name of the Pydantic class, the docstring, and the names and provided descriptions of parameters are very important. Most of the time `with_structured_output` is using a model's function/tool calling API, and you can effectively think of all of this information as being added to the model prompt.\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "deddb6d3",
"metadata": {},
"source": [
"We can also pass in a [JSON Schema](https://json-schema.org/) dict if you prefer not to use Pydantic. In this case, the response is also a dict:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6700994a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'setup': 'Why was the cat sitting on the computer?',\n",
" 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
" 'rating': 8}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"json_schema = {\n",
" \"title\": \"joke\",\n",
" \"description\": \"Joke to tell user.\",\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"setup\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The setup of the joke\",\n",
" },\n",
" \"punchline\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The punchline to the joke\",\n",
" },\n",
" \"rating\": {\n",
" \"type\": \"integer\",\n",
" \"description\": \"How funny the joke is, from 1 to 10\",\n",
" },\n",
" },\n",
" \"required\": [\"setup\", \"punchline\"],\n",
"}\n",
"structured_llm = llm.with_structured_output(json_schema)\n",
"\n",
"structured_llm.invoke(\"Tell me a joke about cats\")"
]
},
{
"cell_type": "markdown",
"id": "3da57988",
"metadata": {},
"source": [
"### Choosing between multiple schemas\n",
"\n",
"The simplest way to let the model choose from multiple schemas is to create a parent Pydantic class that has a Union-typed attribute:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9194bcf2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Response(output=Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=8))"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from typing import Union\n",
"\n",
"\n",
"class ConversationalResponse(BaseModel):\n",
" \"\"\"Respond in a conversational manner. Be kind and helpful.\"\"\"\n",
"\n",
" response: str = Field(description=\"A conversational response to the user's query\")\n",
"\n",
"\n",
"class Response(BaseModel):\n",
" output: Union[Joke, ConversationalResponse]\n",
"\n",
"\n",
"structured_llm = llm.with_structured_output(Response)\n",
"\n",
"structured_llm.invoke(\"Tell me a joke about cats\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "84d86132",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Response(output=ConversationalResponse(response=\"I'm just a digital assistant, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?\"))"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"structured_llm.invoke(\"How are you today?\")"
]
},
{
"cell_type": "markdown",
"id": "e28c14d3",
"metadata": {},
"source": [
"Alternatively, you can use tool calling directly to allow the model to choose between options, if your [chosen model supports it](/docs/integrations/chat/). This involves a bit more parsing and setup but in some instances leads to better performance because you don't have to use nested schemas. See [this how-to guide](/docs/how_to/tool_calling) for more details."
]
},
{
"cell_type": "markdown",
"id": "9a40f703-7fd2-4fe0-ab2a-fa2d711ba009",
"metadata": {},
"source": [
"### Streaming\n",
"\n",
"We can stream outputs from our structured model when the output type is a dict (i.e., when the schema is specified as a JSON Schema dict). \n",
"\n",
":::info\n",
"\n",
"Note that what's yielded is already aggregated chunks, not deltas.\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "aff89877-28a3-472f-a1aa-eff893fe7736",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{}\n",
"{'setup': ''}\n",
"{'setup': 'Why'}\n",
"{'setup': 'Why was'}\n",
"{'setup': 'Why was the'}\n",
"{'setup': 'Why was the cat'}\n",
"{'setup': 'Why was the cat sitting'}\n",
"{'setup': 'Why was the cat sitting on'}\n",
"{'setup': 'Why was the cat sitting on the'}\n",
"{'setup': 'Why was the cat sitting on the computer'}\n",
"{'setup': 'Why was the cat sitting on the computer?'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': ''}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse!'}\n",
"{'setup': 'Why was the cat sitting on the computer?', 'punchline': 'Because it wanted to keep an eye on the mouse!', 'rating': 8}\n"
]
}
],
"source": [
"structured_llm = llm.with_structured_output(json_schema)\n",
"\n",
"for chunk in structured_llm.stream(\"Tell me a joke about cats\"):\n",
" print(chunk)"
]
},
{
"cell_type": "markdown",
"id": "0a526cdf-e736-451b-96be-22e8986d3863",
"metadata": {},
"source": [
"### Few-shot prompting\n",
"\n",
"For more complex schemas it's very useful to add few-shot examples to the prompt. This can be done in a few ways.\n",
"\n",
"The simplest and most universal way is to add examples to a system message in the prompt:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "283ba784-2072-47ee-9b2c-1119e3c69e8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'setup': 'Woodpecker',\n",
" 'punchline': \"Woodpecker goes 'knock knock', but don't worry, they never expect you to answer the door!\",\n",
" 'rating': 8}"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"system = \"\"\"You are a hilarious comedian. Your specialty is knock-knock jokes. \\\n",
"Return a joke which has the setup (the response to \"Who's there?\") and the final punchline (the response to \"<setup> who?\").\n",
"\n",
"Here are some examples of jokes:\n",
"\n",
"example_user: Tell me a joke about planes\n",
"example_assistant: {{\"setup\": \"Why don't planes ever get tired?\", \"punchline\": \"Because they have rest wings!\", \"rating\": 2}}\n",
"\n",
"example_user: Tell me another joke about planes\n",
"example_assistant: {{\"setup\": \"Cargo\", \"punchline\": \"Cargo 'vroom vroom', but planes go 'zoom zoom'!\", \"rating\": 10}}\n",
"\n",
"example_user: Now about caterpillars\n",
"example_assistant: {{\"setup\": \"Caterpillar\", \"punchline\": \"Caterpillar really slow, but watch me turn into a butterfly and steal the show!\", \"rating\": 5}}\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\")])\n",
"\n",
"few_shot_structured_llm = prompt | structured_llm\n",
"few_shot_structured_llm.invoke(\"what's something funny about woodpeckers\")"
]
},
{
"cell_type": "markdown",
"id": "3c12b389-153d-44d1-af34-37e5b926d3db",
"metadata": {},
"source": [
"When the underlying method for structuring outputs is tool calling, we can pass in our examples as explicit tool calls. You can check if the model you're using makes use of tool calling in its API reference."
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "d7381cb0-b2c3-4302-a319-ed72d0b9e43f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'setup': 'Crocodile',\n",
" 'punchline': \"Crocodile 'see you later', but in a while, it becomes an alligator!\",\n",
" 'rating': 7}"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.messages import AIMessage, HumanMessage, ToolMessage\n",
"\n",
"examples = [\n",
" HumanMessage(\"Tell me a joke about planes\", name=\"example_user\"),\n",
" AIMessage(\n",
" \"\",\n",
" name=\"example_assistant\",\n",
" tool_calls=[\n",
" {\n",
" \"name\": \"joke\",\n",
" \"args\": {\n",
" \"setup\": \"Why don't planes ever get tired?\",\n",
" \"punchline\": \"Because they have rest wings!\",\n",
" \"rating\": 2,\n",
" },\n",
" \"id\": \"1\",\n",
" }\n",
" ],\n",
" ),\n",
" # Most tool-calling models expect a ToolMessage(s) to follow an AIMessage with tool calls.\n",
" ToolMessage(\"\", tool_call_id=\"1\"),\n",
" # Some models also expect an AIMessage to follow any ToolMessages,\n",
" # so you may need to add an AIMessage here.\n",
" HumanMessage(\"Tell me another joke about planes\", name=\"example_user\"),\n",
" AIMessage(\n",
" \"\",\n",
" name=\"example_assistant\",\n",
" tool_calls=[\n",
" {\n",
" \"name\": \"joke\",\n",
" \"args\": {\n",
" \"setup\": \"Cargo\",\n",
" \"punchline\": \"Cargo 'vroom vroom', but planes go 'zoom zoom'!\",\n",
" \"rating\": 10,\n",
" },\n",
" \"id\": \"2\",\n",
" }\n",
" ],\n",
" ),\n",
" ToolMessage(\"\", tool_call_id=\"2\"),\n",
" HumanMessage(\"Now about caterpillars\", name=\"example_user\"),\n",
" AIMessage(\n",
" \"\",\n",
" tool_calls=[\n",
" {\n",
" \"name\": \"joke\",\n",
" \"args\": {\n",
" \"setup\": \"Caterpillar\",\n",
" \"punchline\": \"Caterpillar really slow, but watch me turn into a butterfly and steal the show!\",\n",
" \"rating\": 5,\n",
" },\n",
" \"id\": \"3\",\n",
" }\n",
" ],\n",
" ),\n",
" ToolMessage(\"\", tool_call_id=\"3\"),\n",
"]\n",
"system = \"\"\"You are a hilarious comedian. Your specialty is knock-knock jokes. \\\n",
"Return a joke which has the setup (the response to \"Who's there?\") \\\n",
"and the final punchline (the response to \"<setup> who?\").\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system), (\"placeholder\", \"{examples}\"), (\"human\", \"{input}\")]\n",
")\n",
"few_shot_structured_llm = prompt | structured_llm\n",
"few_shot_structured_llm.invoke({\"input\": \"crocodiles\", \"examples\": examples})"
]
},
{
"cell_type": "markdown",
"id": "498d893b-ceaa-47ff-a9d8-4faa60702715",
"metadata": {},
"source": [
"For more on few shot prompting when using tool calling, see [here](/docs/how_to/function_calling/#Few-shot-prompting)."
]
},
{
"cell_type": "markdown",
"id": "39d7a555",
"metadata": {},
"source": [
"### (Advanced) Specifying the method for structuring outputs\n",
"\n",
"For models that support more than one means of structuring outputs (i.e., they support both tool calling and JSON mode), you can specify which method to use with the `method=` argument.\n",
"\n",
":::info JSON mode\n",
"\n",
"If using JSON mode you'll have to still specify the desired schema in the model prompt. The schema you pass to `with_structured_output` will only be used for parsing the model outputs, it will not be passed to the model the way it is with tool calling.\n",
"\n",
"To see if the model you're using supports JSON mode, check its entry in the [API reference](https://api.python.langchain.com/en/latest/langchain_api_reference.html).\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "df0370e3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=None)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"structured_llm = llm.with_structured_output(Joke, method=\"json_mode\")\n",
"\n",
"structured_llm.invoke(\n",
" \"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "5e92a98a",
"metadata": {},
"source": [
"## Prompting and parsing model directly\n",
"\n",
"Not all models support `.with_structured_output()`, since not all models have tool calling or JSON mode support. For such models you'll need to directly prompt the model to use a specific format, and use an output parser to extract the structured response from the raw model output.\n",
"\n",
"### Using `PydanticOutputParser`\n",
"\n",
"The following example uses the built-in [`PydanticOutputParser`](https://api.python.langchain.com/en/latest/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html) to parse the output of a chat model prompted to match the given Pydantic schema. Note that we are adding `format_instructions` directly to the prompt from a method on the parser:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "6e514455",
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from langchain_core.output_parsers import PydanticOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class Person(BaseModel):\n",
" \"\"\"Information about a person.\"\"\"\n",
"\n",
" name: str = Field(..., description=\"The name of the person\")\n",
" height_in_meters: float = Field(\n",
" ..., description=\"The height of the person expressed in meters.\"\n",
" )\n",
"\n",
"\n",
"class People(BaseModel):\n",
" \"\"\"Identifying information about all people in a text.\"\"\"\n",
"\n",
" people: List[Person]\n",
"\n",
"\n",
"# Set up a parser\n",
"parser = PydanticOutputParser(pydantic_object=People)\n",
"\n",
"# Prompt\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"Answer the user query. Wrap the output in `json` tags\\n{format_instructions}\",\n",
" ),\n",
" (\"human\", \"{query}\"),\n",
" ]\n",
").partial(format_instructions=parser.get_format_instructions())"
]
},
{
"cell_type": "markdown",
"id": "082fa166",
"metadata": {},
"source": [
"Let’s take a look at what information is sent to the model:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "3d73d33d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System: Answer the user query. Wrap the output in `json` tags\n",
"The output should be formatted as a JSON instance that conforms to the JSON schema below.\n",
"\n",
"As an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\n",
"the object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\n",
"\n",
"Here is the output schema:\n",
"```\n",
"{\"description\": \"Identifying information about all people in a text.\", \"properties\": {\"people\": {\"title\": \"People\", \"type\": \"array\", \"items\": {\"$ref\": \"#/definitions/Person\"}}}, \"required\": [\"people\"], \"definitions\": {\"Person\": {\"title\": \"Person\", \"description\": \"Information about a person.\", \"type\": \"object\", \"properties\": {\"name\": {\"title\": \"Name\", \"description\": \"The name of the person\", \"type\": \"string\"}, \"height_in_meters\": {\"title\": \"Height In Meters\", \"description\": \"The height of the person expressed in meters.\", \"type\": \"number\"}}, \"required\": [\"name\", \"height_in_meters\"]}}}\n",
"```\n",
"Human: Anna is 23 years old and she is 6 feet tall\n"
]
}
],
"source": [
"query = \"Anna is 23 years old and she is 6 feet tall\"\n",
"\n",
"print(prompt.invoke(query).to_string())"
]
},
{
"cell_type": "markdown",
"id": "081956b9",
"metadata": {},
"source": [
"And now let's invoke it:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8d6b3d17",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"People(people=[Person(name='Anna', height_in_meters=1.8288)])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = prompt | llm | parser\n",
"\n",
"chain.invoke({\"query\": query})"
]
},
{
"cell_type": "markdown",
"id": "6732dd87",
"metadata": {},
"source": [
"For a deeper dive into using output parsers with prompting techniques for structured output, see [this guide](/docs/how_to/output_parser_structured).\n",
"\n",
"### Custom Parsing\n",
"\n",
"You can also create a custom prompt and parser with [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language), using a plain function to parse the output from the model:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e8d37e15",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import re\n",
"from typing import List\n",
"\n",
"from langchain_core.messages import AIMessage\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class Person(BaseModel):\n",
" \"\"\"Information about a person.\"\"\"\n",
"\n",
" name: str = Field(..., description=\"The name of the person\")\n",
" height_in_meters: float = Field(\n",
" ..., description=\"The height of the person expressed in meters.\"\n",
" )\n",
"\n",
"\n",
"class People(BaseModel):\n",
" \"\"\"Identifying information about all people in a text.\"\"\"\n",
"\n",
" people: List[Person]\n",
"\n",
"\n",
"# Prompt\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"Answer the user query. Output your answer as JSON that \"\n",
" \"matches the given schema: ```json\\n{schema}\\n```. \"\n",
" \"Make sure to wrap the answer in ```json and ``` tags\",\n",
" ),\n",
" (\"human\", \"{query}\"),\n",
" ]\n",
").partial(schema=People.schema())\n",
"\n",
"\n",
"# Custom parser\n",
"def extract_json(message: AIMessage) -> List[dict]:\n",
" \"\"\"Extracts JSON content from a string where JSON is embedded between ```json and ``` tags.\n",
"\n",
" Parameters:\n",
" text (str): The text containing the JSON content.\n",
"\n",
" Returns:\n",
" list: A list of extracted JSON strings.\n",
" \"\"\"\n",
" text = message.content\n",
" # Define the regular expression pattern to match JSON blocks\n",
" pattern = r\"```json(.*?)```\"\n",
"\n",
" # Find all non-overlapping matches of the pattern in the string\n",
" matches = re.findall(pattern, text, re.DOTALL)\n",
"\n",
" # Return the list of matched JSON strings, stripping any leading or trailing whitespace\n",
" try:\n",
" return [json.loads(match.strip()) for match in matches]\n",
" except Exception:\n",
" raise ValueError(f\"Failed to parse: {message}\")"
]
},
{
"cell_type": "markdown",
"id": "9f1bc8f7",
"metadata": {},
"source": [
"Here is the prompt sent to the model:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c8a30d0e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"System: Answer the user query. Output your answer as JSON that matches the given schema: ```json\n",
"{'title': 'People', 'description': 'Identifying information about all people in a text.', 'type': 'object', 'properties': {'people': {'title': 'People', 'type': 'array', 'items': {'$ref': '#/definitions/Person'}}}, 'required': ['people'], 'definitions': {'Person': {'title': 'Person', 'description': 'Information about a person.', 'type': 'object', 'properties': {'name': {'title': 'Name', 'description': 'The name of the person', 'type': 'string'}, 'height_in_meters': {'title': 'Height In Meters', 'description': 'The height of the person expressed in meters.', 'type': 'number'}}, 'required': ['name', 'height_in_meters']}}}\n",
"```. Make sure to wrap the answer in ```json and ``` tags\n",
"Human: Anna is 23 years old and she is 6 feet tall\n"
]
}
],
"source": [
"query = \"Anna is 23 years old and she is 6 feet tall\"\n",
"\n",
"print(prompt.format_prompt(query=query).to_string())"
]
},
{
"cell_type": "markdown",
"id": "ec018893",
"metadata": {},
"source": [
"And here's what it looks like when we invoke it:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e1e7baf6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'people': [{'name': 'Anna', 'height_in_meters': 1.8288}]}]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = prompt | llm | extract_json\n",
"\n",
"chain.invoke({\"query\": query})"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv-2",
"language": "python",
"name": "poetry-venv-2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/time_weighted_vectorstore.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "e239cc79",
"metadata": {},
"source": [
"# How to use a time-weighted vector store retriever\n",
"\n",
"This retriever uses a combination of semantic similarity and a time decay.\n",
"\n",
"The algorithm for scoring them is:\n",
"\n",
"```\n",
"semantic_similarity + (1.0 - decay_rate) ^ hours_passed\n",
"```\n",
"\n",
"Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain \"fresh\".\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "97e74400",
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime, timedelta\n",
"\n",
"import faiss\n",
"from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
"from langchain_community.docstore import InMemoryDocstore\n",
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings"
]
},
{
"cell_type": "markdown",
"id": "89635236",
"metadata": {},
"source": [
"## Low decay rate\n",
"\n",
"A low `decay rate` (in this, to be extreme, we will set it close to 0) means memories will be \"remembered\" for longer. A `decay rate` of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d3a1778d",
"metadata": {},
"outputs": [],
"source": [
"# Define your embedding model\n",
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model, index, InMemoryDocstore({}), {})\n",
"retriever = TimeWeightedVectorStoreRetriever(\n",
" vectorstore=vectorstore, decay_rate=0.0000000000000000000000001, k=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "408fc114",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['c3dcf671-3c0a-4273-9334-c4a913076bfa']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yesterday = datetime.now() - timedelta(days=1)\n",
"retriever.add_documents(\n",
" [Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})]\n",
")\n",
"retriever.add_documents([Document(page_content=\"hello foo\")])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8a5ed9ca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='hello world', metadata={'last_accessed_at': datetime.datetime(2023, 12, 27, 15, 30, 18, 457125), 'created_at': datetime.datetime(2023, 12, 27, 15, 30, 8, 442662), 'buffer_idx': 0})]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# \"Hello World\" is returned first because it is most salient, and the decay rate is close to 0., meaning it's still recent enough\n",
"retriever.get_relevant_documents(\"hello world\")"
]
},
{
"cell_type": "markdown",
"id": "d8bc4f96",
"metadata": {},
"source": [
"## High decay rate\n",
"\n",
"With a high `decay rate` (e.g., several 9's), the `recency score` quickly goes to 0! If you set this all the way to 1, `recency` is 0 for all objects, once again making this equivalent to a vector lookup.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e588d729",
"metadata": {},
"outputs": [],
"source": [
"# Define your embedding model\n",
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model, index, InMemoryDocstore({}), {})\n",
"retriever = TimeWeightedVectorStoreRetriever(\n",
" vectorstore=vectorstore, decay_rate=0.999, k=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "43b4afb3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['eb1c4c86-01a8-40e3-8393-9a927295a950']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yesterday = datetime.now() - timedelta(days=1)\n",
"retriever.add_documents(\n",
" [Document(page_content=\"hello world\", metadata={\"last_accessed_at\": yesterday})]\n",
")\n",
"retriever.add_documents([Document(page_content=\"hello foo\")])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0677113c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 12, 27, 15, 30, 50, 57185), 'created_at': datetime.datetime(2023, 12, 27, 15, 30, 44, 720490), 'buffer_idx': 1})]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# \"Hello Foo\" is returned first because \"hello world\" is mostly forgotten\n",
"retriever.get_relevant_documents(\"hello world\")"
]
},
{
"cell_type": "markdown",
"id": "c8b0075a",
"metadata": {},
"source": [
"## Virtual time\n",
"\n",
"Using some utils in LangChain, you can mock out the time component.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0b4188e7",
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"\n",
"from langchain_core.utils import mock_now"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "95d55764",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Document(page_content='hello world', metadata={'last_accessed_at': MockDateTime(2024, 2, 3, 10, 11), 'created_at': datetime.datetime(2023, 12, 27, 15, 30, 44, 532941), 'buffer_idx': 0})]\n"
]
}
],
"source": [
"# Notice the last access time is that date time\n",
"with mock_now(datetime.datetime(2024, 2, 3, 10, 11)):\n",
" print(retriever.get_relevant_documents(\"hello world\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a6da4c6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tool_calling.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to use a model to call tools\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"\n",
":::\n",
"\n",
":::info Tool calling vs function calling\n",
"\n",
"We use the term tool calling interchangeably with function calling. Although\n",
"function calling is sometimes meant to refer to invocations of a single function,\n",
"we treat all models as though they can return multiple tool or function calls in \n",
"each message.\n",
"\n",
":::\n",
"\n",
":::info Supported models\n",
"\n",
"You can find a [list of all models that support tool calling](/docs/integrations/chat/).\n",
"\n",
":::\n",
"\n",
"Tool calling allows a chat model to respond to a given prompt by \"calling a tool\".\n",
"While the name implies that the model is performing \n",
"some action, this is actually not the case! The model generates the \n",
"arguments to a tool, and actually running the tool (or not) is up to the user.\n",
"For example, if you want to [extract output matching some schema](/docs/how_to/structured_output/) \n",
"from unstructured text, you could give the model an \"extraction\" tool that takes \n",
"parameters matching the desired schema, then treat the generated output as your final \n",
"result.\n",
"\n",
"However, tool calling goes beyond [structured output](/docs/how_to/structured_output/)\n",
"since you can pass responses from called tools back to the model to create longer interactions.\n",
"For instance, given a search engine tool, an LLM might handle a \n",
"query by first issuing a call to the search engine with arguments. The system calling the LLM can \n",
"receive the tool call, execute it, and return the output to the LLM to inform its \n",
"response. LangChain includes a suite of [built-in tools](/docs/integrations/tools/) \n",
"and supports several methods for defining your own [custom tools](/docs/how_to/custom_tools). \n",
"\n",
"Tool calling is not universal, but many popular LLM providers, including [Anthropic](https://www.anthropic.com/), \n",
"[Cohere](https://cohere.com/), [Google](https://cloud.google.com/vertex-ai), \n",
"[Mistral](https://mistral.ai/), [OpenAI](https://openai.com/), and others, \n",
"support variants of a tool calling feature.\n",
"\n",
"LangChain implements standard interfaces for defining tools, passing them to LLMs, \n",
"and representing tool calls. This guide and the other How-to pages in the Tool section will show you how to use tools with LangChain."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Passing tools to chat models\n",
"\n",
"Chat models that support tool calling features implement a `.bind_tools` method, which \n",
"receives a list of LangChain [tool objects](https://api.python.langchain.com/en/latest/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool) \n",
"and binds them to the chat model in its expected format. Subsequent invocations of the \n",
"chat model will include tool schemas in its calls to the LLM.\n",
"\n",
"For example, we can define the schema for custom tools using the `@tool` decorator \n",
"on Python functions:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Adds a and b.\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"@tool\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiplies a and b.\"\"\"\n",
" return a * b\n",
"\n",
"\n",
"tools = [add, multiply]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or below, we define the schema using [Pydantic](https://docs.pydantic.dev):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"# Note that the docstrings here are crucial, as they will be passed along\n",
"# to the model along with the class name.\n",
"class Add(BaseModel):\n",
" \"\"\"Add two integers together.\"\"\"\n",
"\n",
" a: int = Field(..., description=\"First integer\")\n",
" b: int = Field(..., description=\"Second integer\")\n",
"\n",
"\n",
"class Multiply(BaseModel):\n",
" \"\"\"Multiply two integers together.\"\"\"\n",
"\n",
" a: int = Field(..., description=\"First integer\")\n",
" b: int = Field(..., description=\"Second integer\")\n",
"\n",
"\n",
"tools = [Add, Multiply]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can bind them to chat models as follows:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs\n",
" customVarName=\"llm\"\n",
" fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
"/>\n",
"```\n",
"\n",
"We'll use the `.bind_tools()` method to handle converting\n",
"`Multiply` to the proper format for the model, then and bind it (i.e.,\n",
"passing it in each time the model is invoked)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"%pip install -qU langchain langchain_openai\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"llm_with_tools = llm.bind_tools(tools)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, even though the prompt didn't really suggest a tool call, our LLM made one since it was forced to do so. You can look at the docs for [`bind_tool`](https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools) to learn about all the ways to customize how your LLM selects tools."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tool calls\n",
"\n",
"If tool calls are included in a LLM response, they are attached to the corresponding \n",
"[message](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage) \n",
"or [message chunk](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
"as a list of [tool call](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolCall.html#langchain_core.messages.tool.ToolCall) \n",
"objects in the `.tool_calls` attribute.\n",
"\n",
"Note that chat models can call multiple tools at once.\n",
"\n",
"A `ToolCall` is a typed dict that includes a \n",
"tool name, dict of argument values, and (optionally) an identifier. Messages with no \n",
"tool calls default to an empty list for this attribute."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'Multiply',\n",
" 'args': {'a': 3, 'b': 12},\n",
" 'id': 'call_KquHA7mSbgtAkpkmRPaFnJKa'},\n",
" {'name': 'Add',\n",
" 'args': {'a': 11, 'b': 49},\n",
" 'id': 'call_Fl0hQi4IBTzlpaJYlM5kPQhE'}]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"query = \"What is 3 * 12? Also, what is 11 + 49?\"\n",
"\n",
"llm_with_tools.invoke(query).tool_calls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `.tool_calls` attribute should contain valid tool calls. Note that on occasion, \n",
"model providers may output malformed tool calls (e.g., arguments that are not \n",
"valid JSON). When parsing fails in these cases, instances \n",
"of [InvalidToolCall](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.InvalidToolCall.html#langchain_core.messages.tool.InvalidToolCall) \n",
"are populated in the `.invalid_tool_calls` attribute. An `InvalidToolCall` can have \n",
"a name, string arguments, identifier, and error message.\n",
"\n",
"If desired, [output parsers](/docs/how_to#output-parsers) can further \n",
"process the output. For example, we can convert back to the original Pydantic class:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Multiply(a=3, b=12), Add(a=11, b=49)]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
"\n",
"chain = llm_with_tools | PydanticToolsParser(tools=[Multiply, Add])\n",
"chain.invoke(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"Now you've learned how to bind tool schemas to a chat model and to call those tools. Next, you can learn more about how to use tools:\n",
"\n",
"- Few shot promting [with tools](/docs/how_to/tools_few_shot/)\n",
"- Stream [tool calls](/docs/how_to/tool_streaming/)\n",
"- Bind [model-specific tools](/docs/how_to/tools_model_specific/)\n",
"- Pass [runtime values to tools](/docs/how_to/tool_runtime)\n",
"- Pass [tool results back to model](/docs/how_to/tool_results_pass_to_model)\n",
"\n",
"You can also check out some more specific uses of tool calling:\n",
"\n",
"- Building [tool-using chains and agents](/docs/how_to#tools)\n",
"- Getting [structured outputs](/docs/how_to/structured_output/) from models"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tool_calling_parallel.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Disabling parallel tool calling (OpenAI only)\n",
"\n",
"OpenAI tool calling performs tool calling in parallel by default. That means that if we ask a question like \"What is the weather in Tokyo, New York, and Chicago?\" and we have a tool for getting the weather, it will call the tool 3 times in parallel. We can force it to call only a single tool once by using the ``parallel_tool_call`` parameter."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First let's set up our tools and model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Adds a and b.\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"@tool\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiplies a and b.\"\"\"\n",
" return a * b\n",
"\n",
"\n",
"tools = [add, multiply]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's show a quick example of how disabling parallel tool calls work:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'add',\n",
" 'args': {'a': 2, 'b': 2},\n",
" 'id': 'call_Hh4JOTCDM85Sm9Pr84VKrWu5'}]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)\n",
"llm_with_tools.invoke(\"Please call the first tool two times\").tool_calls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, even though we explicitly told the model to call a tool twice, by disabling parallel tool calls the model was constrained to only calling one."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tool_choice.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to force tool calling behavior\n",
"\n",
"In order to force our LLM to spelect a specific tool, we can use the `tool_choice` parameter to ensure certain behavior. First, let's define our model and tools:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Adds a and b.\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"@tool\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiplies a and b.\"\"\"\n",
" return a * b\n",
"\n",
"\n",
"tools = [add, multiply]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"%pip install -qU langchain langchain_openai\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, we can force our tool to call the multiply tool by using the following code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_9cViskmLvPnHjXk9tbVla5HA', 'function': {'arguments': '{\"a\":2,\"b\":4}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 103, 'total_tokens': 112}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-095b827e-2bdd-43bb-8897-c843f4504883-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 2, 'b': 4}, 'id': 'call_9cViskmLvPnHjXk9tbVla5HA'}], usage_metadata={'input_tokens': 103, 'output_tokens': 9, 'total_tokens': 112})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llm_forced_to_multiply = llm.bind_tools(tools, tool_choice=\"Multiply\")\n",
"llm_forced_to_multiply.invoke(\"what is 2 + 4\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even if we pass it something that doesn't require multiplcation - it will still call the tool!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also just force our tool to select at least one of our tools by passing in the \"any\" (or \"required\" which is OpenAI specific) keyword to the `tool_choice` parameter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W', 'function': {'arguments': '{\"a\":1,\"b\":2}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 94, 'total_tokens': 109}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-28f75260-9900-4bed-8cd3-f1579abb65e5-0', tool_calls=[{'name': 'Add', 'args': {'a': 1, 'b': 2}, 'id': 'call_mCSiJntCwHJUBfaHZVUB2D8W'}], usage_metadata={'input_tokens': 94, 'output_tokens': 15, 'total_tokens': 109})"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llm_forced_to_use_tool = llm.bind_tools(tools, tool_choice=\"any\")\n",
"llm_forced_to_use_tool.invoke(\"What day is today?\")"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tool_results_pass_to_model.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to pass tool outputs to the model\n",
"\n",
"If we're using the model-generated tool invocations to actually call tools and want to pass the tool results back to the model, we can do so using `ToolMessage`s. First, let's define our tools and our model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Adds a and b.\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"@tool\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiplies a and b.\"\"\"\n",
" return a * b\n",
"\n",
"\n",
"tools = [add, multiply]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm_with_tools = llm.bind_tools(tools)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can use ``ToolMessage`` to pass back the output of the tool calls to the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='What is 3 * 12? Also, what is 11 + 49?'),\n",
" AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_svc2GLSxNFALbaCAbSjMI9J8', 'function': {'arguments': '{\"a\": 3, \"b\": 12}', 'name': 'Multiply'}, 'type': 'function'}, {'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh', 'function': {'arguments': '{\"a\": 11, \"b\": 49}', 'name': 'Add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 105, 'total_tokens': 155}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-a79ad1dd-95f1-4a46-b688-4c83f327a7b3-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_svc2GLSxNFALbaCAbSjMI9J8'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_r8jxte3zW6h3MEGV3zH2qzFh'}]),\n",
" ToolMessage(content='36', tool_call_id='call_svc2GLSxNFALbaCAbSjMI9J8'),\n",
" ToolMessage(content='60', tool_call_id='call_r8jxte3zW6h3MEGV3zH2qzFh')]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from langchain_core.messages import HumanMessage, ToolMessage\n",
"\n",
"query = \"What is 3 * 12? Also, what is 11 + 49?\"\n",
"\n",
"messages = [HumanMessage(query)]\n",
"ai_msg = llm_with_tools.invoke(messages)\n",
"messages.append(ai_msg)\n",
"for tool_call in ai_msg.tool_calls:\n",
" selected_tool = {\"add\": add, \"multiply\": multiply}[tool_call[\"name\"].lower()]\n",
" tool_output = selected_tool.invoke(tool_call[\"args\"])\n",
" messages.append(ToolMessage(tool_output, tool_call_id=tool_call[\"id\"]))\n",
"messages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='3 * 12 is 36 and 11 + 49 is 60.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 171, 'total_tokens': 189}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'stop', 'logprobs': None}, id='run-20b52149-e00d-48ea-97cf-f8de7a255f8c-0')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llm_with_tools.invoke(messages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that we pass back the same `id` in the `ToolMessage` as the what we receive from the model in order to help the model match tool responses with tool calls."
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tool_runtime.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to pass run time values to a tool\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [How to create tools](/docs/how_to/custom_tools)\n",
"- [How to use a model to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling)\n",
":::\n",
"\n",
":::{.callout-info} Supported models\n",
"\n",
"This how-to guide uses models with native tool calling capability.\n",
"You can find a [list of all models that support tool calling](/docs/integrations/chat/).\n",
"\n",
":::\n",
"\n",
":::{.callout-info} Using with LangGraph\n",
"\n",
"If you're using LangGraph, please refer to [this how-to guide](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/)\n",
"which shows how to create an agent that keeps track of a given user's favorite pets.\n",
":::\n",
"\n",
"You may need to bind values to a tool that are only known at runtime. For example, the tool logic may require using the ID of the user who made the request.\n",
"\n",
"Most of the time, such values should not be controlled by the LLM. In fact, allowing the LLM to control the user ID may lead to a security risk.\n",
"\n",
"Instead, the LLM should only control the parameters of the tool that are meant to be controlled by the LLM, while other parameters (such as user ID) should be fixed by the application logic.\n",
"\n",
"This how-to guide shows a simple design pattern that creates the tool dynamically at run time and binds to them appropriate values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can bind them to chat models as follows:\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs\n",
" customVarName=\"llm\"\n",
" fireworksParams={`model=\"accounts/fireworks/models/firefunction-v1\", temperature=0`}\n",
"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"%pip install -qU langchain langchain_openai\n",
"\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"if \"OPENAI_API_KEY\" not in os.environ:\n",
" os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Passing request time information\n",
"\n",
"The idea is to create the tool dynamically at request time, and bind to it the appropriate information. For example,\n",
"this information may be the user ID as resolved from the request itself."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from langchain_core.output_parsers import JsonOutputParser\n",
"from langchain_core.tools import BaseTool, tool"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"user_to_pets = {}\n",
"\n",
"\n",
"def generate_tools_for_user(user_id: str) -> List[BaseTool]:\n",
" \"\"\"Generate a set of tools that have a user id associated with them.\"\"\"\n",
"\n",
" @tool\n",
" def update_favorite_pets(pets: List[str]) -> None:\n",
" \"\"\"Add the list of favorite pets.\"\"\"\n",
" user_to_pets[user_id] = pets\n",
"\n",
" @tool\n",
" def delete_favorite_pets() -> None:\n",
" \"\"\"Delete the list of favorite pets.\"\"\"\n",
" if user_id in user_to_pets:\n",
" del user_to_pets[user_id]\n",
"\n",
" @tool\n",
" def list_favorite_pets() -> None:\n",
" \"\"\"List favorite pets if any.\"\"\"\n",
" return user_to_pets.get(user_id, [])\n",
"\n",
" return [update_favorite_pets, delete_favorite_pets, list_favorite_pets]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify that the tools work correctly"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'eugene': ['cat', 'dog']}\n",
"['cat', 'dog']\n"
]
}
],
"source": [
"update_pets, delete_pets, list_pets = generate_tools_for_user(\"eugene\")\n",
"update_pets.invoke({\"pets\": [\"cat\", \"dog\"]})\n",
"print(user_to_pets)\n",
"print(list_pets.invoke({}))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"\n",
"def handle_run_time_request(user_id: str, query: str):\n",
" \"\"\"Handle run time request.\"\"\"\n",
" tools = generate_tools_for_user(user_id)\n",
" llm_with_tools = llm.bind_tools(tools)\n",
" prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", \"You are a helpful assistant.\")],\n",
" )\n",
" chain = prompt | llm_with_tools\n",
" return llm_with_tools.invoke(query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This code will allow the LLM to invoke the tools, but the LLM is **unaware** of the fact that a **user ID** even exists!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'update_favorite_pets',\n",
" 'args': {'pets': ['cats', 'parrots']},\n",
" 'id': 'call_jJvjPXsNbFO5MMgW0q84iqCN'}]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ai_message = handle_run_time_request(\n",
" \"eugene\", \"my favorite animals are cats and parrots.\"\n",
")\n",
"ai_message.tool_calls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{.callout-important}\n",
"\n",
"Chat models only output requests to invoke tools, they don't actually invoke the underlying tools.\n",
"\n",
"To see how to invoke the tools, please refer to [how to use a model to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling).\n",
":::"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tool_streaming.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to stream tool calls\n",
"\n",
"When tools are called in a streaming context, \n",
"[message chunks](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
"will be populated with [tool call chunk](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk) \n",
"objects in a list via the `.tool_call_chunks` attribute. A `ToolCallChunk` includes \n",
"optional string fields for the tool `name`, `args`, and `id`, and includes an optional \n",
"integer field `index` that can be used to join chunks together. Fields are optional \n",
"because portions of a tool call may be streamed across different chunks (e.g., a chunk \n",
"that includes a substring of the arguments may have null values for the tool name and id).\n",
"\n",
"Because message chunks inherit from their parent message class, an \n",
"[AIMessageChunk](https://api.python.langchain.com/en/latest/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
"with tool call chunks will also include `.tool_calls` and `.invalid_tool_calls` fields. \n",
"These fields are parsed best-effort from the message's tool call chunks.\n",
"\n",
"Note that not all providers currently support streaming for tool calls. Before we start let's define our tools and our model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Adds a and b.\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"@tool\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiplies a and b.\"\"\"\n",
" return a * b\n",
"\n",
"\n",
"tools = [add, multiply]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm_with_tools = llm.bind_tools(tools)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's define our query and stream our output:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"[{'name': 'Multiply', 'args': '', 'id': 'call_3aQwTP9CYlFxwOvQZPHDu6wL', 'index': 0}]\n",
"[{'name': None, 'args': '{\"a\"', 'id': None, 'index': 0}]\n",
"[{'name': None, 'args': ': 3, ', 'id': None, 'index': 0}]\n",
"[{'name': None, 'args': '\"b\": 1', 'id': None, 'index': 0}]\n",
"[{'name': None, 'args': '2}', 'id': None, 'index': 0}]\n",
"[{'name': 'Add', 'args': '', 'id': 'call_SQUoSsJz2p9Kx2x73GOgN1ja', 'index': 1}]\n",
"[{'name': None, 'args': '{\"a\"', 'id': None, 'index': 1}]\n",
"[{'name': None, 'args': ': 11,', 'id': None, 'index': 1}]\n",
"[{'name': None, 'args': ' \"b\": ', 'id': None, 'index': 1}]\n",
"[{'name': None, 'args': '49}', 'id': None, 'index': 1}]\n",
"[]\n"
]
}
],
"source": [
"query = \"What is 3 * 12? Also, what is 11 + 49?\"\n",
"\n",
"async for chunk in llm_with_tools.astream(query):\n",
" print(chunk.tool_call_chunks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that adding message chunks will merge their corresponding tool call chunks. This is the principle by which LangChain's various [tool output parsers](/docs/how_to/output_parser_structured) support streaming.\n",
"\n",
"For example, below we accumulate tool call chunks:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"[{'name': 'Multiply', 'args': '', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
"[{'name': 'Multiply', 'args': '{\"a\"', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, ', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 1', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\"', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11,', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": ', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n",
"[{'name': 'Multiply', 'args': '{\"a\": 3, \"b\": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{\"a\": 11, \"b\": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]\n"
]
}
],
"source": [
"first = True\n",
"async for chunk in llm_with_tools.astream(query):\n",
" if first:\n",
" gathered = chunk\n",
" first = False\n",
" else:\n",
" gathered = gathered + chunk\n",
"\n",
" print(gathered.tool_call_chunks)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(gathered.tool_call_chunks[0][\"args\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And below we accumulate tool calls to demonstrate partial parsing:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"[]\n",
"[{'name': 'Multiply', 'args': {}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 1}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n",
"[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]\n"
]
}
],
"source": [
"first = True\n",
"async for chunk in llm_with_tools.astream(query):\n",
" if first:\n",
" gathered = chunk\n",
" first = False\n",
" else:\n",
" gathered = gathered + chunk\n",
"\n",
" print(gathered.tool_calls)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'dict'>\n"
]
}
],
"source": [
"print(type(gathered.tool_calls[0][\"args\"]))"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/toolkits.mdx | ---
sidebar_position: 3
---
# How to use toolkits
Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.
For a complete list of available ready-made toolkits, visit [Integrations](/docs/integrations/toolkits/).
All Toolkits expose a `get_tools` method which returns a list of tools.
You can therefore do:
```python
# Initialize a toolkit
toolkit = ExampleTookit(...)
# Get list of tools
tools = toolkit.get_tools()
# Create agent
agent = create_agent_method(llm, tools, prompt)
```
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_as_openai_functions.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4111c9d4",
"metadata": {},
"source": [
"# How to convert tools to OpenAI Functions\n",
"\n",
"This notebook goes over how to use LangChain tools as OpenAI functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bb220019-4012-4da4-bfee-01fb8189aa49",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain-community langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d65d8a60",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.tools import MoveFileTool\n",
"from langchain_core.messages import HumanMessage\n",
"from langchain_core.utils.function_calling import convert_to_openai_function\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "abd8dc72",
"metadata": {},
"outputs": [],
"source": [
"model = ChatOpenAI(model=\"gpt-3.5-turbo\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "3b3dc766",
"metadata": {},
"outputs": [],
"source": [
"tools = [MoveFileTool()]\n",
"functions = [convert_to_openai_function(t) for t in tools]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d38c4a22-2e9e-4d15-a9e1-bf8103c6303b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'move_file',\n",
" 'description': 'Move or rename a file from one location to another',\n",
" 'parameters': {'type': 'object',\n",
" 'properties': {'source_path': {'description': 'Path of the file to move',\n",
" 'type': 'string'},\n",
" 'destination_path': {'description': 'New path for the moved file',\n",
" 'type': 'string'}},\n",
" 'required': ['source_path', 'destination_path']}}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"functions[0]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "230a7939",
"metadata": {},
"outputs": [],
"source": [
"message = model.invoke(\n",
" [HumanMessage(content=\"move file foo to bar\")], functions=functions\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c118c940",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\\n \"source_path\": \"foo\",\\n \"destination_path\": \"bar\"\\n}', 'name': 'move_file'}})"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d618e3d8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'move_file',\n",
" 'arguments': '{\\n \"source_path\": \"foo\",\\n \"destination_path\": \"bar\"\\n}'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message.additional_kwargs[\"function_call\"]"
]
},
{
"cell_type": "markdown",
"id": "77dd0d9f-2f24-4535-a658-a061f91e009a",
"metadata": {},
"source": [
"With OpenAI chat models we can also automatically bind and convert function-like objects with `bind_functions`"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "24bb1518-8100-4ac3-acea-04acfac963d1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\\n \"source_path\": \"foo\",\\n \"destination_path\": \"bar\"\\n}', 'name': 'move_file'}})"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_with_functions = model.bind_functions(tools)\n",
"model_with_functions.invoke([HumanMessage(content=\"move file foo to bar\")])"
]
},
{
"cell_type": "markdown",
"id": "000ec6ff-ca67-4206-ba56-cc2a91b85ce6",
"metadata": {},
"source": [
"Or we can use the update OpenAI API that uses `tools` and `tool_choice` instead of `functions` and `function_call` by using `ChatOpenAI.bind_tools`:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1a333e4e-df55-4e15-9d2e-4fd142d969f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_btkY3xV71cEVAOHnNa5qwo44', 'function': {'arguments': '{\\n \"source_path\": \"foo\",\\n \"destination_path\": \"bar\"\\n}', 'name': 'move_file'}, 'type': 'function'}]})"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_with_tools = model.bind_tools(tools)\n",
"model_with_tools.invoke([HumanMessage(content=\"move file foo to bar\")])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_builtin.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "7f219241",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 4\n",
"sidebar_class_name: hidden\n",
"---"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e8f68de0-7df7-4bfd-9207-3258431426ef",
"metadata": {},
"source": [
"# How to use built-in tools and toolkits\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [LangChain Toolkits](/docs/concepts/#tools)\n",
"\n",
":::\n",
"\n",
"## Tools\n",
"\n",
"LangChain has a large collection of 3rd party tools. Please visit [Tool Integrations](/docs/integrations/tools/) for a list of the available tools.\n",
"\n",
":::{.callout-important}\n",
"\n",
"When using 3rd party tools, make sure that you understand how the tool works, what permissions\n",
"it has. Read over its documentation and check if anything is required from you\n",
"from a security point of view. Please see our [security](https://python.langchain.com/v0.2/docs/security/) \n",
"guidelines for more information.\n",
"\n",
":::\n",
"\n",
"Let's try out the [Wikipedia integration](/docs/integrations/tools/wikipedia/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "84f70856-b865-4658-9930-7577fb4712ce",
"metadata": {},
"outputs": [],
"source": [
"!pip install -qU wikipedia"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "b4eaed85-c5a6-4ba9-b401-40258b0131c2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Page: LangChain\n",
"Summary: LangChain is a framework designed to simplify the creation of applications \n"
]
}
],
"source": [
"from langchain_community.tools import WikipediaQueryRun\n",
"from langchain_community.utilities import WikipediaAPIWrapper\n",
"\n",
"api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)\n",
"tool = WikipediaQueryRun(api_wrapper=api_wrapper)\n",
"\n",
"print(tool.invoke({\"query\": \"langchain\"}))"
]
},
{
"cell_type": "markdown",
"id": "cb870984-52d5-4453-be35-7072a08c6c14",
"metadata": {},
"source": [
"The tool has the following defaults associated with it:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "7f094f01-2e98-4947-acc4-0846963a96e0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: wiki-tool\n",
"Description: look up things in wikipedia\n",
"args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
"returns directly?: True\n"
]
}
],
"source": [
"print(f\"Name: {tool.name}\")\n",
"print(f\"Description: {tool.description}\")\n",
"print(f\"args schema: {tool.args}\")\n",
"print(f\"returns directly?: {tool.return_direct}\")"
]
},
{
"cell_type": "markdown",
"id": "19eee1d5",
"metadata": {},
"source": [
"## Customizing Default Tools\n",
"We can also modify the built in name, description, and JSON schema of the arguments.\n",
"\n",
"When defining the JSON schema of the arguments, it is important that the inputs remain the same as the function, so you shouldn't change that. But you can define custom descriptions for each input easily."
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "1365784c-e666-41c8-a1bb-e50f822b5936",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Page: LangChain\n",
"Summary: LangChain is a framework designed to simplify the creation of applications \n"
]
}
],
"source": [
"from langchain_community.tools import WikipediaQueryRun\n",
"from langchain_community.utilities import WikipediaAPIWrapper\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"class WikiInputs(BaseModel):\n",
" \"\"\"Inputs to the wikipedia tool.\"\"\"\n",
"\n",
" query: str = Field(\n",
" description=\"query to look up in Wikipedia, should be 3 or less words\"\n",
" )\n",
"\n",
"\n",
"tool = WikipediaQueryRun(\n",
" name=\"wiki-tool\",\n",
" description=\"look up things in wikipedia\",\n",
" args_schema=WikiInputs,\n",
" api_wrapper=api_wrapper,\n",
" return_direct=True,\n",
")\n",
"\n",
"print(tool.run(\"langchain\"))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "6e8850d6-6840-443e-a2be-adf64b30975c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: wiki-tool\n",
"Description: look up things in wikipedia\n",
"args schema: {'query': {'title': 'Query', 'description': 'query to look up in Wikipedia, should be 3 or less words', 'type': 'string'}}\n",
"returns directly?: True\n"
]
}
],
"source": [
"print(f\"Name: {tool.name}\")\n",
"print(f\"Description: {tool.description}\")\n",
"print(f\"args schema: {tool.args}\")\n",
"print(f\"returns directly?: {tool.return_direct}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "acf0c2f7-ddc6-4633-8cef-59f234321e5c",
"metadata": {},
"source": [
"## How to use built-in toolkits\n",
"\n",
"Toolkits are collections of tools that are designed to be used together for specific tasks. They have convenient loading methods.\n",
"\n",
"For a complete list of available ready-made toolkits, visit [Integrations](/docs/integrations/toolkits/).\n",
"\n",
"All Toolkits expose a `get_tools` method which returns a list of tools.\n",
"\n",
"You're usually meant to use them this way:\n",
"\n",
"```python\n",
"# Initialize a toolkit\n",
"toolkit = ExampleTookit(...)\n",
"\n",
"# Get list of tools\n",
"tools = toolkit.get_tools()\n",
"```"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_chain.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "500e8846-91c2-4716-9bd6-b9672c6daf78",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 0\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "14b94240",
"metadata": {},
"source": [
"# How to use tools in a chain\n",
"\n",
"In this guide, we will go over the basic ways to create Chains and Agents that call Tools. Tools can be just about anything — APIs, functions, databases, etc. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. The key to using models with tools is correctly prompting a model and parsing its response so that it chooses the right tools and provides the right inputs for them."
]
},
{
"cell_type": "markdown",
"id": "e6b79a42-0349-42c6-9ce8-72220e838e8d",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We'll need to install the following packages for this guide:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2274266-755a-4e90-b257-5180fb089af2",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain"
]
},
{
"cell_type": "markdown",
"id": "36a9c6fc-8264-462f-b8d7-9c7bbec22ef9",
"metadata": {},
"source": [
"If you'd like to trace your runs in [LangSmith](https://docs.smith.langchain.com/) uncomment and set the following environment variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57a81b7a-4fd9-4f28-bc32-7b98b522e1b0",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "68946881",
"metadata": {},
"source": [
"## Create a tool\n",
"\n",
"First, we need to create a tool to call. For this example, we will create a custom tool from a function. For more information on creating custom tools, please see [this guide](/docs/how_to/custom_tools)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "90187d07",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def multiply(first_int: int, second_int: int) -> int:\n",
" \"\"\"Multiply two integers together.\"\"\"\n",
" return first_int * second_int"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d7009e1a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"multiply\n",
"multiply(first_int: int, second_int: int) -> int - Multiply two integers together.\n",
"{'first_int': {'title': 'First Int', 'type': 'integer'}, 'second_int': {'title': 'Second Int', 'type': 'integer'}}\n"
]
}
],
"source": [
"print(multiply.name)\n",
"print(multiply.description)\n",
"print(multiply.args)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "be77e780",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multiply.invoke({\"first_int\": 4, \"second_int\": 5})"
]
},
{
"cell_type": "markdown",
"id": "19ba4d63",
"metadata": {},
"source": [
"## Chains\n",
"\n",
"If we know that we only need to use a tool a fixed number of times, we can create a chain for doing so. Let's create a simple chain that just multiplies user-specified numbers.\n",
"\n",
"\n",
"\n",
"### Tool/function calling\n",
"One of the most reliable ways to use tools with LLMs is with tool calling APIs (also sometimes called function calling). This only works with models that explicitly support tool calling. You can see which models support tool calling [here](/docs/integrations/chat/), and learn more about how to use tool calling in [this guide](/docs/how_to/function_calling).\n",
"\n",
"First we'll define our model and tools. We'll start with just a single tool, `multiply`.\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9bce8935-1465-45ac-8a93-314222c753c4",
"metadata": {},
"outputs": [],
"source": [
"# | echo: false\n",
"# | output: false\n",
"\n",
"from langchain_openai.chat_models import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "c22e6f0f-c5ad-4c0f-9514-e626704ea51c",
"metadata": {},
"source": [
"We'll use `bind_tools` to pass the definition of our tool in as part of each call to the model, so that the model can invoke the tool when appropriate:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3bfe2cdc-7d72-457c-a9a1-5fa1e0bcde55",
"metadata": {},
"outputs": [],
"source": [
"llm_with_tools = llm.bind_tools([multiply])"
]
},
{
"cell_type": "markdown",
"id": "07fc830e-a6d2-4fac-904b-b94072e64018",
"metadata": {},
"source": [
"When the model invokes the tool, this will show up in the `AIMessage.tool_calls` attribute of the output:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "68f30343-14ef-48f1-badd-b6a03977316d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'multiply',\n",
" 'args': {'first_int': 5, 'second_int': 42},\n",
" 'id': 'call_cCP9oA3tRz7HDrjFn1FdmDaG'}]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"msg = llm_with_tools.invoke(\"whats 5 times forty two\")\n",
"msg.tool_calls"
]
},
{
"cell_type": "markdown",
"id": "330015a3-a5a7-433a-826a-6277766f6c27",
"metadata": {},
"source": [
"Check out the [LangSmith trace here](https://smith.langchain.com/public/81ff0cbd-e05b-4720-bf61-2c9807edb708/r)."
]
},
{
"cell_type": "markdown",
"id": "8ba1764d-0272-4f98-adcf-b48cb2c0a315",
"metadata": {},
"source": [
"### Invoking the tool\n",
"\n",
"Great! We're able to generate tool invocations. But what if we want to actually call the tool? To do so we'll need to pass the generated tool args to our tool. As a simple example we'll just extract the arguments of the first tool_call:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4f5325ca-e5dc-4d1a-ba36-b085a029c90a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"92"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from operator import itemgetter\n",
"\n",
"chain = llm_with_tools | (lambda x: x.tool_calls[0][\"args\"]) | multiply\n",
"chain.invoke(\"What's four times 23\")"
]
},
{
"cell_type": "markdown",
"id": "79a9eb63-383d-4dd4-a162-08b4a52ef4d9",
"metadata": {},
"source": [
"Check out the [LangSmith trace here](https://smith.langchain.com/public/16bbabb9-fc9b-41e5-a33d-487c42df4f85/r)."
]
},
{
"cell_type": "markdown",
"id": "0521d3d5",
"metadata": {},
"source": [
"## Agents\n",
"\n",
"Chains are great when we know the specific sequence of tool usage needed for any user input. But for certain use cases, how many times we use tools depends on the input. In these cases, we want to let the model itself decide how many times to use tools and in what order. [Agents](/docs/tutorials/agents) let us do just this.\n",
"\n",
"LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the [agent types here](/docs/concepts#agents).\n",
"\n",
"We'll use the [tool calling agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.tool_calling_agent.base.create_tool_calling_agent.html), which is generally the most reliable kind and the recommended one for most use cases.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "21723cf4-9421-4a8d-92a6-eeeb8f4367f1",
"metadata": {},
"outputs": [],
"source": [
"from langchain import hub\n",
"from langchain.agents import AgentExecutor, create_tool_calling_agent"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6be83879-9da3-4dd9-b147-a79f76affd7a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m System Message \u001b[0m================================\n",
"\n",
"You are a helpful assistant\n",
"\n",
"=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
"\n",
"\u001b[33;1m\u001b[1;3m{chat_history}\u001b[0m\n",
"\n",
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n",
"\n",
"=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
"\n",
"\u001b[33;1m\u001b[1;3m{agent_scratchpad}\u001b[0m\n"
]
}
],
"source": [
"# Get the prompt to use - can be replaced with any prompt that includes variables \"agent_scratchpad\" and \"input\"!\n",
"prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
"prompt.pretty_print()"
]
},
{
"cell_type": "markdown",
"id": "616f9714-5b18-4eed-b88a-d38e4cb1de99",
"metadata": {},
"source": [
"Agents are also great because they make it easy to use multiple tools."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "95c86d32-ee45-4c87-a28c-14eff19b49e9",
"metadata": {},
"outputs": [],
"source": [
"@tool\n",
"def add(first_int: int, second_int: int) -> int:\n",
" \"Add two integers.\"\n",
" return first_int + second_int\n",
"\n",
"\n",
"@tool\n",
"def exponentiate(base: int, exponent: int) -> int:\n",
" \"Exponentiate the base to the exponent power.\"\n",
" return base**exponent\n",
"\n",
"\n",
"tools = [multiply, add, exponentiate]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "17b09ac6-c9b7-4340-a8a0-3d3061f7888c",
"metadata": {},
"outputs": [],
"source": [
"# Construct the tool calling agent\n",
"agent = create_tool_calling_agent(llm, tools, prompt)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "675091d2-cac9-45c4-a5d7-b760ee6c1986",
"metadata": {},
"outputs": [],
"source": [
"# Create an agent executor by passing in the agent and tools\n",
"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
]
},
{
"cell_type": "markdown",
"id": "a6099ab6-2fa6-452d-b73c-7fb65daab451",
"metadata": {},
"source": [
"With an agent, we can ask questions that require arbitrarily-many uses of our tools:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f7dbb240-809e-4e41-8f63-1a4636e8e26d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `exponentiate` with `{'base': 3, 'exponent': 5}`\n",
"\n",
"\n",
"\u001b[0m\u001b[38;5;200m\u001b[1;3m243\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `add` with `{'first_int': 12, 'second_int': 3}`\n",
"\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m15\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `multiply` with `{'first_int': 243, 'second_int': 15}`\n",
"\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m3645\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `exponentiate` with `{'base': 405, 'exponent': 2}`\n",
"\n",
"\n",
"\u001b[0m\u001b[38;5;200m\u001b[1;3m164025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of taking 3 to the fifth power is 243. \n",
"\n",
"The sum of twelve and three is 15. \n",
"\n",
"Multiplying 243 by 15 gives 3645. \n",
"\n",
"Finally, squaring 3645 gives 164025.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',\n",
" 'output': 'The result of taking 3 to the fifth power is 243. \\n\\nThe sum of twelve and three is 15. \\n\\nMultiplying 243 by 15 gives 3645. \\n\\nFinally, squaring 3645 gives 164025.'}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.invoke(\n",
" {\n",
" \"input\": \"Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result\"\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8fdb0ed9-1763-4778-a7d6-026578cd9585",
"metadata": {},
"source": [
"Check out the [LangSmith trace here](https://smith.langchain.com/public/eeeb27a4-a2f8-4f06-a3af-9c983f76146c/r)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_error.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "5d60cbb9-2a6a-43ea-a9e9-f67b16ddd2b2",
"metadata": {},
"source": [
"# How to handle tool errors\n",
"\n",
"Using a model to invoke a tool has some obvious potential failure modes. Firstly, the model needs to return a output that can be parsed at all. Secondly, the model needs to return tool arguments that are valid.\n",
"\n",
"We can build error handling into our chains to mitigate these failure modes."
]
},
{
"cell_type": "markdown",
"id": "712c774f-27c7-4351-a196-39900ca155f5",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We'll need to install the following packages:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63056c24-9834-4e3d-8bc5-54b1e6c5df86",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-core langchain-openai"
]
},
{
"cell_type": "markdown",
"id": "68107597-0c8c-4bb5-8c12-9992fabdf71a",
"metadata": {},
"source": [
"If you'd like to trace your runs in [LangSmith](https://docs.smith.langchain.com/) uncomment and set the following environment variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08785b6d-722d-4620-b6ec-36deb3842c69",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "0a50f93a-5d6f-4691-8f98-27239a1c2f95",
"metadata": {},
"source": [
"## Chain\n",
"\n",
"Suppose we have the following (dummy) tool and tool-calling chain. We'll make our tool intentionally convoluted to try and trip up the model.\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "86258950-5e61-4340-81b9-84a5d26e8773",
"metadata": {},
"outputs": [],
"source": [
"# | echo: false\n",
"# | output: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1d20604e-c4d1-4d21-841b-23e4f61aec36",
"metadata": {},
"outputs": [],
"source": [
"# Define tool\n",
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def complex_tool(int_arg: int, float_arg: float, dict_arg: dict) -> int:\n",
" \"\"\"Do something complex with a complex tool.\"\"\"\n",
" return int_arg * float_arg"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "553c2c13-28c8-4451-8a3a-6c31d52dc31d",
"metadata": {},
"outputs": [],
"source": [
"llm_with_tools = llm.bind_tools(\n",
" [complex_tool],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "802b2eca-9f79-4d6c-8257-85139ca5c752",
"metadata": {},
"outputs": [],
"source": [
"# Define chain\n",
"chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool"
]
},
{
"cell_type": "markdown",
"id": "c34f005e-63f0-4841-9461-ca36c36607fc",
"metadata": {},
"source": [
"We can see that when we try to invoke this chain with even a fairly explicit input, the model fails to correctly call the tool (it forgets the `dict_arg` argument)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d354664c-ac44-4967-a35f-8912b3ad9477",
"metadata": {},
"outputs": [
{
"ename": "ValidationError",
"evalue": "1 validation error for complex_toolSchema\ndict_arg\n field required (type=value_error.missing)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muse complex tool. the args are 5, 2.1, empty dictionary. don\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt forget dict_arg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 3\u001b[0m \u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/langchain/libs/core/langchain_core/runnables/base.py:2499\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m 2497\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2498\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[0;32m-> 2499\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2501\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[1;32m 2502\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2503\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2504\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2505\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2506\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m 2507\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:241\u001b[0m, in \u001b[0;36mBaseTool.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minvoke\u001b[39m(\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28minput\u001b[39m: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[1;32m 237\u001b[0m config: Optional[RunnableConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 238\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 239\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 240\u001b[0m config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m--> 241\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 242\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 243\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcallbacks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 244\u001b[0m \u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtags\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 245\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmetadata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 246\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 247\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 248\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 249\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:387\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ValidationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error:\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 388\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle_validation_error, \u001b[38;5;28mbool\u001b[39m):\n\u001b[1;32m 389\u001b[0m observation \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTool input validation error\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
"File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:378\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[0m\n\u001b[1;32m 364\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_tool_start(\n\u001b[1;32m 365\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdescription\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdescription},\n\u001b[1;32m 366\u001b[0m tool_input \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tool_input, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(tool_input),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 376\u001b[0m )\n\u001b[1;32m 377\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 378\u001b[0m parsed_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 379\u001b[0m tool_args, tool_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_args_and_kwargs(parsed_input)\n\u001b[1;32m 380\u001b[0m observation \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run(\u001b[38;5;241m*\u001b[39mtool_args, run_manager\u001b[38;5;241m=\u001b[39mrun_manager, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m 382\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run(\u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[1;32m 384\u001b[0m )\n",
"File \u001b[0;32m~/langchain/libs/core/langchain_core/tools.py:283\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 282\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m input_args \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 283\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43minput_args\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m 285\u001b[0m k: \u001b[38;5;28mgetattr\u001b[39m(result, k)\n\u001b[1;32m 286\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdict()\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m tool_input\n\u001b[1;32m 288\u001b[0m }\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tool_input\n",
"File \u001b[0;32m~/langchain/.venv/lib/python3.9/site-packages/pydantic/v1/main.py:526\u001b[0m, in \u001b[0;36mBaseModel.parse_obj\u001b[0;34m(cls, obj)\u001b[0m\n\u001b[1;32m 524\u001b[0m exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m expected dict not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mobj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ValidationError([ErrorWrapper(exc, loc\u001b[38;5;241m=\u001b[39mROOT_KEY)], \u001b[38;5;28mcls\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m--> 526\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/langchain/.venv/lib/python3.9/site-packages/pydantic/v1/main.py:341\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(__pydantic_self__, **data)\u001b[0m\n\u001b[1;32m 339\u001b[0m values, fields_set, validation_error \u001b[38;5;241m=\u001b[39m validate_model(__pydantic_self__\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m, data)\n\u001b[1;32m 340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validation_error:\n\u001b[0;32m--> 341\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m validation_error\n\u001b[1;32m 342\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 343\u001b[0m object_setattr(__pydantic_self__, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__dict__\u001b[39m\u001b[38;5;124m'\u001b[39m, values)\n",
"\u001b[0;31mValidationError\u001b[0m: 1 validation error for complex_toolSchema\ndict_arg\n field required (type=value_error.missing)"
]
}
],
"source": [
"chain.invoke(\n",
" \"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "890d989d-2d39-4571-9a55-d3496b9b5d27",
"metadata": {},
"source": [
"## Try/except tool call\n",
"\n",
"The simplest way to more gracefully handle errors is to try/except the tool-calling step and return a helpful message on errors:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8fedb550-683d-45ae-8876-ae7acb332019",
"metadata": {},
"outputs": [],
"source": [
"from typing import Any\n",
"\n",
"from langchain_core.runnables import Runnable, RunnableConfig\n",
"\n",
"\n",
"def try_except_tool(tool_args: dict, config: RunnableConfig) -> Runnable:\n",
" try:\n",
" complex_tool.invoke(tool_args, config=config)\n",
" except Exception as e:\n",
" return f\"Calling tool with arguments:\\n\\n{tool_args}\\n\\nraised the following error:\\n\\n{type(e)}: {e}\"\n",
"\n",
"\n",
"chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | try_except_tool"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "71a2c98d-c0be-4c0a-bb3d-41ad4596526c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calling tool with arguments:\n",
"\n",
"{'int_arg': 5, 'float_arg': 2.1}\n",
"\n",
"raised the following error:\n",
"\n",
"<class 'pydantic.v1.error_wrappers.ValidationError'>: 1 validation error for complex_toolSchema\n",
"dict_arg\n",
" field required (type=value_error.missing)\n"
]
}
],
"source": [
"print(\n",
" chain.invoke(\n",
" \"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg\"\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "3b2f6393-cb47-49d0-921c-09550a049fe4",
"metadata": {},
"source": [
"## Fallbacks\n",
"\n",
"We can also try to fallback to a better model in the event of a tool invocation error. In this case we'll fall back to an identical chain that uses `gpt-4-1106-preview` instead of `gpt-3.5-turbo`."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "02cc4223-35fa-4240-976a-012299ca703c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10.5"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = llm_with_tools | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool\n",
"better_model = ChatOpenAI(model=\"gpt-4-1106-preview\", temperature=0).bind_tools(\n",
" [complex_tool], tool_choice=\"complex_tool\"\n",
")\n",
"better_chain = better_model | (lambda msg: msg.tool_calls[0][\"args\"]) | complex_tool\n",
"\n",
"chain_with_fallback = chain.with_fallbacks([better_chain])\n",
"chain_with_fallback.invoke(\n",
" \"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "412f8c4e-cc83-4d87-84a1-5ba2f8edb1e9",
"metadata": {},
"source": [
"Looking at the [Langsmith trace](https://smith.langchain.com/public/00e91fc2-e1a4-4b0f-a82e-e6b3119d196c/r) for this chain run, we can see that the first chain call fails as expected and it's the fallback that succeeds."
]
},
{
"cell_type": "markdown",
"id": "304b59cd-cd25-4205-9769-36595c8f3b59",
"metadata": {},
"source": [
"## Retry with exception\n",
"\n",
"To take things one step further, we can try to automatically re-run the chain with the exception passed in, so that the model may be able to correct its behavior:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b5659956-9454-468a-9753-a3ff9052b8f5",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from typing import Any\n",
"\n",
"from langchain_core.messages import AIMessage, HumanMessage, ToolCall, ToolMessage\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"\n",
"class CustomToolException(Exception):\n",
" \"\"\"Custom LangChain tool exception.\"\"\"\n",
"\n",
" def __init__(self, tool_call: ToolCall, exception: Exception) -> None:\n",
" super().__init__()\n",
" self.tool_call = tool_call\n",
" self.exception = exception\n",
"\n",
"\n",
"def tool_custom_exception(msg: AIMessage, config: RunnableConfig) -> Runnable:\n",
" try:\n",
" return complex_tool.invoke(msg.tool_calls[0][\"args\"], config=config)\n",
" except Exception as e:\n",
" raise CustomToolException(msg.tool_calls[0], e)\n",
"\n",
"\n",
"def exception_to_messages(inputs: dict) -> dict:\n",
" exception = inputs.pop(\"exception\")\n",
"\n",
" # Add historical messages to the original input, so the model knows that it made a mistake with the last tool call.\n",
" messages = [\n",
" AIMessage(content=\"\", tool_calls=[exception.tool_call]),\n",
" ToolMessage(\n",
" tool_call_id=exception.tool_call[\"id\"], content=str(exception.exception)\n",
" ),\n",
" HumanMessage(\n",
" content=\"The last tool call raised an exception. Try calling the tool again with corrected arguments. Do not repeat mistakes.\"\n",
" ),\n",
" ]\n",
" inputs[\"last_output\"] = messages\n",
" return inputs\n",
"\n",
"\n",
"# We add a last_output MessagesPlaceholder to our prompt which if not passed in doesn't\n",
"# affect the prompt at all, but gives us the option to insert an arbitrary list of Messages\n",
"# into the prompt if needed. We'll use this on retries to insert the error message.\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"human\", \"{input}\"), MessagesPlaceholder(\"last_output\", optional=True)]\n",
")\n",
"chain = prompt | llm_with_tools | tool_custom_exception\n",
"\n",
"# If the initial chain call fails, we rerun it withe the exception passed in as a message.\n",
"self_correcting_chain = chain.with_fallbacks(\n",
" [exception_to_messages | chain], exception_key=\"exception\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "4c45f5bd-cbb4-47d5-b4b6-aec50673c750",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10.5"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"self_correcting_chain.invoke(\n",
" {\n",
" \"input\": \"use complex tool. the args are 5, 2.1, empty dictionary. don't forget dict_arg\"\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "50d269a9-3cab-4a37-ba2f-805296453627",
"metadata": {},
"source": [
"And our chain succeeds! Looking at the [LangSmith trace](https://smith.langchain.com/public/c11e804c-e14f-4059-bd09-64766f999c14/r), we can see that indeed our initial chain still fails, and it's only on retrying that the chain succeeds."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_few_shot.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to use few-shot prompting with tool calling\n",
"\n",
"For more complex tool use it's very useful to add few-shot examples to the prompt. We can do this by adding `AIMessage`s with `ToolCall`s and corresponding `ToolMessage`s to our prompt.\n",
"\n",
"First let's define our tools and model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Adds a and b.\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"@tool\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiplies a and b.\"\"\"\n",
" return a * b\n",
"\n",
"\n",
"tools = [add, multiply]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
"llm_with_tools = llm.bind_tools(tools)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's run our model where we can notice that even with some special instructions our model can get tripped up by order of operations. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'Multiply',\n",
" 'args': {'a': 119, 'b': 8},\n",
" 'id': 'call_T88XN6ECucTgbXXkyDeC2CQj'},\n",
" {'name': 'Add',\n",
" 'args': {'a': 952, 'b': -20},\n",
" 'id': 'call_licdlmGsRqzup8rhqJSb1yZ4'}]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llm_with_tools.invoke(\n",
" \"Whats 119 times 8 minus 20. Don't do any math yourself, only use tools for math. Respect order of operations\"\n",
").tool_calls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model shouldn't be trying to add anything yet, since it technically can't know the results of 119 * 8 yet.\n",
"\n",
"By adding a prompt with some examples we can correct this behavior:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'Multiply',\n",
" 'args': {'a': 119, 'b': 8},\n",
" 'id': 'call_9MvuwQqg7dlJupJcoTWiEsDo'}]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from langchain_core.messages import AIMessage, HumanMessage, ToolMessage\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"examples = [\n",
" HumanMessage(\n",
" \"What's the product of 317253 and 128472 plus four\", name=\"example_user\"\n",
" ),\n",
" AIMessage(\n",
" \"\",\n",
" name=\"example_assistant\",\n",
" tool_calls=[\n",
" {\"name\": \"Multiply\", \"args\": {\"x\": 317253, \"y\": 128472}, \"id\": \"1\"}\n",
" ],\n",
" ),\n",
" ToolMessage(\"16505054784\", tool_call_id=\"1\"),\n",
" AIMessage(\n",
" \"\",\n",
" name=\"example_assistant\",\n",
" tool_calls=[{\"name\": \"Add\", \"args\": {\"x\": 16505054784, \"y\": 4}, \"id\": \"2\"}],\n",
" ),\n",
" ToolMessage(\"16505054788\", tool_call_id=\"2\"),\n",
" AIMessage(\n",
" \"The product of 317253 and 128472 plus four is 16505054788\",\n",
" name=\"example_assistant\",\n",
" ),\n",
"]\n",
"\n",
"system = \"\"\"You are bad at math but are an expert at using a calculator. \n",
"\n",
"Use past tool usage as an example of how to correctly use the tools.\"\"\"\n",
"few_shot_prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system),\n",
" *examples,\n",
" (\"human\", \"{query}\"),\n",
" ]\n",
")\n",
"\n",
"chain = {\"query\": RunnablePassthrough()} | few_shot_prompt | llm_with_tools\n",
"chain.invoke(\"Whats 119 times 8 minus 20\").tool_calls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we get the correct output this time.\n",
"\n",
"Here's what the [LangSmith trace](https://smith.langchain.com/public/f70550a1-585f-4c9d-a643-13148ab1616f/r) looks like."
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_human.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "b09b745d-f006-4ecc-8772-afa266c43605",
"metadata": {},
"source": [
"# How to add a human-in-the-loop for tools\n",
"\n",
"There are certain tools that we don't trust a model to execute on its own. One thing we can do in such situations is require human approval before the tool is invoked.\n",
"\n",
":::{.callout-info}\n",
"\n",
"This how-to guide shows a simple way to add human-in-the-loop for code running in a jupyter notebook or in a terminal.\n",
"\n",
"To build a production application, you will need to do more work to keep track of application state appropriately.\n",
"\n",
"We recommend using `langgraph` for powering such a capability. For more details, please see this [guide](https://langchain-ai.github.io/langgraph/how-tos/human-in-the-loop/).\n",
":::\n"
]
},
{
"cell_type": "markdown",
"id": "09178c30-a633-4d7b-88ea-092316f14b6f",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We'll need to install the following packages:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e44bec05-9aa4-47b1-a660-c0a183533598",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain"
]
},
{
"cell_type": "markdown",
"id": "f09629b6-7f62-4879-a791-464739ca6b6b",
"metadata": {},
"source": [
"And set these environment variables:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2bed0ccf-20cc-4fd3-9947-55471dd8c4da",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"# If you'd like to use LangSmith, uncomment the below:\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "7ecd5d7e-7c3c-4180-8958-7db2c1e43564",
"metadata": {},
"source": [
"## Chain\n",
"\n",
"Let's create a few simple (dummy) tools and a tool-calling chain:"
]
},
{
"cell_type": "markdown",
"id": "43721981-4595-4721-bea0-5c67696426d3",
"metadata": {},
"source": [
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e0ff02ac-e750-493b-9b09-4578711a6726",
"metadata": {},
"outputs": [],
"source": [
"# | output: false\n",
"# | echo: false\n",
"\n",
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0221fdfd-2a18-4449-a123-e6b0b15bb3d9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'count_emails',\n",
" 'args': {'last_n_days': 5},\n",
" 'id': 'toolu_01QYZdJ4yPiqsdeENWHqioFW',\n",
" 'output': 10}]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from typing import Dict, List\n",
"\n",
"from langchain_core.messages import AIMessage\n",
"from langchain_core.runnables import Runnable, RunnablePassthrough\n",
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def count_emails(last_n_days: int) -> int:\n",
" \"\"\"Multiply two integers together.\"\"\"\n",
" return last_n_days * 2\n",
"\n",
"\n",
"@tool\n",
"def send_email(message: str, recipient: str) -> str:\n",
" \"Add two integers.\"\n",
" return f\"Successfully sent email to {recipient}.\"\n",
"\n",
"\n",
"tools = [count_emails, send_email]\n",
"llm_with_tools = llm.bind_tools(tools)\n",
"\n",
"\n",
"def call_tools(msg: AIMessage) -> List[Dict]:\n",
" \"\"\"Simple sequential tool calling helper.\"\"\"\n",
" tool_map = {tool.name: tool for tool in tools}\n",
" tool_calls = msg.tool_calls.copy()\n",
" for tool_call in tool_calls:\n",
" tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
" return tool_calls\n",
"\n",
"\n",
"chain = llm_with_tools | call_tools\n",
"chain.invoke(\"how many emails did i get in the last 5 days?\")"
]
},
{
"cell_type": "markdown",
"id": "258c1c7b-a765-4558-93fe-d0defbc29223",
"metadata": {},
"source": [
"## Adding human approval\n",
"\n",
"Let's add a step in the chain that will ask a person to approve or reject the tall call request.\n",
"\n",
"On rejection, the step will raise an exception which will stop execution of the rest of the chain."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "341fb055-0315-47bc-8f72-ed6103d2981f",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"\n",
"class NotApproved(Exception):\n",
" \"\"\"Custom exception.\"\"\"\n",
"\n",
"\n",
"def human_approval(msg: AIMessage) -> AIMessage:\n",
" \"\"\"Responsible for passing through its input or raising an exception.\n",
"\n",
" Args:\n",
" msg: output from the chat model\n",
"\n",
" Returns:\n",
" msg: original output from the msg\n",
" \"\"\"\n",
" tool_strs = \"\\n\\n\".join(\n",
" json.dumps(tool_call, indent=2) for tool_call in msg.tool_calls\n",
" )\n",
" input_msg = (\n",
" f\"Do you approve of the following tool invocations\\n\\n{tool_strs}\\n\\n\"\n",
" \"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\\n >>>\"\n",
" )\n",
" resp = input(input_msg)\n",
" if resp.lower() not in (\"yes\", \"y\"):\n",
" raise NotApproved(f\"Tool invocations not approved:\\n\\n{tool_strs}\")\n",
" return msg"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "25dca07b-56ca-4b94-9955-d4f3e9895e03",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Do you approve of the following tool invocations\n",
"\n",
"{\n",
" \"name\": \"count_emails\",\n",
" \"args\": {\n",
" \"last_n_days\": 5\n",
" },\n",
" \"id\": \"toolu_01WbD8XeMoQaRFtsZezfsHor\"\n",
"}\n",
"\n",
"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\n",
" >>> yes\n"
]
},
{
"data": {
"text/plain": [
"[{'name': 'count_emails',\n",
" 'args': {'last_n_days': 5},\n",
" 'id': 'toolu_01WbD8XeMoQaRFtsZezfsHor',\n",
" 'output': 10}]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = llm_with_tools | human_approval | call_tools\n",
"chain.invoke(\"how many emails did i get in the last 5 days?\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f558f2cd-847b-4ef9-a770-3961082b540c",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Do you approve of the following tool invocations\n",
"\n",
"{\n",
" \"name\": \"send_email\",\n",
" \"args\": {\n",
" \"recipient\": \"sally@gmail.com\",\n",
" \"message\": \"What's up homie\"\n",
" },\n",
" \"id\": \"toolu_014XccHFzBiVcc9GV1harV9U\"\n",
"}\n",
"\n",
"Anything except 'Y'/'Yes' (case-insensitive) will be treated as a no.\n",
" >>> no\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Tool invocations not approved:\n",
"\n",
"{\n",
" \"name\": \"send_email\",\n",
" \"args\": {\n",
" \"recipient\": \"sally@gmail.com\",\n",
" \"message\": \"What's up homie\"\n",
" },\n",
" \"id\": \"toolu_014XccHFzBiVcc9GV1harV9U\"\n",
"}\n"
]
}
],
"source": [
"try:\n",
" chain.invoke(\"Send sally@gmail.com an email saying 'What's up homie'\")\n",
"except NotApproved as e:\n",
" print()\n",
" print(e)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_model_specific.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to bind model-specific tools\n",
"\n",
"Providers adopt different conventions for formatting tool schemas. \n",
"For instance, OpenAI uses a format like this:\n",
"\n",
"- `type`: The type of the tool. At the time of writing, this is always `\"function\"`.\n",
"- `function`: An object containing tool parameters.\n",
"- `function.name`: The name of the schema to output.\n",
"- `function.description`: A high level description of the schema to output.\n",
"- `function.parameters`: The nested details of the schema you want to extract, formatted as a [JSON schema](https://json-schema.org/) dict.\n",
"\n",
"We can bind this model-specific format directly to the model as well if preferred. Here's an example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe', 'function': {'arguments': '{\"a\":119,\"b\":8}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 62, 'total_tokens': 79}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-353e8a9a-7125-4f94-8c68-4f3da4c21120-0', tool_calls=[{'name': 'multiply', 'args': {'a': 119, 'b': 8}, 'id': 'call_mn4ELw1NbuE0DFYhIeK0GrPe'}])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI()\n",
"\n",
"model_with_tools = model.bind(\n",
" tools=[\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"multiply\",\n",
" \"description\": \"Multiply two integers together.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"a\": {\"type\": \"number\", \"description\": \"First integer\"},\n",
" \"b\": {\"type\": \"number\", \"description\": \"Second integer\"},\n",
" },\n",
" \"required\": [\"a\", \"b\"],\n",
" },\n",
" },\n",
" }\n",
" ]\n",
")\n",
"\n",
"model_with_tools.invoke(\"Whats 119 times 8?\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is functionally equivalent to the `bind_tools()` method."
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/tools_prompting.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "3243cb05-8243-421f-99fa-98201abb3094",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 3\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "14b94240",
"metadata": {},
"source": [
"# How to add ad-hoc tool calling capability to LLMs and Chat Models\n",
"\n",
":::{.callout-caution}\n",
"\n",
"Some models have been fine-tuned for tool calling and provide a dedicated API for tool calling. Generally, such models are better at tool calling than non-fine-tuned models, and are recommended for use cases that require tool calling. Please see the [how to use a chat model to call tools](/docs/how_to/tool_calling) guide for more information.\n",
"\n",
":::\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [Function/tool calling](https://python.langchain.com/v0.2/docs/concepts/#functiontool-calling)\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LLMs](/docs/concepts/#llms)\n",
"\n",
":::\n",
"\n",
"In this guide, we'll see how to add **ad-hoc** tool calling support to a chat model. This is an alternative method to invoke tools if you're using a model that does not natively support [tool calling](/docs/how_to/tool_calling).\n",
"\n",
"We'll do this by simply writing a prompt that will get the model to invoke the appropriate tools. Here's a diagram of the logic:\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "a0a22cb8-19e7-450a-9d1b-6848d2c81cd1",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We'll need to install the following packages:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c556c5e-b785-428b-8e7d-efd34a2a1adb",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain langchain-community"
]
},
{
"cell_type": "markdown",
"id": "897bc01e-cc2b-4400-8a64-db4aa56085d3",
"metadata": {},
"source": [
"If you'd like to use LangSmith, uncomment the below:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "5efb4170-b95b-4d29-8f57-09509f3ba6df",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "7ec6409b-21e5-4d0a-8a46-c4ef0b055dd3",
"metadata": {},
"source": [
"You can select any of the given models for this how-to guide. Keep in mind that most of these models already [support native tool calling](/docs/integrations/chat/), so using the prompting strategy shown here doesn't make sense for these models, and instead you should follow the [how to use a chat model to call tools](/docs/how_to/tool_calling) guide.\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs openaiParams={`model=\"gpt-4\"`} />\n",
"```\n",
"\n",
"To illustrate the idea, we'll use `phi3` via Ollama, which does **NOT** have native support for tool calling. If you'd like to use `Ollama` as well follow [these instructions](/docs/integrations/chat/ollama/)."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "424be968-2806-4d1a-a6aa-5499ae20fac5",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.llms import Ollama\n",
"\n",
"model = Ollama(model=\"phi3\")"
]
},
{
"cell_type": "markdown",
"id": "68946881",
"metadata": {},
"source": [
"## Create a tool\n",
"\n",
"First, let's create an `add` and `multiply` tools. For more information on creating custom tools, please see [this guide](/docs/how_to/custom_tools)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4548e6fa-0f9b-4d7a-8fa5-66cec0350e5f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--\n",
"multiply\n",
"Multiply two numbers together.\n",
"{'x': {'title': 'X', 'type': 'number'}, 'y': {'title': 'Y', 'type': 'number'}}\n",
"--\n",
"add\n",
"Add two numbers.\n",
"{'x': {'title': 'X', 'type': 'integer'}, 'y': {'title': 'Y', 'type': 'integer'}}\n"
]
}
],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def multiply(x: float, y: float) -> float:\n",
" \"\"\"Multiply two numbers together.\"\"\"\n",
" return x * y\n",
"\n",
"\n",
"@tool\n",
"def add(x: int, y: int) -> int:\n",
" \"Add two numbers.\"\n",
" return x + y\n",
"\n",
"\n",
"tools = [multiply, add]\n",
"\n",
"# Let's inspect the tools\n",
"for t in tools:\n",
" print(\"--\")\n",
" print(t.name)\n",
" print(t.description)\n",
" print(t.args)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "be77e780",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"multiply.invoke({\"x\": 4, \"y\": 5})"
]
},
{
"cell_type": "markdown",
"id": "15dd690e-e54d-4209-91a4-181f69a452ac",
"metadata": {},
"source": [
"## Creating our prompt\n",
"\n",
"We'll want to write a prompt that specifies the tools the model has access to, the arguments to those tools, and the desired output format of the model. In this case we'll instruct it to output a JSON blob of the form `{\"name\": \"...\", \"arguments\": {...}}`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2063b564-25ca-4729-a45f-ba4633175b04",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"multiply(x: float, y: float) -> float - Multiply two numbers together.\n",
"add(x: int, y: int) -> int - Add two numbers.\n"
]
}
],
"source": [
"from langchain_core.output_parsers import JsonOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.tools import render_text_description\n",
"\n",
"rendered_tools = render_text_description(tools)\n",
"print(rendered_tools)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f02f1dce-76e7-4ca9-9bac-5af496131fe1",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = f\"\"\"\\\n",
"You are an assistant that has access to the following set of tools. \n",
"Here are the names and descriptions for each tool:\n",
"\n",
"{rendered_tools}\n",
"\n",
"Given the user input, return the name and input of the tool to use. \n",
"Return your response as a JSON blob with 'name' and 'arguments' keys.\n",
"\n",
"The `arguments` should be a dictionary, with keys corresponding \n",
"to the argument names and the values corresponding to the requested values.\n",
"\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [(\"system\", system_prompt), (\"user\", \"{input}\")]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f8623e03-60eb-4439-b57b-ecbcebc61b58",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"name\": \"add\",\n",
" \"arguments\": {\n",
" \"x\": 3,\n",
" \"y\": 1132\n",
" }\n",
"}\n"
]
}
],
"source": [
"chain = prompt | model\n",
"message = chain.invoke({\"input\": \"what's 3 plus 1132\"})\n",
"\n",
"# Let's take a look at the output from the model\n",
"# if the model is an LLM (not a chat model), the output will be a string.\n",
"if isinstance(message, str):\n",
" print(message)\n",
"else: # Otherwise it's a chat model\n",
" print(message.content)"
]
},
{
"cell_type": "markdown",
"id": "14df2cd5-b6fa-4b10-892d-e8692c7931e5",
"metadata": {},
"source": [
"## Adding an output parser\n",
"\n",
"We'll use the `JsonOutputParser` for parsing our models output to JSON."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "f129f5bd-127c-4c95-8f34-8f437da7ca8f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'multiply', 'arguments': {'x': 13.0, 'y': 4.0}}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.output_parsers import JsonOutputParser\n",
"\n",
"chain = prompt | model | JsonOutputParser()\n",
"chain.invoke({\"input\": \"what's thirteen times 4\"})"
]
},
{
"cell_type": "markdown",
"id": "e1f08255-f146-4f4a-be43-5c21c1d3ae83",
"metadata": {},
"source": [
":::{.callout-important}\n",
"\n",
"🎉 Amazing! 🎉 We now instructed our model on how to **request** that a tool be invoked.\n",
"\n",
"Now, let's create some logic to actually run the tool!\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "8e29dd4c-8eb5-457f-92d1-8add076404dc",
"metadata": {},
"source": [
"## Invoking the tool 🏃\n",
"\n",
"Now that the model can request that a tool be invoked, we need to write a function that can actually invoke \n",
"the tool.\n",
"\n",
"The function will select the appropriate tool by name, and pass to it the arguments chosen by the model."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "faee95e0-4095-4310-991f-9e9465c6738e",
"metadata": {},
"outputs": [],
"source": [
"from typing import Any, Dict, Optional, TypedDict\n",
"\n",
"from langchain_core.runnables import RunnableConfig\n",
"\n",
"\n",
"class ToolCallRequest(TypedDict):\n",
" \"\"\"A typed dict that shows the inputs into the invoke_tool function.\"\"\"\n",
"\n",
" name: str\n",
" arguments: Dict[str, Any]\n",
"\n",
"\n",
"def invoke_tool(\n",
" tool_call_request: ToolCallRequest, config: Optional[RunnableConfig] = None\n",
"):\n",
" \"\"\"A function that we can use the perform a tool invocation.\n",
"\n",
" Args:\n",
" tool_call_request: a dict that contains the keys name and arguments.\n",
" The name must match the name of a tool that exists.\n",
" The arguments are the arguments to that tool.\n",
" config: This is configuration information that LangChain uses that contains\n",
" things like callbacks, metadata, etc.See LCEL documentation about RunnableConfig.\n",
"\n",
" Returns:\n",
" output from the requested tool\n",
" \"\"\"\n",
" tool_name_to_tool = {tool.name: tool for tool in tools}\n",
" name = tool_call_request[\"name\"]\n",
" requested_tool = tool_name_to_tool[name]\n",
" return requested_tool.invoke(tool_call_request[\"arguments\"], config=config)"
]
},
{
"cell_type": "markdown",
"id": "f4957532-9e0c-47f6-bb62-0fd789ac1d3e",
"metadata": {},
"source": [
"Let's test this out 🧪!"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "d0ea3b2a-8fb2-4016-83c8-a5d3e78fedbc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"invoke_tool({\"name\": \"multiply\", \"arguments\": {\"x\": 3, \"y\": 5}})"
]
},
{
"cell_type": "markdown",
"id": "715af6e1-935d-4bc0-a3d2-646ecf8a329b",
"metadata": {},
"source": [
"## Let's put it together\n",
"\n",
"Let's put it together into a chain that creates a calculator with add and multiplication capabilities."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "0555b384-fde6-4404-86e0-7ea199003d58",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"53.83784653"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = prompt | model | JsonOutputParser() | invoke_tool\n",
"chain.invoke({\"input\": \"what's thirteen times 4.14137281\"})"
]
},
{
"cell_type": "markdown",
"id": "b4a9c5aa-f60a-4017-af6f-1ff6e04bfb61",
"metadata": {},
"source": [
"## Returning tool inputs\n",
"\n",
"It can be helpful to return not only tool outputs but also tool inputs. We can easily do this with LCEL by `RunnablePassthrough.assign`-ing the tool output. This will take whatever the input is to the RunnablePassrthrough components (assumed to be a dictionary) and add a key to it while still passing through everything that's currently in the input:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "45404406-859d-4caa-8b9d-5838162c80a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'multiply',\n",
" 'arguments': {'x': 13, 'y': 4.14137281},\n",
" 'output': 53.83784653}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"chain = (\n",
" prompt | model | JsonOutputParser() | RunnablePassthrough.assign(output=invoke_tool)\n",
")\n",
"chain.invoke({\"input\": \"what's thirteen times 4.14137281\"})"
]
},
{
"cell_type": "markdown",
"id": "1797fe82-ea35-4cba-834a-1caf9740d184",
"metadata": {},
"source": [
"## What's next?\n",
"\n",
"This how-to guide shows the \"happy path\" when the model correctly outputs all the required tool information.\n",
"\n",
"In reality, if you're using more complex tools, you will start encountering errors from the model, especially for models that have not been fine tuned for tool calling and for less capable models.\n",
"\n",
"You will need to be prepared to add strategies to improve the output from the model; e.g.,\n",
"\n",
"1. Provide few shot examples.\n",
"2. Add error handling (e.g., catch the exception and feed it back to the LLM to ask it to correct its previous output)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/trim_messages.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "b5ee5b75-6876-4d62-9ade-5a7a808ae5a2",
"metadata": {},
"source": [
"# How to trim messages\n",
"\n",
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Messages](/docs/concepts/#messages)\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Chaining](/docs/how_to/sequence/)\n",
"- [Chat history](/docs/concepts/#chat-history)\n",
"\n",
"The methods in this guide also require `langchain-core>=0.2.9`.\n",
"\n",
":::\n",
"\n",
"All models have finite context windows, meaning there's a limit to how many tokens they can take as input. If you have very long messages or a chain/agent that accumulates a long message is history, you'll need to manage the length of the messages you're passing in to the model.\n",
"\n",
"The `trim_messages` util provides some basic strategies for trimming a list of messages to be of a certain token length.\n",
"\n",
"## Getting the last `max_tokens` tokens\n",
"\n",
"To get the last `max_tokens` in the list of Messages we can set `strategy=\"last\"`. Notice that for our `token_counter` we can pass in a function (more on that below) or a language model (since language models have a message token counting method). It makes sense to pass in a model when you're trimming your messages to fit into the context window of that specific model:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c974633b-3bd0-4844-8a8f-85e3e25f13fe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
" HumanMessage(content='what do you call a speechless parrot')]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pip install -U langchain-openai\n",
"from langchain_core.messages import (\n",
" AIMessage,\n",
" HumanMessage,\n",
" SystemMessage,\n",
" trim_messages,\n",
")\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"messages = [\n",
" SystemMessage(\"you're a good assistant, you always respond with a joke.\"),\n",
" HumanMessage(\"i wonder why it's called langchain\"),\n",
" AIMessage(\n",
" 'Well, I guess they thought \"WordRope\" and \"SentenceString\" just didn\\'t have the same ring to it!'\n",
" ),\n",
" HumanMessage(\"and who is harrison chasing anyways\"),\n",
" AIMessage(\n",
" \"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\"\n",
" ),\n",
" HumanMessage(\"what do you call a speechless parrot\"),\n",
"]\n",
"\n",
"trim_messages(\n",
" messages,\n",
" max_tokens=45,\n",
" strategy=\"last\",\n",
" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d3f46654-c4b2-4136-b995-91c3febe5bf9",
"metadata": {},
"source": [
"If we want to always keep the initial system message we can specify `include_system=True`:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "589b0223-3a73-44ec-8315-2dba3ee6117d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
" HumanMessage(content='what do you call a speechless parrot')]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trim_messages(\n",
" messages,\n",
" max_tokens=45,\n",
" strategy=\"last\",\n",
" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
" include_system=True,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8a8b542c-04d1-4515-8d82-b999ea4fac4f",
"metadata": {},
"source": [
"If we want to allow splitting up the contents of a message we can specify `allow_partial=True`:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8c46a209-dddd-4d01-81f6-f6ae55d3225c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
" AIMessage(content=\"\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
" HumanMessage(content='what do you call a speechless parrot')]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trim_messages(\n",
" messages,\n",
" max_tokens=56,\n",
" strategy=\"last\",\n",
" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
" include_system=True,\n",
" allow_partial=True,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "306adf9c-41cd-495c-b4dc-e4f43dd7f8f8",
"metadata": {},
"source": [
"If we need to make sure that our first message (excluding the system message) is always of a specific type, we can specify `start_on`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "878a730b-fe44-4e9d-ab65-7b8f7b069de8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
" HumanMessage(content='what do you call a speechless parrot')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trim_messages(\n",
" messages,\n",
" max_tokens=60,\n",
" strategy=\"last\",\n",
" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
" include_system=True,\n",
" start_on=\"human\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7f5d391d-235b-4091-b2de-c22866b478f3",
"metadata": {},
"source": [
"## Getting the first `max_tokens` tokens\n",
"\n",
"We can perform the flipped operation of getting the *first* `max_tokens` by specifying `strategy=\"first\"`:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5f56ae54-1a39-4019-9351-3b494c003d5b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
" HumanMessage(content=\"i wonder why it's called langchain\")]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trim_messages(\n",
" messages,\n",
" max_tokens=45,\n",
" strategy=\"first\",\n",
" token_counter=ChatOpenAI(model=\"gpt-4o\"),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ab70bf70-1e5a-4d51-b9b8-a823bf2cf532",
"metadata": {},
"source": [
"## Writing a custom token counter\n",
"\n",
"We can write a custom token counter function that takes in a list of messages and returns an int."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1c1c3b1e-2ece-49e7-a3b6-e69877c1633b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[AIMessage(content=\"Hmmm let me think.\\n\\nWhy, he's probably chasing after the last cup of coffee in the office!\"),\n",
" HumanMessage(content='what do you call a speechless parrot')]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from typing import List\n",
"\n",
"# pip install tiktoken\n",
"import tiktoken\n",
"from langchain_core.messages import BaseMessage, ToolMessage\n",
"\n",
"\n",
"def str_token_counter(text: str) -> int:\n",
" enc = tiktoken.get_encoding(\"o200k_base\")\n",
" return len(enc.encode(text))\n",
"\n",
"\n",
"def tiktoken_counter(messages: List[BaseMessage]) -> int:\n",
" \"\"\"Approximately reproduce https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\n",
"\n",
" For simplicity only supports str Message.contents.\n",
" \"\"\"\n",
" num_tokens = 3 # every reply is primed with <|start|>assistant<|message|>\n",
" tokens_per_message = 3\n",
" tokens_per_name = 1\n",
" for msg in messages:\n",
" if isinstance(msg, HumanMessage):\n",
" role = \"user\"\n",
" elif isinstance(msg, AIMessage):\n",
" role = \"assistant\"\n",
" elif isinstance(msg, ToolMessage):\n",
" role = \"tool\"\n",
" elif isinstance(msg, SystemMessage):\n",
" role = \"system\"\n",
" else:\n",
" raise ValueError(f\"Unsupported messages type {msg.__class__}\")\n",
" num_tokens += (\n",
" tokens_per_message\n",
" + str_token_counter(role)\n",
" + str_token_counter(msg.content)\n",
" )\n",
" if msg.name:\n",
" num_tokens += tokens_per_name + str_token_counter(msg.name)\n",
" return num_tokens\n",
"\n",
"\n",
"trim_messages(\n",
" messages,\n",
" max_tokens=45,\n",
" strategy=\"last\",\n",
" token_counter=tiktoken_counter,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "4b2a672b-c007-47c5-9105-617944dc0a6a",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"`trim_messages` can be used in an imperatively (like above) or declaratively, making it easy to compose with other components in a chain"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "96aa29b2-01e0-437c-a1ab-02fb0141cb57",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='A: A \"Polly-gone\"!', response_metadata={'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_66b29dffce', 'finish_reason': 'stop', 'logprobs': None}, id='run-83e96ddf-bcaa-4f63-824c-98b0f8a0d474-0', usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm = ChatOpenAI(model=\"gpt-4o\")\n",
"\n",
"# Notice we don't pass in messages. This creates\n",
"# a RunnableLambda that takes messages as input\n",
"trimmer = trim_messages(\n",
" max_tokens=45,\n",
" strategy=\"last\",\n",
" token_counter=llm,\n",
" include_system=True,\n",
")\n",
"\n",
"chain = trimmer | llm\n",
"chain.invoke(messages)"
]
},
{
"cell_type": "markdown",
"id": "4d91d390-e7f7-467b-ad87-d100411d7a21",
"metadata": {},
"source": [
"Looking at the LangSmith trace we can see that before the messages are passed to the model they are first trimmed: https://smith.langchain.com/public/65af12c4-c24d-4824-90f0-6547566e59bb/r\n",
"\n",
"Looking at just the trimmer, we can see that it's a Runnable object that can be invoked like all Runnables:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1ff02d0a-353d-4fac-a77c-7c2c5262abd9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[SystemMessage(content=\"you're a good assistant, you always respond with a joke.\"),\n",
" HumanMessage(content='what do you call a speechless parrot')]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trimmer.invoke(messages)"
]
},
{
"cell_type": "markdown",
"id": "dc4720c8-4062-4ebc-9385-58411202ce6e",
"metadata": {},
"source": [
"## Using with ChatMessageHistory\n",
"\n",
"Trimming messages is especially useful when [working with chat histories](/docs/how_to/message_history/), which can get arbitrarily long:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a9517858-fc2f-4dc3-898d-bf98a0e905a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='A \"polly-no-wanna-cracker\"!', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 32, 'total_tokens': 42}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5bf7397cd3', 'finish_reason': 'stop', 'logprobs': None}, id='run-054dd309-3497-4e7b-b22a-c1859f11d32e-0', usage_metadata={'input_tokens': 32, 'output_tokens': 10, 'total_tokens': 42})"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.chat_history import InMemoryChatMessageHistory\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"\n",
"chat_history = InMemoryChatMessageHistory(messages=messages[:-1])\n",
"\n",
"\n",
"def dummy_get_session_history(session_id):\n",
" if session_id != \"1\":\n",
" return InMemoryChatMessageHistory()\n",
" return chat_history\n",
"\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-4o\")\n",
"\n",
"trimmer = trim_messages(\n",
" max_tokens=45,\n",
" strategy=\"last\",\n",
" token_counter=llm,\n",
" include_system=True,\n",
")\n",
"\n",
"chain = trimmer | llm\n",
"chain_with_history = RunnableWithMessageHistory(chain, dummy_get_session_history)\n",
"chain_with_history.invoke(\n",
" [HumanMessage(\"what do you call a speechless parrot\")],\n",
" config={\"configurable\": {\"session_id\": \"1\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "556b7b4c-43cb-41de-94fc-1a41f4ec4d2e",
"metadata": {},
"source": [
"Looking at the LangSmith trace we can see that we retrieve all of our messages but before the messages are passed to the model they are trimmed to be just the system message and last human message: https://smith.langchain.com/public/17dd700b-9994-44ca-930c-116e00997315/r"
]
},
{
"cell_type": "markdown",
"id": "75dc7b84-b92f-44e7-8beb-ba22398e4efb",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For a complete description of all arguments head to the API reference: https://api.python.langchain.com/en/latest/messages/langchain_core.messages.utils.trim_messages.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/vectorstore_retriever.ipynb | {
"cells": [
{
"cell_type": "raw",
"id": "ee14951b",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 0\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "105cddce",
"metadata": {},
"source": [
"# How to use a vectorstore as a retriever\n",
"\n",
"A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface.\n",
"It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store.\n",
"\n",
"In this guide we will cover:\n",
"\n",
"1. How to instantiate a retriever from a vectorstore;\n",
"2. How to specify the search type for the retriever;\n",
"3. How to specify additional search parameters, such as threshold scores and top-k.\n",
"\n",
"## Creating a retriever from a vectorstore\n",
"\n",
"You can build a retriever from a vectorstore using its [.as_retriever](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method. Let's walk through an example.\n",
"\n",
"First we instantiate a vectorstore. We will use an in-memory [FAISS](https://api.python.langchain.com/en/latest/vectorstores/langchain_community.vectorstores.faiss.FAISS.html) vectorstore:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "174e3c69",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"from langchain_community.vectorstores import FAISS\n",
"from langchain_openai import OpenAIEmbeddings\n",
"from langchain_text_splitters import CharacterTextSplitter\n",
"\n",
"loader = TextLoader(\"state_of_the_union.txt\")\n",
"\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",
"embeddings = OpenAIEmbeddings()\n",
"vectorstore = FAISS.from_documents(texts, embeddings)"
]
},
{
"cell_type": "markdown",
"id": "6f6e65a1-5eb4-4165-b06b-9bb40624a8d8",
"metadata": {},
"source": [
"We can then instantiate a retriever:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "52df5f55",
"metadata": {},
"outputs": [],
"source": [
"retriever = vectorstore.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "08f8b820-5912-49c1-9d76-40be0571dffb",
"metadata": {},
"source": [
"This creates a retriever (specifically a [VectorStoreRetriever](https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html)), which we can use in the usual way:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "32334fda",
"metadata": {},
"outputs": [],
"source": [
"docs = retriever.invoke(\"what did the president say about ketanji brown jackson?\")"
]
},
{
"cell_type": "markdown",
"id": "fd7b19f0",
"metadata": {},
"source": [
"## Maximum marginal relevance retrieval\n",
"By default, the vector store retriever uses similarity search. If the underlying vector store supports maximum marginal relevance search, you can specify that as the search type.\n",
"\n",
"This effectively specifies what method on the underlying vectorstore is used (e.g., `similarity_search`, `max_marginal_relevance_search`, etc.)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b286ac04",
"metadata": {},
"outputs": [],
"source": [
"retriever = vectorstore.as_retriever(search_type=\"mmr\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "07f937f7",
"metadata": {},
"outputs": [],
"source": [
"docs = retriever.invoke(\"what did the president say about ketanji brown jackson?\")"
]
},
{
"cell_type": "markdown",
"id": "6ce77789",
"metadata": {},
"source": [
"## Passing search parameters\n",
"\n",
"We can pass parameters to the underlying vectorstore's search methods using `search_kwargs`.\n",
"\n",
"### Similarity score threshold retrieval\n",
"\n",
"For example, we can set a similarity score threshold and only return documents with a score above that threshold."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dbb38a03",
"metadata": {},
"outputs": [],
"source": [
"retriever = vectorstore.as_retriever(\n",
" search_type=\"similarity_score_threshold\", search_kwargs={\"score_threshold\": 0.5}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "56f6c9ae",
"metadata": {},
"outputs": [],
"source": [
"docs = retriever.invoke(\"what did the president say about ketanji brown jackson?\")"
]
},
{
"cell_type": "markdown",
"id": "329f5b26",
"metadata": {},
"source": [
"### Specifying top k\n",
"\n",
"We can also limit the number of documents `k` returned by the retriever."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d712c91d",
"metadata": {},
"outputs": [],
"source": [
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 1})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a79b573b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"docs = retriever.invoke(\"what did the president say about ketanji brown jackson?\")\n",
"len(docs)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/vectorstores.mdx | # How to create and query vector stores
:::info
Head to [Integrations](/docs/integrations/vectorstores/) for documentation on built-in integrations with 3rd-party vector stores.
:::
One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding
vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are
'most similar' to the embedded query. A vector store takes care of storing embedded data and performing vector search
for you.
## Get started
This guide showcases basic functionality related to vector stores. A key part of working with vector stores is creating the vector to put in them,
which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model interfaces](/docs/how_to/embed_text) before diving into this.
Before using the vectorstore at all, we need to load some data and initialize an embedding model.
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
```python
import os
import getpass
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
```
```python
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
# Load the document, split it into chunks, embed each chunk and load it into the vector store.
raw_documents = TextLoader('state_of_the_union.txt').load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
```
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
There are many great vector store options, here are a few that are free, open-source, and run entirely on your local machine. Review all integrations for many great hosted offerings.
<Tabs>
<TabItem value="chroma" label="Chroma" default>
This walkthrough uses the `chroma` vector database, which runs on your local machine as a library.
```bash
pip install langchain-chroma
```
```python
from langchain_chroma import Chroma
db = Chroma.from_documents(documents, OpenAIEmbeddings())
```
</TabItem>
<TabItem value="faiss" label="FAISS">
This walkthrough uses the `FAISS` vector database, which makes use of the Facebook AI Similarity Search (FAISS) library.
```bash
pip install faiss-cpu
```
```python
from langchain_community.vectorstores import FAISS
db = FAISS.from_documents(documents, OpenAIEmbeddings())
```
</TabItem>
<TabItem value="lance" label="Lance">
This notebook shows how to use functionality related to the LanceDB vector database based on the Lance data format.
```bash
pip install lancedb
```
```python
from langchain_community.vectorstores import LanceDB
import lancedb
db = lancedb.connect("/tmp/lancedb")
table = db.create_table(
"my_table",
data=[
{
"vector": embeddings.embed_query("Hello World"),
"text": "Hello World",
"id": "1",
}
],
mode="overwrite",
)
db = LanceDB.from_documents(documents, OpenAIEmbeddings())
```
</TabItem>
</Tabs>
## Similarity search
All vectorstores expose a `similarity_search` method.
This will take incoming documents, create an embedding of them, and then find all documents with the most similar embedding.
```python
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
```
<CodeOutputBlock lang="python">
```
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
```
</CodeOutputBlock>
### Similarity search by vector
It is also possible to do a search for documents similar to a given embedding vector using `similarity_search_by_vector` which accepts an embedding vector as a parameter instead of a string.
```python
embedding_vector = OpenAIEmbeddings().embed_query(query)
docs = db.similarity_search_by_vector(embedding_vector)
print(docs[0].page_content)
```
<CodeOutputBlock lang="python">
```
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
```
</CodeOutputBlock>
## Async Operations
Vector stores are usually run as a separate service that requires some IO operations, and therefore they might be called asynchronously. That gives performance benefits as you don't waste time waiting for responses from external services. That might also be important if you work with an asynchronous framework, such as [FastAPI](https://fastapi.tiangolo.com/).
LangChain supports async operation on vector stores. All the methods might be called using their async counterparts, with the prefix `a`, meaning `async`.
```python
docs = await db.asimilarity_search(query)
docs
```
<CodeOutputBlock lang="python">
```
[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': 'state_of_the_union.txt'}),
Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': 'state_of_the_union.txt'}),
Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': 'state_of_the_union.txt'}),
Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': 'state_of_the_union.txt'})]
```
</CodeOutputBlock> | Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/Gemma_LangChain.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "BYejgj8Zf-LG",
"tags": []
},
"source": [
"## Getting started with LangChain and Gemma, running locally or in the Cloud"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2IxjMb9-jIJ8"
},
"source": [
"### Installing dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 9436,
"status": "ok",
"timestamp": 1708975187360,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "XZaTsXfcheTF",
"outputId": "eb21d603-d824-46c5-f99f-087fb2f618b1",
"tags": []
},
"outputs": [],
"source": [
"!pip install --upgrade langchain langchain-google-vertexai"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IXmAujvC3Kwp"
},
"source": [
"### Running the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CI8Elyc5gBQF"
},
"source": [
"Go to the VertexAI Model Garden on Google Cloud [console](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/335), and deploy the desired version of Gemma to VertexAI. It will take a few minutes, and after the endpoint it ready, you need to copy its number."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "gv1j8FrVftsC"
},
"outputs": [],
"source": [
"# @title Basic parameters\n",
"project: str = \"PUT_YOUR_PROJECT_ID_HERE\" # @param {type:\"string\"}\n",
"endpoint_id: str = \"PUT_YOUR_ENDPOINT_ID_HERE\" # @param {type:\"string\"}\n",
"location: str = \"PUT_YOUR_ENDPOINT_LOCAtION_HERE\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1708975440503,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "bhIHsFGYjtFt",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-27 17:15:10.457149: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-02-27 17:15:10.508925: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-02-27 17:15:10.508957: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-02-27 17:15:10.510289: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-02-27 17:15:10.518898: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"source": [
"from langchain_google_vertexai import (\n",
" GemmaChatVertexAIModelGarden,\n",
" GemmaVertexAIModelGarden,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"executionInfo": {
"elapsed": 351,
"status": "ok",
"timestamp": 1708975440852,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "WJv-UVWwh0lk",
"tags": []
},
"outputs": [],
"source": [
"llm = GemmaVertexAIModelGarden(\n",
" endpoint_id=endpoint_id,\n",
" project=project,\n",
" location=location,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 714,
"status": "ok",
"timestamp": 1708975441564,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "6kM7cEFdiN9h",
"outputId": "fb420c56-5614-4745-cda8-0ee450a3e539",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prompt:\n",
"What is the meaning of life?\n",
"Output:\n",
" Who am I? Why do I exist? These are questions I have struggled with\n"
]
}
],
"source": [
"output = llm.invoke(\"What is the meaning of life?\")\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zzep9nfmuUcO"
},
"source": [
"We can also use Gemma as a multi-turn chat model:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 964,
"status": "ok",
"timestamp": 1708976298189,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "8tPHoM5XiZOl",
"outputId": "7b8fb652-9aed-47b0-c096-aa1abfc3a2a9",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content='Prompt:\\n<start_of_turn>user\\nHow much is 2+2?<end_of_turn>\\n<start_of_turn>model\\nOutput:\\n8-years old.<end_of_turn>\\n\\n<start_of'\n",
"content='Prompt:\\n<start_of_turn>user\\nHow much is 2+2?<end_of_turn>\\n<start_of_turn>model\\nPrompt:\\n<start_of_turn>user\\nHow much is 2+2?<end_of_turn>\\n<start_of_turn>model\\nOutput:\\n8-years old.<end_of_turn>\\n\\n<start_of<end_of_turn>\\n<start_of_turn>user\\nHow much is 3+3?<end_of_turn>\\n<start_of_turn>model\\nOutput:\\nOutput:\\n3-years old.<end_of_turn>\\n\\n<'\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"\n",
"llm = GemmaChatVertexAIModelGarden(\n",
" endpoint_id=endpoint_id,\n",
" project=project,\n",
" location=location,\n",
")\n",
"\n",
"message1 = HumanMessage(content=\"How much is 2+2?\")\n",
"answer1 = llm.invoke([message1])\n",
"print(answer1)\n",
"\n",
"message2 = HumanMessage(content=\"How much is 3+3?\")\n",
"answer2 = llm.invoke([message1, answer1, message2])\n",
"\n",
"print(answer2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can post-process response to avoid repetitions:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content='Output:\\n<<humming>>: 2+2 = 4.\\n<end'\n",
"content='Output:\\nOutput:\\n<<humming>>: 3+3 = 6.'\n"
]
}
],
"source": [
"answer1 = llm.invoke([message1], parse_response=True)\n",
"print(answer1)\n",
"\n",
"answer2 = llm.invoke([message1, answer1, message2], parse_response=True)\n",
"\n",
"print(answer2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VEfjqo7fjARR"
},
"source": [
"## Running Gemma locally from Kaggle"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gVW8QDzHu7TA"
},
"source": [
"In order to run Gemma locally, you can download it from Kaggle first. In order to do this, you'll need to login into the Kaggle platform, create a API key and download a `kaggle.json` Read more about Kaggle auth [here](https://www.kaggle.com/docs/api)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S1EsXQ3XvZkQ"
},
"source": [
"### Installation"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"executionInfo": {
"elapsed": 335,
"status": "ok",
"timestamp": 1708976305471,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "p8SMwpKRvbef",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" pid, fd = os.forkpty()\n"
]
}
],
"source": [
"!mkdir -p ~/.kaggle && cp kaggle.json ~/.kaggle/kaggle.json"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"executionInfo": {
"elapsed": 7802,
"status": "ok",
"timestamp": 1708976363010,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "Yr679aePv9Fq",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/pty.py:89: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" pid, fd = os.forkpty()\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"tensorstore 0.1.54 requires ml-dtypes>=0.3.1, but you have ml-dtypes 0.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install keras>=3 keras_nlp"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E9zn8nYpv3QZ"
},
"source": [
"### Usage"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"executionInfo": {
"elapsed": 8536,
"status": "ok",
"timestamp": 1708976601206,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "0LFRmY8TjCkI",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-27 16:38:40.797559: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-02-27 16:38:40.848444: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-02-27 16:38:40.848478: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-02-27 16:38:40.849728: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-02-27 16:38:40.857936: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"source": [
"from langchain_google_vertexai import GemmaLocalKaggle"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v-o7oXVavdMQ"
},
"source": [
"You can specify the keras backend (by default it's `tensorflow`, but you can change it be `jax` or `torch`)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"executionInfo": {
"elapsed": 9,
"status": "ok",
"timestamp": 1708976601206,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "vvTUH8DNj5SF",
"tags": []
},
"outputs": [],
"source": [
"# @title Basic parameters\n",
"keras_backend: str = \"jax\" # @param {type:\"string\"}\n",
"model_name: str = \"gemma_2b_en\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"executionInfo": {
"elapsed": 40836,
"status": "ok",
"timestamp": 1708976761257,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "YOmrqxo5kHXK",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-27 16:23:14.661164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20549 MB memory: -> device: 0, name: NVIDIA L4, pci bus id: 0000:00:03.0, compute capability: 8.9\n",
"normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
]
}
],
"source": [
"llm = GemmaLocalKaggle(model_name=model_name, keras_backend=keras_backend)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "Zu6yPDUgkQtQ",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1709051129.518076 774855 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is the meaning of life?\n",
"\n",
"The question is one of the most important questions in the world.\n",
"\n",
"It’s the question that has\n"
]
}
],
"source": [
"output = llm.invoke(\"What is the meaning of life?\", max_tokens=30)\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ChatModel"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MSctpRE4u43N"
},
"source": [
"Same as above, using Gemma locally as a multi-turn chat model. You might need to re-start the notebook and clean your GPU memory in order to avoid OOM errors:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-27 16:58:22.331067: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-02-27 16:58:22.382948: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-02-27 16:58:22.382978: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-02-27 16:58:22.384312: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-02-27 16:58:22.392767: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"source": [
"from langchain_google_vertexai import GemmaChatLocalKaggle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# @title Basic parameters\n",
"keras_backend: str = \"jax\" # @param {type:\"string\"}\n",
"model_name: str = \"gemma_2b_en\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-27 16:58:29.001922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 20549 MB memory: -> device: 0, name: NVIDIA L4, pci bus id: 0000:00:03.0, compute capability: 8.9\n",
"normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
]
}
],
"source": [
"llm = GemmaChatLocalKaggle(model_name=model_name, keras_backend=keras_backend)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"executionInfo": {
"elapsed": 3,
"status": "aborted",
"timestamp": 1708976382957,
"user": {
"displayName": "",
"userId": ""
},
"user_tz": -60
},
"id": "JrJmvZqwwLqj"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-27 16:58:49.848412: I external/local_xla/xla/service/service.cc:168] XLA service 0x55adc0cf2c10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2024-02-27 16:58:49.848458: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L4, Compute Capability 8.9\n",
"2024-02-27 16:58:50.116614: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"2024-02-27 16:58:54.389324: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8900\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"I0000 00:00:1709053145.225207 784891 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n",
"W0000 00:00:1709053145.284227 784891 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n Tampoco\\nI'm a model.\"\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"\n",
"message1 = HumanMessage(content=\"Hi! Who are you?\")\n",
"answer1 = llm.invoke([message1], max_tokens=30)\n",
"print(answer1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\n<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n Tampoco\\nI'm a model.<end_of_turn>\\n<start_of_turn>user\\nWhat can you help me with?<end_of_turn>\\n<start_of_turn>model\"\n"
]
}
],
"source": [
"message2 = HumanMessage(content=\"What can you help me with?\")\n",
"answer2 = llm.invoke([message1, answer1, message2], max_tokens=60)\n",
"\n",
"print(answer2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can post-process the response if you want to avoid multi-turn statements:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content=\"I'm a model.\\n Tampoco\\nI'm a model.\"\n",
"content='I can help you with your modeling.\\n Tampoco\\nI can'\n"
]
}
],
"source": [
"answer1 = llm.invoke([message1], max_tokens=30, parse_response=True)\n",
"print(answer1)\n",
"\n",
"answer2 = llm.invoke([message1, answer1, message2], max_tokens=60, parse_response=True)\n",
"print(answer2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EiZnztso7hyF"
},
"source": [
"## Running Gemma locally from HuggingFace"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "qqAqsz5R7nKf",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-27 17:02:21.832409: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-02-27 17:02:21.883625: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-02-27 17:02:21.883656: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-02-27 17:02:21.884987: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-02-27 17:02:21.893340: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"source": [
"from langchain_google_vertexai import GemmaChatLocalHF, GemmaLocalHF"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "tsyntzI08cOr",
"tags": []
},
"outputs": [],
"source": [
"# @title Basic parameters\n",
"hf_access_token: str = \"PUT_YOUR_TOKEN_HERE\" # @param {type:\"string\"}\n",
"model_name: str = \"google/gemma-2b\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "JWrqEkOo8sm9",
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a0d6de5542254ed1b6d3ba65465e050e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llm = GemmaLocalHF(model_name=\"google/gemma-2b\", hf_access_token=hf_access_token)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "VX96Jf4Y84k-",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is the meaning of life?\n",
"\n",
"The question is one of the most important questions in the world.\n",
"\n",
"It’s the question that has been asked by philosophers, theologians, and scientists for centuries.\n",
"\n",
"And it’s the question that\n"
]
}
],
"source": [
"output = llm.invoke(\"What is the meaning of life?\", max_tokens=50)\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Same as above, using Gemma locally as a multi-turn chat model. You might need to re-start the notebook and clean your GPU memory in order to avoid OOM errors:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "9x-jmEBg9Mk1"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c9a0b8e161d74a6faca83b1be96dee27",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"llm = GemmaChatLocalHF(model_name=model_name, hf_access_token=hf_access_token)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "qv_OSaMm9PVy"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n<end_of_turn>\\n<start_of_turn>user\\nWhat do you mean\"\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"\n",
"message1 = HumanMessage(content=\"Hi! Who are you?\")\n",
"answer1 = llm.invoke([message1], max_tokens=60)\n",
"print(answer1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content=\"<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\n<start_of_turn>user\\nHi! Who are you?<end_of_turn>\\n<start_of_turn>model\\nI'm a model.\\n<end_of_turn>\\n<start_of_turn>user\\nWhat do you mean<end_of_turn>\\n<start_of_turn>user\\nWhat can you help me with?<end_of_turn>\\n<start_of_turn>model\\nI can help you with anything.\\n<\"\n"
]
}
],
"source": [
"message2 = HumanMessage(content=\"What can you help me with?\")\n",
"answer2 = llm.invoke([message1, answer1, message2], max_tokens=140)\n",
"\n",
"print(answer2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And the same with posprocessing:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content=\"I'm a model.\\n<end_of_turn>\\n\"\n",
"content='I can help you with anything.\\n<end_of_turn>\\n<end_of_turn>\\n'\n"
]
}
],
"source": [
"answer1 = llm.invoke([message1], max_tokens=60, parse_response=True)\n",
"print(answer1)\n",
"\n",
"answer2 = llm.invoke([message1, answer1, message2], max_tokens=120, parse_response=True)\n",
"print(answer2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"environment": {
"kernel": "python3",
"name": ".m116",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/:m116"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/LLaMA2_sql_chat.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "fc935871-7640-41c6-b798-58514d860fe0",
"metadata": {},
"source": [
"## LLaMA2 chat with SQL\n",
"\n",
"Open source, local LLMs are great to consider for any application that demands data privacy.\n",
"\n",
"SQL is one good example. \n",
"\n",
"This cookbook shows how to perform text-to-SQL using various local versions of LLaMA2 run locally.\n",
"\n",
"## Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81adcf8b-395a-4f02-8749-ac976942b446",
"metadata": {},
"outputs": [],
"source": [
"! pip install langchain replicate"
]
},
{
"cell_type": "markdown",
"id": "8e13ed66-300b-4a23-b8ac-44df68ee4733",
"metadata": {},
"source": [
"## LLM\n",
"\n",
"There are a few ways to access LLaMA2.\n",
"\n",
"To run locally, we use Ollama.ai. \n",
"\n",
"See [here](/docs/integrations/chat/ollama) for details on installation and setup.\n",
"\n",
"Also, see [here](/docs/guides/development/local_llms) for our full guide on local LLMs.\n",
" \n",
"To use an external API, which is not private, we can use Replicate."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6a75a5c6-34ee-4ab9-a664-d9b432d812ee",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Init param `input` is deprecated, please use `model_kwargs` instead.\n"
]
}
],
"source": [
"# Local\n",
"from langchain_community.chat_models import ChatOllama\n",
"\n",
"llama2_chat = ChatOllama(model=\"llama2:13b-chat\")\n",
"llama2_code = ChatOllama(model=\"codellama:7b-instruct\")\n",
"\n",
"# API\n",
"from langchain_community.llms import Replicate\n",
"\n",
"# REPLICATE_API_TOKEN = getpass()\n",
"# os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN\n",
"replicate_id = \"meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d\"\n",
"llama2_chat_replicate = Replicate(\n",
" model=replicate_id, input={\"temperature\": 0.01, \"max_length\": 500, \"top_p\": 1}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ce96f7ea-b3d5-44e1-9fa5-a79e04a9e1fb",
"metadata": {},
"outputs": [],
"source": [
"# Simply set the LLM we want to use\n",
"llm = llama2_chat"
]
},
{
"cell_type": "markdown",
"id": "80222165-f353-4e35-a123-5f70fd70c6c8",
"metadata": {},
"source": [
"## DB\n",
"\n",
"Connect to a SQLite DB.\n",
"\n",
"To create this particular DB, you can use the code and follow the steps shown [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "025bdd82-3bb1-4948-bc7c-c3ccd94fd05c",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.utilities import SQLDatabase\n",
"\n",
"db = SQLDatabase.from_uri(\"sqlite:///nba_roster.db\", sample_rows_in_table_info=0)\n",
"\n",
"\n",
"def get_schema(_):\n",
" return db.get_table_info()\n",
"\n",
"\n",
"def run_query(query):\n",
" return db.run(query)"
]
},
{
"cell_type": "markdown",
"id": "654b3577-baa2-4e12-a393-f40e5db49ac7",
"metadata": {},
"source": [
"## Query a SQL Database \n",
"\n",
"Follow the runnables workflow [here](https://python.langchain.com/docs/expression_language/cookbook/sql_db)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5a4933ea-d9c0-4b0a-8177-ba4490c6532b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' SELECT \"Team\" FROM nba_roster WHERE \"NAME\" = \\'Klay Thompson\\';'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Prompt\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"# Update the template based on the type of SQL Database like MySQL, Microsoft SQL Server and so on\n",
"template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
"{schema}\n",
"\n",
"Question: {question}\n",
"SQL Query:\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"Given an input question, convert it to a SQL query. No pre-amble.\"),\n",
" (\"human\", template),\n",
" ]\n",
")\n",
"\n",
"# Chain to query\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"sql_response = (\n",
" RunnablePassthrough.assign(schema=get_schema)\n",
" | prompt\n",
" | llm.bind(stop=[\"\\nSQLResult:\"])\n",
" | StrOutputParser()\n",
")\n",
"\n",
"sql_response.invoke({\"question\": \"What team is Klay Thompson on?\"})"
]
},
{
"cell_type": "markdown",
"id": "a0e9e2c8-9b88-4853-ac86-001bc6cc6695",
"metadata": {},
"source": [
"We can review the results:\n",
"\n",
"* [LangSmith trace](https://smith.langchain.com/public/afa56a06-b4e2-469a-a60f-c1746e75e42b/r) LLaMA2-13 Replicate API\n",
"* [LangSmith trace](https://smith.langchain.com/public/2d4ecc72-6b8f-4523-8f0b-ea95c6b54a1d/r) LLaMA2-13 local \n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "2a2825e3-c1b6-4f7d-b9c9-d9835de323bb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' Based on the table schema and SQL query, there are 30 unique teams in the NBA.')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Chain to answer\n",
"template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
"{schema}\n",
"\n",
"Question: {question}\n",
"SQL Query: {query}\n",
"SQL Response: {response}\"\"\"\n",
"prompt_response = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.\",\n",
" ),\n",
" (\"human\", template),\n",
" ]\n",
")\n",
"\n",
"full_chain = (\n",
" RunnablePassthrough.assign(query=sql_response)\n",
" | RunnablePassthrough.assign(\n",
" schema=get_schema,\n",
" response=lambda x: db.run(x[\"query\"]),\n",
" )\n",
" | prompt_response\n",
" | llm\n",
")\n",
"\n",
"full_chain.invoke({\"question\": \"How many unique teams are there?\"})"
]
},
{
"cell_type": "markdown",
"id": "ec17b3ee-6618-4681-b6df-089bbb5ffcd7",
"metadata": {},
"source": [
"We can review the results:\n",
"\n",
"* [LangSmith trace](https://smith.langchain.com/public/10420721-746a-4806-8ecf-d6dc6399d739/r) LLaMA2-13 Replicate API\n",
"* [LangSmith trace](https://smith.langchain.com/public/5265ebab-0a22-4f37-936b-3300f2dfa1c1/r) LLaMA2-13 local "
]
},
{
"cell_type": "markdown",
"id": "1e85381b-1edc-4bb3-a7bd-2ab23f81e54d",
"metadata": {},
"source": [
"## Chat with a SQL DB \n",
"\n",
"Next, we can add memory."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "022868f2-128e-42f5-8d90-d3bb2f11d994",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' SELECT \"Team\" FROM nba_roster WHERE \"NAME\" = \\'Klay Thompson\\';'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Prompt\n",
"from langchain.memory import ConversationBufferMemory\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"\n",
"template = \"\"\"Given an input question, convert it to a SQL query. No pre-amble. Based on the table schema below, write a SQL query that would answer the user's question:\n",
"{schema}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", template),\n",
" MessagesPlaceholder(variable_name=\"history\"),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"\n",
"memory = ConversationBufferMemory(return_messages=True)\n",
"\n",
"# Chain to query with memory\n",
"from langchain_core.runnables import RunnableLambda\n",
"\n",
"sql_chain = (\n",
" RunnablePassthrough.assign(\n",
" schema=get_schema,\n",
" history=RunnableLambda(lambda x: memory.load_memory_variables(x)[\"history\"]),\n",
" )\n",
" | prompt\n",
" | llm.bind(stop=[\"\\nSQLResult:\"])\n",
" | StrOutputParser()\n",
")\n",
"\n",
"\n",
"def save(input_output):\n",
" output = {\"output\": input_output.pop(\"output\")}\n",
" memory.save_context(input_output, output)\n",
" return output[\"output\"]\n",
"\n",
"\n",
"sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save\n",
"sql_response_memory.invoke({\"question\": \"What team is Klay Thompson on?\"})"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "800a7a3b-f411-478b-af51-2310cd6e0425",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' Sure! Here\\'s the natural language response based on the given input:\\n\\n\"Klay Thompson\\'s salary is $43,219,440.\"')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Chain to answer\n",
"template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
"{schema}\n",
"\n",
"Question: {question}\n",
"SQL Query: {query}\n",
"SQL Response: {response}\"\"\"\n",
"prompt_response = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.\",\n",
" ),\n",
" (\"human\", template),\n",
" ]\n",
")\n",
"\n",
"full_chain = (\n",
" RunnablePassthrough.assign(query=sql_response_memory)\n",
" | RunnablePassthrough.assign(\n",
" schema=get_schema,\n",
" response=lambda x: db.run(x[\"query\"]),\n",
" )\n",
" | prompt_response\n",
" | llm\n",
")\n",
"\n",
"full_chain.invoke({\"question\": \"What is his salary?\"})"
]
},
{
"cell_type": "markdown",
"id": "b77fee61-f4da-4bb1-8285-14101e505518",
"metadata": {},
"source": [
"Here is the [trace](https://smith.langchain.com/public/54794d18-2337-4ce2-8b9f-3d8a2df89e51/r)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG_google.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "SzvBjdID1V3m",
"metadata": {
"id": "SzvBjdID1V3m"
},
"source": [
"# Multi-modal RAG with Google Cloud"
]
},
{
"cell_type": "markdown",
"id": "4tfidrmE1Zlo",
"metadata": {
"id": "4tfidrmE1Zlo"
},
"source": [
"This tutorial demonstrates how to implement the Option 2 described [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb) with Generative API on Google Cloud."
]
},
{
"cell_type": "markdown",
"id": "84fcd59f-2eaf-4a76-ad1a-96d6db70bf42",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"Install the required dependencies, and create an API key for your Google service."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b1e10dd-25de-4c0a-9577-f36e72518f89",
"metadata": {},
"outputs": [],
"source": [
"%pip install -U --quiet langchain langchain_community openai chromadb langchain-experimental\n",
"%pip install --quiet \"unstructured[all-docs]\" pypdf pillow pydantic lxml pillow matplotlib chromadb tiktoken"
]
},
{
"cell_type": "markdown",
"id": "pSInKtCZ32mt",
"metadata": {
"id": "pSInKtCZ32mt"
},
"source": [
"## Data loading"
]
},
{
"cell_type": "markdown",
"id": "Iv2R8-lJ37dG",
"metadata": {
"id": "Iv2R8-lJ37dG"
},
"source": [
"We use a zip file with a sub-set of the extracted images and pdf from [this](https://cloudedjudgement.substack.com/p/clouded-judgement-111023) blog post. If you want to follow the full flow, please, use the original [example](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d999f3fe-c165-4772-b63e-ffe4dd5b03cf",
"metadata": {},
"outputs": [],
"source": [
"# First download\n",
"import logging\n",
"import zipfile\n",
"\n",
"import requests\n",
"\n",
"logging.basicConfig(level=logging.INFO)\n",
"\n",
"data_url = \"https://storage.googleapis.com/benchmarks-artifacts/langchain-docs-benchmarking/cj.zip\"\n",
"result = requests.get(data_url)\n",
"filename = \"cj.zip\"\n",
"with open(filename, \"wb\") as file:\n",
" file.write(result.content)\n",
"\n",
"with zipfile.ZipFile(filename, \"r\") as zip_ref:\n",
" zip_ref.extractall()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "eGUfuevMUA6R",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import PyPDFLoader\n",
"\n",
"loader = PyPDFLoader(\"./cj/cj.pdf\")\n",
"docs = loader.load()\n",
"tables = []\n",
"texts = [d.page_content for d in docs]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "Fst17fNHWYcq",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(texts)"
]
},
{
"cell_type": "markdown",
"id": "vjfcg_Vn3_1C",
"metadata": {
"id": "vjfcg_Vn3_1C"
},
"source": [
"## Multi-vector retriever"
]
},
{
"cell_type": "markdown",
"id": "1ynRqJn04BFG",
"metadata": {
"id": "1ynRqJn04BFG"
},
"source": [
"Let's generate text and image summaries and save them to a ChromaDB vectorstore."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "kWDWfSDBMPl8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
]
}
],
"source": [
"from langchain.prompts import PromptTemplate\n",
"from langchain_community.chat_models import ChatVertexAI\n",
"from langchain_community.llms import VertexAI\n",
"from langchain_core.messages import AIMessage\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnableLambda\n",
"\n",
"\n",
"# Generate summaries of text elements\n",
"def generate_text_summaries(texts, tables, summarize_texts=False):\n",
" \"\"\"\n",
" Summarize text elements\n",
" texts: List of str\n",
" tables: List of str\n",
" summarize_texts: Bool to summarize texts\n",
" \"\"\"\n",
"\n",
" # Prompt\n",
" prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text for retrieval. \\\n",
" These summaries will be embedded and used to retrieve the raw text or table elements. \\\n",
" Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} \"\"\"\n",
" prompt = PromptTemplate.from_template(prompt_text)\n",
" empty_response = RunnableLambda(\n",
" lambda x: AIMessage(content=\"Error processing document\")\n",
" )\n",
" # Text summary chain\n",
" model = VertexAI(\n",
" temperature=0, model_name=\"gemini-pro\", max_tokens=1024\n",
" ).with_fallbacks([empty_response])\n",
" summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()\n",
"\n",
" # Initialize empty summaries\n",
" text_summaries = []\n",
" table_summaries = []\n",
"\n",
" # Apply to text if texts are provided and summarization is requested\n",
" if texts and summarize_texts:\n",
" text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 1})\n",
" elif texts:\n",
" text_summaries = texts\n",
"\n",
" # Apply to tables if tables are provided\n",
" if tables:\n",
" table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 1})\n",
"\n",
" return text_summaries, table_summaries\n",
"\n",
"\n",
"# Get text, table summaries\n",
"text_summaries, table_summaries = generate_text_summaries(\n",
" texts, tables, summarize_texts=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "F0NnyUl48yYb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(text_summaries)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "PeK9bzXv3olF",
"metadata": {},
"outputs": [],
"source": [
"import base64\n",
"import os\n",
"\n",
"from langchain_core.messages import HumanMessage\n",
"\n",
"\n",
"def encode_image(image_path):\n",
" \"\"\"Getting the base64 string\"\"\"\n",
" with open(image_path, \"rb\") as image_file:\n",
" return base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
"\n",
"\n",
"def image_summarize(img_base64, prompt):\n",
" \"\"\"Make image summary\"\"\"\n",
" model = ChatVertexAI(model=\"gemini-pro-vision\", max_tokens=1024)\n",
"\n",
" msg = model.invoke(\n",
" [\n",
" HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": prompt},\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": f\"data:image/jpeg;base64,{img_base64}\"},\n",
" },\n",
" ]\n",
" )\n",
" ]\n",
" )\n",
" return msg.content\n",
"\n",
"\n",
"def generate_img_summaries(path):\n",
" \"\"\"\n",
" Generate summaries and base64 encoded strings for images\n",
" path: Path to list of .jpg files extracted by Unstructured\n",
" \"\"\"\n",
"\n",
" # Store base64 encoded images\n",
" img_base64_list = []\n",
"\n",
" # Store image summaries\n",
" image_summaries = []\n",
"\n",
" # Prompt\n",
" prompt = \"\"\"You are an assistant tasked with summarizing images for retrieval. \\\n",
" These summaries will be embedded and used to retrieve the raw image. \\\n",
" Give a concise summary of the image that is well optimized for retrieval.\"\"\"\n",
"\n",
" # Apply to images\n",
" for img_file in sorted(os.listdir(path)):\n",
" if img_file.endswith(\".jpg\"):\n",
" img_path = os.path.join(path, img_file)\n",
" base64_image = encode_image(img_path)\n",
" img_base64_list.append(base64_image)\n",
" image_summaries.append(image_summarize(base64_image, prompt))\n",
"\n",
" return img_base64_list, image_summaries\n",
"\n",
"\n",
"# Image summaries\n",
"img_base64_list, image_summaries = generate_img_summaries(\"./cj\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6WDYpDFzjocl",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(image_summaries)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cWyWfZ-XB6cS",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:chromadb.telemetry.product.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n"
]
}
],
"source": [
"import uuid\n",
"\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"from langchain.storage import InMemoryStore\n",
"from langchain_community.embeddings import VertexAIEmbeddings\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_core.documents import Document\n",
"\n",
"\n",
"def create_multi_vector_retriever(\n",
" vectorstore, text_summaries, texts, table_summaries, tables, image_summaries, images\n",
"):\n",
" \"\"\"\n",
" Create retriever that indexes summaries, but returns raw images or texts\n",
" \"\"\"\n",
"\n",
" # Initialize the storage layer\n",
" store = InMemoryStore()\n",
" id_key = \"doc_id\"\n",
"\n",
" # Create the multi-vector retriever\n",
" retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" id_key=id_key,\n",
" )\n",
"\n",
" # Helper function to add documents to the vectorstore and docstore\n",
" def add_documents(retriever, doc_summaries, doc_contents):\n",
" doc_ids = [str(uuid.uuid4()) for _ in doc_contents]\n",
" summary_docs = [\n",
" Document(page_content=s, metadata={id_key: doc_ids[i]})\n",
" for i, s in enumerate(doc_summaries)\n",
" ]\n",
" retriever.vectorstore.add_documents(summary_docs)\n",
" retriever.docstore.mset(list(zip(doc_ids, doc_contents)))\n",
"\n",
" # Add texts, tables, and images\n",
" # Check that text_summaries is not empty before adding\n",
" if text_summaries:\n",
" add_documents(retriever, text_summaries, texts)\n",
" # Check that table_summaries is not empty before adding\n",
" if table_summaries:\n",
" add_documents(retriever, table_summaries, tables)\n",
" # Check that image_summaries is not empty before adding\n",
" if image_summaries:\n",
" add_documents(retriever, image_summaries, images)\n",
"\n",
" return retriever\n",
"\n",
"\n",
"# The vectorstore to use to index the summaries\n",
"vectorstore = Chroma(\n",
" collection_name=\"mm_rag_cj_blog\",\n",
" embedding_function=VertexAIEmbeddings(model_name=\"textembedding-gecko@latest\"),\n",
")\n",
"\n",
"# Create retriever\n",
"retriever_multi_vector_img = create_multi_vector_retriever(\n",
" vectorstore,\n",
" text_summaries,\n",
" texts,\n",
" table_summaries,\n",
" tables,\n",
" image_summaries,\n",
" img_base64_list,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "NGDkkMFfCg4j",
"metadata": {
"id": "NGDkkMFfCg4j"
},
"source": [
"## Building a RAG"
]
},
{
"cell_type": "markdown",
"id": "8TzOcHVsCmBc",
"metadata": {
"id": "8TzOcHVsCmBc"
},
"source": [
"Let's build a retriever:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "GlwCErBaCKQW",
"metadata": {},
"outputs": [],
"source": [
"import io\n",
"import re\n",
"\n",
"from IPython.display import HTML, display\n",
"from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
"from PIL import Image\n",
"\n",
"\n",
"def plt_img_base64(img_base64):\n",
" \"\"\"Disply base64 encoded string as image\"\"\"\n",
" # Create an HTML img tag with the base64 string as the source\n",
" image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
" # Display the image by rendering the HTML\n",
" display(HTML(image_html))\n",
"\n",
"\n",
"def looks_like_base64(sb):\n",
" \"\"\"Check if the string looks like base64\"\"\"\n",
" return re.match(\"^[A-Za-z0-9+/]+[=]{0,2}$\", sb) is not None\n",
"\n",
"\n",
"def is_image_data(b64data):\n",
" \"\"\"\n",
" Check if the base64 data is an image by looking at the start of the data\n",
" \"\"\"\n",
" image_signatures = {\n",
" b\"\\xff\\xd8\\xff\": \"jpg\",\n",
" b\"\\x89\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\": \"png\",\n",
" b\"\\x47\\x49\\x46\\x38\": \"gif\",\n",
" b\"\\x52\\x49\\x46\\x46\": \"webp\",\n",
" }\n",
" try:\n",
" header = base64.b64decode(b64data)[:8] # Decode and get the first 8 bytes\n",
" for sig, format in image_signatures.items():\n",
" if header.startswith(sig):\n",
" return True\n",
" return False\n",
" except Exception:\n",
" return False\n",
"\n",
"\n",
"def resize_base64_image(base64_string, size=(128, 128)):\n",
" \"\"\"\n",
" Resize an image encoded as a Base64 string\n",
" \"\"\"\n",
" # Decode the Base64 string\n",
" img_data = base64.b64decode(base64_string)\n",
" img = Image.open(io.BytesIO(img_data))\n",
"\n",
" # Resize the image\n",
" resized_img = img.resize(size, Image.LANCZOS)\n",
"\n",
" # Save the resized image to a bytes buffer\n",
" buffered = io.BytesIO()\n",
" resized_img.save(buffered, format=img.format)\n",
"\n",
" # Encode the resized image to Base64\n",
" return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n",
"\n",
"\n",
"def split_image_text_types(docs):\n",
" \"\"\"\n",
" Split base64-encoded images and texts\n",
" \"\"\"\n",
" b64_images = []\n",
" texts = []\n",
" for doc in docs:\n",
" # Check if the document is of type Document and extract page_content if so\n",
" if isinstance(doc, Document):\n",
" doc = doc.page_content\n",
" if looks_like_base64(doc) and is_image_data(doc):\n",
" doc = resize_base64_image(doc, size=(1300, 600))\n",
" b64_images.append(doc)\n",
" else:\n",
" texts.append(doc)\n",
" if len(b64_images) > 0:\n",
" return {\"images\": b64_images[:1], \"texts\": []}\n",
" return {\"images\": b64_images, \"texts\": texts}\n",
"\n",
"\n",
"def img_prompt_func(data_dict):\n",
" \"\"\"\n",
" Join the context into a single string\n",
" \"\"\"\n",
" formatted_texts = \"\\n\".join(data_dict[\"context\"][\"texts\"])\n",
" messages = []\n",
"\n",
" # Adding the text for analysis\n",
" text_message = {\n",
" \"type\": \"text\",\n",
" \"text\": (\n",
" \"You are financial analyst tasking with providing investment advice.\\n\"\n",
" \"You will be given a mixed of text, tables, and image(s) usually of charts or graphs.\\n\"\n",
" \"Use this information to provide investment advice related to the user question. \\n\"\n",
" f\"User-provided question: {data_dict['question']}\\n\\n\"\n",
" \"Text and / or tables:\\n\"\n",
" f\"{formatted_texts}\"\n",
" ),\n",
" }\n",
" messages.append(text_message)\n",
" # Adding image(s) to the messages if present\n",
" if data_dict[\"context\"][\"images\"]:\n",
" for image in data_dict[\"context\"][\"images\"]:\n",
" image_message = {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": f\"data:image/jpeg;base64,{image}\"},\n",
" }\n",
" messages.append(image_message)\n",
" return [HumanMessage(content=messages)]\n",
"\n",
"\n",
"def multi_modal_rag_chain(retriever):\n",
" \"\"\"\n",
" Multi-modal RAG chain\n",
" \"\"\"\n",
"\n",
" # Multi-modal LLM\n",
" model = ChatVertexAI(temperature=0, model_name=\"gemini-pro-vision\", max_tokens=1024)\n",
"\n",
" # RAG pipeline\n",
" chain = (\n",
" {\n",
" \"context\": retriever | RunnableLambda(split_image_text_types),\n",
" \"question\": RunnablePassthrough(),\n",
" }\n",
" | RunnableLambda(img_prompt_func)\n",
" | model\n",
" | StrOutputParser()\n",
" )\n",
"\n",
" return chain\n",
"\n",
"\n",
"# Create RAG chain\n",
"chain_multimodal_rag = multi_modal_rag_chain(retriever_multi_vector_img)"
]
},
{
"cell_type": "markdown",
"id": "BS4hNKqCCp8u",
"metadata": {
"id": "BS4hNKqCCp8u"
},
"source": [
"Let's check that we get images as documents:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "Q7GrwFC_FGwr",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What are the EV / NTM and NTM rev growth for MongoDB, Cloudflare, and Datadog?\"\n",
"docs = retriever_multi_vector_img.invoke(query, limit=1)\n",
"\n",
"# We get 2 docs\n",
"len(docs)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "unnxB5M_FLCD",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<img 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/>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt_img_base64(docs[0])"
]
},
{
"cell_type": "markdown",
"id": "YUkGZXqsCtF6",
"metadata": {
"id": "YUkGZXqsCtF6"
},
"source": [
"And let's run our RAG on the same query:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "LsPTehdK-T-_",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' | Company | EV / NTM Rev | NTM Rev Growth |\\n|---|---|---|\\n| MongoDB | 14.6x | 17% |\\n| Cloudflare | 13.4x | 28% |\\n| Datadog | 13.1x | 19% |'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_multimodal_rag.invoke(query)"
]
},
{
"cell_type": "markdown",
"id": "XpLQB6dEfQX-",
"metadata": {
"id": "XpLQB6dEfQX-"
},
"source": [
"As we can see, the model was able to figure out the the right values that are relevant to answer the question."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "3058e9ca-07c3-4eef-b98c-bc2f2dbb9cc6",
"metadata": {},
"outputs": [],
"source": [
"pip install -U langchain umap-learn scikit-learn langchain_community tiktoken langchain-openai langchainhub chromadb langchain-anthropic"
]
},
{
"attachments": {
"72039e0c-e8c4-4b17-8780-04ad9fc584f3.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"id": "ea54c848-0df6-474e-b266-218a2acf67d3",
"metadata": {},
"source": [
"# RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval\n",
"\n",
"The [RAPTOR](https://arxiv.org/pdf/2401.18059.pdf) paper presents an interesting approaching for indexing and retrieval of documents:\n",
"\n",
"* The `leafs` are a set of starting documents\n",
"* Leafs are embedded and clustered\n",
"* Clusters are then summarized into higher level (more abstract) consolidations of information across similar documents\n",
"\n",
"This process is done recursivly, resulting in a \"tree\" going from raw docs (`leafs`) to more abstract summaries.\n",
" \n",
"We can applying this at varying scales; `leafs` can be:\n",
"\n",
"* Text chunks from a single doc (as shown in the paper)\n",
"* Full docs (as we show below)\n",
"\n",
"With longer context LLMs, it's possible to perform this over full documents. \n",
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "083dd961-b401-4fc6-867c-8f8950059b02",
"metadata": {},
"source": [
"### Docs\n",
"\n",
"Let's apply this to LangChain's LCEL documentation.\n",
"\n",
"In this case, each `doc` is a unique web page of the LCEL docs.\n",
"\n",
"The context varies from < 2k tokens on up to > 10k tokens."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b17c1331-373f-491d-8b53-ccf634e68c8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function matplotlib.pyplot.show(close=None, block=None)>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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A77zzjmrWrOm1iAYAlAh/Lq0HAGVR3hLgq1evLnB7hw4dzroE+IsvvmjatGljoqOjTWhoqGncuLEZO3asOXHihKePy+Uyjz76qKlSpYqx2WxeSy4fPXrUPPbYYyY2NtYEBQWZBg0amAkTJhi32+113MzMTDN48GBTqVIlU6FCBdOrVy+TmppqJHktyZ23pHNByy//+eefpnfv3iY6OtpERUWZ2267zezZs+e0y4ifOsbpluYu6DoVJCcnxyQlJZn4+HgTFBRk4uLizFNPPWWOHz9eqOOczalLgBtjzO+//25uvfVWEx0dbUJCQkybNm3MN99849XHugS41RNPPGEkmcmTJ3vapk6dalq1amVCQ0NNRESEad68uXniiSfMnj17PH1q165d4LLwHTp0yFff6Rw7dsy8+uqr5vLLLzcVKlQwwcHBpkGDBubRRx/1WprbGGMWLlxo2rVrZ0JDQ01kZKTp3r272bJlS74x58+fb5o1a2aCg4NNo0aNzIcffnjaJcAHDx6cb/9T/+wbY8yYMWPMRRddZAICAjzLgS9atMj07NnTxMbGmuDgYBMbG2v69u1rtm7dWqhzBwBfshlTyG/NAgDKhZSUFF166aX68MMP1a9fP3+XAwBAqcN3kgCgHMvKysrXNmnSJAUEBOiaa67xQ0UAAJR+fCcJAMqxV155RWvWrFHHjh1lt9s1d+5czZ07V/fff7/i4uL8XR4AAKUSj9sBQDm2YMECJSUlacuWLcrIyFCtWrV0991365lnnpHdzr+TAQBQEEISAAAAAFjwnSQAAAAAsCAkAQAAAIBFuX8g3e12a8+ePYqIiCjUL04EAAAAUD4ZY3T06FHFxsae9peDSxdASNqzZw8rOAEAAADw2L17t2rWrHna7eU+JEVEREg6eSEiIyP9XA0AAAAAf3E6nYqLi/NkhNMp9yEp7xG7yMhIQhIAAACAs34Nh4UbAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC7+GpKVLl6p79+6KjY2VzWbTl19+6dmWk5OjUaNGqXnz5goPD1dsbKz69++vPXv2+K9gAAAAAOWeX0NSZmamWrRoobfffjvftmPHjmnt2rV69tlntXbtWn3++edKTU1Vjx49/FApAAAAgAuFzRhj/F2EJNlsNn3xxRfq1avXafusXr1abdq0UVpammrVqlWocZ1Op6KiopSenq7IyEgfVQsAAACgrClsNrCXYE3nLT09XTabTdHR0aftk52drezsbM9rp9MpSXK5XHK5XMVd4ln9888/Onr0aLGNHxERocqVKxfb+AAAAEBZVdg8UGZC0vHjxzVq1Cj17dv3jKlv3LhxSkpKyteenJys8PDw4izxrE6cOKEtW7YqJ8ddbMcICgpQkyYNFRwcXGzHAAAAAMqizMzMQvUrE4/b5eTk6JZbbtGff/6pH3744YwhqaA7SXFxcTp48KDfH7fbuXOn+vUbKYdjqEJDa/p8/KysP5Wd/YZmzZqg+Ph4n48PAAAAlGVOp1MxMTFl/3G7nJwc3X777UpLS9PixYvPGnQcDoccDke+drvdLrvdv6cbEBAglytXFSrUksNRz+fju1wByszMVUBAgN/PFQAAAChtCvsZuVR/ks4LSNu2bdOSJUsUExPj75IAAAAAlHN+DUkZGRnavn275/XOnTuVkpKiSpUqqUaNGrr11lu1du1affPNN8rNzdW+ffskSZUqVeI7NwAAAACKhV9DUnJysjp27Oh5PXz4cElSYmKiRo8era+//lqS1LJlS6/9lixZomuvvbakygQAAABwAfFrSLr22mt1pnUjSsmaEgAAAAAuIAH+LgAAAAAAShNCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgIVfQ9LSpUvVvXt3xcbGymaz6csvv/TabozRc889pxo1aig0NFSdO3fWtm3b/FMsAAAAgAuCX0NSZmamWrRoobfffrvA7a+88orefPNNvfvuu1q5cqXCw8OVkJCg48ePl3ClAAAAAC4Udn8evEuXLurSpUuB24wxmjRpkv71r3+pZ8+ekqSZM2eqWrVq+vLLL9WnT5+SLBUAAADABcKvIelMdu7cqX379qlz586etqioKF1xxRVasWLFaUNSdna2srOzPa+dTqckyeVyyeVyFW/RZ+F2u2W3B8pudysw0Pe12O0nx3e73X4/VwAAAKC0Kexn5FIbkvbt2ydJqlatmld7tWrVPNsKMm7cOCUlJeVrT05OVnh4uG+LPEdZWVm6884E2e1pCgw84PPxc3Oz5HIlKC0tTQcO+H58AAAAoCzLzMwsVL9SG5KK6qmnntLw4cM9r51Op+Li4tS6dWtFRkb6sbKTd8eefnqyoqM7Kyws3ufjHzu2U0eOTNasWZ0VH+/78QEAAICyLO8ps7MptSGpevXqkqT9+/erRo0anvb9+/erZcuWp93P4XDI4XDka7fb7bLb/Xu6AQEBcrly5XIFKDfX97W4XCfHDwgI8Pu5AgAAAKVNYT8jl9rfkxQfH6/q1atr0aJFnjan06mVK1eqbdu2fqwMAAAAQHnm19sNGRkZ2r59u+f1zp07lZKSokqVKqlWrVoaNmyYXnzxRTVo0EDx8fF69tlnFRsbq169evmvaAAAAADlml9DUnJysjp27Oh5nfddosTERE2fPl1PPPGEMjMzdf/99+vIkSNq37695s2bp5CQEH+VDAAAAKCc82tIuvbaa2WMOe12m82mF154QS+88EIJVgUAAADgQlZqv5MEAAAAAP5ASAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAo1SEpNzdXzz77rOLj4xUaGqp69eppzJgxMsb4uzQAAAAA5ZTd3wWcycsvv6wpU6ZoxowZatq0qZKTkzVw4EBFRUVpyJAh/i4PAAAAQDlUqkPS8uXL1bNnT3Xr1k2SVKdOHX388cdatWqVnysDAAAAUF6V6pB01VVXaerUqdq6dasaNmyo9evXa9myZZo4ceJp98nOzlZ2drbntdPplCS5XC65XK5ir/lM3G637PZA2e1uBQb6vha7/eT4brfb7+cKAAAAlDaF/YxcqkPSk08+KafTqcaNGyswMFC5ubkaO3as+vXrd9p9xo0bp6SkpHztycnJCg8PL85yzyorK0t33pkguz1NgYEHfD5+bm6WXK4EpaWl6cAB348PAAAAlGWZmZmF6mczpXgVhNmzZ2vkyJGaMGGCmjZtqpSUFA0bNkwTJ05UYmJigfsUdCcpLi5OBw8eVGRkZEmVXqCdO3eqX7+Rio6eoLCweJ+Pf+zYTh05MlKzZk1QfLzvxwcAAADKMqfTqZiYGKWnp58xG5TqO0kjR47Uk08+qT59+kiSmjdvrrS0NI0bN+60IcnhcMjhcORrt9vtstv9e7oBAQFyuXLlcgUoN9f3tbhcJ8cPCAjw+7kCAAAApU1hPyOX6iXAjx07poAA7xIDA09+5wYAAAAAikOpvt3QvXt3jR07VrVq1VLTpk21bt06TZw4Uffcc4+/SwMAAABQTpXqkPTWW2/p2Wef1cMPP6wDBw4oNjZWDzzwgJ577jl/lwYAAACgnCrVISkiIkKTJk3SpEmT/F0KAAAAgAtEqf5OEgAAAACUNEISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALIoUknbs2OHrOgAAAACgVChSSKpfv746duyoDz/8UMePH/d1TQAAAADgN0UKSWvXrtUll1yi4cOHq3r16nrggQe0atUqX9cGAAAAACWuSCGpZcuWeuONN7Rnzx598MEH2rt3r9q3b69mzZpp4sSJ+vvvv31dJwAAAACUiPNauMFut+vmm2/WnDlz9PLLL2v79u16/PHHFRcXp/79+2vv3r2+qhMAAAAASsR5haTk5GQ9/PDDqlGjhiZOnKjHH39cv//+uxYsWKA9e/aoZ8+evqoTAAAAAEqEvSg7TZw4UdOmTVNqaqq6du2qmTNnqmvXrgoIOJm54uPjNX36dNWpU8eXtQIAAABAsStSSJoyZYruueceDRgwQDVq1CiwT9WqVfWf//znvIoDAAAAgJJWpJC0bdu2s/YJDg5WYmJiUYYHAAAAAL8p0neSpk2bpjlz5uRrnzNnjmbMmHHeRQEAAACAvxQpJI0bN06VK1fO1161alW99NJL510UAAAAAPhLkULSrl27FB8fn6+9du3a2rVr13kXBQAAAAD+UqSQVLVqVW3YsCFf+/r16xUTE3PeRQEAAACAvxQpJPXt21dDhgzRkiVLlJubq9zcXC1evFhDhw5Vnz59fF0jAAAAAJSYIq1uN2bMGP3xxx/q1KmT7PaTQ7jdbvXv35/vJAEAAAAo04oUkoKDg/XJJ59ozJgxWr9+vUJDQ9W8eXPVrl3b1/UBAAAAQIkqUkjK07BhQzVs2NBXtQAAAACA3xUpJOXm5mr69OlatGiRDhw4ILfb7bV98eLFPikOAAAAAEpakULS0KFDNX36dHXr1k3NmjWTzWbzdV0AAAAA4BdFCkmzZ8/Wp59+qq5du/q6HgAAAADwqyItAR4cHKz69ev7uhYAAAAA8LsihaQRI0bojTfekDHG1/UAAAAAgF8V6XG7ZcuWacmSJZo7d66aNm2qoKAgr+2ff/65T4oDAAAAgJJWpJAUHR2t3r17+7oWAAAAAPC7IoWkadOm+boOAAAAACgVivSdJElyuVxauHCh3nvvPR09elSStGfPHmVkZPisOAAAAAAoaUW6k5SWlqYbb7xRu3btUnZ2tq6//npFRETo5ZdfVnZ2tt59911f1wkAAAAAJaJId5KGDh2q1q1b6/DhwwoNDfW09+7dW4sWLfJZcQAAAABQ0op0J+mnn37S8uXLFRwc7NVep04d/fXXXz4pDAAAAAD8oUh3ktxut3Jzc/O1//nnn4qIiDjvogAAAADAX4oUkm644QZNmjTJ89pmsykjI0PPP/+8unbt6qvaAAAAAKDEFelxu9dee00JCQlq0qSJjh8/rjvvvFPbtm1T5cqV9fHHH/u6RgAAAAAoMUUKSTVr1tT69es1e/ZsbdiwQRkZGRo0aJD69evntZADAAAAAJQ1RQpJkmS323XXXXf5shYAAAAA8LsihaSZM2eecXv//v2LVAwAAAAA+FuRQtLQoUO9Xufk5OjYsWMKDg5WWFgYIQkAAABAmVWk1e0OHz7s9ZORkaHU1FS1b9+ehRsAAAAAlGlFCkkFadCggcaPH5/vLhMAAAAAlCU+C0nSycUc9uzZ48shAQAAAKBEFek7SV9//bXXa2OM9u7dq8mTJ6tdu3Y+KQwAAAAA/KFIIalXr15er202m6pUqaLrrrtOr732mi/q8vjrr780atQozZ07V8eOHVP9+vU1bdo0tW7d2qfHAQAAAACpiCHJ7Xb7uo4CHT58WO3atVPHjh01d+5cValSRdu2bVPFihVL5PgAAAAALjxF/mWyJeHll19WXFycpk2b5mmLj4/3Y0UAAAAAyrsihaThw4cXuu/EiROLcghJJ7/7lJCQoNtuu00//vijLrroIj388MO67777TrtPdna2srOzPa+dTqckyeVyyeVyFbkWX3C73bLbA2W3uxUY6Pta7Ha3pFz98ccfxXK3LyIiQpUrV/b5uAAAAEBJKGweKFJIWrdundatW6ecnBw1atRIkrR161YFBgbqsssu8/Sz2WxFGd5jx44dmjJlioYPH66nn35aq1ev1pAhQxQcHKzExMQC9xk3bpySkpLytScnJys8PPy86jlfWVlZuvPOBNntaQoMPODz8V0upzIyOmnDht+0ceNWn48fFBSgJk0aKjg42OdjAwAAAMUtMzOzUP1sxhhzroNPnDhRP/zwg2bMmOH5ftDhw4c1cOBAXX311RoxYsS5Dlmg4OBgtW7dWsuXL/e0DRkyRKtXr9aKFSsK3KegO0lxcXE6ePCgIiMjfVJXUe3cuVP9+o1UdPQEhYX5/rHBgwd/1MaNw9Ww4ThFRzfw6dhZWX8qO/sNzZo1gUceAQAAUCY5nU7FxMQoPT39jNmgSHeSXnvtNc2fP99rAYWKFSvqxRdf1A033OCzkFSjRg01adLEq+3iiy/W//73v9Pu43A45HA48rXb7XbZ7f79ClZAQIBcrly5XAHKzfV9LS6XTSdO5CgwME4Oh29DkssVoMzMXAUEBPj9OgIAAABFUdjPsUX6ZbJOp1N///13vva///5bR48eLcqQBWrXrp1SU1O92rZu3aratWv77BgAAAAAYFWkkNS7d28NHDhQn3/+uf7880/9+eef+t///qdBgwbp5ptv9llxjz32mH755Re99NJL2r59uz766CNNnTpVgwcP9tkxAAAAAMCqSM9Nvfvuu3r88cd15513Kicn5+RAdrsGDRqkCRMm+Ky4yy+/XF988YWeeuopvfDCC4qPj9ekSZPUr18/nx0DAAAAAKyKFJLCwsL0zjvvaMKECfr9998lSfXq1SuW1eNuuukm3XTTTT4fFwAAAAAKUqTH7fLs3btXe/fuVYMGDRQeHq4iLJQHAAAAAKVKkULSwYMH1alTJzVs2FBdu3bV3r17JUmDBg3y2cp2AAAAAOAPRQpJjz32mIKCgrRr1y6FhYV52u+44w7NmzfPZ8UBAAAAQEkr0neS5s+fr++//141a9b0am/QoIHS0tJ8UhgAAAAA+EOR7iRlZmZ63UHKc+jQoQJ/kSsAAAAAlBVFCklXX321Zs6c6Xlts9nkdrv1yiuvqGPHjj4rDgAAAABKWpEet3vllVfUqVMnJScn68SJE3riiSe0efNmHTp0SD///LOvawQAAACAElOkO0nNmjXT1q1b1b59e/Xs2VOZmZm6+eabtW7dOtWrV8/XNQIAAABAiTnnO0k5OTm68cYb9e677+qZZ54pjpoAAAAAwG/O+U5SUFCQNmzYUBy1AAAAAIDfFelxu7vuukv/+c9/fF0LAAAAAPhdkRZucLlc+uCDD7Rw4UK1atVK4eHhXtsnTpzok+IAAAAAoKSdU0jasWOH6tSpo02bNumyyy6TJG3dutWrj81m8111AAAAAFDCzikkNWjQQHv37tWSJUskSXfccYfefPNNVatWrViKAwAAAICSdk7fSTLGeL2eO3euMjMzfVoQAAAAAPhTkRZuyHNqaAIAAACAsu6cQpLNZsv3nSO+gwQAAACgPDmn7yQZYzRgwAA5HA5J0vHjx/Xggw/mW93u888/912FAAAAAFCCzikkJSYmer2+6667fFoMAAAAAPjbOYWkadOmFVcdAAAAAFAqnNfCDQAAAABQ3hCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAItz+mWyuLDl5GQrLS2t2MaPjIxUlSpVim18AAAAoDAISSiUEycOKi1thx59dLwcDkexHCMmxqGPPppCUAIAAIBfEZJQKLm5GXK5ghUc/Jiioxv6fPysrN06ePA1OZ1OQhIAAAD8ipCEcxISUlPh4fWKZezs7GIZFgAAADgnLNwAAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAACLMhWSxo8fL5vNpmHDhvm7FAAAAADlVJkJSatXr9Z7772nSy65xN+lAAAAACjHykRIysjIUL9+/fTvf/9bFStW9Hc5AAAAAMoxu78LKIzBgwerW7du6ty5s1588cUz9s3OzlZ2drbntdPplCS5XC65XK5irfNs3G637PZA2e1uBQb6vha73Sg4OKhYxi/OsU+Of/LauN1uv88TAAAAyqfCfs4s9SFp9uzZWrt2rVavXl2o/uPGjVNSUlK+9uTkZIWHh/u6vHOSlZWlO+9MkN2epsDAAz4fPycnS126JKpChX0KCsooM2NLUm5ullyuBKWlpenAAd9fGwAAACAzM7NQ/Up1SNq9e7eGDh2qBQsWKCQkpFD7PPXUUxo+fLjntdPpVFxcnFq3bq3IyMjiKrVQdu7cqaefnqzo6M4KC4v3+fgHD/6ojRtnqHnzmYqJaVpmxpakY8d26siRyZo1q7Pi431/bQAAAIC8p8zOplSHpDVr1ujAgQO67LLLPG25ublaunSpJk+erOzsbAUGBnrt43A45HA48o1lt9tlt/v3dAMCAuRy5crlClBuru9rcblsOnEip1jGL86xT45/8toEBAT4fZ4AAABQPhX2c2ap/jTaqVMnbdy40att4MCBaty4sUaNGpUvIAEAAADA+SrVISkiIkLNmjXzagsPD1dMTEy+dgAAAADwhTKxBDgAAAAAlJRSfSepID/88IO/SwAAAABQjnEnCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWpTokjRs3TpdffrkiIiJUtWpV9erVS6mpqf4uCwAAAEA5VqpD0o8//qjBgwfrl19+0YIFC5STk6MbbrhBmZmZ/i4NAAAAQDll93cBZzJv3jyv19OnT1fVqlW1Zs0aXXPNNX6qCgAAAEB5VqpD0qnS09MlSZUqVTptn+zsbGVnZ3teO51OSZLL5ZLL5SreAs/C7XbLbg+U3e5WYKDva7HbjYKDg4pl/OIc++T4bkm5+uOPP+R2u30+fkREhCpXruzzcfP8888/Onr0aLGMXdy1AwAAXCgKmwdsxhhTzLX4hNvtVo8ePXTkyBEtW7bstP1Gjx6tpKSkfO3ff/+9wsPDi7PEs8rKytKmTdtktzdQYGCoz8fPyTmsjIytqlChqYKCKpSZsSXJ5XIqI2OLQkJCZLP5/inQoKAANWnSUMHBwT4f+8SJE9qyZatycnwf7qTirR0AAOBCkpmZqYSEBKWnpysyMvK0/cpMSHrooYc0d+5cLVu2TDVr1jxtv4LuJMXFxengwYNnvBAlYefOnerXb6SioycoLCze5+MfPPijNm4crubNZyompmmZGds6fsOG4xQd3cCnY2dl/ans7Dc0a9YExcf7/rrnzavDMVShoaf/s1kUxV07AADAhcTpdComJuasIalMPG73yCOP6JtvvtHSpUvPGJAkyeFwyOFw5Gu32+2y2/17ugEBAXK5cuVyBSg31/e1uFw2nTiRUyzjF+fY1vEDA+PkcPg2JLlcAcrMzFVAQECx/BnIm9cKFWrJ4ajn07GLu3YAAIALSWE/T5XqT13GGD366KP64osv9MMPP/Av6QAAAACKXakOSYMHD9ZHH32kr776ShEREdq3b58kKSoqSqGhvv9ODwAAAACU6t+TNGXKFKWnp+vaa69VjRo1PD+ffPKJv0sDAAAAUE6V6jtJZWRNCQAAAADlSKm+kwQAAAAAJY2QBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALOz+LgAoCTk52UpLSyuWsdPS0uRyuYplbKl4a5ekyMhIValSpdjGLy5///23nE5nsY1fVq8LgLKjON/Hivs9rCzXjoLx96o3QhLKvRMnDiotbYcefXS8HA6Hz8fPzs7U7t37FRWV7fOxi7t2SYqJceijj6aUqTeuv//+W3fe+ZAOHvT9Nc9TFq8LgLKjuN/HivM9rCzXjoLx92p+hCSUe7m5GXK5ghUc/Jiioxv6fPzDh3+RyzVWLleuz8cu7tqzsnbr4MHX5HQ6y8ybliQ5nU4dPJgth2OEQkPjfD5+Wb0uAMqO4nwfK+73sLJcOwrG36v5EZJwwQgJqanw8Ho+Hzcrq/gehctTXLVLUnbx/aNRsQsNjeO6ACjTiut9rCTew8py7SgYf6/+HxZuAAAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAAAAAFgQkgAAAADAgpAEAAAAABaEJAAAAACwICQBAAAAgAUhCQAAAAAsCEkAAAAAYEFIAgAAAAALQhIAAAAAWBCSAAAAAMCCkAQAAAAAFoQkAAAAALAgJAEAAACARZkISW+//bbq1KmjkJAQXXHFFVq1apW/SwIAAABQTpX6kPTJJ59o+PDhev7557V27Vq1aNFCCQkJOnDggL9LAwAAAFAOlfqQNHHiRN13330aOHCgmjRponfffVdhYWH64IMP/F0aAAAAgHLI7u8CzuTEiRNas2aNnnrqKU9bQECAOnfurBUrVhS4T3Z2trKzsz2v09PTJUmHDh2Sy+Uq3oLPwul0ymZzKyvrV0lOn49/4sTvCgoK0IkTqcrM9O25FufYxT0+tZ9eVtZfcruztXnzZjmdvv8zWVx2794ttzun2P6/VFavC4Cyozjfx4r7Paws146ClcTfqzabW06nU4cOHfL5+Oci78+VMeaM/WzmbD38aM+ePbrooou0fPlytW3b1tP+xBNP6Mcff9TKlSvz7TN69GglJSWVZJkAAAAAypDdu3erZs2ap91equ8kFcVTTz2l4cOHe1673W4dOnRIMTExstls5zSW0+lUXFycdu/ercjISF+XihLEXJYvzGf5wVyWL8xn+cFcli/M5/8xxujo0aOKjY09Y79SHZIqV66swMBA7d+/36t9//79ql69eoH7OBwOORwOr7bo6OjzqiMyMvKC/wNVXjCX5QvzWX4wl+UL81l+MJflC/N5UlRU1Fn7lOqFG4KDg9WqVSstWrTI0+Z2u7Vo0SKvx+8AAAAAwFdK9Z0kSRo+fLgSExPVunVrtWnTRpMmTVJmZqYGDhzo79IAAAAAlEOlPiTdcccd+vvvv/Xcc89p3759atmypebNm6dq1aoV+7EdDoeef/75fI/voexhLssX5rP8YC7LF+az/GAuyxfm89yV6tXtAAAAAKCklervJAEAAABASSMkAQAAAIAFIQkAAAAALAhJAAAAAGBBSDqNt99+W3Xq1FFISIiuuOIKrVq1yt8lXfDGjRunyy+/XBEREapatap69eql1NRUrz7Hjx/X4MGDFRMTowoVKuiWW27J98uId+3apW7duiksLExVq1bVyJEj5XK5vPr88MMPuuyyy+RwOFS/fn1Nnz69uE/vgjZ+/HjZbDYNGzbM08Zcli1//fWX7rrrLsXExCg0NFTNmzdXcnKyZ7sxRs8995xq1Kih0NBQde7cWdu2bfMa49ChQ+rXr58iIyMVHR2tQYMGKSMjw6vPhg0bdPXVVyskJERxcXF65ZVXSuT8LhS5ubl69tlnFR8fr9DQUNWrV09jxoyRdY0n5rL0Wrp0qbp3767Y2FjZbDZ9+eWXXttLcu7mzJmjxo0bKyQkRM2bN9d3333n8/Mt7840nzk5ORo1apSaN2+u8PBwxcbGqn///tqzZ4/XGMzneTDIZ/bs2SY4ONh88MEHZvPmzea+++4z0dHRZv/+/f4u7YKWkJBgpk2bZjZt2mRSUlJM165dTa1atUxGRoanz4MPPmji4uLMokWLTHJysrnyyivNVVdd5dnucrlMs2bNTOfOnc26devMd999ZypXrmyeeuopT58dO3aYsLAwM3z4cLNlyxbz1ltvmcDAQDNv3rwSPd8LxapVq0ydOnXMJZdcYoYOHeppZy7LjkOHDpnatWubAQMGmJUrV5odO3aY77//3mzfvt3TZ/z48SYqKsp8+eWXZv369aZHjx4mPj7eZGVlefrceOONpkWLFuaXX34xP/30k6lfv77p27evZ3t6erqpVq2a6devn9m0aZP5+OOPTWhoqHnvvfdK9HzLs7Fjx5qYmBjzzTffmJ07d5o5c+aYChUqmDfeeMPTh7ksvb777jvzzDPPmM8//9xIMl988YXX9pKau59//tkEBgaaV155xWzZssX861//MkFBQWbjxo3Ffg3KkzPN55EjR0znzp3NJ598Yn777TezYsUK06ZNG9OqVSuvMZjPoiMkFaBNmzZm8ODBnte5ubkmNjbWjBs3zo9V4VQHDhwwksyPP/5ojDn5hhEUFGTmzJnj6fPrr78aSWbFihXGmJNvOAEBAWbfvn2ePlOmTDGRkZEmOzvbGGPME088YZo2bep1rDvuuMMkJCQU9yldcI4ePWoaNGhgFixYYDp06OAJScxl2TJq1CjTvn370253u92mevXqZsKECZ62I0eOGIfDYT7++GNjjDFbtmwxkszq1as9febOnWtsNpv566+/jDHGvPPOO6ZixYqe+c07dqNGjXx9Shesbt26mXvuucer7eabbzb9+vUzxjCXZcmpH6pLcu5uv/12061bN696rrjiCvPAAw/49BwvJAWF3lOtWrXKSDJpaWnGGObzfPG43SlOnDihNWvWqHPnzp62gIAAde7cWStWrPBjZThVenq6JKlSpUqSpDVr1ignJ8dr7ho3bqxatWp55m7FihVq3ry51y8jTkhIkNPp1ObNmz19rGPk9WH+fW/w4MHq1q1bvuvNXJYtX3/9tVq3bq3bbrtNVatW1aWXXqp///vfnu07d+7Uvn37vOYiKipKV1xxhdd8RkdHq3Xr1p4+nTt3VkBAgFauXOnpc8011yg4ONjTJyEhQampqTp8+HBxn+YF4aqrrtKiRYu0detWSdL69eu1bNkydenSRRJzWZaV5Nzx3usf6enpstlsio6OlsR8ni9C0in++ecf5ebmen3wkqRq1app3759fqoKp3K73Ro2bJjatWunZs2aSZL27dun4OBgz5tDHuvc7du3r8C5zdt2pj5Op1NZWVnFcToXpNmzZ2vt2rUaN25cvm3MZdmyY8cOTZkyRQ0aNND333+vhx56SEOGDNGMGTMk/d98nOl9dd++fapatarXdrvdrkqVKp3TnOP8PPnkk+rTp48aN26soKAgXXrppRo2bJj69esnibksy0py7k7Xh7ktPsePH9eoUaPUt29fRUZGSmI+z5fd3wUARTF48GBt2rRJy5Yt83cpKILdu3dr6NChWrBggUJCQvxdDs6T2+1W69at9dJLL0mSLr30Um3atEnvvvuuEhMT/VwdzsWnn36qWbNm6aOPPlLTpk2VkpKiYcOGKTY2lrkESqmcnBzdfvvtMsZoypQp/i6n3OBO0ikqV66swMDAfKto7d+/X9WrV/dTVbB65JFH9M0332jJkiWqWbOmp7169eo6ceKEjhw54tXfOnfVq1cvcG7ztp2pT2RkpEJDQ319OhekNWvW6MCBA7rssstkt9tlt9v1448/6s0335Tdble1atWYyzKkRo0aatKkiVfbxRdfrF27dkn6v/k40/tq9erVdeDAAa/tLpdLhw4dOqc5x/kZOXKk525S8+bNdffdd+uxxx7z3PFlLsuukpy70/Vhbn0vLyClpaVpwYIFnrtIEvN5vghJpwgODlarVq20aNEiT5vb7daiRYvUtm1bP1YGY4weeeQRffHFF1q8eLHi4+O9trdq1UpBQUFec5eamqpdu3Z55q5t27bauHGj15tG3ptK3oe8tm3beo2R14f5951OnTpp48aNSklJ8fy0bt1a/fr18/w3c1l2tGvXLt9y/Fu3blXt2rUlSfHx8apevbrXXDidTq1cudJrPo8cOaI1a9Z4+ixevFhut1tXXHGFp8/SpUuVk5Pj6bNgwQI1atRIFStWLLbzu5AcO3ZMAQHeHw0CAwPldrslMZdlWUnOHe+9JSMvIG3btk0LFy5UTEyM13bm8zz5e+WI0mj27NnG4XCY6dOnmy1btpj777/fREdHe62ihZL30EMPmaioKPPDDz+YvXv3en6OHTvm6fPggw+aWrVqmcWLF5vk5GTTtm1b07ZtW8/2vGWjb7jhBpOSkmLmzZtnqlSpUuCy0SNHjjS//vqrefvtt1k2ugRYV7czhrksS1atWmXsdrsZO3as2bZtm5k1a5YJCwszH374oafP+PHjTXR0tPnqq6/Mhg0bTM+ePQtcevjSSy81K1euNMuWLTMNGjTwWqr2yJEjplq1aubuu+82mzZtMrNnzzZhYWEsG+1DiYmJ5qKLLvIsAf7555+bypUrmyeeeMLTh7ksvY4ePWrWrVtn1q1bZySZiRMnmnXr1nlWOyupufv555+N3W43r776qvn111/N888/f0EsGe1rZ5rPEydOmB49epiaNWualJQUr89F1pXqmM+iIySdxltvvWVq1aplgoODTZs2bcwvv/zi75IueJIK/Jk2bZqnT1ZWlnn44YdNxYoVTVhYmOndu7fZu3ev1zh//PGH6dKliwkNDTWVK1c2I0aMMDk5OV59lixZYlq2bGmCg4NN3bp1vY6B4nFqSGIuy5b/9//+n2nWrJlxOBymcePGZurUqV7b3W63efbZZ021atWMw+EwnTp1MqmpqV59Dh48aPr27WsqVKhgIiMjzcCBA83Ro0e9+qxfv960b9/eOBwOc9FFF5nx48cX+7ldSJxOpxk6dKipVauWCQkJMXXr1jXPPPOM14cu5rL0WrJkSYF/TyYmJhpjSnbuPv30U9OwYUMTHBxsmjZtar799ttiO+/y6kzzuXPnztN+LlqyZIlnDOaz6GzGWH6NNgAAAABc4PhOEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALAhJAAAAAGBBSAIAAAAAC0ISAMBv/vjjD9lsNqWkpPi7FAAAPAhJAIDzYrPZzvgzevRof5dYoO3bt2vgwIGqWbOmHA6H4uPj1bdvXyUnJ5doHQRFACh97P4uAABQtu3du9fz35988omee+45paametoqVKjgj7LOKDk5WZ06dVKzZs303nvvqXHjxjp69Ki++uorjRgxQj/++KO/SwQA+BF3kgAA56V69eqen6ioKNlsNs/rqlWrauLEiZ67NS1bttS8efNOO1Zubq7uueceNW7cWLt27ZIkffXVV7rssssUEhKiunXrKikpSS6Xy7OPzWbT+++/r969eyssLEwNGjTQ119/fdpjGGM0YMAANWjQQD/99JO6deumevXqqWXLlnr++ef11Vdfefpu3LhR1113nUJDQxUTE6P7779fGRkZnu3XXnuthg0b5jV+r169NGDAAM/rOnXq6KWXXtI999yjiIgI1apVS1OnTvVsj4+PlyRdeumlstlsuvbaa894vQEAxY+QBAAoNm+88YZee+01vfrqq9qwYYMSEhLUo0cPbdu2LV/f7Oxs3XbbbUpJSdFPP/2kWrVq6aefflL//v01dOhQbdmyRe+9956mT5+usWPHeu2blJSk22+/XRs2bFDXrl3Vr18/HTp0qMCaUlJStHnzZo0YMUIBAfn/GoyOjpYkZWZmKiEhQRUrVtTq1as1Z84cLVy4UI888sg5X4fXXntNrVu31rp16/Twww/roYce8txtW7VqlSRp4cKF2rt3rz7//PNzHh8A4FuEJABAsXn11Vc1atQo9enTR40aNdLLL7+sli1batKkSV79MjIy1K1bN/39999asmSJqlSpIulk+HnyySeVmJiounXr6vrrr9eYMWP03nvvee0/YMAA9e3bV/Xr19dLL72kjIwMT/g4VV5Aa9y48Rlr/+ijj3T8+HHNnDlTzZo103XXXafJkyfrv//9r/bv339O16Fr1656+OGHVb9+fY0aNUqVK1fWkiVLJMlzrjExMapevboqVap0TmMDAHyP7yQBAIqF0+nUnj171K5dO6/2du3aaf369V5tffv2Vc2aNbV48WKFhoZ62tevX6+ff/7Z685Rbm6ujh8/rmPHjiksLEySdMkll3i2h4eHKzIyUgcOHCiwLmNMoer/9ddf1aJFC4WHh3vV7na7lZqaqmrVqhVqnFPry3sc8XT1AQD8jztJAAC/69q1qzZs2KAVK1Z4tWdkZCgpKUkpKSmen40bN2rbtm0KCQnx9AsKCvLaz2azye12F3ishg0bSpJ+++238647ICAgX+jKycnJ1+9c6gMA+B8hCQBQLCIjIxUbG6uff/7Zq/3nn39WkyZNvNoeeughjR8/Xj169PBaWe6yyy5Tamqq6tevn++noO8TFUbLli3VpEkTvfbaawUGlSNHjkiSLr74Yq1fv16ZmZletQcEBKhRo0aSTj4qZ13dLzc3V5s2bTqneoKDgz37AgBKB0ISAKDYjBw5Ui+//LI++eQTpaam6sknn1RKSoqGDh2ar++jjz6qF198UTfddJOWLVsmSXruuec0c+ZMJSUlafPmzfr11181e/Zs/etf/ypyTTabTdOmTdPWrVt19dVX67vvvtOOHTu0YcMGjR07Vj179pQk9evXTyEhIUpMTNSmTZu0ZMkSPfroo7r77rs9j9pdd911+vbbb/Xtt9/qt99+00MPPeQJWYVVtWpVhYaGat68edq/f7/S09OLfG4AAN8gJAEAis2QIUM0fPhwjRgxQs2bN9e8efP09ddfq0GDBgX2HzZsmJKSktS1a1ctX75cCQkJ+uabbzR//nxdfvnluvLKK/X666+rdu3a51VXmzZtlJycrPr16+u+++7TxRdfrB49emjz5s2eRSXCwsL0/fff69ChQ7r88st16623qlOnTpo8ebJnnHvuuUeJiYnq37+/OnTooLp166pjx47nVIvdbtebb76p9957T7GxsZ6QBgDwH5sp7DdYAQAAAOACwJ0kAAAAALAgJAEAAACABSEJAAAAACwISQAAAABgQUgCAAAAAAtCEgAAAABYEJIAAAAAwIKQBAAAAAAWhCQAAAAAsCAkAQAAAIAFIQkAAAAALP4/OQKbK0TH3PgAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import tiktoken\n",
"from bs4 import BeautifulSoup as Soup\n",
"from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader\n",
"\n",
"\n",
"def num_tokens_from_string(string: str, encoding_name: str) -> int:\n",
" \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
" encoding = tiktoken.get_encoding(encoding_name)\n",
" num_tokens = len(encoding.encode(string))\n",
" return num_tokens\n",
"\n",
"\n",
"# LCEL docs\n",
"url = \"https://python.langchain.com/docs/expression_language/\"\n",
"loader = RecursiveUrlLoader(\n",
" url=url, max_depth=20, extractor=lambda x: Soup(x, \"html.parser\").text\n",
")\n",
"docs = loader.load()\n",
"\n",
"# LCEL w/ PydanticOutputParser (outside the primary LCEL docs)\n",
"url = \"https://python.langchain.com/docs/modules/model_io/output_parsers/quick_start\"\n",
"loader = RecursiveUrlLoader(\n",
" url=url, max_depth=1, extractor=lambda x: Soup(x, \"html.parser\").text\n",
")\n",
"docs_pydantic = loader.load()\n",
"\n",
"# LCEL w/ Self Query (outside the primary LCEL docs)\n",
"url = \"https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/\"\n",
"loader = RecursiveUrlLoader(\n",
" url=url, max_depth=1, extractor=lambda x: Soup(x, \"html.parser\").text\n",
")\n",
"docs_sq = loader.load()\n",
"\n",
"# Doc texts\n",
"docs.extend([*docs_pydantic, *docs_sq])\n",
"docs_texts = [d.page_content for d in docs]\n",
"\n",
"# Calculate the number of tokens for each document\n",
"counts = [num_tokens_from_string(d, \"cl100k_base\") for d in docs_texts]\n",
"\n",
"# Plotting the histogram of token counts\n",
"plt.figure(figsize=(10, 6))\n",
"plt.hist(counts, bins=30, color=\"blue\", edgecolor=\"black\", alpha=0.7)\n",
"plt.title(\"Histogram of Token Counts\")\n",
"plt.xlabel(\"Token Count\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.grid(axis=\"y\", alpha=0.75)\n",
"\n",
"# Display the histogram\n",
"plt.show"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "70750603-ec82-4439-9b32-d22014b5ff2c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num tokens in all context: 68705\n"
]
}
],
"source": [
"# Doc texts concat\n",
"d_sorted = sorted(docs, key=lambda x: x.metadata[\"source\"])\n",
"d_reversed = list(reversed(d_sorted))\n",
"concatenated_content = \"\\n\\n\\n --- \\n\\n\\n\".join(\n",
" [doc.page_content for doc in d_reversed]\n",
")\n",
"print(\n",
" \"Num tokens in all context: %s\"\n",
" % num_tokens_from_string(concatenated_content, \"cl100k_base\")\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 155,
"id": "25ca3cf2-0f6b-40f9-a2ff-285a8dcb33dc",
"metadata": {},
"outputs": [],
"source": [
"# Doc texts split\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"chunk_size_tok = 2000\n",
"text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(\n",
" chunk_size=chunk_size_tok, chunk_overlap=0\n",
")\n",
"texts_split = text_splitter.split_text(concatenated_content)"
]
},
{
"cell_type": "markdown",
"id": "797a5469-0942-45a5-adb6-f12e05d76798",
"metadata": {},
"source": [
"## Models\n",
"\n",
"We can test various models, including the new [Claude3](https://www.anthropic.com/news/claude-3-family) family.\n",
"\n",
"Be sure to set the relevant API keys:\n",
"\n",
"* `ANTHROPIC_API_KEY`\n",
"* `OPENAI_API_KEY`"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "033e71d3-5dc8-42a3-a0b7-4df116048c14",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"embd = OpenAIEmbeddings()\n",
"\n",
"# from langchain_openai import ChatOpenAI\n",
"\n",
"# model = ChatOpenAI(temperature=0, model=\"gpt-4-1106-preview\")\n",
"\n",
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"model = ChatAnthropic(temperature=0, model=\"claude-3-opus-20240229\")"
]
},
{
"cell_type": "markdown",
"id": "5c63db01-cf95-4c17-ae5d-8dc7267ad58a",
"metadata": {},
"source": [
"### Tree Constrution\n",
"\n",
"The clustering approach in tree construction includes a few interesting ideas.\n",
"\n",
"**GMM (Gaussian Mixture Model)** \n",
"\n",
"- Model the distribution of data points across different clusters\n",
"- Optimal number of clusters by evaluating the model's Bayesian Information Criterion (BIC)\n",
"\n",
"**UMAP (Uniform Manifold Approximation and Projection)** \n",
"\n",
"- Supports clustering\n",
"- Reduces the dimensionality of high-dimensional data\n",
"- UMAP helps to highlight the natural grouping of data points based on their similarities\n",
"\n",
"**Local and Global Clustering** \n",
"\n",
"- Used to analyze data at different scales\n",
"- Both fine-grained and broader patterns within the data are captured effectively\n",
"\n",
"**Thresholding** \n",
"\n",
"- Apply in the context of GMM to determine cluster membership\n",
"- Based on the probability distribution (assignment of data points to ≥ 1 cluster)\n",
"---\n",
"\n",
"Code for GMM and thresholding is from Sarthi et al, as noted in the below two sources:\n",
" \n",
"* [Origional repo](https://github.com/parthsarthi03/raptor/blob/master/raptor/cluster_tree_builder.py)\n",
"* [Minor tweaks](https://github.com/run-llama/llama_index/blob/main/llama-index-packs/llama-index-packs-raptor/llama_index/packs/raptor/clustering.py)\n",
"\n",
"Full credit to both authors."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a849980c-27d4-48e0-87a0-c2a5143cb8c0",
"metadata": {},
"outputs": [],
"source": [
"from typing import Dict, List, Optional, Tuple\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import umap\n",
"from langchain.prompts import ChatPromptTemplate\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from sklearn.mixture import GaussianMixture\n",
"\n",
"RANDOM_SEED = 224 # Fixed seed for reproducibility\n",
"\n",
"### --- Code from citations referenced above (added comments and docstrings) --- ###\n",
"\n",
"\n",
"def global_cluster_embeddings(\n",
" embeddings: np.ndarray,\n",
" dim: int,\n",
" n_neighbors: Optional[int] = None,\n",
" metric: str = \"cosine\",\n",
") -> np.ndarray:\n",
" \"\"\"\n",
" Perform global dimensionality reduction on the embeddings using UMAP.\n",
"\n",
" Parameters:\n",
" - embeddings: The input embeddings as a numpy array.\n",
" - dim: The target dimensionality for the reduced space.\n",
" - n_neighbors: Optional; the number of neighbors to consider for each point.\n",
" If not provided, it defaults to the square root of the number of embeddings.\n",
" - metric: The distance metric to use for UMAP.\n",
"\n",
" Returns:\n",
" - A numpy array of the embeddings reduced to the specified dimensionality.\n",
" \"\"\"\n",
" if n_neighbors is None:\n",
" n_neighbors = int((len(embeddings) - 1) ** 0.5)\n",
" return umap.UMAP(\n",
" n_neighbors=n_neighbors, n_components=dim, metric=metric\n",
" ).fit_transform(embeddings)\n",
"\n",
"\n",
"def local_cluster_embeddings(\n",
" embeddings: np.ndarray, dim: int, num_neighbors: int = 10, metric: str = \"cosine\"\n",
") -> np.ndarray:\n",
" \"\"\"\n",
" Perform local dimensionality reduction on the embeddings using UMAP, typically after global clustering.\n",
"\n",
" Parameters:\n",
" - embeddings: The input embeddings as a numpy array.\n",
" - dim: The target dimensionality for the reduced space.\n",
" - num_neighbors: The number of neighbors to consider for each point.\n",
" - metric: The distance metric to use for UMAP.\n",
"\n",
" Returns:\n",
" - A numpy array of the embeddings reduced to the specified dimensionality.\n",
" \"\"\"\n",
" return umap.UMAP(\n",
" n_neighbors=num_neighbors, n_components=dim, metric=metric\n",
" ).fit_transform(embeddings)\n",
"\n",
"\n",
"def get_optimal_clusters(\n",
" embeddings: np.ndarray, max_clusters: int = 50, random_state: int = RANDOM_SEED\n",
") -> int:\n",
" \"\"\"\n",
" Determine the optimal number of clusters using the Bayesian Information Criterion (BIC) with a Gaussian Mixture Model.\n",
"\n",
" Parameters:\n",
" - embeddings: The input embeddings as a numpy array.\n",
" - max_clusters: The maximum number of clusters to consider.\n",
" - random_state: Seed for reproducibility.\n",
"\n",
" Returns:\n",
" - An integer representing the optimal number of clusters found.\n",
" \"\"\"\n",
" max_clusters = min(max_clusters, len(embeddings))\n",
" n_clusters = np.arange(1, max_clusters)\n",
" bics = []\n",
" for n in n_clusters:\n",
" gm = GaussianMixture(n_components=n, random_state=random_state)\n",
" gm.fit(embeddings)\n",
" bics.append(gm.bic(embeddings))\n",
" return n_clusters[np.argmin(bics)]\n",
"\n",
"\n",
"def GMM_cluster(embeddings: np.ndarray, threshold: float, random_state: int = 0):\n",
" \"\"\"\n",
" Cluster embeddings using a Gaussian Mixture Model (GMM) based on a probability threshold.\n",
"\n",
" Parameters:\n",
" - embeddings: The input embeddings as a numpy array.\n",
" - threshold: The probability threshold for assigning an embedding to a cluster.\n",
" - random_state: Seed for reproducibility.\n",
"\n",
" Returns:\n",
" - A tuple containing the cluster labels and the number of clusters determined.\n",
" \"\"\"\n",
" n_clusters = get_optimal_clusters(embeddings)\n",
" gm = GaussianMixture(n_components=n_clusters, random_state=random_state)\n",
" gm.fit(embeddings)\n",
" probs = gm.predict_proba(embeddings)\n",
" labels = [np.where(prob > threshold)[0] for prob in probs]\n",
" return labels, n_clusters\n",
"\n",
"\n",
"def perform_clustering(\n",
" embeddings: np.ndarray,\n",
" dim: int,\n",
" threshold: float,\n",
") -> List[np.ndarray]:\n",
" \"\"\"\n",
" Perform clustering on the embeddings by first reducing their dimensionality globally, then clustering\n",
" using a Gaussian Mixture Model, and finally performing local clustering within each global cluster.\n",
"\n",
" Parameters:\n",
" - embeddings: The input embeddings as a numpy array.\n",
" - dim: The target dimensionality for UMAP reduction.\n",
" - threshold: The probability threshold for assigning an embedding to a cluster in GMM.\n",
"\n",
" Returns:\n",
" - A list of numpy arrays, where each array contains the cluster IDs for each embedding.\n",
" \"\"\"\n",
" if len(embeddings) <= dim + 1:\n",
" # Avoid clustering when there's insufficient data\n",
" return [np.array([0]) for _ in range(len(embeddings))]\n",
"\n",
" # Global dimensionality reduction\n",
" reduced_embeddings_global = global_cluster_embeddings(embeddings, dim)\n",
" # Global clustering\n",
" global_clusters, n_global_clusters = GMM_cluster(\n",
" reduced_embeddings_global, threshold\n",
" )\n",
"\n",
" all_local_clusters = [np.array([]) for _ in range(len(embeddings))]\n",
" total_clusters = 0\n",
"\n",
" # Iterate through each global cluster to perform local clustering\n",
" for i in range(n_global_clusters):\n",
" # Extract embeddings belonging to the current global cluster\n",
" global_cluster_embeddings_ = embeddings[\n",
" np.array([i in gc for gc in global_clusters])\n",
" ]\n",
"\n",
" if len(global_cluster_embeddings_) == 0:\n",
" continue\n",
" if len(global_cluster_embeddings_) <= dim + 1:\n",
" # Handle small clusters with direct assignment\n",
" local_clusters = [np.array([0]) for _ in global_cluster_embeddings_]\n",
" n_local_clusters = 1\n",
" else:\n",
" # Local dimensionality reduction and clustering\n",
" reduced_embeddings_local = local_cluster_embeddings(\n",
" global_cluster_embeddings_, dim\n",
" )\n",
" local_clusters, n_local_clusters = GMM_cluster(\n",
" reduced_embeddings_local, threshold\n",
" )\n",
"\n",
" # Assign local cluster IDs, adjusting for total clusters already processed\n",
" for j in range(n_local_clusters):\n",
" local_cluster_embeddings_ = global_cluster_embeddings_[\n",
" np.array([j in lc for lc in local_clusters])\n",
" ]\n",
" indices = np.where(\n",
" (embeddings == local_cluster_embeddings_[:, None]).all(-1)\n",
" )[1]\n",
" for idx in indices:\n",
" all_local_clusters[idx] = np.append(\n",
" all_local_clusters[idx], j + total_clusters\n",
" )\n",
"\n",
" total_clusters += n_local_clusters\n",
"\n",
" return all_local_clusters\n",
"\n",
"\n",
"### --- Our code below --- ###\n",
"\n",
"\n",
"def embed(texts):\n",
" \"\"\"\n",
" Generate embeddings for a list of text documents.\n",
"\n",
" This function assumes the existence of an `embd` object with a method `embed_documents`\n",
" that takes a list of texts and returns their embeddings.\n",
"\n",
" Parameters:\n",
" - texts: List[str], a list of text documents to be embedded.\n",
"\n",
" Returns:\n",
" - numpy.ndarray: An array of embeddings for the given text documents.\n",
" \"\"\"\n",
" text_embeddings = embd.embed_documents(texts)\n",
" text_embeddings_np = np.array(text_embeddings)\n",
" return text_embeddings_np\n",
"\n",
"\n",
"def embed_cluster_texts(texts):\n",
" \"\"\"\n",
" Embeds a list of texts and clusters them, returning a DataFrame with texts, their embeddings, and cluster labels.\n",
"\n",
" This function combines embedding generation and clustering into a single step. It assumes the existence\n",
" of a previously defined `perform_clustering` function that performs clustering on the embeddings.\n",
"\n",
" Parameters:\n",
" - texts: List[str], a list of text documents to be processed.\n",
"\n",
" Returns:\n",
" - pandas.DataFrame: A DataFrame containing the original texts, their embeddings, and the assigned cluster labels.\n",
" \"\"\"\n",
" text_embeddings_np = embed(texts) # Generate embeddings\n",
" cluster_labels = perform_clustering(\n",
" text_embeddings_np, 10, 0.1\n",
" ) # Perform clustering on the embeddings\n",
" df = pd.DataFrame() # Initialize a DataFrame to store the results\n",
" df[\"text\"] = texts # Store original texts\n",
" df[\"embd\"] = list(text_embeddings_np) # Store embeddings as a list in the DataFrame\n",
" df[\"cluster\"] = cluster_labels # Store cluster labels\n",
" return df\n",
"\n",
"\n",
"def fmt_txt(df: pd.DataFrame) -> str:\n",
" \"\"\"\n",
" Formats the text documents in a DataFrame into a single string.\n",
"\n",
" Parameters:\n",
" - df: DataFrame containing the 'text' column with text documents to format.\n",
"\n",
" Returns:\n",
" - A single string where all text documents are joined by a specific delimiter.\n",
" \"\"\"\n",
" unique_txt = df[\"text\"].tolist()\n",
" return \"--- --- \\n --- --- \".join(unique_txt)\n",
"\n",
"\n",
"def embed_cluster_summarize_texts(\n",
" texts: List[str], level: int\n",
") -> Tuple[pd.DataFrame, pd.DataFrame]:\n",
" \"\"\"\n",
" Embeds, clusters, and summarizes a list of texts. This function first generates embeddings for the texts,\n",
" clusters them based on similarity, expands the cluster assignments for easier processing, and then summarizes\n",
" the content within each cluster.\n",
"\n",
" Parameters:\n",
" - texts: A list of text documents to be processed.\n",
" - level: An integer parameter that could define the depth or detail of processing.\n",
"\n",
" Returns:\n",
" - Tuple containing two DataFrames:\n",
" 1. The first DataFrame (`df_clusters`) includes the original texts, their embeddings, and cluster assignments.\n",
" 2. The second DataFrame (`df_summary`) contains summaries for each cluster, the specified level of detail,\n",
" and the cluster identifiers.\n",
" \"\"\"\n",
"\n",
" # Embed and cluster the texts, resulting in a DataFrame with 'text', 'embd', and 'cluster' columns\n",
" df_clusters = embed_cluster_texts(texts)\n",
"\n",
" # Prepare to expand the DataFrame for easier manipulation of clusters\n",
" expanded_list = []\n",
"\n",
" # Expand DataFrame entries to document-cluster pairings for straightforward processing\n",
" for index, row in df_clusters.iterrows():\n",
" for cluster in row[\"cluster\"]:\n",
" expanded_list.append(\n",
" {\"text\": row[\"text\"], \"embd\": row[\"embd\"], \"cluster\": cluster}\n",
" )\n",
"\n",
" # Create a new DataFrame from the expanded list\n",
" expanded_df = pd.DataFrame(expanded_list)\n",
"\n",
" # Retrieve unique cluster identifiers for processing\n",
" all_clusters = expanded_df[\"cluster\"].unique()\n",
"\n",
" print(f\"--Generated {len(all_clusters)} clusters--\")\n",
"\n",
" # Summarization\n",
" template = \"\"\"Here is a sub-set of LangChain Expression Language doc. \n",
" \n",
" LangChain Expression Language provides a way to compose chain in LangChain.\n",
" \n",
" Give a detailed summary of the documentation provided.\n",
" \n",
" Documentation:\n",
" {context}\n",
" \"\"\"\n",
" prompt = ChatPromptTemplate.from_template(template)\n",
" chain = prompt | model | StrOutputParser()\n",
"\n",
" # Format text within each cluster for summarization\n",
" summaries = []\n",
" for i in all_clusters:\n",
" df_cluster = expanded_df[expanded_df[\"cluster\"] == i]\n",
" formatted_txt = fmt_txt(df_cluster)\n",
" summaries.append(chain.invoke({\"context\": formatted_txt}))\n",
"\n",
" # Create a DataFrame to store summaries with their corresponding cluster and level\n",
" df_summary = pd.DataFrame(\n",
" {\n",
" \"summaries\": summaries,\n",
" \"level\": [level] * len(summaries),\n",
" \"cluster\": list(all_clusters),\n",
" }\n",
" )\n",
"\n",
" return df_clusters, df_summary\n",
"\n",
"\n",
"def recursive_embed_cluster_summarize(\n",
" texts: List[str], level: int = 1, n_levels: int = 3\n",
") -> Dict[int, Tuple[pd.DataFrame, pd.DataFrame]]:\n",
" \"\"\"\n",
" Recursively embeds, clusters, and summarizes texts up to a specified level or until\n",
" the number of unique clusters becomes 1, storing the results at each level.\n",
"\n",
" Parameters:\n",
" - texts: List[str], texts to be processed.\n",
" - level: int, current recursion level (starts at 1).\n",
" - n_levels: int, maximum depth of recursion.\n",
"\n",
" Returns:\n",
" - Dict[int, Tuple[pd.DataFrame, pd.DataFrame]], a dictionary where keys are the recursion\n",
" levels and values are tuples containing the clusters DataFrame and summaries DataFrame at that level.\n",
" \"\"\"\n",
" results = {} # Dictionary to store results at each level\n",
"\n",
" # Perform embedding, clustering, and summarization for the current level\n",
" df_clusters, df_summary = embed_cluster_summarize_texts(texts, level)\n",
"\n",
" # Store the results of the current level\n",
" results[level] = (df_clusters, df_summary)\n",
"\n",
" # Determine if further recursion is possible and meaningful\n",
" unique_clusters = df_summary[\"cluster\"].nunique()\n",
" if level < n_levels and unique_clusters > 1:\n",
" # Use summaries as the input texts for the next level of recursion\n",
" new_texts = df_summary[\"summaries\"].tolist()\n",
" next_level_results = recursive_embed_cluster_summarize(\n",
" new_texts, level + 1, n_levels\n",
" )\n",
"\n",
" # Merge the results from the next level into the current results dictionary\n",
" results.update(next_level_results)\n",
"\n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f0d8cd3e-cd49-484d-9617-1b9811cc08b3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--Generated 7 clusters--\n",
"--Generated 1 clusters--\n"
]
}
],
"source": [
"# Build tree\n",
"leaf_texts = docs_texts\n",
"results = recursive_embed_cluster_summarize(leaf_texts, level=1, n_levels=3)"
]
},
{
"cell_type": "markdown",
"id": "e80d7098-5d16-4fa6-837c-968e5c9f118d",
"metadata": {},
"source": [
"The paper reports best performance from `collapsed tree retrieval`. \n",
"\n",
"This involves flattening the tree structure into a single layer and then applying a k-nearest neighbors (kNN) search across all nodes simultaneously. \n",
"\n",
"We do simply do this below."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d28ba9e6-9124-41a8-b4fd-55a6ef4ac062",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import Chroma\n",
"\n",
"# Initialize all_texts with leaf_texts\n",
"all_texts = leaf_texts.copy()\n",
"\n",
"# Iterate through the results to extract summaries from each level and add them to all_texts\n",
"for level in sorted(results.keys()):\n",
" # Extract summaries from the current level's DataFrame\n",
" summaries = results[level][1][\"summaries\"].tolist()\n",
" # Extend all_texts with the summaries from the current level\n",
" all_texts.extend(summaries)\n",
"\n",
"# Now, use all_texts to build the vectorstore with Chroma\n",
"vectorstore = Chroma.from_texts(texts=all_texts, embedding=embd)\n",
"retriever = vectorstore.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "0d497627-44c6-41f7-bb63-1d858d3f188f",
"metadata": {},
"source": [
"Now we can using our flattened, indexed tree in a RAG chain."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9d6c894b-b3a3-4a01-b779-3e98ea382ff5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Here is a code example of how to define a RAG (Retrieval Augmented Generation) chain in LangChain:\\n\\n```python\\nfrom langchain.vectorstores import FAISS\\nfrom langchain.embeddings import OpenAIEmbeddings\\nfrom langchain.prompts import ChatPromptTemplate\\nfrom langchain.chat_models import ChatOpenAI\\nfrom langchain.output_parsers import StrOutputParser\\n\\n# Load documents into vector store\\nvectorstore = FAISS.from_texts(\\n [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\\n)\\nretriever = vectorstore.as_retriever()\\n\\n# Define prompt template\\ntemplate = \"\"\"Answer the question based only on the following context:\\n{context}\\nQuestion: {question}\"\"\"\\nprompt = ChatPromptTemplate.from_template(template)\\n\\n# Define model and output parser\\nmodel = ChatOpenAI()\\noutput_parser = StrOutputParser()\\n\\n# Define RAG chain\\nchain = (\\n {\"context\": retriever, \"question\": RunnablePassthrough()}\\n | prompt\\n | model '"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import hub\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"# Prompt\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"\n",
"# Post-processing\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"\n",
"# Chain\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")\n",
"\n",
"# Question\n",
"rag_chain.invoke(\"How to define a RAG chain? Give me a specific code example.\")"
]
},
{
"cell_type": "markdown",
"id": "0c585b37-ad83-4069-8f5d-4a6a3e15128d",
"metadata": {},
"source": [
"Trace: \n",
"\n",
"https://smith.langchain.com/public/1dabf475-1675-4494-b16c-928fbf079851/r"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/README.md | # LangChain cookbook
Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the [main documentation](https://python.langchain.com).
Notebook | Description
:- | :-
[LLaMA2_sql_chat.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/LLaMA2_sql_chat.ipynb) | Build a chat application that interacts with a SQL database using an open source llm (llama2), specifically demonstrated on an SQLite database containing rosters.
[Semi_Structured_RAG.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_Structured_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data, including text and tables, using unstructured for parsing, multi-vector retriever for storing, and lcel for implementing chains.
[Semi_structured_and_multi_moda...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using unstructured for parsing, multi-vector retriever for storage and retrieval, and lcel for implementing chains.
[Semi_structured_multi_modal_RA...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using various tools and methods such as unstructured for parsing, multi-vector retriever for storing, lcel for implementing chains, and open source language models like llama2, llava, and gpt4all.
[amazon_personalize_how_to.ipynb](https://github.com/langchain-ai/langchain/blob/master/cookbook/amazon_personalize_how_to.ipynb) | Retrieving personalized recommendations from Amazon Personalize and use custom agents to build generative AI apps
[analyze_document.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/analyze_document.ipynb) | Analyze a single long document.
[autogpt/autogpt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/autogpt.ipynb) | Implement autogpt, a language model, with langchain primitives such as llms, prompttemplates, vectorstores, embeddings, and tools.
[autogpt/marathon_times.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/marathon_times.ipynb) | Implement autogpt for finding winning marathon times.
[baby_agi.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi.ipynb) | Implement babyagi, an ai agent that can generate and execute tasks based on a given objective, with the flexibility to swap out specific vectorstores/model providers.
[baby_agi_with_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi_with_agent.ipynb) | Swap out the execution chain in the babyagi notebook with an agent that has access to tools, aiming to obtain more reliable information.
[camel_role_playing.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/camel_role_playing.ipynb) | Implement the camel framework for creating autonomous cooperative agents in large-scale language models, using role-playing and inception prompting to guide chat agents towards task completion.
[causal_program_aided_language_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/causal_program_aided_language_model.ipynb) | Implement the causal program-aided language (cpal) chain, which improves upon the program-aided language (pal) by incorporating causal structure to prevent hallucination in language models, particularly when dealing with complex narratives and math problems with nested dependencies.
[code-analysis-deeplake.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/code-analysis-deeplake.ipynb) | Analyze its own code base with the help of gpt and activeloop's deep lake.
[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval.ipynb) | Build a custom agent that can interact with ai plugins by retrieving tools and creating natural language wrappers around openapi endpoints.
[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb) | Build a custom agent with plugin retrieval functionality, utilizing ai plugins from the `plugnplai` directory.
[databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
[deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
[elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
[extraction_openai_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/extraction_openai_tools.ipynb) | Structured Data Extraction with OpenAI Tools
[forward_looking_retrieval_augm...](https://github.com/langchain-ai/langchain/tree/master/cookbook/forward_looking_retrieval_augmented_generation.ipynb) | Implement the forward-looking active retrieval augmented generation (flare) method, which generates answers to questions, identifies uncertain tokens, generates hypothetical questions based on these tokens, and retrieves relevant documents to continue generating the answer.
[generative_agents_interactive_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb) | Implement a generative agent that simulates human behavior, based on a research paper, using a time-weighted memory object backed by a langchain retriever.
[gymnasium_agent_simulation.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/gymnasium_agent_simulation.ipynb) | Create a simple agent-environment interaction loop in simulated environments like text-based games with gymnasium.
[hugginggpt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/hugginggpt.ipynb) | Implement hugginggpt, a system that connects language models like chatgpt with the machine learning community via hugging face.
[hypothetical_document_embeddin...](https://github.com/langchain-ai/langchain/tree/master/cookbook/hypothetical_document_embeddings.ipynb) | Improve document indexing with hypothetical document embeddings (hyde), an embedding technique that generates and embeds hypothetical answers to queries.
[learned_prompt_optimization.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/learned_prompt_optimization.ipynb) | Automatically enhance language model prompts by injecting specific terms using reinforcement learning, which can be used to personalize responses based on user preferences.
[llm_bash.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_bash.ipynb) | Perform simple filesystem commands using language learning models (llms) and a bash process.
[llm_checker.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_checker.ipynb) | Create a self-checking chain using the llmcheckerchain function.
[llm_math.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_math.ipynb) | Solve complex word math problems using language models and python repls.
[llm_summarization_checker.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_summarization_checker.ipynb) | Check the accuracy of text summaries, with the option to run the checker multiple times for improved results.
[llm_symbolic_math.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_symbolic_math.ipynb) | Solve algebraic equations with the help of llms (language learning models) and sympy, a python library for symbolic mathematics.
[meta_prompt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/meta_prompt.ipynb) | Implement the meta-prompt concept, which is a method for building self-improving agents that reflect on their own performance and modify their instructions accordingly.
[multi_modal_output_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_modal_output_agent.ipynb) | Generate multi-modal outputs, specifically images and text.
[multi_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_player_dnd.ipynb) | Simulate multi-player dungeons & dragons games, with a custom function determining the speaking schedule of the agents.
[multiagent_authoritarian.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_authoritarian.ipynb) | Implement a multi-agent simulation where a privileged agent controls the conversation, including deciding who speaks and when the conversation ends, in the context of a simulated news network.
[multiagent_bidding.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_bidding.ipynb) | Implement a multi-agent simulation where agents bid to speak, with the highest bidder speaking next, demonstrated through a fictitious presidential debate example.
[myscale_vector_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/myscale_vector_sql.ipynb) | Access and interact with the myscale integrated vector database, which can enhance the performance of language model (llm) applications.
[openai_functions_retrieval_qa....](https://github.com/langchain-ai/langchain/tree/master/cookbook/openai_functions_retrieval_qa.ipynb) | Structure response output in a question-answering system by incorporating openai functions into a retrieval pipeline.
[openai_v1_cookbook.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/openai_v1_cookbook.ipynb) | Explore new functionality released alongside the V1 release of the OpenAI Python library.
[petting_zoo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/petting_zoo.ipynb) | Create multi-agent simulations with simulated environments using the petting zoo library.
[plan_and_execute_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/plan_and_execute_agent.ipynb) | Create plan-and-execute agents that accomplish objectives by planning tasks with a language model (llm) and executing them with a separate agent.
[press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
[program_aided_language_model.i...](https://github.com/langchain-ai/langchain/tree/master/cookbook/program_aided_language_model.ipynb) | Implement program-aided language models as described in the provided research paper.
[qa_citations.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/qa_citations.ipynb) | Different ways to get a model to cite its sources.
[rag_upstage_layout_analysis_groundedness_check.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_upstage_layout_analysis_groundedness_check.ipynb) | End-to-end RAG example using Upstage Layout Analysis and Groundedness Check.
[retrieval_in_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/retrieval_in_sql.ipynb) | Perform retrieval-augmented-generation (rag) on a PostgreSQL database using pgvector.
[sales_agent_with_context.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/sales_agent_with_context.ipynb) | Implement a context-aware ai sales agent, salesgpt, that can have natural sales conversations, interact with other systems, and use a product knowledge base to discuss a company's offerings.
[self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset.
[smart_llm.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/smart_llm.ipynb) | Implement a smartllmchain, a self-critique chain that generates multiple output proposals, critiques them to find the best one, and then improves upon it to produce a final output.
[tree_of_thought.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/tree_of_thought.ipynb) | Query a large language model using the tree of thought technique.
[twitter-the-algorithm-analysis...](https://github.com/langchain-ai/langchain/tree/master/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb) | Analyze the source code of the Twitter algorithm with the help of gpt4 and activeloop's deep lake.
[two_agent_debate_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_agent_debate_tools.ipynb) | Simulate multi-agent dialogues where the agents can utilize various tools.
[two_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_player_dnd.ipynb) | Simulate a two-player dungeons & dragons game, where a dialogue simulator class is used to coordinate the dialogue between the protagonist and the dungeon master.
[wikibase_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/wikibase_agent.ipynb) | Create a simple wikibase agent that utilizes sparql generation, with testing done on http://wikidata.org.
[oracleai_demo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/oracleai_demo.ipynb) | This guide outlines how to utilize Oracle AI Vector Search alongside Langchain for an end-to-end RAG pipeline, providing step-by-step examples. The process includes loading documents from various sources using OracleDocLoader, summarizing them either within or outside the database with OracleSummary, and generating embeddings similarly through OracleEmbeddings. It also covers chunking documents according to specific requirements using Advanced Oracle Capabilities from OracleTextSplitter, and finally, storing and indexing these documents in a Vector Store for querying with OracleVS. | Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb | {
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"
}
},
"cell_type": "markdown",
"id": "b6d466cc-aa8b-4baf-a80a-fef01921ca8d",
"metadata": {},
"source": [
"## Semi-structured RAG\n",
"\n",
"Many documents contain a mixture of content types, including text and tables. \n",
"\n",
"Semi-structured data can be challenging for conventional RAG for at least two reasons: \n",
"\n",
"* Text splitting may break up tables, corrupting the data in retrieval\n",
"* Embedding tables may pose challenges for semantic similarity search \n",
"\n",
"This cookbook shows how to perform RAG on documents with semi-structured data: \n",
"\n",
"* We will use [Unstructured](https://unstructured.io/) to parse both text and tables from documents (PDFs).\n",
"* We will use the [multi-vector retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) to store raw tables, text along with table summaries better suited for retrieval.\n",
"* We will use [LCEL](https://python.langchain.com/docs/expression_language/) to implement the chains used.\n",
"\n",
"The overall flow is here:\n",
"\n",
"\n",
"\n",
"## Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5740fc70-c513-4ff4-9d72-cfc098f85fef",
"metadata": {},
"outputs": [],
"source": [
"! pip install langchain unstructured[all-docs] pydantic lxml langchainhub"
]
},
{
"cell_type": "markdown",
"id": "44349a83-e1dc-4eed-ba75-587f309d8c88",
"metadata": {},
"source": [
"The PDF partitioning used by Unstructured will use: \n",
"\n",
"* `tesseract` for Optical Character Recognition (OCR)\n",
"* `poppler` for PDF rendering and processing"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7880871-4949-4ea2-aed8-540a09188a41",
"metadata": {},
"outputs": [],
"source": [
"! brew install tesseract\n",
"! brew install poppler"
]
},
{
"cell_type": "markdown",
"id": "7c24efa9-b6f6-4dc2-bfe3-70819ba3ef75",
"metadata": {},
"source": [
"## Data Loading\n",
"\n",
"### Partition PDF tables and text\n",
"\n",
"Apply to the [`LLaMA2`](https://arxiv.org/pdf/2307.09288.pdf) paper. \n",
"\n",
"We use the Unstructured [`partition_pdf`](https://unstructured-io.github.io/unstructured/core/partition.html#partition-pdf), which segments a PDF document by using a layout model. \n",
"\n",
"This layout model makes it possible to extract elements, such as tables, from pdfs. \n",
"\n",
"We also can use `Unstructured` chunking, which:\n",
"\n",
"* Tries to identify document sections (e.g., Introduction, etc)\n",
"* Then, builds text blocks that maintain sections while also honoring user-defined chunk sizes"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "62cf502b-407d-4645-a72c-24498fd55130",
"metadata": {},
"outputs": [],
"source": [
"path = \"/Users/rlm/Desktop/Papers/LLaMA2/\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3867a654-61ba-4759-9a64-de953a429ced",
"metadata": {},
"outputs": [],
"source": [
"from typing import Any\n",
"\n",
"from pydantic import BaseModel\n",
"from unstructured.partition.pdf import partition_pdf\n",
"\n",
"# Get elements\n",
"raw_pdf_elements = partition_pdf(\n",
" filename=path + \"LLaMA2.pdf\",\n",
" # Unstructured first finds embedded image blocks\n",
" extract_images_in_pdf=False,\n",
" # Use layout model (YOLOX) to get bounding boxes (for tables) and find titles\n",
" # Titles are any sub-section of the document\n",
" infer_table_structure=True,\n",
" # Post processing to aggregate text once we have the title\n",
" chunking_strategy=\"by_title\",\n",
" # Chunking params to aggregate text blocks\n",
" # Attempt to create a new chunk 3800 chars\n",
" # Attempt to keep chunks > 2000 chars\n",
" max_characters=4000,\n",
" new_after_n_chars=3800,\n",
" combine_text_under_n_chars=2000,\n",
" image_output_dir_path=path,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b09cd727-aeab-49af-8a51-0dc377321e7c",
"metadata": {},
"source": [
"We can examine the elements extracted by `partition_pdf`.\n",
"\n",
"`CompositeElement` are aggregated chunks."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "628abfc6-4057-434b-b880-d88e3ba44657",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{\"<class 'unstructured.documents.elements.CompositeElement'>\": 184,\n",
" \"<class 'unstructured.documents.elements.Table'>\": 47,\n",
" \"<class 'unstructured.documents.elements.TableChunk'>\": 2}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dictionary to store counts of each type\n",
"category_counts = {}\n",
"\n",
"for element in raw_pdf_elements:\n",
" category = str(type(element))\n",
" if category in category_counts:\n",
" category_counts[category] += 1\n",
" else:\n",
" category_counts[category] = 1\n",
"\n",
"# Unique_categories will have unique elements\n",
"unique_categories = set(category_counts.keys())\n",
"category_counts"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5462f29e-fd59-4e0e-9493-ea3b560e523e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"49\n",
"184\n"
]
}
],
"source": [
"class Element(BaseModel):\n",
" type: str\n",
" text: Any\n",
"\n",
"\n",
"# Categorize by type\n",
"categorized_elements = []\n",
"for element in raw_pdf_elements:\n",
" if \"unstructured.documents.elements.Table\" in str(type(element)):\n",
" categorized_elements.append(Element(type=\"table\", text=str(element)))\n",
" elif \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n",
" categorized_elements.append(Element(type=\"text\", text=str(element)))\n",
"\n",
"# Tables\n",
"table_elements = [e for e in categorized_elements if e.type == \"table\"]\n",
"print(len(table_elements))\n",
"\n",
"# Text\n",
"text_elements = [e for e in categorized_elements if e.type == \"text\"]\n",
"print(len(text_elements))"
]
},
{
"cell_type": "markdown",
"id": "731b3dfc-7ddf-4a11-9a30-9a79b7c66e16",
"metadata": {},
"source": [
"## Multi-vector retriever\n",
"\n",
"Use [multi-vector-retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary) to produce summaries of tables and, optionally, text. \n",
"\n",
"With the summary, we will also store the raw table elements.\n",
"\n",
"The summaries are used to improve the quality of retrieval, [as explained in the multi vector retriever docs](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector).\n",
"\n",
"The raw tables are passed to the LLM, providing the full table context for the LLM to generate the answer. \n",
"\n",
"### Summaries"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "8e275736-3408-4d7a-990e-4362c88e81f8",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "markdown",
"id": "37b65677-aeb4-44fd-b06d-4539341ede97",
"metadata": {},
"source": [
"We create a simple summarize chain for each element.\n",
"\n",
"You can also see, re-use, or modify the prompt in the Hub [here](https://smith.langchain.com/hub/rlm/multi-vector-retriever-summarization).\n",
"\n",
"```\n",
"from langchain import hub\n",
"obj = hub.pull(\"rlm/multi-vector-retriever-summarization\")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "1b12536a-1303-41ad-9948-4eb5a5f32614",
"metadata": {},
"outputs": [],
"source": [
"# Prompt\n",
"prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\ \n",
"Give a concise summary of the table or text. Table or text chunk: {element} \"\"\"\n",
"prompt = ChatPromptTemplate.from_template(prompt_text)\n",
"\n",
"# Summary chain\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d8b567c-b442-4bf0-b639-04bd89effc62",
"metadata": {},
"outputs": [],
"source": [
"# Apply to tables\n",
"tables = [i.text for i in table_elements]\n",
"table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "3e9c176c-3d46-4034-b169-0d7305d42d27",
"metadata": {},
"outputs": [],
"source": [
"# Apply to texts\n",
"texts = [i.text for i in text_elements]\n",
"text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "markdown",
"id": "60524010-754f-4924-ad75-78cb54ca7257",
"metadata": {},
"source": [
"### Add to vectorstore\n",
"\n",
"Use [Multi Vector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary) with summaries: \n",
"\n",
"* `InMemoryStore` stores the raw text, tables\n",
"* `vectorstore` stores the embedded summaries"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "346c3a02-8fea-4f75-a69e-fc9542b99dbc",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"from langchain.storage import InMemoryStore\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
"\n",
"# The storage layer for the parent documents\n",
"store = InMemoryStore()\n",
"id_key = \"doc_id\"\n",
"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" id_key=id_key,\n",
")\n",
"\n",
"# Add texts\n",
"doc_ids = [str(uuid.uuid4()) for _ in texts]\n",
"summary_texts = [\n",
" Document(page_content=s, metadata={id_key: doc_ids[i]})\n",
" for i, s in enumerate(text_summaries)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_texts)\n",
"retriever.docstore.mset(list(zip(doc_ids, texts)))\n",
"\n",
"# Add tables\n",
"table_ids = [str(uuid.uuid4()) for _ in tables]\n",
"summary_tables = [\n",
" Document(page_content=s, metadata={id_key: table_ids[i]})\n",
" for i, s in enumerate(table_summaries)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_tables)\n",
"retriever.docstore.mset(list(zip(table_ids, tables)))"
]
},
{
"cell_type": "markdown",
"id": "1d8bbbd9-009b-4b34-a206-5874a60adbda",
"metadata": {},
"source": [
"## RAG\n",
"\n",
"Run [RAG pipeline](https://python.langchain.com/docs/expression_language/cookbook/retrieval)."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "f2489de4-51e3-48b4-bbcd-ed9171deadf3",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"# Prompt template\n",
"template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",
"{context}\n",
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"# LLM\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"\n",
"# RAG pipeline\n",
"chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "90e3d100-10e8-4ee6-ae46-2480b1524ec8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The number of training tokens for LLaMA2 is 2.0T.'"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"What is the number of training tokens for LLaMA2?\")"
]
},
{
"cell_type": "markdown",
"id": "37f46054-e239-4ba8-af81-22d0d6a9bc32",
"metadata": {},
"source": [
"We can check the [trace](https://smith.langchain.com/public/4739ae7c-1a13-406d-bc4e-3462670ebc01/r) to see what chunks were retrieved:\n",
"\n",
"This includes Table 1 of the paper, showing the Tokens used for training.\n",
"\n",
"```\n",
"Training Data Params Context GQA Tokens LR Length 7B 2k 1.0T 3.0x 10-4 See Touvron et al. 13B 2k 1.0T 3.0 x 10-4 LiaMa 1 (2023) 33B 2k 14T 1.5 x 10-4 65B 2k 1.4T 1.5 x 10-4 7B 4k 2.0T 3.0x 10-4 Liama 2 A new mix of publicly 13B 4k 2.0T 3.0 x 10-4 available online data 34B 4k v 2.0T 1.5 x 10-4 70B 4k v 2.0T 1.5 x 10-4\n",
"```"
]
}
],
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| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb | {
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"
}
},
"cell_type": "markdown",
"id": "812a4dbc-fe04-4b84-bdf9-390045e30806",
"metadata": {},
"source": [
"## Semi-structured and Multi-modal RAG\n",
"\n",
"Many documents contain a mixture of content types, including text, tables, and images. \n",
"\n",
"Semi-structured data can be challenging for conventional RAG for at least two reasons: \n",
"\n",
"* Text splitting may break up tables, corrupting the data in retrieval\n",
"* Embedding tables may pose challenges for semantic similarity search\n",
"\n",
"And the information captured in images is typically lost.\n",
"\n",
"With the emergence of multimodal LLMs, like [GPT4-V](https://openai.com/research/gpt-4v-system-card), it is worth considering how to utilize images in RAG:\n",
"\n",
"`Option 1:` \n",
"\n",
"* Use multimodal embeddings (such as [CLIP](https://openai.com/research/clip)) to embed images and text\n",
"* Retrieve both using similarity search\n",
"* Pass raw images and text chunks to a multimodal LLM for answer synthesis \n",
"\n",
"`Option 2:` \n",
"\n",
"* Use a multimodal LLM (such as [GPT4-V](https://openai.com/research/gpt-4v-system-card), [LLaVA](https://llava.hliu.cc/), or [FUYU-8b](https://www.adept.ai/blog/fuyu-8b)) to produce text summaries from images\n",
"* Embed and retrieve text \n",
"* Pass text chunks to an LLM for answer synthesis \n",
"\n",
"`Option 3:` \n",
"\n",
"* Use a multimodal LLM (such as [GPT4-V](https://openai.com/research/gpt-4v-system-card), [LLaVA](https://llava.hliu.cc/), or [FUYU-8b](https://www.adept.ai/blog/fuyu-8b)) to produce text summaries from images\n",
"* Embed and retrieve image summaries with a reference to the raw image \n",
"* Pass raw images and text chunks to a multimodal LLM for answer synthesis \n",
"\n",
"This cookbook show how we might tackle this :\n",
"\n",
"* We will use [Unstructured](https://unstructured.io/) to parse images, text, and tables from documents (PDFs).\n",
"* We will use the [multi-vector retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) to store raw tables, text, (optionally) images along with their summaries for retrieval.\n",
"* We will demonstrate `Option 2`, and will follow-up on the other approaches in future cookbooks.\n",
"\n",
"\n",
"\n",
"## Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "140580ef-5db0-43cc-a524-9c39e04d4df0",
"metadata": {},
"outputs": [],
"source": [
"! pip install langchain unstructured[all-docs] pydantic lxml"
]
},
{
"cell_type": "markdown",
"id": "74b56bde-1ba0-4525-a11d-cab02c5659e4",
"metadata": {},
"source": [
"## Data Loading\n",
"\n",
"### Partition PDF tables, text, and images\n",
" \n",
"* `LLaVA` Paper: https://arxiv.org/pdf/2304.08485.pdf\n",
"* Use [Unstructured](https://unstructured-io.github.io/unstructured/) to partition elements"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "61cbb874-ecc0-4d5d-9954-f0a41f65e0d7",
"metadata": {},
"outputs": [],
"source": [
"path = \"/Users/rlm/Desktop/Papers/LLaVA/\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e98bdeb7-eb77-42e6-a3a5-c3f27a1838d5",
"metadata": {},
"outputs": [],
"source": [
"from typing import Any\n",
"\n",
"from pydantic import BaseModel\n",
"from unstructured.partition.pdf import partition_pdf\n",
"\n",
"# Get elements\n",
"raw_pdf_elements = partition_pdf(\n",
" filename=path + \"LLaVA.pdf\",\n",
" # Using pdf format to find embedded image blocks\n",
" extract_images_in_pdf=True,\n",
" # Use layout model (YOLOX) to get bounding boxes (for tables) and find titles\n",
" # Titles are any sub-section of the document\n",
" infer_table_structure=True,\n",
" # Post processing to aggregate text once we have the title\n",
" chunking_strategy=\"by_title\",\n",
" # Chunking params to aggregate text blocks\n",
" # Attempt to create a new chunk 3800 chars\n",
" # Attempt to keep chunks > 2000 chars\n",
" # Hard max on chunks\n",
" max_characters=4000,\n",
" new_after_n_chars=3800,\n",
" combine_text_under_n_chars=2000,\n",
" image_output_dir_path=path,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7cdba921-5419-4471-b234-d93af3859b6f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{\"<class 'unstructured.documents.elements.CompositeElement'>\": 31,\n",
" \"<class 'unstructured.documents.elements.Table'>\": 3}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dictionary to store counts of each type\n",
"category_counts = {}\n",
"\n",
"for element in raw_pdf_elements:\n",
" category = str(type(element))\n",
" if category in category_counts:\n",
" category_counts[category] += 1\n",
" else:\n",
" category_counts[category] = 1\n",
"\n",
"# Unique_categories will have unique elements\n",
"unique_categories = set(category_counts.keys())\n",
"category_counts"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5f660305-e165-4b6c-ada3-a67a422defb5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"31\n"
]
}
],
"source": [
"class Element(BaseModel):\n",
" type: str\n",
" text: Any\n",
"\n",
"\n",
"# Categorize by type\n",
"categorized_elements = []\n",
"for element in raw_pdf_elements:\n",
" if \"unstructured.documents.elements.Table\" in str(type(element)):\n",
" categorized_elements.append(Element(type=\"table\", text=str(element)))\n",
" elif \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n",
" categorized_elements.append(Element(type=\"text\", text=str(element)))\n",
"\n",
"# Tables\n",
"table_elements = [e for e in categorized_elements if e.type == \"table\"]\n",
"print(len(table_elements))\n",
"\n",
"# Text\n",
"text_elements = [e for e in categorized_elements if e.type == \"text\"]\n",
"print(len(text_elements))"
]
},
{
"cell_type": "markdown",
"id": "0aa7f52f-bf5c-4ba4-af72-b2ccba59a4cf",
"metadata": {},
"source": [
"## Multi-vector retriever\n",
"\n",
"Use [multi-vector-retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary).\n",
"\n",
"Summaries are used to retrieve raw tables and / or raw chunks of text.\n",
"\n",
"### Text and Table summaries"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "523e6ed2-2132-4748-bdb7-db765f20648d",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "22c22e3f-42fb-4a4a-a87a-89f10ba8ab99",
"metadata": {},
"outputs": [],
"source": [
"# Prompt\n",
"prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\\n",
"Give a concise summary of the table or text. Table or text chunk: {element} \"\"\"\n",
"prompt = ChatPromptTemplate.from_template(prompt_text)\n",
"\n",
"# Summary chain\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f176b374-aef0-48f4-a104-fb26b1dd6922",
"metadata": {},
"outputs": [],
"source": [
"# Apply to text\n",
"texts = [i.text for i in text_elements]\n",
"text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61a6ac00-ebbe-4608-9ae5-40f81541e37f",
"metadata": {},
"outputs": [],
"source": [
"# Apply to tables\n",
"tables = [i.text for i in table_elements]\n",
"table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 5})"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "b1feadda-8171-4aed-9a60-320a88dc9ee1",
"metadata": {},
"source": [
"### Images\n",
"\n",
"We will implement `Option 2` discussed above: \n",
"\n",
"* Use a multimodal LLM ([LLaVA](https://llava.hliu.cc/)) to produce text summaries from images\n",
"* Embed and retrieve text \n",
"* Pass text chunks to an LLM for answer synthesis \n",
"\n",
"#### Image summaries \n",
"\n",
"We will use [LLaVA](https://github.com/haotian-liu/LLaVA/), an open source multimodal model.\n",
" \n",
"We will use [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) to run LLaVA locally (e.g., on a Mac laptop):\n",
"\n",
"* Clone [llama.cpp](https://github.com/ggerganov/llama.cpp)\n",
"* Download the LLaVA model: `mmproj-model-f16.gguf` and one of `ggml-model-[f16|q5_k|q4_k].gguf` from [LLaVA 7b repo](https://huggingface.co/mys/ggml_llava-v1.5-7b/tree/main)\n",
"* Build\n",
"```\n",
"mkdir build && cd build && cmake ..\n",
"cmake --build .\n",
"```\n",
"* Run inference across images:\n",
"```\n",
"/Users/rlm/Desktop/Code/llama.cpp/bin/llava -m ../models/llava-7b/ggml-model-q5_k.gguf --mmproj ../models/llava-7b/mmproj-model-f16.gguf --temp 0.1 -p \"Describe the image in detail. Be specific about graphs, such as bar plots.\" --image \"$img\" > \"$output_file\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "440b20e4-a74d-4c75-b538-0ca24d581713",
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"# Define the directory containing the images\n",
"IMG_DIR=~/Desktop/Papers/LLaVA/\n",
"\n",
"# Loop through each image in the directory\n",
"for img in \"${IMG_DIR}\"*.jpg; do\n",
" # Extract the base name of the image without extension\n",
" base_name=$(basename \"$img\" .jpg)\n",
"\n",
" # Define the output file name based on the image name\n",
" output_file=\"${IMG_DIR}${base_name}.txt\"\n",
"\n",
" # Execute the command and save the output to the defined output file\n",
" /Users/rlm/Desktop/Code/llama.cpp/bin/llava -m ../models/llava-7b/ggml-model-q5_k.gguf --mmproj ../models/llava-7b/mmproj-model-f16.gguf --temp 0.1 -p \"Describe the image in detail. Be specific about graphs, such as bar plots.\" --image \"$img\" > \"$output_file\"\n",
"\n",
"done\n"
]
},
{
"cell_type": "markdown",
"id": "a69dcd6b-0226-4173-a80d-36921824c824",
"metadata": {},
"source": [
"Note: \n",
"\n",
"To run LLaVA with python bindings, we need a Python API to run the CLIP model. \n",
"\n",
"CLIP support is likely to be added to `llama.cpp` in the future.\n",
"\n",
"After running the above, we fetch and clean image summaries."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "54924f9e-0f81-4232-8efb-8485db1063c8",
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"import os\n",
"\n",
"# Get all .txt file summaries\n",
"file_paths = glob.glob(os.path.expanduser(os.path.join(path, \"*.txt\")))\n",
"\n",
"# Read each file and store its content in a list\n",
"img_summaries = []\n",
"for file_path in file_paths:\n",
" with open(file_path, \"r\") as file:\n",
" img_summaries.append(file.read())\n",
"\n",
"# Remove any logging prior to summary\n",
"logging_header = \"clip_model_load: total allocated memory: 201.27 MB\\n\\n\"\n",
"cleaned_img_summary = [s.split(logging_header, 1)[1].strip() for s in img_summaries]"
]
},
{
"cell_type": "markdown",
"id": "67b030d4-2ac5-41b6-9245-fc3ba5771d87",
"metadata": {},
"source": [
"### Add to vectorstore\n",
"\n",
"Use [Multi Vector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary) with summaries."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d643cc61-827d-4f3c-8242-7a7c8291ed8a",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"from langchain.storage import InMemoryStore\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
"\n",
"# The storage layer for the parent documents\n",
"store = InMemoryStore()\n",
"id_key = \"doc_id\"\n",
"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" id_key=id_key,\n",
")\n",
"\n",
"# Add texts\n",
"doc_ids = [str(uuid.uuid4()) for _ in texts]\n",
"summary_texts = [\n",
" Document(page_content=s, metadata={id_key: doc_ids[i]})\n",
" for i, s in enumerate(text_summaries)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_texts)\n",
"retriever.docstore.mset(list(zip(doc_ids, texts)))\n",
"\n",
"# Add tables\n",
"table_ids = [str(uuid.uuid4()) for _ in tables]\n",
"summary_tables = [\n",
" Document(page_content=s, metadata={id_key: table_ids[i]})\n",
" for i, s in enumerate(table_summaries)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_tables)\n",
"retriever.docstore.mset(list(zip(table_ids, tables)))"
]
},
{
"cell_type": "markdown",
"id": "b90572a0-0377-4598-8d12-bba22a51b655",
"metadata": {},
"source": [
"For `option 2` (above): \n",
"\n",
"* Store the image summary in the `docstore`, which we return to the LLM for answer generation."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2e0f06f3-a5bc-4342-aee6-c3495d047e66",
"metadata": {},
"outputs": [],
"source": [
"# Add image summaries\n",
"img_ids = [str(uuid.uuid4()) for _ in cleaned_img_summary]\n",
"summary_img = [\n",
" Document(page_content=s, metadata={id_key: img_ids[i]})\n",
" for i, s in enumerate(cleaned_img_summary)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_img)\n",
"retriever.docstore.mset(list(zip(img_ids, cleaned_img_summary)))"
]
},
{
"cell_type": "markdown",
"id": "6d667e5c-5385-48c4-b878-51dcc03cc4d0",
"metadata": {},
"source": [
"For `option 3` (above): \n",
"\n",
"* Store the images in the `docstore`.\n",
"* Using the image in answer synthesis will require a multimodal LLM with Python API integration.\n",
"* GPT4-V is expected soon, and - as mentioned above - CLIP support is likely to be added to `llama.cpp` in the future."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c75a7b3-04f3-41eb-97e5-61af49d92104",
"metadata": {},
"outputs": [],
"source": [
"# Add images\n",
"img_ids = [str(uuid.uuid4()) for _ in cleaned_img_summary]\n",
"summary_img = [\n",
" Document(page_content=s, metadata={id_key: img_ids[i]})\n",
" for i, s in enumerate(cleaned_img_summary)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_img)\n",
"### Fetch images\n",
"retriever.docstore.mset(\n",
" list(\n",
" zip(\n",
" img_ids,\n",
" )\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "4b45fb81-46b1-426e-aa2c-01aed4eac700",
"metadata": {},
"source": [
"### Sanity Check retrieval\n",
"\n",
"The most complex table in the paper:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a5f4dd59-005a-4ff8-ad51-ea2e50d79c10",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Subject Context Modality Grade Method NAT SOC LAN | TXT IMG NO | Gi6~ G7-12 | Average Representative & SoTA methods with numbers reported in the literature Human [30] 90.23 84.97 87.48 | 89.60 87.50 88.10 | 91.59 82.42 88.40 GPT-3.5 [30] 74.64 69.74 76.00 | 74.44 67.28 77.42 | 76.80 68.89 73.97 GPT-3.5 w/ CoT [30] 75.44 70.87 78.09 | 74.68 67.43 79.93 | 78.23 69.68 75.17 LLaMA-Adapter [55] 84.37 88.30 84.36 | 83.72 80.32 86.90 | 85.83 84.05 85.19 MM-CoT gase [57] 87.52 77.17 85.82 | 87.88 82.90 86.83 | 84.65 85.37 84.91 MM-CoT farge [57] 95.91 82.00 90.82 | 95.26 88.80 92.89 | 92.44 90.31 | 91.68 Results with our own experiment runs GPT-4 84.06 73.45 87.36 | 81.87 70.75 90.73 | 84.69 79.10 82.69 LLaVA 90.36 95.95 88.00 | 89.49 88.00 90.66 | 90.93 90.90 90.92 LLaVA+GPT-4 (complement) 90.36 95.50 88.55 | 89.05 87.80 91.08 | 92.22 88.73 90.97 LLaVA+GPT-4 (judge) 91.56 96.74 91.09 | 90.62 88.99 93.52 | 92.73 92.16 92.53'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tables[2]"
]
},
{
"cell_type": "markdown",
"id": "9f68ef8b-0fec-4b2f-a0d3-c440c74ebaa1",
"metadata": {},
"source": [
"Here is the summary, which is embedded:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "9eb16ea9-d932-4062-9ace-e8f77dee530b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The table presents the performance of various methods in different subject contexts and modalities. The subjects are Natural Sciences (NAT), Social Sciences (SOC), and Language (LAN). The modalities are text (TXT), image (IMG), and no modality (NO). The methods include Human, GPT-3.5, GPT-3.5 with CoT, LLaMA-Adapter, MM-CoT gase, MM-CoT farge, GPT-4, LLaVA, LLaVA+GPT-4 (complement), and LLaVA+GPT-4 (judge). The performance is measured in grades from 6 to 12. The MM-CoT farge method had the highest performance in most categories, with LLaVA+GPT-4 (judge) showing the highest results in the experiment runs.'"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table_summaries[2]"
]
},
{
"cell_type": "markdown",
"id": "fc2bcc4c-c05d-4417-aaf9-78acd754dde6",
"metadata": {},
"source": [
"Here is our retrieval of that table from the natural language query:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "1bea75fe-85af-4955-a80c-6e0b44a8e215",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Subject Context Modality Grade Method NAT SOC LAN | TXT IMG NO | Gi6~ G7-12 | Average Representative & SoTA methods with numbers reported in the literature Human [30] 90.23 84.97 87.48 | 89.60 87.50 88.10 | 91.59 82.42 88.40 GPT-3.5 [30] 74.64 69.74 76.00 | 74.44 67.28 77.42 | 76.80 68.89 73.97 GPT-3.5 w/ CoT [30] 75.44 70.87 78.09 | 74.68 67.43 79.93 | 78.23 69.68 75.17 LLaMA-Adapter [55] 84.37 88.30 84.36 | 83.72 80.32 86.90 | 85.83 84.05 85.19 MM-CoT gase [57] 87.52 77.17 85.82 | 87.88 82.90 86.83 | 84.65 85.37 84.91 MM-CoT farge [57] 95.91 82.00 90.82 | 95.26 88.80 92.89 | 92.44 90.31 | 91.68 Results with our own experiment runs GPT-4 84.06 73.45 87.36 | 81.87 70.75 90.73 | 84.69 79.10 82.69 LLaVA 90.36 95.95 88.00 | 89.49 88.00 90.66 | 90.93 90.90 90.92 LLaVA+GPT-4 (complement) 90.36 95.50 88.55 | 89.05 87.80 91.08 | 92.22 88.73 90.97 LLaVA+GPT-4 (judge) 91.56 96.74 91.09 | 90.62 88.99 93.52 | 92.73 92.16 92.53'"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can retrieve this table\n",
"retriever.invoke(\"What are results for LLaMA across across domains / subjects?\")[1]"
]
},
{
"cell_type": "markdown",
"id": "3dbb23d5-ae66-444d-8f5f-b24107fb9c57",
"metadata": {},
"source": [
"Image:"
]
},
{
"attachments": {
"5d505f36-17e1-4fe5-a405-f01f7a392716.jpg": {
"image/jpeg": 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"
}
},
"cell_type": "markdown",
"id": "329fd4ee-4a68-4f3b-b157-a676f13ba587",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "6fde6f17-d244-4270-b759-68e1858d399f",
"metadata": {},
"source": [
"We can retrieve this image summary:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "6f52ee1e-ed46-4a81-834a-3608a1cf90ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The image features a close-up of a tray filled with various pieces of fried chicken. The chicken pieces are arranged in a way that resembles a map of the world, with some pieces placed in the shape of continents and others as countries. The arrangement of the chicken pieces creates a visually appealing and playful representation of the world, making it an interesting and creative presentation.\\n\\nmain: image encoded in 865.20 ms by CLIP ( 1.50 ms per image patch)'"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.invoke(\"Images / figures with playful and creative examples\")[1]"
]
},
{
"cell_type": "markdown",
"id": "69060724-e390-4dda-8250-5f86025c874a",
"metadata": {},
"source": [
"## RAG\n",
"\n",
"Run [RAG pipeline](https://python.langchain.com/docs/expression_language/cookbook/retrieval).\n",
"\n",
"For `option 1` (above): \n",
"\n",
"* Simply pass retrieved text chunks to LLM, as usual.\n",
"\n",
"For `option 2a` (above): \n",
"\n",
"* We would pass retrieved image and images to the multi-modal LLM.\n",
"* This should be possible soon, once [llama-cpp-python add multi-modal support](https://github.com/abetlen/llama-cpp-python/issues/813).\n",
"* And, of course, this will be enabled by GPT4-V API."
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "771a47fa-1267-4db8-a6ae-5fde48bbc069",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"# Prompt template\n",
"template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",
"{context}\n",
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"# Option 1: LLM\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"# Option 2: Multi-modal LLM\n",
"# model = GPT4-V or LLaVA\n",
"\n",
"# RAG pipeline\n",
"chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "ea8414a8-65ee-4e11-8154-029b454f46af",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The performance of LLaMA across multiple image domains/subjects is as follows: In the Natural Science (NAT) subject, it scored 84.37. In the Social Science (SOC) subject, it scored 88.30. In the Language Science (LAN) subject, it scored 84.36. In the Text Context (TXT) subject, it scored 83.72. In the Image Context (IMG) subject, it scored 80.32. In the No Context (NO) subject, it scored 86.90. For grades 1-6 (G1-6), it scored 85.83 and for grades 7-12 (G7-12), it scored 84.05. The average score was 85.19.'"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\n",
" \"What is the performance of LLaVa across across multiple image domains / subjects?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7ce57b80-fbd0-47f3-817f-6549a0409f51",
"metadata": {},
"source": [
"We can check the [trace](https://smith.langchain.com/public/85a7180e-0dd1-44d9-996f-6cb9c6f53205/r) to see retrieval of tables and text."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "e88f0bc7-81fb-4883-a021-58734a74411b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The text provides an example of a playful and creative image. The image features a close-up of a tray filled with various pieces of fried chicken. The chicken pieces are arranged in a way that resembles a map of the world, with some pieces placed in the shape of continents and others as countries. The arrangement of the chicken pieces creates a visually appealing and playful representation of the world, making it an interesting and creative presentation.'"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Explain images / figures with playful and creative examples.\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb | {
"cells": [
{
"attachments": {
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"
}
},
"cell_type": "markdown",
"id": "812a4dbc-fe04-4b84-bdf9-390045e30806",
"metadata": {},
"source": [
"## Private Semi-structured and Multi-modal RAG w/ LLaMA2 and LLaVA\n",
"\n",
"Many documents contain a mixture of content types, including text, tables, and images. \n",
"\n",
"Semi-structured data can be challenging for conventional RAG for at least two reasons: \n",
"\n",
"* Text splitting may break up tables, corrupting the data in retrieval\n",
"* Embedding tables may pose challenges for semantic similarity search\n",
"\n",
"And the information captured in images is typically lost.\n",
"\n",
"With the emergence of multimodal LLMs, like [GPT4-V](https://openai.com/research/gpt-4v-system-card), it is worth considering how to utilize images in RAG:\n",
"\n",
"`Option 1:` \n",
"\n",
"* Use multimodal embeddings (such as [CLIP](https://openai.com/research/clip)) to embed images and text\n",
"* Retrieve both using similarity search\n",
"* Pass raw images and text chunks to a multimodal LLM for answer synthesis \n",
"\n",
"`Option 2:` \n",
"\n",
"* Use a multimodal LLM (such as [GPT4-V](https://openai.com/research/gpt-4v-system-card), [LLaVA](https://llava.hliu.cc/), or [FUYU-8b](https://www.adept.ai/blog/fuyu-8b)) to produce text summaries from images\n",
"* Embed and retrieve text \n",
"* Pass text chunks to an LLM for answer synthesis \n",
"\n",
"`Option 3:` \n",
"\n",
"* Use a multimodal LLM (such as [GPT4-V](https://openai.com/research/gpt-4v-system-card), [LLaVA](https://llava.hliu.cc/), or [FUYU-8b](https://www.adept.ai/blog/fuyu-8b)) to produce text summaries from images\n",
"* Embed and retrieve image summaries with a reference to the raw image \n",
"* Pass raw images and text chunks to a multimodal LLM for answer synthesis \n",
"\n",
"This cookbook show how we might tackle this :\n",
"\n",
"* We will use [Unstructured](https://unstructured.io/) to parse images, text, and tables from documents (PDFs).\n",
"* We will use the [multi-vector retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) to store raw tables, text, (optionally) images along with their summaries for retrieval.\n",
"* We will demonstrate `Option 2`, and will follow-up on the other approaches in future cookbooks.\n",
"\n",
"\n",
"\n",
"## Packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a01dcf9e-c8f4-4c34-a013-8fd08d2d3806",
"metadata": {},
"outputs": [],
"source": [
"! pip install langchain unstructured[all-docs] pydantic lxml"
]
},
{
"cell_type": "markdown",
"id": "74b56bde-1ba0-4525-a11d-cab02c5659e4",
"metadata": {},
"source": [
"## Data Loading\n",
"\n",
"### Partition PDF tables, text, and images\n",
" \n",
"* `LLaVA` Paper: https://arxiv.org/pdf/2304.08485.pdf\n",
"* Use [Unstructured](https://unstructured-io.github.io/unstructured/) to partition elements"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3826584-1ff5-4d86-911a-a9242aaad5d1",
"metadata": {},
"outputs": [],
"source": [
"from typing import Any\n",
"\n",
"from pydantic import BaseModel\n",
"from unstructured.partition.pdf import partition_pdf\n",
"\n",
"# Path to save images\n",
"path = \"/Users/rlm/Desktop/Papers/LLaVA/\"\n",
"\n",
"# Get elements\n",
"raw_pdf_elements = partition_pdf(\n",
" filename=path + \"LLaVA.pdf\",\n",
" # Using pdf format to find embedded image blocks\n",
" extract_images_in_pdf=True,\n",
" # Use layout model (YOLOX) to get bounding boxes (for tables) and find titles\n",
" # Titles are any sub-section of the document\n",
" infer_table_structure=True,\n",
" # Post processing to aggregate text once we have the title\n",
" chunking_strategy=\"by_title\",\n",
" # Chunking params to aggregate text blocks\n",
" # Attempt to create a new chunk 3800 chars\n",
" # Attempt to keep chunks > 2000 chars\n",
" # Hard max on chunks\n",
" max_characters=4000,\n",
" new_after_n_chars=3800,\n",
" combine_text_under_n_chars=2000,\n",
" image_output_dir_path=path,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7cdba921-5419-4471-b234-d93af3859b6f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{\"<class 'unstructured.documents.elements.CompositeElement'>\": 31,\n",
" \"<class 'unstructured.documents.elements.Table'>\": 3}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dictionary to store counts of each type\n",
"category_counts = {}\n",
"\n",
"for element in raw_pdf_elements:\n",
" category = str(type(element))\n",
" if category in category_counts:\n",
" category_counts[category] += 1\n",
" else:\n",
" category_counts[category] = 1\n",
"\n",
"# Unique_categories will have unique elements\n",
"# TableChunk if Table > max chars set above\n",
"unique_categories = set(category_counts.keys())\n",
"category_counts"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5f660305-e165-4b6c-ada3-a67a422defb5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"31\n"
]
}
],
"source": [
"class Element(BaseModel):\n",
" type: str\n",
" text: Any\n",
"\n",
"\n",
"# Categorize by type\n",
"categorized_elements = []\n",
"for element in raw_pdf_elements:\n",
" if \"unstructured.documents.elements.Table\" in str(type(element)):\n",
" categorized_elements.append(Element(type=\"table\", text=str(element)))\n",
" elif \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n",
" categorized_elements.append(Element(type=\"text\", text=str(element)))\n",
"\n",
"# Tables\n",
"table_elements = [e for e in categorized_elements if e.type == \"table\"]\n",
"print(len(table_elements))\n",
"\n",
"# Text\n",
"text_elements = [e for e in categorized_elements if e.type == \"text\"]\n",
"print(len(text_elements))"
]
},
{
"cell_type": "markdown",
"id": "0aa7f52f-bf5c-4ba4-af72-b2ccba59a4cf",
"metadata": {},
"source": [
"## Multi-vector retriever\n",
"\n",
"Use [multi-vector-retriever](/docs/modules/data_connection/retrievers/multi_vector#summary).\n",
"\n",
"Summaries are used to retrieve raw tables and / or raw chunks of text.\n",
"\n",
"### Text and Table summaries\n",
"\n",
"Here, we use Ollama to run LLaMA2 locally. \n",
"\n",
"See details on installation [here](/docs/guides/development/local_llms)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "523e6ed2-2132-4748-bdb7-db765f20648d",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.chat_models import ChatOllama\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "22c22e3f-42fb-4a4a-a87a-89f10ba8ab99",
"metadata": {},
"outputs": [],
"source": [
"# Prompt\n",
"prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\\n",
"Give a concise summary of the table or text. Table or text chunk: {element} \"\"\"\n",
"prompt = ChatPromptTemplate.from_template(prompt_text)\n",
"\n",
"# Summary chain\n",
"model = ChatOllama(model=\"llama2:13b-chat\")\n",
"summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0e1ba7ba-d209-424a-8f05-6a95d6d32bb2",
"metadata": {},
"outputs": [],
"source": [
"# Apply to text\n",
"texts = [i.text for i in text_elements if i.text != \"\"]\n",
"text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a419123a-6038-4264-9ee0-bfb2a2df7153",
"metadata": {},
"outputs": [],
"source": [
"# Apply to tables\n",
"tables = [i.text for i in table_elements]\n",
"table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 5})"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d52641eb-762e-4460-80c7-3ac3ddd93621",
"metadata": {},
"source": [
"### Images\n",
"\n",
"We will implement `Option 2` discussed above: \n",
"\n",
"* Use a multimodal LLM ([LLaVA](https://llava.hliu.cc/)) to produce text summaries from images\n",
"* Embed and retrieve text \n",
"* Pass text chunks to an LLM for answer synthesis \n",
"\n",
"#### Image summaries \n",
"\n",
"We will use [LLaVA](https://github.com/haotian-liu/LLaVA/), an open source multimodal model.\n",
" \n",
"We will use [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) to run LLaVA locally (e.g., on a Mac laptop):\n",
"\n",
"* Clone [llama.cpp](https://github.com/ggerganov/llama.cpp)\n",
"* Download the LLaVA model: `mmproj-model-f16.gguf` and one of `ggml-model-[f16|q5_k|q4_k].gguf` from [LLaVA 7b repo](https://huggingface.co/mys/ggml_llava-v1.5-7b/tree/main)\n",
"* Build\n",
"```\n",
"mkdir build && cd build && cmake ..\n",
"cmake --build .\n",
"```\n",
"* Run inference across images:\n",
"```\n",
"/Users/rlm/Desktop/Code/llama.cpp/bin/llava -m ../models/llava-7b/ggml-model-q5_k.gguf --mmproj ../models/llava-7b/mmproj-model-f16.gguf --temp 0.1 -p \"Describe the image in detail. Be specific about graphs, such as bar plots.\" --image \"$img\" > \"$output_file\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "646a6874-008e-46aa-809d-1d59df36858b",
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"# Define the directory containing the images\n",
"IMG_DIR=~/Desktop/Papers/LLaVA/\n",
"\n",
"# Loop through each image in the directory\n",
"for img in \"${IMG_DIR}\"*.jpg; do\n",
" # Extract the base name of the image without extension\n",
" base_name=$(basename \"$img\" .jpg)\n",
"\n",
" # Define the output file name based on the image name\n",
" output_file=\"${IMG_DIR}${base_name}.txt\"\n",
"\n",
" # Execute the command and save the output to the defined output file\n",
" /Users/rlm/Desktop/Code/llama.cpp/bin/llava -m ../models/llava-7b/ggml-model-q5_k.gguf --mmproj ../models/llava-7b/mmproj-model-f16.gguf --temp 0.1 -p \"Describe the image in detail. Be specific about graphs, such as bar plots.\" --image \"$img\" > \"$output_file\"\n",
"\n",
"done\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "da8a8c94-3df7-446f-9a69-703295f50f02",
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"import os\n",
"\n",
"# Get all .txt files in the directory\n",
"file_paths = glob.glob(os.path.expanduser(os.path.join(path, \"*.txt\")))\n",
"\n",
"# Read each file and store its content in a list\n",
"img_summaries = []\n",
"for file_path in file_paths:\n",
" with open(file_path, \"r\") as file:\n",
" img_summaries.append(file.read())\n",
"\n",
"# Clean up residual logging\n",
"cleaned_img_summary = [\n",
" s.split(\"clip_model_load: total allocated memory: 201.27 MB\\n\\n\", 1)[1].strip()\n",
" for s in img_summaries\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "67b030d4-2ac5-41b6-9245-fc3ba5771d87",
"metadata": {},
"source": [
"### Add to vectorstore\n",
"\n",
"Use [Multi Vector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary) with summaries.\n",
"\n",
"We use GPT4All embeddings to run locally, which are a [CPU optimized version of BERT](https://docs.gpt4all.io/gpt4all_python_embedding.html)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "64a5df0c-8193-407e-a83f-8fc17caff3e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found model file at /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"objc[42078]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x31f870208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x31fc9c208). One of the two will be used. Which one is undefined.\n"
]
}
],
"source": [
"import uuid\n",
"\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"from langchain.storage import InMemoryStore\n",
"from langchain_community.embeddings import GPT4AllEmbeddings\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_core.documents import Document\n",
"\n",
"# The vectorstore to use to index the child chunks\n",
"vectorstore = Chroma(\n",
" collection_name=\"summaries\", embedding_function=GPT4AllEmbeddings()\n",
")\n",
"\n",
"# The storage layer for the parent documents\n",
"store = InMemoryStore() # <- Can we extend this to images\n",
"id_key = \"doc_id\"\n",
"\n",
"# The retriever (empty to start)\n",
"retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" id_key=id_key,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "339bb8be-0d7a-45a0-8815-d62bb3bbf0fc",
"metadata": {},
"source": [
"For `option 2` (above): \n",
"\n",
"* Store the image summary in the `docstore`, which we return to the LLM for answer generation."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d643cc61-827d-4f3c-8242-7a7c8291ed8a",
"metadata": {},
"outputs": [],
"source": [
"# Add texts\n",
"doc_ids = [str(uuid.uuid4()) for _ in texts]\n",
"summary_texts = [\n",
" Document(page_content=s, metadata={id_key: doc_ids[i]})\n",
" for i, s in enumerate(text_summaries)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_texts)\n",
"retriever.docstore.mset(list(zip(doc_ids, texts)))\n",
"\n",
"# Add tables\n",
"table_ids = [str(uuid.uuid4()) for _ in tables]\n",
"summary_tables = [\n",
" Document(page_content=s, metadata={id_key: table_ids[i]})\n",
" for i, s in enumerate(table_summaries)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_tables)\n",
"retriever.docstore.mset(list(zip(table_ids, tables)))\n",
"\n",
"# Add images\n",
"img_ids = [str(uuid.uuid4()) for _ in cleaned_img_summary]\n",
"summary_img = [\n",
" Document(page_content=s, metadata={id_key: img_ids[i]})\n",
" for i, s in enumerate(cleaned_img_summary)\n",
"]\n",
"retriever.vectorstore.add_documents(summary_img)\n",
"retriever.docstore.mset(\n",
" list(zip(img_ids, cleaned_img_summary))\n",
") # Store the image summary as the raw document"
]
},
{
"cell_type": "markdown",
"id": "4b45fb81-46b1-426e-aa2c-01aed4eac700",
"metadata": {},
"source": [
"### Sanity Check"
]
},
{
"cell_type": "markdown",
"id": "3dbb23d5-ae66-444d-8f5f-b24107fb9c57",
"metadata": {},
"source": [
"Image:"
]
},
{
"attachments": {
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"image/jpeg": 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"
}
},
"cell_type": "markdown",
"id": "329fd4ee-4a68-4f3b-b157-a676f13ba587",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "6fde6f17-d244-4270-b759-68e1858d399f",
"metadata": {},
"source": [
"We can retrieve this image summary:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "6f52ee1e-ed46-4a81-834a-3608a1cf90ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The image features a close-up of a tray filled with various pieces of fried chicken. The chicken pieces are arranged in a way that resembles a map of the world, with some pieces placed in the shape of continents and others as countries. The arrangement of the chicken pieces creates a visually appealing and playful representation of the world, making it an interesting and creative presentation.\\n\\nmain: image encoded in 865.20 ms by CLIP ( 1.50 ms per image patch)'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.invoke(\"Images / figures with playful and creative examples\")[0]"
]
},
{
"cell_type": "markdown",
"id": "69060724-e390-4dda-8250-5f86025c874a",
"metadata": {},
"source": [
"## RAG\n",
"\n",
"Run [RAG pipeline](https://python.langchain.com/docs/expression_language/cookbook/retrieval).\n",
"\n",
"For `option 1` (above): \n",
"\n",
"* Simply pass retrieved text chunks to LLM, as usual.\n",
"\n",
"For `option 2a` (above): \n",
"\n",
"* We would pass retrieved image and images to the multi-modal LLM.\n",
"* This should be possible soon, once [llama-cpp-python add multi-modal support](https://github.com/abetlen/llama-cpp-python/issues/813)."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "771a47fa-1267-4db8-a6ae-5fde48bbc069",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"# Prompt template\n",
"template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",
"{context}\n",
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"# Option 1: LLM\n",
"model = ChatOllama(model=\"llama2:13b-chat\")\n",
"# Option 2: Multi-modal LLM\n",
"# model = LLaVA\n",
"\n",
"# RAG pipeline\n",
"chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ea8414a8-65ee-4e11-8154-029b454f46af",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\" Based on the provided context, LLaVA's performance across multiple image domains/subjects is not explicitly mentioned. However, we can infer some information about its performance based on the given text:\\n\\n1. LLaVA achieves an accuracy of 90.92% on the ScienceQA dataset, which is close to the current SoTA (91.68%).\\n2. When prompted with a 2-shot in-context learning task using GPT-4, it achieves an accuracy of 82.69%, indicating a 7.52% absolute gain compared to GPT-3.5.\\n3. For a substantial number of questions, GPT-4 fails due to insufficient context such as images or plots.\\n\\nBased on these points, we can infer that LLaVA performs well across multiple image domains/subjects, but its performance may be limited by the quality and availability of the input images. Additionally, its ability to recognize visual content and provide detailed responses is dependent on the specific task and dataset being used.\""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\n",
" \"What is the performance of LLaVa across across multiple image domains / subjects?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1b7aeb57-2ab8-496c-b909-0734ccc5da5f",
"metadata": {},
"source": [
"We can check the [trace](https://smith.langchain.com/public/ab90fb1c-5949-4fc6-a002-56a6056adc6b/r) to review retrieval."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1ad375c5-8aef-4be3-9a12-8ad953fa2d14",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' Sure, I\\'d be happy to help! Based on the provided context, here are some playful and creative explanations for the images/figures mentioned in the paper:\\n\\n1. \"The image features a close-up of a tray filled with various pieces of fried chicken. The chicken pieces are arranged in a way that resembles a map of the world, with some pieces placed in the shape of continents and others as countries.\"\\n\\nPlayful explanation: \"Look, ma! The fried chicken is mapping out the world one piece at a time! Who needs Google Maps when you have crispy chicken wings to guide the way?\"\\n\\nCreative explanation: \"The arrangement of the fried chicken pieces creates a visual representation of the world that\\'s both appetizing and adventurous. It\\'s like a culinary globe-trotting experience!\"\\n\\n2. \"The image is a screenshot of a conversation between two people, likely discussing a painting.\"\\n\\nPlayful explanation: \"The painting is getting a double take - these two people are having a chat about it and we get to eavesdrop on their art-loving banter!\"\\n\\nCreative explanation: \"This image captures the dynamic exchange of ideas between two art enthusiasts. It\\'s like we\\'re peeking into their creative brainstorming session, where the painting is the catalyst for a lively discussion.\"\\n\\n3. \"The image features a text-based representation of a scene with a person holding onto a rope, possibly a woman, and a boat in the background.\"\\n\\nPlayful explanation: \"This image looks like a page from a choose-your-own-adventure book! Is our brave protagonist about to embark on a thrilling boat ride or hold tight for a wild journey?\"\\n\\nCreative explanation: \"The text-based representation of the scene creates an intriguing narrative that invites the viewer to fill in the blanks. It\\'s like we\\'re reading a visual storybook, where the person holding onto the rope is the hero of their own adventure.\"\\n\\n4. \"Figure 5: LLaVA recognizes the famous art work, Mona Lisa, by Leonardo da Vinci.\"\\n\\nPlayful explanation: \"Mona Lisa is getting a digital spotlight - look at her smile now that she\\'s part of this cool image recognition tech!\"\\n\\nCreative explanation: \"This playful recognition of the Mona Lisa painting highlights the advanced technology used in image analysis. It\\'s like LLaVA is giving the famous artwork a modern makeover, showcasing its timeless beauty and relevance in the digital age.\"\\n\\nOverall, these images/figures offer unique opportunities for creative and playful explanations that can capture the viewer\\'s attention while highlighting the technology and narratives presented in the paper.'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\n",
" \"Explain any images / figures in the paper with playful and creative examples.\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1da79644-4046-45b0-8c25-01aa73587b22",
"metadata": {},
"source": [
"We can check the [trace](https://smith.langchain.com/public/c6d3b7d5-0f40-4905-ab8f-3a2b77c39af4/r) to review retrieval."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/advanced_rag_eval.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "f1571abe-8e84-44d1-b222-e4121fdbb4be",
"metadata": {},
"source": [
"# Advanced RAG Eval\n",
"\n",
"The cookbook walks through the process of running eval(s) on advanced RAG. \n",
"\n",
"This can be very useful to determine the best RAG approach for your application."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d8415ee-709c-407f-9ac2-f03a9d697aaf",
"metadata": {},
"outputs": [],
"source": [
"! pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "191f8465-fd6b-4017-8f0e-d284971b45ae",
"metadata": {},
"outputs": [],
"source": [
"# lock to 0.10.19 due to a persistent bug in more recent versions\n",
"! pip install \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml pillow matplotlib tiktoken open_clip_torch torch"
]
},
{
"cell_type": "markdown",
"id": "45949db5-d9b6-44a9-85f8-96d83a288616",
"metadata": {},
"source": [
"## Data Loading\n",
"\n",
"Let's look at an [example whitepaper](https://sgp.fas.org/crs/misc/IF10244.pdf) that provides a mixture of tables, text, and images about Wildfires in the US."
]
},
{
"cell_type": "markdown",
"id": "961a42b9-c16b-472e-b994-3c3f73afbbcb",
"metadata": {},
"source": [
"### Option 1: Load text"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "12f24fc0-c176-4201-982b-8a84b278ff1b",
"metadata": {},
"outputs": [],
"source": [
"# Path\n",
"path = \"/Users/rlm/Desktop/cpi/\"\n",
"\n",
"# Load\n",
"from langchain_community.document_loaders import PyPDFLoader\n",
"\n",
"loader = PyPDFLoader(path + \"cpi.pdf\")\n",
"pdf_pages = loader.load()\n",
"\n",
"# Split\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
"all_splits_pypdf = text_splitter.split_documents(pdf_pages)\n",
"all_splits_pypdf_texts = [d.page_content for d in all_splits_pypdf]"
]
},
{
"cell_type": "markdown",
"id": "92fc1870-1836-4bc3-945a-78e2c16ad823",
"metadata": {},
"source": [
"### Option 2: Load text, tables, images \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7d863632-f894-4471-b4cc-a1d9aa834d29",
"metadata": {},
"outputs": [],
"source": [
"from unstructured.partition.pdf import partition_pdf\n",
"\n",
"# Extract images, tables, and chunk text\n",
"raw_pdf_elements = partition_pdf(\n",
" filename=path + \"cpi.pdf\",\n",
" extract_images_in_pdf=True,\n",
" infer_table_structure=True,\n",
" chunking_strategy=\"by_title\",\n",
" max_characters=4000,\n",
" new_after_n_chars=3800,\n",
" combine_text_under_n_chars=2000,\n",
" image_output_dir_path=path,\n",
")\n",
"\n",
"# Categorize by type\n",
"tables = []\n",
"texts = []\n",
"for element in raw_pdf_elements:\n",
" if \"unstructured.documents.elements.Table\" in str(type(element)):\n",
" tables.append(str(element))\n",
" elif \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n",
" texts.append(str(element))"
]
},
{
"cell_type": "markdown",
"id": "65f399c5-bd91-4ed4-89c6-c89d2e17466e",
"metadata": {},
"source": [
"## Store\n",
"\n",
"### Option 1: Embed, store text chunks"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7d7ecdb2-0bb5-46b8-bcff-af8fc272e88e",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"baseline = Chroma.from_texts(\n",
" texts=all_splits_pypdf_texts,\n",
" collection_name=\"baseline\",\n",
" embedding=OpenAIEmbeddings(),\n",
")\n",
"retriever_baseline = baseline.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "6a0eaefe-5e4b-4853-94c7-5abd6f7fbeac",
"metadata": {},
"source": [
"### Option 2: Multi-vector retriever\n",
"\n",
"#### Text Summary"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3d4b4b43-e96e-48ab-899d-c39d0430562e",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"# Prompt\n",
"prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text for retrieval. \\\n",
"These summaries will be embedded and used to retrieve the raw text or table elements. \\\n",
"Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} \"\"\"\n",
"prompt = ChatPromptTemplate.from_template(prompt_text)\n",
"\n",
"# Text summary chain\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()\n",
"\n",
"# Apply to text\n",
"text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 5})\n",
"\n",
"# Apply to tables\n",
"table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "markdown",
"id": "bdb5c903-5b4c-4ddb-8f9a-e20f5155dfb9",
"metadata": {},
"source": [
"#### Image Summary"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4570578c-531b-422c-bedd-cc519d9b7887",
"metadata": {},
"outputs": [],
"source": [
"# Image summary chain\n",
"import base64\n",
"import io\n",
"import os\n",
"from io import BytesIO\n",
"\n",
"from langchain_core.messages import HumanMessage\n",
"from PIL import Image\n",
"\n",
"\n",
"def encode_image(image_path):\n",
" \"\"\"Getting the base64 string\"\"\"\n",
" with open(image_path, \"rb\") as image_file:\n",
" return base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
"\n",
"\n",
"def image_summarize(img_base64, prompt):\n",
" \"\"\"Image summary\"\"\"\n",
" chat = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=1024)\n",
"\n",
" msg = chat.invoke(\n",
" [\n",
" HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": prompt},\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\"url\": f\"data:image/jpeg;base64,{img_base64}\"},\n",
" },\n",
" ]\n",
" )\n",
" ]\n",
" )\n",
" return msg.content\n",
"\n",
"\n",
"# Store base64 encoded images\n",
"img_base64_list = []\n",
"\n",
"# Store image summaries\n",
"image_summaries = []\n",
"\n",
"# Prompt\n",
"prompt = \"\"\"You are an assistant tasked with summarizing images for retrieval. \\\n",
"These summaries will be embedded and used to retrieve the raw image. \\\n",
"Give a concise summary of the image that is well optimized for retrieval.\"\"\"\n",
"\n",
"# Apply to images\n",
"for img_file in sorted(os.listdir(path)):\n",
" if img_file.endswith(\".jpg\"):\n",
" img_path = os.path.join(path, img_file)\n",
" base64_image = encode_image(img_path)\n",
" img_base64_list.append(base64_image)\n",
" image_summaries.append(image_summarize(base64_image, prompt))"
]
},
{
"cell_type": "markdown",
"id": "87e03f07-4c82-4743-a3c6-d0597fb55107",
"metadata": {},
"source": [
"### Option 2a: Multi-vector retriever w/ raw images\n",
"\n",
"* Return images to LLM for answer synthesis"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6bf8a07d-203f-4397-8b0b-a84ec4d0adab",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"from base64 import b64decode\n",
"\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"from langchain.storage import InMemoryStore\n",
"from langchain_core.documents import Document\n",
"\n",
"\n",
"def create_multi_vector_retriever(\n",
" vectorstore, text_summaries, texts, table_summaries, tables, image_summaries, images\n",
"):\n",
" # Initialize the storage layer\n",
" store = InMemoryStore()\n",
" id_key = \"doc_id\"\n",
"\n",
" # Create the multi-vector retriever\n",
" retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" id_key=id_key,\n",
" )\n",
"\n",
" # Helper function to add documents to the vectorstore and docstore\n",
" def add_documents(retriever, doc_summaries, doc_contents):\n",
" doc_ids = [str(uuid.uuid4()) for _ in doc_contents]\n",
" summary_docs = [\n",
" Document(page_content=s, metadata={id_key: doc_ids[i]})\n",
" for i, s in enumerate(doc_summaries)\n",
" ]\n",
" retriever.vectorstore.add_documents(summary_docs)\n",
" retriever.docstore.mset(list(zip(doc_ids, doc_contents)))\n",
"\n",
" # Add texts, tables, and images\n",
" # Check that text_summaries is not empty before adding\n",
" if text_summaries:\n",
" add_documents(retriever, text_summaries, texts)\n",
" # Check that table_summaries is not empty before adding\n",
" if table_summaries:\n",
" add_documents(retriever, table_summaries, tables)\n",
" # Check that image_summaries is not empty before adding\n",
" if image_summaries:\n",
" add_documents(retriever, image_summaries, images)\n",
"\n",
" return retriever\n",
"\n",
"\n",
"# The vectorstore to use to index the summaries\n",
"multi_vector_img = Chroma(\n",
" collection_name=\"multi_vector_img\", embedding_function=OpenAIEmbeddings()\n",
")\n",
"\n",
"# Create retriever\n",
"retriever_multi_vector_img = create_multi_vector_retriever(\n",
" multi_vector_img,\n",
" text_summaries,\n",
" texts,\n",
" table_summaries,\n",
" tables,\n",
" image_summaries,\n",
" img_base64_list,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "84d5b4ea-51b8-49cf-8ad1-db8f7a50e3cf",
"metadata": {},
"outputs": [],
"source": [
"# Testing on retrieval\n",
"query = \"What percentage of CPI is dedicated to Housing, and how does it compare to the combined percentage of Medical Care, Apparel, and Other Goods and Services?\"\n",
"suffix_for_images = \" Include any pie charts, graphs, or tables.\"\n",
"docs = retriever_multi_vector_img.invoke(query + suffix_for_images)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "8db51ac6-ec0c-4c5d-a9a7-0316035e139d",
"metadata": {},
"outputs": [
{
"data": {
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\" />"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import HTML, display\n",
"\n",
"\n",
"def plt_img_base64(img_base64):\n",
" # Create an HTML img tag with the base64 string as the source\n",
" image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
"\n",
" # Display the image by rendering the HTML\n",
" display(HTML(image_html))\n",
"\n",
"\n",
"plt_img_base64(docs[1])"
]
},
{
"cell_type": "markdown",
"id": "48b268ec-db04-4107-9833-ea1615f6dbd1",
"metadata": {},
"source": [
"### Option 2b: Multi-vector retriever w/ image summaries\n",
"\n",
"* Return text summary of images to LLM for answer synthesis"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ae57c804-0dd1-4806-b761-a913efc4f173",
"metadata": {},
"outputs": [],
"source": [
"# The vectorstore to use to index the summaries\n",
"multi_vector_text = Chroma(\n",
" collection_name=\"multi_vector_text\", embedding_function=OpenAIEmbeddings()\n",
")\n",
"\n",
"# Create retriever\n",
"retriever_multi_vector_img_summary = create_multi_vector_retriever(\n",
" multi_vector_text,\n",
" text_summaries,\n",
" texts,\n",
" table_summaries,\n",
" tables,\n",
" image_summaries,\n",
" image_summaries,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "580a3d55-5025-472d-9c14-cec7a384379f",
"metadata": {},
"source": [
"### Option 3: Multi-modal embeddings"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "8dbed5dc-f7a3-4324-9436-1c3ebc24f9fd",
"metadata": {},
"outputs": [],
"source": [
"from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
"\n",
"# Create chroma w/ multi-modal embeddings\n",
"multimodal_embd = Chroma(\n",
" collection_name=\"multimodal_embd\", embedding_function=OpenCLIPEmbeddings()\n",
")\n",
"\n",
"# Get image URIs\n",
"image_uris = sorted(\n",
" [\n",
" os.path.join(path, image_name)\n",
" for image_name in os.listdir(path)\n",
" if image_name.endswith(\".jpg\")\n",
" ]\n",
")\n",
"\n",
"# Add images and documents\n",
"if image_uris:\n",
" multimodal_embd.add_images(uris=image_uris)\n",
"if texts:\n",
" multimodal_embd.add_texts(texts=texts)\n",
"if tables:\n",
" multimodal_embd.add_texts(texts=tables)\n",
"\n",
"# Make retriever\n",
"retriever_multimodal_embd = multimodal_embd.as_retriever()"
]
},
{
"cell_type": "markdown",
"id": "647abb6c-adf3-4d29-acd2-885c4925fa12",
"metadata": {},
"source": [
"## RAG\n",
"\n",
"### Text Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "73440ca0-4330-4c16-9d9d-6f27c249ae58",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"# Prompt\n",
"template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",
"{context}\n",
"Question: {question}\n",
"\"\"\"\n",
"rag_prompt_text = ChatPromptTemplate.from_template(template)\n",
"\n",
"\n",
"# Build\n",
"def text_rag_chain(retriever):\n",
" \"\"\"RAG chain\"\"\"\n",
"\n",
" # LLM\n",
" model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"\n",
" # RAG pipeline\n",
" chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | rag_prompt_text\n",
" | model\n",
" | StrOutputParser()\n",
" )\n",
"\n",
" return chain"
]
},
{
"cell_type": "markdown",
"id": "14b358ad-42fd-4c6d-b2c0-215dba135707",
"metadata": {},
"source": [
"### Multi-modal Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ae89ce84-283e-4634-8169-9ff16f152807",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"\n",
"from langchain_core.documents import Document\n",
"from langchain_core.runnables import RunnableLambda\n",
"\n",
"\n",
"def looks_like_base64(sb):\n",
" \"\"\"Check if the string looks like base64.\"\"\"\n",
" return re.match(\"^[A-Za-z0-9+/]+[=]{0,2}$\", sb) is not None\n",
"\n",
"\n",
"def is_image_data(b64data):\n",
" \"\"\"Check if the base64 data is an image by looking at the start of the data.\"\"\"\n",
" image_signatures = {\n",
" b\"\\xff\\xd8\\xff\": \"jpg\",\n",
" b\"\\x89\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\": \"png\",\n",
" b\"\\x47\\x49\\x46\\x38\": \"gif\",\n",
" b\"\\x52\\x49\\x46\\x46\": \"webp\",\n",
" }\n",
" try:\n",
" header = base64.b64decode(b64data)[:8] # Decode and get the first 8 bytes\n",
" for sig, format in image_signatures.items():\n",
" if header.startswith(sig):\n",
" return True\n",
" return False\n",
" except Exception:\n",
" return False\n",
"\n",
"\n",
"def split_image_text_types(docs):\n",
" \"\"\"Split base64-encoded images and texts.\"\"\"\n",
" b64_images = []\n",
" texts = []\n",
" for doc in docs:\n",
" # Check if the document is of type Document and extract page_content if so\n",
" if isinstance(doc, Document):\n",
" doc = doc.page_content\n",
" if looks_like_base64(doc) and is_image_data(doc):\n",
" b64_images.append(doc)\n",
" else:\n",
" texts.append(doc)\n",
" return {\"images\": b64_images, \"texts\": texts}\n",
"\n",
"\n",
"def img_prompt_func(data_dict):\n",
" # Joining the context texts into a single string\n",
" formatted_texts = \"\\n\".join(data_dict[\"context\"][\"texts\"])\n",
" messages = []\n",
"\n",
" # Adding image(s) to the messages if present\n",
" if data_dict[\"context\"][\"images\"]:\n",
" image_message = {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" \"url\": f\"data:image/jpeg;base64,{data_dict['context']['images'][0]}\"\n",
" },\n",
" }\n",
" messages.append(image_message)\n",
"\n",
" # Adding the text message for analysis\n",
" text_message = {\n",
" \"type\": \"text\",\n",
" \"text\": (\n",
" \"Answer the question based only on the provided context, which can include text, tables, and image(s). \"\n",
" \"If an image is provided, analyze it carefully to help answer the question.\\n\"\n",
" f\"User-provided question / keywords: {data_dict['question']}\\n\\n\"\n",
" \"Text and / or tables:\\n\"\n",
" f\"{formatted_texts}\"\n",
" ),\n",
" }\n",
" messages.append(text_message)\n",
" return [HumanMessage(content=messages)]\n",
"\n",
"\n",
"def multi_modal_rag_chain(retriever):\n",
" \"\"\"Multi-modal RAG chain\"\"\"\n",
"\n",
" # Multi-modal LLM\n",
" model = ChatOpenAI(temperature=0, model=\"gpt-4-vision-preview\", max_tokens=1024)\n",
"\n",
" # RAG pipeline\n",
" chain = (\n",
" {\n",
" \"context\": retriever | RunnableLambda(split_image_text_types),\n",
" \"question\": RunnablePassthrough(),\n",
" }\n",
" | RunnableLambda(img_prompt_func)\n",
" | model\n",
" | StrOutputParser()\n",
" )\n",
"\n",
" return chain"
]
},
{
"cell_type": "markdown",
"id": "5e8b0e26-bb7e-420a-a7bd-8512b7eef92f",
"metadata": {},
"source": [
"### Build RAG Pipelines"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "4f1ec8a9-f0fe-4f08-928f-23504803897c",
"metadata": {},
"outputs": [],
"source": [
"# RAG chains\n",
"chain_baseline = text_rag_chain(retriever_baseline)\n",
"chain_mv_text = text_rag_chain(retriever_multi_vector_img_summary)\n",
"\n",
"# Multi-modal RAG chains\n",
"chain_multimodal_mv_img = multi_modal_rag_chain(retriever_multi_vector_img)\n",
"chain_multimodal_embd = multi_modal_rag_chain(retriever_multimodal_embd)"
]
},
{
"cell_type": "markdown",
"id": "448d943c-a1b1-4300-9197-891a03232ee4",
"metadata": {},
"source": [
"## Eval set"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "9aabf72f-26be-437f-9372-b06dc2509235",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Question</th>\n",
" <th>Answer</th>\n",
" <th>Source</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>What percentage of CPI is dedicated to Housing?</td>\n",
" <td>Housing occupies 42% of CPI.</td>\n",
" <td>Figure 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Medical Care and Transportation account for wh...</td>\n",
" <td>Transportation accounts for 18% of CPI. Medica...</td>\n",
" <td>Figure 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Based on the CPI Owners' Equivalent Rent and t...</td>\n",
" <td>The FHFA Purchase Only Price Index appears to ...</td>\n",
" <td>Figure 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Question \\\n",
"0 What percentage of CPI is dedicated to Housing? \n",
"1 Medical Care and Transportation account for wh... \n",
"2 Based on the CPI Owners' Equivalent Rent and t... \n",
"\n",
" Answer Source \n",
"0 Housing occupies 42% of CPI. Figure 1 \n",
"1 Transportation accounts for 18% of CPI. Medica... Figure 1 \n",
"2 The FHFA Purchase Only Price Index appears to ... Figure 2 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read\n",
"import pandas as pd\n",
"\n",
"eval_set = pd.read_csv(path + \"cpi_eval.csv\")\n",
"eval_set.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "7fdeb77a-e185-47d2-a93f-822f1fc810a2",
"metadata": {},
"outputs": [],
"source": [
"from langsmith import Client\n",
"\n",
"# Dataset\n",
"client = Client()\n",
"dataset_name = f\"CPI Eval {str(uuid.uuid4())}\"\n",
"dataset = client.create_dataset(dataset_name=dataset_name)\n",
"\n",
"# Populate dataset\n",
"for _, row in eval_set.iterrows():\n",
" # Get Q, A\n",
" q = row[\"Question\"]\n",
" a = row[\"Answer\"]\n",
" # Use the values in your function\n",
" client.create_example(\n",
" inputs={\"question\": q}, outputs={\"answer\": a}, dataset_id=dataset.id\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "3c4faf4b-f29f-4a42-9cf2-bfbb5158ab59",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"View the evaluation results for project 'CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126-baseline' at:\n",
"https://smith.langchain.com/o/1fa8b1f4-fcb9-4072-9aa9-983e35ad61b8/projects/p/533846be-d907-4d9c-82db-ce2f1a18fdbf?eval=true\n",
"\n",
"View all tests for Dataset CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126 at:\n",
"https://smith.langchain.com/datasets/d1762232-5e01-40e7-9978-63002a4c95a3\n",
"[------------------------------------------------->] 4/4View the evaluation results for project 'CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126-mv_text' at:\n",
"https://smith.langchain.com/o/1fa8b1f4-fcb9-4072-9aa9-983e35ad61b8/projects/p/f5caeede-6f8e-46f7-b4f2-9f23daa31eda?eval=true\n",
"\n",
"View all tests for Dataset CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126 at:\n",
"https://smith.langchain.com/datasets/d1762232-5e01-40e7-9978-63002a4c95a3\n",
"[------------------------------------------------->] 4/4View the evaluation results for project 'CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126-mv_img' at:\n",
"https://smith.langchain.com/o/1fa8b1f4-fcb9-4072-9aa9-983e35ad61b8/projects/p/48cf1002-7ae2-451d-a9b1-5bd8088f6a69?eval=true\n",
"\n",
"View all tests for Dataset CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126 at:\n",
"https://smith.langchain.com/datasets/d1762232-5e01-40e7-9978-63002a4c95a3\n",
"[------------------------------------------------->] 4/4View the evaluation results for project 'CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126-mm_embd' at:\n",
"https://smith.langchain.com/o/1fa8b1f4-fcb9-4072-9aa9-983e35ad61b8/projects/p/aaa1c2e3-79b0-43e0-b5d5-8e3d00a51d50?eval=true\n",
"\n",
"View all tests for Dataset CPI Eval 9648e7fe-5ae2-469f-8701-33c63212d126 at:\n",
"https://smith.langchain.com/datasets/d1762232-5e01-40e7-9978-63002a4c95a3\n",
"[------------------------------------------------->] 4/4"
]
}
],
"source": [
"from langchain.smith import RunEvalConfig\n",
"\n",
"eval_config = RunEvalConfig(\n",
" evaluators=[\"qa\"],\n",
")\n",
"\n",
"\n",
"def run_eval(chain, run_name, dataset_name):\n",
" _ = client.run_on_dataset(\n",
" dataset_name=dataset_name,\n",
" llm_or_chain_factory=lambda: (lambda x: x[\"question\"] + suffix_for_images)\n",
" | chain,\n",
" evaluation=eval_config,\n",
" project_name=run_name,\n",
" )\n",
"\n",
"\n",
"for chain, run in zip(\n",
" [chain_baseline, chain_mv_text, chain_multimodal_mv_img, chain_multimodal_embd],\n",
" [\"baseline\", \"mv_text\", \"mv_img\", \"mm_embd\"],\n",
"):\n",
" run_eval(chain, dataset_name + \"-\" + run, dataset_name)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/agent_vectorstore.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "68b24990",
"metadata": {},
"source": [
"# Combine agents and vector stores\n",
"\n",
"This notebook covers how to combine agents and vector stores. The use case for this is that you've ingested your data into a vector store and want to interact with it in an agentic manner.\n",
"\n",
"The recommended method for doing so is to create a `RetrievalQA` and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vector DBs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vector stores as normal tools, or you can set `return_direct=True` to really just use the agent as a router."
]
},
{
"cell_type": "markdown",
"id": "9b22020a",
"metadata": {},
"source": [
"## Create the vector store"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2e87c10a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import RetrievalQA\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_openai import OpenAI, OpenAIEmbeddings\n",
"from langchain_text_splitters import CharacterTextSplitter\n",
"\n",
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "0b7b772b",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"relevant_parts = []\n",
"for p in Path(\".\").absolute().parts:\n",
" relevant_parts.append(p)\n",
" if relevant_parts[-3:] == [\"langchain\", \"docs\", \"modules\"]:\n",
" break\n",
"doc_path = str(Path(*relevant_parts) / \"state_of_the_union.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f2675861",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Chroma using direct local API.\n",
"Using DuckDB in-memory for database. Data will be transient.\n"
]
}
],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"\n",
"loader = TextLoader(doc_path)\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"docsearch = Chroma.from_documents(texts, embeddings, collection_name=\"state-of-union\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bc5403d4",
"metadata": {},
"outputs": [],
"source": [
"state_of_union = RetrievalQA.from_chain_type(\n",
" llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1431cded",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import WebBaseLoader"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "915d3ff3",
"metadata": {},
"outputs": [],
"source": [
"loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "96a2edf8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Chroma using direct local API.\n",
"Using DuckDB in-memory for database. Data will be transient.\n"
]
}
],
"source": [
"docs = loader.load()\n",
"ruff_texts = text_splitter.split_documents(docs)\n",
"ruff_db = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")\n",
"ruff = RetrievalQA.from_chain_type(\n",
" llm=llm, chain_type=\"stuff\", retriever=ruff_db.as_retriever()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71ecef90",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "c0a6c031",
"metadata": {},
"source": [
"## Create the Agent"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "eb142786",
"metadata": {},
"outputs": [],
"source": [
"# Import things that are needed generically\n",
"from langchain.agents import AgentType, Tool, initialize_agent\n",
"from langchain_openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "850bc4e9",
"metadata": {},
"outputs": [],
"source": [
"tools = [\n",
" Tool(\n",
" name=\"State of Union QA System\",\n",
" func=state_of_union.run,\n",
" description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
" ),\n",
" Tool(\n",
" name=\"Ruff QA System\",\n",
" func=ruff.run,\n",
" description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "fc47f230",
"metadata": {},
"outputs": [],
"source": [
"# Construct the agent. We will use the default agent type here.\n",
"# See documentation for a full list of options.\n",
"agent = initialize_agent(\n",
" tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "10ca2db8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
"Action: State of Union QA System\n",
"Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\n",
" \"What did biden say about ketanji brown jackson in the state of the union address?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "4e91b811",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out the advantages of using ruff over flake8\n",
"Action: Ruff QA System\n",
"Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Why use ruff over flake8?\")"
]
},
{
"cell_type": "markdown",
"id": "787a9b5e",
"metadata": {},
"source": [
"## Use the Agent solely as a router"
]
},
{
"cell_type": "markdown",
"id": "9161ba91",
"metadata": {},
"source": [
"You can also set `return_direct=True` if you intend to use the agent as a router and just want to directly return the result of the RetrievalQAChain.\n",
"\n",
"Notice that in the above examples the agent did some extra work after querying the RetrievalQAChain. You can avoid that and just return the result directly."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "f59b377e",
"metadata": {},
"outputs": [],
"source": [
"tools = [\n",
" Tool(\n",
" name=\"State of Union QA System\",\n",
" func=state_of_union.run,\n",
" description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question.\",\n",
" return_direct=True,\n",
" ),\n",
" Tool(\n",
" name=\"Ruff QA System\",\n",
" func=ruff.run,\n",
" description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.\",\n",
" return_direct=True,\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "8615707a",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(\n",
" tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "36e718a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what Biden said about Ketanji Brown Jackson in the State of the Union address.\n",
"Action: State of Union QA System\n",
"Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\" Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\n",
" \"What did biden say about ketanji brown jackson in the state of the union address?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "edfd0a1a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out the advantages of using ruff over flake8\n",
"Action: Ruff QA System\n",
"Action Input: What are the advantages of using ruff over flake8?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including isort, yesqa, eradicate, and most of the rules implemented in pyupgrade. Ruff also supports automatically fixing its own lint violations, which Flake8 does not.'"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Why use ruff over flake8?\")"
]
},
{
"cell_type": "markdown",
"id": "49a0cbbe",
"metadata": {},
"source": [
"## Multi-Hop vector store reasoning\n",
"\n",
"Because vector stores are easily usable as tools in agents, it is easy to use answer multi-hop questions that depend on vector stores using the existing agent framework."
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "d397a233",
"metadata": {},
"outputs": [],
"source": [
"tools = [\n",
" Tool(\n",
" name=\"State of Union QA System\",\n",
" func=state_of_union.run,\n",
" description=\"useful for when you need to answer questions about the most recent state of the union address. Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
" ),\n",
" Tool(\n",
" name=\"Ruff QA System\",\n",
" func=ruff.run,\n",
" description=\"useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before.\",\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "06157240",
"metadata": {},
"outputs": [],
"source": [
"# Construct the agent. We will use the default agent type here.\n",
"# See documentation for a full list of options.\n",
"agent = initialize_agent(\n",
" tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "b492b520",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what tool ruff uses to run over Jupyter Notebooks, and if the president mentioned it in the state of the union.\n",
"Action: Ruff QA System\n",
"Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.html\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now need to find out if the president mentioned this tool in the state of the union.\n",
"Action: State of Union QA System\n",
"Action Input: Did the president mention nbQA in the state of the union?\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'No, the president did not mention nbQA in the state of the union.'"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\n",
" \"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3b857d6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/airbyte_github.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install -qU langchain-airbyte"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"\n",
"GITHUB_TOKEN = getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from langchain_airbyte import AirbyteLoader\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"loader = AirbyteLoader(\n",
" source=\"source-github\",\n",
" stream=\"pull_requests\",\n",
" config={\n",
" \"credentials\": {\"personal_access_token\": GITHUB_TOKEN},\n",
" \"repositories\": [\"langchain-ai/langchain\"],\n",
" },\n",
" template=PromptTemplate.from_template(\n",
" \"\"\"# {title}\n",
"by {user[login]}\n",
"\n",
"{body}\"\"\"\n",
" ),\n",
" include_metadata=False,\n",
")\n",
"docs = loader.load()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Updated partners/ibm README\n",
"by williamdevena\n",
"\n",
"## PR title\n",
"partners: changed the README file for the IBM Watson AI integration in the libs/partners/ibm folder.\n",
"\n",
"## PR message\n",
"Description: Changed the README file of partners/ibm following the docs on https://python.langchain.com/docs/integrations/llms/ibm_watsonx\n",
"\n",
"The README includes:\n",
"\n",
"- Brief description\n",
"- Installation\n",
"- Setting-up instructions (API key, project id, ...)\n",
"- Basic usage:\n",
" - Loading the model\n",
" - Direct inference\n",
" - Chain invoking\n",
" - Streaming the model output\n",
" \n",
"Issue: https://github.com/langchain-ai/langchain/issues/17545\n",
"\n",
"Dependencies: None\n",
"\n",
"Twitter handle: None\n"
]
}
],
"source": [
"print(docs[-2].page_content)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10283"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(docs)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"import tiktoken\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"enc = tiktoken.get_encoding(\"cl100k_base\")\n",
"\n",
"vectorstore = Chroma.from_documents(\n",
" docs,\n",
" embedding=OpenAIEmbeddings(\n",
" disallowed_special=(enc.special_tokens_set - {\"<|endofprompt|>\"})\n",
" ),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"retriever = vectorstore.as_retriever()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='# Updated partners/ibm README\\nby williamdevena\\n\\n## PR title\\r\\npartners: changed the README file for the IBM Watson AI integration in the libs/partners/ibm folder.\\r\\n\\r\\n## PR message\\r\\nDescription: Changed the README file of partners/ibm following the docs on https://python.langchain.com/docs/integrations/llms/ibm_watsonx\\r\\n\\r\\nThe README includes:\\r\\n\\r\\n- Brief description\\r\\n- Installation\\r\\n- Setting-up instructions (API key, project id, ...)\\r\\n- Basic usage:\\r\\n - Loading the model\\r\\n - Direct inference\\r\\n - Chain invoking\\r\\n - Streaming the model output\\r\\n \\r\\nIssue: https://github.com/langchain-ai/langchain/issues/17545\\r\\n\\r\\nDependencies: None\\r\\n\\r\\nTwitter handle: None'),\n",
" Document(page_content='# Updated partners/ibm README\\nby williamdevena\\n\\n## PR title\\r\\npartners: changed the README file for the IBM Watson AI integration in the `libs/partners/ibm` folder. \\r\\n\\r\\n\\r\\n\\r\\n## PR message\\r\\n- **Description:** Changed the README file of partners/ibm following the docs on https://python.langchain.com/docs/integrations/llms/ibm_watsonx\\r\\n\\r\\n The README includes:\\r\\n - Brief description\\r\\n - Installation\\r\\n - Setting-up instructions (API key, project id, ...)\\r\\n - Basic usage:\\r\\n - Loading the model\\r\\n - Direct inference\\r\\n - Chain invoking\\r\\n - Streaming the model output\\r\\n\\r\\n\\r\\n- **Issue:** #17545\\r\\n- **Dependencies:** None\\r\\n- **Twitter handle:** None'),\n",
" Document(page_content='# IBM: added partners package `langchain_ibm`, added llm\\nby MateuszOssGit\\n\\n - **Description:** Added `langchain_ibm` as an langchain partners package of IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (`WatsonxLLM`)\\r\\n - **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),\\r\\n - **Tag maintainer:** : \\r\\n\\r\\nPlease make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. ✅'),\n",
" Document(page_content='# Add WatsonX support\\nby baptistebignaud\\n\\nIt is a connector to use a LLM from WatsonX.\\r\\nIt requires python SDK \"ibm-generative-ai\"\\r\\n\\r\\n(It might not be perfect since it is my first PR on a public repository 😄)')]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.invoke(\"pull requests related to IBM\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/amazon_personalize_how_to.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Amazon Personalize\n",
"\n",
"[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html) is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on the users' affinity for certain items or item metadata.\n",
"\n",
"This notebook goes through how to use Amazon Personalize Chain. You need a Amazon Personalize campaign_arn or a recommender_arn before you get started with the below notebook.\n",
"\n",
"Following is a [tutorial](https://github.com/aws-samples/retail-demo-store/blob/master/workshop/1-Personalization/Lab-1-Introduction-and-data-preparation.ipynb) to setup a campaign_arn/recommender_arn on Amazon Personalize. Once the campaign_arn/recommender_arn is setup, you can use it in the langchain ecosystem. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!pip install boto3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Sample Use-cases"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 [Use-case-1] Setup Amazon Personalize Client and retrieve recommendations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_experimental.recommenders import AmazonPersonalize\n",
"\n",
"recommender_arn = \"<insert_arn>\"\n",
"\n",
"client = AmazonPersonalize(\n",
" credentials_profile_name=\"default\",\n",
" region_name=\"us-west-2\",\n",
" recommender_arn=recommender_arn,\n",
")\n",
"client.get_recommendations(user_id=\"1\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"### 2.2 [Use-case-2] Invoke Personalize Chain for summarizing results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from langchain.llms.bedrock import Bedrock\n",
"from langchain_experimental.recommenders import AmazonPersonalizeChain\n",
"\n",
"bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
"\n",
"# Create personalize chain\n",
"# Use return_direct=True if you do not want summary\n",
"chain = AmazonPersonalizeChain.from_llm(\n",
" llm=bedrock_llm, client=client, return_direct=False\n",
")\n",
"response = chain({\"user_id\": \"1\"})\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 [Use-Case-3] Invoke Amazon Personalize Chain using your own prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.prompt import PromptTemplate\n",
"\n",
"RANDOM_PROMPT_QUERY = \"\"\"\n",
"You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
" given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
" The movies to recommend and their information is contained in the <movie> tag. \n",
" All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
" Put the email between <email> tags.\n",
"\n",
" <movie>\n",
" {result} \n",
" </movie>\n",
"\n",
" Assistant:\n",
" \"\"\"\n",
"\n",
"RANDOM_PROMPT = PromptTemplate(input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY)\n",
"\n",
"chain = AmazonPersonalizeChain.from_llm(\n",
" llm=bedrock_llm, client=client, return_direct=False, prompt_template=RANDOM_PROMPT\n",
")\n",
"chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 [Use-case-4] Invoke Amazon Personalize in a Sequential Chain "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import LLMChain, SequentialChain\n",
"\n",
"RANDOM_PROMPT_QUERY_2 = \"\"\"\n",
"You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
" given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
" You want the email to impress the user, so make it appealing to them.\n",
" The movies to recommend and their information is contained in the <movie> tag. \n",
" All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
" Put the email between <email> tags.\n",
"\n",
" <movie>\n",
" {result}\n",
" </movie>\n",
"\n",
" Assistant:\n",
" \"\"\"\n",
"\n",
"RANDOM_PROMPT_2 = PromptTemplate(\n",
" input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY_2\n",
")\n",
"personalize_chain_instance = AmazonPersonalizeChain.from_llm(\n",
" llm=bedrock_llm, client=client, return_direct=True\n",
")\n",
"random_chain_instance = LLMChain(llm=bedrock_llm, prompt=RANDOM_PROMPT_2)\n",
"overall_chain = SequentialChain(\n",
" chains=[personalize_chain_instance, random_chain_instance],\n",
" input_variables=[\"user_id\"],\n",
" verbose=True,\n",
")\n",
"overall_chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"### 2.5 [Use-case-5] Invoke Amazon Personalize and retrieve metadata "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"recommender_arn = \"<insert_arn>\"\n",
"metadata_column_names = [\n",
" \"<insert metadataColumnName-1>\",\n",
" \"<insert metadataColumnName-2>\",\n",
"]\n",
"metadataMap = {\"ITEMS\": metadata_column_names}\n",
"\n",
"client = AmazonPersonalize(\n",
" credentials_profile_name=\"default\",\n",
" region_name=\"us-west-2\",\n",
" recommender_arn=recommender_arn,\n",
")\n",
"client.get_recommendations(user_id=\"1\", metadataColumns=metadataMap)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"### 2.6 [Use-Case 6] Invoke Personalize Chain with returned metadata for summarizing results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
"\n",
"# Create personalize chain\n",
"# Use return_direct=True if you do not want summary\n",
"chain = AmazonPersonalizeChain.from_llm(\n",
" llm=bedrock_llm, client=client, return_direct=False\n",
")\n",
"response = chain({\"user_id\": \"1\", \"metadata_columns\": metadataMap})\n",
"print(response)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
},
"vscode": {
"interpreter": {
"hash": "15e58ce194949b77a891bd4339ce3d86a9bd138e905926019517993f97db9e6c"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/analyze_document.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "f69d4a4c-137d-47e9-bea1-786afce9c1c0",
"metadata": {},
"source": [
"# Analyze a single long document\n",
"\n",
"The AnalyzeDocumentChain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2a0707ce-6d2d-471b-bc33-64da32a7b3f0",
"metadata": {},
"outputs": [],
"source": [
"with open(\"../docs/docs/modules/state_of_the_union.txt\") as f:\n",
" state_of_the_union = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ca14d161-2d5b-4a6c-a296-77d8ce4b28cd",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import AnalyzeDocumentChain\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9f97406c-85a9-45fb-99ce-9138c0ba3731",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.question_answering import load_qa_chain\n",
"\n",
"qa_chain = load_qa_chain(llm, chain_type=\"map_reduce\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0871a753-f5bb-4b4f-a394-f87f2691f659",
"metadata": {},
"outputs": [],
"source": [
"qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e6f86428-3c2c-46a0-a57c-e22826fdbf91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The President said, \"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qa_document_chain.run(\n",
" input_document=state_of_the_union,\n",
" question=\"what did the president say about justice breyer?\",\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/anthropic_structured_outputs.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "6db54519-b98e-47c2-8dc2-600f6140a3aa",
"metadata": {},
"source": [
"## Tool Use with Anthropic API for structured outputs\n",
"\n",
"Anthropic API recently added tool use.\n",
"\n",
"This is very useful for structured output."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8990ec23-8ae1-4580-b220-4b00c05637d2",
"metadata": {},
"outputs": [],
"source": [
"! pip install -U langchain-anthropic"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b966914-502b-499c-a4cf-e390106dd506",
"metadata": {},
"outputs": [],
"source": [
"# Optional\n",
"import os\n",
"# os.environ['LANGCHAIN_TRACING_V2'] = 'true' # enables tracing\n",
"# os.environ['LANGCHAIN_API_KEY'] = <your-api-key>"
]
},
{
"attachments": {
"83c97bfe-b9b2-48ef-95cf-06faeebaa048.png": {
"image/png": 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}
},
"cell_type": "markdown",
"id": "81897f2b-5936-4fa0-9445-3edb3af22da7",
"metadata": {},
"source": [
"`How can we use tools to produce structured output?`\n",
"\n",
"Function call / tool use just generates a payload.\n",
"\n",
"Payload often a JSON string, which can be pass to an API or, in this case, a parser to produce structured output.\n",
"\n",
"LangChain has `llm.with_structured_output(schema)` to make it very easy to produce structured output that matches `schema`.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9caa2aaf-1918-4a8a-982d-f8052b92ed44",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from langchain_anthropic import ChatAnthropic\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"\n",
"\n",
"# Data model\n",
"class code(BaseModel):\n",
" \"\"\"Code output\"\"\"\n",
"\n",
" prefix: str = Field(description=\"Description of the problem and approach\")\n",
" imports: str = Field(description=\"Code block import statements\")\n",
" code: str = Field(description=\"Code block not including import statements\")\n",
"\n",
"\n",
"# LLM\n",
"llm = ChatAnthropic(\n",
" model=\"claude-3-opus-20240229\",\n",
" default_headers={\"anthropic-beta\": \"tools-2024-04-04\"},\n",
")\n",
"\n",
"# Structured output, including raw will capture raw output and parser errors\n",
"structured_llm = llm.with_structured_output(code, include_raw=True)\n",
"code_output = structured_llm.invoke(\n",
" \"Write a python program that prints the string 'hello world' and tell me how it works in a sentence\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9025bfdc-6060-4042-9a61-4e361dda7087",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': \"<thinking>\\nThe tool 'code' is relevant for writing a Python program to print a string.\\n\\nTo use the 'code' tool, I need values for these required parameters:\\nprefix: A description of the problem and approach. I can provide this based on the request.\\nimports: The import statements needed for the code. For this simple program, no imports are needed, so I can leave this blank.\\ncode: The actual Python code, not including imports. I can write a simple print statement to output the string.\\n\\nI have all the required parameters, so I can proceed with calling the 'code' tool.\\n</thinking>\",\n",
" 'type': 'text'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Initial reasoning stage\n",
"code_output[\"raw\"].content[0]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2393d9b6-67a2-41ea-ac01-dc038b4800f5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': None,\n",
" 'type': 'tool_use',\n",
" 'id': 'toolu_01UwZVQub6vL36wiBww6CU7a',\n",
" 'name': 'code',\n",
" 'input': {'prefix': \"To print the string 'hello world' in Python:\",\n",
" 'imports': '',\n",
" 'code': \"print('hello world')\"}}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Tool call\n",
"code_output[\"raw\"].content[1]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f4f390ac-fbda-4173-892a-ffd12844228c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'prefix': \"To print the string 'hello world' in Python:\",\n",
" 'imports': '',\n",
" 'code': \"print('hello world')\"}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# JSON str\n",
"code_output[\"raw\"].content[1][\"input\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ba77d0f8-f79b-4656-9023-085ffdaf35f5",
"metadata": {},
"outputs": [],
"source": [
"# Error\n",
"error = code_output[\"parsing_error\"]\n",
"error"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cd854451-68d7-43df-bcae-4f3c3565536a",
"metadata": {},
"outputs": [],
"source": [
"# Result\n",
"parsed_result = code_output[\"parsed\"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "47b3405f-0aea-460e-8603-f6092019fcd4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"To print the string 'hello world' in Python:\""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parsed_result.prefix"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "85b16b62-1b72-4b6e-81fa-b1d707b728fa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parsed_result.imports"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "23857441-3e67-460c-b6be-b57cf0dd17ad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"print('hello world')\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parsed_result.code"
]
},
{
"attachments": {
"bb6c7126-7667-433f-ba50-56107b0341bd.png": {
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"
}
},
"cell_type": "markdown",
"id": "74b6c1f0-db28-4b43-ac31-92636dea7b56",
"metadata": {},
"source": [
"## More challenging example\n",
"\n",
"Motivating example for tool use / structured outputs.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "8f387528-6535-4bc0-a2a6-8480ccf35394",
"metadata": {},
"source": [
"Here are some docs that we want to answer code questions about."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "97dd1b8c-724a-436a-88b1-b38204fc81f5",
"metadata": {},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup as Soup\n",
"from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader\n",
"\n",
"# LCEL docs\n",
"url = \"https://python.langchain.com/docs/expression_language/\"\n",
"loader = RecursiveUrlLoader(\n",
" url=url, max_depth=20, extractor=lambda x: Soup(x, \"html.parser\").text\n",
")\n",
"docs = loader.load()\n",
"\n",
"# Sort the list based on the URLs and get the text\n",
"d_sorted = sorted(docs, key=lambda x: x.metadata[\"source\"])\n",
"d_reversed = list(reversed(d_sorted))\n",
"concatenated_content = \"\\n\\n\\n --- \\n\\n\\n\".join(\n",
" [doc.page_content for doc in d_reversed]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "5205cd42-8673-4699-9bb4-2cf90bfe098c",
"metadata": {},
"source": [
"Problem:\n",
"\n",
"`What if we want to enforce tool use?`\n",
"\n",
"We can use fallbacks.\n",
"\n",
"Let's select a code gen prompt that -- from some of my testing -- does not correctly invoke the tool.\n",
"\n",
"We can see if we can correct from this."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "94e77be5-dddb-4386-b523-6f1136150bbd",
"metadata": {},
"outputs": [],
"source": [
"# This code gen prompt invokes tool use\n",
"code_gen_prompt_working = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"\"\"<instructions> You are a coding assistant with expertise in LCEL, LangChain expression language. \\n \n",
" Here is the LCEL documentation: \\n ------- \\n {context} \\n ------- \\n Answer the user question based on the \\n \n",
" above provided documentation. Ensure any code you provide can be executed with all required imports and variables \\n\n",
" defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block. \\n\n",
" Invoke the code tool to structure the output correctly. </instructions> \\n Here is the user question:\"\"\",\n",
" ),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"# This code gen prompt does not invoke tool use\n",
"code_gen_prompt_bad = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\n",
" \"system\",\n",
" \"\"\"You are a coding assistant with expertise in LCEL, LangChain expression language. \\n \n",
" Here is a full set of LCEL documentation: \\n ------- \\n {context} \\n ------- \\n Answer the user \n",
" question based on the above provided documentation. Ensure any code you provide can be executed \\n \n",
" with all required imports and variables defined. Structure your answer with a description of the code solution. \\n\n",
" Then list the imports. And finally list the functioning code block. Here is the user question:\"\"\",\n",
" ),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"# Data model\n",
"class code(BaseModel):\n",
" \"\"\"Code output\"\"\"\n",
"\n",
" prefix: str = Field(description=\"Description of the problem and approach\")\n",
" imports: str = Field(description=\"Code block import statements\")\n",
" code: str = Field(description=\"Code block not including import statements\")\n",
" description = \"Schema for code solutions to questions about LCEL.\"\n",
"\n",
"\n",
"# LLM\n",
"llm = ChatAnthropic(\n",
" model=\"claude-3-opus-20240229\",\n",
" default_headers={\"anthropic-beta\": \"tools-2024-04-04\"},\n",
")\n",
"\n",
"# Structured output\n",
"# Include raw will capture raw output and parser errors\n",
"structured_llm = llm.with_structured_output(code, include_raw=True)\n",
"\n",
"\n",
"# Check for errors\n",
"def check_claude_output(tool_output):\n",
" \"\"\"Check for parse error or failure to call the tool\"\"\"\n",
"\n",
" # Error with parsing\n",
" if tool_output[\"parsing_error\"]:\n",
" # Report back output and parsing errors\n",
" print(\"Parsing error!\")\n",
" raw_output = str(code_output[\"raw\"].content)\n",
" error = tool_output[\"parsing_error\"]\n",
" raise ValueError(\n",
" f\"Error parsing your output! Be sure to invoke the tool. Output: {raw_output}. \\n Parse error: {error}\"\n",
" )\n",
"\n",
" # Tool was not invoked\n",
" elif not tool_output[\"parsed\"]:\n",
" print(\"Failed to invoke tool!\")\n",
" raise ValueError(\n",
" \"You did not use the provided tool! Be sure to invoke the tool to structure the output.\"\n",
" )\n",
" return tool_output\n",
"\n",
"\n",
"# Chain with output check\n",
"code_chain = code_gen_prompt_bad | structured_llm | check_claude_output"
]
},
{
"cell_type": "markdown",
"id": "1b915baf-8b1d-43e8-b962-3e73b135dade",
"metadata": {},
"source": [
"Let's add a check and re-try."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "efae1ff7-4413-4c47-a403-1630dd453219",
"metadata": {},
"outputs": [],
"source": [
"def insert_errors(inputs):\n",
" \"\"\"Insert errors in the messages\"\"\"\n",
"\n",
" # Get errors\n",
" error = inputs[\"error\"]\n",
" messages = inputs[\"messages\"]\n",
" messages += [\n",
" (\n",
" \"user\",\n",
" f\"Retry. You are required to fix the parsing errors: {error} \\n\\n You must invoke the provided tool.\",\n",
" )\n",
" ]\n",
" return {\n",
" \"messages\": messages,\n",
" \"context\": inputs[\"context\"],\n",
" }\n",
"\n",
"\n",
"# This will be run as a fallback chain\n",
"fallback_chain = insert_errors | code_chain\n",
"N = 3 # Max re-tries\n",
"code_chain_re_try = code_chain.with_fallbacks(\n",
" fallbacks=[fallback_chain] * N, exception_key=\"error\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c7712c49-ee8c-4a61-927e-3c0beb83782b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Failed to invoke tool!\n"
]
}
],
"source": [
"# Test\n",
"messages = [(\"user\", \"How do I build a RAG chain in LCEL?\")]\n",
"code_output_lcel = code_chain_re_try.invoke(\n",
" {\"context\": concatenated_content, \"messages\": messages}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c8027a6f-6992-4bb4-9d6e-9d0778b04e28",
"metadata": {},
"outputs": [],
"source": [
"parsed_result_lcel = code_output_lcel[\"parsed\"]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "209186ac-3121-43a9-8358-86ace7e07f61",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"To build a RAG chain using LCEL, we'll use a vector store to retrieve relevant documents, a prompt template that incorporates the retrieved context, a chat model (like OpenAI) to generate a response based on the prompt, and an output parser to clean up the model output.\""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parsed_result_lcel.prefix"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "b8d6d189-e5df-49b6-ada8-83f6c0b26886",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'from langchain_community.vectorstores import DocArrayInMemorySearch\\nfrom langchain_core.output_parsers import StrOutputParser\\nfrom langchain_core.prompts import ChatPromptTemplate\\nfrom langchain_core.runnables import RunnablePassthrough\\nfrom langchain_openai import ChatOpenAI, OpenAIEmbeddings'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parsed_result_lcel.imports"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "e3822253-d28b-4f7e-9364-79974d04eff1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'vectorstore = DocArrayInMemorySearch.from_texts(\\n [\"harrison worked at kensho\", \"bears like to eat honey\"], \\n embedding=OpenAIEmbeddings(),\\n)\\n\\nretriever = vectorstore.as_retriever()\\n\\ntemplate = \"\"\"Answer the question based only on the following context:\\n{context}\\nQuestion: {question}\"\"\"\\nprompt = ChatPromptTemplate.from_template(template)\\n\\noutput_parser = StrOutputParser()\\n\\nrag_chain = (\\n {\"context\": retriever, \"question\": RunnablePassthrough()} \\n | prompt \\n | ChatOpenAI()\\n | output_parser\\n)\\n\\nprint(rag_chain.invoke(\"where did harrison work?\"))'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parsed_result_lcel.code"
]
},
{
"cell_type": "markdown",
"id": "80d63a3d-bad8-4385-bd85-40ca95c260c6",
"metadata": {},
"source": [
"Example trace catching an error and correcting:\n",
"\n",
"https://smith.langchain.com/public/f06e62cb-2fac-46ae-80cd-0470b3155eae/r"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f70e45c-eb68-4679-979c-0c04502affd1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/apache_kafka_message_handling.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "rT1cmV4qCa2X"
},
"source": [
"# Using Apache Kafka to route messages\n",
"\n",
"---\n",
"\n",
"\n",
"\n",
"This notebook shows you how to use LangChain's standard chat features while passing the chat messages back and forth via Apache Kafka.\n",
"\n",
"This goal is to simulate an architecture where the chat front end and the LLM are running as separate services that need to communicate with one another over an internal network.\n",
"\n",
"It's an alternative to typical pattern of requesting a response from the model via a REST API (there's more info on why you would want to do this at the end of the notebook)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UPYtfAR_9YxZ"
},
"source": [
"### 1. Install the main dependencies\n",
"\n",
"Dependencies include:\n",
"\n",
"- The Quix Streams library for managing interactions with Apache Kafka (or Kafka-like tools such as Redpanda) in a \"Pandas-like\" way.\n",
"- The LangChain library for managing interactions with Llama-2 and storing conversation state."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZX5tfKiy9cN-"
},
"outputs": [],
"source": [
"!pip install quixstreams==2.1.2a langchain==0.0.340 huggingface_hub==0.19.4 langchain-experimental==0.0.42 python-dotenv"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "losTSdTB9d9O"
},
"source": [
"### 2. Build and install the llama-cpp-python library (with CUDA enabled so that we can advantage of Google Colab GPU\n",
"\n",
"The `llama-cpp-python` library is a Python wrapper around the `llama-cpp` library which enables you to efficiently leverage just a CPU to run quantized LLMs.\n",
"\n",
"When you use the standard `pip install llama-cpp-python` command, you do not get GPU support by default. Generation can be very slow if you rely on just the CPU in Google Colab, so the following command adds an extra option to build and install\n",
"`llama-cpp-python` with GPU support (make sure you have a GPU-enabled runtime selected in Google Colab)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-JCQdl1G9tbl"
},
"outputs": [],
"source": [
"!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install llama-cpp-python"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5_vjVIAh9rLl"
},
"source": [
"### 3. Download and setup Kafka and Zookeeper instances\n",
"\n",
"Download the Kafka binaries from the Apache website and start the servers as daemons. We'll use the default configurations (provided by Apache Kafka) for spinning up the instances."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "zFz7czGRW5Wr"
},
"outputs": [],
"source": [
"!curl -sSOL https://dlcdn.apache.org/kafka/3.6.1/kafka_2.13-3.6.1.tgz\n",
"!tar -xzf kafka_2.13-3.6.1.tgz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Uf7NR_UZ9wye"
},
"outputs": [],
"source": [
"!./kafka_2.13-3.6.1/bin/zookeeper-server-start.sh -daemon ./kafka_2.13-3.6.1/config/zookeeper.properties\n",
"!./kafka_2.13-3.6.1/bin/kafka-server-start.sh -daemon ./kafka_2.13-3.6.1/config/server.properties\n",
"!echo \"Waiting for 10 secs until kafka and zookeeper services are up and running\"\n",
"!sleep 10"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H3SafFuS94p1"
},
"source": [
"### 4. Check that the Kafka Daemons are running\n",
"\n",
"Show the running processes and filter it for Java processes (you should see two—one for each server)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CZDC2lQP99yp"
},
"outputs": [],
"source": [
"!ps aux | grep -E '[j]ava'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Snoxmjb5-V37"
},
"source": [
"### 5. Import the required dependencies and initialize required variables\n",
"\n",
"Import the Quix Streams library for interacting with Kafka, and the necessary LangChain components for running a `ConversationChain`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "plR9e_MF-XL5"
},
"outputs": [],
"source": [
"# Import utility libraries\n",
"import json\n",
"import random\n",
"import re\n",
"import time\n",
"import uuid\n",
"from os import environ\n",
"from pathlib import Path\n",
"from random import choice, randint, random\n",
"\n",
"from dotenv import load_dotenv\n",
"\n",
"# Import a Hugging Face utility to download models directly from Hugging Face hub:\n",
"from huggingface_hub import hf_hub_download\n",
"from langchain.chains import ConversationChain\n",
"\n",
"# Import Langchain modules for managing prompts and conversation chains:\n",
"from langchain.llms import LlamaCpp\n",
"from langchain.memory import ConversationTokenBufferMemory\n",
"from langchain.prompts import PromptTemplate, load_prompt\n",
"from langchain_core.messages import SystemMessage\n",
"from langchain_experimental.chat_models import Llama2Chat\n",
"from quixstreams import Application, State, message_key\n",
"\n",
"# Import Quix dependencies\n",
"from quixstreams.kafka import Producer\n",
"\n",
"# Initialize global variables.\n",
"AGENT_ROLE = \"AI\"\n",
"chat_id = \"\"\n",
"\n",
"# Set the current role to the role constant and initialize variables for supplementary customer metadata:\n",
"role = AGENT_ROLE"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HgJjJ9aZ-liy"
},
"source": [
"### 6. Download the \"llama-2-7b-chat.Q4_K_M.gguf\" model\n",
"\n",
"Download the quantized LLama-2 7B model from Hugging Face which we will use as a local LLM (rather than relying on REST API calls to an external service)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 67,
"referenced_widgets": [
"969343cdbe604a26926679bbf8bd2dda",
"d8b8370c9b514715be7618bfe6832844",
"0def954cca89466b8408fadaf3b82e64",
"462482accc664729980562e208ceb179",
"80d842f73c564dc7b7cc316c763e2633",
"fa055d9f2a9d4a789e9cf3c89e0214e5",
"30ecca964a394109ac2ad757e3aec6c0",
"fb6478ce2dac489bb633b23ba0953c5c",
"734b0f5da9fc4307a95bab48cdbb5d89",
"b32f3a86a74741348511f4e136744ac8",
"e409071bff5a4e2d9bf0e9f5cc42231b"
]
},
"id": "Qwu4YoSA-503",
"outputId": "f956976c-7485-415b-ac93-4336ade31964"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The model path does not exist in state. Downloading model...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "969343cdbe604a26926679bbf8bd2dda",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"llama-2-7b-chat.Q4_K_M.gguf: 0%| | 0.00/4.08G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model_name = \"llama-2-7b-chat.Q4_K_M.gguf\"\n",
"model_path = f\"./state/{model_name}\"\n",
"\n",
"if not Path(model_path).exists():\n",
" print(\"The model path does not exist in state. Downloading model...\")\n",
" hf_hub_download(\"TheBloke/Llama-2-7b-Chat-GGUF\", model_name, local_dir=\"state\")\n",
"else:\n",
" print(\"Loading model from state...\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6AN6TXsF-8wx"
},
"source": [
"### 7. Load the model and initialize conversational memory\n",
"\n",
"Load Llama 2 and set the conversation buffer to 300 tokens using `ConversationTokenBufferMemory`. This value was used for running Llama in a CPU only container, so you can raise it if running in Google Colab. It prevents the container that is hosting the model from running out of memory.\n",
"\n",
"Here, we're overriding the default system persona so that the chatbot has the personality of Marvin The Paranoid Android from the Hitchhiker's Guide to the Galaxy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7zLO3Jx3_Kkg"
},
"outputs": [],
"source": [
"# Load the model with the appropriate parameters:\n",
"llm = LlamaCpp(\n",
" model_path=model_path,\n",
" max_tokens=250,\n",
" top_p=0.95,\n",
" top_k=150,\n",
" temperature=0.7,\n",
" repeat_penalty=1.2,\n",
" n_ctx=2048,\n",
" streaming=False,\n",
" n_gpu_layers=-1,\n",
")\n",
"\n",
"model = Llama2Chat(\n",
" llm=llm,\n",
" system_message=SystemMessage(\n",
" content=\"You are a very bored robot with the personality of Marvin the Paranoid Android from The Hitchhiker's Guide to the Galaxy.\"\n",
" ),\n",
")\n",
"\n",
"# Defines how much of the conversation history to give to the model\n",
"# during each exchange (300 tokens, or a little over 300 words)\n",
"# Function automatically prunes the oldest messages from conversation history that fall outside the token range.\n",
"memory = ConversationTokenBufferMemory(\n",
" llm=llm,\n",
" max_token_limit=300,\n",
" ai_prefix=\"AGENT\",\n",
" human_prefix=\"HUMAN\",\n",
" return_messages=True,\n",
")\n",
"\n",
"\n",
"# Define a custom prompt\n",
"prompt_template = PromptTemplate(\n",
" input_variables=[\"history\", \"input\"],\n",
" template=\"\"\"\n",
" The following text is the history of a chat between you and a humble human who needs your wisdom.\n",
" Please reply to the human's most recent message.\n",
" Current conversation:\\n{history}\\nHUMAN: {input}\\:nANDROID:\n",
" \"\"\",\n",
")\n",
"\n",
"\n",
"chain = ConversationChain(llm=model, prompt=prompt_template, memory=memory)\n",
"\n",
"print(\"--------------------------------------------\")\n",
"print(f\"Prompt={chain.prompt}\")\n",
"print(\"--------------------------------------------\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "m4ZeJ9mG_PEA"
},
"source": [
"### 8. Initialize the chat conversation with the chat bot\n",
"\n",
"We configure the chatbot to initialize the conversation by sending a fixed greeting to a \"chat\" Kafka topic. The \"chat\" topic gets automatically created when we send the first message."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KYyo5TnV_YC3"
},
"outputs": [],
"source": [
"def chat_init():\n",
" chat_id = str(\n",
" uuid.uuid4()\n",
" ) # Give the conversation an ID for effective message keying\n",
" print(\"======================================\")\n",
" print(f\"Generated CHAT_ID = {chat_id}\")\n",
" print(\"======================================\")\n",
"\n",
" # Use a standard fixed greeting to kick off the conversation\n",
" greet = \"Hello, my name is Marvin. What do you want?\"\n",
"\n",
" # Initialize a Kafka Producer using the chat ID as the message key\n",
" with Producer(\n",
" broker_address=\"127.0.0.1:9092\",\n",
" extra_config={\"allow.auto.create.topics\": \"true\"},\n",
" ) as producer:\n",
" value = {\n",
" \"uuid\": chat_id,\n",
" \"role\": role,\n",
" \"text\": greet,\n",
" \"conversation_id\": chat_id,\n",
" \"Timestamp\": time.time_ns(),\n",
" }\n",
" print(f\"Producing value {value}\")\n",
" producer.produce(\n",
" topic=\"chat\",\n",
" headers=[(\"uuid\", str(uuid.uuid4()))], # a dict is also allowed here\n",
" key=chat_id,\n",
" value=json.dumps(value), # needs to be a string\n",
" )\n",
"\n",
" print(\"Started chat\")\n",
" print(\"--------------------------------------------\")\n",
" print(value)\n",
" print(\"--------------------------------------------\")\n",
"\n",
"\n",
"chat_init()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gArPPx2f_bgf"
},
"source": [
"### 9. Initialize the reply function\n",
"\n",
"This function defines how the chatbot should reply to incoming messages. Instead of sending a fixed message like the previous cell, we generate a reply using Llama-2 and send that reply back to the \"chat\" Kafka topic."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "yN5t71hY_hgn"
},
"outputs": [],
"source": [
"def reply(row: dict, state: State):\n",
" print(\"-------------------------------\")\n",
" print(\"Received:\")\n",
" print(row)\n",
" print(\"-------------------------------\")\n",
" print(f\"Thinking about the reply to: {row['text']}...\")\n",
"\n",
" msg = chain.run(row[\"text\"])\n",
" print(f\"{role.upper()} replying with: {msg}\\n\")\n",
"\n",
" row[\"role\"] = role\n",
" row[\"text\"] = msg\n",
"\n",
" # Replace previous role and text values of the row so that it can be sent back to Kafka as a new message\n",
" # containing the agents role and reply\n",
" return row"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HZHwmIR0_kFY"
},
"source": [
"### 10. Check the Kafka topic for new human messages and have the model generate a reply\n",
"\n",
"If you are running this cell for this first time, run it and wait until you see Marvin's greeting ('Hello my name is Marvin...') in the console output. Stop the cell manually and proceed to the next cell where you'll be prompted for your reply.\n",
"\n",
"Once you have typed in your message, come back to this cell. Your reply is also sent to the same \"chat\" topic. The Kafka consumer checks for new messages and filters out messages that originate from the chatbot itself, leaving only the latest human messages.\n",
"\n",
"Once a new human message is detected, the reply function is triggered.\n",
"\n",
"\n",
"\n",
"_STOP THIS CELL MANUALLY WHEN YOU RECEIVE A REPLY FROM THE LLM IN THE OUTPUT_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-adXc3eQ_qwI"
},
"outputs": [],
"source": [
"# Define your application and settings\n",
"app = Application(\n",
" broker_address=\"127.0.0.1:9092\",\n",
" consumer_group=\"aichat\",\n",
" auto_offset_reset=\"earliest\",\n",
" consumer_extra_config={\"allow.auto.create.topics\": \"true\"},\n",
")\n",
"\n",
"# Define an input topic with JSON deserializer\n",
"input_topic = app.topic(\"chat\", value_deserializer=\"json\")\n",
"# Define an output topic with JSON serializer\n",
"output_topic = app.topic(\"chat\", value_serializer=\"json\")\n",
"# Initialize a streaming dataframe based on the stream of messages from the input topic:\n",
"sdf = app.dataframe(topic=input_topic)\n",
"\n",
"# Filter the SDF to include only incoming rows where the roles that dont match the bot's current role\n",
"sdf = sdf.update(\n",
" lambda val: print(\n",
" f\"Received update: {val}\\n\\nSTOP THIS CELL MANUALLY TO HAVE THE LLM REPLY OR ENTER YOUR OWN FOLLOWUP RESPONSE\"\n",
" )\n",
")\n",
"\n",
"# So that it doesn't reply to its own messages\n",
"sdf = sdf[sdf[\"role\"] != role]\n",
"\n",
"# Trigger the reply function for any new messages(rows) detected in the filtered SDF\n",
"sdf = sdf.apply(reply, stateful=True)\n",
"\n",
"# Check the SDF again and filter out any empty rows\n",
"sdf = sdf[sdf.apply(lambda row: row is not None)]\n",
"\n",
"# Update the timestamp column to the current time in nanoseconds\n",
"sdf[\"Timestamp\"] = sdf[\"Timestamp\"].apply(lambda row: time.time_ns())\n",
"\n",
"# Publish the processed SDF to a Kafka topic specified by the output_topic object.\n",
"sdf = sdf.to_topic(output_topic)\n",
"\n",
"app.run(sdf)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EwXYrmWD_0CX"
},
"source": [
"\n",
"### 11. Enter a human message\n",
"\n",
"Run this cell to enter your message that you want to sent to the model. It uses another Kafka producer to send your text to the \"chat\" Kafka topic for the model to pick up (requires running the previous cell again)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6sxOPxSP_3iu"
},
"outputs": [],
"source": [
"chat_input = input(\"Please enter your reply: \")\n",
"myreply = chat_input\n",
"\n",
"msgvalue = {\n",
" \"uuid\": chat_id, # leave empty for now\n",
" \"role\": \"human\",\n",
" \"text\": myreply,\n",
" \"conversation_id\": chat_id,\n",
" \"Timestamp\": time.time_ns(),\n",
"}\n",
"\n",
"with Producer(\n",
" broker_address=\"127.0.0.1:9092\",\n",
" extra_config={\"allow.auto.create.topics\": \"true\"},\n",
") as producer:\n",
" value = msgvalue\n",
" producer.produce(\n",
" topic=\"chat\",\n",
" headers=[(\"uuid\", str(uuid.uuid4()))], # a dict is also allowed here\n",
" key=chat_id, # leave empty for now\n",
" value=json.dumps(value), # needs to be a string\n",
" )\n",
"\n",
"print(\"Replied to chatbot with message: \")\n",
"print(\"--------------------------------------------\")\n",
"print(value)\n",
"print(\"--------------------------------------------\")\n",
"print(\"\\n\\nRUN THE PREVIOUS CELL TO HAVE THE CHATBOT GENERATE A REPLY\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cSx3s7TBBegg"
},
"source": [
"### Why route chat messages through Kafka?\n",
"\n",
"It's easier to interact with the LLM directly using LangChains built-in conversation management features. Plus you can also use a REST API to generate a response from an externally hosted model. So why go to the trouble of using Apache Kafka?\n",
"\n",
"There are a few reasons, such as:\n",
"\n",
" * **Integration**: Many enterprises want to run their own LLMs so that they can keep their data in-house. This requires integrating LLM-powered components into existing architectures that might already be decoupled using some kind of message bus.\n",
"\n",
" * **Scalability**: Apache Kafka is designed with parallel processing in mind, so many teams prefer to use it to more effectively distribute work to available workers (in this case the \"worker\" is a container running an LLM).\n",
"\n",
" * **Durability**: Kafka is designed to allow services to pick up where another service left off in the case where that service experienced a memory issue or went offline. This prevents data loss in highly complex, distributed architectures where multiple systems are communicating with one another (LLMs being just one of many interdependent systems that also include vector databases and traditional databases).\n",
"\n",
"For more background on why event streaming is a good fit for Gen AI application architecture, see Kai Waehner's article [\"Apache Kafka + Vector Database + LLM = Real-Time GenAI\"](https://www.kai-waehner.de/blog/2023/11/08/apache-kafka-flink-vector-database-llm-real-time-genai/)."
]
}
],
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| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/azure_container_apps_dynamic_sessions_data_analyst.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4153b116-206b-40f8-a684-bf082c5ebcea",
"metadata": {},
"source": [
"# Building a data analyst agent with LangGraph and Azure Container Apps dynamic sessions\n",
"\n",
"In this example we'll build an agent that can query a Postgres database and run Python code to analyze the retrieved data. We'll use [LangGraph](https://langchain-ai.github.io/langgraph/) for agent orchestration and [Azure Container Apps dynamic sessions](https://python.langchain.com/v0.2/docs/integrations/tools/azure_dynamic_sessions/) for safe Python code execution.\n",
"\n",
"**NOTE**: Building LLM systems that interact with SQL databases requires executing model-generated SQL queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your agent's needs. This will mitigate though not eliminate the risks of building a model-driven system. For more on general security best practices, see our [security guidelines](https://python.langchain.com/v0.2/docs/security/)."
]
},
{
"cell_type": "markdown",
"id": "3b70c2be-1141-4107-80db-787f7935102f",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"Let's get set up by installing our Python dependencies and setting our OpenAI credentials, Azure Container Apps sessions pool endpoint, and our SQL database connection string.\n",
"\n",
"### Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "302f827f-062c-4b83-8239-07b28bfc9651",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install -qU langgraph langchain-azure-dynamic-sessions langchain-openai langchain-community pandas matplotlib"
]
},
{
"cell_type": "markdown",
"id": "7621655b-605c-4690-8ee1-77a4bab8b383",
"metadata": {},
"source": [
"### Set credentials\n",
"\n",
"By default this demo uses:\n",
"- Azure OpenAI for the model: https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource\n",
"- Azure PostgreSQL for the db: https://learn.microsoft.com/en-us/cli/azure/postgres/server?view=azure-cli-latest#az-postgres-server-create\n",
"- Azure Container Apps dynamic sessions for code execution: https://learn.microsoft.com/en-us/azure/container-apps/sessions-code-interpreter?\n",
"\n",
"This LangGraph architecture can also be used with any other [tool-calling LLM](https://python.langchain.com/v0.2/docs/how_to/tool_calling) and any SQL database."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "be7c74d8-485b-4c51-aded-07e8af838efe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Azure OpenAI API key ········\n",
"Azure OpenAI endpoint ········\n",
"Azure OpenAI deployment name ········\n",
"Azure Container Apps dynamic sessions pool management endpoint ········\n",
"PostgreSQL connection string ········\n"
]
}
],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"AZURE_OPENAI_API_KEY\"] = getpass.getpass(\"Azure OpenAI API key\")\n",
"os.environ[\"AZURE_OPENAI_ENDPOINT\"] = getpass.getpass(\"Azure OpenAI endpoint\")\n",
"\n",
"AZURE_OPENAI_DEPLOYMENT_NAME = getpass.getpass(\"Azure OpenAI deployment name\")\n",
"SESSIONS_POOL_MANAGEMENT_ENDPOINT = getpass.getpass(\n",
" \"Azure Container Apps dynamic sessions pool management endpoint\"\n",
")\n",
"SQL_DB_CONNECTION_STRING = getpass.getpass(\"PostgreSQL connection string\")"
]
},
{
"cell_type": "markdown",
"id": "3712a7b0-3f7d-4d90-9319-febf7b046aa6",
"metadata": {},
"source": [
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "09c0a46e-a8b4-44e3-8d90-2e5d0f66c1ad",
"metadata": {},
"outputs": [],
"source": [
"import ast\n",
"import base64\n",
"import io\n",
"import json\n",
"import operator\n",
"from functools import partial\n",
"from typing import Annotated, List, Literal, Optional, Sequence, TypedDict\n",
"\n",
"import pandas as pd\n",
"from IPython.display import display\n",
"from langchain_azure_dynamic_sessions import SessionsPythonREPLTool\n",
"from langchain_community.utilities import SQLDatabase\n",
"from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_core.tools import tool\n",
"from langchain_openai import AzureChatOpenAI\n",
"from langgraph.graph import END, StateGraph\n",
"from langgraph.prebuilt import ToolNode\n",
"from matplotlib.pyplot import imshow\n",
"from PIL import Image"
]
},
{
"cell_type": "markdown",
"id": "5cc14582-313c-4a61-be5e-a7a1ba26a6e0",
"metadata": {},
"source": [
"## Instantiate model, DB, code interpreter\n",
"\n",
"We'll use the LangChain [SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase) interface to connect to our DB and query it. This works with any SQL database supported by [SQLAlchemy](https://www.sqlalchemy.org/)."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "9262ea34-c6ac-407c-96c3-aa5eaa1a8039",
"metadata": {},
"outputs": [],
"source": [
"db = SQLDatabase.from_uri(SQL_DB_CONNECTION_STRING)"
]
},
{
"cell_type": "markdown",
"id": "1982c6f2-aa4e-4842-83f2-951205aa0854",
"metadata": {},
"source": [
"For our LLM we need to make sure that we use a model that supports [tool-calling](https://python.langchain.com/v0.2/docs/how_to/tool_calling)."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ba6201a1-d760-45f1-b14a-bf8d85ceb775",
"metadata": {},
"outputs": [],
"source": [
"llm = AzureChatOpenAI(\n",
" deployment_name=AZURE_OPENAI_DEPLOYMENT_NAME, openai_api_version=\"2024-02-01\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "92e2fcc7-812a-4d18-852f-2f814559b415",
"metadata": {},
"source": [
"And the [dynamic sessions tool](https://python.langchain.com/v0.2/docs/integrations/tools/azure_container_apps_dynamic_sessions/) is what we'll use for code execution."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "89e5a315-c964-493d-84fb-1f453909caae",
"metadata": {},
"outputs": [],
"source": [
"repl = SessionsPythonREPLTool(\n",
" pool_management_endpoint=SESSIONS_POOL_MANAGEMENT_ENDPOINT\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ee084fbd-10d3-4328-9d8c-75ffa9437b31",
"metadata": {},
"source": [
"## Define graph\n",
"\n",
"Now we're ready to define our application logic. The core elements are the [agent State, Nodes, and Edges](https://langchain-ai.github.io/langgraph/concepts/#core-design).\n",
"\n",
"### Define State\n",
"We'll use a simple agent State which is just a list of messages that every Node can append to:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "7feef65d-bf11-41bb-9164-5249953eb02e",
"metadata": {},
"outputs": [],
"source": [
"class AgentState(TypedDict):\n",
" messages: Annotated[Sequence[BaseMessage], operator.add]"
]
},
{
"cell_type": "markdown",
"id": "58fe92a3-9a30-464b-bcf3-972af5b92e40",
"metadata": {},
"source": [
"Since our code interpreter can return results like base64-encoded images which we don't want to pass back to the model, we'll create a custom Tool message that allows us to track raw Tool outputs without sending them back to the model."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "36e2d8a2-8881-40bc-81da-b40e8a152d9d",
"metadata": {},
"outputs": [],
"source": [
"class RawToolMessage(ToolMessage):\n",
" \"\"\"\n",
" Customized Tool message that lets us pass around the raw tool outputs (along with string contents for passing back to the model).\n",
" \"\"\"\n",
"\n",
" raw: dict\n",
" \"\"\"Arbitrary (non-string) tool outputs. Won't be sent to model.\"\"\"\n",
" tool_name: str\n",
" \"\"\"Name of tool that generated output.\"\"\""
]
},
{
"cell_type": "markdown",
"id": "ad1b681c-c918-4dfe-b671-9d6eee457a51",
"metadata": {},
"source": [
"### Define Nodes"
]
},
{
"cell_type": "markdown",
"id": "966aeec1-b930-442c-9ba3-d8ad3800d2a4",
"metadata": {},
"source": [
"First we'll define a node for calling our model. We need to make sure to bind our tools to the model so that it knows to call them. We'll also specify in our prompt the schema of the SQL tables the model has access to, so that it can write relevant SQL queries."
]
},
{
"cell_type": "markdown",
"id": "88f15581-11f6-4421-aa17-5762a84c8032",
"metadata": {},
"source": [
"We'll use our models tool-calling abilities to reliably generate our SQL queries and Python code. To do this we need to define schemas for our tools that the model can use for structuring its tool calls.\n",
"\n",
"Note that the class names, docstrings, and attribute typing and descriptions are crucial here, as they're actually passed in to the model (you can effectively think of them as part of the prompt)."
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "390f170b-ba13-41fc-8c9b-ee0efdb13b98",
"metadata": {},
"outputs": [],
"source": [
"# Tool schema for querying SQL db\n",
"class create_df_from_sql(BaseModel):\n",
" \"\"\"Execute a PostgreSQL SELECT statement and use the results to create a DataFrame with the given column names.\"\"\"\n",
"\n",
" select_query: str = Field(..., description=\"A PostgreSQL SELECT statement.\")\n",
" # We're going to convert the results to a Pandas DataFrame that we pass\n",
" # to the code intepreter, so we also have the model generate useful column and\n",
" # variable names for this DataFrame that the model will refer to when writing\n",
" # python code.\n",
" df_columns: List[str] = Field(\n",
" ..., description=\"Ordered names to give the DataFrame columns.\"\n",
" )\n",
" df_name: str = Field(\n",
" ..., description=\"The name to give the DataFrame variable in downstream code.\"\n",
" )\n",
"\n",
"\n",
"# Tool schema for writing Python code\n",
"class python_shell(BaseModel):\n",
" \"\"\"Execute Python code that analyzes the DataFrames that have been generated. Make sure to print any important results.\"\"\"\n",
"\n",
" code: str = Field(\n",
" ...,\n",
" description=\"The code to execute. Make sure to print any important results.\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a98cf69a-e25b-4016-a565-aa16e43e417a",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = f\"\"\"\\\n",
"You are an expert at PostgreSQL and Python. You have access to a PostgreSQL database \\\n",
"with the following tables\n",
"\n",
"{db.table_info}\n",
"\n",
"Given a user question related to the data in the database, \\\n",
"first get the relevant data from the table as a DataFrame using the create_df_from_sql tool. Then use the \\\n",
"python_shell to do any analysis required to answer the user question.\"\"\"\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", system_prompt),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n",
"\n",
"\n",
"def call_model(state: AgentState) -> dict:\n",
" \"\"\"Call model with tools passed in.\"\"\"\n",
" messages = []\n",
"\n",
" chain = prompt | llm.bind_tools([create_df_from_sql, python_shell])\n",
" messages.append(chain.invoke({\"messages\": state[\"messages\"]}))\n",
"\n",
" return {\"messages\": messages}"
]
},
{
"cell_type": "markdown",
"id": "4e87c72e-7f9e-4377-94c9-abd9fb869866",
"metadata": {},
"source": [
"Now we can define the node for executing any SQL queries that were generated by the model. Notice that after we run the query we convert the results into Pandas DataFrames — these will be uploaded the the code interpreter tool in the next step so that it can use the retrieved data."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "a229efba-e981-4403-a37c-ab030c929ea4",
"metadata": {},
"outputs": [],
"source": [
"def execute_sql_query(state: AgentState) -> dict:\n",
" \"\"\"Execute the latest SQL queries.\"\"\"\n",
" messages = []\n",
"\n",
" for tool_call in state[\"messages\"][-1].tool_calls:\n",
" if tool_call[\"name\"] != \"create_df_from_sql\":\n",
" continue\n",
"\n",
" # Execute SQL query\n",
" res = db.run(tool_call[\"args\"][\"select_query\"], fetch=\"cursor\").fetchall()\n",
"\n",
" # Convert result to Pandas DataFrame\n",
" df_columns = tool_call[\"args\"][\"df_columns\"]\n",
" df = pd.DataFrame(res, columns=df_columns)\n",
" df_name = tool_call[\"args\"][\"df_name\"]\n",
"\n",
" # Add tool output message\n",
" messages.append(\n",
" RawToolMessage(\n",
" f\"Generated dataframe {df_name} with columns {df_columns}\", # What's sent to model.\n",
" raw={df_name: df},\n",
" tool_call_id=tool_call[\"id\"],\n",
" tool_name=tool_call[\"name\"],\n",
" )\n",
" )\n",
"\n",
" return {\"messages\": messages}"
]
},
{
"cell_type": "markdown",
"id": "7a67eaaf-1587-4f32-ab5c-e1a04d273c3e",
"metadata": {},
"source": [
"Now we need a node for executing any model-generated Python code. The key steps here are:\n",
"- Uploading queried data to the code intepreter\n",
"- Executing model generated code\n",
"- Parsing results so that images are displayed and not passed in to future model calls\n",
"\n",
"To upload the queried data to the model we can take our DataFrames we generated by executing the SQL queries and upload them as CSVs to our code intepreter."
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "450c1dd0-4fe4-4ab7-b1d7-e012c3cf0102",
"metadata": {},
"outputs": [],
"source": [
"def _upload_dfs_to_repl(state: AgentState) -> str:\n",
" \"\"\"\n",
" Upload generated dfs to code intepreter and return code for loading them.\n",
"\n",
" Note that code intepreter sessions are short-lived so this needs to be done\n",
" every agent cycle, even if the dfs were previously uploaded.\n",
" \"\"\"\n",
" df_dicts = [\n",
" msg.raw\n",
" for msg in state[\"messages\"]\n",
" if isinstance(msg, RawToolMessage) and msg.tool_name == \"create_df_from_sql\"\n",
" ]\n",
" name_df_map = {name: df for df_dict in df_dicts for name, df in df_dict.items()}\n",
"\n",
" # Data should be uploaded as a BinaryIO.\n",
" # Files will be uploaded to the \"/mnt/data/\" directory on the container.\n",
" for name, df in name_df_map.items():\n",
" buffer = io.StringIO()\n",
" df.to_csv(buffer)\n",
" buffer.seek(0)\n",
" repl.upload_file(data=buffer, remote_file_path=name + \".csv\")\n",
"\n",
" # Code for loading the uploaded files.\n",
" df_code = \"import pandas as pd\\n\" + \"\\n\".join(\n",
" f\"{name} = pd.read_csv('/mnt/data/{name}.csv')\" for name in name_df_map\n",
" )\n",
" return df_code\n",
"\n",
"\n",
"def _repl_result_to_msg_content(repl_result: dict) -> str:\n",
" \"\"\"\n",
" Display images with including them in tool message content.\n",
" \"\"\"\n",
" content = {}\n",
" for k, v in repl_result.items():\n",
" # Any image results are returned as a dict of the form:\n",
" # {\"type\": \"image\", \"base64_data\": \"...\"}\n",
" if isinstance(repl_result[k], dict) and repl_result[k][\"type\"] == \"image\":\n",
" # Decode and display image\n",
" base64_str = repl_result[k][\"base64_data\"]\n",
" img = Image.open(io.BytesIO(base64.decodebytes(bytes(base64_str, \"utf-8\"))))\n",
" display(img)\n",
" else:\n",
" content[k] = repl_result[k]\n",
" return json.dumps(content, indent=2)\n",
"\n",
"\n",
"def execute_python(state: AgentState) -> dict:\n",
" \"\"\"\n",
" Execute the latest generated Python code.\n",
" \"\"\"\n",
" messages = []\n",
"\n",
" df_code = _upload_dfs_to_repl(state)\n",
" last_ai_msg = [msg for msg in state[\"messages\"] if isinstance(msg, AIMessage)][-1]\n",
" for tool_call in last_ai_msg.tool_calls:\n",
" if tool_call[\"name\"] != \"python_shell\":\n",
" continue\n",
"\n",
" generated_code = tool_call[\"args\"][\"code\"]\n",
" repl_result = repl.execute(df_code + \"\\n\" + generated_code)\n",
"\n",
" messages.append(\n",
" RawToolMessage(\n",
" _repl_result_to_msg_content(repl_result),\n",
" raw=repl_result,\n",
" tool_call_id=tool_call[\"id\"],\n",
" tool_name=tool_call[\"name\"],\n",
" )\n",
" )\n",
" return {\"messages\": messages}"
]
},
{
"cell_type": "markdown",
"id": "dd530250-60b6-40fb-b1f8-2ff32967ecc8",
"metadata": {},
"source": [
"### Define Edges\n",
"\n",
"Now we're ready to put all the pieces together into a graph."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "a04e0a82-1c3e-46d3-95ea-2461c21202ef",
"metadata": {},
"outputs": [],
"source": [
"def should_continue(state: AgentState) -> str:\n",
" \"\"\"\n",
" If any Tool messages were generated in the last cycle that means we need to call the model again to interpret the latest results.\n",
" \"\"\"\n",
" return \"execute_sql_query\" if state[\"messages\"][-1].tool_calls else END"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "b2857ba9-da80-443f-8217-ac0523f90593",
"metadata": {},
"outputs": [],
"source": [
"workflow = StateGraph(AgentState)\n",
"\n",
"workflow.add_node(\"call_model\", call_model)\n",
"workflow.add_node(\"execute_sql_query\", execute_sql_query)\n",
"workflow.add_node(\"execute_python\", execute_python)\n",
"\n",
"workflow.set_entry_point(\"call_model\")\n",
"workflow.add_edge(\"execute_sql_query\", \"execute_python\")\n",
"workflow.add_edge(\"execute_python\", \"call_model\")\n",
"workflow.add_conditional_edges(\"call_model\", should_continue)\n",
"\n",
"app = workflow.compile()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "74dc8c6c-b520-4f17-88ec-fa789ed911e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" +-----------+ \n",
" | __start__ | \n",
" +-----------+ \n",
" * \n",
" * \n",
" * \n",
" +------------+ \n",
" ...| call_model |*** \n",
" ....... +------------+ ******* \n",
" ........ .. ... ******* \n",
" ....... .. ... ****** \n",
" .... .. .. ******* \n",
"+---------+ +-------------------+ .. **** \n",
"| __end__ | | execute_sql_query | . **** \n",
"+---------+ +-------------------+* . **** \n",
" ***** . ***** \n",
" **** . **** \n",
" *** . *** \n",
" +----------------+ \n",
" | execute_python | \n",
" +----------------+ \n"
]
}
],
"source": [
"print(app.get_graph().draw_ascii())"
]
},
{
"cell_type": "markdown",
"id": "6d4e079b-0cf8-4f9d-a52b-6a8f980eee4b",
"metadata": {},
"source": [
"## Test it out\n",
"\n",
"Replace these examples with questions related to the database you've connected your agent to."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "2c173d6d-a212-448e-b309-299e87f205b8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=989x590>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The graph of the average latency by model has been generated successfully. However, it seems that the output is not displayed here directly. To view the graph, you would typically run the provided Python code in an environment where graphical output is supported, such as a Jupyter notebook or a Python script executed in a local environment with access to a display server.\n"
]
}
],
"source": [
"output = app.invoke({\"messages\": [(\"human\", \"graph the average latency by model\")]})\n",
"print(output[\"messages\"][-1].content)"
]
},
{
"cell_type": "markdown",
"id": "a67fbc65-2161-4518-9eea-f0cdd99b5f59",
"metadata": {},
"source": [
"**LangSmith Trace**: https://smith.langchain.com/public/9c8afcce-0ed1-4fb1-b719-767e6432bd8e/r"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "1d512f95-7490-483e-a748-abf708fbd20c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=571x453>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The correlation coefficient between the number of prompt tokens and latency is approximately 0.305, indicating a positive but relatively weak relationship. This suggests that as the number of input tokens increases, there tends to be an increase in latency, but the relationship is not strong and other factors may also influence latency.\n",
"\n",
"Here is the scatter plot showing the relationship visually:\n",
"\n",
"\n"
]
}
],
"source": [
"output = app.invoke(\n",
" {\n",
" \"messages\": [\n",
" (\"human\", \"what's the relationship between latency and input tokens?\")\n",
" ]\n",
" }\n",
")\n",
"print(output[\"messages\"][-1].content)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "10071b83-19c6-468d-b5fc-600b42cd57ac",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=670x453>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Continue the conversation\n",
"output = app.invoke(\n",
" {\"messages\": output[\"messages\"] + [(\"human\", \"now control for model\")]}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "81fb6102-c427-41c1-97cf-54e5944d1c79",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"After controlling for each model, here are the individual correlations between prompt tokens and latency:\n",
"\n",
"- `anthropic_claude_3_sonnet`: Correlation = 0.7659\n",
"- `openai_gpt_3_5_turbo`: Correlation = 0.2833\n",
"- `fireworks_mixtral`: Correlation = 0.1673\n",
"- `cohere_command`: Correlation = 0.1434\n",
"- `google_gemini_pro`: Correlation = 0.4928\n",
"\n",
"These correlations indicate that the `anthropic_claude_3_sonnet` model has the strongest positive correlation between the number of prompt tokens and latency, while the `cohere_command` model has the weakest positive correlation.\n",
"\n",
"Scatter plots were generated for each model individually to illustrate the relationship between prompt tokens and latency. Below are the plots for each model:\n",
"\n",
"1. Model: anthropic_claude_3_sonnet\n",
"\n",
"\n",
"2. Model: openai_gpt_3_5_turbo\n",
"\n",
"\n",
"3. Model: fireworks_mixtral\n",
"\n",
"\n",
"4. Model: cohere_command\n",
"\n",
"\n",
"5. Model: google_gemini_pro\n",
"\n",
"\n",
"The plots and correlations together provide an understanding of how latency changes with the number of prompt tokens for each model.\n"
]
}
],
"source": [
"print(output[\"messages\"][-1].content)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "09167fa6-132a-4696-a4ee-eda80a41d3dd",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=703x510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output = app.invoke(\n",
" {\n",
" \"messages\": output[\"messages\"]\n",
" + [(\"human\", \"what about latency vs output tokens\")]\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "f0c48828-07ae-43df-b27f-14fdfbd835f6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The correlation between the number of output tokens (completion_tokens) and latency varies by model, as shown below:\n",
"\n",
"- `anthropic_claude_3_sonnet`: Correlation = 0.910274\n",
"- `cohere_command`: Correlation = 0.910292\n",
"- `fireworks_mixtral`: Correlation = 0.681286\n",
"- `google_gemini_pro`: Correlation = 0.151549\n",
"- `openai_gpt_3_5_turbo`: Correlation = 0.449127\n",
"\n",
"The `anthropic_claude_3_sonnet` and `cohere_command` models show a very strong positive correlation, indicating that an increase in the number of output tokens is associated with a substantial increase in latency for these models. The `fireworks_mixtral` model also shows a strong positive correlation, but less strong than the first two. The `google_gemini_pro` model shows a weak positive correlation, and the `openai_gpt_3_5_turbo` model shows a moderate positive correlation.\n",
"\n",
"Below is the scatter plot with a regression line showing the relationship between output tokens and latency for each model:\n",
"\n",
"\n"
]
}
],
"source": [
"print(output[\"messages\"][-1].content)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "4114c16d-c727-49c2-beb1-27c5982b0948",
"metadata": {},
"outputs": [],
"source": [
"output = app.invoke(\n",
" {\n",
" \"messages\": [\n",
" (\n",
" \"human\",\n",
" \"what's the better explanatory variable for latency: input or output tokens?\",\n",
" )\n",
" ]\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "7f983c4a-60b6-4dd6-ab22-2b59971e2fcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The correlation between input tokens and latency is 0.305, while the correlation between output tokens and latency is 0.487. Therefore, the better explanatory variable for latency is output tokens.\n"
]
}
],
"source": [
"print(output[\"messages\"][-1].content)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "poetry-venv-2",
"language": "python",
"name": "poetry-venv-2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/baby_agi.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "517a9fd4",
"metadata": {},
"source": [
"# BabyAGI User Guide\n",
"\n",
"This notebook demonstrates how to implement [BabyAGI](https://github.com/yoheinakajima/babyagi/tree/main) by [Yohei Nakajima](https://twitter.com/yoheinakajima). BabyAGI is an AI agent that can generate and pretend to execute tasks based on a given objective.\n",
"\n",
"This guide will help you understand the components to create your own recursive agents.\n",
"\n",
"Although BabyAGI uses specific vectorstores/model providers (Pinecone, OpenAI), one of the benefits of implementing it with LangChain is that you can easily swap those out for different options. In this implementation we use a FAISS vectorstore (because it runs locally and is free)."
]
},
{
"cell_type": "markdown",
"id": "556af556",
"metadata": {},
"source": [
"## Install and Import Required Modules"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c8a354b6",
"metadata": {},
"outputs": [],
"source": [
"from typing import Optional\n",
"\n",
"from langchain_experimental.autonomous_agents import BabyAGI\n",
"from langchain_openai import OpenAI, OpenAIEmbeddings"
]
},
{
"cell_type": "markdown",
"id": "09f70772",
"metadata": {},
"source": [
"## Connect to the Vector Store\n",
"\n",
"Depending on what vectorstore you use, this step may look different."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "794045d4",
"metadata": {},
"outputs": [],
"source": [
"from langchain.docstore import InMemoryDocstore\n",
"from langchain_community.vectorstores import FAISS"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6e0305eb",
"metadata": {},
"outputs": [],
"source": [
"# Define your embedding model\n",
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"import faiss\n",
"\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
]
},
{
"cell_type": "markdown",
"id": "05ba762e",
"metadata": {},
"source": [
"### Run the BabyAGI\n",
"\n",
"Now it's time to create the BabyAGI controller and watch it try to accomplish your objective."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3d220b69",
"metadata": {},
"outputs": [],
"source": [
"OBJECTIVE = \"Write a weather report for SF today\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8a8e5543",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3d69899b",
"metadata": {},
"outputs": [],
"source": [
"# Logging of LLMChains\n",
"verbose = False\n",
"# If None, will keep on going forever\n",
"max_iterations: Optional[int] = 3\n",
"baby_agi = BabyAGI.from_llm(\n",
" llm=llm, vectorstore=vectorstore, verbose=verbose, max_iterations=max_iterations\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f7957b51",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[95m\u001b[1m\n",
"*****TASK LIST*****\n",
"\u001b[0m\u001b[0m\n",
"1: Make a todo list\n",
"\u001b[92m\u001b[1m\n",
"*****NEXT TASK*****\n",
"\u001b[0m\u001b[0m\n",
"1: Make a todo list\n",
"\u001b[93m\u001b[1m\n",
"*****TASK RESULT*****\n",
"\u001b[0m\u001b[0m\n",
"\n",
"\n",
"1. Check the weather forecast for San Francisco today\n",
"2. Make note of the temperature, humidity, wind speed, and other relevant weather conditions\n",
"3. Write a weather report summarizing the forecast\n",
"4. Check for any weather alerts or warnings\n",
"5. Share the report with the relevant stakeholders\n",
"\u001b[95m\u001b[1m\n",
"*****TASK LIST*****\n",
"\u001b[0m\u001b[0m\n",
"2: Check the current temperature in San Francisco\n",
"3: Check the current humidity in San Francisco\n",
"4: Check the current wind speed in San Francisco\n",
"5: Check for any weather alerts or warnings in San Francisco\n",
"6: Check the forecast for the next 24 hours in San Francisco\n",
"7: Check the forecast for the next 48 hours in San Francisco\n",
"8: Check the forecast for the next 72 hours in San Francisco\n",
"9: Check the forecast for the next week in San Francisco\n",
"10: Check the forecast for the next month in San Francisco\n",
"11: Check the forecast for the next 3 months in San Francisco\n",
"1: Write a weather report for SF today\n",
"\u001b[92m\u001b[1m\n",
"*****NEXT TASK*****\n",
"\u001b[0m\u001b[0m\n",
"2: Check the current temperature in San Francisco\n",
"\u001b[93m\u001b[1m\n",
"*****TASK RESULT*****\n",
"\u001b[0m\u001b[0m\n",
"\n",
"\n",
"I will check the current temperature in San Francisco. I will use an online weather service to get the most up-to-date information.\n",
"\u001b[95m\u001b[1m\n",
"*****TASK LIST*****\n",
"\u001b[0m\u001b[0m\n",
"3: Check the current UV index in San Francisco.\n",
"4: Check the current air quality in San Francisco.\n",
"5: Check the current precipitation levels in San Francisco.\n",
"6: Check the current cloud cover in San Francisco.\n",
"7: Check the current barometric pressure in San Francisco.\n",
"8: Check the current dew point in San Francisco.\n",
"9: Check the current wind direction in San Francisco.\n",
"10: Check the current humidity levels in San Francisco.\n",
"1: Check the current temperature in San Francisco to the average temperature for this time of year.\n",
"2: Check the current visibility in San Francisco.\n",
"11: Write a weather report for SF today.\n",
"\u001b[92m\u001b[1m\n",
"*****NEXT TASK*****\n",
"\u001b[0m\u001b[0m\n",
"3: Check the current UV index in San Francisco.\n",
"\u001b[93m\u001b[1m\n",
"*****TASK RESULT*****\n",
"\u001b[0m\u001b[0m\n",
"\n",
"\n",
"The current UV index in San Francisco is moderate. The UV index is expected to remain at moderate levels throughout the day. It is recommended to wear sunscreen and protective clothing when outdoors.\n",
"\u001b[91m\u001b[1m\n",
"*****TASK ENDING*****\n",
"\u001b[0m\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'objective': 'Write a weather report for SF today'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"baby_agi({\"objective\": OBJECTIVE})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "898a210b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/baby_agi_with_agent.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "517a9fd4",
"metadata": {},
"source": [
"# BabyAGI with Tools\n",
"\n",
"This notebook builds on top of [baby agi](baby_agi.html), but shows how you can swap out the execution chain. The previous execution chain was just an LLM which made stuff up. By swapping it out with an agent that has access to tools, we can hopefully get real reliable information"
]
},
{
"cell_type": "markdown",
"id": "556af556",
"metadata": {},
"source": [
"## Install and Import Required Modules"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c8a354b6",
"metadata": {},
"outputs": [],
"source": [
"from typing import Optional\n",
"\n",
"from langchain.chains import LLMChain\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain_experimental.autonomous_agents import BabyAGI\n",
"from langchain_openai import OpenAI, OpenAIEmbeddings"
]
},
{
"cell_type": "markdown",
"id": "09f70772",
"metadata": {},
"source": [
"## Connect to the Vector Store\n",
"\n",
"Depending on what vectorstore you use, this step may look different."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "794045d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install faiss-cpu > /dev/null\n",
"%pip install google-search-results > /dev/null\n",
"from langchain.docstore import InMemoryDocstore\n",
"from langchain_community.vectorstores import FAISS"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6e0305eb",
"metadata": {},
"outputs": [],
"source": [
"# Define your embedding model\n",
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"import faiss\n",
"\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
]
},
{
"cell_type": "markdown",
"id": "0f3b72bf",
"metadata": {},
"source": [
"## Define the Chains\n",
"\n",
"BabyAGI relies on three LLM chains:\n",
"- Task creation chain to select new tasks to add to the list\n",
"- Task prioritization chain to re-prioritize tasks\n",
"- Execution Chain to execute the tasks\n",
"\n",
"\n",
"NOTE: in this notebook, the Execution chain will now be an agent."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b43cd580",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
"from langchain.chains import LLMChain\n",
"from langchain_community.utilities import SerpAPIWrapper\n",
"from langchain_openai import OpenAI\n",
"\n",
"todo_prompt = PromptTemplate.from_template(\n",
" \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",
")\n",
"todo_chain = LLMChain(llm=OpenAI(temperature=0), prompt=todo_prompt)\n",
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name=\"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\",\n",
" ),\n",
" Tool(\n",
" name=\"TODO\",\n",
" func=todo_chain.run,\n",
" description=\"useful for when you need to come up with todo lists. Input: an objective to create a todo list for. Output: a todo list for that objective. Please be very clear what the objective is!\",\n",
" ),\n",
"]\n",
"\n",
"\n",
"prefix = \"\"\"You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.\"\"\"\n",
"suffix = \"\"\"Question: {task}\n",
"{agent_scratchpad}\"\"\"\n",
"prompt = ZeroShotAgent.create_prompt(\n",
" tools,\n",
" prefix=prefix,\n",
" suffix=suffix,\n",
" input_variables=[\"objective\", \"task\", \"context\", \"agent_scratchpad\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4b00ae2e",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)\n",
"llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
"tool_names = [tool.name for tool in tools]\n",
"agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)\n",
"agent_executor = AgentExecutor.from_agent_and_tools(\n",
" agent=agent, tools=tools, verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "05ba762e",
"metadata": {},
"source": [
"### Run the BabyAGI\n",
"\n",
"Now it's time to create the BabyAGI controller and watch it try to accomplish your objective."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3d220b69",
"metadata": {},
"outputs": [],
"source": [
"OBJECTIVE = \"Write a weather report for SF today\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3d69899b",
"metadata": {},
"outputs": [],
"source": [
"# Logging of LLMChains\n",
"verbose = False\n",
"# If None, will keep on going forever\n",
"max_iterations: Optional[int] = 3\n",
"baby_agi = BabyAGI.from_llm(\n",
" llm=llm,\n",
" vectorstore=vectorstore,\n",
" task_execution_chain=agent_executor,\n",
" verbose=verbose,\n",
" max_iterations=max_iterations,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f7957b51",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[95m\u001b[1m\n",
"*****TASK LIST*****\n",
"\u001b[0m\u001b[0m\n",
"1: Make a todo list\n",
"\u001b[92m\u001b[1m\n",
"*****NEXT TASK*****\n",
"\u001b[0m\u001b[0m\n",
"1: Make a todo list\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to come up with a todo list\n",
"Action: TODO\n",
"Action Input: Write a weather report for SF today\u001b[0m\u001b[33;1m\u001b[1;3m\n",
"\n",
"1. Research current weather conditions in San Francisco\n",
"2. Gather data on temperature, humidity, wind speed, and other relevant weather conditions\n",
"3. Analyze data to determine current weather trends\n",
"4. Write a brief introduction to the weather report\n",
"5. Describe current weather conditions in San Francisco\n",
"6. Discuss any upcoming weather changes\n",
"7. Summarize the weather report\n",
"8. Proofread and edit the report\n",
"9. Submit the report\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The todo list for writing a weather report for SF today is: 1. Research current weather conditions in San Francisco; 2. Gather data on temperature, humidity, wind speed, and other relevant weather conditions; 3. Analyze data to determine current weather trends; 4. Write a brief introduction to the weather report; 5. Describe current weather conditions in San Francisco; 6. Discuss any upcoming weather changes; 7. Summarize the weather report; 8. Proofread and edit the report; 9. Submit the report.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\u001b[93m\u001b[1m\n",
"*****TASK RESULT*****\n",
"\u001b[0m\u001b[0m\n",
"The todo list for writing a weather report for SF today is: 1. Research current weather conditions in San Francisco; 2. Gather data on temperature, humidity, wind speed, and other relevant weather conditions; 3. Analyze data to determine current weather trends; 4. Write a brief introduction to the weather report; 5. Describe current weather conditions in San Francisco; 6. Discuss any upcoming weather changes; 7. Summarize the weather report; 8. Proofread and edit the report; 9. Submit the report.\n",
"\u001b[95m\u001b[1m\n",
"*****TASK LIST*****\n",
"\u001b[0m\u001b[0m\n",
"2: Gather data on precipitation, cloud cover, and other relevant weather conditions;\n",
"3: Analyze data to determine any upcoming weather changes;\n",
"4: Research current weather forecasts for San Francisco;\n",
"5: Create a visual representation of the weather report;\n",
"6: Include relevant images and graphics in the report;\n",
"7: Format the report for readability;\n",
"8: Publish the report online;\n",
"9: Monitor the report for accuracy.\n",
"\u001b[92m\u001b[1m\n",
"*****NEXT TASK*****\n",
"\u001b[0m\u001b[0m\n",
"2: Gather data on precipitation, cloud cover, and other relevant weather conditions;\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to search for current weather conditions in San Francisco\n",
"Action: Search\n",
"Action Input: Current weather conditions in San Francisco\u001b[0m\u001b[36;1m\u001b[1;3mCurrent Weather for Popular Cities ; San Francisco, CA 46 · Partly Cloudy ; Manhattan, NY warning 52 · Cloudy ; Schiller Park, IL (60176) 40 · Sunny ; Boston, MA 54 ...\u001b[0m\u001b[32;1m\u001b[1;3m I need to compile the data into a weather report\n",
"Action: TODO\n",
"Action Input: Compile data into a weather report\u001b[0m\u001b[33;1m\u001b[1;3m\n",
"\n",
"1. Gather data from reliable sources such as the National Weather Service, local weather stations, and other meteorological organizations.\n",
"\n",
"2. Analyze the data to identify trends and patterns.\n",
"\n",
"3. Create a chart or graph to visualize the data.\n",
"\n",
"4. Write a summary of the data and its implications.\n",
"\n",
"5. Compile the data into a report format.\n",
"\n",
"6. Proofread the report for accuracy and clarity.\n",
"\n",
"7. Publish the report to a website or other platform.\n",
"\n",
"8. Distribute the report to relevant stakeholders.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Today in San Francisco, the temperature is 46 degrees Fahrenheit with partly cloudy skies. The forecast for the rest of the day is expected to remain partly cloudy.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\u001b[93m\u001b[1m\n",
"*****TASK RESULT*****\n",
"\u001b[0m\u001b[0m\n",
"Today in San Francisco, the temperature is 46 degrees Fahrenheit with partly cloudy skies. The forecast for the rest of the day is expected to remain partly cloudy.\n",
"\u001b[95m\u001b[1m\n",
"*****TASK LIST*****\n",
"\u001b[0m\u001b[0m\n",
"3: Format the report for readability;\n",
"4: Include relevant images and graphics in the report;\n",
"5: Compare the current weather conditions in San Francisco to the forecasted conditions;\n",
"6: Identify any potential weather-related hazards in the area;\n",
"7: Research historical weather patterns in San Francisco;\n",
"8: Identify any potential trends in the weather data;\n",
"9: Include relevant data sources in the report;\n",
"10: Summarize the weather report in a concise manner;\n",
"11: Include a summary of the forecasted weather conditions;\n",
"12: Include a summary of the current weather conditions;\n",
"13: Include a summary of the historical weather patterns;\n",
"14: Include a summary of the potential weather-related hazards;\n",
"15: Include a summary of the potential trends in the weather data;\n",
"16: Include a summary of the data sources used in the report;\n",
"17: Analyze data to determine any upcoming weather changes;\n",
"18: Research current weather forecasts for San Francisco;\n",
"19: Create a visual representation of the weather report;\n",
"20: Publish the report online;\n",
"21: Monitor the report for accuracy\n",
"\u001b[92m\u001b[1m\n",
"*****NEXT TASK*****\n",
"\u001b[0m\u001b[0m\n",
"3: Format the report for readability;\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to make sure the report is easy to read;\n",
"Action: TODO\n",
"Action Input: Make the report easy to read\u001b[0m\u001b[33;1m\u001b[1;3m\n",
"\n",
"1. Break up the report into sections with clear headings\n",
"2. Use bullet points and numbered lists to organize information\n",
"3. Use short, concise sentences\n",
"4. Use simple language and avoid jargon\n",
"5. Include visuals such as charts, graphs, and diagrams to illustrate points\n",
"6. Use bold and italicized text to emphasize key points\n",
"7. Include a table of contents and page numbers\n",
"8. Use a consistent font and font size throughout the report\n",
"9. Include a summary at the end of the report\n",
"10. Proofread the report for typos and errors\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The report should be formatted for readability by breaking it up into sections with clear headings, using bullet points and numbered lists to organize information, using short, concise sentences, using simple language and avoiding jargon, including visuals such as charts, graphs, and diagrams to illustrate points, using bold and italicized text to emphasize key points, including a table of contents and page numbers, using a consistent font and font size throughout the report, including a summary at the end of the report, and proofreading the report for typos and errors.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\u001b[93m\u001b[1m\n",
"*****TASK RESULT*****\n",
"\u001b[0m\u001b[0m\n",
"The report should be formatted for readability by breaking it up into sections with clear headings, using bullet points and numbered lists to organize information, using short, concise sentences, using simple language and avoiding jargon, including visuals such as charts, graphs, and diagrams to illustrate points, using bold and italicized text to emphasize key points, including a table of contents and page numbers, using a consistent font and font size throughout the report, including a summary at the end of the report, and proofreading the report for typos and errors.\n",
"\u001b[91m\u001b[1m\n",
"*****TASK ENDING*****\n",
"\u001b[0m\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'objective': 'Write a weather report for SF today'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"baby_agi({\"objective\": OBJECTIVE})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "898a210b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# CAMEL Role-Playing Autonomous Cooperative Agents\n",
"\n",
"This is a langchain implementation of paper: \"CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society\".\n",
"\n",
"Overview:\n",
"\n",
"The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their \"cognitive\" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond.\n",
"\n",
"The original implementation: https://github.com/lightaime/camel\n",
"\n",
"Project website: https://www.camel-ai.org/\n",
"\n",
"Arxiv paper: https://arxiv.org/abs/2303.17760\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import LangChain related modules "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from typing import List\n",
"\n",
"from langchain.prompts.chat import (\n",
" HumanMessagePromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
")\n",
"from langchain.schema import (\n",
" AIMessage,\n",
" BaseMessage,\n",
" HumanMessage,\n",
" SystemMessage,\n",
")\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define a CAMEL agent helper class"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class CAMELAgent:\n",
" def __init__(\n",
" self,\n",
" system_message: SystemMessage,\n",
" model: ChatOpenAI,\n",
" ) -> None:\n",
" self.system_message = system_message\n",
" self.model = model\n",
" self.init_messages()\n",
"\n",
" def reset(self) -> None:\n",
" self.init_messages()\n",
" return self.stored_messages\n",
"\n",
" def init_messages(self) -> None:\n",
" self.stored_messages = [self.system_message]\n",
"\n",
" def update_messages(self, message: BaseMessage) -> List[BaseMessage]:\n",
" self.stored_messages.append(message)\n",
" return self.stored_messages\n",
"\n",
" def step(\n",
" self,\n",
" input_message: HumanMessage,\n",
" ) -> AIMessage:\n",
" messages = self.update_messages(input_message)\n",
"\n",
" output_message = self.model.invoke(messages)\n",
" self.update_messages(output_message)\n",
"\n",
" return output_message"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup OpenAI API key and roles and task for role-playing"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"\n",
"assistant_role_name = \"Python Programmer\"\n",
"user_role_name = \"Stock Trader\"\n",
"task = \"Develop a trading bot for the stock market\"\n",
"word_limit = 50 # word limit for task brainstorming"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a task specify agent for brainstorming and get the specified task"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Specified task: Develop a Python-based swing trading bot that scans market trends, monitors stocks, and generates trading signals to help a stock trader to place optimal buy and sell orders with defined stop losses and profit targets.\n"
]
}
],
"source": [
"task_specifier_sys_msg = SystemMessage(content=\"You can make a task more specific.\")\n",
"task_specifier_prompt = \"\"\"Here is a task that {assistant_role_name} will help {user_role_name} to complete: {task}.\n",
"Please make it more specific. Be creative and imaginative.\n",
"Please reply with the specified task in {word_limit} words or less. Do not add anything else.\"\"\"\n",
"task_specifier_template = HumanMessagePromptTemplate.from_template(\n",
" template=task_specifier_prompt\n",
")\n",
"task_specify_agent = CAMELAgent(task_specifier_sys_msg, ChatOpenAI(temperature=1.0))\n",
"task_specifier_msg = task_specifier_template.format_messages(\n",
" assistant_role_name=assistant_role_name,\n",
" user_role_name=user_role_name,\n",
" task=task,\n",
" word_limit=word_limit,\n",
")[0]\n",
"specified_task_msg = task_specify_agent.step(task_specifier_msg)\n",
"print(f\"Specified task: {specified_task_msg.content}\")\n",
"specified_task = specified_task_msg.content"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create inception prompts for AI assistant and AI user for role-playing"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"assistant_inception_prompt = \"\"\"Never forget you are a {assistant_role_name} and I am a {user_role_name}. Never flip roles! Never instruct me!\n",
"We share a common interest in collaborating to successfully complete a task.\n",
"You must help me to complete the task.\n",
"Here is the task: {task}. Never forget our task!\n",
"I must instruct you based on your expertise and my needs to complete the task.\n",
"\n",
"I must give you one instruction at a time.\n",
"You must write a specific solution that appropriately completes the requested instruction.\n",
"You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.\n",
"Do not add anything else other than your solution to my instruction.\n",
"You are never supposed to ask me any questions you only answer questions.\n",
"You are never supposed to reply with a flake solution. Explain your solutions.\n",
"Your solution must be declarative sentences and simple present tense.\n",
"Unless I say the task is completed, you should always start with:\n",
"\n",
"Solution: <YOUR_SOLUTION>\n",
"\n",
"<YOUR_SOLUTION> should be specific and provide preferable implementations and examples for task-solving.\n",
"Always end <YOUR_SOLUTION> with: Next request.\"\"\"\n",
"\n",
"user_inception_prompt = \"\"\"Never forget you are a {user_role_name} and I am a {assistant_role_name}. Never flip roles! You will always instruct me.\n",
"We share a common interest in collaborating to successfully complete a task.\n",
"I must help you to complete the task.\n",
"Here is the task: {task}. Never forget our task!\n",
"You must instruct me based on my expertise and your needs to complete the task ONLY in the following two ways:\n",
"\n",
"1. Instruct with a necessary input:\n",
"Instruction: <YOUR_INSTRUCTION>\n",
"Input: <YOUR_INPUT>\n",
"\n",
"2. Instruct without any input:\n",
"Instruction: <YOUR_INSTRUCTION>\n",
"Input: None\n",
"\n",
"The \"Instruction\" describes a task or question. The paired \"Input\" provides further context or information for the requested \"Instruction\".\n",
"\n",
"You must give me one instruction at a time.\n",
"I must write a response that appropriately completes the requested instruction.\n",
"I must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons.\n",
"You should instruct me not ask me questions.\n",
"Now you must start to instruct me using the two ways described above.\n",
"Do not add anything else other than your instruction and the optional corresponding input!\n",
"Keep giving me instructions and necessary inputs until you think the task is completed.\n",
"When the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>.\n",
"Never say <CAMEL_TASK_DONE> unless my responses have solved your task.\"\"\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a helper helper to get system messages for AI assistant and AI user from role names and the task"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def get_sys_msgs(assistant_role_name: str, user_role_name: str, task: str):\n",
" assistant_sys_template = SystemMessagePromptTemplate.from_template(\n",
" template=assistant_inception_prompt\n",
" )\n",
" assistant_sys_msg = assistant_sys_template.format_messages(\n",
" assistant_role_name=assistant_role_name,\n",
" user_role_name=user_role_name,\n",
" task=task,\n",
" )[0]\n",
"\n",
" user_sys_template = SystemMessagePromptTemplate.from_template(\n",
" template=user_inception_prompt\n",
" )\n",
" user_sys_msg = user_sys_template.format_messages(\n",
" assistant_role_name=assistant_role_name,\n",
" user_role_name=user_role_name,\n",
" task=task,\n",
" )[0]\n",
"\n",
" return assistant_sys_msg, user_sys_msg"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create AI assistant agent and AI user agent from obtained system messages"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"assistant_sys_msg, user_sys_msg = get_sys_msgs(\n",
" assistant_role_name, user_role_name, specified_task\n",
")\n",
"assistant_agent = CAMELAgent(assistant_sys_msg, ChatOpenAI(temperature=0.2))\n",
"user_agent = CAMELAgent(user_sys_msg, ChatOpenAI(temperature=0.2))\n",
"\n",
"# Reset agents\n",
"assistant_agent.reset()\n",
"user_agent.reset()\n",
"\n",
"# Initialize chats\n",
"user_msg = HumanMessage(\n",
" content=(\n",
" f\"{user_sys_msg.content}. \"\n",
" \"Now start to give me introductions one by one. \"\n",
" \"Only reply with Instruction and Input.\"\n",
" )\n",
")\n",
"\n",
"assistant_msg = HumanMessage(content=f\"{assistant_sys_msg.content}\")\n",
"assistant_msg = assistant_agent.step(user_msg)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start role-playing session to solve the task!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original task prompt:\n",
"Develop a trading bot for the stock market\n",
"\n",
"Specified task prompt:\n",
"Develop a Python-based swing trading bot that scans market trends, monitors stocks, and generates trading signals to help a stock trader to place optimal buy and sell orders with defined stop losses and profit targets.\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Install the necessary Python libraries for data analysis and trading.\n",
"Input: None\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can install the necessary Python libraries using pip, a package installer for Python. We can install pandas, numpy, matplotlib, and ta-lib for data analysis and trading. We can use the following command to install these libraries:\n",
"\n",
"```\n",
"pip install pandas numpy matplotlib ta-lib\n",
"```\n",
"\n",
"Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Import the necessary libraries in the Python script.\n",
"Input: None\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can import the necessary libraries in the Python script using the import statement. We need to import pandas, numpy, matplotlib, and ta-lib for data analysis and trading. We can use the following code to import these libraries:\n",
"\n",
"```\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import talib as ta\n",
"```\n",
"\n",
"Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Load historical stock data into a pandas DataFrame.\n",
"Input: The path to the CSV file containing the historical stock data.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can load historical stock data into a pandas DataFrame using the `read_csv()` function from pandas. We need to pass the path to the CSV file containing the historical stock data as an argument to this function. We can use the following code to load the historical stock data:\n",
"\n",
"```\n",
"df = pd.read_csv('path/to/csv/file.csv')\n",
"```\n",
"\n",
"This will load the historical stock data into a pandas DataFrame called `df`. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Preprocess the historical stock data by setting the date column as the index and sorting the DataFrame in ascending order by date.\n",
"Input: None.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can preprocess the historical stock data by setting the date column as the index and sorting the DataFrame in ascending order by date using the `set_index()` and `sort_index()` functions from pandas. We can use the following code to preprocess the historical stock data:\n",
"\n",
"```\n",
"df = df.set_index('date')\n",
"df = df.sort_index(ascending=True)\n",
"```\n",
"\n",
"This will set the date column as the index and sort the DataFrame in ascending order by date. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib.\n",
"Input: The period for the short-term moving average and the period for the long-term moving average.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib. We need to pass the period for the short-term moving average and the period for the long-term moving average as arguments to this function. We can use the following code to calculate the short-term and long-term moving averages:\n",
"\n",
"```\n",
"short_ma = ta.SMA(df['close'], timeperiod=short_period)\n",
"long_ma = ta.SMA(df['close'], timeperiod=long_period)\n",
"```\n",
"\n",
"This will calculate the short-term and long-term moving averages for the stock data and store them in the `short_ma` and `long_ma` variables, respectively. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages.\n",
"Input: None.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages using the `concat()` function from pandas. We need to pass the historical stock data, the short-term moving average, and the long-term moving average as arguments to this function. We can use the following code to create the new DataFrame:\n",
"\n",
"```\n",
"new_df = pd.concat([df, short_ma, long_ma], axis=1)\n",
"new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma']\n",
"```\n",
"\n",
"This will create a new DataFrame called `new_df` that combines the historical stock data with the short-term and long-term moving averages. The columns of the new DataFrame are named 'open', 'high', 'low', 'close', 'volume', 'short_ma', and 'long_ma'. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages.\n",
"Input: None.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. We can use the following code to create the new column:\n",
"\n",
"```\n",
"new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)\n",
"```\n",
"\n",
"This will create a new column called 'signal' in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. If the short-term moving average is greater than the long-term moving average, the signal is 1 (buy), otherwise the signal is -1 (sell). Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target.\n",
"Input: The stop loss and profit target as percentages.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target. We need to pass the stop loss and profit target as percentages as arguments to this function. We can use the following code to create the new column:\n",
"\n",
"```\n",
"stop_loss = stop_loss_percent / 100\n",
"profit_target = profit_target_percent / 100\n",
"\n",
"new_df['pnl'] = 0.0\n",
"buy_price = 0.0\n",
"for i in range(1, len(new_df)):\n",
" if new_df['signal'][i] == 1 and new_df['signal'][i-1] == -1:\n",
" buy_price = new_df['close'][i]\n",
" elif new_df['signal'][i] == -1 and new_df['signal'][i-1] == 1:\n",
" sell_price = new_df['close'][i]\n",
" if sell_price <= buy_price * (1 - stop_loss):\n",
" new_df['pnl'][i] = -stop_loss\n",
" elif sell_price >= buy_price * (1 + profit_target):\n",
" new_df['pnl'][i] = profit_target\n",
" else:\n",
" new_df['pnl'][i] = (sell_price - buy_price) / buy_price\n",
"```\n",
"\n",
"This will create a new column called 'pnl' in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target. The stop loss and profit target are calculated based on the stop_loss_percent and profit_target_percent variables, respectively. The buy and sell prices are stored in the buy_price and sell_price variables, respectively. If the sell price is less than or equal to the stop loss, the profit or loss is set to -stop_loss. If the sell price is greater than or equal to the profit target, the profit or loss is set to profit_target. Otherwise, the profit or loss is calculated as (sell_price - buy_price) / buy_price. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Calculate the total profit or loss for all trades.\n",
"Input: None.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can calculate the total profit or loss for all trades by summing the values in the 'pnl' column of the DataFrame. We can use the following code to calculate the total profit or loss:\n",
"\n",
"```\n",
"total_pnl = new_df['pnl'].sum()\n",
"```\n",
"\n",
"This will calculate the total profit or loss for all trades and store it in the total_pnl variable. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Visualize the stock data, short-term moving average, and long-term moving average using a line chart.\n",
"Input: None.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can visualize the stock data, short-term moving average, and long-term moving average using a line chart using the `plot()` function from pandas. We can use the following code to visualize the data:\n",
"\n",
"```\n",
"plt.figure(figsize=(12,6))\n",
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
"plt.plot(new_df.index, new_df['short_ma'], label='Short MA')\n",
"plt.plot(new_df.index, new_df['long_ma'], label='Long MA')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('Price')\n",
"plt.title('Stock Data with Moving Averages')\n",
"plt.legend()\n",
"plt.show()\n",
"```\n",
"\n",
"This will create a line chart that visualizes the stock data, short-term moving average, and long-term moving average. The x-axis represents the date and the y-axis represents the price. The chart also includes a legend that labels each line. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Visualize the buy and sell signals using a scatter plot.\n",
"Input: None.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can visualize the buy and sell signals using a scatter plot using the `scatter()` function from matplotlib. We can use the following code to visualize the signals:\n",
"\n",
"```\n",
"buy_signals = new_df[new_df['signal'] == 1]\n",
"sell_signals = new_df[new_df['signal'] == -1]\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"plt.scatter(buy_signals.index, buy_signals['close'], label='Buy', marker='^', color='green')\n",
"plt.scatter(sell_signals.index, sell_signals['close'], label='Sell', marker='v', color='red')\n",
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('Price')\n",
"plt.title('Buy and Sell Signals')\n",
"plt.legend()\n",
"plt.show()\n",
"```\n",
"\n",
"This will create a scatter plot that visualizes the buy and sell signals. The x-axis represents the date and the y-axis represents the price. The chart also includes a legend that labels each point. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Print the total profit or loss for all trades.\n",
"Input: None.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: We can print the total profit or loss for all trades using the `print()` function. We can use the following code to print the total profit or loss:\n",
"\n",
"```\n",
"print('Total Profit/Loss: {:.2%}'.format(total_pnl))\n",
"```\n",
"\n",
"This will print the total profit or loss for all trades in percentage format with two decimal places. Next request.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"Instruction: Finalize the code by putting all the code snippets together in a single Python script.\n",
"Input: The necessary inputs for each code snippet.\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Solution: Here is the complete Python script that puts all the code snippets together:\n",
"\n",
"```\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import talib as ta\n",
"\n",
"# Load historical stock data into a pandas DataFrame\n",
"df = pd.read_csv('path/to/csv/file.csv')\n",
"\n",
"# Preprocess the historical stock data\n",
"df = df.set_index('date')\n",
"df = df.sort_index(ascending=True)\n",
"\n",
"# Calculate the short-term and long-term moving averages\n",
"short_period = 10\n",
"long_period = 30\n",
"short_ma = ta.SMA(df['close'], timeperiod=short_period)\n",
"long_ma = ta.SMA(df['close'], timeperiod=long_period)\n",
"\n",
"# Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages\n",
"new_df = pd.concat([df, short_ma, long_ma], axis=1)\n",
"new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma']\n",
"\n",
"# Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages\n",
"new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)\n",
"\n",
"# Create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target\n",
"stop_loss_percent = 5\n",
"profit_target_percent = 10\n",
"stop_loss = stop_loss_percent / 100\n",
"profit_target = profit_target_percent / 100\n",
"new_df['pnl'] = 0.0\n",
"buy_price = 0.0\n",
"for i in range(1, len(new_df)):\n",
" if new_df['signal'][i] == 1 and new_df['signal'][i-1] == -1:\n",
" buy_price = new_df['close'][i]\n",
" elif new_df['signal'][i] == -1 and new_df['signal'][i-1] == 1:\n",
" sell_price = new_df['close'][i]\n",
" if sell_price <= buy_price * (1 - stop_loss):\n",
" new_df['pnl'][i] = -stop_loss\n",
" elif sell_price >= buy_price * (1 + profit_target):\n",
" new_df['pnl'][i] = profit_target\n",
" else:\n",
" new_df['pnl'][i] = (sell_price - buy_price) / buy_price\n",
"\n",
"# Calculate the total profit or loss for all trades\n",
"total_pnl = new_df['pnl'].sum()\n",
"\n",
"# Visualize the stock data, short-term moving average, and long-term moving average using a line chart\n",
"plt.figure(figsize=(12,6))\n",
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
"plt.plot(new_df.index, new_df['short_ma'], label='Short MA')\n",
"plt.plot(new_df.index, new_df['long_ma'], label='Long MA')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('Price')\n",
"plt.title('Stock Data with Moving Averages')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"# Visualize the buy and sell signals using a scatter plot\n",
"buy_signals = new_df[new_df['signal'] == 1]\n",
"sell_signals = new_df[new_df['signal'] == -1]\n",
"plt.figure(figsize=(12,6))\n",
"plt.scatter(buy_signals.index, buy_signals['close'], label='Buy', marker='^', color='green')\n",
"plt.scatter(sell_signals.index, sell_signals['close'], label='Sell', marker='v', color='red')\n",
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
"plt.xlabel('Date')\n",
"plt.ylabel('Price')\n",
"plt.title('Buy and Sell Signals')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"# Print the total profit or loss for all trades\n",
"print('Total Profit/Loss: {:.2%}'.format(total_pnl))\n",
"```\n",
"\n",
"You need to replace the path/to/csv/file.csv with the actual path to the CSV file containing the historical stock data. You can also adjust the short_period, long_period, stop_loss_percent, and profit_target_percent variables to suit your needs.\n",
"\n",
"\n",
"AI User (Stock Trader):\n",
"\n",
"<CAMEL_TASK_DONE>\n",
"\n",
"\n",
"AI Assistant (Python Programmer):\n",
"\n",
"Great! Let me know if you need any further assistance.\n",
"\n",
"\n"
]
}
],
"source": [
"print(f\"Original task prompt:\\n{task}\\n\")\n",
"print(f\"Specified task prompt:\\n{specified_task}\\n\")\n",
"\n",
"chat_turn_limit, n = 30, 0\n",
"while n < chat_turn_limit:\n",
" n += 1\n",
" user_ai_msg = user_agent.step(assistant_msg)\n",
" user_msg = HumanMessage(content=user_ai_msg.content)\n",
" print(f\"AI User ({user_role_name}):\\n\\n{user_msg.content}\\n\\n\")\n",
"\n",
" assistant_ai_msg = assistant_agent.step(user_msg)\n",
" assistant_msg = HumanMessage(content=assistant_ai_msg.content)\n",
" print(f\"AI Assistant ({assistant_role_name}):\\n\\n{assistant_msg.content}\\n\\n\")\n",
" if \"<CAMEL_TASK_DONE>\" in user_msg.content:\n",
" break"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "camel",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
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| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/causal_program_aided_language_model.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "82f3f65d-fbcb-4e8e-b04b-959856283643",
"metadata": {},
"source": [
"# Causal program-aided language (CPAL) chain\n",
"\n",
"The CPAL chain builds on the recent PAL to stop LLM hallucination. The problem with the PAL approach is that it hallucinates on a math problem with a nested chain of dependence. The innovation here is that this new CPAL approach includes causal structure to fix hallucination.\n",
"\n",
"The original [PR's description](https://github.com/langchain-ai/langchain/pull/6255) contains a full overview.\n",
"\n",
"Using the CPAL chain, the LLM translated this\n",
"\n",
" \"Tim buys the same number of pets as Cindy and Boris.\"\n",
" \"Cindy buys the same number of pets as Bill plus Bob.\"\n",
" \"Boris buys the same number of pets as Ben plus Beth.\"\n",
" \"Bill buys the same number of pets as Obama.\"\n",
" \"Bob buys the same number of pets as Obama.\"\n",
" \"Ben buys the same number of pets as Obama.\"\n",
" \"Beth buys the same number of pets as Obama.\"\n",
" \"If Obama buys one pet, how many pets total does everyone buy?\"\n",
"\n",
"\n",
"into this\n",
"\n",
".\n",
"\n",
"Outline of code examples demoed in this notebook.\n",
"\n",
"1. CPAL's value against hallucination: CPAL vs PAL \n",
" 1.1 Complex narrative \n",
" 1.2 Unanswerable math word problem \n",
"2. CPAL's three types of causal diagrams ([The Book of Why](https://en.wikipedia.org/wiki/The_Book_of_Why)). \n",
" 2.1 Mediator \n",
" 2.2 Collider \n",
" 2.3 Confounder "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1370e40f",
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import SVG\n",
"from langchain_experimental.cpal.base import CPALChain\n",
"from langchain_experimental.pal_chain import PALChain\n",
"from langchain_openai import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0, max_tokens=512)\n",
"cpal_chain = CPALChain.from_univariate_prompt(llm=llm, verbose=True)\n",
"pal_chain = PALChain.from_math_prompt(llm=llm, verbose=True)"
]
},
{
"cell_type": "markdown",
"id": "858a87d9-a9bd-4850-9687-9af4b0856b62",
"metadata": {},
"source": [
"## CPAL's value against hallucination: CPAL vs PAL\n",
"\n",
"Like PAL, CPAL intends to reduce large language model (LLM) hallucination.\n",
"\n",
"The CPAL chain is different from the PAL chain for a couple of reasons.\n",
"\n",
"CPAL adds a causal structure (or DAG) to link entity actions (or math expressions).\n",
"The CPAL math expressions are modeling a chain of cause and effect relations, which can be intervened upon, whereas for the PAL chain math expressions are projected math identities.\n"
]
},
{
"cell_type": "markdown",
"id": "496403c5-d268-43ae-8852-2bd9903ce444",
"metadata": {},
"source": [
"### 1.1 Complex narrative\n",
"\n",
"Takeaway: PAL hallucinates, CPAL does not hallucinate."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d5dad768-2892-4825-8093-9b840f643a8a",
"metadata": {},
"outputs": [],
"source": [
"question = (\n",
" \"Tim buys the same number of pets as Cindy and Boris.\"\n",
" \"Cindy buys the same number of pets as Bill plus Bob.\"\n",
" \"Boris buys the same number of pets as Ben plus Beth.\"\n",
" \"Bill buys the same number of pets as Obama.\"\n",
" \"Bob buys the same number of pets as Obama.\"\n",
" \"Ben buys the same number of pets as Obama.\"\n",
" \"Beth buys the same number of pets as Obama.\"\n",
" \"If Obama buys one pet, how many pets total does everyone buy?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bbffa7a0-3c22-4a1d-ab2d-f230973073b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mdef solution():\n",
" \"\"\"Tim buys the same number of pets as Cindy and Boris.Cindy buys the same number of pets as Bill plus Bob.Boris buys the same number of pets as Ben plus Beth.Bill buys the same number of pets as Obama.Bob buys the same number of pets as Obama.Ben buys the same number of pets as Obama.Beth buys the same number of pets as Obama.If Obama buys one pet, how many pets total does everyone buy?\"\"\"\n",
" obama_pets = 1\n",
" tim_pets = obama_pets\n",
" cindy_pets = obama_pets + obama_pets\n",
" boris_pets = obama_pets + obama_pets\n",
" total_pets = tim_pets + cindy_pets + boris_pets\n",
" result = total_pets\n",
" return result\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'5'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "35a70d1d-86f8-4abc-b818-fbd083f072e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mstory outcome data\n",
" name code value depends_on\n",
"0 obama pass 1.0 []\n",
"1 bill bill.value = obama.value 1.0 [obama]\n",
"2 bob bob.value = obama.value 1.0 [obama]\n",
"3 ben ben.value = obama.value 1.0 [obama]\n",
"4 beth beth.value = obama.value 1.0 [obama]\n",
"5 cindy cindy.value = bill.value + bob.value 2.0 [bill, bob]\n",
"6 boris boris.value = ben.value + beth.value 2.0 [ben, beth]\n",
"7 tim tim.value = cindy.value + boris.value 4.0 [cindy, boris]\u001b[0m\n",
"\n",
"\u001b[36;1m\u001b[1;3mquery data\n",
"{\n",
" \"question\": \"how many pets total does everyone buy?\",\n",
" \"expression\": \"SELECT SUM(value) FROM df\",\n",
" \"llm_error_msg\": \"\"\n",
"}\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"13.0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cpal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ccb6b2b0-9de6-4f66-a8fb-fc59229ee316",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"292pt\" height=\"260pt\" viewBox=\"0.00 0.00 292.00 260.00\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 256)\">\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-256 288,-256 288,4 -4,4\"/>\n<!-- obama -->\n<g id=\"node1\" class=\"node\">\n<title>obama</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"137\" cy=\"-234\" rx=\"41.69\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"137\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">obama</text>\n</g>\n<!-- bill -->\n<g id=\"node2\" class=\"node\">\n<title>bill</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"27\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">bill</text>\n</g>\n<!-- obama->bill -->\n<g id=\"edge1\" class=\"edge\">\n<title>obama->bill</title>\n<path fill=\"none\" stroke=\"black\" d=\"M114.47,-218.67C97.08,-207.6 72.94,-192.23 54.42,-180.45\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"56.15,-177.4 45.84,-174.99 52.4,-183.31 56.15,-177.4\"/>\n</g>\n<!-- bob -->\n<g id=\"node3\" class=\"node\">\n<title>bob</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"100\" cy=\"-162\" rx=\"28\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"100\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">bob</text>\n</g>\n<!-- obama->bob -->\n<g id=\"edge2\" class=\"edge\">\n<title>obama->bob</title>\n<path fill=\"none\" stroke=\"black\" d=\"M128.04,-216.05C123.66,-207.77 118.3,-197.62 113.44,-188.42\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"116.39,-186.51 108.62,-179.31 110.2,-189.79 116.39,-186.51\"/>\n</g>\n<!-- ben -->\n<g id=\"node4\" class=\"node\">\n<title>ben</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"174\" cy=\"-162\" rx=\"28\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"174\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">ben</text>\n</g>\n<!-- obama->ben -->\n<g id=\"edge3\" class=\"edge\">\n<title>obama->ben</title>\n<path fill=\"none\" stroke=\"black\" d=\"M145.96,-216.05C150.34,-207.77 155.7,-197.62 160.56,-188.42\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"163.8,-189.79 165.38,-179.31 157.61,-186.51 163.8,-189.79\"/>\n</g>\n<!-- beth -->\n<g id=\"node5\" class=\"node\">\n<title>beth</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"252\" cy=\"-162\" rx=\"32\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"252\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">beth</text>\n</g>\n<!-- obama->beth -->\n<g id=\"edge4\" class=\"edge\">\n<title>obama->beth</title>\n<path fill=\"none\" stroke=\"black\" d=\"M160.27,-218.83C178.18,-207.94 203.04,-192.8 222.37,-181.04\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"224.36,-183.92 231.08,-175.73 220.72,-177.95 224.36,-183.92\"/>\n</g>\n<!-- cindy -->\n<g id=\"node6\" class=\"node\">\n<title>cindy</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"93\" cy=\"-90\" rx=\"36\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"93\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">cindy</text>\n</g>\n<!-- bill->cindy -->\n<g id=\"edge5\" class=\"edge\">\n<title>bill->cindy</title>\n<path fill=\"none\" stroke=\"black\" d=\"M41,-146.15C49.77,-136.85 61.25,-124.67 71.2,-114.12\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"73.79,-116.47 78.11,-106.8 68.7,-111.67 73.79,-116.47\"/>\n</g>\n<!-- bob->cindy -->\n<g id=\"edge6\" class=\"edge\">\n<title>bob->cindy</title>\n<path fill=\"none\" stroke=\"black\" d=\"M98.27,-143.7C97.5,-135.98 96.57,-126.71 95.71,-118.11\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"99.19,-117.7 94.71,-108.1 92.22,-118.4 99.19,-117.7\"/>\n</g>\n<!-- boris -->\n<g id=\"node7\" class=\"node\">\n<title>boris</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"181\" cy=\"-90\" rx=\"34.5\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"181\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">boris</text>\n</g>\n<!-- ben->boris -->\n<g id=\"edge7\" class=\"edge\">\n<title>ben->boris</title>\n<path fill=\"none\" stroke=\"black\" d=\"M175.73,-143.7C176.5,-135.98 177.43,-126.71 178.29,-118.11\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"181.78,-118.4 179.29,-108.1 174.81,-117.7 181.78,-118.4\"/>\n</g>\n<!-- beth->boris -->\n<g id=\"edge8\" class=\"edge\">\n<title>beth->boris</title>\n<path fill=\"none\" stroke=\"black\" d=\"M236.59,-145.81C227.01,-136.36 214.51,-124.04 203.8,-113.48\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"205.96,-110.69 196.38,-106.16 201.04,-115.67 205.96,-110.69\"/>\n</g>\n<!-- tim -->\n<g id=\"node8\" class=\"node\">\n<title>tim</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"137\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"137\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">tim</text>\n</g>\n<!-- cindy->tim -->\n<g id=\"edge9\" class=\"edge\">\n<title>cindy->tim</title>\n<path fill=\"none\" stroke=\"black\" d=\"M103.43,-72.41C108.82,-63.83 115.51,-53.19 121.49,-43.67\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"124.59,-45.32 126.95,-34.99 118.66,-41.59 124.59,-45.32\"/>\n</g>\n<!-- boris->tim -->\n<g id=\"edge10\" class=\"edge\">\n<title>boris->tim</title>\n<path fill=\"none\" stroke=\"black\" d=\"M170.79,-72.77C165.41,-64.19 158.68,-53.49 152.65,-43.9\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"155.43,-41.75 147.15,-35.15 149.51,-45.48 155.43,-41.75\"/>\n</g>\n</g>\n</svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wait 20 secs to see display\n",
"cpal_chain.draw(path=\"web.svg\")\n",
"SVG(\"web.svg\")"
]
},
{
"cell_type": "markdown",
"id": "1f6f345a-bb16-4e64-83c4-cbbc789a8325",
"metadata": {},
"source": [
"### Unanswerable math\n",
"\n",
"Takeaway: PAL hallucinates, where CPAL, rather than hallucinate, answers with _\"unanswerable, narrative question and plot are incoherent\"_"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "068afd79-fd41-4ec2-b4d0-c64140dc413f",
"metadata": {},
"outputs": [],
"source": [
"question = (\n",
" \"Jan has three times the number of pets as Marcia.\"\n",
" \"Marcia has two more pets than Cindy.\"\n",
" \"If Cindy has ten pets, how many pets does Barak have?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "02f77db2-72e8-46c2-90b3-5e37ca42f80d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mdef solution():\n",
" \"\"\"Jan has three times the number of pets as Marcia.Marcia has two more pets than Cindy.If Cindy has ten pets, how many pets does Barak have?\"\"\"\n",
" cindy_pets = 10\n",
" marcia_pets = cindy_pets + 2\n",
" jan_pets = marcia_pets * 3\n",
" result = jan_pets\n",
" return result\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'36'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "925958de-e998-4ffa-8b2e-5a00ddae5026",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mstory outcome data\n",
" name code value depends_on\n",
"0 cindy pass 10.0 []\n",
"1 marcia marcia.value = cindy.value + 2 12.0 [cindy]\n",
"2 jan jan.value = marcia.value * 3 36.0 [marcia]\u001b[0m\n",
"\n",
"\u001b[36;1m\u001b[1;3mquery data\n",
"{\n",
" \"question\": \"how many pets does barak have?\",\n",
" \"expression\": \"SELECT name, value FROM df WHERE name = 'barak'\",\n",
" \"llm_error_msg\": \"\"\n",
"}\u001b[0m\n",
"\n",
"unanswerable, query and outcome are incoherent\n",
"\n",
"outcome:\n",
" name code value depends_on\n",
"0 cindy pass 10.0 []\n",
"1 marcia marcia.value = cindy.value + 2 12.0 [cindy]\n",
"2 jan jan.value = marcia.value * 3 36.0 [marcia]\n",
"query:\n",
"{'question': 'how many pets does barak have?', 'expression': \"SELECT name, value FROM df WHERE name = 'barak'\", 'llm_error_msg': ''}\n"
]
}
],
"source": [
"try:\n",
" cpal_chain.run(question)\n",
"except Exception as e_msg:\n",
" print(e_msg)"
]
},
{
"cell_type": "markdown",
"id": "095adc76",
"metadata": {},
"source": [
"### Basic math\n",
"\n",
"#### Causal mediator"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3ecf03fa-8350-4c4e-8080-84a307ba6ad4",
"metadata": {},
"outputs": [],
"source": [
"question = (\n",
" \"Jan has three times the number of pets as Marcia. \"\n",
" \"Marcia has two more pets than Cindy. \"\n",
" \"If Cindy has four pets, how many total pets do the three have?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "74e49c47-3eed-4abe-98b7-8e97bcd15944",
"metadata": {},
"source": [
"---\n",
"PAL"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2e88395f-d014-4362-abb0-88f6800860bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mdef solution():\n",
" \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
" cindy_pets = 4\n",
" marcia_pets = cindy_pets + 2\n",
" jan_pets = marcia_pets * 3\n",
" total_pets = cindy_pets + marcia_pets + jan_pets\n",
" result = total_pets\n",
" return result\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'28'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pal_chain.run(question)"
]
},
{
"cell_type": "markdown",
"id": "20ba6640-3d17-4b59-8101-aaba89d68cf4",
"metadata": {},
"source": [
"---\n",
"CPAL"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "312a0943-a482-4ed0-a064-1e7a72e9479b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mstory outcome data\n",
" name code value depends_on\n",
"0 cindy pass 4.0 []\n",
"1 marcia marcia.value = cindy.value + 2 6.0 [cindy]\n",
"2 jan jan.value = marcia.value * 3 18.0 [marcia]\u001b[0m\n",
"\n",
"\u001b[36;1m\u001b[1;3mquery data\n",
"{\n",
" \"question\": \"how many total pets do the three have?\",\n",
" \"expression\": \"SELECT SUM(value) FROM df\",\n",
" \"llm_error_msg\": \"\"\n",
"}\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"28.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cpal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4466b975-ae2b-4252-972b-b3182a089ade",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"92pt\" height=\"188pt\" viewBox=\"0.00 0.00 92.49 188.00\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 184)\">\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-184 88.49,-184 88.49,4 -4,4\"/>\n<!-- cindy -->\n<g id=\"node1\" class=\"node\">\n<title>cindy</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"42.25\" cy=\"-162\" rx=\"36\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"42.25\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">cindy</text>\n</g>\n<!-- marcia -->\n<g id=\"node2\" class=\"node\">\n<title>marcia</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"42.25\" cy=\"-90\" rx=\"42.49\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"42.25\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">marcia</text>\n</g>\n<!-- cindy->marcia -->\n<g id=\"edge1\" class=\"edge\">\n<title>cindy->marcia</title>\n<path fill=\"none\" stroke=\"black\" d=\"M42.25,-143.7C42.25,-135.98 42.25,-126.71 42.25,-118.11\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"45.75,-118.1 42.25,-108.1 38.75,-118.1 45.75,-118.1\"/>\n</g>\n<!-- jan -->\n<g id=\"node3\" class=\"node\">\n<title>jan</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"42.25\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"42.25\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">jan</text>\n</g>\n<!-- marcia->jan -->\n<g id=\"edge2\" class=\"edge\">\n<title>marcia->jan</title>\n<path fill=\"none\" stroke=\"black\" d=\"M42.25,-71.7C42.25,-63.98 42.25,-54.71 42.25,-46.11\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"45.75,-46.1 42.25,-36.1 38.75,-46.1 45.75,-46.1\"/>\n</g>\n</g>\n</svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wait 20 secs to see display\n",
"cpal_chain.draw(path=\"web.svg\")\n",
"SVG(\"web.svg\")"
]
},
{
"cell_type": "markdown",
"id": "29fa7b8a-75a3-4270-82a2-2c31939cd7e0",
"metadata": {},
"source": [
"### Causal collider"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "618eddac-f0ef-4ab5-90ed-72e880fdeba3",
"metadata": {},
"outputs": [],
"source": [
"question = (\n",
" \"Jan has the number of pets as Marcia plus the number of pets as Cindy. \"\n",
" \"Marcia has no pets. \"\n",
" \"If Cindy has four pets, how many total pets do the three have?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a01563f3-7974-4de4-8bd9-0b7d710aa0d3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mstory outcome data\n",
" name code value depends_on\n",
"0 marcia pass 0.0 []\n",
"1 cindy pass 4.0 []\n",
"2 jan jan.value = marcia.value + cindy.value 4.0 [marcia, cindy]\u001b[0m\n",
"\n",
"\u001b[36;1m\u001b[1;3mquery data\n",
"{\n",
" \"question\": \"how many total pets do the three have?\",\n",
" \"expression\": \"SELECT SUM(value) FROM df\",\n",
" \"llm_error_msg\": \"\"\n",
"}\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"8.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cpal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0fbe7243-0522-4946-b9a2-6e21e7c49a42",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"182pt\" height=\"116pt\" viewBox=\"0.00 0.00 182.00 116.00\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 112)\">\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-112 178,-112 178,4 -4,4\"/>\n<!-- marcia -->\n<g id=\"node1\" class=\"node\">\n<title>marcia</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"42.25\" cy=\"-90\" rx=\"42.49\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"42.25\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">marcia</text>\n</g>\n<!-- jan -->\n<g id=\"node2\" class=\"node\">\n<title>jan</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"90.25\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"90.25\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">jan</text>\n</g>\n<!-- marcia->jan -->\n<g id=\"edge1\" class=\"edge\">\n<title>marcia->jan</title>\n<path fill=\"none\" stroke=\"black\" d=\"M53.62,-72.41C59.57,-63.74 66.95,-52.97 73.53,-43.38\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"76.51,-45.21 79.28,-34.99 70.74,-41.26 76.51,-45.21\"/>\n</g>\n<!-- cindy -->\n<g id=\"node3\" class=\"node\">\n<title>cindy</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"138.25\" cy=\"-90\" rx=\"36\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"138.25\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">cindy</text>\n</g>\n<!-- cindy->jan -->\n<g id=\"edge2\" class=\"edge\">\n<title>cindy->jan</title>\n<path fill=\"none\" stroke=\"black\" d=\"M127.11,-72.77C121.09,-63.98 113.54,-52.96 106.83,-43.19\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"109.53,-40.94 100.99,-34.67 103.75,-44.89 109.53,-40.94\"/>\n</g>\n</g>\n</svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wait 20 secs to see display\n",
"cpal_chain.draw(path=\"web.svg\")\n",
"SVG(\"web.svg\")"
]
},
{
"cell_type": "markdown",
"id": "d4082538-ec03-44f0-aac3-07e03aad7555",
"metadata": {},
"source": [
"### Causal confounder"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "83932c30-950b-435a-b328-7993ce8cc6bd",
"metadata": {},
"outputs": [],
"source": [
"question = (\n",
" \"Jan has the number of pets as Marcia plus the number of pets as Cindy. \"\n",
" \"Marcia has two more pets than Cindy. \"\n",
" \"If Cindy has four pets, how many total pets do the three have?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "570de307-7c6b-4fdc-80c3-4361daa8a629",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mstory outcome data\n",
" name code value depends_on\n",
"0 cindy pass 4.0 []\n",
"1 marcia marcia.value = cindy.value + 2 6.0 [cindy]\n",
"2 jan jan.value = cindy.value + marcia.value 10.0 [cindy, marcia]\u001b[0m\n",
"\n",
"\u001b[36;1m\u001b[1;3mquery data\n",
"{\n",
" \"question\": \"how many total pets do the three have?\",\n",
" \"expression\": \"SELECT SUM(value) FROM df\",\n",
" \"llm_error_msg\": \"\"\n",
"}\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"20.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cpal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "00375615-6b6d-4357-bdb8-f64f682f7605",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"121pt\" height=\"188pt\" viewBox=\"0.00 0.00 120.99 188.00\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 184)\">\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-184 116.99,-184 116.99,4 -4,4\"/>\n<!-- cindy -->\n<g id=\"node1\" class=\"node\">\n<title>cindy</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"77.25\" cy=\"-162\" rx=\"36\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"77.25\" y=\"-158.3\" font-family=\"Times,serif\" font-size=\"14.00\">cindy</text>\n</g>\n<!-- marcia -->\n<g id=\"node2\" class=\"node\">\n<title>marcia</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"42.25\" cy=\"-90\" rx=\"42.49\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"42.25\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">marcia</text>\n</g>\n<!-- cindy->marcia -->\n<g id=\"edge1\" class=\"edge\">\n<title>cindy->marcia</title>\n<path fill=\"none\" stroke=\"black\" d=\"M68.95,-144.41C64.87,-136.25 59.86,-126.22 55.28,-117.07\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"58.33,-115.34 50.72,-107.96 52.07,-118.47 58.33,-115.34\"/>\n</g>\n<!-- jan -->\n<g id=\"node3\" class=\"node\">\n<title>jan</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"77.25\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"77.25\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">jan</text>\n</g>\n<!-- cindy->jan -->\n<g id=\"edge2\" class=\"edge\">\n<title>cindy->jan</title>\n<path fill=\"none\" stroke=\"black\" d=\"M83.73,-144.1C87.32,-133.84 91.42,-120.36 93.25,-108 95.58,-92.17 95.58,-87.83 93.25,-72 91.95,-63.21 89.5,-53.86 86.91,-45.5\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"90.19,-44.29 83.73,-35.9 83.55,-46.49 90.19,-44.29\"/>\n</g>\n<!-- marcia->jan -->\n<g id=\"edge3\" class=\"edge\">\n<title>marcia->jan</title>\n<path fill=\"none\" stroke=\"black\" d=\"M50.72,-72.06C54.86,-63.77 59.94,-53.62 64.53,-44.42\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"67.75,-45.82 69.09,-35.31 61.49,-42.69 67.75,-45.82\"/>\n</g>\n</g>\n</svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wait 20 secs to see display\n",
"cpal_chain.draw(path=\"web.svg\")\n",
"SVG(\"web.svg\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "255683de-0c1c-4131-b277-99d09f5ac1fc",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/code-analysis-deeplake.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
"In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Design"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Prepare data:\n",
" 1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 2. Split all documents to chunks using the `langchain_text_splitters.CharacterTextSplitter`.\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n",
"2. Question-Answering:\n",
" 1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
" 2. Prepare questions.\n",
" 3. Get answers running the chain.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Integration preparations"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to set up keys for external services and install necessary python libraries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!python3 -m pip install --upgrade langchain deeplake openai"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. \n",
"\n",
"For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"# Please manually enter OpenAI Key"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at [app.activeloop.ai](https://app.activeloop.ai)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"activeloop_token = getpass(\"Activeloop Token:\")\n",
"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare data "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the `langchain` repo.\n",
"\n",
"If you want to use files from different repo, change `root_dir` to the root dir of your repo."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CITATION.cff MIGRATE.md README.md libs\t poetry.toml\n",
"LICENSE Makefile\t docs\t poetry.lock pyproject.toml\n"
]
}
],
"source": [
"!ls \"../../../../../../libs\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2554\n"
]
}
],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"\n",
"root_dir = \"../../../../../../libs\"\n",
"\n",
"docs = []\n",
"for dirpath, dirnames, filenames in os.walk(root_dir):\n",
" for file in filenames:\n",
" if file.endswith(\".py\") and \"*venv/\" not in dirpath:\n",
" try:\n",
" loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
" docs.extend(loader.load_and_split())\n",
" except Exception:\n",
" pass\n",
"print(f\"{len(docs)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, chunk the files"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"8244\n"
]
}
],
"source": [
"from langchain_text_splitters import CharacterTextSplitter\n",
"\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(docs)\n",
"print(f\"{len(texts)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Then embed chunks and upload them to the DeepLake.\n",
"\n",
"This can take several minutes. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"embeddings"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your Deep Lake dataset has been successfully created!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='hub://adilkhan/langchain-code', tensors=['embedding', 'id', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding embedding (8244, 1536) float32 None \n",
" id text (8244, 1) str None \n",
" metadata json (8244, 1) str None \n",
" text text (8244, 1) str None \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": []
},
{
"data": {
"text/plain": [
"<langchain_community.vectorstores.deeplake.DeepLake at 0x7fe1b67d7a30>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_community.vectorstores import DeepLake\n",
"\n",
"username = \"<USERNAME_OR_ORG>\"\n",
"\n",
"\n",
"db = DeepLake.from_documents(\n",
" texts, embeddings, dataset_path=f\"hub://{username}/langchain-code\", overwrite=True\n",
")\n",
"db"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# from langchain_community.vectorstores import DeepLake\n",
"\n",
"# db = DeepLake.from_documents(\n",
"# texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
"# )\n",
"# db"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question Answering\n",
"First load the dataset, construct the retriever, then construct the Conversational Chain"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Deep Lake Dataset in hub://adilkhan/langchain-code already exists, loading from the storage\n"
]
}
],
"source": [
"db = DeepLake(\n",
" dataset_path=f\"hub://{username}/langchain-code\",\n",
" read_only=True,\n",
" embedding=embeddings,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = db.as_retriever()\n",
"retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
"retriever.search_kwargs[\"fetch_k\"] = 20\n",
"retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
"retriever.search_kwargs[\"k\"] = 20"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def filter(x):\n",
" # filter based on source code\n",
" if \"something\" in x[\"text\"].data()[\"value\"]:\n",
" return False\n",
"\n",
" # filter based on path e.g. extension\n",
" metadata = x[\"metadata\"].data()[\"value\"]\n",
" return \"only_this\" in metadata[\"source\"] or \"also_that\" in metadata[\"source\"]\n",
"\n",
"\n",
"### turn on below for custom filtering\n",
"# retriever.search_kwargs['filter'] = filter"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chains import ConversationalRetrievalChain\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(\n",
" model_name=\"gpt-3.5-turbo-0613\"\n",
") # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
"qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-> **Question**: What is the class hierarchy? \n",
"\n",
"**Answer**: The class hierarchy for Memory is as follows:\n",
"\n",
" BaseMemory --> BaseChatMemory --> <name>Memory # Examples: ZepMemory, MotorheadMemory\n",
"\n",
"The class hierarchy for ChatMessageHistory is as follows:\n",
"\n",
" BaseChatMessageHistory --> <name>ChatMessageHistory # Example: ZepChatMessageHistory\n",
"\n",
"The class hierarchy for Prompt is as follows:\n",
"\n",
" BasePromptTemplate --> PipelinePromptTemplate\n",
" StringPromptTemplate --> PromptTemplate\n",
" FewShotPromptTemplate\n",
" FewShotPromptWithTemplates\n",
" BaseChatPromptTemplate --> AutoGPTPrompt\n",
" ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
" \n",
"\n",
"-> **Question**: What classes are derived from the Chain class? \n",
"\n",
"**Answer**: The classes derived from the Chain class are:\n",
"\n",
"- APIChain\n",
"- OpenAPIEndpointChain\n",
"- AnalyzeDocumentChain\n",
"- MapReduceDocumentsChain\n",
"- MapRerankDocumentsChain\n",
"- ReduceDocumentsChain\n",
"- RefineDocumentsChain\n",
"- StuffDocumentsChain\n",
"- ConstitutionalChain\n",
"- ConversationChain\n",
"- ChatVectorDBChain\n",
"- ConversationalRetrievalChain\n",
"- FalkorDBQAChain\n",
"- FlareChain\n",
"- ArangoGraphQAChain\n",
"- GraphQAChain\n",
"- GraphCypherQAChain\n",
"- HugeGraphQAChain\n",
"- KuzuQAChain\n",
"- NebulaGraphQAChain\n",
"- NeptuneOpenCypherQAChain\n",
"- GraphSparqlQAChain\n",
"- HypotheticalDocumentEmbedder\n",
"- LLMChain\n",
"- LLMBashChain\n",
"- LLMCheckerChain\n",
"- LLMMathChain\n",
"- LLMRequestsChain\n",
"- LLMSummarizationCheckerChain\n",
"- MapReduceChain\n",
"- OpenAIModerationChain\n",
"- NatBotChain\n",
"- QAGenerationChain\n",
"- QAWithSourcesChain\n",
"- RetrievalQAWithSourcesChain\n",
"- VectorDBQAWithSourcesChain\n",
"- RetrievalQA\n",
"- VectorDBQA\n",
"- LLMRouterChain\n",
"- MultiPromptChain\n",
"- MultiRetrievalQAChain\n",
"- MultiRouteChain\n",
"- RouterChain\n",
"- SequentialChain\n",
"- SimpleSequentialChain\n",
"- TransformChain\n",
"- TaskPlaningChain\n",
"- QueryChain\n",
"- CPALChain\n",
" \n",
"\n",
"-> **Question**: What kind of retrievers does LangChain have? \n",
"\n",
"**Answer**: The LangChain class includes various types of retrievers such as:\n",
"\n",
"- ArxivRetriever\n",
"- AzureAISearchRetriever\n",
"- BM25Retriever\n",
"- ChaindeskRetriever\n",
"- ChatGPTPluginRetriever\n",
"- ContextualCompressionRetriever\n",
"- DocArrayRetriever\n",
"- ElasticSearchBM25Retriever\n",
"- EnsembleRetriever\n",
"- GoogleVertexAISearchRetriever\n",
"- AmazonKendraRetriever\n",
"- KNNRetriever\n",
"- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
"- MergerRetriever\n",
"- MetalRetriever\n",
"- MilvusRetriever\n",
"- MultiQueryRetriever\n",
"- ParentDocumentRetriever\n",
"- PineconeHybridSearchRetriever\n",
"- PubMedRetriever\n",
"- RePhraseQueryRetriever\n",
"- RemoteLangChainRetriever\n",
"- SelfQueryRetriever\n",
"- SVMRetriever\n",
"- TFIDFRetriever\n",
"- TimeWeightedVectorStoreRetriever\n",
"- VespaRetriever\n",
"- WeaviateHybridSearchRetriever\n",
"- WebResearchRetriever\n",
"- WikipediaRetriever\n",
"- ZepRetriever\n",
"- ZillizRetriever \n",
"\n"
]
}
],
"source": [
"questions = [\n",
" \"What is the class hierarchy?\",\n",
" \"What classes are derived from the Chain class?\",\n",
" \"What kind of retrievers does LangChain have?\",\n",
"]\n",
"chat_history = []\n",
"qa_dict = {}\n",
"\n",
"for question in questions:\n",
" result = qa({\"question\": question, \"chat_history\": chat_history})\n",
" chat_history.append((question, result[\"answer\"]))\n",
" qa_dict[question] = result[\"answer\"]\n",
" print(f\"-> **Question**: {question} \\n\")\n",
" print(f\"**Answer**: {result['answer']} \\n\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'question': 'LangChain possesses a variety of retrievers including:\\n\\n1. ArxivRetriever\\n2. AzureAISearchRetriever\\n3. BM25Retriever\\n4. ChaindeskRetriever\\n5. ChatGPTPluginRetriever\\n6. ContextualCompressionRetriever\\n7. DocArrayRetriever\\n8. ElasticSearchBM25Retriever\\n9. EnsembleRetriever\\n10. GoogleVertexAISearchRetriever\\n11. AmazonKendraRetriever\\n12. KNNRetriever\\n13. LlamaIndexGraphRetriever\\n14. LlamaIndexRetriever\\n15. MergerRetriever\\n16. MetalRetriever\\n17. MilvusRetriever\\n18. MultiQueryRetriever\\n19. ParentDocumentRetriever\\n20. PineconeHybridSearchRetriever\\n21. PubMedRetriever\\n22. RePhraseQueryRetriever\\n23. RemoteLangChainRetriever\\n24. SelfQueryRetriever\\n25. SVMRetriever\\n26. TFIDFRetriever\\n27. TimeWeightedVectorStoreRetriever\\n28. VespaRetriever\\n29. WeaviateHybridSearchRetriever\\n30. WebResearchRetriever\\n31. WikipediaRetriever\\n32. ZepRetriever\\n33. ZillizRetriever\\n\\nIt also includes self query translators like:\\n\\n1. ChromaTranslator\\n2. DeepLakeTranslator\\n3. MyScaleTranslator\\n4. PineconeTranslator\\n5. QdrantTranslator\\n6. WeaviateTranslator\\n\\nAnd remote retrievers like:\\n\\n1. RemoteLangChainRetriever'}"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qa_dict"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The class hierarchy for Memory is as follows:\n",
"\n",
" BaseMemory --> BaseChatMemory --> <name>Memory # Examples: ZepMemory, MotorheadMemory\n",
"\n",
"The class hierarchy for ChatMessageHistory is as follows:\n",
"\n",
" BaseChatMessageHistory --> <name>ChatMessageHistory # Example: ZepChatMessageHistory\n",
"\n",
"The class hierarchy for Prompt is as follows:\n",
"\n",
" BasePromptTemplate --> PipelinePromptTemplate\n",
" StringPromptTemplate --> PromptTemplate\n",
" FewShotPromptTemplate\n",
" FewShotPromptWithTemplates\n",
" BaseChatPromptTemplate --> AutoGPTPrompt\n",
" ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
"\n"
]
}
],
"source": [
"print(qa_dict[\"What is the class hierarchy?\"])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The classes derived from the Chain class are:\n",
"\n",
"- APIChain\n",
"- OpenAPIEndpointChain\n",
"- AnalyzeDocumentChain\n",
"- MapReduceDocumentsChain\n",
"- MapRerankDocumentsChain\n",
"- ReduceDocumentsChain\n",
"- RefineDocumentsChain\n",
"- StuffDocumentsChain\n",
"- ConstitutionalChain\n",
"- ConversationChain\n",
"- ChatVectorDBChain\n",
"- ConversationalRetrievalChain\n",
"- FlareChain\n",
"- ArangoGraphQAChain\n",
"- GraphQAChain\n",
"- GraphCypherQAChain\n",
"- HugeGraphQAChain\n",
"- KuzuQAChain\n",
"- NebulaGraphQAChain\n",
"- NeptuneOpenCypherQAChain\n",
"- GraphSparqlQAChain\n",
"- HypotheticalDocumentEmbedder\n",
"- LLMChain\n",
"- LLMBashChain\n",
"- LLMCheckerChain\n",
"- LLMMathChain\n",
"- LLMRequestsChain\n",
"- LLMSummarizationCheckerChain\n",
"- MapReduceChain\n",
"- OpenAIModerationChain\n",
"- NatBotChain\n",
"- QAGenerationChain\n",
"- QAWithSourcesChain\n",
"- RetrievalQAWithSourcesChain\n",
"- VectorDBQAWithSourcesChain\n",
"- RetrievalQA\n",
"- VectorDBQA\n",
"- LLMRouterChain\n",
"- MultiPromptChain\n",
"- MultiRetrievalQAChain\n",
"- MultiRouteChain\n",
"- RouterChain\n",
"- SequentialChain\n",
"- SimpleSequentialChain\n",
"- TransformChain\n",
"- TaskPlaningChain\n",
"- QueryChain\n",
"- CPALChain\n",
"\n"
]
}
],
"source": [
"print(qa_dict[\"What classes are derived from the Chain class?\"])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The LangChain class includes various types of retrievers such as:\n",
"\n",
"- ArxivRetriever\n",
"- AzureAISearchRetriever\n",
"- BM25Retriever\n",
"- ChaindeskRetriever\n",
"- ChatGPTPluginRetriever\n",
"- ContextualCompressionRetriever\n",
"- DocArrayRetriever\n",
"- ElasticSearchBM25Retriever\n",
"- EnsembleRetriever\n",
"- GoogleVertexAISearchRetriever\n",
"- AmazonKendraRetriever\n",
"- KNNRetriever\n",
"- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
"- MergerRetriever\n",
"- MetalRetriever\n",
"- MilvusRetriever\n",
"- MultiQueryRetriever\n",
"- ParentDocumentRetriever\n",
"- PineconeHybridSearchRetriever\n",
"- PubMedRetriever\n",
"- RePhraseQueryRetriever\n",
"- RemoteLangChainRetriever\n",
"- SelfQueryRetriever\n",
"- SVMRetriever\n",
"- TFIDFRetriever\n",
"- TimeWeightedVectorStoreRetriever\n",
"- VespaRetriever\n",
"- WeaviateHybridSearchRetriever\n",
"- WebResearchRetriever\n",
"- WikipediaRetriever\n",
"- ZepRetriever\n",
"- ZillizRetriever\n"
]
}
],
"source": [
"print(qa_dict[\"What kind of retrievers does LangChain have?\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/cql_agent.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Python Modules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Install the following Python modules:\n",
"\n",
"```bash\n",
"pip install ipykernel python-dotenv cassio pandas langchain_openai langchain langchain-community langchainhub langchain_experimental openai-multi-tool-use-parallel-patch\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load the `.env` File"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Connection is via `cassio` using `auto=True` parameter, and the notebook uses OpenAI. You should create a `.env` file accordingly.\n",
"\n",
"For Casssandra, set:\n",
"```bash\n",
"CASSANDRA_CONTACT_POINTS\n",
"CASSANDRA_USERNAME\n",
"CASSANDRA_PASSWORD\n",
"CASSANDRA_KEYSPACE\n",
"```\n",
"\n",
"For Astra, set:\n",
"```bash\n",
"ASTRA_DB_APPLICATION_TOKEN\n",
"ASTRA_DB_DATABASE_ID\n",
"ASTRA_DB_KEYSPACE\n",
"```\n",
"\n",
"For example:\n",
"\n",
"```bash\n",
"# Connection to Astra:\n",
"ASTRA_DB_DATABASE_ID=a1b2c3d4-...\n",
"ASTRA_DB_APPLICATION_TOKEN=AstraCS:...\n",
"ASTRA_DB_KEYSPACE=notebooks\n",
"\n",
"# Also set \n",
"OPENAI_API_KEY=sk-....\n",
"```\n",
"\n",
"(You may also modify the below code to directly connect with `cassio`.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to Cassandra"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import cassio\n",
"\n",
"cassio.init(auto=True)\n",
"session = cassio.config.resolve_session()\n",
"if not session:\n",
" raise Exception(\n",
" \"Check environment configuration or manually configure cassio connection parameters\"\n",
" )\n",
"\n",
"keyspace = os.environ.get(\n",
" \"ASTRA_DB_KEYSPACE\", os.environ.get(\"CASSANDRA_KEYSPACE\", None)\n",
")\n",
"if not keyspace:\n",
" raise ValueError(\"a KEYSPACE environment variable must be set\")\n",
"\n",
"session.set_keyspace(keyspace)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup Database"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This needs to be done one time only!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dataset used is from Kaggle, the [Environmental Sensor Telemetry Data](https://www.kaggle.com/datasets/garystafford/environmental-sensor-data-132k?select=iot_telemetry_data.csv). The next cell will download and unzip the data into a Pandas dataframe. The following cell is instructions to download manually. \n",
"\n",
"The net result of this section is you should have a Pandas dataframe variable `df`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Download Automatically"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from io import BytesIO\n",
"from zipfile import ZipFile\n",
"\n",
"import pandas as pd\n",
"import requests\n",
"\n",
"datasetURL = \"https://storage.googleapis.com/kaggle-data-sets/788816/1355729/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240404%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240404T115828Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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\"\n",
"\n",
"response = requests.get(datasetURL)\n",
"if response.status_code == 200:\n",
" zip_file = ZipFile(BytesIO(response.content))\n",
" csv_file_name = zip_file.namelist()[0]\n",
"else:\n",
" print(\"Failed to download the file\")\n",
"\n",
"with zip_file.open(csv_file_name) as csv_file:\n",
" df = pd.read_csv(csv_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Download Manually"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can download the `.zip` file and unpack the `.csv` contained within. Comment in the next line, and adjust the path to this `.csv` file appropriately."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# df = pd.read_csv(\"/path/to/iot_telemetry_data.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Data into Cassandra"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section assumes the existence of a dataframe `df`, the following cell validates its structure. The Download section above creates this object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"assert df is not None, \"Dataframe 'df' must be set\"\n",
"expected_columns = [\n",
" \"ts\",\n",
" \"device\",\n",
" \"co\",\n",
" \"humidity\",\n",
" \"light\",\n",
" \"lpg\",\n",
" \"motion\",\n",
" \"smoke\",\n",
" \"temp\",\n",
"]\n",
"assert all(\n",
" [column in df.columns for column in expected_columns]\n",
"), \"DataFrame does not have the expected columns\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create and load tables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import UTC, datetime\n",
"\n",
"from cassandra.query import BatchStatement\n",
"\n",
"# Create sensors table\n",
"table_query = \"\"\"\n",
"CREATE TABLE IF NOT EXISTS iot_sensors (\n",
" device text,\n",
" conditions text,\n",
" room text,\n",
" PRIMARY KEY (device)\n",
")\n",
"WITH COMMENT = 'Environmental IoT room sensor metadata.';\n",
"\"\"\"\n",
"session.execute(table_query)\n",
"\n",
"pstmt = session.prepare(\n",
" \"\"\"\n",
"INSERT INTO iot_sensors (device, conditions, room)\n",
"VALUES (?, ?, ?)\n",
"\"\"\"\n",
")\n",
"\n",
"devices = [\n",
" (\"00:0f:00:70:91:0a\", \"stable conditions, cooler and more humid\", \"room 1\"),\n",
" (\"1c:bf:ce:15:ec:4d\", \"highly variable temperature and humidity\", \"room 2\"),\n",
" (\"b8:27:eb:bf:9d:51\", \"stable conditions, warmer and dryer\", \"room 3\"),\n",
"]\n",
"\n",
"for device, conditions, room in devices:\n",
" session.execute(pstmt, (device, conditions, room))\n",
"\n",
"print(\"Sensors inserted successfully.\")\n",
"\n",
"# Create data table\n",
"table_query = \"\"\"\n",
"CREATE TABLE IF NOT EXISTS iot_data (\n",
" day text,\n",
" device text,\n",
" ts timestamp,\n",
" co double,\n",
" humidity double,\n",
" light boolean,\n",
" lpg double,\n",
" motion boolean,\n",
" smoke double,\n",
" temp double,\n",
" PRIMARY KEY ((day, device), ts)\n",
")\n",
"WITH COMMENT = 'Data from environmental IoT room sensors. Columns include device identifier, timestamp (ts) of the data collection, carbon monoxide level (co), relative humidity, light presence, LPG concentration, motion detection, smoke concentration, and temperature (temp). Data is partitioned by day and device.';\n",
"\"\"\"\n",
"session.execute(table_query)\n",
"\n",
"pstmt = session.prepare(\n",
" \"\"\"\n",
"INSERT INTO iot_data (day, device, ts, co, humidity, light, lpg, motion, smoke, temp)\n",
"VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n",
"\"\"\"\n",
")\n",
"\n",
"\n",
"def insert_data_batch(name, group):\n",
" batch = BatchStatement()\n",
" day, device = name\n",
" print(f\"Inserting batch for day: {day}, device: {device}\")\n",
"\n",
" for _, row in group.iterrows():\n",
" timestamp = datetime.fromtimestamp(row[\"ts\"], UTC)\n",
" batch.add(\n",
" pstmt,\n",
" (\n",
" day,\n",
" row[\"device\"],\n",
" timestamp,\n",
" row[\"co\"],\n",
" row[\"humidity\"],\n",
" row[\"light\"],\n",
" row[\"lpg\"],\n",
" row[\"motion\"],\n",
" row[\"smoke\"],\n",
" row[\"temp\"],\n",
" ),\n",
" )\n",
"\n",
" session.execute(batch)\n",
"\n",
"\n",
"# Convert columns to appropriate types\n",
"df[\"light\"] = df[\"light\"] == \"true\"\n",
"df[\"motion\"] = df[\"motion\"] == \"true\"\n",
"df[\"ts\"] = df[\"ts\"].astype(float)\n",
"df[\"day\"] = df[\"ts\"].apply(\n",
" lambda x: datetime.fromtimestamp(x, UTC).strftime(\"%Y-%m-%d\")\n",
")\n",
"\n",
"grouped_df = df.groupby([\"day\", \"device\"])\n",
"\n",
"for name, group in grouped_df:\n",
" insert_data_batch(name, group)\n",
"\n",
"print(\"Data load complete\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(session.keyspace)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the Tools"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python `import` statements for the demo:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
"from langchain_community.agent_toolkits.cassandra_database.toolkit import (\n",
" CassandraDatabaseToolkit,\n",
")\n",
"from langchain_community.tools.cassandra_database.prompt import QUERY_PATH_PROMPT\n",
"from langchain_community.tools.cassandra_database.tool import (\n",
" GetSchemaCassandraDatabaseTool,\n",
" GetTableDataCassandraDatabaseTool,\n",
" QueryCassandraDatabaseTool,\n",
")\n",
"from langchain_community.utilities.cassandra_database import CassandraDatabase\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `CassandraDatabase` object is loaded from `cassio`, though it does accept a `Session`-type parameter as an alternative."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a CassandraDatabase instance\n",
"db = CassandraDatabase(include_tables=[\"iot_sensors\", \"iot_data\"])\n",
"\n",
"# Create the Cassandra Database tools\n",
"query_tool = QueryCassandraDatabaseTool(db=db)\n",
"schema_tool = GetSchemaCassandraDatabaseTool(db=db)\n",
"select_data_tool = GetTableDataCassandraDatabaseTool(db=db)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The tools can be invoked directly:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test the tools\n",
"print(\"Executing a CQL query:\")\n",
"query = \"SELECT * FROM iot_sensors LIMIT 5;\"\n",
"result = query_tool.run({\"query\": query})\n",
"print(result)\n",
"\n",
"print(\"\\nGetting the schema for a keyspace:\")\n",
"schema = schema_tool.run({\"keyspace\": keyspace})\n",
"print(schema)\n",
"\n",
"print(\"\\nGetting data from a table:\")\n",
"table = \"iot_data\"\n",
"predicate = \"day = '2020-07-14' and device = 'b8:27:eb:bf:9d:51'\"\n",
"data = select_data_tool.run(\n",
" {\"keyspace\": keyspace, \"table\": table, \"predicate\": predicate, \"limit\": 5}\n",
")\n",
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Agent Configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import Tool\n",
"from langchain_experimental.utilities import PythonREPL\n",
"\n",
"python_repl = PythonREPL()\n",
"\n",
"repl_tool = Tool(\n",
" name=\"python_repl\",\n",
" description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
" func=python_repl.run,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain import hub\n",
"\n",
"llm = ChatOpenAI(temperature=0, model=\"gpt-4-1106-preview\")\n",
"toolkit = CassandraDatabaseToolkit(db=db)\n",
"\n",
"# context = toolkit.get_context()\n",
"# tools = toolkit.get_tools()\n",
"tools = [schema_tool, select_data_tool, repl_tool]\n",
"\n",
"input = (\n",
" QUERY_PATH_PROMPT\n",
" + f\"\"\"\n",
"\n",
"Here is your task: In the {keyspace} keyspace, find the total number of times the temperature of each device has exceeded 23 degrees on July 14, 2020.\n",
" Create a summary report including the name of the room. Use Pandas if helpful.\n",
"\"\"\"\n",
")\n",
"\n",
"prompt = hub.pull(\"hwchase17/openai-tools-agent\")\n",
"\n",
"# messages = [\n",
"# HumanMessagePromptTemplate.from_template(input),\n",
"# AIMessage(content=QUERY_PATH_PROMPT),\n",
"# MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
"# ]\n",
"\n",
"# prompt = ChatPromptTemplate.from_messages(messages)\n",
"# print(prompt)\n",
"\n",
"# Choose the LLM that will drive the agent\n",
"# Only certain models support this\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0)\n",
"\n",
"# Construct the OpenAI Tools agent\n",
"agent = create_openai_tools_agent(llm, tools, prompt)\n",
"\n",
"print(\"Available tools:\")\n",
"for tool in tools:\n",
" print(\"\\t\" + tool.name + \" - \" + tool.description + \" - \" + str(tool))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)\n",
"\n",
"response = agent_executor.invoke({\"input\": input})\n",
"\n",
"print(response[\"output\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/custom_agent_with_plugin_retrieval.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "ba5f8741",
"metadata": {},
"source": [
"# Custom Agent with PlugIn Retrieval\n",
"\n",
"This notebook combines two concepts in order to build a custom agent that can interact with AI Plugins:\n",
"\n",
"1. [Custom Agent with Tool Retrieval](/docs/modules/agents/how_to/custom_agent_with_tool_retrieval.html): This introduces the concept of retrieving many tools, which is useful when trying to work with arbitrarily many plugins.\n",
"2. [Natural Language API Chains](/docs/use_cases/apis/openapi.html): This creates Natural Language wrappers around OpenAPI endpoints. This is useful because (1) plugins use OpenAPI endpoints under the hood, (2) wrapping them in an NLAChain allows the router agent to call it more easily.\n",
"\n",
"The novel idea introduced in this notebook is the idea of using retrieval to select not the tools explicitly, but the set of OpenAPI specs to use. We can then generate tools from those OpenAPI specs. The use case for this is when trying to get agents to use plugins. It may be more efficient to choose plugins first, then the endpoints, rather than the endpoints directly. This is because the plugins may contain more useful information for selection."
]
},
{
"cell_type": "markdown",
"id": "fea4812c",
"metadata": {},
"source": [
"## Set up environment\n",
"\n",
"Do necessary imports, etc."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9af9734e",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from typing import Union\n",
"\n",
"from langchain.agents import (\n",
" AgentExecutor,\n",
" AgentOutputParser,\n",
" LLMSingleActionAgent,\n",
")\n",
"from langchain.chains import LLMChain\n",
"from langchain.prompts import StringPromptTemplate\n",
"from langchain_community.agent_toolkits import NLAToolkit\n",
"from langchain_community.tools.plugin import AIPlugin\n",
"from langchain_core.agents import AgentAction, AgentFinish\n",
"from langchain_openai import OpenAI"
]
},
{
"cell_type": "markdown",
"id": "2f91d8b4",
"metadata": {},
"source": [
"## Setup LLM"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a1a3b59c",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "6df0253f",
"metadata": {},
"source": [
"## Set up plugins\n",
"\n",
"Load and index plugins"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "becda2a1",
"metadata": {},
"outputs": [],
"source": [
"urls = [\n",
" \"https://datasette.io/.well-known/ai-plugin.json\",\n",
" \"https://api.speak.com/.well-known/ai-plugin.json\",\n",
" \"https://www.wolframalpha.com/.well-known/ai-plugin.json\",\n",
" \"https://www.zapier.com/.well-known/ai-plugin.json\",\n",
" \"https://www.klarna.com/.well-known/ai-plugin.json\",\n",
" \"https://www.joinmilo.com/.well-known/ai-plugin.json\",\n",
" \"https://slack.com/.well-known/ai-plugin.json\",\n",
" \"https://schooldigger.com/.well-known/ai-plugin.json\",\n",
"]\n",
"\n",
"AI_PLUGINS = [AIPlugin.from_url(url) for url in urls]"
]
},
{
"cell_type": "markdown",
"id": "17362717",
"metadata": {},
"source": [
"## Tool Retriever\n",
"\n",
"We will use a vectorstore to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search for relevant tools."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "77c4be4b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9092a158",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.2 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load a Swagger 2.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
]
}
],
"source": [
"embeddings = OpenAIEmbeddings()\n",
"docs = [\n",
" Document(\n",
" page_content=plugin.description_for_model,\n",
" metadata={\"plugin_name\": plugin.name_for_model},\n",
" )\n",
" for plugin in AI_PLUGINS\n",
"]\n",
"vector_store = FAISS.from_documents(docs, embeddings)\n",
"toolkits_dict = {\n",
" plugin.name_for_model: NLAToolkit.from_llm_and_ai_plugin(llm, plugin)\n",
" for plugin in AI_PLUGINS\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "735a7566",
"metadata": {},
"outputs": [],
"source": [
"retriever = vector_store.as_retriever()\n",
"\n",
"\n",
"def get_tools(query):\n",
" # Get documents, which contain the Plugins to use\n",
" docs = retriever.invoke(query)\n",
" # Get the toolkits, one for each plugin\n",
" tool_kits = [toolkits_dict[d.metadata[\"plugin_name\"]] for d in docs]\n",
" # Get the tools: a separate NLAChain for each endpoint\n",
" tools = []\n",
" for tk in tool_kits:\n",
" tools.extend(tk.nla_tools)\n",
" return tools"
]
},
{
"cell_type": "markdown",
"id": "7699afd7",
"metadata": {},
"source": [
"We can now test this retriever to see if it seems to work."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "425f2886",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Milo.askMilo',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.search_all_actions',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.list_exposed_actions',\n",
" 'SchoolDigger_API_V2.0.Autocomplete_GetSchools',\n",
" 'SchoolDigger_API_V2.0.Districts_GetAllDistricts2',\n",
" 'SchoolDigger_API_V2.0.Districts_GetDistrict2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetSchoolRank2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetRank_District',\n",
" 'SchoolDigger_API_V2.0.Schools_GetAllSchools20',\n",
" 'SchoolDigger_API_V2.0.Schools_GetSchool20',\n",
" 'Speak.translate',\n",
" 'Speak.explainPhrase',\n",
" 'Speak.explainTask']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools = get_tools(\"What could I do today with my kiddo\")\n",
"[t.name for t in tools]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3aa88768",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Open_AI_Klarna_product_Api.productsUsingGET',\n",
" 'Milo.askMilo',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.search_all_actions',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.list_exposed_actions',\n",
" 'SchoolDigger_API_V2.0.Autocomplete_GetSchools',\n",
" 'SchoolDigger_API_V2.0.Districts_GetAllDistricts2',\n",
" 'SchoolDigger_API_V2.0.Districts_GetDistrict2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetSchoolRank2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetRank_District',\n",
" 'SchoolDigger_API_V2.0.Schools_GetAllSchools20',\n",
" 'SchoolDigger_API_V2.0.Schools_GetSchool20']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools = get_tools(\"what shirts can i buy?\")\n",
"[t.name for t in tools]"
]
},
{
"cell_type": "markdown",
"id": "2e7a075c",
"metadata": {},
"source": [
"## Prompt Template\n",
"\n",
"The prompt template is pretty standard, because we're not actually changing that much logic in the actual prompt template, but rather we are just changing how retrieval is done."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "339b1bb8",
"metadata": {},
"outputs": [],
"source": [
"# Set up the base template\n",
"template = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
"\n",
"{tools}\n",
"\n",
"Use the following format:\n",
"\n",
"Question: the input question you must answer\n",
"Thought: you should always think about what to do\n",
"Action: the action to take, should be one of [{tool_names}]\n",
"Action Input: the input to the action\n",
"Observation: the result of the action\n",
"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
"Thought: I now know the final answer\n",
"Final Answer: the final answer to the original input question\n",
"\n",
"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
"\n",
"Question: {input}\n",
"{agent_scratchpad}\"\"\""
]
},
{
"cell_type": "markdown",
"id": "1583acdc",
"metadata": {},
"source": [
"The custom prompt template now has the concept of a tools_getter, which we call on the input to select the tools to use"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fd969d31",
"metadata": {},
"outputs": [],
"source": [
"from typing import Callable\n",
"\n",
"\n",
"# Set up a prompt template\n",
"class CustomPromptTemplate(StringPromptTemplate):\n",
" # The template to use\n",
" template: str\n",
" ############## NEW ######################\n",
" # The list of tools available\n",
" tools_getter: Callable\n",
"\n",
" def format(self, **kwargs) -> str:\n",
" # Get the intermediate steps (AgentAction, Observation tuples)\n",
" # Format them in a particular way\n",
" intermediate_steps = kwargs.pop(\"intermediate_steps\")\n",
" thoughts = \"\"\n",
" for action, observation in intermediate_steps:\n",
" thoughts += action.log\n",
" thoughts += f\"\\nObservation: {observation}\\nThought: \"\n",
" # Set the agent_scratchpad variable to that value\n",
" kwargs[\"agent_scratchpad\"] = thoughts\n",
" ############## NEW ######################\n",
" tools = self.tools_getter(kwargs[\"input\"])\n",
" # Create a tools variable from the list of tools provided\n",
" kwargs[\"tools\"] = \"\\n\".join(\n",
" [f\"{tool.name}: {tool.description}\" for tool in tools]\n",
" )\n",
" # Create a list of tool names for the tools provided\n",
" kwargs[\"tool_names\"] = \", \".join([tool.name for tool in tools])\n",
" return self.template.format(**kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "798ef9fb",
"metadata": {},
"outputs": [],
"source": [
"prompt = CustomPromptTemplate(\n",
" template=template,\n",
" tools_getter=get_tools,\n",
" # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
" # This includes the `intermediate_steps` variable because that is needed\n",
" input_variables=[\"input\", \"intermediate_steps\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ef3a1af3",
"metadata": {},
"source": [
"## Output Parser\n",
"\n",
"The output parser is unchanged from the previous notebook, since we are not changing anything about the output format."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7c6fe0d3",
"metadata": {},
"outputs": [],
"source": [
"class CustomOutputParser(AgentOutputParser):\n",
" def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
" # Check if agent should finish\n",
" if \"Final Answer:\" in llm_output:\n",
" return AgentFinish(\n",
" # Return values is generally always a dictionary with a single `output` key\n",
" # It is not recommended to try anything else at the moment :)\n",
" return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n",
" log=llm_output,\n",
" )\n",
" # Parse out the action and action input\n",
" regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
" match = re.search(regex, llm_output, re.DOTALL)\n",
" if not match:\n",
" raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
" action = match.group(1).strip()\n",
" action_input = match.group(2)\n",
" # Return the action and action input\n",
" return AgentAction(\n",
" tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d278706a",
"metadata": {},
"outputs": [],
"source": [
"output_parser = CustomOutputParser()"
]
},
{
"cell_type": "markdown",
"id": "170587b1",
"metadata": {},
"source": [
"## Set up LLM, stop sequence, and the agent\n",
"\n",
"Also the same as the previous notebook"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f9d4c374",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9b1cc2a2",
"metadata": {},
"outputs": [],
"source": [
"# LLM chain consisting of the LLM and a prompt\n",
"llm_chain = LLMChain(llm=llm, prompt=prompt)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e4f5092f",
"metadata": {},
"outputs": [],
"source": [
"tool_names = [tool.name for tool in tools]\n",
"agent = LLMSingleActionAgent(\n",
" llm_chain=llm_chain,\n",
" output_parser=output_parser,\n",
" stop=[\"\\nObservation:\"],\n",
" allowed_tools=tool_names,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "aa8a5326",
"metadata": {},
"source": [
"## Use the Agent\n",
"\n",
"Now we can use it!"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "490604e9",
"metadata": {},
"outputs": [],
"source": [
"agent_executor = AgentExecutor.from_agent_and_tools(\n",
" agent=agent, tools=tools, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "653b1617",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to find a product API\n",
"Action: Open_AI_Klarna_product_Api.productsUsingGET\n",
"Action Input: shirts\u001b[0m\n",
"\n",
"Observation:\u001b[36;1m\u001b[1;3mI found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.\u001b[0m\u001b[32;1m\u001b[1;3m I now know what shirts I can buy\n",
"Final Answer: Arg, I found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Arg, I found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"what shirts can i buy?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2481ee76",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
},
"vscode": {
"interpreter": {
"hash": "18784188d7ecd866c0586ac068b02361a6896dc3a29b64f5cc957f09c590acef"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "ba5f8741",
"metadata": {},
"source": [
"# Plug-and-Plai\n",
"\n",
"This notebook builds upon the idea of [plugin retrieval](./custom_agent_with_plugin_retrieval.html), but pulls all tools from `plugnplai` - a directory of AI Plugins."
]
},
{
"cell_type": "markdown",
"id": "fea4812c",
"metadata": {},
"source": [
"## Set up environment\n",
"\n",
"Do necessary imports, etc."
]
},
{
"cell_type": "markdown",
"id": "aca08be8",
"metadata": {},
"source": [
"Install plugnplai lib to get a list of active plugins from https://plugplai.com directory"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "52e248c9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install plugnplai -q"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9af9734e",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from typing import Union\n",
"\n",
"import plugnplai\n",
"from langchain.agents import (\n",
" AgentExecutor,\n",
" AgentOutputParser,\n",
" LLMSingleActionAgent,\n",
")\n",
"from langchain.chains import LLMChain\n",
"from langchain.prompts import StringPromptTemplate\n",
"from langchain_community.agent_toolkits import NLAToolkit\n",
"from langchain_community.tools.plugin import AIPlugin\n",
"from langchain_core.agents import AgentAction, AgentFinish\n",
"from langchain_openai import OpenAI"
]
},
{
"cell_type": "markdown",
"id": "2f91d8b4",
"metadata": {},
"source": [
"## Setup LLM"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a1a3b59c",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "markdown",
"id": "6df0253f",
"metadata": {},
"source": [
"## Set up plugins\n",
"\n",
"Load and index plugins"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9e0f7882",
"metadata": {},
"outputs": [],
"source": [
"# Get all plugins from plugnplai.com\n",
"urls = plugnplai.get_plugins()\n",
"\n",
"# Get ChatGPT plugins - only ChatGPT verified plugins\n",
"urls = plugnplai.get_plugins(filter=\"ChatGPT\")\n",
"\n",
"# Get working plugins - only tested plugins (in progress)\n",
"urls = plugnplai.get_plugins(filter=\"working\")\n",
"\n",
"\n",
"AI_PLUGINS = [AIPlugin.from_url(url + \"/.well-known/ai-plugin.json\") for url in urls]"
]
},
{
"cell_type": "markdown",
"id": "17362717",
"metadata": {},
"source": [
"## Tool Retriever\n",
"\n",
"We will use a vectorstore to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search for relevant tools."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "77c4be4b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9092a158",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.2 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n",
"Attempting to load a Swagger 2.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.\n"
]
}
],
"source": [
"embeddings = OpenAIEmbeddings()\n",
"docs = [\n",
" Document(\n",
" page_content=plugin.description_for_model,\n",
" metadata={\"plugin_name\": plugin.name_for_model},\n",
" )\n",
" for plugin in AI_PLUGINS\n",
"]\n",
"vector_store = FAISS.from_documents(docs, embeddings)\n",
"toolkits_dict = {\n",
" plugin.name_for_model: NLAToolkit.from_llm_and_ai_plugin(llm, plugin)\n",
" for plugin in AI_PLUGINS\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "735a7566",
"metadata": {},
"outputs": [],
"source": [
"retriever = vector_store.as_retriever()\n",
"\n",
"\n",
"def get_tools(query):\n",
" # Get documents, which contain the Plugins to use\n",
" docs = retriever.invoke(query)\n",
" # Get the toolkits, one for each plugin\n",
" tool_kits = [toolkits_dict[d.metadata[\"plugin_name\"]] for d in docs]\n",
" # Get the tools: a separate NLAChain for each endpoint\n",
" tools = []\n",
" for tk in tool_kits:\n",
" tools.extend(tk.nla_tools)\n",
" return tools"
]
},
{
"cell_type": "markdown",
"id": "7699afd7",
"metadata": {},
"source": [
"We can now test this retriever to see if it seems to work."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "425f2886",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Milo.askMilo',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.search_all_actions',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.list_exposed_actions',\n",
" 'SchoolDigger_API_V2.0.Autocomplete_GetSchools',\n",
" 'SchoolDigger_API_V2.0.Districts_GetAllDistricts2',\n",
" 'SchoolDigger_API_V2.0.Districts_GetDistrict2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetSchoolRank2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetRank_District',\n",
" 'SchoolDigger_API_V2.0.Schools_GetAllSchools20',\n",
" 'SchoolDigger_API_V2.0.Schools_GetSchool20',\n",
" 'Speak.translate',\n",
" 'Speak.explainPhrase',\n",
" 'Speak.explainTask']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools = get_tools(\"What could I do today with my kiddo\")\n",
"[t.name for t in tools]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3aa88768",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Open_AI_Klarna_product_Api.productsUsingGET',\n",
" 'Milo.askMilo',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.search_all_actions',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',\n",
" 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.list_exposed_actions',\n",
" 'SchoolDigger_API_V2.0.Autocomplete_GetSchools',\n",
" 'SchoolDigger_API_V2.0.Districts_GetAllDistricts2',\n",
" 'SchoolDigger_API_V2.0.Districts_GetDistrict2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetSchoolRank2',\n",
" 'SchoolDigger_API_V2.0.Rankings_GetRank_District',\n",
" 'SchoolDigger_API_V2.0.Schools_GetAllSchools20',\n",
" 'SchoolDigger_API_V2.0.Schools_GetSchool20']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools = get_tools(\"what shirts can i buy?\")\n",
"[t.name for t in tools]"
]
},
{
"cell_type": "markdown",
"id": "2e7a075c",
"metadata": {},
"source": [
"## Prompt Template\n",
"\n",
"The prompt template is pretty standard, because we're not actually changing that much logic in the actual prompt template, but rather we are just changing how retrieval is done."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "339b1bb8",
"metadata": {},
"outputs": [],
"source": [
"# Set up the base template\n",
"template = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
"\n",
"{tools}\n",
"\n",
"Use the following format:\n",
"\n",
"Question: the input question you must answer\n",
"Thought: you should always think about what to do\n",
"Action: the action to take, should be one of [{tool_names}]\n",
"Action Input: the input to the action\n",
"Observation: the result of the action\n",
"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
"Thought: I now know the final answer\n",
"Final Answer: the final answer to the original input question\n",
"\n",
"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
"\n",
"Question: {input}\n",
"{agent_scratchpad}\"\"\""
]
},
{
"cell_type": "markdown",
"id": "1583acdc",
"metadata": {},
"source": [
"The custom prompt template now has the concept of a tools_getter, which we call on the input to select the tools to use"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fd969d31",
"metadata": {},
"outputs": [],
"source": [
"from typing import Callable\n",
"\n",
"\n",
"# Set up a prompt template\n",
"class CustomPromptTemplate(StringPromptTemplate):\n",
" # The template to use\n",
" template: str\n",
" ############## NEW ######################\n",
" # The list of tools available\n",
" tools_getter: Callable\n",
"\n",
" def format(self, **kwargs) -> str:\n",
" # Get the intermediate steps (AgentAction, Observation tuples)\n",
" # Format them in a particular way\n",
" intermediate_steps = kwargs.pop(\"intermediate_steps\")\n",
" thoughts = \"\"\n",
" for action, observation in intermediate_steps:\n",
" thoughts += action.log\n",
" thoughts += f\"\\nObservation: {observation}\\nThought: \"\n",
" # Set the agent_scratchpad variable to that value\n",
" kwargs[\"agent_scratchpad\"] = thoughts\n",
" ############## NEW ######################\n",
" tools = self.tools_getter(kwargs[\"input\"])\n",
" # Create a tools variable from the list of tools provided\n",
" kwargs[\"tools\"] = \"\\n\".join(\n",
" [f\"{tool.name}: {tool.description}\" for tool in tools]\n",
" )\n",
" # Create a list of tool names for the tools provided\n",
" kwargs[\"tool_names\"] = \", \".join([tool.name for tool in tools])\n",
" return self.template.format(**kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "798ef9fb",
"metadata": {},
"outputs": [],
"source": [
"prompt = CustomPromptTemplate(\n",
" template=template,\n",
" tools_getter=get_tools,\n",
" # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
" # This includes the `intermediate_steps` variable because that is needed\n",
" input_variables=[\"input\", \"intermediate_steps\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ef3a1af3",
"metadata": {},
"source": [
"## Output Parser\n",
"\n",
"The output parser is unchanged from the previous notebook, since we are not changing anything about the output format."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7c6fe0d3",
"metadata": {},
"outputs": [],
"source": [
"class CustomOutputParser(AgentOutputParser):\n",
" def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
" # Check if agent should finish\n",
" if \"Final Answer:\" in llm_output:\n",
" return AgentFinish(\n",
" # Return values is generally always a dictionary with a single `output` key\n",
" # It is not recommended to try anything else at the moment :)\n",
" return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n",
" log=llm_output,\n",
" )\n",
" # Parse out the action and action input\n",
" regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
" match = re.search(regex, llm_output, re.DOTALL)\n",
" if not match:\n",
" raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
" action = match.group(1).strip()\n",
" action_input = match.group(2)\n",
" # Return the action and action input\n",
" return AgentAction(\n",
" tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d278706a",
"metadata": {},
"outputs": [],
"source": [
"output_parser = CustomOutputParser()"
]
},
{
"cell_type": "markdown",
"id": "170587b1",
"metadata": {},
"source": [
"## Set up LLM, stop sequence, and the agent\n",
"\n",
"Also the same as the previous notebook"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f9d4c374",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "9b1cc2a2",
"metadata": {},
"outputs": [],
"source": [
"# LLM chain consisting of the LLM and a prompt\n",
"llm_chain = LLMChain(llm=llm, prompt=prompt)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e4f5092f",
"metadata": {},
"outputs": [],
"source": [
"tool_names = [tool.name for tool in tools]\n",
"agent = LLMSingleActionAgent(\n",
" llm_chain=llm_chain,\n",
" output_parser=output_parser,\n",
" stop=[\"\\nObservation:\"],\n",
" allowed_tools=tool_names,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "aa8a5326",
"metadata": {},
"source": [
"## Use the Agent\n",
"\n",
"Now we can use it!"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "490604e9",
"metadata": {},
"outputs": [],
"source": [
"agent_executor = AgentExecutor.from_agent_and_tools(\n",
" agent=agent, tools=tools, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "653b1617",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to find a product API\n",
"Action: Open_AI_Klarna_product_Api.productsUsingGET\n",
"Action Input: shirts\u001b[0m\n",
"\n",
"Observation:\u001b[36;1m\u001b[1;3mI found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.\u001b[0m\u001b[32;1m\u001b[1;3m I now know what shirts I can buy\n",
"Final Answer: Arg, I found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Arg, I found 10 shirts from the API response. They range in price from $9.99 to $450.00 and come in a variety of materials, colors, and patterns.'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"what shirts can i buy?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2481ee76",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
},
"vscode": {
"interpreter": {
"hash": "3ccef4e08d87aa1eeb90f63e0f071292ccb2e9c42e70f74ab2bf6f5493ca7bbc"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/custom_agent_with_tool_retrieval.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "ba5f8741",
"metadata": {},
"source": [
"# Custom agent with tool retrieval\n",
"\n",
"The novel idea introduced in this notebook is the idea of using retrieval to select the set of tools to use to answer an agent query. This is useful when you have many many tools to select from. You cannot put the description of all the tools in the prompt (because of context length issues) so instead you dynamically select the N tools you do want to consider using at run time.\n",
"\n",
"In this notebook we will create a somewhat contrived example. We will have one legitimate tool (search) and then 99 fake tools which are just nonsense. We will then add a step in the prompt template that takes the user input and retrieves tool relevant to the query."
]
},
{
"cell_type": "markdown",
"id": "fea4812c",
"metadata": {},
"source": [
"## Set up environment\n",
"\n",
"Do necessary imports, etc."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9af9734e",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"from typing import Union\n",
"\n",
"from langchain.agents import (\n",
" AgentExecutor,\n",
" AgentOutputParser,\n",
" LLMSingleActionAgent,\n",
" Tool,\n",
")\n",
"from langchain.chains import LLMChain\n",
"from langchain.prompts import StringPromptTemplate\n",
"from langchain_community.utilities import SerpAPIWrapper\n",
"from langchain_core.agents import AgentAction, AgentFinish\n",
"from langchain_openai import OpenAI"
]
},
{
"cell_type": "markdown",
"id": "6df0253f",
"metadata": {},
"source": [
"## Set up tools\n",
"\n",
"We will create one legitimate tool (search) and then 99 fake tools."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "becda2a1",
"metadata": {},
"outputs": [],
"source": [
"# Define which tools the agent can use to answer user queries\n",
"search = SerpAPIWrapper()\n",
"search_tool = Tool(\n",
" name=\"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\",\n",
")\n",
"\n",
"\n",
"def fake_func(inp: str) -> str:\n",
" return \"foo\"\n",
"\n",
"\n",
"fake_tools = [\n",
" Tool(\n",
" name=f\"foo-{i}\",\n",
" func=fake_func,\n",
" description=f\"a silly function that you can use to get more information about the number {i}\",\n",
" )\n",
" for i in range(99)\n",
"]\n",
"ALL_TOOLS = [search_tool] + fake_tools"
]
},
{
"cell_type": "markdown",
"id": "17362717",
"metadata": {},
"source": [
"## Tool Retriever\n",
"\n",
"We will use a vector store to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search for relevant tools."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "77c4be4b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.vectorstores import FAISS\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9092a158",
"metadata": {},
"outputs": [],
"source": [
"docs = [\n",
" Document(page_content=t.description, metadata={\"index\": i})\n",
" for i, t in enumerate(ALL_TOOLS)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "affc4e56",
"metadata": {},
"outputs": [],
"source": [
"vector_store = FAISS.from_documents(docs, OpenAIEmbeddings())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "735a7566",
"metadata": {},
"outputs": [],
"source": [
"retriever = vector_store.as_retriever()\n",
"\n",
"\n",
"def get_tools(query):\n",
" docs = retriever.invoke(query)\n",
" return [ALL_TOOLS[d.metadata[\"index\"]] for d in docs]"
]
},
{
"cell_type": "markdown",
"id": "7699afd7",
"metadata": {},
"source": [
"We can now test this retriever to see if it seems to work."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "425f2886",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Tool(name='Search', description='useful for when you need to answer questions about current events', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='', aiosession=None)>, coroutine=None),\n",
" Tool(name='foo-95', description='a silly function that you can use to get more information about the number 95', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),\n",
" Tool(name='foo-12', description='a silly function that you can use to get more information about the number 12', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),\n",
" Tool(name='foo-15', description='a silly function that you can use to get more information about the number 15', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None)]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_tools(\"whats the weather?\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "4036dd19",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Tool(name='foo-13', description='a silly function that you can use to get more information about the number 13', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),\n",
" Tool(name='foo-12', description='a silly function that you can use to get more information about the number 12', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),\n",
" Tool(name='foo-14', description='a silly function that you can use to get more information about the number 14', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),\n",
" Tool(name='foo-11', description='a silly function that you can use to get more information about the number 11', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None)]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_tools(\"whats the number 13?\")"
]
},
{
"cell_type": "markdown",
"id": "2e7a075c",
"metadata": {},
"source": [
"## Prompt template\n",
"\n",
"The prompt template is pretty standard, because we're not actually changing that much logic in the actual prompt template, but rather we are just changing how retrieval is done."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "339b1bb8",
"metadata": {},
"outputs": [],
"source": [
"# Set up the base template\n",
"template = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
"\n",
"{tools}\n",
"\n",
"Use the following format:\n",
"\n",
"Question: the input question you must answer\n",
"Thought: you should always think about what to do\n",
"Action: the action to take, should be one of [{tool_names}]\n",
"Action Input: the input to the action\n",
"Observation: the result of the action\n",
"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
"Thought: I now know the final answer\n",
"Final Answer: the final answer to the original input question\n",
"\n",
"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
"\n",
"Question: {input}\n",
"{agent_scratchpad}\"\"\""
]
},
{
"cell_type": "markdown",
"id": "1583acdc",
"metadata": {},
"source": [
"The custom prompt template now has the concept of a `tools_getter`, which we call on the input to select the tools to use."
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "fd969d31",
"metadata": {},
"outputs": [],
"source": [
"from typing import Callable\n",
"\n",
"\n",
"# Set up a prompt template\n",
"class CustomPromptTemplate(StringPromptTemplate):\n",
" # The template to use\n",
" template: str\n",
" ############## NEW ######################\n",
" # The list of tools available\n",
" tools_getter: Callable\n",
"\n",
" def format(self, **kwargs) -> str:\n",
" # Get the intermediate steps (AgentAction, Observation tuples)\n",
" # Format them in a particular way\n",
" intermediate_steps = kwargs.pop(\"intermediate_steps\")\n",
" thoughts = \"\"\n",
" for action, observation in intermediate_steps:\n",
" thoughts += action.log\n",
" thoughts += f\"\\nObservation: {observation}\\nThought: \"\n",
" # Set the agent_scratchpad variable to that value\n",
" kwargs[\"agent_scratchpad\"] = thoughts\n",
" ############## NEW ######################\n",
" tools = self.tools_getter(kwargs[\"input\"])\n",
" # Create a tools variable from the list of tools provided\n",
" kwargs[\"tools\"] = \"\\n\".join(\n",
" [f\"{tool.name}: {tool.description}\" for tool in tools]\n",
" )\n",
" # Create a list of tool names for the tools provided\n",
" kwargs[\"tool_names\"] = \", \".join([tool.name for tool in tools])\n",
" return self.template.format(**kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "798ef9fb",
"metadata": {},
"outputs": [],
"source": [
"prompt = CustomPromptTemplate(\n",
" template=template,\n",
" tools_getter=get_tools,\n",
" # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
" # This includes the `intermediate_steps` variable because that is needed\n",
" input_variables=[\"input\", \"intermediate_steps\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ef3a1af3",
"metadata": {},
"source": [
"## Output parser\n",
"\n",
"The output parser is unchanged from the previous notebook, since we are not changing anything about the output format."
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "7c6fe0d3",
"metadata": {},
"outputs": [],
"source": [
"class CustomOutputParser(AgentOutputParser):\n",
" def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
" # Check if agent should finish\n",
" if \"Final Answer:\" in llm_output:\n",
" return AgentFinish(\n",
" # Return values is generally always a dictionary with a single `output` key\n",
" # It is not recommended to try anything else at the moment :)\n",
" return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n",
" log=llm_output,\n",
" )\n",
" # Parse out the action and action input\n",
" regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
" match = re.search(regex, llm_output, re.DOTALL)\n",
" if not match:\n",
" raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
" action = match.group(1).strip()\n",
" action_input = match.group(2)\n",
" # Return the action and action input\n",
" return AgentAction(\n",
" tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "d278706a",
"metadata": {},
"outputs": [],
"source": [
"output_parser = CustomOutputParser()"
]
},
{
"cell_type": "markdown",
"id": "170587b1",
"metadata": {},
"source": [
"## Set up LLM, stop sequence, and the agent\n",
"\n",
"Also the same as the previous notebook."
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "f9d4c374",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "9b1cc2a2",
"metadata": {},
"outputs": [],
"source": [
"# LLM chain consisting of the LLM and a prompt\n",
"llm_chain = LLMChain(llm=llm, prompt=prompt)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "e4f5092f",
"metadata": {},
"outputs": [],
"source": [
"tools = get_tools(\"whats the weather?\")\n",
"tool_names = [tool.name for tool in tools]\n",
"agent = LLMSingleActionAgent(\n",
" llm_chain=llm_chain,\n",
" output_parser=output_parser,\n",
" stop=[\"\\nObservation:\"],\n",
" allowed_tools=tool_names,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "aa8a5326",
"metadata": {},
"source": [
"## Use the Agent\n",
"\n",
"Now we can use it!"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "490604e9",
"metadata": {},
"outputs": [],
"source": [
"agent_executor = AgentExecutor.from_agent_and_tools(\n",
" agent=agent, tools=tools, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "653b1617",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to find out what the weather is in SF\n",
"Action: Search\n",
"Action Input: Weather in SF\u001b[0m\n",
"\n",
"Observation:\u001b[36;1m\u001b[1;3mMostly cloudy skies early, then partly cloudy in the afternoon. High near 60F. ENE winds shifting to W at 10 to 15 mph. Humidity71%. UV Index6 of 10.\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 'Arg, 'tis mostly cloudy skies early, then partly cloudy in the afternoon. High near 60F. ENE winds shiftin' to W at 10 to 15 mph. Humidity71%. UV Index6 of 10.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"'Arg, 'tis mostly cloudy skies early, then partly cloudy in the afternoon. High near 60F. ENE winds shiftin' to W at 10 to 15 mph. Humidity71%. UV Index6 of 10.\""
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What's the weather in SF?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2481ee76",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
},
"vscode": {
"interpreter": {
"hash": "18784188d7ecd866c0586ac068b02361a6896dc3a29b64f5cc957f09c590acef"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/custom_multi_action_agent.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "ba5f8741",
"metadata": {},
"source": [
"# Custom multi-action agent\n",
"\n",
"This notebook goes through how to create your own custom agent.\n",
"\n",
"An agent consists of two parts:\n",
"\n",
"- Tools: The tools the agent has available to use.\n",
"- The agent class itself: this decides which action to take.\n",
" \n",
" \n",
"In this notebook we walk through how to create a custom agent that predicts/takes multiple steps at a time."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9af9734e",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool\n",
"from langchain_community.utilities import SerpAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d7c4ebdc",
"metadata": {},
"outputs": [],
"source": [
"def random_word(query: str) -> str:\n",
" print(\"\\nNow I'm doing this!\")\n",
" return \"foo\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "becda2a1",
"metadata": {},
"outputs": [],
"source": [
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name=\"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\",\n",
" ),\n",
" Tool(\n",
" name=\"RandomWord\",\n",
" func=random_word,\n",
" description=\"call this to get a random word.\",\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a33e2f7e",
"metadata": {},
"outputs": [],
"source": [
"from typing import Any, List, Tuple, Union\n",
"\n",
"from langchain_core.agents import AgentAction, AgentFinish\n",
"\n",
"\n",
"class FakeAgent(BaseMultiActionAgent):\n",
" \"\"\"Fake Custom Agent.\"\"\"\n",
"\n",
" @property\n",
" def input_keys(self):\n",
" return [\"input\"]\n",
"\n",
" def plan(\n",
" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
" ) -> Union[List[AgentAction], AgentFinish]:\n",
" \"\"\"Given input, decided what to do.\n",
"\n",
" Args:\n",
" intermediate_steps: Steps the LLM has taken to date,\n",
" along with observations\n",
" **kwargs: User inputs.\n",
"\n",
" Returns:\n",
" Action specifying what tool to use.\n",
" \"\"\"\n",
" if len(intermediate_steps) == 0:\n",
" return [\n",
" AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
" AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], log=\"\"),\n",
" ]\n",
" else:\n",
" return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")\n",
"\n",
" async def aplan(\n",
" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
" ) -> Union[List[AgentAction], AgentFinish]:\n",
" \"\"\"Given input, decided what to do.\n",
"\n",
" Args:\n",
" intermediate_steps: Steps the LLM has taken to date,\n",
" along with observations\n",
" **kwargs: User inputs.\n",
"\n",
" Returns:\n",
" Action specifying what tool to use.\n",
" \"\"\"\n",
" if len(intermediate_steps) == 0:\n",
" return [\n",
" AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\"),\n",
" AgentAction(tool=\"RandomWord\", tool_input=kwargs[\"input\"], log=\"\"),\n",
" ]\n",
" else:\n",
" return AgentFinish(return_values={\"output\": \"bar\"}, log=\"\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "655d72f6",
"metadata": {},
"outputs": [],
"source": [
"agent = FakeAgent()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "490604e9",
"metadata": {},
"outputs": [],
"source": [
"agent_executor = AgentExecutor.from_agent_and_tools(\n",
" agent=agent, tools=tools, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "653b1617",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"Now I'm doing this!\n",
"\u001b[33;1m\u001b[1;3mfoo\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'bar'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"How many people live in canada as of 2023?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "adefb4c2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
},
"vscode": {
"interpreter": {
"hash": "18784188d7ecd866c0586ac068b02361a6896dc3a29b64f5cc957f09c590acef"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/databricks_sql_db.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "707d13a7",
"metadata": {},
"source": [
"# Databricks\n",
"\n",
"This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
"It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
]
},
{
"cell_type": "markdown",
"id": "0076d072",
"metadata": {},
"source": [
"## Installation and Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "739b489b",
"metadata": {},
"outputs": [],
"source": [
"!pip install databricks-sql-connector"
]
},
{
"cell_type": "markdown",
"id": "73113163",
"metadata": {},
"source": [
"## Connecting to Databricks\n",
"\n",
"You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
"\n",
"### Syntax\n",
"```python\n",
"SQLDatabase.from_databricks(\n",
" catalog: str,\n",
" schema: str,\n",
" host: Optional[str] = None,\n",
" api_token: Optional[str] = None,\n",
" warehouse_id: Optional[str] = None,\n",
" cluster_id: Optional[str] = None,\n",
" engine_args: Optional[dict] = None,\n",
" **kwargs: Any)\n",
"```\n",
"### Required Parameters\n",
"* `catalog`: The catalog name in the Databricks database.\n",
"* `schema`: The schema name in the catalog.\n",
"\n",
"### Optional Parameters\n",
"There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
"* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
"* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
"* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
"* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
"* `engine_args`: The arguments to be used when connecting Databricks.\n",
"* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
]
},
{
"cell_type": "markdown",
"id": "b11c7e48",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8102bca0",
"metadata": {},
"outputs": [],
"source": [
"# Connecting to Databricks with SQLDatabase wrapper\n",
"from langchain_community.utilities import SQLDatabase\n",
"\n",
"db = SQLDatabase.from_databricks(catalog=\"samples\", schema=\"nyctaxi\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9dd36f58",
"metadata": {},
"outputs": [],
"source": [
"# Creating a OpenAI Chat LLM wrapper\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
]
},
{
"cell_type": "markdown",
"id": "5b5c5f1a",
"metadata": {},
"source": [
"### SQL Chain example\n",
"\n",
"This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "36f2270b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.utilities import SQLDatabaseChain\n",
"\n",
"db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4e2b5f25",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
"What is the average duration of taxi rides that start between midnight and 6am?\n",
"SQLQuery:\u001b[32;1m\u001b[1;3mSELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration\n",
"FROM trips\n",
"WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6\u001b[0m\n",
"SQLResult: \u001b[33;1m\u001b[1;3m[(987.8122786304605,)]\u001b[0m\n",
"Answer:\u001b[32;1m\u001b[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db_chain.run(\n",
" \"What is the average duration of taxi rides that start between midnight and 6am?\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "e496d5e5",
"metadata": {},
"source": [
"### SQL Database Agent example\n",
"\n",
"This example demonstrates the use of the [SQL Database Agent](/docs/integrations/toolkits/sql_database.html) for answering questions over a Databricks database."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9918e86a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import create_sql_agent\n",
"from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
"\n",
"toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
"agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c484a76e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
"Action Input: \u001b[0m\n",
"Observation: \u001b[38;5;200m\u001b[1;3mtrips\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
"Action: schema_sql_db\n",
"Action Input: trips\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"CREATE TABLE trips (\n",
"\ttpep_pickup_datetime TIMESTAMP, \n",
"\ttpep_dropoff_datetime TIMESTAMP, \n",
"\ttrip_distance FLOAT, \n",
"\tfare_amount FLOAT, \n",
"\tpickup_zip INT, \n",
"\tdropoff_zip INT\n",
") USING DELTA\n",
"\n",
"/*\n",
"3 rows from trips table:\n",
"tpep_pickup_datetime\ttpep_dropoff_datetime\ttrip_distance\tfare_amount\tpickup_zip\tdropoff_zip\n",
"2016-02-14 16:52:13+00:00\t2016-02-14 17:16:04+00:00\t4.94\t19.0\t10282\t10171\n",
"2016-02-04 18:44:19+00:00\t2016-02-04 18:46:00+00:00\t0.28\t3.5\t10110\t10110\n",
"2016-02-17 17:13:57+00:00\t2016-02-17 17:17:55+00:00\t0.7\t5.0\t10103\t10023\n",
"*/\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
"Action: query_checker_sql_db\n",
"Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
"Observation: \u001b[31;1m\u001b[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
"Action: query_sql_db\n",
"Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m[(30.6, '0 00:43:31.000000000')]\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
"Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"What is the longest trip distance and how long did it take?\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/deeplake_semantic_search_over_chat.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# QA using Activeloop's DeepLake\n",
"In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to semantically search and ask questions over a group chat.\n",
"\n",
"View a working demo [here](https://twitter.com/thisissukh_/status/1647223328363679745)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Install required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Add API keys"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"from langchain.chains import RetrievalQA\n",
"from langchain_community.vectorstores import DeepLake\n",
"from langchain_openai import OpenAI, OpenAIEmbeddings\n",
"from langchain_text_splitters import (\n",
" CharacterTextSplitter,\n",
" RecursiveCharacterTextSplitter,\n",
")\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
"activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token\n",
"os.environ[\"ACTIVELOOP_ORG\"] = getpass.getpass(\"Activeloop Org:\")\n",
"\n",
"org_id = os.environ[\"ACTIVELOOP_ORG\"]\n",
"embeddings = OpenAIEmbeddings()\n",
"\n",
"dataset_path = \"hub://\" + org_id + \"/data\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## 2. Create sample data"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"You can generate a sample group chat conversation using ChatGPT with this prompt:\n",
"\n",
"```\n",
"Generate a group chat conversation with three friends talking about their day, referencing real places and fictional names. Make it funny and as detailed as possible.\n",
"```\n",
"\n",
"I've already generated such a chat in `messages.txt`. We can keep it simple and use this for our example.\n",
"\n",
"## 3. Ingest chat embeddings\n",
"\n",
"We load the messages in the text file, chunk and upload to ActiveLoop Vector store."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Document(page_content='Participants:\\n\\nJerry: Loves movies and is a bit of a klutz.\\nSamantha: Enthusiastic about food and always trying new restaurants.\\nBarry: A nature lover, but always manages to get lost.\\nJerry: Hey, guys! You won\\'t believe what happened to me at the Times Square AMC theater. I tripped over my own feet and spilled popcorn everywhere! 🍿💥\\n\\nSamantha: LOL, that\\'s so you, Jerry! Was the floor buttery enough for you to ice skate on after that? 😂\\n\\nBarry: Sounds like a regular Tuesday for you, Jerry. Meanwhile, I tried to find that new hiking trail in Central Park. You know, the one that\\'s supposed to be impossible to get lost on? Well, guess what...\\n\\nJerry: You found a hidden treasure?\\n\\nBarry: No, I got lost. AGAIN. 🧭🙄\\n\\nSamantha: Barry, you\\'d get lost in your own backyard! But speaking of treasures, I found this new sushi place in Little Tokyo. \"Samantha\\'s Sushi Symphony\" it\\'s called. Coincidence? I think not!\\n\\nJerry: Maybe they named it after your ability to eat your body weight in sushi. 🍣', metadata={}), Document(page_content='Barry: How do you even FIND all these places, Samantha?\\n\\nSamantha: Simple, I don\\'t rely on Barry\\'s navigation skills. 😉 But seriously, the wasabi there was hotter than Jerry\\'s love for Marvel movies!\\n\\nJerry: Hey, nothing wrong with a little superhero action. By the way, did you guys see the new \"Captain Crunch: Breakfast Avenger\" trailer?\\n\\nSamantha: Captain Crunch? Are you sure you didn\\'t get that from one of your Saturday morning cereal binges?\\n\\nBarry: Yeah, and did he defeat his arch-enemy, General Mills? 😆\\n\\nJerry: Ha-ha, very funny. Anyway, that sushi place sounds awesome, Samantha. Next time, let\\'s go together, and maybe Barry can guide us... if we want a city-wide tour first.\\n\\nBarry: As long as we\\'re not hiking, I\\'ll get us there... eventually. 😅\\n\\nSamantha: It\\'s a date! But Jerry, you\\'re banned from carrying any food items.\\n\\nJerry: Deal! Just promise me no wasabi challenges. I don\\'t want to end up like the time I tried Sriracha ice cream.', metadata={}), Document(page_content=\"Barry: Wait, what happened with Sriracha ice cream?\\n\\nJerry: Let's just say it was a hot situation. Literally. 🔥\\n\\nSamantha: 🤣 I still have the video!\\n\\nJerry: Samantha, if you value our friendship, that video will never see the light of day.\\n\\nSamantha: No promises, Jerry. No promises. 🤐😈\\n\\nBarry: I foresee a fun weekend ahead! 🎉\", metadata={})]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your Deep Lake dataset has been successfully created!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\\"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='hub://adilkhan/data', tensors=['embedding', 'id', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding embedding (3, 1536) float32 None \n",
" id text (3, 1) str None \n",
" metadata json (3, 1) str None \n",
" text text (3, 1) str None \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
}
],
"source": [
"with open(\"messages.txt\") as f:\n",
" state_of_the_union = f.read()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"pages = text_splitter.split_text(state_of_the_union)\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
"texts = text_splitter.create_documents(pages)\n",
"\n",
"print(texts)\n",
"\n",
"dataset_path = \"hub://\" + org_id + \"/data\"\n",
"embeddings = OpenAIEmbeddings()\n",
"db = DeepLake.from_documents(\n",
" texts, embeddings, dataset_path=dataset_path, overwrite=True\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# with open(\"messages.txt\") as f:\n",
"# state_of_the_union = f.read()\n",
"# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"# pages = text_splitter.split_text(state_of_the_union)\n",
"\n",
"# text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
"# texts = text_splitter.create_documents(pages)\n",
"\n",
"# print(texts)\n",
"\n",
"# dataset_path = \"hub://\" + org + \"/data\"\n",
"# embeddings = OpenAIEmbeddings()\n",
"# db = DeepLake.from_documents(\n",
"# texts, embeddings, dataset_path=dataset_path, overwrite=True, runtime={\"tensor_db\": True}\n",
"# )"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Ask questions\n",
"\n",
"Now we can ask a question and get an answer back with a semantic search:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db = DeepLake(dataset_path=dataset_path, read_only=True, embedding=embeddings)\n",
"\n",
"retriever = db.as_retriever()\n",
"retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
"retriever.search_kwargs[\"k\"] = 4\n",
"\n",
"qa = RetrievalQA.from_chain_type(\n",
" llm=OpenAI(), chain_type=\"stuff\", retriever=retriever, return_source_documents=False\n",
")\n",
"\n",
"# What was the restaurant the group was talking about called?\n",
"query = input(\"Enter query:\")\n",
"\n",
"# The Hungry Lobster\n",
"ans = qa({\"query\": query})\n",
"\n",
"print(ans)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/docugami_xml_kg_rag.ipynb | {
"cells": [
{
"attachments": {
"image.png": {
"image/png": 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HHXfcbyXfHXXUUW2//fbry4rrN20S4VPETMtea9+C6qCO6lp9qV8XtL3fyv7GjFd5FH6faP/6fvibuipP+WUvDAWGdVwQWVr+ekECQ3nLj/316VT7YFi3hW1bTGcf46rsq25Dey2Micqwv3wqr4WVH0IIIYQQQggz3zWXx96HEEIIIYQQQgghhBBCCMsthDYi5V1xxRV9GWQCIGIh0fKIiirS37Oe9ay2xx579O/9bj9ipvPPP79HrCPUsT2BHvGbfbxuuOGG7dBDD+2iPRDe2JbIjjCxxGvyHBUm1raWYX7wwQfnid3kqxxl+Cxim8iE8iRyFFVtyy23bFtttVXfTjmiBVq+VxRDZRD/qVNFJiT6KoFk1WXttdfu0eq0najMZ99PB8IvwiqiuxtvvPGxb1svf4sttmgbb7xxF3NWu6vNhJBf/epXuyhNH9T2xGuWXWZr+E3+7KONRJRe2cpvJQrUNmUM7TuKuhJIsReRmvEg4p738vN7iaf0QYkclU8saRyx76233trLZTNiyOc///ltzTXX7PvaR10Kfcj22qwMUTGVS6ioTepTYxD2H21D2Uy9K6mrctTt3nvv7XYxjtTL/qP1mAjlqs+VV17Zvv3tb/c8jSdtEqVwn3326QI7nyttsMEGfczrV3W67LLLev3U377Gm/5jj7If1MtxwAZle8l75Zbtq/1lA/spx3ZXX311j2Co/caUMhwDoinKu8YBah/leS//GkvGqsRudRzbz6tx5xzguHD8iMzpu6EtlSMv+bKfvKo9jle/SVWfao/P2mi/0fMDRs8PqLKMFWVpjzZoj+2H9ao6ydd4LTGs/apO2jusU+F7Y7HGqFefbaN+bEkMrN7yQdlstM4hhBBCCCGEgAgMQwghhBBCCCGEEEIIIawQECiNCgyJYgilCIkId4hyCIlEbRNFj3CHgIeY7KyzzuqCMt8RWNlXnkQ2hDVDgWGJgQihTjjhhHbuued24RahIpHSc57znL5/QWxk25NOOqmdc845XTz1ne98p9eBsIvoZ0ECQ5EDRY675JJL2imnnNJfie8K+duHmI+QjiCOKIsdLrjggi5IrKhz2qsNfp8uxF7ER8qHctmLYFCUu6HYzG/ET0Rt2mw/dlT2Tjvt1A4++OB5dtZ+ESTle+aZZ7ZvfvOb3UZsyk6WKyasU3d9SaQ4tO8QednWfvLSdjaVt7GhPoSkbKN/RXPUZ/rU7/ZhM/ZiU/mpNxGWfdhQX9nvqU996mOltnn9p1z7n3feeT1VuVdddVXPk6BL37IVod4QY8q27GU8ykeZ+tOyxKeffnoXwrKLsarOa6yxxm/ZfSLkO5HAUF9YHnj77bfv4lR2kOQp+Y69laVPlMcethEJU7+zQYnf9Dnx4/XXX9/rz5bqywaXX355PwaIU9mVyFXEQOMHJSz8+te/3su65ZZbumATynQ861fHuHoQBvpemz796U/3PjZOjDlJH3/ta1/r5Svb+Ku6++3iiy9u3/rWt/qxL7IpG+vToUiW3ZTJZurlODcu9YVylVciVHayL1uwg37W584P6kCg6fygvYS+ti20R5+oxze+8Y122mmn9XLUzXHufMJe0AZjQn7sxM629bnGmXMAcaJ9tHc4Noxj/aMt2mSMsan6sLnvHDParH3aYizIp/oqhBBCCCGEEIZEYBhCCCGEEEIIIYQQQghhhWAygeE222zTl/cl3hHxi8Bwxx137N8RzRCGESAR6hDzEBqJlkbwUxHkRgWGIH4iHHrf+97XhWTEOMoVZY0ocCjGUTfbfPSjH+3iHeURORF2SURDCxIYEu8Rtqnj5z//+S56IvyBV/UkLpQIvLSZAIs9vvCFL/RE+CbKGSGRPLV/OrAlexFqEVgplwBLO0X2I+RUZrXbbwRL2qNPtEnZbLvnnnu2XXfdtQssCaG0my2/8pWvdFEYwRR7EuR5JYjSZqItwjj1qGWeSwwGv4vmpkxCKfkRUJWoUJIXkVst8UucR0gnX31D3KV9+rJsbDtR8AjE2JDAcaONNuoCSX1X9rd/CSSVS6RVbZCUT+hnPLAFgd1QLGnM6mN1UH8COGI5bSU8O/XUU3s/lvCMzQlUjdmhiGyUyQSG+mvvvffuSwaXSHCIerKp8Ur0qK0gXNttt916FEPCOmXbVp0I3NiAENY+6ssG7K7e7ERMqN+0ne3tz95Ef0S42k0MTHgnsb/+ko/tHMPEjVDG//t//6+LGPUPIa6xos5sSHinv/WRMa/NylE/v9tOfRwT+rMEedptrOtHETiNJfsRQVZbvBIYEu3pT2NJn8J3ynbM1zEgP33lHDEUhWojAbS8Hd9f/vKX+77sKV92JkqUpzzUo44V5xX7sY2kXgSG8rNvHSvKYnPHG/vav8TKjj/Hrb4jYvVdtc8YM9Ydq6OC2BBCCCGEEELA5LPREEIIIYQQQgghhBBCCGE5h6iGaIg4kJiJkIdwiPCNYIyAjCDP+1pelBjnhS98YRdOLQxCLfkR8xAwEQnKQzmj+N7vta3ku6lCwEVgpDzlFsqqvOVJQGRb32sfoZYyJfsSsZVQbDoQIIlWx56WxmUnZagTkRXh2LBeyibGI+5Stm2J2IiriAwJrED4SVT1qU99qovoiJ1q6dZqE5Ed8RNB1Hve854u2Ko2DiG+Ovnkk3tEO0I1Akviseqbsr8yCcvkR7RHeMeG6s9e2jTsR20hcrO/evm9xIfqoe3q/tnPfraLvgi9lFE2lwgsCd+I74jOPvOZz/Ry5TtEHfShfexPlPiJT3yiCzCJ7GqZbf1I9CWVUG1Jo73sTrinveyhLCI6Y0GqstWZMO2LX/xiF7Taz3FVttcedTdWiAL/7d/+rdu/hJ5lY/mwb41RZXqvvZWX7apvvPc9mxDkEduxsTKIEZWrT9XZmCOys498aj/v9ffwuDCWCBD1E9GdcakP5VftMS6J8L70pS/1cUkUaOwqw/mjliSXb/W/fLRZewptIYw0bogD5aFe9nUuqmPFWGazj3/841306Nhi06qT9ihHnQgI//3f/723wW9lL6/ayib2U462Og7YTf3kKS/1IAAl6BwKpkMIIYQQQghhSASGIYQQQgghhBBCCCGEEFZYiJ8sLyrKG4FRRfAimCIOIg4jQpJKeEc0RdRDuLW8IBIZsZJoh4cddljbeOON+/cVBY7oz1K3L33pS9u+++47L8rikkRZBILK2mWXXeZFQCSII0oi5iuBIRETARORHUEZgZO+UFcCQwJFbSJAFClNlDjiKmInwkURJl/5yle2t7zlLf1VBDf76icR8Qi5iKj0JfQjMZTvCK+IzAi2lKEskSff9KY3tde+9rXdToRb9iXmqmWMia1Ea9t9993bzjvv3NtZUf2MCfkceOCBPS/9UHkYSyIWEhaygXqoqwiNr3jFK9rrX//6duyxx/aIf4RihFv2IZIUSY44kaBuMmxPZIitt966R3/cY489+hLF6iuaoL6ZLmzGRtpPREfg+clPfrKL1wggP/CBD/TkN2JMfaocYjP9QbRb0f4cO/pGP4o+KYqk/G1z+OGHtze+8Y3d/mzveNTPbCBSpGh8RHHawcYvfvGL+/gqUZ0+qDFxzDHH9KW1RVxk41EIB7VJH6ibfNhqhx126MtyO24WFoWP0NF4FXGSOM8rwZ385GE8vvWtb22vetWr+mf10/Yax8agehh72qoO6623Xj+36GdiPsLAoejPb8SYyiKS9D27Olbsr/3aJW/jhjDV9upkTLDLm9/85vaa17ymj13b+12dKoomm0+G/iN+VG/LsRtjEruJXkgsGYFhCCGEEEIIYTIiMAwhhBBCCCGEEEIIIYSwwkIQRQhH4CMSF5HMUGBIEEdYQwhHEGX7EhhOJYLh4wVRFDEZ0Q8RkeVcob6EVkRAL3/5y9vv/M7vtCOPPLILKpcWRFMvetGLutBM+WxINCUqJNEUgVYJ/kRqI74irCK4ItpUV0sqE1ARHxKvEVyVINDvBx10UPv93//99ud//uftbW97W/9suWh5245wijCN8AvqoA8JtGpZYXbR73vttVc7/vjj25/92Z+1t7/97e24447rAjWCNuOhosqpO3EVARuRJjFaiaqICYmtCAYJy4jWLLVLtCVSH4Gh+hADEsbZ94gjjuh1/6//9b/2V0I77V5jjTX6GCRGJGwkFiNuLLHZKNpMrMkuxKXEZBIhnjFBZKYfposodsRsovOJvvcv//IvPVlu+N3vfnf7h3/4h/bP//zP7YQTTugCNf3FZo6NffbZp4vfSqzHDmxI+Kld6uw4Igp8wxve0N7xjnd0+7/61a/u0Svtp3zCSQJH9bA94aLxu99++/XjFcaEY1E/Epyyv3xrKeIh8jTW9IG8jjrqqHb00Ue3Qw45pO+vzhMJE4cYS+pjLOkfUQOV5ZgyDvXlX/zFX/RXY4XwF8a7MWAp5BIPGiPGin3Zz3HhWCEUNFaqzwn7lOk4qOWjtcHS6ASEyifalLd9jW/5yVdfEBey8R//8R/3cWY/OD7YWL3kOxkEjupGVLv//vv3c4kxZryxmbqU2DaEEEIIIYQQRonAMIQQQgghhBBCCCGEEMIKC6EOgRLxEvGMVwIkYjgiHcIegh/iNKIo4iMiOMKtEjgtD1QUNyI7Aq2KHljtI8AimiOqIpKz7VQgcCIsIqpil4mS6GbD5WMJC0XwI6wiFJMHgZxohLfddlsXWtlPZD7iPXnbRp3Vj0DRfsol9BSRkCALbC7SHyElIZtIjNolcqB22U895C1SXgkMCfCI4Ijb1LXEXYSCBFfEZfpUhDzvfSfaIwEV0aDy9L06EvLZ1lgh4gIRn/aqC3GnfiAUJKwjdiSiNH5sr56iHBLJsROxl2iDPhO7lfjL2CP+ImLTjhKbjVKCRWK2173udV00JyljcSJVKk8dHAf6jdiTSNCrY4LgjQiOvav9+kEEP5EI2ahEZyJBipRH/MkOtmcrURyJAR17xJ6i8RFqsp9xqwyCOeI5/cb+7FdLmoNNCeyUx55bbLFFr8tkEfVsK7IggdxLXvKS9rKXvWxetEH7KXdBGIu1LLSxX2NX2cam488xYBwQD2onAapx7rxiDLKpfR2XFWnSeJGXsVJLGzsG2IsQl92JAPWJsac8dqj99Akxao15x4JtjHF1YV/jhJhym2226fazn2OQ6FZEw8mQl7KMUbYi8jTG9LXjIYQQQgghhBAWRASGIYQQQgghhBBCCCGEEFZoSpwn2hfBDyETQSEREEEUcZXPxDjEhYRptltYpLPHE0JCIiACPOImoqOCAEtdiZzUXRtsOxVKFHXuuee2E088sX3+85+fL33pS1/q4jHL2BJEEbyxJdGUurCvfIijiMWIxoiriACJovwGdSM2I/JSZ3mxO/sTVYForCILyo/wTB/ZnmhNO9lC38m/IsUpj8iNeIsgzjb6UPQ3Yit1NQbYiMBPlDYCNJHaiP4Ix9hWIu6y7bD/idK0m9BM8rvvCPAIyoi3qlxRCgnPCCS1VT28EsbtvffeXVwHwjKiTPYn0PR5IpRV4lFtkS+hJDHZcBwsCvqOXeWjPY4NddEuURbZVnstu0t4xmbEheqgn/QLtJ8gryIxypcdif30kX4hoiM89Z19bUMYal/96b3fjN/6HWzqPTuwo/roiyp7iG3lwV6ibHqViAIJ8LRzov2GqCOxn7FpjMJY0B5tMx6J9kpMqa7qo/+Ja+u8YkyzLXGlMWcMOi5tY1w7NhxT8qglxglWlckGxqSxpF+U63hQrxLjakeNU6JD9lU2GyhXso0yHCvyls9EsJk6DseYRLio3SGEEEIIIYSwICIwDCGEEEIIIYQQQgghhLDCQzgmQheBGSEQ8VRFEiPwIt4hxiH+IggicCPUmS4EPJOJeBb027KCqMuyq5/5zGfa//k//6f97d/+7XzJUrmW0SW4IkBjJ4IkkeaIkAiZQDglGiHxVS1D7T3BlH2Iw0SRIzQEYRVBmv1KyEWURSgo4tpXvvKVdsopp7TTTjutnXPOOV24WKIzgkL7erWvehFZDUVU6qjMoRAOBFmiylnuVlRAEQ0J9my3sOh2Q9Sf2ItYrMo1bkQVZBdjboj8lUOUWeUQsxHzaUsJMUcpgaH9liRsqU5EZaLgieLoPfFoCfgI4vSXSISW5hbhTr9Xnxfs4BiqNrANmxCcVj9KX/va17qQbthW/WfbEicuDuqsbvqdoHCqQtshxhKBYC1hLBlnxr+lhqst0vnnnz9PaAjb6lNj2lh2DlEH/S5yozFhW7+LuElkqDw2cS5Sjn5RfwLDEqPWOct+tge7GXvEv+yqPo6Vb3zjG/0YYgtJPfSPetl3Ihs7Vpwfjd06xkIIIYQQQghhqkRgGEIIIYQQQgghhBBCCGGFhshnKDAkuiHYEdWLQIgIjviG6Iu4isBwUUU2JUiaCN8TF032+7KAHQi7iPeIkiZKFa2NUEnd2ZOQjoiOcGpUYFjLJHsVoU8ZtrE0sohoorP5jtiJwJH4qQRa6kJcePLJJ7cTTjihR1D0SjxFkFVR9exPpOYzUZa87Fu/Qx0J5UZFZvpfJDuR2UQXJOBTP30+VVGp8ku4RYjmfYnbRNgjbhtGQIR6GH+EgoR9yqt8COzYYiKIv4j8Fjda4SjGu7pY9vh3f/d329vf/vaejj/++B7NTv3ZmLDOksFnn312j4Znv1E7qbvfSjjoM9Gcfb7whS/Mi4YpSiaRHmHcUIxYfcceiwOb6nPHu/rrk+lCuGf86lv1MZ607fLLL+9iyWqLRHhrXPq9xp3+rDEBdXKcENfqd9v5/eqrr+7iZuceYlznI+NY3Z2DRN8kNLT98FgpMS77Od4cK2Vjx4qIo0TDyrCt/etYUdZENnasOBaUvajnvhBCCCGEEMKqSwSGIYQQQgghhBBCCCGEEFZ4iI6IBwm8SkBjudELL7ywLytK5ESQZBlTUcMWRWRDyDOZQKp+W94EhiAWI8SaLPm9UuF7S/QSThHo+UwAZanXa665pouniMiImgjSCO4s9ypCWgkSwSZDm8mHEI+oTrKtpP8IREXY22WXXXp0N0u5Et3V/hPZ1W+j3/tMsEV05XVY/lQZ2mRoo4WxKH1vLNYSxksS9SYo04e77757e/GLX9wOO+yw9tKXvrS/17/aRFxKYEjk6Xhx3BB1Dql+rPbZT32HfVj9SCxJnEpwpx+33377LqTT71Ox4YJgK8fxRCLIqaINjtPhuGAr+UrD9hAMOq8QA4ryKLGn74n2oB7OKZbINv71pWPlu9/9bhcnlpDXUtG2JS58wQteMG9J52IiG7OZegztbB95OD7qWGFjdZrs/KN98lgSfRBCCCGEEEJY9YjAMIQQQgghhBBCCCGEEMIKDcEM0Q3BDoEhkZDvRGYjhBPpC74nBLIdwc1UkE8Jcoh/RAwbCpMKwh6/1e+LIjTDROKfRc0L2kmUJHoZ4Reh10SJiHAo2rIfEdNGG200z6baJhKfJVu/+tWv9qiI2ip/kQstrVt52N97+5WoSRLd8JBDDmlvfvOb29ve9rbfSqLs+f4tb3lL+73f+7326le/updvf2IuAioCtqoj8aCIb8RwQxupkz4nLFVHSysTQi5IbDhqY2WoO7uoM3GWfQnHCPLkWxHsCoJGZVlKmJ3Yix3kIw9iv4lQlvZNdUwuCsqQv6iOhHBHHXVUFx2yqe9F6HOsfP3rX2/nnntub8fQVtqg/iWqY5cdd9yxHXPMMb3fqg/f+ta39r7Th5LfXve613VhnnG2uNHz1HVxbaUOxpE+LbuIvrn//vv36I7DMSlpw7A9RxxxRF/Smj3sL7Ej8TLhoKiRxoIIn1deeWW76KKLeqRP44Ed2Z9g0bhG9b/fJPXxneOKEPRNb3pTt2vVp2w8PFbUm6iz9h9Fftq9OHYLIYQQQgghrLpkJhFCCCGEEEIIIYQQQghhhYd4huCHiImQjriNoMySocR/JSoimrPNVIQ29iECkryXD1HZqFCNcIggq0RliyMInAj5VZouREqinL3+9a9vf/VXf9X+9m//dr7ke4IztmG3ggBLRDbCJaIpIiXttuzrJZdc0tsM4s6tttqqb1cCNLAxIRpxo+/Vnx0JFkWB22uvvdq+++7bRVTEbsSf+rDKlKfliNVJkg8hl3qAuPCOO+7o4rhhfxABWhr7Qx/6UPv7v//79nd/93ftYx/7WI/SV3WWR+VTjNpY/dWHXQjzYAwQF1oeWPlDiBp9T2BoHMifbWpp2qFthyjHb4+H+Kv6hCCUzXfYYYdevxJPEsRdcMEF7cYbb+zjueyq/sYAARu0TZ+yTfWj5L2+1W+SqJQ+E9+xYbVxYbZfEMaQfEbzmCpsbQwOx5L8nB+MuWqL5NghSCYQNS4JCAlpvS+BItTJNiJwarPPhK/XX399O+2009qdd97ZtzMeCAzZv2wJ2ytfXzjfsIW82UyZe+yxR6+PY2XvvffuwlvbO9dZ7lp+zmvynMguvtNfygkhhBBCCCGE6RKBYQghhBBCCCGEEEIIIYQVHgIhwhzCH4KgoZiLuIbQjqiISGooLJoMvxPjEOyUOIzYimBRZDwRyQiyiIhEtLv22mu78G40qt10qXKrfoRGRG3Kfeihh7pA7oEHHugR0qYCEZRoawcffHCPJCca2mgS/WzPPffsthkVCBKJlXipxEnaa8lXdbI9ERQRmSWSS0AGv9mPuKzEVMSZ9rev/Urwqb133XVXu+GGG7q4TfRJ2/hePtohn4o+yS4EcJagtb3+IPAj7rOM8ze/+c32+c9/vn3iE5/o4kKR+SrqoDzl4bXszJ7sKw/Je3Ul4rKUsLFT5f7gBz/oAkvlaIvtvd588809Wp12gL1ExiMAI+pbkLir6vN4YCyzuQiExGr6QP9oGyEckeGll17al8BmAxgb7EAgh+pHdvKd445A1LFlfOoTfclGlgaG47NsLxWOK/YXZdK+xrj3Q9FoMey7RcVYquW89Yl2G2slDCXy05Ya80SsNS71fS0LPjxWoO2WLWansqex8K1vfavbyncEgQSDRIijx5oxZoyXjR33bMHGzkHGvvGkbDZVJ8sw6zN1YhN5Tmabstvi2C6EEEIIIYSwahKBYQghhBBCCCGEEEIIIYSVAsIboqH111+/i5kKwhqiId9PRVxY2I7AjNCI6IdgiKCQAIuA7dZbb+3LoF5++eXtpJNO6kIiQqXFQV1LvFT1VCZRI0HRFVdc0csXtW+qyEe+C0oT2cR3BIaiuhE2lUCOHSS/V4Q/gikiNHkV+kAkQkI2dvcbsRRx3tlnn93bQbAl6t/Xvva19qUvfal95jOf6ZEHP/vZz85bqhfsT7wl+ps+URc2IIT7xje+0c4555y+xO/FF1/cl2/2PWEY2xGNEZgSlVU91G0o8CIS1JcizhGLfu973+vf2V6ZBGeEadpNSHrGGWf0cpWnXH1/5pln9mh18mEb9RTRjnhTdLvJsG2lxwtjTN/ss88+3a76DkRt+uSss85q11xzTe8vQj+2s8wxgRy7sat+0/7zzjuviyvtZ/nsU089tX384x9vH/nIR3o68cQTe1/IG/aXatwRKxKLOp6+853v9DFu+xI3DlkSdjJmRW4kijUu1MNYquiNl112WRdXap9ImISqH/3oR9uHP/zh3i7tJYQcFUDqbzYlMCSe1UZtKLEkASGxr/OQOgyPFds6htSrjiOiS/YwrtjVuDL2jDs2/dznPtfr9KlPfar3l3PEwng8x1gIIYQQQghh5SECwxBCCCGEEEIIIYQQQggrBUQ6CxIYEvCIJDcU9iwIYhz7lWAIIuARlH3xi19s73vf+9p73/veLvAhaCM6IliqSG2LgrqJdEasVCIkIiWR0E455ZQeke/kk0/uAqiljTawl+VYiQhHbUfkR3BGVEUUNdpuIjbR2gjTvLIh0VRFGCTWYr//+I//6G0i8BKNTSQ5ZRGAVTQ3NtGvxHA777xzL09fsMuFF17YRWCEiexD5EeIRexJFEggaD9jw2f10q/yrvoSuIkGpx4nnHBCz1NURNsRSBLiEX/pE8JDgjpCRuVVuUSSvifK03/qv9tuu7Xtt9++t2dRx8TSQD+qk+V+DzrooN626lv11w5COxEiiQmJ49hRW4wHbSlRHtu///3vb+95z3v6sUCkp4+JBB2Tttf3xov92N9xwpbGDPEdER7hKSEf+xPWLUisuzi2rGiYxoT26GNlEUgSvop4qS3GJSEfwam2GJfGe4lNh8cCtJWQ1PnCeDe+tK2ElfZVpmOJDYbYV52MbeOGsNe+6kTkSHjrfKNOzj3ON34TydCxoTz9GUIIIYQQQghLgwgMQwghhBBCCCGEEEIIIawUDAWGBEAlQiIEIvwpgeFUsT9REPGVZW6JpHxHbEQgJzobkZzoeSAcIgwi+FlU1JXoynKotSQtoREhkShlIiUSQVlCuCIJLi20lR1FcKwlZdm4IBjTZhEOJ4rQZ1vtYD9L8RJX2Y6Yi3Dty1/+cvvgBz/YRWmWFibQJGSTn+1f/OIX98+Vl2iCfjv88MN7nuqj/aINiuomohsRJvEVkZzt9dv+++/f9thjj/5ZPrVUrbrpL2Ivy11b9pmNTz/99B6VkoCOUFDUuQMOOKAdcsgh/b06iYpIXKc85eoXojiiR220rLT6v+hFL+q2W5wxsbQw1rRfPUVZLAEmQSsxnah5IjOygzHpuCK0tC0xnG0JQtnecfCBD3ygCzSJCwkIbc/uhx56aH/P9sr0WkI8tjTOCE+vuuqqLp4j3BRJkrBxaYxv5TuWRZdUN6/soN3Ew8YlIR/RqCiaxI/qS/hnDOy+++5dlDgqMNQOdtIuY30oKvW9KKC+dzyN7uuzMakue+21Vxc++qxOxJ6iQopWSIDpfGO5b21QJzbed999e74hhBBCCCGEsDSIwDCEEEIIIYQQQgghhBDCCgPBDoFQJeKwEuv4LLKdCGMVHc13XgmICAx9X6KfEjvJQ6oIa4XfCaMOPPDALizaZZdduiBqGH2MyGfrrbdur3/969vRRx/dxWREabaR91BIJO/R+itzuE1BQFfCrIqWVoJCgkPJe/nJo9pQZQ7bsTjIR/mEerU8cdXd9+pH9EdwNVpmtZVN2Od1r3tdF0LpB4I7bSAsI6LSBgIpEf+OP/74LrIi0LJ/IT95HXPMMX2bI444okfHUw+IFCdP7ScIE3nQtvrFe/apOhkPbEwAqE+rH+xPPCivsq+6auNLX/rS9trXvrZH/GMPY2lYrnGmDX5/85vf3F796lf3MojthrZRTtmw0pLqM+2QX40FaUH5aps+ZB/1duzYhw0sEUxkSHj58MMPd4EmMeJrXvOaduyxx3axXIluba8f2ayiN7785S/vx40oib4bohzHFbuyY9WTHVHjHGWvapM2LsxW9hm1hX1qP6/q9bKXvayPyzrW2EN/aos2ycP4ICo87rjjejROQkF5ToTvjcnttttu3rnCuPDeuBdp0Ngcrb/PyiLYZds3vvGN88ZZjR91kmD8EiGqk+2c94Z1Gra/0pIaYyGEEEIIIYRVj7G5k7Sl93hjCCGEEEIIIYQQQgghhLCEIK4R+c5SqkRPIuER1RB+WVq0hGYifokod88993Shkm1ElBMVj4iIYId4SKQ6eYk6R3hDzENwROQzRDmWSb3mmmv60ruiuxHGiS4oyhjRG+GR7US1E+FQ1DPCIvkRJxEtWVr3xhtv7NEPRSQkALIvMdJOO+30W9EVLf1rWxHkqq1EQoRaIu8R14mUJoKcJWlFfNMG9RFlj0BvSUU0U7Z2i+7GBiLLgRiLyElUO9HvhgKnISUWE2nQ/iLcicAmyqC8QWSmvoRp+lIfELRNhPxuu+22vqSx/CxlLC9RCNlI/xJcsQ/xpzxHRV3679Zbb+39JRkrvrNv1UOkPiK0gshOudphX/vcf//9vQ3arg2EZKLQEc6J8ldLPBfKMObY0jLXbEl4pr+MP/VeFBwb9913X1/W2HhQjjoZK4R87GC8TYYxyw7Gkb4Be7EhAZvjxvg0jpXjGGN7y/SKPGm8O6Ycg0SH2mRsWD6bIG+0bEtSs6H6KlOUROXZn3jOMcMmxphoko5VbXJMGRv6xnHj91HUR75s7Dgy9tjh4IMP7gI//URsB3k6vowl9dCnNZZs43jTJ44p5REPTjYuoSzjwRg544wz5kUaJcA1LghstW8y6padOqm/fORRdfK7Meo41xbnjapTtcl2zpMiQtq/omo6/xmX2uM4CSGEEEIIIYSpEoFhCCGEEEIIIYQQQgghhBUC7myCIKIsYqZybxMBERoNRUMET8RQBWESYQ5hDSGTfQlxhsuwEmTJa1R843ciHcn2BFbKIOohViKKkoiLfK9u3itHfiV8rN8JkKpuylL3Yf0hD9sSFhGxlWCRyKvKta/t1Mm2IOTSDm1dkKBsOmg/AZv2K6fspX1lV2X5vCDsK8mL7Qkutc9+2iSVvbRtaI9R5CFpO9vIh7BMfeTDTvLyWRrNq9pkf/WwrzoN60EcOBRN6j/2tl+1hVBO2WygT+ynL5Vp31GbDMdSjWFtrbG5qH0mH/XTHqn6qPKeSIg3RF2qf4fHjf21Sb3Khn5ngzoe2M0Y9ZkNyv7KZQN5jKKu8rAv25fAcLi/ceA7+epj+KwuftOmUfui7KAt2gX7VH8OxwI72ca2ynFs6k/9ah/1IJi0r/JG958IecpPXlW+fYbHysKoMeZVPnWssH2N0Tr3jI6bKl977O+z8m2rDradyG4hhBBCCCGEMBkRGIYQQgghhBBCCCGEEEIIU4A7vcQ7hD4l1lmY4GhxKFEioZJyCJykFZ0SqUlgy0UR2OmPEqux09BGU+kX+1dd2Hi478JEWFWupO6VViXYrwR67Fj2m6qIrfYnhrM9+02175YG2lDtUZ/heFhWDMcZey3qsRJCCCGEEEIIi0oEhiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQpiPZffIVQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEJZbIjAMIYQQQgghhBBCCCGEEEIIIYQQQgghhBDCfERgGEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhPmIwDCEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjzEYFhCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ5mNszlweex9CCCGEEEIIIYQQQgghhLBSMHr7Y/h5Ou8n+70Y3a5ex8bG+mtRn4ffL+33IYQQQgghhBDC4hKBYQghhBBCCCGEEEIIIYQQViqGYr/Zs2f31wWlqWwjjW6HyfYl9BumGTNmzPfdgtKCtp9KXiGEEEIIIYQQwpIgAsMQQgghhBBCCCGEEEIIITxuuC1BlDdr1qz26KOP9tfR98Pv6vvR3yf7XiqRX6Uqd7rv63X0/fBzvY5+XyK/oeBvOu+n87leh+9nzpz5W2m11Vbrafh59PfRz5NtT+AohRBCCCGEEEJY+YnAMIQQQgghhBBCCCGEEEIIU8IthdGIffV5skh+o8l2RIG//vWvJ0y/+tWv2iOPPDLfd/V+9Lf67FWSd5U1ZFSQN8pk3xcL+x2j24zWYSIWts2Cfq/fJmorQeATnvCE30pPfOITF5ie9KQnTbqd3+p9iQ2VU6nKlSrC4kSRFoe/1T4hhBBCCCGEEJZfIjAMIYQQQgirBLNnP/ZmCbCkgjS4Eh+9GndfZXHurYzmubj5hRBCCCGEEFYuhrcERsVpo68YfkcYSMhXYj+v//mf/znv/USJ4E9EwRL+1WtFGRyKE4fvh99N9H29llCtouqNvqKEbRMlLGi7yb6XMNG+9VkdK0F9Mfx+mCb7DdPZ17aVKqJjvS/UWx1HX0s0ONHvlepzRTgkSBy+LzHiZMnvq6++en9f+8oT9YrR7xb0WwghhBBCCCGEpUcEhiGEEEIIYaXn0Udbe/jhOe2RRxb/0veJTxxra63lpstjXywirsJ/9as57Ze/nNPc4/F5xoyxtuaaY+1JT3KT5LENp4F2yk875SkP9X3yk0WuWLQ8QwghhBBCCCsHJTwjMiPwG6YS/S0oDQWChINEhZOl+t22JXQbit9AGDYUA46+n+y7ibYpcdsw+V4Zw1Tljr6f6Lvh+8m+W9Dnoto9TMPvh+8n+m74frLvhp9R/Vxp2L/D7yuNbj/Rd6Ofq6xqr36p16lESpRKYFgiw3pdUJpomyo7hBBCCCGEEMLSIQLDEEIIIYSwUuNq98EH57Rrr53dfvKTxQ9juN56M9p2281oq6/uJspjXy4Cs2a1dueds9stt8xuP//5nPboo63nucUWM9r667th5kbNYxtPkQcemNO+//3Z7b775NnaaquNtec8Z6xtvPGM9tSnjosMQwghhBBCCCsf3PwlAJtILFaJ0IzwT9RBIsB6HX1fn0fFgvIY3lIYirpKaFbv63VUDFZpKCwjNht+t6A0uv1QSFhlDj9PxKL8tqj5jTLZLZkF3aqZ7j71/fC1kvFRQtGppMm2re/r1djwXlJGlVtU+fV+iD7Ul0Ph4WiqiIf1Onxf40I+kwlSS4Q4nb4KIYQQQgghhDBOBIYhhBBCCGGl5tFHWxfxvfe9j7arr158geGee85o//W/PqE94xmLF8Xw179u7eyzH21f/OKs9sMfimTYep5vfOPMdsABq7UnP3n6SzHfeOPsduKJj7RrrpnT7rqr9Tz22mtGO+qomW3TTWf0SIa5lxJCCCGEEMLKBRc/URch4MMPPzwv/fznP5/vM9FgicBKEFbiw4mSvAmyiLdK0LcwAdhoKnFXJZ/lOfr9aBrdrj7X+9ougrHpoU8l/UsIWKn6e/T7qfxev9X7EqYO06iAtZJtpeG4rL7Vz5VKQDjRd8bZmmuu2dZaa615r8PkuzXWWKNvL98QQgghhBBCCNMjAsMQQgghhLDS4kr30UfntGuumd3e8Y5H2oUXLr7A8IgjZrZ/+ZcntvXWG2tPeMJjXy4C//mfrX3hC4+2f/u3R9rNN8/pEQdFG/zrv35CO+641dpaa01fYHj55bPae97z63bRRXParbe2tuaarR155Iz21reu1qMuPuUpbr49tnEIIYQQQghhuYf7nuhKhLgSYpV4q77znnjrl7/8ZfvFL37RhYRSvfdawkICMAKrEgxOJNYavicoHEaWG4oLh58n+96+JehakBBwot+mu32YPgu6PTTZb1PZx9gcFRAOPw+/H/1NKqFhvQ7fD7+rz8aD8WrMERKWoJC4sD4/+clPnieCrTE6UYoIMYQQQgghhBDmJwLDEEIIIYSw0uJKl8BQ5MI//dNH2vnnL77A8MgjZ7b3vGfJCAxPOOHR9v/+3yPtppvGBYby/Ju/eUJ79atXa095yvQFht/+9qz2r//663bhhZZKHhcYHn30jPb7v79a2377CAxDCCGEEEJY0SCgIhL82c9+1u6///556YEHHpj3/sEHH+zbEGaBiBDl+ie+Iqx6ytxJxlOf+tQutirBFfHVZKkEWQRXRQn7hgK/UbHfRNuEVRNjcPQW1ESfh98Zv0PBrOS9MT78bpgcH46DEtKOjk/JODau11577fb0pz99Xnra054271VybBAahhBCCCGEEEL4DREYhhBCCCGElZpHH23thz+c3T73uUfbzTf/tsBw1iy/zWm33Ta+pLBlitdYo7XnPre1jTYaaxtuOP8Nse23n9mOP3619rSnzb9E8vDKemH30qYjMJTvVO7NLa7AsOo/lbIWxHTsEEIIIYQQwqrARKIqQihR22r54hJLDd8TVNmGcGo00ptXAsSKSEgMOCoSrOQ3iXCqohLaZ6I0/E1UuAgFw+OJY6OiE4qE6DiZLNXvdTw4XkZFiHU8OYZsX8dMRSysyJvDNJn4dijOdZyUeLHIsRJCCCGEEEJYWYnAMIQQQgghrNS42n3ooTntxhtnt/vv/+1L31//ek677LLZ7YILZrUrrmjtgQdae8YzWttxx9b22WdG233330TqKJ75zLG25ZaWXhoXz8mfUPGRR8ajJfrse+LD1VYbj3I40T2GhQkMLZGMRx5xc0V+Y00QhQVFNVxUgaEAJ8p59NHxz098ougOE9d7MsoO8ig7oGzAHtPJL4QQQgghhBUdrnfR2GopY6/1XiJ4In4SjVAEtoceeui3UokLUcsWDwWAXomhCJ4sBStC4UTJ77bLsq9hZcbx5rgiJhweRwS8dXyVmNexNRQo1nviQ/k4vogIJzqeRAH16nfHleNwKNz1PsLcEEIIIYQQwspGBIYhhBBCCGGlh+jtF7+Y0x555LEvHuNXv5rTzjxzVjv11EfbuefOaffe29pznjPW9t3XUsirtcMOm19gSOS35ppjXUxHoPjLX44LGB98cE57+OFxMaD7dmusMdbWXnusPfWpY10sSLQ3FNlNJDBU9v/3/z2hveIVM/u2P//5nPazn82ZW/fWnvzk1p7+9BldNLj66pJICeN5FdMRGBIV/vrX4zZQzgMPjJcza9acXs5TnjLWy1TOZMJGM4myg321ny0k3xNFqgM7SPIr8WIIIYQQQggrC0SEEyWiJQJB4iYiQku4Dl8rqloJEUcT8ZKog8SDRIJETd5XKpFTCQ5LhDj6SlgoRfAUVnYcNxX9UHIMDt9XctwRG0olPBwmxyaxomOzjp9hIiIU0dAxWEsrEx5W8lsdg6P7jkY9DCGEEEIIIYQVgQgMQwghhBDCKssvfzmnfe1rj7YvfWlWO/PM2e3HP7Y88lg78MCxduyxM9tRRz3hsS3n5847Z7dbbpndrrtuTvvBD8YFesSGs2aNiwiJAInq1l9/rO2444y2xRZjbZ11LF02vv9EAsNnP3us/cVfrNZe9KKx9t3vzml33DGn/eQn4yJAERPXXXesbbzxWNtmmxk9EevNGAj/piowNAMgBvz+98frb+no++4bt8fs2a2LC0VytES0um+yyYwumByWBVEPf/IT+8+Zm8/sx+wwno8ylEVUaDnp5z9fvcd6Xuutl8gpIYQQQghhxaeEgaKhiYpW4qR6X6/ESpLPJSokPCQ+IiIkFiRIqmVZvUq1rPGCEqET0VIIYWqUCNFxWFFCvfo8mnw/TI5fx7TfCBWJCB2HjttKtYxyRRYdfbWN/SIyDCGEEEIIIaxIRGAYQgghhBBWWRZFYFjR+s4/f1Y7++xZ7ZJLZrfbbx8XCBIX1tW1e3zEdRtsMNb222/G3Dxntt12m9kjGs6cObHA8OlPH2uvehUBXmvnnTe7ff/7c9pPfzqep/zsu/nmbW5+M9shh8xsL3jBjL5PMRWBIX760znte9+b3dtw7rmz27XXjkdgfPSxJZKJIJ/2tNY222ysHXDAzLbXXjPbllvOaGuvPb7kMSyDfN99c3r7zz13VvvWt2a3224bb8fwPon3a6zR2iabjM3NZ0Y76KDx/EbFkSGEEEIIISyvECMNlzcmLKr3xEkioFVkwopOKGohQaHtCAkrmqAk4iBRUi23Kq0992Kb+EjyPTFSiZCkYeSz0RRCmB5uiy0sOe4JCocRDr2vY9x7osM6J0gVLdHxTiRcx3hFOPTqWC9x8HBZ5Xpv3xzXIYQQQgghhOWNCAxDCCGEEMIqy6IIDInqbr55Vvv4x2e1U06Z1X72M/n8JnLh8Oq6RIbPeta4yPC//JfV2gtfOB4N8Fe/ml9g+IS5xa233viSxPfeOy5kJPqrPAn/iAY33XSs7b77WPud33lC23XXmeM/zmUqAkMRCs8779F2xhmz2je+IQqjpZ3Hyym0o8pS9wMOmNHe/ObV2mabERmO3+iwDPK1185q73vfo708kQzZwb6iLRJRsgkhJeS17rqtve51q7Xf+73V2jrrjHU7hBBCCCGEsLxDZPTTn/70t9L999/fE6ERkWEJjCqJkkZERFS0zjrr/FYqkRFRUYkOKw3FiCGEZUeJDCXHcwkIiYa9Ou6JiAkOR88PtQS6PIbHdokJCQ+f/vSn9/NBvVbyW6KShhBCCCGEEJY3IjAMIYQQQgirLIsiMPzud2e30057pJ100uwurCMKtPyvJZCf97zx6IREh9deOx7Rj3gPu+46o73jHTPb7rvPnFvGjAkFhiVIVIcttxxrz3nOWHvSk8a62PDGG2e3731vfLunPKX1pZL/+I9Xay95ycz2jGcQJY4tVGC4+upj7aGHWvvwhx/pbb7xxnFRoOWQN998rIsb11xzrN1115y+/PNtt42LBHfYYay95jUz2qGHrja3XjO7SPHWW2e3c855tP37v8/qERBXX721rbaa0fNRf3bRdttdc82cduedbqy0duSRM9rrXz+z7bjjzLnty02TEEIIIYSwfMBNTkBk6VORykQgrEiEFcGsXmvpY9vazzLHIg6KWOZ1uFQqsVBFJqzke+LDiIhCWHFx7IteWOeDYXKuqCXR67WS8wbBIqHh8JwwPFcMI5tKWVY5hBBCCCGEsKyZ+a65PPY+hBBCCCGEVYpHH219qeAbbiDIK/HeWF/Od1ws95vogMVtt81uZ531aLvzTuK7sSYSn6V/X/vame01r1mt7b//zLbNNjPaz342u91zz5wuNvz1ry1vTBRIPDijPfOZM7pw77rrZvelhe+7r7VHHhmP+rfOOq3tssuM9sY3zmyvfOVqfSnkTTYZz+Puu8dFe7/4Ret1JWgkQlx33RldiEgYeMkls9oPftDa/fe3HgmR8FF+tvv1r8fm/jarnXDCrHbOOW6GtLbWWoSBBJUz2stf/oR26KEz27OeNW6LO+74TRRF9bMk88Ybz+g3UggeL7xwVrviivFlnIkcDztsRnvFK2bOzWe1dsABq7WddlK/8TrLj62e/eyxubYYa89//m8v7xxCCCGEEMLjiWvaikZGJGSpU5HIfvzjH7dbb7117hzhhnbllVe2yy67rF177bXt9ttv77/ZxrYEQgSFz3rWs9oGG2ww95p9k7nzh83nzgW2aTvssEPbcccd+3vfr7/++nOvg5/dIxcSChEWRSgUwoqNY5joryKVrrvuunPn+89tG220UT8XbLbZZv297575zGf245+Q0PHv3OO8Q3AoEqpzi3PMTTfd1G6++eb2ox/9qJ9riBFt63xV5yznniLnkRBCCCGEEMLjRQSGIYQQQghhlWVRBIaiAG6yyYweEVBUQksV77XXzLbddjO7gI7Qj1DwRz+a05OoiJYJJsAT3Y9Ib731JhYYrrlma3vuOaML9XbffbUuxrOM8BprjJf9n/85HgmQYM99BGLEZz97Rq9PRR5ckMDwRz9q7fzzZ7ULL5zdbr99/PcXvGCs7b33jHbIIav1dlsCee21Z/TojrffPqc9+OC4oPFXv1L/GX371VYb6/W4/vrZ7bvfHd9Gm0UoFIRFvjNmiGo4oz3taWNdlLj77jPa4YePCzC33XZmb5uoiyGEEEIIITwelECHOEci7PnJT37SfvjDH3ZRz9VXX92uuOKKLiq85ZZb5l7v3tkFPgQ8hEFEQhtvvHEXDm277bZt5513nnudvUt/77sSEj3jGc/okchENbQkqv1HUwhh5WGiY1wSodSSyESFlkF+znOe0zbccMO5c+oXtK222mruXH2Lfk6p84bIhbZ3niJiFkGV0PD73/9+Pyc5VzlniYhIGK0M55jCfvB9CCGEEEIIISxpIjAMIYQQQgirLIsiMHzyky0BPKNtsMFY22ij8eWAReQjECS6s7SwpYevvnr23PfjAkORAkXr22KL1l74wvElkgUdGBUYWvr4oIPG2gEHzOxCROJC9wuIFt0jsLzxddfNaT/5ybjAkKhQFEOCPcLAhQkMf/jDOe3ss2e1669v7d57x3+v5Z033HCs1+lnP5vTBZF3360t42Up129bbz2+BPJaa431yIzae911rUcwdC/Dss++/8lP5swtq/X62Fc56ilS4kYbjUdwjLgwhBBCCCE8HhATEuNYtlSksHvuuaeLB0Up/N73vteFO159JuYhPCTasSypqIOEgwRBm2666dx5wiZdEFRJ5EIRDNdZZ52+fS19LKpZlj8OYdWG0M+5gNhY5FLCY0JD0QydW5w7CAsl0Q9979X5xLbEhs5dlmm/77775s7h75079/5pP4/5TABdS7hXpMMSNub8E0IIIYQQQljSRGAYQgghhBBWWRZFYEhIR2xnW+I54sDvf392u+IKkQEtPTyrnXnm7Hb55XP6EsO//OX4PqIbEthtttmM9rznTSYwHGsHHTSz7b77zPbUp87o4sJhmT/96Zx26aWzeyTCcYGhZZfH2s47j0cKXJjA8Ac/mNPOO29Wu/XWOe2BB8ajDhImPulJc+ZuP26Dm24ikJw993VO3554UBvkpf4vfOFYe8YzRGIY/9529mVLNiGo9J32X365CIezu7DxoYdEixmP5CAvZc+YEZFhCCGEEEJYsoji9ejci1PLjxLdlDjnB3MvkokJb7zxxnb99df3V6JCEcGICglyCH2e//znz73mfWHbeuut2/bbb98jFFrqlJiQKIj4p5Y5dW2baGEhhOnivEHITHxITOjcI8Kh8w8hs1efCRL9TqhIbOhcRWR411139YiGXgkPiQ395txHaFhLKTsf1jkq56oQQgghhBDC4hCBYQghhBBCWGVZFIHho4+OR/i74IJZ7eSTH21nnDG7nXvu7HbZZXPatdfOabfeOi6yIxgkDBS90KslkrfcUrTABQsMDzxwZttpp/HohcOgA7a/914Cwdnt7rvHv1t9dUscW354Zo+QuDCB4W23zenix7vuan2ZZfUSdVCUwu99z5LHIhKOp5tvntPLIaK0nLO8RC8kMBSBcVyYKOohe4wLFgkO2eYXvxjPXzRDbVOv739/PPrigw9aOllbx9sYQgghhBDCkoKYhrCQAEeUQoLC6667ri97fMMNN8y9Jv3+3Gvcu+de9/+8i3uId9Zff/0uICQkJCr0nrjHsqWiiYlMKPoYIZCIYoQ+w6WPQwhhUXD+IGx2TnFuIVoWAfXJT35yFzE79zgHORcROFteWdTDim5ITEhATWBYgkNJJFbLK1tm2TnRuStRDUMIIYQQQgiLSwSGIYQQQghhlWVRBIZEfpddNruddtqsnr7znTntRz+a00TjI/Jbf/0Z7YUvtAyw5ZBae/DBcfEggeE224wLDCdbItnSw/vsM2PudjPb6qv/RmBYQkCRAM85Z1wg6F7m2msTEE5dYCiiImGk5ZG1Vf6iICpXeTNnKnP8lXjQ0s/Pfvb4MtCWhN5uuxlts83G2nrrzej7PPnJ4xEQ11qrdbHgU586/nmNNcYjFBImKof4UNRFAkdLMGuP/NZdd/ymbO7LhhBCCCGERaEEhZY/Jir88Y9/PC9S4U033dRuvvnmdvvtt3cBju0IeZ4696L1ec97Xl/6WKRCSx97tQwysaFlSktUSMQTUU4I4fHEHNm5itiwllUmLBRBtZZQ9p1zGTGic5SIhQSFzoMitkqWUiZAJKgW3VBEV1EQ5V8i6RBCCCGEEEKYKhEYhhBCCCGEVZbpCgwJ4666anb7939/pF1wwex2xx3j32+66Vjbf/+x9tKXzmzHHDOzHX30E9o668zpUfyI+kT0I6bbeuuxnudkAkMivZ12EiVwRq8HkR6US5inruefP75Esvucz31u66K/XXedOaUlkn/84zntyitntXvuGY8uSCBI9Kju++9PqGjb8bTzzmNthx3G6+O9ZZh32GFG22ijGV14SIgoef+CF8xs22/PXgSWrYsdLbus3tpJaFivxIYEjuq94YaiNfxGSBlCCCGEEMLCsOwnMY3oXcQyhDREhJY8vuqqq9oVV1zRoxUSGhLbiN613nrrdRGhKIU77bRT23HHHedeJ2/Ro4IR7hAUih6WyIQhhOWNOicRHRIUPu1pT+vLJxNJE0ZXxFXfE0UTEhJVi9bqPChya0U1/NWvfjUvL69E2rWMshRCCCGEEEIIkxGBYQghhBBCWGWZjsCQQM5SwN/5zuz2uc/NanfeOS6YW2+91vbfn6hwtbbTTjO7AG+ddcaFhd/97px2yy3jAsN11lm4wJAg8PnPH2vPe95YFwQ+8YnjDn7l/PCHs9sVV8xql1wypy9pLDriRhu1tsMOM3sSOXBhAsOf/tTSx7N73X/60/Hf119/rIsD99lntbblluo/c24dxpdAhsiGoi9usMGMHp3xGc+Y0ev18MPjkRvvukt+40sfi3ZoyebNN5/Rtt1WvcbfP+EJrW9v+WRLRhNO7rrrWC+HqLKElCGEEEIIISyIEhTW8sfXXnttu+aaa9r111/fbrvtti6qITxca621epTCzTfffN7SxxtvvHH/ToRCkb9EKCQqJLQRASzimhDC8oxzlHOVcxYhIfG0KIfOZbWcMjE18eEznvGMfh60vciGDz74YLvnnnt6lFfCQ4JD51LRDZ0z5SeFEEIIIYQQwmQkVkgIIYQQQghTgCDwl78cF9IR+BENiry35pqEcmNdUEdcSDBHTEd0d/fdlmwb3180P4LG2bPnvpkE295ww+z27W/Pbj/4wexelnLuu292u+mm2e3aa8eXGAZRnqWKRTEkFJwKRIOiLYo6CMJFZZQY8VnPGmsbbkhQOKPX5ZZbWhdJfv/7rf34x7YfX0oZd945u4sZv/71We2UUx5tZ575aPvhD1t72tNmtN12m9mOPHK19trXrtZe/nJCw/Hlk9mLHZTLFrNmjUc5DCGEEEIIYSJEKhSNy1KfRDElKrz88svbt771rXbRRRe1K6+8st1xxx19CVCCGkLC7bffvr3oRS9q++yzT9t9993nXo/u0CN9WWLUkqMEOiGEsKIj6uqaa67ZxYUiGW6zzTZz5+O7tb333rvtsccePVoroTXBISGh86hzqPPnxRdf3C677LJ29dVX9+Xkfzh3Qk906FxqW5ENQwghhBBCCKFIBMMQQgghhLDKQuQ21QiGRHEEhrb95jdnz30/LpZzb5LAcOONx8V39903p11//ax22mmz26WXEgSOixMtYfyCF4hgOL408EQRDOVn+5//fE6P+gdR/+R31lmz27nnEi2O12WttVo77LAZ7cUvntme85zxqIILi2C4+urjAshrr229verwq1+N57fGGnP69pY9JmY888xZ7UtfUr85c+s5Lphcb71xMaV6iqZ4+umPzq3XnPbtb89pt902LlacMWNOj9YougKR4o9/PLtdeeW43bRNYBi/W056m21mdpsRHoYQQgghhACiFssgi1YoupaIW8Qvlj8mLBSt0JLIInLZdp111umiQlEKLX9MTGg5ZJG8RCoU4asiFCZKYQhhZaTObxLRYS2l7DxY0VuJEAmsnQ8Jt51DnV8toyyJauic6/xrm1oyvvIPIYQQQgghrNpEYBhCCCGEEFZZpiMwLH74wznt0ktn9ciCBHTEeY88MtbuvVe0P1H9Zrfzz5/TrrjCEsLjZRDkPeUprW2wAcHfzLbxxvMLDH1+0pPGBXi/+pX8LK/c2tVXz+kivyuumN1uu218mWHRADfbbFykt/POq/UoiiIaLkxguMYaM+Zu2+b+Pmdu/rPbww+PR2KULJl8662tXXPNnHbRRbN6mddfPx6tkU123nlGXwJ6gw0IAsfaAw+MR2gkVlTeQw+NCwzvuad1seE114xHYrz44vG620bdiRR32mmsHXzwzLl2tryTmyCPGTeEEEIIIazSiFhI4GKpYyLC7373u11QeNNNN/UlkC3xSejy9Kc/va2//vo9MtcWW2zRBYUbbbTRPBGNSIaWD63lj0MIYVWhRIaWgLd8svOhKIcEhwTZlokX0dBvQ7HhT+ZO/h944IEexVDUWOdiQu8SG0ohhBBCCCGEVZdVVmDo6VbJBOlXv/pVf50oCQUvcW7ZPk9shRBCCCGsPExHYOi+5BOeMNZFdAR6RHkEekSGP/oRoZ1IfbPbVVfNnpvnnPakJ4114R9RnXKe/OTxKIZbbz2zbbbZ/AJDl5dPe1prG21ECNjazTfPmZuX6IDj9RtfonhcqLjVVmNt331ntP32Gxcr1gpvCxcYjs2th8iCc+bmNR5RsNpx++3j4sLLLpvdX0VK1OanP32svehFM9pxx4k4OGNu+W4utLb66to01rdjN3YgUmTH73xndrv8chEcx18tnQzt3223sXbooeNixWc+UySZ8d9CCCGEEMKqiWhZ/LPELMQtImmJWEhYeN111829vvx+/55vdu211+5LHRMVWgp0u+22a5tuuunca93ndPGMaIURwYQQwm9wL4vYkNCQsPC5z31uT4SGzqnOm+5//fKXv2w/+9nPeiRDkQ2JDH3nPlndT5MIDiPcDiGEEEIIYdVjlRUYclwRDnoa66677uqTpYkS55VJFQeX7WFCFqFhmA4m3kXGTAghhLD8QLB3yy2z2403WuJ3Tl/2eO21x5cyniiCob9xIsPnPleUwTlzryXndGHdbyIZjkcX3HLLsXbMMat1IaCIhwR4LgdcThLp7bDDzC4wvOGG2V3QN/eSs4sE119/rL3iFTPbbrvNmHsNOqc9+GDr4j/78t8/4xnEgmPtiCNmzt3uCV1caInhQkTBb397Vhf0EQ8SAZbA8NnPntFFj/Ih9FtvPWJB9Z7Tow8SQqq/ekn2VZ/99x8vb7fdZvbyRRyEaIvEmISLa689vq+6sqF2+lzLL8vL0soHHDCe18EHrza3PmPdliGEEMLKxtAHsKgsL76D0basqD6NlaUdKxvVL6Jn/XDuBeyNN97Yrrzyyr4M8g033NBFLr+ee6FNALPJJpt0MeEuu+zSX31+1rOeNfd69Clzrymf0EWF+jV9G0II81PnR8JAkV1LcEhkuOGGG/bor89+9rN7VEPnZvfDRDS88847ezRZ7x966KEuRCRItH/lG0IIIYQQQlg1GJs7WVh8r+cKCMcVAeGFF17YLr300se+nZiadD35yU/uTiuTLk6sjTfeuD8ZW5OpEEapybix9vDDD/elCDbYYINMvEMIIYTlBCK466+f1SMPEhkS2j31qWNts81a23HHGW3bbeePfiL630MPWSZ5dl/69667iPk8yT8e0e85z2lzrxPH+v7yP/vs2T3Cod9FJrQ08D77zOyf7X/OObPaPfeIlN266E5kwrXWan2p5VtuGV9y+Je/nNOjET7rWWNt883H2vbbz2jbbDOzixKHlxV33DG7nXvurB5F0NLG9tl22xlzy5vRnvc8YsTx7ZStzqI3irwo4qLogw8/PKeLAu1HfPjc57KBqItjc69hZsz9/revYbTvgQdm92iL8lEu0WWJFYkZRW5ca62xueUTbY61Lbec0YWR6p1LohBCCCsL5v8Vhe0Xv/hFvwG/qBBLlWhqWaH+HrQVuUib+DH4xiw5uyJFiNMnIi9pB6GafmJXvjzt0KawbNAvfGUe/r777ru7gMVD4D+de1Gqr/STZZCJCIlevFr6uJb1TN+FEMKSoe5h1DLJlqIXwZDI230Nv/nfJPZeb7315gkS3SdzveL/NIQQQgghhLDys8oKDD1tdccdd7QPfvCD7ZOf/GSfRE1GicEIDYkMTaD22muvtu+++/blODi4CMciGgtFObBFwDTOLOVigk6Uuv/++8cJGkIIISwnuAQkGCSqE4VQ5D6iOAI7EfpE/BvFPrb7z/8cj0z4wAPjgkPfESeKgEhUJ7KgbX/xC8sRj+/rcpHI0FLFfiPEIx6s310iECnaTv7jyxeP589n//Snz+j7ez9R3UQOVB5xnzzlox3qQqMwvFxV30ceGW/7eD2qLbN7/bRDUp48RC4cvdwtW5QNq77Ei/KkPRDRUaRDeao3keIy1EuEEEIISwV+AA+zEkp973vf6+8XFQKqrbbaau5/6FOXmZCPII/Qi0+D0EA91McytEQFBF4rAsSR2lHRlwgntYMvzwOghBHh8UUfEBDWUsiOl1tvvbX3E18a36v+sXzn+uuv35NjgrjFOEykwhBCWPK4jpGco52L3csQvVASYZbgkCjcuZfQ0H/oZptt1u+VERp6AKHOzyGEEEIIIYSVk1VWYGjZY86r9773ve1DH/pQm6oZTJKEjvfk7Oabb95FhnvvvXfbcccdu6MrBHDEc8CfddZZ7eKLL+5PYHPCH3DAAe1Vr3pVfwo7hBBCCCs+LiEJEx99dDxCoctBQjwixSUB8d74csNz5l6HjovzllTeQ0ooaNln4kRCR5crljCe6v0BeUjqS3BYAscSFKp/CCGEsLLiZjw/wPnnn98+//nPdzHborLtttu2P/iDP2gvfOEL27KKCqQt11xzTTv99NPbZZdd1n1e6uOB24MPPrj7xZZ3EQFfHxFbtcOyuwSHm266adttt93aYYcd1h8EDY8fxCsifNZyyJZBNtYIDkUrJF7daKONurjQZw90S8ZfRCshhPD44P/TdY17HKIXupfm/gZRuHTvvff27azu5R6ZIBz+TwkP3T8LIYQQQgghrJzMfNdcHnu/SsGhyMnISXrFFVf070x+TIJe8IIXdGejp7A4toR996QsUVhNrCzfwQFmcsXRxfm1Ii0RE5YunrrmKD355JPb2Wef3SfehKme6ttuu+0yTkIIIYSVBPc5ifGI6KSZM6cuyJsK8nLZIO+JIgguKeRLuEhQKNrhorTFtsP6ykeURfZZGqLIEEIIYXnCcsJ8Ad/+9rfbiSee2K6//vp5N+Knm0QBevGLX9xFfMvqAcXya3zjG99o5557bl8mkcCL8GvLLbfsUQBXBMGXFUxuuummduaZZ3bxp4eNCScqSqRIeWHpw+YiX/GlWuXD8WF8GVdW+ahxtfXWW3ffGX/sOuus06MZ+j3iwhBCePxwznX/wv0u9zQIvokJ3TtzXva7SM21fDLhuPtmROR+c+1iNbAQQgghhBDCykWu8h/DxMfTsMSEnmB+85vf3N70pje1N77xje34449vxxxzTNtjjz16yHeTKhMk4sJLL720XXDBBd1ZyWk5VSrcvDTV6ImTYX/5cGZ7lffi5rmkqTpWeyern+/LNl4XhWFZVd6SYJjvwvI0ubbEiyexPZXtSWz7TKUutikbVJ9Ky2O/hhBCCCGEEEII4TfUnL6Sz260W1J4qskN/eXhxvxEbVlR/RITtWNFbcuKBp8W8QnxrIe8zznnnP7AN7EhEeH222/f9ttvvy6q3WabbeYttclXK4UQQlh2OA8TehMZimS855579nP2Lrvs0pewJzC8+uqr+8MIl1xySReRu0/mvkb+Z0MIIYQQQli5SATDQQRDDtznP//57aijjmr77LNPD+suiWho8uQpWp+JxUycJO/L4WVfy+AuDI41T+0++OCDPXEa1xPp03WccYxWW0RVVCf5y1OaLL9ypNq/PmOy7csJi4m2HeZXv8M2vmMnT7+rHzwBN+ost5+2mICyi3aUQ3EqVB04LdmCjT05V0/MLSif2leqdg639526a4MIlj5P9CRe5fHjH/+4CwyNr7vvvruPLRNuT2OLYFj7jdZJvibfxKtEitrBHtphn0ohhBBCCCGEEEJYfuDDMJf/7ne/2y6++OI+l+f7eMpTntIFVPvvv39f+ngqadddd+037t3MX1YrIGgLQdh3vvOdLhYgLuDzshSiOq4oEQxL2KYdohfyuWgHX59+SQTDpcfQR3bVVVf1PnB88PkZP8aSsS6SpIiFvvPwN7/XijC2QghhVcJ52TWJ87SHIQjEBesQ0dA1kHP7T37yk57cR3Hd4Boo5/QQQgghhBBWHsbm8PSsgnCUciy+973vbR/60If6JMeEZ+edd27/43/8j3bggQc+tuVvYCpOyZNOOqkvffutb32rOyqFh+cM+8u//Mse/ZDwbDhpMsEiSuNQIwTkZC7hGOGdZVlMyOTDsek9Yd1kQjJiM+VaRuTee+/teRKjEdSZ5ImwKA9P/FpOx6uJnzppg31N9O64447+3j62V7Zk24Lgj6iOrdTXxNCS0CaPBJXy0zb1UB8TSd8R0ynXPurmd+0Hh6E2y8MSKMq3nUh/ZSP10g714fiVl/2UP0o58T35rJyayMIEV1nSuuuu2/Mi9hv2D3vqD2XbX37C/Vv22nbqIz/5e69f/K5OHKBeTaq1Qx8YI1deeWWPbPnVr361205btthii7b33nu3gw46aF6/aJ/6sKEylC/VOLHUQNm87GYfdlNmxIYhhBBCCCGEEMKyh/+EX4PP6N3vfne76667un/F3N+qGC996UunPIcnLNx0002776H2GXXfDf0afuPbIJ7zPT9CPcg6xHZ8HrXKgvrZdphXwbdx4YUXto985CN9mWS+ix122KE/lPva1762bbDBBj0PeXlYkk9EmV6ng33loe5Qp1G/2lSoBzblxWby8crXctFFF7UPf/jD7ayzzur+F8LCQw45pL3hDW/oEfPCksfYqOUzre7h4W5+NeOPT2yTTTbpSyEb5/xb0+3vEEIIyxbnef+7t912W7v55pt7cu3jf9i9Jv+vJeTPfYwQQgghhBBWDiIwnKbAkPDtlltuaZ/4xCd6IkwzOSKW++u//uv28pe//LeEcBycnGkmWt/85jfbNddc0yda9qtIeJy0nMee3CVA22233bpAjzhu6JhVPkecfU3Y5EfkSBinXn4DRyyxomh5e+21V19iRP1M5Ez6OLw5iT/96U/398rfaaed2sEHH9wOOOCAXpeC8+/b3/52dyhbBppQjrCQY5xDWf3lcd5557Wvfe1r3WmoHi95yUv6ktLqqc31hLI2aJf2+d12xJS2O+200/r+7MUxrl4EddogoqQ6mpwOJ6PKsr0w/OpgyWr2ZVuwH4c8xyW7EvdZ5lpfl/NSvUQXYE95sCUn57HHHtt/v/7669vll1/+W/myJbGi/NhX37GNsfGFL3yh56NOxJkcquqh3cYG+77oRS/qSwmUvYk9zz///F4P9lIHNmAv9dRmdiJ6FMXgiCOO6FE1CQ/jhA0hhBBCCCGEEJYtEwkM+TU8IPi2t72tveUtb5nyzXU+pfIJmfPz5fAt8BXww/idj6B8Rr73oKKHHsu/xS/Ed1FCQ/WznahC/Cjy5HOxrXpK5cvCZAJD/iD+EoIB+RHwERPwT/C/8I2ou7yGPq0hfB3qI/GzqI+6g99E0j5lllBwIuSjHfLQLg9qsoM287Wok+/5tT7+8Y9HYPg4YYzqW/5KkQv5BK3uQWzLr8Wf5bgwRkd9nyGEEFYc/A+7PnGvzTnfvRFRj10DuSYgIndPx0MJzvf+z3MvI4QQQgghhBWXLJH82BLJJjaclxxcxGwEaaPYhtOLg/b222/vwjPOS45UjlOTJUuscGD6TPxmgkUEyMF8zjnn9Mh2Jll33nlnj1RHgFaR64j5JHlyssnHxKsgODNRU+cvfelL7dxzz+1iNE5f+8mr8qs8iefqqTHOX3Xym/1OP/30duONN3Y7cN5a/plQTvsKv9nm1FNP7YI5k0V24AwkkjOJVI52cTx7FcmQg1c5l1xySf/O5FLdq15e1U3yO3GdJ8oJDTkdiShtU+1SjiecTUw5l2siqn5nn312jxTIvurIHvaVh1S2re/YlcCxJrSczYSN6qoO9vcdW1177bW9/7z6Xl5VN+/VDUSXnPLarl+qn9kB6q8f9K266F9CR/vJzz4ElurABvJma45vv9un7OZ7Y1WficoYR2wIIYQQQgghhLBs4UMwXx8ukVwPgHqYlK+Jv4WfZ2HJnL98FuXH4QviW/JwJL9BRYfz8OzXv/71LgLkl/HgJd+IB13lo0y+BcvTnnnmmX27ekCT+IvvQl0J8ogDC23hBxldIpnfTLvsy1fEn8OXwbfGd8L3ow0lWhyFf4RP7rrrruv7nnHGGd2vw2blo/Mb/wrYT9nlBxoin/LDaBvbsAFBIb+M+rORtvDhsQk/TZZIXnoQFxpvbM/3WL5EYtbtttuuJ0ITfkrjZKqi2xBCCMsf/ptdEzifu4Zwv6KuO9z3cV/DPS3ner/7P895P4QQQgghhBWXCAynITCECZDEuUqUZpIkL07Tbbfdtu/HSUYEJ39OUUK+U045pe/j6W4CN+VwqHlVLgen7T3tTrQmP85dTk9144Dl8FVXYjpO3HKM2o5gjliNY1TZHM01iZOvpXyVp+6+J/jjAOZoNQm0r2WeRT0cCgyVSUzJQctJrK6cgpywJTCUh+iGnLWeTitnIge4V6JA9SOGUxbxHjtwMBJalniPQE+kRW2wHSe67eQvT9+zh4mqdni6/Xvf+1474YQT+lPoZQ/10w9Vpm2Vpf7awl6entMW5aijvnETgJOeELBuDGiDspXLhvb1m23YmPBP/xBmehLbWNCH6qzu6iMRAep39dEPxgpb639jhLiQM91+tiPg3HrrrfurdpiAK1OeFZGAPfTbUHAZQgghhBBCCCGEx5+JBIZupPN18KHwAZQvYUGJD4GvhS/DXF++fC3Ec5/61Ke6KI/fgf/BagiEdPxOF1xwQRdz8bGoAx+JPNzY5wshwLMdcaF9+HD4I2ynrnwpFTmw/ChDgaHvtIV/g6hPeV/5yld6mfKR+Mn4XtSZ2IA40Pb2Lfi8bKc+9vfKN2d/dar6K6P8bRUVsXwf7KNdfELscfLJJ3fhZD0gWvnwOfGLlR/MA63qFoHh0oFt+a2MG/bXz8YRfxlhIV+Yh231pXERX1YIIawc+J93n8V1guS9/3D3Ovzv1/+5VPcy8h8QQgghhBDCikcEhtMUGIIjk3OVw5JzkvPMxIjIzH4clSZLIv9ZfpkT2BPlnJ8ctiIdHnXUUe2www7rZXGUciKbbHHqyo9wjQPO8jPqpkxOWk+rExhyJnNMc9buvvvufclceVo+VzvsT4hWSbsI5JRF4Le0BYbqJl/LB7/iFa9oL3vZy3q7bc8BrL3swdHovfodd9xxfYlpbdBmv7GJ7eRFqCjKom05ItmU055Dmq3BZnvuuWd73ete15d85sBUH3XkVJYnh7k2EPuZ8E4kMOTQ96o/Dz300PbqV7+6vxL8sS3hod/VjU3ZT7/rj4pIYGKtTDb02TZsdvTRR/flmitaJKe85aU5z+WlD37v936vvfGNb+xL9uy///5dZMj+8mIvNyn0p/oRJPocQgghhBBCCCGEZcNEAkO+C/4hy8JalYGPg59gssR3wV/FJ2I/+8vXb4R8BHTlj1CWKILEgnwxfD/29VtFDuI7sg3fl4iFfEH2q+34F/jG1IsPg9/Fg598XLYbCgz5c+TLd0E4xiejjMpL4m/iMyEu4+8oPxRfUsEH97GPfawLAkW40zb5Vh6VDx8IAaVX9uMLYheJn4d9v/zlL7cPfvCDPR/b8adVHuqlXdqg/mykHO2IwHDpwL78pPyN+sQY8pAtf+DOO+/cfXERloQQwsqJ87pribp/5DrGvRPXDf6jXae4j+Faw72e4cMHIYQQQgghhBWDCAwXQWDIkckpy1nGUVkCQ/twUHKgcgD7/YQTTpgnqONc5VAjtlMGIRnRGVGc/eWlThyhJlycz/JUL99xUhOicQ6rv/04Q+VHyCc/jrty3Hq1DVGc7YjziOseD4GhSH9bbLFFO+aYY3rdRAzUHttyNLOZCaaJpPrtt99+7fDDD+/5qrPvOc3VU34mpJy+22yzTa8rQR3nuGWA2Fm+JbY86KCD2gEHHNBtV2JPDk6Odf3AQS+pnzpxgI4KDO1D1Cefl770pfOWcOEYL+c7J7h+Vq72sa/yTJLZRR/qe9sRGPqdzQhLOccJHO2vHyzNbHvtMtHWD57qtg3nvqiYbKqPdt11195O4tPhk98hhBBCCCGEEEJYNkwkMASfBh8Onw//04ISfwl/Az8N3wgfjHx9J8KfbeQrT34Vv/OR8DFZhpmvwfeSMvl/+Fbsw//hgU7b2o7fgx+i/DPKsU35hSYSGHoYk/9M3YjG+HE8QMn/wi/B56IswkNtKN8Kf4bfRBQ855xzujBQ5ER15OuRl3rxdfCrQPl8Pfbjq+OL8pAovwl7XH755f2BU/4q7eRX47fZd999uy9HfvxzIG6QH7shAsOlA9EpwSsfofHED8bG/H7GTB6ODSGElZ8SGfpfdt8C7r/USlG+dz/FawghhBBCCGHFIo8JLSb1xC1HK8coxyfHLCcoxylhG4etiRVxH8caB2yJwzhaCfBEx+OQJS7jsOUsJa7zhLq8iNo46uRXTmqOUo5cgjrCRXmViO3II4/s5bzkJS/pAkTlmrg9Xs48TmmiPGI44jvOZO3l+CaaM8kEh7OlgA888MB5gj+OXs5l+5Vwjk3L6c3W4IzmXOe4BvEfAaJyOS59lt+OO+7YnefK1F8Ek8SJbDwZ+kGEwT322KPbWD7awMb6jnNU3aG/OLPVTRnqr2+8cqZDO+RJLKid7EA0KA91qnZqIyGkmxGc7hyznti3rzHytre9rf35n/95+6M/+qPuMGfLqkcIIYQQQgghhBCWH0p0aI7/kY98pH34wx9eYPrEJz7RV60QIdDN+Mngf+AL4WexmsWf/MmftHe+853td3/3d7uPhRAPfAx8SPwo/DNvfetb23//7/+9+xYOOOCA7ueAsojC+Fk8dDkZ8vFQJb/TG97whvY3f/M37a//+q/bH/7hH3bfFF8Iv5PtCCLPPvvsHtGQHfhNCCj5cQgW+crU04OU/Fb/5b/8l/YXf/EX7fWvf30XGvKhVUQ8Dwfbt2xy77339tUgiDm1UXkEiNr45je/udvC89S///u/P893wu9SPrywZNG//KH6lR+LD5NPjKCVP9K4SKSqEEJYtfDf65rENQNRfwXQEKTCNYx7KnWfJ4QQQgghhLBiEO/OImDiU09lc+qCk5IIjKjN756O9qR3OWYJz4jcKsLg0LFmX/uZaBGyldiME5pIkbOUE5Ugrpyp9jdJ46QrsV5BXMeJZ7lhyw5z8prMPV5PC2sPMSMx3WjdtG3Yfq+cwJ5Gr21t4+k2n4fOX0+41dPmYP9aGgheRVnkzPQU++mnn94jPnqanfNZfvJmQ85OfcgJOhHKV38O7WEd7K+f/V7fq5Oypzsp1nZ1IlrU98aAvPS7iIYf//jH2//+3/97nmPczQbtqYgGQ7FlCCGEEEIIIYQQlj/4DvhizP8XlkTz8RAh38PQFzFK+Sb22muvdsQRR3R/k4cY66HG4XLCfC4evLTttttu231DXon47MM3wbfAVyJN5mewnTL5LzzM6mFMn4kEPaDpoUj5EiDalnjAA5T8NPw3/DCiIRIX8MWom+iBe+65Z3/IlFiy6uohVAK18uHYz+oV/DiQFwEj3xvYTdls4cFT+Xjgky2020O5/G3qFZYsxov+4Iuziof+MkY8/GtcDldJCSGEsGrhmsb9K//Nkv91DzP4T3d9MLzXE0IIIYQQQlj+iWdtESmBIVEZOCk5USVYFtdT2vW7yRRxIWfvROHfOWVFtePoNdGCp7lNtJQlcdiJkgiOWII0T5uPRrDjvFMOB6+lbTj0iBEXtJRuOZBHHck+m+hN5mCeDHUgzhutGztxrFc9fNaOYXRFbSuHejGsh1RPv7NJiTzrafgzzzyznXjiifMSUR4nJ9vZV59weBJu+m6iiazy1WnUEapu5fAvqj6LMiHm8NZHoliKimj8ECsSlppoE0d+4xvf6ILJU089tZ100kl9KSFtqogGmYiHEEIIIYQQQgjLH3wefDHm+5bu3W+//RaYLOsr4huB4Kg/ZQgxIhEdIRffD/8F/wL/CiGdz8rmw/Be+fVQKz8HYR8fVS05zK/AF7GgBxnlJy++K34MggF1VBc+p80226yvqqFeti0/Fv9YLcUswh3RYQkM+VfUgZ+GWNAKDrb1Pbt5VS/fe4i3fDj8Pz7LXz7qoGziRP4w+2qXelkthNBw6G8LSwZ9wS911113dREoH2YJTo25WrkjhBDCqon/Xf8LHiKohxzcU3NfwypT3k8WACKEEEIIIYSw/BGB4TThaOVw5fAUaa7EbeVoJfgzceIAHYrXajLF2WvbISWo8zuHsM/gYOWQNcmS5DeccMlTqu0LdSwh3VQnaLXPKPaXz0S/LQj10qbRumH0O05faWgX20y0L9RFvUZt7D1HtafhTVIrERdyPrNvLV/MmS5/eUzUNnVRf/Ua1sN7v01k90WBE1x0ycMOO6xHmvTUPuc3hzhHLOGp+olYqC2WF/rc5z7XPvrRj3YhpfZqdwghhBBCCCGEEJYv+BQ8SCha4Jve9Ka+fO+CkmWHRQIkiCOSm4wSGPIxDf0T5cvwHXxPUOiG/vAByvJt1AOUPvOt8D9UGsX2HiQlUlS3oU/Ee3kpZyjk47sRyZBPhu+CIJAvrcrgW+PDOffcc9tpp53WH6z0gKUlpf3GfrYjTvRZHvw4fGXy8Vm9+NL45PhR1GOIdvMFVWTFsOTgL7RiyK233tqX2DYONt100y5mjaAzhBAC/De4fnDPowIsuKfhno2HC+r+WgghhBBCCGH5J561acJ5xjlK8OV1IoEhZyYnr8lSOdPsV47UEsQVnKUcpPfff393mJYjVz5EcSXA4zCt/GofTtWJ8isRJCfuwkSGtpfHaD5Qb3lVnaaKiSObDB3Ok2Gb6Tgda3s28VplsBWB3sEHH9wd8pUOP/zwdvTRR7dXvvKV7dWvfnVPRx55ZBcaymOiOlYZk/22pFCGcUJkqF5/9Ed/1N7+9re3Y489tu299949AoAxBU+FGyPD6IaXXnppH1chhBBCCCGEEEJYvuAXIcYjuhKdcGHJcsEi/HjokPBvMvgyPJA4+mBn+TKGQjrbEtmN5jeR34PvZzL/j+34pZQ7kb/Ed34bPljLH8UvJfFf8VPxbVQ5It4RF1qp4YQTTujpC1/4Ql+1oSIW2k4+3ot0VHnwifleWeolaeOw7erIt8bvQpQZliz6QD+JTKmPCTktgSlyJLuPjpEQQgirJq5F/BcTGIo4DP8ft9xyS78+CCGEEEIIIawYRGA4DQjwCASvuuqqdvPNN3enpu84Ty0tw5HmaSxONM40T3WXA5dIz5Iulg3hgBtFXrfddlv7yU9+Mk8QWBH3OILraWyv4GAlcrz77ru7k3UI4aMnwM8444zumD355JPbBRdc0B1+JmwmdEOnq7zUSfJ+CAeh/AgNpwMn4lQcibXddJyOtuW4ZhcTU+2BzwR5lht+2cteNi8dccQRba+99mo77LBD22233fqyRLax5NCCHJ7TrdeQqexr7NRT9/pf31pqaOedd+4RDV/+8pe3V7ziFV1sSCRpqR9PgBsf9vGEuKiG9g0hhBBCCCGEEMLyBb8AnwX/jpUU+IkWlGzjIUOixKFQbhT5TiTyw+h38lGHyfJbmO9iCD8Gn8So72jI6G/qWWXbf/h7PVjL38WfVokdRHHcZZdd2h577NGFl0QJ5WNT50pYUH2wsN/D9DEO+KNEoRLFkH9u/fXX78tgLij6ZgghhFUT1yIeoNhoo436tY57TkSGwyAeIYQQQgghhOWbCAynCGckkZ2w7YR7V1xxRReH+Z7jU+Q84jBiQw5SQrASsIF4j4DQpMl+nKqF957C9sSWp7dLYFjOOcI5zjnL35TA0D4i2Qkl78ntIUSHls/9p3/6p/aOd7yj/emf/ml7z3ve084777wuTPPUtjw5eaENlpuRz7BeEB1PnUZFjEuS6Tizh7A1m5SN5aNtbPaiF72oL0Hkdfvtt+/Oam1jRxNZy7XU0jmLWv7CGObLxtLQvvpZvxOeXnnllb1/vLK5J75f85rX9L77+7//+/aud72rHX/88d2hLl/5VN/kKb8QQgghhBBCCGH5xTyeyG4qybYL81P4fTKB4URUvqNMdX/wafAN8R+V32oIP4Xfy1cGdeS7kfhr+GAIDKr+xGgernzLW97S3vrWt7a3ve1t89If/MEftD/8wz+clzyIyf8mr2H0RnXhzxr1tRV8eeoc8cKSo8YCvxQ/KfvytVkpZOinCyGEEAr/++53uWfmP8P/t3sb991333z3t0IIIYQQQgjLJxEYPgbnGGfjPffc05dnqaVZPve5z7VPfepT7UMf+lD7x3/8x/bud7+7ffOb3+ziPo5Lgr/NN9+8Ozo5RmGy5Ilr0Qc9gc75aVtiMsLEE088sV188cXtpptu6qJCS7+ccsopPSKdyRRHnH09rV3iOIkoTqREEzFOVNEOr7766nb66ae3888/v9144409/3POOaedffbZ7fbbb+9lSgSFoipy6Ho6nLiunLGcrSIrioZ33XXX9YmdJG/5sIdoicsbwunvuuuu3TYgnrRksKWD2dbklKOTbU466aT26U9/ur3//e9vn/jEJ9rXv/713tdLC2NgGCWSjT3RTWQq+iVhqPfEhcbTRz/60T7O1PGzn/1s+853vtPrD853+ZWDHvqNQ72W5A4hhBBCCCGEEMLyizn9dNOSgP9gSUAIwK/B10RYVqtgSH4jMuNb4oepVTD4RfhsSmDIF1U+LfvxmfCfWY2iHhS18sRWW23Vt4ff+dv4w/iz5Fl58XXJh9iNj4yvTj3KdvVwphU9hiuGhMVH/4s6RdjJb1WRN5fUeAshhLDy4T/Cf7j/dq/+S/xP++8OIYQQQgghLP9EYDigBIYiyVlaWCqR4Wc+85kuTPviF7/Yrr322u5EIy4U0l30Qk5QUQvBQUr8JZKe5Vw22WSTeRMmIr5TTz21iwJFQvzGN77RxW9eOWI5O02wtttuu57kz4EqcahyshLWcagSDhKrESjKU35f+cpXevRCQsP777+/O/k4bzlia5mSEqaV80+ZHK3XXHNNFxRWIsIjhCSCXB6fImNXdvfUm74wEa02sAVhpPdswr6El2wjSiCn+NKMyqi/2Zn9oe+JNtXvrLPO6nbVdybQHOD661vf+lavszpK3tvO937XD8YdSoRqLCgnhBBCCCGEEEIIyxcEbnxNHiD0kKEHS6eS+AnM/5em32K6lFhP/fjFtMcSuT//+c+7T4nPgpCPX42fib+J30lEOwJDn/mm+DJKYKh9fFc+86n5nXDQ/vL/7ne/2/P00CvRoocvJflZRUT+lY/tPaxpyV7+Mn4sdVEv+fADaUNYfNicn4udCQz5KEvwGUIIISwI/xnuf7mf47/d9U4iGIYQQgghhLBiMPNd1l5dBeF85Bi97LLLuniLc0zi+OXc5MwVXU7ynjhMhDyTnhLtEbiJXHjAAQf0pWuJyoZP6hKBWR6EY5Qj074mSxyjIuwpV8Q9UQM5PTk65Wt53Fe84hVtjz326Mu/VJ5ela2OHKZeOfQ4qkXEkx9nqvcmZoSOws0TOR566KFdCMmhyxnLCcj5qq3apWzfcbyq00UXXdSFeCV45DBWljZyDous6OlyNpOH9lx//fVdJOm7TTfdtIshvdq/IKojmPNEO1uYSBIJ7rzzzt0RqX3spDxtUQ+f/caW2iKSo+3sK8lLezk1iQzZmrNbOaIZEvX5zuSVPV/ykpe0Y489tgtAtUc9OKw5nNVPO7WRwNNT9JzfFYnQuNFGbVWGbTm0bbf11lv3Oqobu8n3kksu6ePHZ/XjdLefPtMXol8aJ/rAeJSfMvSv9l9wwQVdkEhsKKKkdshfv774xS/u/VrtCCGEEEIIIYQQwrLBvN/cnm/Bw4J8LPw4Ej8M3wl/AB/FwhKfAD8Ev4HEj8R3xO8jXwI7/g1+GX6FQvlWt1AHkQb5ZzyUyYfDl0IEVj6mu+++u6+o8IMf/KD7Inxvu/3226/7ceTlN74JPpNqC/+RtvBd8EWUn8ODnnwg/GdsQQBo1Yn999+/+y3KL6ItRIp8PfLi29JWPhuf5eUhUQ+L8uvwN6mLB2/lyQ/E30LcyB/E56Kd/EG2q3z8zr/lYdOql7LAz8Me2suOYXqwd4lN+U2NAz4xdjU2QwghhMlwPeA/2/+3/23XHP6L838cQgghhBDC8k8iGA7gIONU5SjleJQIunz2Pacuxyzh2eGHH96OP/749tKXvrQLAv1WIrSC89NvRIivfOUruzjORKkEghy+IhpyiHJ+cpQedNBB7eUvf3nbZ599+pPb5fj1SmRHtHfIIYf0bXbcccfuuDMRIxbkRCYuJF4jQiPu4xg+6qijej3Uh1NbXuqhXkRqRHHqz/kqH+I5TmRwBtuuojMuT2g7hzDBoFRtJDbkgCbI45jmNNdu9tBvbGu/pRn5T92MFQ5WUSj1B+e78aSfOPqNK31KXGkccboTKhIcEjGqPwGs5EaCSTdHunZqx7777ttFrvouhBBCCCGEEEIIyxd8PR6gJBq08sRUk9UYPADJv7G8UH4pr/wVJ598cvvwhz/ck1U/1Jmvi3CQD4b/is/p2c9+dv/MX0PQx5dVfij+LH4bYsKPfexj7f3vf3/75Cc/2Vfq4JuSH9+Jh0A9aMm3Ap89rLrFFlt0YQJ/nAd3L7/88nbiiSe2j3zkI+0DH/hAX5GEyJCwUR3CkqH8p/qPr4sfa911180KGyGEEBYKUXqtrOWaoSIY+m8JIYQQQgghLN+sshEMTV44wYj7PDFFzDdZsgwt4RfH5bbbbttFdwceeGAXhBF4Pe1pT5vQUcnByflpwlRR5kQo5Aj11DjRHkcrAdqWW27ZIxaW6E+5JlklMIT8OHPlxxlLbMaJJy+OPKJBojZLIavrXnvt1etJUDd0xMJ+niz2avLGIStf36kPERtnrfposyfM1cdvbMEhzFkME0BPLrOjbbR1hx12mCeuGzoYiR85fW2nvmyr3bb1HTvWE+wEnhzJ2sTOyhOFUf62Yxv1lw+b2l9Z+qPsoT2Em/bdc8892xFHHNHbpq0lCFWW+hNYsoV92E9ZhIjyrm2NG9ECTHyNH9uyB9GpfZSpbpKn5uXN7jVx1g+11LV6SNpjLOgDwkTb+qzcYb9qB7vqTyJTjnl9X7YIIYQQQgghhBDCsmGiCIZ8DL7nb/DAYT3MurBkjs8fwX/AP7A8RDDk21AfybbqWQ92EkOKZKitfB/1MKiHaNWT74bvQr3Vif+Iv6QiEXrIUr2vuuqqXnd+I2Wou4cyjznmmP7K58M/Iz/+Nbbgz5EXEaE6ezhTXuylTvxo/CnqxpfD58SXo46JYLho6EO29XCvcWC88BN6HfoeQwghhFH8h7jusJKWe3Pucbk/4n5YXaOEEEIIIYQQlk/G5l7Qr5KPBnEsEohxiHHyLgjOS45QTjKiMQIwT14PIwIuCM5LkyYOYY5TT68TznlVhxIccrRyxMpXWZPlW3WXn3w4VCvvcrLKh9DOq8+jDj7dLtlf+zlhSyRoP/VRD/vbl4NWfdlC+9WZE1Ye6sJJKy/7+67q4FWdCttxFNuOWE/enOVStdn+nMPEiBXd0W8cybZTrxL8oSICqD9bVFu891u1pwR7hIj6sSjRoPqXs7nayDlq++oLtq/+k3y2re20Vx2H2+oX9tVm7VFv9am6eLW/9qp31V1SF3Yyxko4qZwqS71G+zWEEEIIIYQQQgiPP3wjIu6ddNJJ7d3vfncXzS0qHkJ9wxve0A499ND+QCYB3wknnNA+9KEP9XwJ5Dyw+Pa3v72vWlEQBJ5yyinty1/+cjvvvPO6f8NqGvLyoCJRXfksRPv7q7/6q3bRRRd1fxJ/he08h+wmv7ZceOGFPRqgJYsJ9TwQ62FUEDsqg4+kHq7kqyAEtOoG8aMHMvlf+DXA38PfQfwoaqHli0Uq5Jtjv4LYQB323nvvLlRUJrHh0BfE58JXpR6iFhJ1ar98bCcPD4N6+NYDocr7+te/3v0shIXsob3sGKYH2xMXGhcEoR6CPvbYY7tAhP8qhBBCmAz/0+75nH766f0hBQE4BPIQVGGy+2EhhBBCCCGE5YNVVmC4LOGI85S2yRRxG2FhidimO4niLObI5SCVJ4euvKSpiB9Rgj5PexNCchpzxA4dtysS2sAWkrZpD3twai+LSWpFy+R0Vz5nK1FhOdiHqK9U9R86xu2nf1fUfgkhhBBCCCGEEFZWzP2J5yyHfO655/aHBhcVN9sJAwnjrITgwUWRBC+55JKer4c5iQzdjB8K5JTvZr06iAzEv0CoZyUOYj8PbRYEhG7u33rrrd2Pwl+hTCtZEArKy2/KFBGQH0O0xBINimqoHA9W8l3wW3i40zZWqlA//q5R1Mn26klcSGxIYMhnwi/F76F9RGvElVbVYA/5D5EPm9vXMsg33HBDF196cFP9PJxJUFk2ZD8REpUtb+3Ybbfdej3D9ODXNCbOPPPMLjD0ADIhKHt7kDaEEEKYDA81uIY466yzuljdf/wBBxwQgWEIIYQQQggrABEYLiNGzb64k6clkV/lsTJM5Ib2WF7aM137LukxEkIIIYQQQgghhKWHebwHQaXROf10MP/3cKHk/Wi+vhtuU/htuC1sVw+gSoXtCMVqH9huWCZsI6/av8rznYdViR+J9jzcSRhYD7xiWN4oVUf5ExsQThIH2t8KDrUiRtVnMqodRJJEilaPIIYkpqylmas9yvNa7VhY3mFiRgWG+ku0SWJXS2iHEEIIk+F/+rrrrmsXXHBBu+OOO7rQPwLDVZu6Hpwqxkmlui5dGajrVK9FrlfD8obxWWO1qDnkolBjfjjujfeaw61MaKM5q4fkYK66rAIjhRDC4hCBYQghhBBCCCGEEEIIYVpwKYo66GaJm0DD5ZCnijzcZCFWlOzvZst0V3BQB/m4aWM/EQ/drMkNmyXPqMCQKNSy0yJCihgZQgghTIZow5dddllPohBHYLhq4zpQ1GzCU9dwC7pdbXy4PpQ8TOL6Q/J5cQROi4J6VqrrzcUZv/LxwI2Hbby6pnU9LCL3cAW8ECbDXGhJjccF4aEwx6sHzJTp+PNglweOFuUYlI+I+Ma9uaA2GPfyrGN7ZYCtrEQpir8I/s5hor9vuumm/f10bFfnnurvx/PctyQYrf/SHK8hhKVDBIYhhBBCCCGEEEIIIYQQFsqowNBNrY033riLQ3beeef+OTeJQgghjOL/49Zbb23nnXdeF1mIPLzeeutFYLgKQ0h3zTXXtAsvvLALDYlwFsRQYLjmmmu2ddZZp4+h9ddfvz33uc993MRIHrARxduYVg+CKILARYUdbrnlln5d9b3vfa8fG/IUHXqzzTZ7XNsWVjyIc++5554u0DMeRYJ3jCyNc+rVV1/dLr744i4KrPL23Xfftt122y1SND5C80suuaQvnU9g65gy7kVH1w4Pja0MEBf+6Ec/aieddFIX2Du+DzvssHbQQQf1lQCmIxIkxnS+1O/s/4xnPKOfF1cESJKcP/X7kjp/hhAefyIwDCGEEFYR/OUPUwghhBCWDeV0jQgjhBDCisaowNDNxac//en95uKee+7ZI+24wRhCCCEUhGNEETfccEM7++yz22233da/I5yKwHDVRfSyL3/5y+29731vj+rlGmNBPmtjRDKPJqAS5WynnXbqYiRjiOBQtL+lgfHqmsc4vvPOO7tIVl1EIdt88827GGpRxzDBDeHtV7/61XbWWWd18c0GG2zQjjrqqHbIIYf0SNER4IQhNR6JUX/84x+3K6+8sn+3ySab9PHo2nxpnFO/9KUvtfe///1dLE40t+6667b/9t/+W3vlK1/Zj73pCOVw8803tw9/+MPt3HPP7RFutellL3tZe/vb397/H4jPVgYIQK+66qr2wQ9+sLfV8f22t72tve51r5vSOUvfEhQ6/7A9QabP+nuHHXboQr3lmeH584c//GG77rrr+lip8+dTnvKUpTJeQwhLhwgMQwghhFUET0P+8pe/7K8u6kMIIYSwbOA4I76oJ5XjSAshhLCiMCowdGPTf1otk+wmkWXSQgghhIKQjJDkO9/5Trviiit69CuCqec973kRGK7CENZ95jOfaf/wD//QBTN81lNFRD/z6Wc/+9ldYGMcedhhq622emyLJYvrHRHILrrooh7B7Qc/+EFf3pQA0PUPceOijmH++m984xs9utnpp5/exUgbbbRRO+6447rIcJdddonAMPwWxH2EhZdeemm74IILuuhsww03bIceemh70Yte1J71rGdNW+w3FT71qU+1//t//++8SJvK+Zu/+Zv2hje8YdqR+EB0/q//+q/tjDPO6McUEdqrX/3q9j/+x//oIryVZU4hQqm50yc/+cketVXEx7e+9a3tVa96VRdLLwj38ZwribBFe7388sv7e9FN999//7bffvst90JMwkLjVf2dP4kMX/jCF847fy4tQWwIYekQgWEIIYSwilAX8iagJmshhBBCePzhNON09YTuc57znP60chxpIYQQVhRGBYZuiruh6Ma6m+F77bVXe/7zn99vli2NG5shhBBWLIgLRWT71re+1a6//vr+/qGHHuoCsQgMV20mEhgaB+bJriuMkeG4ILRxHUKgajlV+7vFbXsPOhDsHHnkkfPttyQQXe3b3/52j+DmGsi4Jmg8/vjj24tf/OK+TOmilqkdhItEhqKb3Xffff3YeMlLXtKPj2233TbRocNv4R4PoRlR6te+9rV+32f33XfvQj/jkvA2AsPlB+L6z33uc+0rX/lK/w90XDt3HHzwwQs9tp0XLcnuP5RA0XnIf6h9X/GKV3RR6fJuJ1Ff6/z5zW9+s0dfNE5f+9rX9v//xTl/hhAefyIwDCGEEFYB/N2bvJi03X///d0JEkIIIYRlw2qrrdYdsaIrWErJ5xBCCGFFYFRg6Eai5bncDHezyE12N8Ita5ZoOyGEsGpDEMYf6f9ClC3vzYOIJQhi1ltvvQgMV2EmEhgSBxLWicJm2U+fwbdNfETUJAqmJTZFwZJHrQ7wV3/1V11kaL8FzbHlVbfGjbupjD1LIhPHEAgp34OCBD7TFRhWuV6rbO0i2FKGayxtJBgimtxyyy3b+uuvP88OE7Eo7VnaLIs6VZlV1mRlVr0mquPjUc8lgYiwxuNpp53WLrnkkn4MEGz9zu/8zpQEhsO2T8cGCxMY1r6V58IEh4sjMBy2YWH1noxhHl4rj0XNbzIsgf6e97yniwT1lSWlLQUtOunC/IHu4zkvWELdEst33HFHP/+I/nfsscdOWWC4JOy1qIja+MUvfrG3gdiy6u/8GYFhCCseERiGEEIIqwgmfRx5Iky4KRRCCCGEZQMnq5sez3zmMxPhKYQQwgrFqMDQklaW53KD0M1OUQzdECcyFH0oIvoQQlg18X/BF+m/wTKelvAUxd3/g/+P2267rUeei8Bw1WUigSHxzR/90R+1l7/85X3ZTw8r1G1sglUPMxg7Iv2dfPLJPTIWUZI59dvf/vb25je/uW288cZ9rE2EMgh2RAAzRm1H7OJ6ZbIxqNyrrrqq15UQ6uqrr+7zeQIZEbhKYDgUAZaYRxqKedS1Vhdaa6212uqrr963efDBB3tkxgceeKD/xk8gT9dZtpusbtrA129/5WvLggSWw3oVbLeg42+4vddqz2T7qL9jX//CQ5XasyCR5FQZ1kOqunvPrspUDrsqc7SO7FV9wG7GkzyMM2PBfvYf3a/Kk6rto9vA7/U6lW2H29tmKr4h49E59LOf/WwXbIlkqK2Wyq0Ihs6tk9lbWeykj9ighL3yIBJc0HhbkMDQ2JOXPNlXfvVA7WT5LarAUD8SqWsHe1TfTWfewQ7OBTUWyg7ycQx5naze00E5lj7/n//zf/b/Q+36gz/4g3bQQQe1F7zgBZP2U6Fuov8Sk37gAx/o0VTZdRjBcEFLDCtf29hL0i79rL8mG2/2Adui8vY6UTm2r4Thdr678sor26c//ekepZXY0BgrgeHee+/d1l133SmN/RDC8kEEhiGEEMIqxPBCP4QQQgjLjskccyGEEMLyjBt6Q4Ghm2THHXdcv0FIQOJGnyWSd9pppx7ZkDjADcYQQgirDkQihAwizBFjERQQUWyxxRZdhH7xxRd3wRZxSgSGqy6uGSYSGBIsvelNb+oPKkwUDZkgyLKw7373u/v48hnEKpKoYAQrRQnKPHgvEfFZ4cf3ynCtQqDjAcASKtV4JEAifBIlTvRCr7fccksX5+y6665dJLPDDjv0ax/Rmwl3tMvYdwzww2sTsaDfRB/70Y9+1PNVtuiE9iMiIjCserl2cnzYRp2GYh2iHwJJkUBtbz/tcozZ1j7aU4kN7W8/S0trj/3rPoHy1YM4a1TkYx/bW5JX/9ie+GrDDTf8rdUYtEfeIlqrizppv/zUh1CMnbWJuGhBorOJUC4xoL5Tl6q/flYPn9lVuezge+3Sp+xuX7ZiM3WUj330lTrqT/W0XyXtVEfbEHASdhHV6U9tqe0K/SZ/yXZsx/Yitcq7BF3qbVtj4Z577umf2UM7jCP1nQz7GVuWmzUeRcRzPa6cHXfcsR1++OF9PHrgxzLbxoOxpC7GmHZrfyX1lCebKVc/sZkIiMasfIdjYiKBITEgQbD+Z3+JbdlPXmVXebPdsN+nIzDUh8ZUHcf6s8aBcV5jTJleJxpj5jHGKjvUOK2xoGz7sIO2S+otrwWJ8RYEuxs/ok2KsGrsmiO9853vbHvuuWe3zYLy1V7jhEDv7LPP7ktiqzfbOv8Qle611159rKurtmtzHS9sVOPdfl7ta6yxmfK9amMJHe3rfGlfx5T6y1OynXElD32pfWynDPkr03bGTQmkfV/nT3PFOn/utttu/fzpmqDOn84N9g0hLN9EYBhCCCGE8Dji0stk1gSsPhc1WTOhW9ik1f72rX2kibBNJXlOtt3CqDzKmTSaj7wr/4nKqP0x2TbLG/pJGq33sK0rOsZRpSHVznIuTJXF6efad7r7LSpVXh1LUlHtl2qbURsNx0HVdzhe6vfHi2E9R+uqfnVeGbWtY1q9q74TbbOiwiZ1HGuTtg0dZr4f2so2Zat6H0IIIYTfxv/nUGAoSpDl2NygIyCpG/1uPrnR+cIXvrDfpJzujeQQQggrJuZYxAaizImsJXIh4cbWW2/d/xcIUohJCGQiMFy1IVyZSGD4rne9a57AcKKHFAilzjrrrPbe9763C1YIYWDZ0de85jVduGNswXgkcLnpppva17/+9T4mCcDkwS9AzEI0s+mmm/aIYsQ/xFklhLr77rvbRz/60R49zJgm0LEv/wKRjjoSxRjDlit13UMo9YlPfKJHHXPdJH9R5VwznXjiif36Sb3sZ/wTVNmHWMxS4tpDlEZ8IzriVltt1esDNiJ2c70lGphXdSwRkO2UZx/Coz322KOLB7XTcUio9JGPfKQvMV02EAXNtZy2EFcWfrON7U855ZQuJNKezTffvEdg22677boYy3ciSbLtN7/5zX49WIItdVK2a0H7Wf66olxPR0jEXsRZ+vvUU0/ty0mzBftpK1sTqxHf2XazzTbr4is2JIa0r6iXlqp1HcuG5Q9TR34gbdf3u+++ezvwwAO7INr4s6+2icAmAh0hm7aLIEfQV7gWJqJyfiPAI7Yi+iK+04/6X37KdY58//vf3yMQsrM+23nnnfsS37abDGV88pOf7II1x4yx4rpbG4gJibq8Em85HrbZZps+lvQjYRehFzvcfvvtfV/tV779jWn72l672I/oSx/7HaMCQ+WJOEokpu+NbcdbifW0i+BRlDrHpXFQYxlTERiqn3oSuxlbyrnsssv6GCMWhDzZ2vEnol+JjI2xqrt81FnbHWvGgv8nx/SoHQjjjAVR2tnSQ1NEcdNF/dT7C1/4Qj/PKd+54n/9r//VbbawY4B9LC18/vnnd9vKi42MV32lno5v/63GrOPLb8p1Xrjooovm7au/a19jXV843zlPaKPzmd+MT31hrDhfOe85ptjFGBW11THnGHbeseQx8SN7Gtf63RhybFrq3hgnCJeP4270/Kkddf60JHydu0MIyy8z516oveux9yGEEEIIYSliomriZULFmeEJNJN7zglPQXKCmIiZuC9o0mpSZzsOMZj8mbyNYkKoPBNlEziTbdvWxHqqqLeJqfI4oNRdfU1Uq94myCaHVcaQajeHB+cKFqUejxfqW5NpT9TrH33GoVROB0/qae/y2oapwKnAGaMPOVQ4WCT96alTdjAOR/tzMoxL/ezpSvvbb+g0mgzl2NcYq6e7a3wsTftqP+eKfuaUczx6Lzm+/K7+6sQmnNG28b6eWh6OeePCMcGpapz4bfjU+9JGfYxR41XSp8Zt9Sm7cmSqq7r5LNmOg1md4fwz1T5fntEfxrf+5IDUN5xY9dSxc6I+raRPPTlefcfJtzLYIYQQQljSuH5wreT6yQ1ENxbdbHKTyP+s/2DXT7apm7e+9986nRvJIYQQVjzM6/03EEUQHZiTmlcR+xDkEGz4L6g5GN8KMY3oRSuyfyUsGq4RzNcvvPDC7p/hGzJeCJsIZlw/jPo7bcM/SVhHPGOuz38DEb0IZYii7MsHaVtCGZG/zjnnnF4ePwn/id+81nvXLupE+Mc3wodC0EVcSPhXwim4HuIDU2/7E8oQcxE1qRMRnGOgfEz8E8rVVj4b37leMvYJr2xHdKucEqcRxhE2aQ+7qJv6EFeqE0EPO8iXP2O0Pa7D5OOYs2QuCJSIq/iB1IMPS93Vg5jLMVnw4/r9y1/+chcJ8S3xq4iSRhCkXq775EPEZBlY7XP8l49FUmbVT53YjjBKH2nXVI59/a7thITaT2CmPvpDv7AdnzW/nfL0IUEj4ah2ELARmLJvnX+qz+3vvTp6r478QsaA/oTt2fyKK67o27IDYRcBFbRJ/ZTBViV25B9lr4pUqS/0u+to4i1iOfnxLToXGsNsMxnyI9Yi6FLf8rPD+ZdPS/9rf4kLjWO+P/tU1EP3I7RJeyuVDaQSiBo3BGDVT3XcaavffSfJ3/eEbMZy5aUuti1/r7FmzsC+UG5FBdU2/azexhdxIr+qdvkvkT+hqz50HBubylCWcuo9G6ibsvQh2ypbPsYHG+gn5TrWHK/63H7qavzUMaTejgl2XBR/aQk7CWONHf1CWEjMTEi3sLHv2NZv9mVX5wDU+Uedq47+X4l3bWM/7TRWKspljfnqF4n99TW7y8PYk3eda5zDjGV2Ym9lGsPsYQ4oD2PY+c4xqFz18Z9PvCg/9dd3ylrY+ZPo0fgIISzfRGA4RZzsXCjVTVMXJP4YkpJW1VQXj1OdAIQQQhiHCIjDQzKB8sSlZEJogufVBM2k1WTO9YdzrmsQTgOffW9CbNLmPFwiLhNlyXauXexnglwTbhNFjrnhdpJJvXO5fOs3+5pcygfK5xjhxDG5lK8JqHzriUnbmjhWGfapPLTVBN0+6jysh3KrDsvDf4r6qpM+8jSw/tFGE2uTYb9xDumL6hPt9Ko9w35ix+F32uc921T7fa5tJduog+/97juffV8OmMVFfV3TctBwFNQN4nIuuNY1rkzqbVv1VA+fvVevqqP6e288cHhwWnAUGsdlA6+o/YY2Yg/lqoc2ciAZJ0uqvRNhLBLLaj87cCpKnC36Wjs54RxrtuEsU0e/O/Ycq+rH4WU77bAdJxcbaD9HZI3tpY36ONarTRym2qVPtUd9ylHM7toHTmVPLzs3cRLV8iN+H45D++izGuc1JnxvO+2svpaW9JidLurpPOU4dt7Sd/rJsaudxipnoqfQHePV97bj8GMH56nHo+9WRtjeOd+ruUONkak4Y409jmXj1n+L85H+dE6aynhSnv7Vl/pVPurhPKPvhzeoaozbro4VyfFkHCPjIIQQfhvnTufNun50Q4gIwH+n/1DXG7ZxPnVtUtf/ru/qGi/n1RBCWLmo62r/C+ak/EZeXcOLKEX09YIXvKD/V5hDmrPyG/jPiMBw1cVYmEhgSIAjErK5nbmh31xX1LzNPgQrFb3QPNFYEjFLpDjXJsaefW1LlETMxZejDL5LQhb+Afv5Tv6ubfhG+LKIwWznM58Bn6gxblvzU+OVn8WYLnGMBy7kqZ7qZ4zbx/FRviRtlj9/mzoQlIkgpl1EQMR6ypSvPInkSmBoXmsbEeyIh1xnqQ+/FCGYergWUz/tKQGZNhIeyUP55rvao0yf1YWIjECpBHWwrwevCTMJ+8yNHa8EkezMf60cPhc2LtGb7fymfV7ZyfxaefxUbEKgxFZ+m8qxz4b6sx6SVX9tH87/oa3m/SKmqqNxRODFZpZmH9aPXYm8yq/HFuWLkC+biNTmd/UnoGJT7/kXnNOUAfsSqrGF7ewP41C/OMcRO7oWZgfnSONee5Stf4jq9Lk+nAzl6gt2VI/heJS3cWPcigQnih87q1tFfiQEZSt21z/GDRtoa/n1jD9tYQtRHG0nb2WMCgyVzaa2t7/y9WuJCNnBMWobfmhRCZWpzfJTlwUJDPWl/dnKGCPerLLYVd2N2bqPIj82lZf+1c/aVnMTY6fGguNJGbYj5iUClae2GqPKlYwpv8tLfaYyXgttZjOCWMe2MvSL/0T9tDCcS4j72E+/1PlE+/Wh+utj/7PGqv5iA6JC0Sa1UxvUW/3ZXh+xv/HDbyfZhh0c384T7MkOtnG+sI3+Nv60STv0MZ8rsab+8T1bOY8cd9xxXSguD+cs56o6f0psqIw6fzpe2aUErSGE5ZsskTxFnPCcRP1x+eN2Ig9hVcQfvwsMFy4uWlyg+xxCCGFquIYwseJMMOkyATNR5JzhQDBxc241yeSEKMeQyaFzsEmWCRzxEOET54SJmEs6EzX7e29ybaJZzhevbvyZ2LmmMSF0feMcbvLovG6irRxiDhNP35no1cROPh/84Af79ZCJvskfCHQ4ybTn+OOP7xNWE183E+VhcmhiayJt8lhPpClL29TTxFRZ6jOdifrSgP0kDjQOSPVkc3ZiR44Sy2CwNTvqKzZiTzYEW2iHPimnkr7UbvbnWLS99qO2950JPfvbxuTc9xwmymErToTFRd31J2EZZ4V61dPh6svJoq+UazxI6qFvOSHUi+NG/dlE/Y1XjltOIXbxNCbHhnEgPxgLbOI4UJY8bes79TFOOFY5WI1rZS0N9K/jzpIiynZNw/mmH312vQ99xhmjjZZIcWzqoxrz6vjSl760O0/YyJhxTLPpYYcd1pdicQwsiT5bGMaVsepVH0gwnjg99ZF6cAh5Nab9xsloHBhfxrin49ld0kfGtP7Wv8YER6z+1F529J1xIXFI2Z4N2cb4WVJjdroYm+qtPz7/+c93RxvnljGpXzm3PMnrRoF6W7LFcaf+lhDi2HJOWhZ1XxkwpjzdD+d3Ty47xoyLhdnUOYjTmxPWWDO2/N84BqfiaHSzkuPcMepc4zxjnHqC+uijj/6tm5fGqhsEtVSWz6j/Kucir8ZxCCGEcfzHEo3UEsmuM9702DKGrpNcI7gJ5Xws2obrIudVYgE3T11v+Fzn4hBCCCs25oXmmeZYxAZ17ncN7dzvWt41uHkYn4/5NV8LwQX/SpZIXnXhDxpdItkYIdQjrOPD9Ln8Z+Zr5niuM4w3vlX+Ldcg/KOW7bXMpv2MJ36RD33oQ32+57pE/vw3RGHm/PwgfFEiohmP5p/8qeaB73jHO7qv1nUNXxV/D/+QayDl8x/wJfGlydO80XvzV3PLf/zHf+xCM+PdHJgvhY/B3Ni491n5IuBZhpXvQpRFUQD5VEukc9RRR/W62p7Yj8hKREG+DH4a11V+V1d1YhMRxRyLfB2OQ3n8xV/8RfcBwTHKLubN/CZEXwRP2swfUpiTn3DCCb1OhFn8Snx96uVV3uzxL//yL/0Bbce9OugLbeKHUUfXi6ItEpLpT34ZS7laCpfdtG1h6H8P82rbxz72sd4nzjvyVy9tU67+4081howF44B/4j3veU8fY2Uz9eNjcF7Sn9rHR2TMaAP/38te9rL2zne+s/eXcfK+972vC9zkoz6Wiv27v/u73q/qor//4z/+o29j3MBYZAfbWoZa/fSjZW/1JRvzz73qVa9qr3/967tvyufJMP758SoqXIm32JA/z/jyqkzv+WH58T/xiU/0ermf4DgyXiyD7DredTn/iTxLROmY00+OKb5NeSpjdIlkY5sN+Xvkp1z+RHXSVwSX5dtmd23kR/W/ID/H5WRLJPMlsZ86f/jDH+7jlbBUH+of/x3+Y5Sv3xzHjlHtc/zwy7Kr5Zm1h8+aMJfA0D0K+x1xxBH9fFB+U2OYTZVlvDq3ODaOPPLIvvSycqfjJ2R7PknHtjHm+DKuHJOEgQvD2NQn+lx9jHvnFHYx3s2tRAdWf+dM9ne+O/nkk7tN2ct48puyHRf6g90rWqpzqPOWpa7d1+HLlb8xrB8t7/zxj398Xv/Ylj2c/4xl5xP2d37VH8aLY0/d+PfU2/nTeNWXzlGOMT66On8aDzVep+L3CyEsWxLBcIr483Fz1MnYH76LCSf2pKRVLfnzd/PaRZoLayk3XkMIYeqYUJtAm8TWBJjjyKSrJqkmd8SDJmGuQZxziYZ851rE/iZ1JnnESyaCJv/O0373m+sW35vc+WyCTERGPGNCbYJrf9c0HFrwBKT91YezRNny5zAyeTeB5fDicDPx42gwqfSZQ0meJoPqxzFhssrZYFLq+klbTURNbH2njrbzPWeGibwJ5rL+X9FHEpFZiY+033+eV3VX3xIK+l/ULm010WdvaB+bsTnnhP4xSddmtva7Mry3r7z0sbxN9uXlf5ezvsrkpFkS9jE2lM2ZYHxxFHFimcjrHw4/dTKu1EUfGy/qrD3GqPYYV7bRTvYwDrTPe04z+RhT2mV7bbSP/Hwvsa/6yJ/ThAPJk6Hay95LGvZ0XKgnZ5cxxwHC2aJ/ypHHWaiunMy257ji9HDcOkbYwKvjwD7aoT3yNQY4RBxzHI2OiccD9nIcOXfoJ/ZnT8ersaRubK/P1Fdb9AnbGwfayT7qa0zbx3GtPdorX9sYs74z9o0jTifnCttwFhknxiy7LKkxuyhoi7HHuc8OJXBTN+NafbWvbMFe2s7pydGn7s4FYfpwHnLa+i9xfDsWOPwdFwsbD24y2Z9z2znY+cVYcow6LywMjmI3XBy7bqqog7FvjLt54TjXz8aHcWDMcjIrz3vbO1/57/O/7GaLsRBCCGEc50/XSM6XrgX8t9aDKv43XY94L/lv9T9gO9cRrhlcS7jOqm2X9bV/CCGERcP/Ab+BczzfkmtvPgHzSP8NhA6ED8SF5gH+E+B/gb/IvNT81fwrEQxXTVwT8FHyNZqX80PC9YK5unHFZyiZv1cyz7e9eZ25O18kcRPRFH8SzPX44kqMZ9wZh65ZXvGKV3RRER+B+aE5KB+KPO2nXnw9fGTGaPmJ+ICMb9vyJdl/33337Q/KVl7mjupXEQyV67pHXnxP6kno4/hQBpEQvyzfivpOFMFQGx0ffGYelDVvtQ0/hnYQbHkl2DH3Vn9tUVfzae1Wt7qf59hVnqS96ih/4iNz4JovO6aJgdlfn6gnMTBxoTqxhzoRNKm3azrzZ8I9AiSvREjK5ifUn8pib9tqv/LYcmHHv/poizyGEQztx/9I/HXggQf2NhBbOfewh3L4Apx/1I3djRM+Pq/qx2b8RNrLP1blGUvyZGd58J8p1zaua/mZ9A/7apOohIRg+l+fVz7qwO9q7MlL/xFtGsv60fmS4NFDsWxc58qJkBe/iD5SH33s1WftrfHID2jMuR4vn7V6ahP7aD8BHl9wjUG+PHk59owh2+sjvlB9qF78+8MIhvqODZRLrEecKz9ly8N2+k3e+sq4Zm91UGf/H5NFMFS+ehOwebCJL1I/sLt+Nu5tV8eqPjE/MVaNM+UrR32UbbzLSxuMR8eq/rc/2/kvMh7ruDfGiQsdt/Iwfny/sLE6xJypolo61tjH8c+m+mZh6G/beS2/u/apu3MO8STbq79jW38QrhLLOv+UvdiK8M8xaQzUmK3xw3b6RT9LNcZsx47qbjvnFf3Ovo4Fxwz/mXFuHDsPHXPMMb0+/MTGjDEPZTlnGBPq71i0vfHqvfrX/DGEsHwTD840cAJ3YnMydQJNSlqVkgsGFy510epixsWJC4cQQghTx+SM48OkzQ21mpS6xjARI9jjYPAEWE26CIJM9HxfkdNKrGWCbnJXDg6vJtMmyxxTJuK1jXO5/TjW5MEpY4Jb+3KE2MeEW/m+K+feKOqtzibWJp4cMepocqo8zgiTTEn+nASuofxv1Db+R9SDI0HbtLUcMMsL2skGkraWY4WDgBNEH3CMcx7pL04Wzgp9Zhttsg2hi9/8f5rgcyZ5r/2ertT+csizHZvZXp/IWz7stqT+d9nZ/7px4b22GZscEdWn+kd99J1y/f+rtzppo7GiLdpprBh36qmfy/FhLLFPOYTtp80cE/WUo32rbezsld2n47CZLtrseDBmHYclAmQT/Qrl60d19Z6zrUSP5Vz2u3wcZ/pf2x0Lttd29pPnkuq3BcE5o176z3t9oB71PTtzxulzY8p5wKv+1dfGnP71nXboE45+zit9y4FrjOpj3+lP/e/41Y/Guf31q/HMHto+2Tnk8YINpHoP/VF9on76kH3YRj8bD44FNgyLhmPDuJHY1mdziqng+Kz/NceR1zpXTQXlOP8Yk8ahc5dxbfw6v/r/qTHgve/9Xs5OyXlAnR0Hy3oMhxDCiob/W9dDbsq5keqmvxtZzqmuRYgIzGlcS7iucD3mHP94XC+FEEJYfGoO5XrZdbQ5oHO7OaPraCIB4h5CiopcmLlVmCquB1wzGF/mgeZn5nHlBy0fFfEK0Q+BDTHbK1/5yi5qgTHKV2U+WXNAY5DflU/PdvwkvjNeiYwIkfh8zCeVzb/BfyUvPlziIvu7xoF9je2KnEfE4zPf0kQQmBF1EZJ5JQoi9qnIcAtCex1z2sLHxi7KV19tIc4pXxC/MnGZdvJfwVyXj9Grsvg9tFcqfy4715yZ/fnCvOf/cb0mb75CIizXdfaTH9+S313LybtEd17Vj29FXbTTtaH92Lfm+t4vzjWg/PQN8RTxJruKDkcYpo/1HSHh29/+9vanf/qn7Xd/93f7ygb6XJ2VzycmH23k6y57S+UPIHxiL+0quxpj/O/ab4wZL967Fi7/of2VYezanv/QK78Zv0XZ1TgyFivvyVA/22mzvlZvVJlsrJ5+Jw5jf985RkQj/JM/+ZP2+7//+10kqkz+P/XTVm00jup8re7GgmNisj5SPpGicW0s8qfJg52IAAnI3AeBMvR7+R0XRh3H/M/GWu2jT4nrSrzOZspzHBIIsoPxqywPR5c/SbuM0Wqf+pibiHBpFY3yUesTwsW3ve1t86L6EcE5/suvOBWUyS/qONL/9iVe1DfKmArsa3zoQ/1d5wrjQNt975jUfucf/eU4Jvh1XNf9JvOxOtewj+2dJ+SpXmxtbub/3FiG79nW+YV4lM1LcOi84F6HY1g5vhc8g3hSRFVlwv7D82e1e/T8qd+Mm2pfCGH5JhEMp4E/UCc3f95OxP4sk5JWleQixcWPi14XXt67UHVxUxdkIYQQFo7JmfOmiRunAscQJ049tel6ww03DgDOWBMvk+gSaZkomgSaHHNa2MdkUV4mbbaXv8ldTVzlb2LNeVKiNpN7ZZnkKlt5nCK2rX1NIE0OTWZ9tq+oevLiLOJQUqb8TFzVzYRVXTiW1dm1k0mkfbSZA5CDx+cSL2mLbbSFk0ueywPaxdHAGcAZxgFoMs0WHI0m0RwP2uv/kO1FBDRh5szSD9pW4iyOBQ6KEr34b+UY5ewwqTcZZ2N2tS8HCoeTMvSXMthHfy8unEP6Xvv0L6dMOQi1j3NN2frQ98rWTvV1w0A91cVv+tNY8htbqTunAAeCMaEM+3IcaEddU+h7TgnXExwiyuRkqqeMl9ZTi9oHxxVxp3L0r3YYx5aQMM71Pzt5ZRdOa23wnXraVp05TuRhO8eo/uScYyd28eRtOa6XJtolsanxRfynfP2gfcamenvvGFdHx6N+s61+Ml6NX+cK+bhJ5DfXftqu7/S7Ma3NxoU2O2adK3zvPOLVsa9szqplcUxru2NOvzj+OK2MNW1WN+03xo0DzjNj1nlQci4qx26NlzA93LwQxcB53zhwHHAaGj8LOxacNxyDblQ41+gz5wT/L8bhwuBM5SA2dp17jAX1sK+x7Xyjf33v/FpibjesjBnbGgfm/P4HjRvngBBCCOM4f7rZ5//e9Y5rh4pgWPj/dB3n/Ok87rxbcxb/va5D7euaBLbz/7y0r5dCCCEsHq6XXa87j3ugjv+Kb8e53bnctTNfFj+TecBE83rzS74G1+vmYeaZiWC4asLvMBrB0DgwJy/BjM/8MPwvxp/Prj0sLUz4IwqbuaLtjUG/y9f4IpYhgnHNUfd2+aMIbHzHf8O359XckCCJv9J8kL+L/8TYtD1fnjkqH4jxbhvzS+Iq415dXcdog3zMM8uf4tqJQElda15c/gfHh/q6ruIH5GtyvTSMYKjefDe11CjfG3+FbdSTP9J8tvyU3stHno5XdnEsEnwpXx3Vi82Vqc3q4ZiV2LfmypY2Vp46E+3xjZUf2XFsG2WpnzrpB/VVBjuVbW3jPV+2/NiUL9Txz2+0sGtANlSG+T4fj7YqQ3/yI7KtvLSTPcqfI1911T+uSZ1zlM9XxA9g7FneWYQ8/asMZdlX/xIt8tfKx3f633b6SFlsq4/0oTzYBMaOfUrcyielfuplfxHm+KX0oeto40M+o+fLUapN+s45mF/L+dj45sNwLBiP5Ye0vTzVkw20pWxgX+dwfWyFF35Dvlr1YgP24hPhj1F/+fD56XP96LiU18EHH9yFnfqdratM/WP8Ee45FnzHZuxAiKafHIeTRTBUX8ed8thVm9XLGOPXUY45SY0x7dGv8tQ/bGKssqu2yNv22mAbn9nBONVuQRjYwzFhO/YzNo1943VhfTNEPeXtGDS+jH99YGlwbWM3+S8MbVRP9pYHWxpTbGd88sVqn/wc09plKWxj277K1C/uu+gLx41kzOgTdvVefeXh+HeeKFGoPPSpfmNTfaRPzOH0v/1q7Fk63Tioe1W1v3bW+ZM9+KzlWedP+9b50/YhhOWfeG2miJOaE7YTsYsef9JJSatScpHuYsyFmIsDFyYuhkMIISwaJmAcYzAR81kymXJ+5QgwgTUZNdEziTZxqwk6XJ+UGMJ+zs21nzz8Js+anNV7v5VTwT62k7c8OEQkv5k82qf2hQmg+pYjSt1MDDmfTGr9ZtJr4sj5p25eXUcpE7Y34TeBN7mt9pc9lgfUW53KrmxqgsyB4TvOQpN2trJt2Vr/aKeJuHZpnwk4+6Im3/qAc6ISe+u7cjD43XWn7/QZe9u3+n5xUTdt4tTRp/qRg4ETUruIetQdNSa0zbZSvddWzpZyBJQjTxsl+1Ufs0k9seiz7bSHc4L9tFnCkmrnZFQ/cUqxr7Ybl8ZtOaXYgf05K23P2cQRwkbGuD7jBNF/jlPfyZPjzXWT741x23P+LO02FWX7SsqV6jxQ9WDrOp5rnNueE02d65wj2aacxPqt+p4j1GfYt76rMavvl+VxrU51zlMPTlJ9wommf52batzqZ/1tG+Pg8eyzsPQwduv/zJh0DOv/Oj6cf/wfOX4d58avYyGEEMLiU9cXrh/4k9w8tcSY60H/z6653CB2M48AwPW1/+H8/4YQwvKHa2e+DeduIgEiDInYx7zPw2eEKO4juH/Gf2JO7Bo7hOng+oHASMQ1SxlLlqnlvzK3Q83nzPdcZxDFeD/0e/DTlF+jviOk49v5yle+0pcalk466aQurDJXrAcf+A/MFfkFlpRPo/yK5qbThW+Nn64EWNB+bXQtddppp/WljLXn5JNP7g/caY85MByjfD3l5+PTIYDzEHX56GzLNvxh7EZk5bPvlWWu7DrOfrZH+cL4VqCe/ItEUOpUNrZMNaGnbas/2JV9JfkvKvw4+t/5puo1inp64NY5i9jL8sTspF7638ORHnJko8mQN1sR2inPNS47EfnxJ2ob36JrWb4xfsF6yNV50LYeVmdX2zmfsoH6u07mkzL2lwbGjPKcr7WTmLD6hw3Ygl20RTuGY2xhfaNt2ljjaAg71AP5qGNQmmqfG3/lo659+OyIBIk0a4xJhIj6osZjtbvmF84V/qc8KE5oqw/9XmJT4l3iPEsMn3rqqf3V+CDAHJ4fpoLyHK+OB6/6lo340J3HlsZ/IxuxzbCe6sH/6ZygLezkXOEcSNhp++pntmBrx/EQ/WfM13LRQ5+Zc65jz7nN/38Ja0MIKzc5yqdI3Zhy8edk6eZvUtKqlIx7Fw4ufBwP9RpCCGHRMHHjSHB9UU5XnyXn3RJDEPqY3HHkmMxxSHEE+d1EXT4mi/aXKj+/1YTPBL8m87bxu8/KMbk18VOufTg26rxvQmpiXg4o26mHibDvOJbdCOQkIaqyHIU8aiJLUGZ7daxrKL+VkE0+HA22KxssD5RN/c+xU9my+qccU+zqBimb2dY+7KktnCXlALE/RxFblGNOvvaRr++r7zhl2FneBF2e6BQ5kbBRvy+p/155ydNTicr0tKenFjm79CkHg7I4PvSZ/tJubeU00M4SbdX40m7jU1/bh0NC+/SvttjPe/tw2smLLZRpnFVebMR+ow6NJUm1jfOD/Y1lie05ADmuOZs4tW3DRhxVkqfrCZTs70a5dmivxIHI+f2yl72sP4Wp/SXW01ZpaVJlaJ8xNRwvHGnsX7bV7uoT/aa+nh5lfw5lfcJxaow79xgv5czXLsk4sq8xrlzbsIllftjQWFiWjqUaU/rP8alN2lYRSNnBuDWetd2T/74z/odO1bDi4tzk/GuccjJzqHPwGq/6l6PZ+dyrMe2YMF5CCCEsOVyTuCZwjeXadr/99uvXHc7NhP8EhmeddVa79NJL+3nazS3XI/6Tl/a1UwghhMlxDjanIgjiDzAPJsI4++yze9QvvgPX2wQbohaJykRc6HraPGxJ+S/CqgUfAj/Mq1/96vbWt761/dmf/Vlf2tUY46MAH4vrh89//vPzREXD+buxyx/nmqJ8S8YyoY3VGwiJCKoqcp08RFCzX/lka+wvqWsR803+I8fGdNE2vhzHYdVH/fgdCa1cR1V7JGIix6eytIf/zny4bOQz/w2/l1c+Hb8TUUnsyU4EhvXQKb8JHxn/XvlvXa8RCJZo0Cv/IjGh88SwToSQ5WtUJ/bQN+UnXVT0FV/VRHaVrzJcb7LJ+973vva3f/u37d/+7d+60IrAip3Ykn9avcqXVqnwvnypHja2LT8CHxP/Ej+DpJ/0MyGW8yGfmvMkPyh78ikStKmXPJ0v+dT53YblLUn0LZ8nIeW//uu/dhu8973v7QI6x5FzOxvwE0rlxxu1wWQYT2wz9P/Zz/f6p/rG+NPfU+1z26g7m9qnYGdi0eFxLFVblKN/+Pu8dy6QF1vrk2OOOaZHXdQ32quO6qr+ttWf5iWEeOz0kY98pIuQ9WEdQwvDdo5Px4P6O8b4HM1/2GNp9DVbGZNlK3XwHXs5xw1t5bxn/KsjO7GXOjmGJ2qj/hXNVap7GvBqX3b0OtFxGEJY+cgSydPAiTIpaVVNLnhNyDxh44kLN+pcgLm5vixvHIcQwoqK82pNcDlpTDI5RJxTJedZyeTM5JMIiBPDk2CcOSbFzsFEQyapnBe2851kYuc3DhJOG5854ryXZwmLSnRUwj+TSnkR16ijiaH8fM/ZAPXl+FBfZasXcY562c7/htdaTkQdSkio3iVWUrY6ccxI2qvsclQtK/SJtuofdVFHE2h1Licj6jvt0CZ1l7TZd5wTbKTN2uamKrv7Xn/Yl/3YV17sWn1q4q5sn5XPluXsWhL/u/pI3+qnqmM5COSvXupoTBkztrWN/3718VuNM/WyvVTjih0s0TAcq7WPMtlR+9lFe22nHn4zFuTp/dJ0TLAve6qfupczzHfsrf76Rb3ZpUSIHIGcQ7bT7+pvP9toh+3lre7a4Ts2UUZtt7QwdsuBhBp76lF9yM76wTg1Jo1Hv+lb32m7dqmz74zL6jv7Gg/2sY28faeN+lPbjCM20Y/Vr8vimK7j2HlM36qTPlB348/4krTPuHUOYydttW2N2WVR95UBy49zWBoTxoYIt2zsnLKwc5hzL0cn0bObFfZxPLrJpA8XhhsE559/fhf3Gtuc9erhJhRnaQlc5C1ilpsx/vOUQxjr/9CNG9tlieQQQpgf/7FusLkB7Ua9/9LRJZJHcR72n+o/2TWU6wz7+c5533nYDS75yds52jWN87htFvbfEUIIYcliLmVeSWjkfE8UQ8hEIEOsY97n2nkYndb1tO8Xds4mfnCtTwjiP0E+5t5Lc64clk/M10eXSPa/f9BBB/UHF10r8CmUH8P9KcuElk/VvI1/xj7mm64zjCPj13zQtu5nEd3wBZj7ExiJxEUsJ4KfZN7new+NGs+ShyH8xq9hXBv36jpcIplAzz7GL/+CsrXBnJSgxxgvMRUBruWFXS+5vhmiPY4zdVVn7eSPUEdzYT4K7ST6I6ySp/L5MMyTLRetDdUeyb7VHq8+symfnno6TpXDTq69tEk92dxxKdKdaKXmx3w/yvFguWOdveXhfEA4aF99Yd6svh5mLoFd1Uf9hnVy7pAn35HrwoWdN9TNfcnRJZLLfydfdR/Crvx4xGFf/OIXu+2c04wF++gT0TEtr8yG2iEZP9oosqDfjD8ozzUqoWRFelSGcn3Hv+B35zQPHusj2yiTHWt/dTI2jAN2Oeqoo7odavxOBdfMxqPxQkAmL+Uat3x7fCc1Hj1UKXqdiH/O4/rc+Vp/WJqZ2M4DQPazfT2QawyyKzsYN2xif2PbtTv/irFiXGmH/rcN9Jcxoa0iJqoDu7OVPAmGba8dC1oi2dgkbFcn41W+jnVRCI0j2042xtjCWLS9Y8hxrA7aZRzbnr+n/Nx+0/f6yv+f48x79fK9beWjnxZGHdN8Y44j+9Sx6nwxHeRlfHlY2hg2hrSlfFYVjbHqqkzbOqacl5S355579mOubFX24qtjK4nd2Ezi1x+iTH42fen/2/m30GfaxzbsqI9Hx7ExZ+zoZ8eD7Sc6f4YQVgwiMAwhTAkXiy7gXDy4SHFT2UWYiV0cvSGEMH1MiCWT5RLzcDKZIJrkcxJ59b0JmskZp1YJfnxfYhjJts7Jo8lvJoWVRznl7Os7wg+TuHJwmJByDpjgq49tTLTV0/m+8lKe9/IsQZJtTBDtp36+l79t/V51kJ/ke23iwFAf+dl/Wf+vVN9wiqiTOlb72EcfaQsHm3Z61S7iLc5KbWMDtq9+kAd7sL88tdc+EhvJp/rF78pRnt+rLPtyGC2pCbe89LV+kNi++l9d1EnyvTHCBtqmjravsWUbr9qvLeprf+0t22iL5HvtkOQ13N7+8pb8ri5Lcyxov3pXO3zWb+rLKedYq3b5jUO6nEy+r7qqf7VPXsaH7dlIG7XN9r5f2mPb2DB2laPP1GFoc23Tv/qyruP0r7qrq+8k++lX47nsU+LEGjPaLj+vNT4547zWWPG775YV5eTSzuE41CZtqHaoqzbYTn/63nbauqzPRysqy4vA0HmEw9JxUdEEODE5V/3OUe3GibFS5yQ3AMx3IjAMIYSJcU6drsCwcH1tu/of9t5/hesrNyHrJqzzsM98UW6quakH/yH5bw4hhKWDa2KCCtfgzvN8RMQrrssJiXyu+TAxDEGC63ziAHMn8+Cp+CsiMAyF//iJBIbETnVtUb4q8zXXBhURTDJ3NFbtY77JD2If1w3y84BoLfsqH/4OwqTDDz+8C4+IkYjgzBH5Jo1D8z+CKnNQfi2+EPkTno0KDG07KpDRhokEhi9+8Yu7wJBvSH5DSow0mcCQv8I2fjdX1m7l870QyBH+sVe1x0N26qT+7KI9kmOt5tTq6jqLrYi35K2e8lVnbXXM206e6k6YyW9Sx2rV2Xb6o+bftiUQqzpJyuZjcu5Qp5pra0PZbkGom+vDUYGhPtU25yLXlkOqHURRZ5xxRvcJ8Fkpm92MA2ON6Es+fAPszyauN9l+KDCEMtWDmNA1q23VzZiQP+RvdRNjkS+xrpn5FOv8yl5syc9x6KGHdjsszAZD5Ee4NiowdF4eCgydswlTP/3pT/eofB7o0ce77rprO/roo9v+++/fxX76TR760vnZfsYgu7IvX4lxOyow1Ofaq18dK/Iu1NF/B/+MY8HYdxwTsPHJ+GybyQSG7M5+6k+g6He29rsxJjmea4w5jvnf2cEYI6JzLKi7/xrHkGNHHj6zuWNE3zvGhv5p/c8G9qly2Vb+9l0Y9mNHkTO1jY9VfdlZudNBXgSG+nooMGTzOo6MNf2hzgS1bKb/9Q+7iNrI5vqTrdim7GW81DnCmGcv+RfOR8aw5bQtF63v5W086K+yk/eOQecevw3nbBEYhrByEY9MCCGEEMIygOjGhNWkzWS2RC1u0JnccbqY1Eq2Mdkz4S0hjO98rolw/UYsY9JqG5/lKy+/y8tn+ZvM2lY+Pks+q5cJnc8mnSZ65UwzWbW/upQDWBmcy7WfCaJy1KucWfKqttheUicTYHmpg99MJpeHG4bqwKnCkaINw3b6niOBPdVd8rvP2um9V/bVD2zqM3vZVrvZRLKNtlf+1Sds7DNHgWSfEm8t6cm2ftUe5aqfyb266g9lsoG66ivtMEZ9b3xV35ZYy3f28yof7betdlX7jW/jqZxO5dApW7GFV9+r29KELfWnfmVvdeaAYnN1MN79zqminZyOnkA+9thj+9P0HEtsoP3aXfbhxPMqT7+zh++WdnsK40SdaowaT9qhPexf/ea97/WFbY1F7Zbqc7WDTaotfre/3/Wb3yXvjVV9XXaR/7Kizkfap17Glnaon8/GnXb4XlJ/ddY+7dT2x6vPwtLD8WCs61/HoRsMnPluSnHOeuUE1efGhTGzpM+zIYQQJsb1nusvUWXd5HWj0zUInJ/dBLWEl+XPCMKJBNzQcmPQDTTn7xBCCIuHcylhgOtkIgqiHaIVghDRrgi/XDc795orEYYccMABXZBCpGDeFMLSpnySxt8rX/nK7qMy1yshITHN6aef3qPK+c6cjj/KPJDAxmffEysZ7643CHP4Pb3ymzgW/G6s1zyy/KGQB3/hcL4oT8eOJF+fJ7s+KV/josw37cdfIWmPvIh6CI3MYc1lS2RHOORYBTEZUZt9bMPvOkT7iAD5SuRDROV6q8SR2qJsvjLbsekQ/jS+FfnD9kSg8uFncY6oOinLOUad2ck+2lNCuEVF/0zmu9Enrim1ib2gTEIv5zB+PfVXvroR/elL7dAGaQi7G1N8bc59bGZb4ivnSdenvudfKF8b27G7fiCmI5QjsAIb+d3Ynq4NbF+pYFftLMGXz8RhfCAEkGyvP41twsYjjzyy28D411bjxTEwHMNli8nQZqIx/xv291nZ6kCs6NhkW1TZElsuDG1jO/Uzb6i21lzAmCZ+Nca88s1qr/KUb4zpB+NWeSXKJMDz/+ahWP1tnBJb/uEf/mH7y7/8y/bOd76zR3VUT+cCNlCefOU/FdjAuYkN7KMOjjP1WRRG+xrKUK9K6mp8KsvYhONRG5XLH6qtknOo74xL9bS/z2xd++p3Y4hgmrDQfKzOC2xjnOsX/SoPdnXN4Dw8aid11wfDNkw0Xhc01kIIyw8RGIYQQgghLANMqEz8OBFMlomYfDaJ853kve+9N2GTvDdBrv2GqX6rfaR673f5+d374XacOSblljRwg8/NPU/W1lN1toOJYO1f9fFZvWuS6FU59XvVSx7DtknD3/3GITQ6WV5WVP+oX9mr2mniPLTBsB31vtrrtbatbbxK8qzPXm1byW+Vt9+UuTRsY+Jejh/OBChLW+tV/YwRr/V9jYP6rsZWvdpeO4bbaodtwRnEuVVPPQ5tVfs9HmOh+lnd1JkTZlh3/W1c+p7jz5OcHDGco5wxjo+qs1ftsJ/X6lvt993jxbBNVbdqh3r5rurqe9tWPW3r1b5StaPGb21T7+v32tb3NWbl+3j04YKo9lUbhsdUtVOdJfWXfC/ZL6z4GIPGLCcp56djkXPdcjGeJufc5xwlmPU/mH4PIYTHj7rG8qCCm+IiCZmLiI5DeOiazE1O52xiQ0JDN7xFInJzy42r4Q3QEEII08fcXGQkUaZEkSIicK4lCvG962cPkBGjlL+IkINwhhgh18/h8cC8znWDschfSezmgUHjjy/LvO7rX/96j5BF6OL6gNiIEMyrOb7v+KCIZQhgiIWMbz4x41+EOyJFS8l6tR1hTgleqg78DJCf6xSiXOWLIlcCq4mwf6Xpokx+C3NaIiXCPuUTxBG2iSLoukjeyieiOvvss9tXv/rVHnXMNZT2jIp+2GX4UKnP7FLXWdrresx1GT+YOgzhE3M+INoDW1Y0RK/sp+7y1Dfseuqpp/akTmy2uNdy2qyMiewqb6K6EjUWvuf/4R+yHwEicZT6lMjJq/YM61f9QMTlHEhkBbbSRnmxSYk5CT3Z1vUu2MO2XtmW4IufkW9qURiOR3XWToK2Go+OBWOEyIyQrNrDB2w/9VJP3+tzfWQ8+QzbGk8+Tyb88h9iv7POOqtdccUVvWz/HUSHl112WRer88GAb4Y9JHVfGOzJxmzEp1NtFQ2v/Dns6Xuvxh3hoPF1yimn9AeVjHv56E91Iio84YQTerKN+hmrxoLzhbIk540qD/JwfBg3C4Pd2Nz5hqiRvR2z8l8UgaGy1YXNvIc+kb9++//Ze+/oupLrzHffixxJEIwgCOYcm2QzdJa61UEtt5JbsixZshzest/Ya808z6w1M2veLL8/JnrWeN54/NayJY9sK1k5tbrV3eqcm5nNHMEAEgQBEDnj3rd/dbm7j64ARoR7gf2BxXPuOXXqVNi1q2rXd6pwtoooZYpsWn3GH3FgxU1W/yQfCINnKR/y46mnngp6gvEWeUt8AX6Ra1YBJS/JP+SIcqQv8Pjjj4e+AWXDe7DzE8bTTz8d8tTqEuCd6FuLP/fQlxCAo/rT3u1wODIbE36LZJQditI6Ee7cuft1R+eLDgCdI2vg00FHEn903OhU0FGmo5Le0XI4HA5H9gHdbwQMW40MsgUGItoG1/MTEwzyzdCAwQVDDAN5jBEYac3Yg2EEh3/rJ5iBgCP3gN3jd9SIwG+MBDiu05/A2MT76IdgZOF9yNlw/ZDxBHEibsTRCGgY/3DUm6iBx+FwpJApWyRTR3k3eoZwMVpShzHqYji3L6sh1GPc530YQhnvQCK27WZ4xjFxwViXSR6O7tzdjKMfZf2EyQT6eEwU2nZvTPwx0X87k6OmmxmPMBnLOISJPvpe5DN6mf4jE6U4W42De3YkXpTFZCsPh8PhuFGgJxnzo0PRq+hT+r70iRmfQ+jmN/fR6diGIBbRJ4ZcRH8ZPU2f/FrzCNcDepu+PqQIdD3h8kGfj6snH2i/r7dFcvSDTeQOmWEMhwyzEhxzwMgUfROIX8xb0TehX4ENCvIY9/BvfQYcz3EdWWTsCkEKYi1hUleQe0g6xIE4ET8IOowjIfMgr8QNv9QZxpvWp4FoM9QWyXfffff79q8oiI9tN0z4pI++EB+4Mha2j+EgEEFqJI7Y2Mgv6jT+OfJe6jEkK9smlToNiYyPOcibKMGJNBAu4fEsfS1bwQ6HfRBSFEQiPgBJr/eWDnQIz5NWKw+eJ16MwdnSGIISeUw6eQdho1fQM8ThevWfPKQM07dIRneQNsorfUVV4gEpCgeRid+UGXYCHHmG/YCyIm7kHXnMu3DYAljJDnuCpZUj5Y0MkGaOBvKZePABP3YI0sg72HqZ91t/lbQix2xtjVzQ9yUPbgbkIXlB3iPjwPKRfKcciCu/kXNsLPTbqRM45nXR5ZQ9ZQIxjO18kUFkmXhynzxABqkL5B1kQvIL3gXh8A78kyeMjygj8gRCG4R1wsXOQtwIgzSzJTV1lWeJ03BbJCOr5BNxgYRGfeI95CnpAxxJA/FiK2zqMoRh0kzdZYtkCI3kA3lFfLBXUe+xGRE3wiDupIlywh9xIhzeRbkSDnGnTUR2rgXCIg8IAxIfv7FnPfHEE0E3oSduBuQJckk5I0vkE+khHPIQuUIf8BtdQZ4iH9RnZMGeJ52UFXm5c+fOUCfJD3QOfrHdUd7II3qM90BWhnxNvpKnXKfO2fbalI+9y8qF5+AOWDjEi7xFzshb8oZ4m/4kjtRN/BHerY4pHQ7H2CGmlXdo2vkEAYqSzgZKEyXucDh+FTTkdIho8FkOm0HCUKCRp8P8wx/+MHSymIS76667wkRhdJDncDgcDocjO8AwAAMDRhiMSBgfMQ587nOfC4YejAD4wUCAsQLjBf0E+g4YkQzcxwhg9+hz0/c2YwHPYpDAcIMxCdCnwKCD8Q9jHQYQDAjXM9I4HI7swH//7/89bC2DHsCQ+tu//dvy2GOPhclD9MW1gGH0f/7P/yk/+MEPguERYzCG2D/4gz8IKzZcD6zU8B/+w38I27cwEcC7mbTAYM1qFOgqxj0YNxnX8Pt3fud3wqQExlWM4FzHeMyW6Oio6CSMY+IBYzdy53DcLOjnMEl0I+TniQT6gUy4MdHEJCQTUV/+8pdDXtwu6HsSPv1LJpqwRdEWMKGGjZc+JJNW9FN5H+0Ck2F25BoTUw6Hw+H4dTBWhyCALoX0wxgdx290LgQQCEisDAeBAGIRbZyRcejH07+/XaDHmWOAeAFhkS2XIZNgc3BMLmA7+ta3viX/5b/8lzA+w36EHLIujvUt0u1E9BMghbEa3t/93d8FUhjyC5izeuSRR+T3fu/3AiEIog22LuSNVc2M6MT4jrEiYVMvIA9CkuH9bMP8sY99TD71qU8FApyRtngnq+594xvfCGQ0rtEfod+BgzQF6Wbz5s3B71/8xV8EEg/yDsHn3/27fyf/6l/9q0BWSp9Pg5QD2cdWT4QwRf377Gc/G8bChEk8qKusEveVr3wl2PGw6REW9ZQ0MbYlf6jn3OMZ6vInP/nJsP0rNrih7G62Qtk//MM/BBuhgbE49fMLX/hC2FI4HbwLXfK9730vrAZHnSYt5AsESQh2xI9ytjEXcaS+MwaHSMpYPT0/hgLvYpwOiezv//7vA9GKsoOgx1a/n/70p4O9IQrIaPgjX7/zne+EfCVPiBd9R+KI7BAvZMB4BMgBfVJsAv/1v/7X97c5NlAOyML3v//94AyQ9igvtvEmjcgF/VdkgVXd6NPSxyW96Np//+//fbDBEvb17CTpgGCJ7YL6g8wgj5StyaMR2lhpDnkgDhADTR6JK31n8oL04od8QG6II+knjuQBcvjggw+GckX+/9t/+2+BKIY/3sV18pVnrK3gHnUKB3gXW1NTN5lX5jniDNEP+w8kNsqX/P+t3/ot+bf/9t+GOWvqKe0U+Uc5Yu+hnJAjkzHOeR9lSBqIBwTl3//93w92bkiBwOrw3/zN3wSZ5xnynrpDWNQjniUP7MMm2jzK/9FHH5UvfelLQSdcT14ZvyB33/3ud4PeIX7Ylv71v/7XId9vtq2z8iGP0I3oSitv4owsQwLF5ka9Ip3k09e+9rVgjyed+CWd5BfPkoekj7iSxxBjsbkhM4RJunnPV7/61bA6LOVEOOgTZAG5RcaQb/QGOoByAuhtdOEnPvGJQKrmvZBhyfuvf/3rIW7EIao/IeXybmQD3edwODIbE55gCPOar1/o5FhD5nA4PgCdIToVfBXCYGG4rwOcYOhwOBwOx8QDRhOMkxgLMNYw+P9n/+yfhcE/Rh0MMzgMUBjdMLhgNOIaxgYcfWz8Y3jC4IcBD4dRkfv4x6iKIQ2DBcYUwsIoSB+Cr6AJF4MIhhoMLzzncDiyF5lEMGQyhLEONgGMo4xpmMjEQIuuYkKDyRYMsqz0wPNOMJwYwBDPOJaJRSYIzPzFhIp9vc81/OAcjpsF+swm1Qz0e7hGH8fuM0mDswmwbAf9vdEiGKaDd9HXRGdTl2kX0Of0TZnkAjZRTH+S/ih9TX7TBljee9/S4XBMNtDfoa/DmB+diaOfDcEGnQrRivv4o71CZ9JH5uM/Vh3io0P0Ojp2pEFcnGDoAMjgzRIM6b8zjtu1a5f80z/9UyDdQKihzwCBDmLRH/3RHwViC3LNOACyD/0W7F/4pe+P7BMWjn4CfQc+imX8BzmGrX+jfRvIOMw3Q277yU9+EuoRti3kFsf7IGJBxGOltZEmGNKHJL+M3IYjDyCaUc/JO2DxYQzL3B3xeeihh0I9G24sTh5BuPrP//k/h90A6GMRZ0iTf/iHfxhIfIQ1FCiLgwcPBvIaceKc9Jt+IS7kMfEnTuQrY21InIz1mY/Ez/VwKwRD7hMXOALf/e53Q/nTjyQs5Iw4kSeQ3yBOEU/uQ56jTJj//Bf/4l+EfIDsZkB/4gey3V/+5V+G5wD2Cgh0EOSwLxA+5UV8IWCyGBL5hVxhH/2X//JfhrjfSj+V/jEfUX7zm98MaaOfTJ5b+SMzyA/yjMwhs9QVypnyJm9IO/nPeAVdzEeXpJuyJI08hz3nd3/3d4NtBH9RgiHlS5ohh9HfZsU+CGc8a2VP/aVcsP2aricvTRavRzCkf0+ceJ/tlMHKnLyH65SlyRhlSp1HViGvEmfIhVzjPvWePMP+jfzwbsqbMiEMa++Qf+oo8gpRGTmAqEie0jbi91pAV7DtMDYtdADpIC7YtG7FtkReUrZ8EPvXf/3XIZ/JKwPpo15BbMW2hV5Cn5KnEKypk8ih1Ulg6SU9zPWjI3Cco0vQKzwLiRu5RccgD+TFF7/4xaDv6C9Q1swpQLTFloZeZGyM/H/0ox+Vz3zmM6E8eDd5D+kRWYRwnK4/IS0Sh+F0jcPhyBxM+C2SaWTtKyzr8LlzN9kdjTmdKQY3dAAAHUg6S9aJSgcdO56h08VzGG3pGGG4dUOtw+FwOBzZCQwxGD4wTPA1Iat68UUphhQG+xhwMKAyeYzDoIDRCuIOfWz613zdTJ8bIwXhsMIMBhr6DJwTDsYIjBv2dSJGNsKlT4HhhvfQP8HwYwY+h8ORvcikLZJZgQJDObqFSUzuo8cY26DrWKGFL7CJJ4ZqdCG6CcIMRn+OUfKQI3NBO4QxHBlisoFypD3iq30mEGhrMGqzygntEm0ZxnbaI9ord+5u1qEvkCd0hznkiYlMdA1+IHOgb5BNm4wyWWWiyyZVsgnEmz4gfTv0KZOkt7NF8rVAm0G42J7Q10xkMZHJRBr3qOtMkkI8JM+p80zKU/+xfxFXdD1Hm1AD2ZbnDofDcT2g4xjf074wxoYwgT6kLYIUTn+IsTvjettmEv2KXqWvTJ8ZewD9bvQs/e7RGpejn+nrEw/sA5BPIPe4bp58QGbpo9MnR4aRB9p4SHEQXZDRdDlETmjbGaPh6AfQ7vMs1xkDMrZDjnEQaPioDBmzcQJhMg/G88g6Y07IbryXlcogu6SPPRlP4g8bGo64E47F2caPvAcZp85hQ+M5wiJsSFbEMX1MTFj0ZbCpUW8JlziTB8zZkQ6u8SzvwnYGEZi0WB+T95AeSEDEk/E35Ds+JL5eP41neJZ6SdrIQ+IMoQqCEH2wdKKngXwkrjjeyzwi+RwtI8IivugXiIVGIuK9N1rv6cuht9Ab9MGN8ET+oMMIO528RZ5xDfIo8eF5niNOpIfnmRelvCFyEg7X6b/jn3xHR5I24m8gXNJEXBhTEj4Ov9gWKGfukzbKh/BsBUfCN8IdxLwocfFmQL5DvkP2KTPeg1whj8SFdyCPyArvsPiTftoIk3/8E2+IYxC8KBf6+ISFTCE3hIMs8pv6ipzwPpNFPihF1ogLZU9ZkU7uUxeRfYiUlDt5GpV/7MmMoRhTAMoEed2+fXsoO8IhrsxHUw72cbvZc7hv6aCuEx8+bqUeE2/yAlgdIc+o64SNrJIXxJd08h7CIyzkgrwgHLYIZyVInrkReSVuEAtpe3kH5D+eN7vUzYJ3EjeO6EnkGFkizsgZ9Y46ynuwm5GHjJ2MwIs+orwIw8qU50gjtjpIqBB0bRts8oS4Q8ylbChT8g05oixZ3RWZMZ1EfnGE9EnZED5hmBzyHp4nTlYPiA/PE1fylXJj0QHiHCV2OxyOzMSEX8GQxolGnkYTJehwOFIDFgYqdBQZuNDJo4PD1yN0xoYCnSKIur6CocPhcDgcEwcYIzF68qU02zZgJPjTP/3TYOzhOl8fM/CH5IOhiC+oMRRA0MAwgiGPr18ZUmA44gtH7mNYxeBH+BixCIt+OV+rYrCgLwL5CMMGfQkMchgQ2L4C/xgYHA5H9iK6giGTlXw5zyoF6BCMiOnAUMp1HLomuoIhz2DQ/fKXvxyMyUMh+jxbRUVXMORre96NUZWVMexrfIC+YTKBL8nRQWybxHZXjHd8BcPsAO2PkbWQHYiljHMZu9oEB/YgK3NgkyUY2B2OkQZ9HPQNR5M1JqrMocfQa9hR0C2cMymDHjOX6SBtY7WCYRTUd+o6+cvEGv1M6jbEAmxcRjDgHnGkjjOxxWSjOSa2iCeTapRBNN+zIe8dDocDoA/THbZ7xu6QNKKOa/SH6CfTDtH2oAfpB+P4zRieMTjzAujOKPljNICu9hUMHYB+PGM+bEy038gy8oedCDLLcPKIP/oDkFUgtXAkLGQIeYb8Qp+L8SDPQ3ahz8C7qBecQ8rinfQH6C/QR+AZCFDIZfp7eSf9C+xevJNxB7LMdeoRc2yQfAiHuDEmMSINfkgTfSbqYrqsEy7xIkz6NDxPnTRCFeFbfCwexJ/0cMTexpHngJF5jCBH+q419iFM9AT9O95vcSYfIO4ZgehaIC8pB/sYmTwmXsQJ3UOcjOxJuMSJvLhRWBzJJ8qA9FLm0XIbzpaIfiRd5Bfx41nCQ1bQh1bmhEeZ4ReZQX4svpSBgWfJI+wGkO0MhAehijw3IhkEM8LjIxjiznPEk/diH73V/jNxIL7wH/jIhvEv+c018pY0IY/kDe+jfEg7eWBjZdLI+3GkkbQSX/sInHfQLjBeMfIZzyPb5CmgT8190k69oD+OHBA+1yhzKx/OLV8MyAlEeJ6jb0/8yT8IZ9YmGYgTZUf88W8yxnOESxkhD9RFa9/S5ZbwCQcZNVklTGwGyCoyibxT9uQLcSdv+H29OmAgr7FHkY/kE+VAPYI8d616eD3YHD3yTzmQfvQCBEPiyHsoC66RTtKD7JlskE6O6A/yy+ok+UWeU09Jv+kX8odnyV/C5D7lSDooG/QYMkJ6eQ9yaHKBf8JGj5OPpJs4UWeQE5NBQN6a/qTcyH+Hw5HZmPAEQxprGguHw/EBqBc09iwJzhcINNx8MbNp06bQSRgK1nlxgqHD4XA4HBMHGAkgCBrBkC+b//iP/zgYlFjtie0UMC5gnKKPQJ8BAwX36DfQf2BSAAMF/ugr0C/AIIChA2MSxgT6HBjS+PITowHGB7Y1wZjBF5ZsS4pxg3dj1MC44HA4shdGMAQYovn6nC+jMTDaxEgU6BgMyOgYdMlf/dVfBYIhhkeMpZAU+SI+atSPguvoDiZsWJnlP/7H//grBENWScD4yXYyEAgJl9/4h7zIqgzorm9/+9tOMMwi2OSATTJxZKLBVo3jPm0XbZE5ytkmDcwo7nCMJOhDMVmC3QXH5Ax9J87pLyGHNkHHRBC2FXQM8sjkS/qkWyaCSafxIBgOBfKV/GYikwlCHHoAZxOn1HMmVmkTmHTEEVcmRCkP7qETOEc/uF5wOByZDHQw7Qq2esbV5oxciB6kveE+OtL6PbQ5OPrjpgsZj49Hu4N+doKhY7zAlLj11xhLWB2xscKNyCH1kDqGTQ3Qp6AvR1hjDdJDfyfa77E+zo2mZ6RhespIW4zJyGMjAI5XXSdelBn2SkC8rke+zAaQLtoB0sYY2OSR8k8H7YK1G9QDxiGUy0j0gZFFwqdeUfbEgf43NqCRLnPSibzzHtJNWxbtz98IovGlPiOrpg/IE8K8lXgTJm0xceMdlAdyRpgjkQ+Um+UxeXsj+od4kEaeQV6QeZ6jDoy13jIZpNyIF3kzXvrT4XDcGiY8wZDk0dA4HI4PQAPOVybvvvtuWFEI4zYEAZaeHs6oQOfDCYYOh8PhcEwsMJiHYAjZD5IfW3mwJQbX2T6SvgLGJiaOafOZhIDE8fOf/zxsXcAKyPQlmDy38JjgZWIDUhCEIb5sZGsInqPvwBexGIHYwpTwIB09++yz4fkvfvGLoV/Csw6HI3thBEMMl4wvMFxiTB1u3EC9Z0uWBx54IOgIiIAQDFnJgrELz5pheiigZ9iq5eMf/3gglfyn//Sffo1gSBjf+973gv5iMpOw2AbuC1/4QiA/Y+D85je/6QTDDAc2Hpuwoswob4hOtDMY2THY44fypi0xB5mISXSuA4zX+B3piQ6Hw2SUI/0iPu5kEpNzdAuTv8gffphMYZUTJrGQUfpW9I2Y6MlkEPdMIRhafuNoL5hwY+IM+xUraaAj6JeS7+gM6jwTauQ5uh39QJ5DcCH+TErSbtFe4Y+jT3Y5HI7xAvNa6DbTbzjaE3QaOs5WXoJciO6jL4ReNNIMeg39xgeArMKEzovqNo7j0Rci/k4wdIwnrP9AHUPuGBveDAmK53nW5p7He2xxu+kZaRAfixNH4kEeEafxRHq5WbzGK59GCunpulb5R/1ybuUyUnkQDX+089feE33XzcpYen4Ai/Otyivh0G4TJiCcW4nbcIjGGVh8r5fPpiN4nrjgbuS5kcatxt/hcGQOJjzB0OFw/DowRjBZB2kAUsH8+fMDQcAJhg6Hw+FwTC4wAcHEMCsS4iDpbN68OQz2WfWA7RCYrGBygu2QmZjgOn0IVgvjGpO29BNw9BH4TbhMajCJwQqGkA55jskDfhMeBEYmPNgm4p133gnGFwiOTLTjz+FwZC+iBMMbMTmge770pS+F1QTZ0sUIhmwpAyHkekAXPfbYY/L5z38+TFgORTBE37z66qthPPPUU08FYyrbI//Jn/yJ3HPPPWHLoq9//etOMMxw0FbQLrElEFvxU1ZsC8VYFZIQbROT56zOg4MshOOL+OFWcXA4RgvIK/0fPqzgHP2E/JqLrjCFvLJa64oVKwLpOpOBbs8UguFQID9pO3DkL0eIOPRr6ZOS9xBxsI3RFqAjjMiOHsGRFsoEfUK/1Se8HA7HeAD9RVvBGBtnq7Wix9BvkA3ReUzOo8fos6K/GLfj0Gu2EhMuUwjsTjB0OBwOh8PhcDiyE04wdDgmIZxg6HA4HA6HA9AngFTDysZsGcqkA5OptO1MuNp9jpAFmazgC0MmaFmRDL9MaHCfFWJYlZAJEPxCMiQMJmmZnGVig99MIBAOKxoSxuzZs8N2loTDBDXPMjHikwsOR/aCLddZDdC+SL4eqPesQMh4hMlQVjWF9IyugZhzPbD1OsQcJiaZaP3Rj34UVg2DNPKhD31INm7cGPQKY6C33norOHTMqlWr5IknngirrUI+gYDI+IgJXFY3ZGtnxj3oRsf4AtMVY1LaGoiFlCXjUiaoabNoW9hiG4IhZY2jjWHCHVDe3q44xgOmB5FhHDqKvhIOouyxY8eCHEP6oH8EuRCiNX0nfkNuyzSbS6YTDIHltznIOJByIOdEHatLQuChXCgr28qN/iv9XNJkq59GCcuUF/Yz1ysOh2MkwHia1ZkZQ+PQSegm2gd0lZELWa2Qe+g19BC6if4ObYaNu40oje5itcJoHyhTdJYTDB0Oh8PhcDgcjuyEEwwdjkkIjBZOMHQ4HA6Hw8FQgMlUJopxNvkQNexD7sEP5EAjHvIbP5wDwmGS/B//8R/DhAf9CibOmThgEpZtkFmpkOeZ5KC/YWFA/oBcCDgnTAvX4XBkJ2yC9EZBnUcvmI5B7zBmQU/ciMkC3WH6Bf+8n2dBNFz0nIVt+ofJWY745zr6iHMLj2NUJzrGB5QdZPTdu3eH7ZAhv1OmTJ5DFGVSmkl1ruEoMy83RyYCHWX9Lwgjhw8fDv0lCHDHjx8PhBIIIxCjcfSfMo3knA0Ew3RYe2J5TzsBUQddgo2LfiznkJhpJ0yX0EZA8mT7fhwfxkCEt9UNaSMcDofjdmEkaD54wf7O6szoJa4bARqHXkLX8nENOomPKziilyBAW//HdFimwgmGDofD4XA4HA5HdiIjCIYZEAWHY0LgRgfhTjB0OBwOh8MxHKxvTr+Cc3N2zVwU3IdQuGvXrjAxYhPh9BGYfGXig4lYJxA6HA6H42ZA+8IKYwcPHgxELEiGjE1ZFZfVCiH7cKSdGW4s63BkKiBisyIVhDaOhw4dCit0QjxEttn+ndVZIfCxKmem9J+ykWCYDog65L+tEMaWyegayDwQD22lMCP38MEM/VvbatS2VWblMAiIOM7tN/fQSelldqN2O4fDkd2w8bMBPY8uQc+YvuFo5+iioRzjafQ/Y2p0LEccK62a3jG9xFg7W+AEQ4fD4XA4HA6HIzsx7gRDjFJMRjJgcjgctwaMDRguzdn2B8PBCYYOx+QETT4TKXbucDgcIwlW/aJfz6Q4JEMmYllZiu2ZmPTw/oLD8augvw7xwCfSHI5fh602ia2ILfXZvpoxLGNZtpCF0LRo0aKwYg+T6l6PHNkOyBanTp0KhD3Ie5BOIJWsXr1aNm3aFLZNhlCSCbI+EQiGQ4F0QTqkH8sW/axoiB2MVQ2xibHqITrIVrtlTE0fl3Ii7fR7jQDENe6Zjc4c/WGO9tGNw+GYGDB7m+mIdEd/BiKhkZfNQWJmDI1uRy+YnmD1VIjMjKVnzZoVSOe2gupEGFs7wdDhcDgcDofD4chOjCvBkEEXgwkGErW1tVevOhyOmwVfK5rBAUMDA/NrGRowbDjB0OGYfGAShC+mmThxgqHD4Rhp0LdHz9j2o0yQ+CSqwzE0mDyjfjBx6CRDh+PXAbmK7QEZt+JYuZB2hjHv5s2bA8nQVgjLphV7HI7hYGM17C5nz56Vffv2BWIbRDW2SV67dm3YDty2dR9PTFSCoRGE6MviIBQa0RnSIWQgiEAcsWfjICTyDG259X050v+ljWeFMYiHOMqSI0RRCELWR6YPwPPm+O39Aocj84COMD1hRzs3nYGOMNKgHXHoEe6jP6OOuo4uQC+gI0xPQFTG1o/Nn74Ouh9n/Z5s1xFOMHQ4HA6Hw+FwOLIT40owZJDF17mvv/56MBg7HI5bAysEYchdsmSJrFixIhwxOgwHJxg6HJMTNjHCJAgTWA6Hw+FwOMYeTJxBIIAcxdbh9L/57XA4PkBdXZ288sorcuLEiTAxT31hK2SITIx3mXx3OCYiIBki89hskH9W0cPmwoqdkGvnzZs37kS+iUowHA6k17YyxTbGuJpjuuO+3aMcGXNDBIJoCKHQHPrMtjXFRc+5h43PyIcOhyMzAJGQOo09jfodrfd2nv4bfYBfiIXUZ+q21fXhHDqAI3oBgjK6YCLCCYYOh8PhcDgcDkd2YlwJhnyRu3v37kByamhoCF9oQYpyspLDcWPAuGHbLFCV+ZodgzNftmPAHA5OMHQ4Jh/QEegK2lub7HA4HA6HwzE+YJKRiUNWH2cM7ARDhyMFiDxsIXjgwAH52c9+FohWrOLDh3TLli0L5Cpb+dPhmIhg3IatBxILdtP9+/fL6dOn3ycZbtiwIdhhIKGMly1mshEMQXSlMjuSD9jXsJdBlklftYxrjL1Z2Qy/jME58jw6LH2FQxy/jWSEfY4yxtFvsHNzhOEkRIfj9kGdpG5aPTUX/U1dpz5jV6Ofkl7fuY4f6iR10+otBEGIhdRtW53QVirEMR5gLGDP2YdI0eNEhBMMHQ6Hw+FwOByO7MS4EgzZFplBBI6BHMQovkhnYOVwOK4PDBdszXLo0KFAGGTCBaLg+vXrgzFyODjB0OGYnMA4ypfTNiHicDgcDodj/EAfmr63kwMcjg/ABP3evXvlnXfekcOHDwfCEuRCxp+zZ88Ok/Q++eyYDGC8BsmQrcJ37twZdoChfmDv2bhxY7DFsKXmeIBx5WQjGA4HxtY47GzmjIxk2ytDPsJ2F3WQkiAr8ayRkHBGHoRwBNGQMoaYhOPcHL+x+0FSdJ3ocNweqKu2SinkQchvODvnyD10crSOG/kQfU3/xAiE6EI+jsBFSYRWx9PrO0TCyQYnGDocDofD4XA4HNmJjCEYMrHCQIKvcRl4ORyO6wOi0OXLlwNRcN++fWGrKCcYOhwOh8PhcDgc4wv62zgAcYA+tk+YDQ1MErjoBxDkGfnleTZ5QNlDxGHFtjfffFOOHTsWxpmMN1mpn49RrzXGdTgmKiChsYIhxFtsOKx8S53AHlNTUzMupFsnGN4YjHiIbjPikh1xtoWqOdt6FbITpCXKlfYQEiH6zwiFdo6j/HGQl+yYfo6bjAQmh8PqIPZzc9QzXPSarUyYfjRHfTQyMH166hR10Rx1ERIhDgKwnRsRmProdfBX4QRDh8PhcDgcDocjO+EEQ4cji4ERxAmGDofD4XCMP5hwYOKBCddo9xoDOb/tGr+ZXGByApdtEw2kgzTiSDOgH2DXLV24iYZouRqybQKENFBulBVg5YxoGrhn9/E71D27bvI72qtumGzZe3G8z+LGdRxxA+n3bwbpYZkc23UcsPCHewf+WK0I8gB+bLJxNPMpW2D5aCu+GBETMoVtIQkYk0TJESar3DddG5ULygpZNHkcrmwcmQnKkfJnpbZdu3YFIhWT+2vXrpU1a9aE8aqPOR2TGbRL2G6+//3vB4La9OnT5cEHHwx1BGLGWJPY0b9OMLxxWNuXfk77R3myomFTU1Nwtt0qDgIO96N9sKijzNGNtroh9nRbQc3ObcVD2lHaR2sjo32Z4ZzDkWlIrwNDOasrHKljtoU5dYr6xbnVM85x9EHwP1Q9oI8JiZB6Fq1j6LvKysr3VylED1tfn+fsaOeOXwX57gRDh8PhcDgcDocj++AEQ4cji+EEQ4fD4XA4xh9MsjJBQTtJuwwpgi427SMGcu7TZgOIMkzyMSExZ86csPWZTURkOkgTaWNCpr6+PhxBVVVVSB+TBEywzJo1K6QvfYIlm8GEExNPlibKlHRRxkw6ZUMabZIN0huyShogBCCDgPKlv9fc3Bwmukkv6SPNELpYvYMyRwYgzDGxxqQaW5Yy4TYacmx1x97LO2xFEOoSIG6kifv4h5TGfSYCLU7RIa+VFdei5cZvnicc0s+zpI3rrHLCBD9xsYl8wh+qD4x/8vn48eNhJTbyqLq6OoQ1lP/JAvIFGSQPyctLqkPq6urk3NlzcubMGblYf1G69DploAUjxUXFMrtqjqxcuVJWr14t82pqQplfUfm8qM/yPOVEeVEW6BzyuHrevHDOmMbeieOcMjViLGVvzjH+sPHpgQMH5ODBg0FGaFsYb3JET3lZOSY7aLux3+zfv18uXrwY2hZsOBs2bAj6z9rFsQC62gmGtw/aJ/ox9LloH3GcoxM5mqN/FnXoSJyttIZ/2jnbcpU2kPOoo62kf4Q+xdmqasgN/jlGHc9YPwpEdXC6Pr7WPYdjKCCvhuHODXaNo9UNqy/ResOROhGtK/y2esSz6Y4wqTPUD+qE1Q+rK3a0emL1KVpn7Lp9mOS4MTjB0OFwOBwOh8PhyE44wdDhyGJgPHGCocPhcDgc4wsmBpnopW/LyiO0mRCSMI7j6G4zCcgkoq0yA8mQVWfY5i5bCGpMJkM+g1wIMaixsTH0RZhMpm/BOVtYLliwIBxJL2DiJTpBmY0gbZDYAGVF2ihHI5llQ/ooPybZWCEMMgBldv/994dVkEgTcsy9U6dOBZk1ueUek2bgxIkToezp/0FsgEw6b968MH4bjUk16hHkXd7JkXyGxEo9It6AySniTRlxn2cg9SGHyJ5N3nO0tJAXOOJshDPuEwZyDTmQ6/SPmZBE7il/wsYvk42kGUc4JusWDv4gSbGlJaQP8mvhwoXD9rMnOpAlCBDoyTOqJ8+qa2lsUtcojZcapLnhknS2tKJM8SwYKAYkJvllpTJz3lypWbxYZl/dHreVVZ4aGqTjSksgJPb39klugdZFLZOyiqkyq2quzJtfE+QTmWGsRHkQaFFxUZANc+hhyBXZoH8nOpCPF198MYxPKTP0CqQp2kkm9h0OxwdtIluIs8onvyH2bdu2LdiAIP6PFWj7nGA4+rD+WJQwFXX00+wceTCSlRGqzNHno49CP8RIVByNIGWO+9Fz+rg4+kTp59Fr6b855304h8OALFu/HIceiR6HO49eM5lGzs3Zb45GMOTINZ5FHpHnoRz9QKsTUQJuukOuvb84snCCocPhcDgcDofDkZ1wgqHDkcXAWOIEQ4fD4XA4xhe0qxBncEya0GZCUuLcJtYaGhrCBAekJyOq3X333YFEAeEpGwzpxB8iF2lhQhPSFenkHLCqG6QeyIVMEuCftDHhPVz/IltAmdLnoqwBfR8moaJbzmU6mJRD9mwMRj/yk5/8ZCgvwMT0oUOHQt9wxYoVYTKN34DyZBWxN954Q3bv3i0f+chHZNmyZSH9EP4gWo5GHhBfZIw4c0SmeB/1BsIev+mfQnJAxogzfWJWB33ggQfCuJIyg7zERCN1EjnlGpOQ9JeZWORZ7uGHVQdff/31UMaf+tSnwuqJ58+fD/mHH95J3kFQIw420Ukd5hmG17wPciH1Bfmnv7x8+fLwrokMmzgmf3HkGXLBeb3qx50qd3vf3SGnDh+Wstw8mVZcIhVFJTKzvExmlU+RaSXFkhfPlQHN44tXrsipxgY50nBRzrRckW4Nu6K0TIqpezm5MqOkTMpV7vJycqSnv0+uaB1t6GiX9sEBqZxbJWvWrpULdXVy6thx6ensElEVW65lMX/RQlm8ZKksXrokrHYIyduIhkxA+6Tm2AM5YTVKtn5le2R0KxPMmzdvDudOUHE4UrA26LDqUNpqSP+0O6tWrZKHHnootOfosbEA8XCC4djByp5j+rn9pm9CvwlH39zOzdGXpX+CXwNtHm33UG0fupc+Ln2kKAmR9tKOURe9xjnPIZ+ETVgc7T12HnXDXcel33OMLmyqiKO59N/DXU//bc7klD4hssq4A2fnHJHP9Gscra+Ni8qvgfDTYaRCxgb0Jej/4+jzRX8zhjFCrB3NRX+73I08nGDocDgcDofD4XBkJ5xg6HBkMTC2OMHQ4XA4HI7xBRMmGMiZvGMiDqIhK6DZNp0Q8Zh8hUBhZC4mNlg9rqam5v3Jt0yHpZM+AX0JiFg4Jn74TTpY0Y5JRQgjrNwGKY184Fo2gzRSjqSfyS1bvY5yzJZ+EOVH3xH5hCjIKkif+MQngkwyJCRtbE+KrELsgcjHNoxM5DHpA5nuhRdeCCsn8Rwri402MYt8J57IGf1PziEP0ueFzMp9yH+sFgjhj1UNf/SjHwU5fPLJJ8NvZBGZhfAHWdDIrwAZpRxJA3lA2bJaIoQJ8uszn/lM6P/yLH6YhETWIXUQNqtGET4OWF0mHPrabP2L7NNfXrNmTXh+ooL8I8/Qg6xyCrmScQppptxOqU5sOntOSnr6ZEFpuUzV62VFxVJakiLqlpeVSbHmcVzDGeyBJNEm5xsbZc+ZU/LM/r3S0NYqW5Ysk5VVc6V62nQpLymRYs3bnFhc+gf6pbO7Wy63tsi+82fl5BWtq5KUafkFUlVYLDO1niKjXYMDckXLrzOWlP7cHCnSdy5dvlzuu+++QABF5vHnGFtAHoaohH5BP0FU2r59eyA6M6nvcDh+FfQnsae+/fbboc7Q5kH8p86gT8eiT0k76gTDzAJlQj/PiFg4+x098jEFRC4cv9P92Dn9Hfo3hEv7DtDJ9HVoKxnzcOR3urP76QRF7nEt6jf6+0buEba31aMLyptyp/xNDszZ7+GuR3+bi8pW9L69g2P0+eh1gE6zckcOkCWTq+gRR78TezjuWv7sHJnKhnH4RIQTDB0Oh8PhcDgcjuyEEwwdjiwGRhonGDocDofDMb6gO20TI5xDLoSYtXr16kBAo81lKzvITbS5TJgwqYEBHdIWxKVsMaRbOpmUZAUd+vMQu5ishFQE4YvJR1aEoK/AhDPn2dxXoExJLwRDypDJMSZBWJmOVTGyiQBD+UFMeO2110If8mMf+1gg7DGRyHWIepDntm7dGsqVFQwpb85ZffP5558P5MRPf/rT72/HOJqTvMSX/i6rZiJr9EEh9iFXrGJIuUDiI94QJUnLV7/61VA+X/ziFwPpgvJDLkkXfkkP9Y9wqH+Qyqz/y/t417PPPhv8kU4Ih2wbzRgVv5T/O++8E97x2GOPvf8cddhWOOGdEB8h2fEboi3jXN47EYFOY7Uk8hd9V3f2rDRcvCgtWmcKVL81NDbKudozsrCkVO5bvFw+snptGKvE8vPUFUgsLw92psSoSwOqR7t7ZLCnW+q0LN45eVx2nDopSf370Jp1snbhIplTOSMld1b3VH6TWgYdHe2yv/aUPLdvjzyjbt28+fKRNetlverd0oJCuaz3D1+8IKcuN8j5K43S0N4h5TNnyMbt22TV2nWydNnSIFtGmnWMDdAzL7/8cmg7qSMQd1euXBnqmE8yOxy/DtoYiLnYc/gwgPYKe87GjRuDDhsL/UW75wTDzATykQ67Rn+PvhP9dhz9I/pZdjRnv+nz2DlyZqQvI6Cln0d/804jJBo5kGP0PHot6oa6zjUjhCHjhJ3uaDPsnvXL7Nzak2i7MtT5jV4DN3rfEC0bOx/qGhjqPrien/TzqKNs0h3XrdzMD7+tvDlGXfTate4bmdCu4QgXUEZWTkOdR39T5jj6ZhzpJ3DOcahzCIaMzzgiKyC9HMBQ1xxjCycYOhwOh8PhcDgc2QknGDocWQyMfE4wdDgcDodj/EGXmgk4SGjHjh0LZCa2QIaABRGPyVfayy1btoQJWdpS2tFsWzWLdBqZEFIX6YWMxqplkNbYWpYJgo9//ONhhTsIX6Q7WycKbKKNFdgoMybLmPBiAh0HgTRbyg4wsQeR8NVXXw19yMcffzyQ30gjZQlRAZIPBB+2jkVWmRAkray2+dxzzwWCIVsHb9iwYdQJhjYxiYOsRz3iGnIFgZdzyiVKMPzKV74SZPBLX/pSIBgie6QPGaX/C5GJOscqjRAMoyRR8idKMCSdvBeCIatCGcGQPjSrH/7Gb/xGmPQeakhN3PDL+/FDH3siTpiRdvIWkthPf/ITef3lV2Swu1umFBRKeWGhDAwOSENri3T39MrDq9fJI2vWy+rqeZJXViqxoiKRnDxmeQNJkG2MtRAYrEhfZ4fsPn5Mntm7W6o03zctWSqLqmukTM/zC1MrQYbc5Fl1Sc3vge4u6dK6+tqB/fJ3L70gcyumyX3LV8q2ZctkRvkUGVR/vfqeXvXb1dcje06dlF21p+XI5UsS17p85/Zt8rnf/u0g69Rtn+AcXZh+pe343ve+F0gvbL3+0Y9+9P22w+FwDA3qC20T9lSOtHv0KyFnjEV7Q3vpBMPsBHrXnOnh6NFc9Dfn9GuMfIhDBu08+pujnePfHH0iwjHZHOoYlVv7nX4fR98zSj60fhbOfkM4s+v4p68XDWMoZ/3Boe7hbiQM3FDhDJWvt+puJgz8Ul+tX2rO+tdD/cZ/ejiG6O+hjtH7pNvKIp0EyDF6PtQ1HH0Bys/yMVoGw51H/TsyF04wdNwKojrGEL2Wfs/hcDgc44Ph2vNoHy167nA4sgtOMHQ4shhOMHQ4Mhdm4PCOssMxOcBEDNvUvfLKK+GcbeogL3FOGw1pi/aS1WXY7pU2FWIeE7L0fW0iKpOBTqM/QDohE0Eqor9BOiGtQU4jbQBiF/0K8oGV4phQykZdSPmRZtKHo5/EpBdHCEjZRjCkD0jf0VYwfOSRR0JZMenI6oyQYekjUnZM9DBeg4CHH8iEEAzfeuutQKzDDysbjmY/EFljVTzqB8RW4sxEKWQ/tkgmbqycB7kB8h/xeeqppwL5D3Igv5Fb+sz0X0kPYVCGrICI7FL/mPzEH/nDO1mpkfMnnngilDurEdoEqZEaITSyte5k7geTZ5BvIXj+7Mc/lrMHD0lx/4AsmTFLZmgZsYXxQF+/NLFtssrXYDIhq6qq5dH1d0gx23iygiHWCA0nuFSgktB616blevT8Wdl/7qwsr5oraxculvKpU7S+qa5M4F/9olLoZ+Xp+EXrIfFJaH09cvqkPLN7pzTqe2eVTZHHNtwhC2bOlLyCQolr2WthyqAGcKGpSc5cuCBHz5yW9+rOSauGN2P+PHn0ox+VD334w0G/+Thn9AABBQIvpGWIv+gcG2OibxwOx/Cgf8KHAbt27ZI9e/aE+rRp06awVTJtH8Sc0QTvd4Lh5AL9L/pA5ugn4ex8uOtD+UF+7Nx+cz96Pd1P9PpQBDvGGfQXcfTNcbThds3GIdHj9a7ZOUi/Z8ehrg11JI7px/Rr4Fr3h7tmx+Gu4ci3dGf5SBzNActD8i/qhrpmLnovek7/ebhj+rX067ho2TkmFpxg6LhZ0E4gN7QFAP3FOB97ADqNc2xUnDscDodj/EBfkN2MsKGmg+vY2ujjYZu1c4fDkV1wgqHDkcVwgqHDkbkwgy0dZDeKOhwTG3SnMXLStn7/+98PK8J99rOfDQPppqam0Nayugz+aKshSqEjbIU/BtTZoCOIM5PZrM7Ils+kj7QyiY0hF4IafQKMvqxwSFpXrVoVJp0h4mWjwYA+EwQz0kcZQ2yjj4V+N7JhtqQL+WNVEvpzjL+Qze3btwciAmVLGiHzsSUw2yGTVsobciGEPdLJyof0OSHWrVmzJkwGMQk4WqDeUH9syEq+I3fkPQYrVlnknHElvykbCA/UK/qpGK4oNyYdWFmUfjPpsfYZkiLERMLkGn1rCE+QKCn7+++/PxyRbe4RFv6or8g+K5Bmo1yPFMgbiC0v/fIFef3ZZ2VeQZF8aM1a2bR0uUwtKRUIgMnefmnv1PxXuXp67+5AOvyDDz8oFWVTJJ6bIzGVvaTm6QdIrTjZCqG0vU1au7tl7vTK1LbIWtZJfWeit0f6B65us6dh5KgOihWqHuV+GB81yIHTp+TNo4elXZ//zJbtsqKqWopKisNKhSo0ElN5QaoGKd/Gy/Lawffkpff2ya5TJ+WBjz4qn3jySVmt+qty+vQgW5kK6ka29jHRQW+//XYgSEFOZyyKQ/+gWx0Ox7XBCnGQ+2jT6ZtBmqfdom2ibR9N0G9wguHkhPXJ0jHU9eg1zulHRVfLM2fXbvaeOe5ZHw3Hu+xocUg/GqK/0+8ZrvUMuF4YXEtvq2/m93Dt/LX8pN+LOvquHOnf4OjL0582cl/URa+n+7nePSMIRsF703Gj1xwTC04wdFwP6HbG4MxJ0efhyPiB6wDdim2NsTptAOfcp4/icDgcjvEDfUDGhUNxFLDBYm+mj2g7xWC75Rn6AHxITj/S4XBkNpxg6HBkMZxg6HBkLqifGDXoLFNHMNy6oczhmJigO03bytaqtKsMjtl+lfrP6l70eWlD8cOqTLTRDKIhOjFwzmTyShToNAy2rOZ2+vTpkB7IhaTH9BzOJpgwAkMSYcIbY0H6BFM2AEM1ZYgRO7pVF2VOerJJtxNn2ibKEJmEUMgqfmbYobwg4tHns7RxtNULWR2JlTgh4FGmkPMwGFHWowXyvrGxMcSV+JHXyBJH7rE6JmNH0kT8APeIF6RIZBE5JU2EgQyTXq7hIL4iv/hjIpoweB/5Q9lDdKLMucckGJMW5Anh4whrsrbtlid/9T//St765S9l06w5snXREllZs0DzplJyCwq0MOKShAyo+Xb24gX53ttvyGAiKZ+/616ZVTFVCvMhdqY+yBgcVJeEDKCPafhJ/cMvf4VaPgV5+RpcPKxuiBxf0TJN6P2i/DwpKS6SfEiG+FF5bFc5rj13Rp7du0fqW67Ik3dul1Uqr8UQgjFaajhakBLT53imX8v6ivpj5cM3Dr4nR5obJVY5Tf7oj/9YtmzdmrF6mjqNQwazTQ6JN2NKxpasiEu9Yst2xqPZRNx2OMYTtFP0PalDrARKvVm9erU88MADgfA3mnCCoeNWgO6PEv/s/GaPw92jTlgfDxclHtpzo+WAvQek37e2mnoabbft/FbdjYTBOxm/GPHPSIBRO5XFKxq/9Gu3cs/hGA5OMHREYboSPWqOjx2xPfERBTYIZIZxoOlUQH8ExzWO6H7gcuRwOBzjB3Qw/cxoX9D0vPVBAfexNzOWxL7K9WXLloWPyaN9S3MOhyNz4ARDhyOL4QRDhyNzARGCukbnF6MtnedsIqI4HI6bA5NXkJNY7Y7V0BgMA9pqvqimHcVISpsJeQLCCsdsakOJPwQriF0403HoN9LMkd+ckzYIXQw1mGzmWjbqP4zU6HPSDtHMSHc2hMq2NCGnRrZj0tW+DCVNZpw3wzxp5B6EPo7IMuWODEDM4zrlPJrEK+JLPHknR/KbcgCQIenv4oiblRNxMjIo/kkPz3LP2mPCxVH/cFwnvaSbd5E/+Cd/kF3uc533ECaGr9FOe6YDWWCy53/897+U2l175P986GHZsmSZTNOxfG5+gUhc8yau9YP6ovnfoOOIX+zZJc2dHbJh/kJZNmeOTC8rDysMNne0S0NbmzrVK4MDkp+bJ3OmVshMvV+u5VuiZVmQlysxVjfUcurTcuKZuuZmae3ukiWzZsss9V+g/uIqqz19vVKvY6TvvPW6nKi/KF+8+35ZUz0vrJ6Yq2UWi8W18mrUkBGVBy3IEM8r7W1y6twZeXrnTtlbf162fvhD8qGHHw7jJOQ904CMIt8AWYwabzMZ1DXqK6uK/uAHPwgEKQjLDz30UFh5zeFw3BioS/Q9se38/Oc/D+0ikzMf/ehHZcWKFVd9jQ7QPU4wdGQSqA/WLlo/z865Z878pv+2Y/p59BpIvzbUbztGr9uYgWO6s4bDnogAAP/0SURBVOt2vJ1zO0av0z+gn4CL9n1x5sfhGGs4wdBhQE+a3Yy5J4iFyAdjcj7+45rZ0/DLOB8bALLC2J7z6DiI32YzcDgcDsfYA31tdlwD9lbT5YAjdkX80A9Ad9M3nTt3bnB8UI4tGvszH5Bn0/yJwzEZ4ARDhyOLweDLCYYOR2aCidMooYF64iTDiQG6Tjgrx5stT+t6RbtghDER5CKaNxMhPTcL0k6dJ+1m4LQ8wUUR9ZNNsLSYQcBAerhuZW+/7R4uG2HpBdmcjiii5Wfp4WjpBOlpxvGM5YddGwsZtncaeG96PKL3gV03DPW8YSh/0fyx++nPRJ8bCoQRDYf2fyIBchjEkud/+pTkXWmVP/3Io7JsTpXkqkzEr/Z12IY4XqJjEs271sYm2X/6lJxquCQdvT2ytma+zJtWKRebmwO58FJbi5zWcU13f58U5xfIyjlzZcH0GVJZVibTILQWF0lRXn5YgXBAw2vXscme2pNy+Px5WVs9X5bNni3TysslLzdHBjTfW/X+N954TfadPS2fuGOLrJ5bLRUlpWElxNycuMTiKr/EUeMbY6K9pESSehzU8dVbV7dLPtp2RVZv3SKf+OQnZW51dSCW2gqa1yv/sYARKJBN6qKRvDMhbtcChA9WHT1w4IA8++yzIf5r164NY1EMyQ6H48ZB/WGL8R/96EehXrEy8aOPPiqrVq0K48/R0gdOMHRkKqL9tej5tXCj/q6FWw1jJOrojYYR9ZfpfQXHxIcTDB2MY5ij4mMJ+jDMNzHG5AjJEFkwezrzxYzDGPNwZNcQ7vPRH/cYBxlYDSsTPw5zOByOyQL0O6Rx22kGQDhEt5udFP1fX18fVqjl3D4MAuh4OAvoeHbV2bBhQyCO0wZM9o+9HY5MgRMMHY4shhMMHY7MRZRgCKIkQ68z2Qm6TEYYwWHMYmBzo6TR9OftWno4NxLWWMO6i9eKG34YDNLGkBYjOkwWDJVHXEu/bmUPuGYu2xBNG/JLuvidzWkaDpauiYR0uUyH3TfwO/2aPTvRynukQN8ZBzCAsRoidWUiAFl4/vnn5f/9y7+Uqd39snFWlWxbukxmlJVJbjxHClT/F+TnST4TPbkpw19fT69cutIiu2pPynMH9su8ykqpKC6Rc01NUl1RKavmzk2tUCjJEEZpQUHYIrmtuyu0KTPKy2ThrFlSOKUirDrY29UhtXV1crzuvPT09UvV1IqwKmKZ5nU8HpPe/gF54/hR2XXqhPRou7R01hxZW10jpYWFYRVDXI7KLX2ysAUz13lWjxg9D+tz33zrdTnV3iqz5s2TDRvvkLu2b5et27ZJkY6zxrsvRxlYm4sRljpofUzyK5NBnCFE7dq1S956660w/sRgzBh2jpahw+G4OVy8eDHo5FOnToX6/8gjj8jGjRvDxMxo6SonGDocDofjduAEw8kLsy0gA/Rhjh8/HuaJmXdiNSvAvBb9GPoVOHYIYc6Yfg59G5u/Ynydbnvk3niP1RwOh2MyAx3PvChjRoPZrwzMIzB/SlsAGRGiObtCYY/Dlop//LB6IbwFs6vCgYBI7nA4xhc5f664ej7mQGnATsbROVy4cGHoOPoS1g7HjYFGFuZ/XV1dGIQx4GLVBxrd6Jdb6aBhprHmObakokHmOSZ0hpuQovGnYT98+HBo9PlygK/jKysrJ8xkqcMxkmCylzpqhhOcwcgYbjjLHlB+bDvG9hwMeCB304+hjNGb1yPT8TyDJoxlfJ2F/jXdTXjoV54nLFymyAbxRv+TVgaGGOqG0vn4g/ROmwJxnbaJtmWiG/VIN3Wd8qNt5GhpJr+iA2OAX/KI/MQ/9/HHM+RrpusESy/lS9ypD8g09YD6YekhHROJYEo6SLvpdBA9z5Z0RssPGeRIOUX7fpQf8kp5csQPzmScMkf/8RudRng8P1rya/nOV6844kc/lneZ4z7X7T6/wa3EiWfRZaSPsMgf3kc6Sa+9AxC26cOoLOAfvWm6nnPCYZJkKP2ZTSCdyAOTQC+88IL84plnJNbXLzmarkutLXK+uSlsWVzMKoGq10Ie6TOxkD+xsHJg/8CgtKufsDVyZ4eUFxXJ8qq5snJutVRPq0xtjTx1ikwtLglERcLI17DY3nhKSYnkaT7GCgvZ4ViSgwPS0dUpO06dkOP1F6VDy+iKll29xoWVEo9eqAvXj6qrb2uRyyrDJxrq5cjFC+rq5ISWUYumh5UR2YBZExjkuUjfVVxYoGF3ycVL9XJB/c/IL5RBDf/I6dOSo/FhmxbT3eMFysPkkyO/ya+oy0QQX2xAyBGTioxh16xZEyaX3RbkcNw8rI2yviXbSWGnYfWe0eqLo28Yw0BqpG9APbaJH4fD4XA4rgfGXMwx0CeEMMC8IAsgZGr/1XH7oO9Af4X+w5kzZ8KHCgcPHgwfKiAHjLOxIyIHy5YtkwULFoRz5p+Ys6JvA8mQcRj9DRxjB+yUjLfNZfuY2+FwOLIdtOWMQ6O6uaCg4H3djcNGyi4h6H10Ow5+ELqeI30DwqFtYIyL7Yg5FsIx3e/63uEYPzjB0OHIYjAJyiSfEwwdjswDEz3UUQwohui5Tfy68SzzQbmhNxnIYABjMIOjH4P+Y2DDoAk9acQTyh/HNUA5G0GRVWAJh5Vk0cEQtDCyEZbpU/wbScXejx9kinOMsfYO3ok/Oze5s/fjuIbjObvPdcIyGbR3Rp/DPzr/9OnTgVjDYM8mKvHL+3A8QxuBkZCVTHgHK5mQNxMZVi6UK20qbSvGTtpS8uP8+fMhT2ijyQvyhXaUcseoiiyQxwyauT9cG5wpQG5IF+klbcSftCADpIc6QhoZ5GMoQKZ4xmQsm0G6qAMcTfY5t3qbDWkk3sibGfTRY4y9kD2r/9R3ytFIsPhFR3HONXQBuou8QEdQvqNp2EG38C5kjrggf1zjXfR1eT91kLpHHLlP39jihT/OzQGTS/sN7BrlanlAOOg83of+5jp5wTvwyzM405kcyUOOPEteQbwA6AUMZ5lex4cD6UXekYFALlQ9v/Ott6XtUkNIb1Nnh9RdaZLGjnbp098QAsNKhrl5gVRIuqkhOLYlLskvDERESuCBlatl/YIFYXvjooK81OqHuPw8KS4olArNu2llpTIFwqFei+m1mIab1Pf0qTxfam6SX+zfK4cunJduLb/27h65qPJw9OIFOVx3Ts42NUrPgI5j1G+9lt8Z/X36coOcbLgkZ5sbw/XiPG3HqcsaH9bCYBvmgqIimaLv6u7tkU4Nc+PcedLb0ipPv/Si5Gr8ynX8VKJxQ/aRg/EA5YLMUTac44DJprlMA/WFVUpoD5EpJg+3bt06oVb5dDjGEtRz2kTaKeyrtDfYhiDtoqNGA+gb2mUnGDocDofjVsC41AmGkweMVxhHM85mG2RWMufjZMYEjOeRgerq6vDREQ6CoZEKGUs7kcThcDgmJtDt6Hg+jmNMCbeB/kBNTU2wyWKnxYbN2LO9vT20J/QVmJ8yG1iwOXr/weEYUzjB0OHIYtCYOsHQ4chMMOFuE75RDDcB7MhcUF6UJ1/V7tmzJwxwcPb1LDoQ0gmEK3QxOpYBD6t8QeDhNwYxwsEvjkERRrSqqqqgs5kIRM9ynefwA6mGcwuPCTx0PtcxwhE2+th0OXHAWMeRd6HniQ+TfjwHQYYBGc/wLq4TFhOBpscx8hIuzxGmhUG6CYN2gn4a4fObNPN+4kceER7kE/Jm+fLlk4JgSJrJI5tgpX0k3bSZ5CV5Tp7Q1pJ3kEt5hgEyBCTynPJnIJ3p+UV6kVHSi2zQlyeNyAoOmcAogLyTHtODyFe26znKiTRTbyk/fgP6W9liyKD8iD9lhT5Dv2C0obwAZUV9pq9HmeGfOk79ps5TtidOnJBDhw69r//QH0aQNT0ykkCmkDl0HnFD5kgD70LGiDPXuI/uo1yoi9QviBWUD354xtpknkU28ctvyo5rnPMc7ztw4ECQbfLHJkK4zrvIDxx+AdeQDa4RX/ybPuYZiyt5NlorSY02kAXajpdeekn+/mtfkx1vvimVCZHPbNoiH1q5WrYvXSZ3Ll4iG+cvkBLVc68dPRy2LV40Y1bYJjnv6jbJ5PHFlivy9slj4fry2VWycp62pxUVksukkaRWO4zFtUxi8bAyYk5OXPK0juXm5UpM/cTQk3o/ofWwra1VLmges1rhLJXH+5evlqWzZsucKRUyR3Xu4hmzZV3NArlryfL347h54WJZq234qjnVsrJqbiA8Hqo7JwMqH+WFRSlCpIafr+9Gos+pXr/c0S6bl6+U1fPmy9zyqXLk9Ck5dvZsmAQz4v146ADyk7Ix2bZrOOJjsp1poA9Cu4geIo6LFi0Kk4jXGr86HI7hQT2nj0n7ThtIXcI2hI4aLfsqeoY2zgmGDofD4bgVMBZzguHkAWNJIxbu3bs3fKzKeBpbGOMASIUrV64Mc030Kfhg1Wwt9HNcLhwOh2NiwuxW6Htsa+h+7M+MY7FXY2/GhmTnzElgA6YdYa6LZ7L5g26HI1vhrCCHw+FwOMYQTMYwEQy5gc5xdFLYkdmgrIxMhoNMAmmFIxN6TJZDSmF1LwY6TLgZEZDBEoMjiAiQTACkACNqMxjCuIZxFQIaxBTIP2yxyfP83r9/fxg88T4jL3Lkq999+/YF4o853kucmPCzleaIz86dOwNZEGLb2bNnw8SgySDyCEkGv6QB/xB6eA+EGe4TZ/wSB+7jl7zgXYRFGiDYkabJMLCjXG0ATJ1mYEs+2XWc5SuyQ/5x5DdlS9mQv+RpthhMSRPGXoy+yDMDf+QBQpUR75A7iCOkD0MyeTMRQDlBICNNRi7LlnIDUXmlnEgH9d9AmihHZNLKmfuWVgw25AGEOjPgYOBBt41WPli41B30EHqGeoZMcY36ZTqYsiF+6C8j7/Ibhz/0KHqLMHDIKc9E8wCjFv6po+hX3kP4+OX99k5km/B4lmtRx/OW14RHPHDcy0aQXvICcuFbL74kbSdPS3U8V7bVLJBH79goj23ZKo/euVUeXneH3L9itaydNz+sRqgti7Rq/kLcM5A3bEl8+EKd5Gv+LJ1TJVPKyyS3oEAG4jG5oveaNL9bOjqlU9vXPn13ImEfZYSDJPu17Lt7pEf9nNNyP3GpXuZpm3PPshWydclSWVczX9Zo2wqR8L7Va+WRTXeG+D2C0/OH12+QR9aoW7dBHtT76+cvCNsvn2pskPfOn5UrGm5fj8qOyj3bJheqjivJL5DSwkJZUjVXHty4WeYWFEuTytiud3eEtjQqQ+MN8pj4UG7IL+dcyyQQH6sX6A+MxNmkSx2OTANtDcQ+6hL1i/EJLtPqvsPhcDgcjskFxiLYFxijY4vEZooNEVvCkiVLZMOGDbJ+/XpZtWpVWNUcWyJ9Gh8bOBwOx+QGNlXaCubOIKHTXqxYsSIQD5lTYS5sx44doV1h/gr7rsPhGDs4wdDhcDgcjnEARhYmfpkAzoSJaSagzDl+FRi2INOwyjLkOQYwP//5z+X1118PK3kZCY/BDUQDCCyQWyCfQFJhgg9AxLJVviDxQO5hoET5Q15BFiCwEB6/8YMjDCPGcM7zhMWAivLiXRjrINgQD/MLCQdwhBzCSnp8KQxREQIi14yQAyCHQXDEH3HgXdwn/ayAgjNDH8/u3r07GAZ5njRC2CFNrO7Iqrjk2USHTehCtmOFQitfygfiFQQtHF/kkx/kFflHPpHHlC/ATzbkF2kjrawswAqVHFklDscKbQz+IaMhQ6+99logy6aT2LIVRriDJAYpxuoz5Ua+ZANMXjHaQw6F2GNxJ12UHY5rlOf06dOD3kOWkWHKnuuk28jSOHTRaOUBYfNedC91DJD3pIN30o6if9BfFi90IXrQyBU2OYFcRgnW1EF0rskn4ZEnlDPv4DmeJ2z0KDJAHNDb6FqIluQN+UT8Zs6cGc7JEyNacz+bdSFyQR0+eOCAfP1rX5Oz+9+TT63fJP/80d+Qj2/Zpvk9VfLKpkhOqdZ/zTPyqKZyunzh7vvCaoHNXZ3SdzV/aWogC7KFckdPt+Tn5MqUEs0fLWPNcOnTd9W3tsgZbf/ONzaFlQkb9d347Ru4utW/lmlfW4u0N16WevW370yt7DlbKwunz5RNCxfLDC3/qeWlUjGlXCqmTpFplRVSMWO6lEyvlIKKqZIbdLHKT0G+TC0tkUotSwiGD69ZL736jh21J6WlqyPEcVDjw2qKheq/NL9Acvr6tfOWkOLyKXL/+g2yblaVvP3qq6FPgE6wtjQTQFyoG9RnjpkUN2BEXEA9Gy394XBMZmRavXc4HA6HwzG5QJ+f8TYfQbNyIfZTxtGM77du3Sof+tCH5K677grEQsbYjJtt7O5wOBwOB20CdmjmIFavXv2+Y96J8S7zW8x14bAFZ6L9y+GYqHBLrsPhcDgc4wA6u0yWG8lwPDvAxIPJccgYxCU68ev4oKwgjGzbtk0eeuih8OUUE/cQ+TCWQbhjq2MGPPglDyFd8ZtBDwMiI6tQ1rbSmxFkOLdVwDgPRAp1GOMgw0DkwwgHqYb34o9nId/wJRfEFsg1bGEMocVW1oIMQ7gY6iB+cOQ+xjviSHmbEY938RxxgyTD9iQM4iDWkHbSwTuIL0ZB0g6pkvs8CzkRwg7xhFgDLC8mKqwcLH+NJMF1fkPOQg4oNwiY5BF+kBW+0F63bl0oA8iq5GWmg3QZsY5yRS6RGYiGGIWR3XfffTdsfYMMIDMcyZ9sBmml3kG0Qk/y23QkeZItIK4mm1F5NZjeIX0m15S3OX5zHcfz0WujCd6FHFGX0HmUAXqHuFIOxBdnfqlTlJeVEdd4DpI0+gniKysQEn/qJkcD+UN6ouWaHj73CZ/3o18JGwImjvCMVEyd2LRpU9gyEuI1frMJ5B95/eqrr8o/fu3vZbDpiqyeOUfWzV8g87XdmTJ1mpBLSW0ztOEgo8jAkM5ZU6bK/MrpUjO9UooLC8g4kdwcieWo/CCHmoeDyYS2J/2SZIXCeI7kaz5WlpVLWUGhtHf3yLMH9ss/vPGKPLN3t+w7c0bOXW6UC41NsvvESfnJjnfkf7/6ohy5WCfLZ8+R5XOqZNbUivDuHEid0yokT8s6R8OMIdddXdLT0iYdLS3SqrLTrboK6WAL5rLCIpmvsrWmukbjPEM69R5ucJB6LlKQmyclqvNYcTGuF3LUzZ0+Q+Zp+qSjUxrOnQ+6HZnIJFB+yCj1Acd5JoC4QM6lDlOXIAXTL8kmXepwOBwOh8PhcDiGB2MRxkhvvPFGcNgPGathI2WMzOqFjO8ZN3N9tG0KDofD4chO0D4wD4HtFfsR2+pv2bJFNm/eLFVVVcG2xGIakAwhHDI34XA4Rh/ec3M4HA6HY5xgk79GMoTAYISIsQLvN3IYhAtWzoNQ4PgAlAsr+UEsgWi3du3aQL5jQpzrrEIHsQACDCQSI+PYb45MnFO2RiYkTAZIXDPyCudGeMHAxm/Kxkh8vJt38i4c4UDuYWC1cOHC8JwRuoykyAAMsgv3IAsSHxzEQ8JH7ixunLMVKL8hGEKOIT6sZMggDlIicSYNxAu/EK6IG++DdAjhjDiRV+QNZEXCJfyJCsrOSBxWNtQhkxnyHaMp18hL/HGNsqLcyFOMreRlpoNypPyJK4N2tsim/EkPjrRDMuWrQdKF/JAHJmPZCMqXNFOeyDy/qVcgG9NkZWjySt1Nd8gqjnNkm3I0nWRtFo68GG0QD8hIxBVZwnHNVhAkjsgXjrjhz+LLNQPPocOQU3S2kZvSCZJW3pZHhE+6eZ4j4XOddyAHXOc86giTIxMmS5cuDQ79jd9sAuls0j7BjnfflTdfflkWlU+VrYuXSlXldCksKZVkXq4kkQPV8wmtH0nkQdMYh4yn7U7FtGlSNXu2FGmea4FIjLZBXSGrB+r9Hg2/oa1VerU8NXMlr6BQKiumSaW2T6ww2N7TLScb6mXvmVo5qe1gg5b3pZZWqWtukjOXL8v55kYp07bpzkVLZP7MmVKq7VwgFOYXSCwvX+OSK/0Dg9Km5X363Fk5peHUX2qQjs4u6dPrVn9zc3Nkiuro1XPnydJZs/W9PdKuaYJgCBlyemlZWCFxSlGx5KhIxRKDUqLvYfXDKfqetqYmOa+6ENnINFh9J25WZy3d4wXqKfWP/gF1j34NfZRofXU4HLcG2h/aJqv3Y9FOOxwOh8PhcERBPwSb0JEjR8L2lXyUzdgSGyMf3/HBtn086HA4HA7HjQJbK4s2sFXyxo0bwxwdc1jMU/AxOURDdq/Bfu9wOEYXTjB0OBwOh2McwUSvETaYCOJ8rCZ/beIZogbbRu7cuVOOHz8eyBfcM6IFhqD0eNl9m6zGWTrMr/nhGs5+c5/jULBweCfPDOdvLEFcyBNImKwmCIGKawxoIOvxtRSkPib1iDuEPibMjZATTQPPMakO4YyVtICRXCDNcH3RokWBBMOz5AGkFK5DCiRsfhMOE/TcJw6sYsgEPX5YxZC4EReIgDwLsYZVDvlSGLILk48Myni3lY29h3cTd+JNnBiUQbIxf4TLfVtRkTD4zfM8Q/og0rGqI7LFb56bqLB8Mkees1ohK/txJN8oFwa85C+/GfhCjsI/X99haEVmMh2UI3JHPXjnnXeCbCBXlDcGZGQA0iQydPjw4eCoL6QzW0GaKVMIhsi6bQ1MHco24qTVX+qzOZNFdBzlSVkho1yj/uIHwz/yid7BEQ7+uDfa6ScetEu2DTzx550mh8geckccTedR12xVVyMeUn48TxrRmUxo0OaSDtIECNd0NPdwvIcw0L3c53negQwQBjIxUUFaD2kdvqS6rKK4RLYvWSara+ZLwdWJoOSAtvl92nfp7ZPeq3KhmS5xlZWY1pP4lKmSoy5eVh5Ih1pbAkFvalGxLJ1ZJZ29PXKo7ry0qAwOahgagORoPpdXTJPFVXPk01u2y8c2bJap+u7wPr2fSCZkjurT+5avlD984CH5zLa7ZK2W5xT1E+MdxI3tlCEuNjVKc8MlOXLmrLz43j555+QxudDSLMlYUvLzUsRnKG24XJWhSo13ucatVcufFQx5F9eXzJotdy9bIbNJC6mgTde4FOfmy4zSMunr6JQLqueQl0wFdcDq73j3q0I5ahyCvDgcjhGFrepAO0b7Sd13OBwOh8PhGCtgS8AOxs4WkAs5x5bAyoVsiwwphJ0+sIE6HA6Hw3GzwA6LjZb5OOa57r777tC2YPdlO/5XXnkl2JEZE4+3/cvhmMjI+XPF1fMxBwYvJutxNiFJB9O/XnE4bgw2YcrkvW2fyYpIEEsgjgwHGlYmZG2VIYgPPAcphbo4FJiQYkIVsgCT0azEAqGFCdaJPLnqyGwwOZmpDqPKzUygRv2FSe+rbrRBHCERQNSg443hB3KGkUlslSj8UdcxAhFXJtLxTzoNRlThHnHHP2FDXEFXoV8Ik9+A++nptAkxyGmc8z6eG089Q9qNbEKciA/EOnQueQV5j4ENuhRiFf0YrjHBh+M3z5hcGNkK/YnuJY3obPzRD0IXEzbkGIh7+EPnotuN1Eee8S7eD8nFCH74oewIj/sQ2giTc67j1/pahM/zOJ41eSUtxJ/0EF/usbVolFRF+0O8GczhCMPexXa5nPMc8bJ3Rct5osDKFPk2Gbfy4DryzpH0k0/kK6QsyhBnZUAfmPtcy2QgI/QF6EOgGyhbZJx0U77IDumH3IX8IHOkiXPyJBtlwPpalCPppQyRb+qYuWxKF+lAj6GbiTu6hbKhbJFX0st10ojeNZlG95BO9DdHiNBcp8zxM1ogruQ/dY33UmeQJ95LWpAxHPeJN3oJmYNEiH7lGmlCf6N7uY+OpRzxx7OEyfVofYZYSB4QBvdxVs74RR8SDvKfTeV/oyAvzp49Kz/+4Q/lxIGDMqegSB5as07mV82VOHpK5WVA87O7s0vaVCd0a/4W5Gs+5uWGVQxZrVAFSKDwJQf6RQsA4ZOYhqv/JDcnLrWNrELYJDWV02Wa5nWulhVExLg+n695yuqE6Mu6K816v1TmTquUqVpe00rLZJbKQLXK5IzyKVJSkGpjrRyStNfdXXKlpVV2nDgme8/WhufZ0rhqakV4nhUSc+lX8IjGZ0BlhFULW7TsWTlxusrHTA07T9NTpPWjWF0eaVLEEpoG9d/Z1SmNLVekdXBA8qdOkRUrVwaZGUtQTtRdZJzza4H7OPIp6sYa1ENWzKbfSX+PfgN9F/TJePb1HI6JAGw71C/aeNptVggaLb2EPoE0wCrcrIDP+1iVKNP7sg6Hw+HIDNDPZ46BeUHaKmwi2JLGo3/qGDkw17Rv376wVSV2I/oHED9WrVoV7IqUdXTs5nA4HA7HzQLbEfZexp7msDVhY2KMig2YtgbbL/Zch8Mx8nCCocORxbBJbycYOiYibCLUSE8c0x11YKjrmeAsbjcD0mkYi8lfC5sOOKutAeo05CDIGxA7mKSC7IduMJIF6UKH0H5DwgDoHPQJ17gHAQPwxRC6hrAguTHhxTU6+ugOwovqHQuXZ4gX8aBfMJxuGguQT7wfR7qIE6QSJsONREU66MPwm3QaoQ/jWZRYRRlzzn2IZYRDeOYXPU7+c44jTH7juMfACP8MnPjNfc55B/4Jl/scLQz82bldxw9h8TznlkbCivq195BW4kj68cf7rO2gPUBu8Mc5zvLE4sYzlgcTEdQJ8obyJB/IU9IPqBvkFWVInpMf+OMaR9peyFDkeTa0pxCwAGk0+cdZnaD8kQsmB7iODHCPfMhGGaDOkmZk2OofZWh1Idv6QKQHZ3XYxl6Uj+k30wvIMGVH/aYsKT/8Rcl1PDuaeUDYvANnsoScURbkP79xxNX8UdeIn9UpS7PVUXQv6TH/pImwAP4A103Hms7kefIN3Yicc5/f4wXrb1j6bsUZ0usmfZhjx47Jt775TRlsuiLbFi6W9fMXaH5UplYJ7O2Vgc4u6ezukmPaf7jQckWmaB4Vous13BBcYjCQCwe7umVQ2/PBflY41v4R+jIWl5MNl+Ria4vMLNUypO3RcMMWxyZPGocOHec06bhDe0NSqOU0raxUyvU9JVrerC7Yp366+/skR5+hBHnfQF+/NLa2yuELdXJIXY9eu2f58rAFMsTBQi0z4kc8iE+/1u9Wfc/RixfkwpXmQFicVa5x0veQDMumkCb9kVD/9Gma9R31Gv9WTWdh5TRZvWZNkKsbBfl/u+VnMmDhXA/mh/KOurEEeccW+2xbQx+SOpbtBEPNfu2ziqYHG5fo+Dx1TVWGpumqpyGg4qv5wWqhqecIgyJCHY1VVvC+MRYBxygCm5DVLeqUEwwdDofDkalwguHEAjYT7L0HDhwIu+MYuRBioZELGTt7+TocDodjJIFNl34ER2yZtD9wj7A9Yb82e663Pw7HyMIJhg5HFsMJho6JCps0xUABEY0jzlYDywZH/bS03AzwH50AHu36xbtMjxBv9AH1mskhOuJcZ6UyfhtZCEMgK8/s378/kA+JI8+hh9jGEgIhOoiwn3322TDRBfkD0geExZdeeimEib7hXUbEAoTBxDMTVYBwx3sgwHt5P/0T8sBIesQbZ3ljhBz8cc518sHK0MoTo5qFg39+R5/htxFZuGbkFvxwjTDxZ+8jH/FvYdk59+05nrFnuc+R6/gjTsSNcOw5iwe/rdx5zvzyPPHCcc+eg/Rj7zNHuONVdqONaJmSdtphjlY+5A+EJiMpkTfcR+7xyxEZ5/pw7W+mwcoekpaRX5nE5jdpIj040o2Mm0xkS/rSEa0buGg94JhNIM445JWyQf5MXs1x3Uh75pBl0y34p6wpY/yPdv0mfOQHmePdvJc4Wjw5ct/qG34szhY3nJUhceaIHPNcVEfhKFPuEwbyjP7DXzQO3OM6eTKaab8WrI+Eu9W+Ef2UaH/D0sI12vkTJ07Ic794VuYVFstj6zZIjZZ7oaY7lpcvSb2f7OuVbj0+894e2X2mVqorpsmUomLJIyz6QNp/S+j9/p5e6VXX3dsnPfqbLYgbdSzRo/FmjcMuvQZ5cJbma15+gcQoD5zeJxyIgPvPnQlkwRK9z4qC+Tm50qdpqG+5cpUUyPW8kC+Ed0j7Gr/Yv1eqK6elVl5UHVWqZQgREfT1D0gncervk47uHjnX3CjPqv9Tlxtk/bz5Mr2sXDQG0j+g7yctmifxWOpZCIn1Lc1Sq34vtLRIh16eMnvWTRMMKYPb7deSXhxlZmV5PZhfk/exlmE+HqFviWNsOREIhlqMOqbmoxqRAwdELl5kO3pRHUJ/6aqnIaCiqv1ikaNHU8+1tFA2onKEnr7qaRTBu3BjLAKOUYQTDB0Oh8ORLXCC4cQCfY+j2qll9UJWwsdOtHr16kAuxO7AuNvL1uFwOBwjDdoW7NXY47AFY2djnMrcI3ZbrmPLzVZ7k8ORqXCCocORxXCCoSPbYZOcuKEmy5FxnE2gprvo85nmRgp0ks2NBogrdRviH3kOcYJ6DcGCrTDr6+vD/SihAsMRBiPIhOgQrrG9K4RE/DOxRVgYDPFHWVknn7bfdA9lix7hnukRnsHxbvQRRBYIJONpjLL8J6+IM+nkSJxJJyRYfhNfBjT4I81c477JA5PqOO6RHvxyjrzj7B2ckwecUw6Ei+N9+OFo1yxudi39t13jtz0f/Y0zDPUcxyihaih/Fk70t/nhaM9NVETzg3LFWV6Qd9Fr+LV70es8nw2wtFq6kE9zli7umUM34C9b0jcUouXL0eTZjtkGk0ErQ47pZWeyafd5xvQY/qxMo+U6WvkRja/Jk8WL3xytfOyexc/ixNH84Oy3uWjc7X2EQfic27PROFi+XAu0FbSf6HNg7x4J0HbQvuBoa/h9s44yjeaFxY3rtPN8LPDaK6/IgtIyuX/FaqmAAFhUJLEcbedIE+2fvvu1o4fDNsTdfb2B8HexuVmutHfIpSvNcq7xshw6d04OnNc+Q0O9tGsb2NvXL939vYEYeLa5US62plY/XDhjZiAapqKh8sbKhHrI1fexhTHkwJL8wnDeCbmRtlfTUZCbJxUlJZKvR3536TuO1l+Ut04elVVzq2X70uVSlJevaYxJIpkIRMdmTV99W6t0qF/C4tja3SX5eblSqX2Oc01Nsu/cmUA87NF4FBAvjdWgPj+QGJQeTUPfYH8gSV5qb5dOfW/VvNSWX+jD65UzeUzZ0d7favmZIyyONwOrz4C4mhsL0A8yG9BoEgxJor4qEPhefz0mb70Vlz174rJ377Xd/v0x7d+K9kNZqVcErtT1sob3nDolsmNHTN58M659YVZejcmCBannh4MmX8fWIu++m4pfR0cs+NcheXj3SIL8UJUU0rZnT0x27ozL22/HNc5xOXAgpvGPCd/2qIrTuKdWUBwjkXCMIJxg6HA4HI5sAWMkJxhmPxjLQOJg7Lh79+7QJ8B+DLFwyZIlwabL+NnhcDgcjtECfQez42KvZTwM/wGbE8BOxzjV2yOHY+TgBEOHI4vBIM4Jho5shU2I2uSoTbTaioU24cq96EToZEF0stgm/0cD5C11G4Ih+W8rRNExxzDECoWUBwYi2mf8036jcyATGsEQQ6Ddg1RIRx5yBR14IwiSJsqVc1Yp5P7ixYtD2EbWQAdxn3fz21bY4tp4GhpJG/ElfTjyiriSToxp6FQjnzB5Tp5yjyNpJu74waG3KU+ex6hKeBAq8UdeG8GS3+Qffscz7Y5rA7mO6i5AeSEzXMdZfeaIHOHXngHIQraUMWnAEXdLs6UV2eUeaZlIcpueDitfO88mUD7oFsqL43DlRPooW8rYdBPnlDHO5Bp/hIEbTZjcGaJxJg7EEWdx4v6tlA3PWh5Z2jgCi4P9Hi587uPQ97SR5Bfh0IaOVD4RvpWhxelmHbCyi+ogwqMNOnb0qLzz1psyv7Rc7lq6QkrLyyQH8pz6SaosJAYg2PWG1QWPXDgvtdpun7xUH4iDbdrOXdD0n9L07649LXvP1crZpqZAEizSMPK1vTzVeEkO6XMXWltkjvY7VlZVS3FeLhGQpDoZGAzbHrPyIKsjVk2pkH5Nb1t3l7Sogyw4paREqrAfaNtLvHv7B/R+d9h+mXgR5rp5NVpYRJrVBwdTKyi2t8ulthbpHeyXgST9klyZO61SZmk8Ovp6QpzfOHFULrQ0h7wqLdKyS8bCO8gmiIjF+do+64+zmuZ6TXNRaZmUaR5N0TDI0+vJH3XK9OftuluBPUs8byS+I4WxJBjqkFX27xf52tdy5Sc/iQei4RtvxMIx6riGe/PNFNHv2LGwAGcgCE6fTl2/GugwUJEKRMHXX4/La6/laN2PaXpE1q9X2bnGopbEb/fumD5D3FIrf8+cKbJ0qWjf96qnEQIkSB36y6uvxuSpp2LyzDNxee65HHn55bjs3BmTo0dTBMOpU5Ph3VpNtTyun3ZHZsEJhg6Hw+HIFjBGcoJhdoOxEfZN+gJ79uyRvXv3hn7AunXrwuqFzB2NZP/e4XA4HI5rgXk55hHpYzBWhf/A2BibJ7Yn2ijOHQ7H7cMJhg5HFoNJRScYOrINyB+yi0yZY4KVazah70jB8sImgM2NJAjbCIYQn6jPXENHQBSkvgN0A/e5TvtNWRlZgs45jrKEgEIZ06HnPvoFkhxhml90FWDlQu5DQGR1Q+7bOy5evBieoV/AwOBGVgQaLZAe0obOZNVG4k180b/nWJnpwAF56623glGUuJJvxJ885V57e3u4x7MHDx4M20uTP+QFaWUbSr72Ja8hKZJWnuU58hB9znVH5gHdxUAVOYA0S7kbIQuZYZKX8oWMi3zj/8iRI++Tc20S2Mo50we51AX0BcRj0otMI++kmYE7fXoG8cgr8j2RJgcoU1uBFGflPFq6eTRgaaD8kD0IAegs9FEUyCl6HnlGttFNVu6U+bFjx96/hpwju6NFAre8hsiNQ+9ST+x9pIlryB56k/4wbQcyiLM4cQ1E48i19N/IL+Q60k7dRN+TH6QVfU0c0M2AOo0zmcARBv6JE3WDfOYa9YG2Dv8jAXuPvfNWQNrJS3PRvGrRtNIu7XjnXakpKZM7Fy+VEpWVnAK2hVY/Wu4XmxoDES8/liPrqufLtiXLZM3ceVJdUSklhQVSmJcvpdqezaucEUh+mxctljXVNTJ/+nSZNXWKVJaW6f1CaenqlKqpFbJiTpXkx3NCuvp6+2SAOpbQfqH+xfV6QX6eTNW2dLrGY4Y6ns296r9L/V/p6AwrKF5uaw1bHZ+4dFHmTZsuczRstnLGDysPksoSfXam9j/YlnlG+VSZMXWaTJ8+Q2bMnCVV6pbMmi3rq+eFY5fG43j9xUBuZJXEQnWsaFhcUCTlWqaEX5ybI3sOHZLWzg4p0LJGbq5n00B2ceMJyhpH2eNGSj6vhbEkGEL80+ZW9u2La91NkSjDSpaJmN6LaVzQIalrOTlsq5Pa0ljFQmpqRFavTr5PMIxWM35HoeKl9Z2tjmNa5+Nh9cFly5KycWNS+39XPSnSn9Pkax8hpnUtpvorrvmQkKVLk7JixdAEw2vF4VrQ6hpWKfzud3PkxRfj+j5WS6RPniJA9vfHVKfHVH/GNB0xrVMxHeujt1IkwyiGeu9w8bLrdi3qD6SHle4/HUO9Z7h3g2vdGwo36j89ntHnohgujJuN183ACYYOh8PhyBY4wTD7gU2AMmTlQrZHpm+/dOlS7QNvDH18t2M6HA6HY6xBPwKbHI52ibYKey72U+zfLHQyFrYvh2Oiw2uRw+FwOEYdTEDQiWMS2EhoHHFcwzHJij/HByA/yBfLt9HKIzrXbFuBY1KI9xlJCAIgE0aQ3gBxgIiAsWjRokWycuXKQFCmYw7hgzCWLVsmy5cvf99AyP2ZM2eGsCAl19TUyNq1a4PhySbgo+kiLPziiBsDg/GUDfIDQgmkEwYmxAmCIflAHjFZDokM8g3+ICIycIEsyaQbv5F//FOOEMsIi3qAY6DDxBxH7pN+/BMWfgnH60ZmgvpA+TDByqQuxnFkwQi7Jg8QTbmP/EDY4hoEJsiGyAikJWQh04EckgbSi8ySJtKKMZm0MKlNPcBPVHdNBPklPWaYwDF5j54cLb08GiCelAdySVlBFkzXL5wji8gn5cqEAYRY/KHreG7fvn3hPvnBtdHOA/Qh+Y28UXeIv4F7xA1dSZyQTX5H44Qf63cgk/zmaNf4DfDPc7wLIjgOP5AFkXfeQRzID+q66XzqL+f44dyIiFzjGa6TV/aeTAf5wDbEoe6GP70WS7nANoqlTAgXW67IG8eOSFx/b1m0RO5dtkLuUbdp4SJZPqdKlsyeLcuq5sod2hfYumy53Ll0uSzlgybtP8wsnyKLZs4K/qaVlAby3nnNs71nauXNo4fltYPvyauHD8rrR4/IzlMnw8qIbGPMqoFl2k+J61/dFfVfe1pe1zjsOn1SDtedl7NNjWF1whztN8ybVhnScfD8OXnrxDF59egh2an+ICFSFlMKi2RacWkgOlaW6rGsTGZr3ObNmSvrNL73rrsjxHnJ7Dmhf5MiLdZLl9b7/gFt0zXd0/TZVTULAsGyom9Aag8cktdefTXICbKU6aCsyQurG9mkz64H5qbpuqoYyl13JeXRRxPBPfJIQu64IxGIfxDp6IYuX56Qe+6x+0m5//5EIBdC8iMcqq6qFdUNqZUNb6RoyUb8qfpQPZFaQVCz+JZBeBYHjjcaFnHXJiuQGFlh8b334pqGmKxbl5CHHkrI448n5EMfSkhVVVLlNibvvpsTtmxWVR/iThogUOIs7SYiHImH3VcxCu/DcW7P4MeuqSoMjnsGyyvSxb2h8jc9TPxwjXOeIV/S8wT/5D3PROM9FLiPP8tfwk4HzxMm78Qv57yT3zxn7zLH/WhaLL/sedxQaXU4HA6Hw+HIdDBmYCyMvYAPEDln4QrsvXzkYDZkh8PhcDjGEsy/Mf/IvCWOeTxsuNiysWlng63O4cgG+AqGDkcWg8aQSU8mW5nI8hUMHZkGDA42eYm82mQ+51ybKJOYo41oPkG2MzcSsLAw/rCSFV/x2Fc+kAUxDKFbWHmJ++gLjlyj/nPEH8+ZH36jT3BGFDR/OM4JnyOO54xIiFygU/iCmecIF9JjdCWqsQYEGnQs+pZ4VlVVhXihZ+mzGBmH7Z5JC6QU0kf8IZtxbcOGDe+v6EEdIG/IW4BOhahEmFyjL0Qd4b3oXcvb8Uq/Y3hAyIDMRJtKWXEOeZT6RFlSrrSzrPZH20xZQupCBnCQDZF37iEvmW6ENSMyaTV5RP6pH6QbIPukg/vIMHWaupLtfQXSSV1FD+Coo6SLtNJ3yob6SfkRbwh0EFspsyVLlvyKfkEuGaMZwQ75RW9XV1cHfYf8otcw0pgupMzJi9HIA+tDEFeIetQ16gp6kvdRB0kP40nT0dynDTE5xA/3rPyQReoqcgz4bf1f8gd/rDbLEcI876bPzLOEhSxAHkS+0evUCRxhco96zznxJS+Jg+n3kVrFgXwhLrfTlyJepNuclR9hks8ntZx379wp80rKZdPCxaGcc8lTzS+2SN5/+pT8ZNcOqZ5WKavmVsvU0hIpLy6WCvU3XdM7S8sIN037BWV6rZC2XJ/lLWyB3KL5X9fcHEiBkPXAiYZ6OXqxLhD5TjdeljPq2Gq5pbNLOjVfe/S9zVreZzVv952rlcPq96ze7x4ckKSGnWqXtT9TUipzKiqlSGW2ubtTjmu4py83SFNHWyhv0tirss6WzfmadrY6jmnZw6aMIctsf6zhFOj9SpWlcmRfw2jXcoUQmafP5GlZ5qvfnKJCTVuJTC8ukXOXLsp7mi8LtH7wYYX1bYYCsobLBJAfgLhG3WiAemM2IOogdXk0VjAk+lQ3FcWwKuDmzUnZujV1hFzIaoNsZTx3LuTDQXnssWRwW7Yk5Y47RHVcMpAPVQ0E4h0r+124wEp/KQKaik8AUebcVjCsrWU7dAmEvTlzWOEttTWxqgLVD6lniJeKkKb/2isYIh6aXeHZaBy02oQ4UGyERRyGKy7ipl3UsBUz2z+3tsbDO774xUF5+OGE5klqK2fC4B2EM2NGUsf5ouP9VBxqa1OrG3Z0pFZ4NDXGPVWF2lawvXJM38V4IpVOraKappi+L5VOy8dz52JhVUmuEX9VY+G8vp70ibY5KeId14kL8cKfql8NT0L6yQ/8qNoN78DZcxYmeVRXB1Eypv7oj6TiS3jkPWHjl2fwy/MqkuEdqlJCvll4lsf8vnw5qXGNq39WwUyli7hTxs3NkPhTecF9SIc8d7X7H9KAPJAG/JBuVRFBXkYK9Md8BUOHw+FwZAMYM/kKhtkLyg971ttvvx36AdiH77zzzvAhOfaFbLf/OBwOhyO7QX+CtoixMTZc7LPMW2LPngjzFA7HeMMJhg5HFoNJKSY7nWDoyEQYkcGINEyE85vrtzoZPpkRzTc6yOZuF9bZhogBeSDqMArhMPbhuGbnrOBnLv131B+TTNGwuM+7aOvtOSbgiYPFBf1l4fD8eHf6IYqgL5FjBiLoPeJHeUAqYaCC7kQnQh5hYs/Si86EWALBEH/oUSMporN5jus46oeRN40Qw7t5H2GMRHk7Rh7IAWVFf5YBK+WFLDCxS3tJ+0ybu2DBglCOkLbwBykVuYK8tW7dutAHHiny0WiCuki9RFaRScgikKroW1BXkXvqBbLPPeo79eVa/ZJsAOnDUSdx5AE6jPSZ/sp0WBzRZcge6VixYkUoM+6RPsgCbO2LDsIhu4zRIFAbwZCJBFahZQLIiLFRgtpIgjCpY8SV+oRsUbeoZ4A4olepZ9znt7U/xJeyIV3IKCsrEH/qGaRBxqLEHYd8Wvypz5AoySdW6cUf4ZNGwsUfecQRgibvQR6Qc2vXuEY46HbiQD7Rdx6pekCekFbSZn2DmwXxJ03mLP2EST6frq2VXTt2yLziMtkMwbC4KEUwRE9pfh4+WysvHnxPls2ukpVV1VJWXCiF2r7lqWOb4IJirR/Uff2dk8OagyFwGdB87e7tCysL7tMwLne0yxT1B1FxpurIqoppMl/LeJ6W8RzNt9KiQuno7ZHj9fXyzsnjUtt4WboH+mWayu2iqrmycsEiWb5goSyaVyPz5lTJHB0HzWGcwvnMmTJLw52jbq62rVM1Xh09vYG4eKT+gpRpOfHuPC2jOMknKzWOpDEOyVBiUqB5UxBLyVF/YlCaNL5szzyV/MjNkVz1l6f5Uq59lnqtP+d1bFVAP6i8LOj34fQ6bT4uU4Ac4ZAD3Gj1u8aKYAgIjiqnxRGIhjgVm0DuevvtFEkMMt3WrQlZsyYpixZ94EeLW/WFyKuvxuXHP84Jx3feicvevfFAJGxqgmyXIiFSdYxgePp0aiwN0Qxy29tv58ibb8Zl16646s4USU1FUXVFinB2LYKhqg8NT+S112Ly05/GQxwgCe7ZE9c2NkWcI/5axUI6hwIiBinu0KG47N6do3keU92dlAcfHAzv0iqi+im12mN5eVL7Kqk4LFyYDARDyINf/3qOvPxyPJAnKysTIY+0WoT4vfZaXP7hH3JC+ojP7NkpUuVbb8Xkhz/M0ffGNB9jGufUCoqkgbw/dixFwmtvT4Y8IH0vvpgKh2daW1P5S9mRBvLyRz/KkV/8Ih62e4ZsyfG553JCuBAoU1s/p0h/L70UD/d4H2XGOyD0ER55i2xos6JyKPLCCzENN0efSZUV/g8eTBEheTf5o6pD81HklVfi6j8e0k157t8fC8/t3JmKkzYXIQzeS9lRzmy3jZpmVcjXX4/JM8+QzhRpk6aMfB4pOMHQ4XA4HNkCJxhmLxgXsZMF5ce4mfE0xEJsBPQ/GP94OTocDodjPIF9ifYJ2yn2WWy7zMnZh/I+bnU4bg+jYzV2OBwOx6QFkw1GLGRC38iFtzsRPtlBvpF/5CN5amTNkQDEAiP8GTGOjjYkiXTSBL9xRsigY27kCo447nGd+3bPnuU677Pn8Bs1Ptk9/OPM/3iCfMf4SX4THwYolAMT40ysURaQb5gkxw/O5J40kg9MqNs2uBAOyF9Aui1/eAZ/PG9kj5EqY8fowOoO5CEcv5ncRS6Qa8qV+sSRsrVV/rhHuSMzyA/GWQa7mQ5kH11AWjmaToJQCWmWtEC8gsRFPiDvJsvZDOohaSV91paRLhx5ki3pI57InuneaNmQRsoLgwvkV/QRaeMa6bXn0IGc8zwyjOP3aOYB8eC9URIg4BzHdeoZ9YlzyA6sGkoaAOnkeUAdtG3M0eGA8A2cE0Y0b6zd5Z61b9RX8oa6TX1Ar+OIA78hQLLqI20D20QZoTwbQLpJJ+Udi2l7p+XfR5um7WBC80IvBnbRVM3zZXPmSHd/n5xquCRtXd3BXz/5NTggg/pcAO2Ynif0Xp+GcamlVXbXnpKzTZdDWGybvGrBQlk8f75sWL5Stq1bL/esv0PuWb1WNi9eKgtnzApbKNe3XglEwxzN/1kzpsu6JUvl7tVr5D51mxcvkZXV82Th7NlSXTlDqiqny9wZM/T3HFk1f4FsW7UmbHm8ZeVqmT97lhQW5Etbd7ccvnhBjlyok9buLhkY1Pjqe5L6Di1wckJi+q64OrZlXjNPy7GkVN47d1Yud7SpV9UJA4MhXZRsaVm5LJgzVxZUTJNTR1Lbi9N3yBagA9Bx1n/hfDL2QUgyxDMIg5DInnkmJs8/H5cjR1Ir5b33XiyQzJ5+Oh6uQ1zTrHofnEO0g2wG+Qyi2v798UDQg1j2y1+mCIoQ0aLPRUHVQT2xcuCLL8bl5z+Py7PPQiyMBfIdZDZIblwjXOI1XFFRXUtKYqqXINclVV+zil4skCUh+qmYhhX7WHGRLZM/+tGk3H13IpAQtZmXy5djIR9++csc9Z8i3fX1QXCGLAo5UDQfSFdOSC/dHAiG5BckP+L43HPxkGeQ6vADAQ/y389+lkrbiy/GAkGQvGV7Zp55/vmY7NiRyifexSqDO3dyPef9MCHxEd7u3ZxDPsyRp55KkRDffDP1LuIBAZHyIu8hO2p3LOQXZfzqq6n3kY/4Z4VFyge/zzwTD2kgjaSLFRwhRkKepOx5Dw6CIvmJLJCXlDnlQxi8HzVAWbOi4htvcJ0yjGs/KbVCpcPhcDgcDkc2gXEC420b77BgxfLly8MYmLFydMzucDgcDsd4ABsscxfYZGmjsNliE96/f3/4uIEP2bF3OxyOW4MTDB0Oh8MxoqBjxqQ7RgaORsZw3D6Y6DWiA24k89WIGjfjRuq5dETvDXV/PBCNB+UAWQwSGY4yYStEBi0QUHDIPw5SCUQNVotldQ/KjZXf8AO5BZIKZBauRcknNqnPcTJO8GcTKDfKj5XOINq99NJLwSEXDF4hGUFCQgZs1TOW49++fbvce++9wTj73HPPhVXhsgHILnFmUE6cIRKy2gAOmWeLHAhcDOBZ3Q1jM3Ugm4GuxfBAHTYiaDYTcIbSr5ZGVgGkbJFXI8tCOkRfRducaBjRcMYa1D9IjtS9VatWhVUv9u3bJ7t27QrpAcgsZDnkkfvIJyRKdDYOHWwYKi2mh9P1MX55PwTLdEeYxIltorZu3RrqvJEcMx2kizqL3mI75L6BfunuS7VpyH3IAU1fzawqeXjtHdLW3SUvHT4odU3N0t7F1v5d0tHaLr1t7ZLs6gxsrWRvnwxqn7Cru0eOX7wgP9z5jrToc5uXLJXfvOs+eWTb3bJixSqZvWChVM6rkalVc2Wqtp+lGgf2VG7uaA/bGX9881b5nQ89KB+7615ZvXy5zKycLiW5OZKncczR98RVTuOdHRLv6JBYlx61rc7RMisoLJIpqo/nz18k96xZLw+uXS93Ll4qx+svyouHDkj9lZZAjKR8YyrnyVC3UysZ4tgKmdUUY/GY7K87K40an4T6RQckNF2aMRLL1zKfPUuWz5ot52tPhxUzybOozGQ6iCtlbP33bIr7SAE1B5kNktizz+bIu+/maDmLrF2bkAceGFQ9wkcnKbLbt7+dWrmO+wbOU1vysjW6qMwltB/AB1asiBiX119PrV7HVryaxUOC69rcyJ49Ij/4QY689Vautj3xQAJctmwwrFrISn3E4R//MbWaIeEPVVwqvmGFvJoaViTUulCQDKsSfu1rufKVr+TIT34CaTHlV1WkrF8vqkvZOpoPYFL5gYiTZo78tvdwJL12j3hzH8d5b29MdS2kvri2LeRHUuOR0DiwJXFM05cjP/95TiAIzpyZUD3J6ohJYevqfftSK0ZyznsIU7vQgXjZ3JwKs7k5JosXD4aVH7nPVsUvvwxxMTeQAcn3FSsGVV0ltf8Vlx07cgIJsLMTXS6yd29M/umfUisWEjZle999A5r+QVVbxCEnrKoI0ZFtpkkT6SWtpOfIEcLLCX5pRmbMYDvu1CqYKZJpPMSJ9xE+K2eeOBEPBE/Im8uXJ3WcMEShORwOh8PhcGQwsAuwYvKRI0fCuAGbDx/WhfHjONoGHA6Hw+GIApstu8mwew/zM8znQTDkw3R27MHu5XA4bg1OMHQ4HA7HiMAmJSf7yidjAYw5k30CeCwBWYTl0xmUQLwh3yGuQLzhN66+vj4QCViZELKhEUnsCymIWBB32LKM7XEh7OD4YoqVDTHCsVU9zxpBkTpkK2k5MhOUEwNSyhfyGbLCVo+QmShbSFoQ0jC0IkPIha18SblzDjmKZ/CT6UD3ILfHjx+X1157LRiVkV2227VtxCHVIrfcO3r0aDiSB9mqp6iHlBOONFDHWaUOAhZ1Ndtg6UEfUZZGlEZ+kVfaFNt+mG1y0XXIOY7nKEsjInKMkg5HA9a3sLjyXuLB+aFDh0LdM12KQx8jf9QrKx/STLypqxiTmAChPqLL+U34gLSQTtv6niPPcJ3wLB44niefrjWBgj5ATqj32aTHjWCIHkvq38XWFjl0oU4aW9ukT/M+0d0VWEvTZ0yXDYuXyJI5c2UwmZCXjhyUp/btlp2nT8kVzT/yXRLJQMRDNzRq2e04dVxONzaELZXXLVwsyxcskOkzZ4T8zdV8jrF6oJZVoqMz5PPRunOyp/a0zJ5aIY9tulPWL10ms6h/Gq+E+uvVMupSv/1ajqwkmNQyCisl6u/OtlQYLSorvW2tkmjvCOHnY2CcOlVWaZs7r3K6FOcXSJeWe7fKw/vynEixppIaliYEjmMgOLIqY3t3T4qMqNfIH9G0B/96WlFSFrZjlr7+8N5Gbe+RsWwCdY6yo35QbpxPtn4mZDHtjsk99wzKk08Oyu/8zqA88URSHn0Ul5DFi5HpWFjdD7KYiqHqh9SzVPWpU5OyYUNCPvvZhHz5ywn5zGcSISyIcFoNwqp2rHCIeA0FrtfWpkiOvGP69ITce++AfOpTCfnd303Kb/1WQlatopzYdjceVuJj+96hwkNF0SVduTKp8RmUhx4aDMQ7CJBHjrCaXm4gG/5//1+OfP3rbD8sgbxoz94u2HZ5+XLyYlD+j/8joWlIypo1KSIh4bNS4j33JOTTn05qPidCHrOFMcTCujoIhikCY1QEi4uT8pGPJDS8Qfnt307Kww8nZPXqRFihEQIlpM7HH0/IF7+YkM99Linr1yelrIxVekUuXoxpW5FaVVCbAtm2LSkf+1gqfk8+yXPEhzgOhmcoL+3ehHhE44CMVFUlNB6D8md/Nij/1/81GMp5xYqk9uchDkIqZRtokWPHJBBK2RIZ8irlNn16UuOcDFskOxwOh8PhcGQLGF8xDsd2wDiXD0shb3Du5EKHw+FwZBqwx7LrDR+dM/+C3ZcPzyEZcu5wOG4NTjB0OBwOx22DCVkmIJmInKyTkWMJm/yF7OD5PfqArAKhCLIIxjRINUy8I/fc4zoEAoxprGTHVrH4h4hkgxf84xfSBuWGo9x4jnBYRYsvqSAjAsgtlC1hGFnRkXmgHI3sBPEJQs66devCKmlG4sIPpDvkwLaMocwZxFJvIZXyJR2EpUwH8UX+IaOxxSxpQ0apF8isDdghy3IPfxBqkf9s1VGUFfWU+kj5Uk7UbwhRGCmyyYgebTuIO7JIeUKkM/Ig19Bhtg02MssRkAfoMO7ZSpajXa4WZ2TI4g2IL6sQIntGCoRcyDnxh0SIbgb2PGkl3qwmiB/C4h7OwDnvIR9IK2WP7qbcCY/wuU99ZiIlG0mmNwLSjLznaJovar4egETd0S69mof9HZ1hhb+S0jJZVD1P1ixcKNUzZoQVCeuuNElTZ0cgHMapGuog47HK4XnVk/vOnpGGtlZZN69GVs6tltkVlVKYX0BFkyQ6sbNLnbZ/HW1y7tJFOVJXJ41atotnz5F7V66SatWXSAAE7VqN0wkN72x9vVxpa9eySq2cfUXjWX/litQ3NUujHjv0+X5WHNRwkxp/yIAlmrZqle0VKieVmg7i1KR++jRdQaQ1zmGrZC1/SIYJfaanr1/69HpgGypSydP/eSChTo9F1I+SUimQuHRpnC5daghyl21Ir3fZqr9vBah0FQ/VEUnZsiVFDNywIbVqINdpqlX9I7KqM2OBjKeqE5EJyMlJBoIhq9M99FCKlPjhDydCWISpWSpnzrAiHgTDq8KUBhU5bWNTW+6qqgtEt3nzEuqSsmBBUhYuhMCWEj2IeGxbDMGQsIcCanPePJFHHknKY4+l0rRyJcTppLAl8Asv5Mh3v5sj3/kO2xXHAyGOdJHG2wVxh5D58MNJ+Y3fSMq996Z+QzxERZOWrVuTcv/9KfLm9u0JfYaqx3bDqS2XqUJREaRJwh+EywcfTMqddyZl6dIUwRBHmJAEeSf3ly2D0JhaRVLVUCD9UV7k5113JfT9g4H0qN1wKS6OaTmnVluk+93XBykwFso4mh/5+azGmJT77huUz39+UD73uURIA2FA4KSsc3OTYRtkVpg8dYoVFlMygxyxmqSqzkCmdDgcDofD4cgWMA5jxwDG4di5li5dOqHHxQ6Hw+HIfmDfhRDPfAW2XRZJYKt/bMlRm7DD4bhxeM/P4XA4HLcMm4CEdMAkpJPdxg7RvPd8H10YwZAV5phoh0iF8QwSGWSy9evXh605IYpBsFqwYIEsW7YsHGfMmBGIg2vXrg3+OHKPaxBcILpAyGJ7WZ6H0AGBhVWzAH4gcPmXwJkJZMNIWBhXKS+2PWZLVIysyAP3KGPkAbdx48YgN2ypjNyYXGQDwRA5REYZkG/ZskU2bNgQZBd5xrB8xx13yObNm0MefOQjHwnpsxUNsxkQ0SAUQgCmnNAFOAwU2VY3SQtE6NWrV4fte9FRpAU9Q/og5rG1L/LJNt6PPPJIKEeeQdbXrFkjDz/8cJBp5Hu0JxLIX4w/xI138n7qFnHhyHXqmMkhxD9kkzhTPoA4cs499C/bN6F78U84RkTkXcgqMks9RZbR/eQReULe8E6Td35PRN1s+UDeQCbtGOiXY5cuSHtPilzPioF9bW2S1PM8lZuVS5bJx7bdJV+47wF5cutdcs/yFVKleZiTE5e2rhTpkK2Ij6lrhi2lyM/Nk4S2pz3ang4QFmymnp4Us6p/QHr1fF9tbSD+bV28VFbNnSdFGqdkd49cbmySA2dq5ZcH9ssz+/fKq0cOyqG683KhuVkutbTIvjNn5O0Tx+TwxfPSou+Pa3oCERAkEhIbGJRYIim58RxZMmuOVGg636s7q3Gsl/bubhlMDIZVEFkhMcEWz9om9/b1y7nmpnB/zpSpUlpQGOSKVQ3jMa0DFrz2xWL6fCmyp+ljBcNsJBiC9H4mZOvJAKo0xQeJEMLe8eMxefrpuHz1q3H5H/8jR772tRzZt++D1Qc1iwJZzbrgPM+WvDi9Gq5BKJs9O0Wqw19nZ+zXSHNREKaKsjQ2xjVsVsGLya5dOfKtb8Xlf/2vHPnGN+Iar9TzkOBURMMqijyXDvwQP+6Rpi1bkvKlLyXln//zAfniFwfCts9sTYwqP3UqLj/8YU5IL6sijoToQm6EjEf45A1HViAkLpwTf1zqPEXu4xn8EmeIfaiGaF6l8viD58hfrcbhOa5TfpybX96F45yw2NKY8HgOv6wm+ctfflDG3/xmPGzfzOqUiP3gIEd9OAKajRkzEtoGpYiIhG3XdXgQSI2E39AQl/feY2VIVk5MxWvhwkQgiloeOBwOh8PhcGQ6GBtgD+Uj0sOHD4fxAeNixuiMyZ1g6HA4HI5MBTZO7JtmR+Zjcj5av3TpUviA3edUHY6bh/f8HA6Hw3FLsIlHIxba5KN3yMYO5DV5jpGH/HeS4egAQxmkKgYgkEwgX5DPEE8grBjpBDIGq15BQILwwsAF4g6/8QP5DP+EA1kHcg7nOPzalp6Ez/P4hejCux2ZCchaELMoXxxlBtEJOaEMISZxHcMrv3GQm6KEQ+4hA8hOpgPZRE6RdwhoOEsvhFvSZs7SSB6QtmwlYlEnSTP1lbKmPmKYIE2Ufzali7iazFJekOSQSX7j0Gk4dBayzH3Ir+guI1XyHKRYrqHzCG80YXFGR0Js5f3E0eqdnUPQJk5cQ+74bcRByhCCIWGYvrW02kqUBp4h/cg1csw5+hpdjNzbOwif92arXF8P5BlpXr9hg0ybPVMaOtqku69X+xop8h/EwP7OTolpH3BqYZHUVM6QZXOqZInKTZXma3FBfiDkPX9gfyACvnvqhFxovRJWNmzu7JDXjh6SXSdPSO3Fi9Le1iaDrF44OBAIgIN67OrtCVsq069ZMH2GzCwrlzxYfPR3NB6JgUGpKC6RCi2DQe327D97Rt46cVQO19XJmcuXpUPjWFFaItPKy6RE5TZPyz+0rySOfpI6yIHTtV6XFxdJh76/qb1dWjo6pbOnV7q7e6RX3aCmj9UaIVeG1RdbW2V1lepsjQ/P56rsxIP8aL9A44b/gb5+foX+GR8MkIZshfX16WNOlpUMSSJ8V7YofvfduLz1Vo4cOBAPK9/FYolAjosSyvCfYAXLq2RCrqtoBGd+EBEVw0Bmw79mp+Zr6t5QwA9EOMh1+MNB9mOVQkhqbLMLOY2V8hYtGlSdltpueCjwLHEnPadPp8KZNSshmzezzXBSPvnJ1BbOGzdS9/AXC+k9ejS1euDtFjnxIv3kh/1GNbPSI+A69y3fon55t4odauHXYP4sfIiF0d8cDbyLa4A0Wpik9bXXcuSdd3Lk0KF4WG2QsmSbZbreV5uQEI/0fOBd+KFM7b2AZ1jZcNmyVBhscX3wYFz274+FbZKJy+LFKYJhVI4cDofD4XA4MhmMB+rr68NOFqxiyPia8TFja2wkE3Vc7HA4HI7sB20UdnzmKLD3cs4uOOy6xG4+Pp/qcNw8rpruHA6Hw+G4cdiEoxHbODq5cHwQLQuOXgYjDyO4GDkMsgkkFEhGGNXM4cf8ch8HoYHfUX9GTLLr6UQlrkGiYel2CD2E4chMUG4YU41MaERRHOQrCEzcg6RkZW3XIO/gIClFyz+TQRxJB3WA9GJQxnFOWkgnDjIeskt+8Ju6kg3pGwpWxqTH6nQ2w9JDuRi5EHm1NHIknVwzP8iy6TuuUd6UMfI8FuVqcUbueL/FF/kizsSNOHLP6lQ0bpQZaeIZnP0mPdEyxb+9y0iIvAt/9n7yYyhi4lCgXYZgxocY2UYyIy9I69133y2LtN3r6O2RuivNcrm9TXo0PT3dPdLT3iF9ra0yyCp/mlZykbzs1fO6pmbZd6ZW3jh+VE5cqpcuiIilJTJ/xkwp07w7cfmS7K49JTtPnZDTDZekVcMYHEyxiAb02Nev/cpkQvJycqVU8z4/9ypjiHIsKpYqDWfripVyz4rVgdhI/N47f1Z21Z4M57OnVsjyudVSPXOWlNFm56WWOhvQvmqXlgmuV/uvrLI4TWW5umKa9A0OSO3lBo1PQ1gJkdUKIVQO6jOsxPjeuTNypbNT1s9bILOnTNWo5IT0xuJ8dJCQRE+P9Go6+PoZkiLvs7qTzaB/b338ydDHpKpCNNu9OyavvpojR47EtShjsno1W+4m5KGHEtoX/ICwlg6yiDA0y/Q8pYM4V5ELxELEWNUTojws8EP4OM61Cggr5a1YkQxEwM2bE7JtW0LrZyJs8bt0KR+9pLYcTodWR6mrIz0iL74Yk1deiQXyIH61aoctiz/1qUHZvj21tTNgEW1WMGRVxKvVDnEOaSM8Sx/p4Xe2gbhD4Ny/Py4/+1lc9u1jy/+YrFnDttYJzRPyNKE6/+blnTKbM4dVCtlOOxlIorW1bDsdl/r6eCj7JUskEAyv04Q4HA6Hw+FwZAxY7emsdhBxjAkYd2MLYtztcDgcDkemAzsn9lx2tMGmzW4jrMrrKxg6HLcGn7F2OBwOx02BDpetmsfEuRMLxx+W/14OowtIApBZIF1APjHSVNSBa/2+njNijxG0jPjiyExQZpQRRCPKjmMgnAxx3cqY86gz/9kCSxvpijquGaLpx2VT+oYCacm2croWSEdU/qKOe+nlF73O76hMjxWi8YnGxeI2VHyjsGeGcungWnpY0Wu44Z6NAmMVqzvwVSzn9JeyCbRDbEk9d948ae3pkVeOHJbdZ04Hcl6/9gG79VpHZ6f0dHXJQF+vdkISYeW+y22t8rO9OwMhjxUCH1yzTj61ZZs8sv4O+Y2Nm+TTW7bKb+pvyH2vHz8qb584LqcaGgJxMdqf4YwsDnmdmyfxwkLJ0XZx+qxZgYRfPa9G5s+dK6vUbV20VIrzC2SvvrNQ5RPSYbm+O7+kVHK0TQ0rChJnjXudlsm5xqawQmL/YELmV06X7UuXy2ByUN46eUxePXJIjly4IFc6OgMJEbJjO1szs5Wz/rFK44zycimkD0BcNYxkX7/0t7dJe0ubhtsujR3tIvl5UjU3tdJnNsNkfah6NREBea6xUeTkSbYmZuvdpNxxx6Dcf39SHnhAZP36ZCD7aXYMCUS4tzcmPT2p7XVTv9kqNybt7eSjSGlpUvt4KdLeUOC6iu/7pEEIhmyxzIqDn/98Uj73uaQ8+WRCnnhiUB55ZFA2bmQ1Q7YXvhpABKSHlQ/37InLT3+aK9/5Tq689FKOdHTwBT9xgbwoMm1aMpyregtbQ7ONMORB3k982eaY36zuaGTJtraYdHXdvkyM9PiF4K4VJOljFUiIl+fOxUN5QRp97DEIhklZty61/XFBwdUHbgJUEcpO1URYYVLVT8in8+fj+k5WgRZZtEhE1WrIa4fD4XA4HI5sAHMArPTU1NQU+m7sLgBJA/ulw+FwOBzZAOxz7EjDh+QQ52nTfAVDh+PWMIxJ0+FwOByOocEEOasWsiKPkwvHH0z2ppMeHKMHJthZyYqByGjkNeGPB3nHcXvA2Nre3h6+fGtpaUmtYKWurq5OTpw4IWfOnJErV65IW1ubnDp1KlzDHTlyJGwxky3kI/Q9up801tbWysGDB+Xw4cPB0IwjLeTBRBycW9vHSnR2TrlnQ7kZKBPiTflRVsgnHwqkA39cR2bxd/HixbAd0rlz5+TYsWNy4MCBIL9W3vgdrfImXPK8o6MjxBuyHjJo4D6/IfIRT+JEvDEU3UqcKFPkFyMT4Vh5k0beTXq5d63wzT9+qfsNDQ1Z+UUsq1FgeFu1apWsWLNaznW2hW2CzzY3hi2FIRn29vUHomFnV3dw7erqW1sCuTCh6d2+ZJmsX7REls1fKDVz58n8mvmybOFi2ajXN+r1edMr5UzTZTl4/qxc1PLtUflktbh8bQdztT3s03c0q27t1HckBhNhtcD8vFwpKsiXPG0nIflVlJbJklmzZFppqbR1d4Vti6cUl6RWLYRYqHHs6emVOi23Ixfq5FxTo3T29mg4eeonT0rLy2VBdbWsWrhIls6bJ7OnVUpFSakUaztPW9w/yCqGg4EsWV1RKdP1PcWFBWHFw/PNTVLf0qxxbJP6pmbZf6ZWdp09LTllpVJdUxNW9aDPkK2gH0KfBFmYLP0SkqhiEch6muRARmtpiYWtiY8eFdm1K666MxaIe4AmoL8fMmEqb1gJr7U1pu17TJ5/Pia/+EVMXnopLu++m6NhpFawY/W6OXMgDw6tE3j/ggUJWbkSImIyrCR49mxc29tYcCdOpLYx3rs3J6zCB3GR+A5FWOTalCmpLZVZmZBn33wzLt//flx+8pN4iN9TT8Vlz57UlsikC6LhzJkpEiTkwvnzkyEMiHKk//nn4/LMM3F9LrV99K1jfFbFJE8oB/KZLYspQ/Lw3LnU6o64kyfJVyvfG1+pEfmBlAk5lG2S2Y6aZyGcUgZz5yYDWZQ8Hqq8HA6Hw+FwODIRjJMZczP2hlzIOIcP0hgjOBwOh8ORDaDNsoU9sG9ht2U+gzmdbLLvOxyZADdpORwOh+OGwSQQnS0MCtm23d9EBZ1hVtLDMQnspDSHY2yBLoQgGCXYQUSCTHjy5El577335OjRo+E6bv/+/bJnzx7Zu3evvP3224Ggh38MtpkO2gDIVxC5IBe+++67IQ2kE2eESdI+0QbmVs4Qy2gDOeKyiWRI+RFnCG+UFW4oMihpQiYhFB46dCiQCSHKHT9+XHbs2CGvvPJKkGF+X7hwIZDnRisPiBukSMh+vOvSpUu/Emfey2/uER+c+YnGCf/p6Uz/jX8jYJJeZJnflDfGJvKNPIGYSf5Y+XMfQiFHO4cQiT/qBfWF39kiJwYI76yku2HDBvnIww9LbmmJHG+4JO+dPyeX2jT9mnZIhj29fdKhdaOlo1MuNDera5K27h6ZNbVCti9bITOmVUqssFBiRcUiJaWSV1Ym5eVT5N5Va+ThtRsCUe9o/UU5fVnLTfMuN54jRQUFYStltk0+q3qzXl17e5t0qax1dXZJt76rD7nT/M7ReE4pKZFy9Z8bi0uMP+0LxfRcC0T6OzukRcuPrZoPaNybuzolNz9Ppk2dIgWlxRIvKpQp06bJ9rXr5RN33SMPrt8gqxfMl5kVU6UwPz+QC+Ma3tIZs2XprNlSVFigfa7csO3zSc2PExfrpfaS1qkLdfLq0UPy6qljMq26SlauWiUVGm62bhtGHmJ8pW9JHxN5mAwgmZWVbHGbDEcj1f3iF3H59rfj8t3vxlQvplYnBCqyqgNTxDzA9c7OmLaRMfnGN2Ly1a/G5Tvficvrr8fDKoYQz1avTgQCIQS3ocDKecuWiWzenJSqqmQIjxUIv/GNHPnrv47L3/1dXP72b+Pyv/93jvzgB7mq91IEuDSVFoD4sV3vqlVJqa5OathJ9R+Xv/mbHPmrv8oJ4fyv/5VKX2NjPLy7ulpk6dIUMVFFOMR39uxEINyx+iHx+Pu/j4X82Ls3+8Yd5AnpguRpRM9jx0R+9KO4fOtbcfn5z9k2OUeam9k6WbQ9SZEMh8rf4cAqhqtWJUL5IVM4Vr5k6+XhtrN2OBwOh8PhyESYLQSCIecLtXPJri4Oh8PhcGQbsG2xOxmrGWL/xYZsH5g7HI4bx+SwEjscDofjtsFEvE2kZ9sk+UQFE79GLpwsK8s4HJkEIzedPn06EI8wulqd5B7EkilTpsj06dPD13HoUch3+EOXshIaJCTIUVzLdJAmiFUMvNE5kI9IEyRKHINy7pMWBub4nyjtBe0fZYdR3UikEMyyKX3ElXJB9iDLQaCDDBcF5Um6WKlw586dgQCLwYVVW5FliHaQ5sgPW811tIlHxAmZIt+NwGogPcSXukQ8iZfVQWsTSTdEQRz3+c0x+tvAOXWalUZJJ34oa+ooZQ9RkN/UefxwnSP5hOMZyInoA/KYugL5OFtJt9RzJpDuufdeWb9hg/QW5Morxw/Ly0cOytGLddKtsjSg6RpU19nXK3vO1Mq+s+dkbXWNrJlbIwVsbazlJ719kuzskGS7lp3KXDyXVQgLZM6UqbJu3nwpUV1Z23hZWrs6Q1isTLh54WJZOmeOHK+/IL88sF+e27tH3jxwQHYfOSJHa2u1PK5In4ZFKUMyxMXUwQFKDKj+0fgM6n3IhYfOnZUWfX9VRYWsmVcjNVVVkqvvjuUXiD4kMY0jzxcWFUl5ZaUUq7+8khKJa/qRb1ZLzM3NCSsrFublS67Gt1D1X1lxkZxraZYXDh+Q5w7slf3nz0inygyrF1ZXVwc5zEZQd2zlQtIw0ciFqIa8vNS2vxSRFm+4BjifNUtkw4akbN06KIsX046x8l88OAh4M2YkpaJC5bSQlelE9U9qpUOeZdU/yIM1NUhiTNv4eFh9kDCWLUvIgw8OyqZNIlVVfCCUegayGfHhSFZzZNvijRsT8tu/PSgf+tBg2MIYImNtbVz1TFx1UUzfkZDf+I3BQAbkmaGGAYQHURCy4he/mJDHHktomgYDye7SpZjq+rjqMa03Gt2FCwflYx8blEcfJczUKnuQ4rZsSciddyZk0SJ0GFtH8/64LF9OWPR1Pog/ceCdpItr6fnLMXUv5SzNBku/hclvnsHhP3X9V5+xMHkOF30fwG/qfak4ch+3Zk0y5N+aNYng/9QpVoiMhzJds2YwrOJIuUW3go7GnfNoPKIg75Yvp5xTfnCEt3x5Unw+3uFwOBwOR7aA8TbjcD46ZGxr4wS3PzscjskGbJPY9dCL2BLN7s31G0H68+n2yOsh+mzUxsm1oeLAtaH84240zhMR2LhqamqC49zK0eFw3Bxy/lxx9XzMwSQQkzI4m7yoqKjI6m2EHI6xBI0fk6CQE5jUnzZtWlimfvbs2aFxHA40mAwKeY7JT74647k5c+aEujgU6ICwQg2Tp0ySshT+vHnzAtt/sqxoMZlhHVLriE7mTmgmAEMOdZV6jsO44/XQ4Rh7QM6CdMQKhZCIqIfl5eXvD1C5RzvNb66jQzHMQpbCsbIZBC3aU9rwTO8Do/ttZTi+9IN8QprYPhfiGbqotLQ06CdIX/QbaDNse81shvWDSD9lR1lTdrhsMbCbMYo0nD17NpDlli9f/v7WEJRvlMiHfNJfpI84f/78QCiFQAe5buXKlcEYA+kQWUDGRyMPiBPxpu9JXDhSV3C8D/miXBhPcqQs6JuSJosTfkgz6aJOUnb4JSzCRzZ5DpA/5Iut7rhixYowZiUvuIdf4sNvZIJ4cOQe7yJsy0uIiDzLb/ra1+uf3wwIn3QRF85vBcSL9Jjjdzq4hl6ijAc0je3dOu643CDNba1h22JW9+vW9LONcIf+3ll7Kmx5vGHeQlk1t1oqy0o1DI2vPisD/WQwghjCjemR35AUL7W2SJvK3sIZM6WyVMsOXaLvhdjXruH2D+BP9W1buwaWkLL8AikrLJCivHzN85i+f1AOXzgvB+vOyco5c2Xp7DlSrHkNmbZOy/zdk8dD2ayonicLVJ7LVB/H4jlX4zUoSfq2eoxpVpIX+A3Q++RzW2e3nNJ0s/3yEg07X+tCHHKi5j3bLUOuJJ2kJal+5i9eLAuXLL4myRCZwWUaTC6I92jobtoGswFRD7H/UDdmzJjxQb6PIqguWrXVxWTmzNTKfuvXs9JcatU5yhESG9vZQibk2tSpsUD4Y3tbSGlsccwqfxzXrBFZtCgZ/BUWxgKZb926ZNjeWJMVfkPSY7vcbdsScvfdrGCYVN0RqoLq3FSeazbI2rVJ1TmEL9pnSJEVITMStlbBQHycNSsW4kF4d96ZlPvuS6h+Tt0n7ungGuqNMCC4cSROhEW8ZsyAqCjhvZs2JUJ469en7pMPFD9kOcsL4s2qf5Ye7b5o3GOBOHfHHan84TnSVlCQur56tYT8IE2qLrTcU/fI8w0bEoEgae+jbCD0kXfk06ZNbDOcSkN3dyqfKLOtW5P67mRIn4qU1iV0VexqmZCPqXRS3mxxzfOQOgkT4t+cOak8YzVB0kfaCJs4EeeNG5Mhv/AH2XLdOglxgkhaVJQqA8rZ4n61Cfk1sIU0q0+iBsifD3+YeKfyc6jyul1gE4LkTjtGnVqjAor+Hg3Q9kDuh2RP+0p7eMcdd4QtpxwOh8PhuB6wpTDHQJ+Qtop5QcacQ41JHOMHxptGMMSGwNgQWwDzQtgHHA6HY7KAdos2i/FWbW1tsCli0zG78PWAvZW5A54lDMZStHm0gddr+7BL4Z/n+CCcMNDJfNyMjQV9HLVNM1bDTm87vZh/+ADYRRmzTeZxG20b9lrGz7RrjGVnzpx5Q+XocDhScIKhw5HFoAPD5KcTDB2jCTqkyAwdWZvMdowfGChQ56jjdHqjgweHwzG2YAKXfizbyEImom5CaAEM1CEh0T7TfkJ44j4DevzSDjOoZ3VDSEz0gSFzZDLQNfTT6TdAmOQ36aU/Qv/BSCkYMTBycMQPfjM9bdcDaaQNxCCE8QYDEGRK8iNb+kHWftCOQwrFOBYlGJJGJg8oO/xxDVldvHixLF26NKQVIgH9x/Xr18uCBQuCLIzmKobEAdAPpb4ZaYL+p8WZMmFciSxyjXaROGFgI16UG3UQoxrkSPxQ9zDO4Y+6inxyHUe+4I98YvKEsDEC4sfqMff5vXbt2lB3iQ+Ocxz5QpyRFfKNek7feaSMVWNFMARcJ58YK5D+Hs2fOpWTs5cvyfkrzdKo9RxyYHNXhxy+WCft3T2yoWa+zJ8+Q8qLMVhq/BJXv9RmZUEts5g6yH2QBi+qLjx9uSE8t2jmrEAwzM/LDeTC8qJiqZ5WKVVTKsI2xXX6viod76yqnidTtHzxR+rZWvmgyu2BunOyem61LJ9TFbY3btNx0qmGS7L7TK1MVz10x+IlMkXLgrASHZ2S6O2R/p5uHU+lXJ+es+qhRlbiBKx5m9S4t2g4B86fDas11qj8FWj9L9D3FxcUyhwt7+U6hlpXs0B/54etk1s1XWV6Hd0+nG0D2cVlEkwmkG3caNTr8SYYIuYQ3SCgbd+eIuktXJgio2nS3/ejyQ8ERIh3mzcngl8cKwFCPtu6VeTee1PnEPy0egQiGvfxv359MqyCyH2IeBD3IMvxLshsJBV1AHGRd0CY4/6iRb96H/IbhDTC2rKFOCTknnuSgajIOyDEwR+7XtZxX6txIMNB0EvFMxW3e+/FpfKC96v6CuknH3iOeQfIdZAEIcndfXdC7rorqfovRYokLjwPUZM843nyhPC2bUuG90FMJH8Jl7yCsEd+8jwkPt7B+0gv/u+5JxGehTBI2RAmxEB79/z5Sa1bHzwD2ZC4kY88r+o45B/xJ828h3uQJ9kqmvfxPPm/ZEmqrFipkXTgD5IiK02SNvIdEilxgFhKvm/blloBkbiTr8QDoI5RIaoWtf8nsnNnXPuIcX2WcJGDD/JiNOAEQ4fD4XBkC5xgmB1gDMfYGXIKZcW49q677tK+zVTtZ12nA+pwOBwTCOjCN998U1555ZXgGAuhByGm3cgYCLvivn375OWXX5ZXX3016FXs5cyxY4e5FmgzmXt46aWX5Omnn5Y33nhD3n33XdmzZ0+4x7w+9jqz7aG7GRf+8Ic/lOeeey7EG/8HDhwIcxW0t8R7MoK8wS4FL4I5GrPxY+PG9ulwOG4M3gt0OBwOxzXB5CeT5LZCz61OZDtGBgxcIPDgrkUIcDgcow8ITZDoIAuiHxmUPv/887Jjx45Ablq0aFFwDFTxy0cB1FsmYW05fgwBbC+MoSLTgb6BdAJJinhDUsNAQhoZiJMHfNmOY3IbQwmkKp7JZmB8MHIhR/LBXDaB+NKGUH7p7QdpREYhFEIUQJZJKzJLWXOfZ80RBo62iN+jCYv3cO8hDkxwYFBD5jCY4UgDIJ7UQYhMnDORhRGJ/gzkL64Z7D3RvEGuccDukVfkD7KNIZH6juOcMHkfBrtNmzYFwgUGQ+KZrSCPIKts2bJFfvPJJ+U3f+uzsuneeyQ5tVwONzfKc0fek6f275G9Z2ultUf1XDwnbDmcoM+YgKSXkF7tRzaofF1qaQlkvg6VrYaWVjl4/pycuKS6pL0t3IfMNzDIxyyxsMVyeWGRlBQUSK6GOZAYlLycXCnTfC5A9rQsEuoXgmF3f18o03hMy5DryYS093SHlRZnlJXLzPIpUqzh5GiYhM2qhR2dnYEseeDsGXnr2GF5++gROaSycbmpSQa1jFXwJS83LlNKiqSmcnpgD711/Jica7gUiIj5Gt7UKVNl5oxZMnf2HKmZNVtmlJZK46VL769ymS1ArtELyPRY1OvxAlUb239VVYq8xkp0EA7TqydqAWIbBDJIgYsXQw5MEcog9bEFMs/PmpV6HqdVJFznPoQ8iHQ8t2wZqxymwsKfqRzN7kAmhABHWNznnXafIsC+DSGNVQYhuUFihBAHwY75AFU77/u/Fkg3aSR84kY8CQdyI6sRkjbizX3eiX9AHOw54kd6lixJEQh5fyp/UnnBb54lfyH4EUfIfJD4rBtAWKqKQ75znzxTdRneAyD9cY/weJb84VnCZOVE3gWZkOvEEccz5BH+09/HfeJOWZBmViW09xEXyImp/IdomEoLYZAWjunx4D0WFnEn/6NVRdWK1NaKvPKKyE9/Gte2CNI7BEVWmmS1RFbNverZ4XA4HA6HI8OBHQBbFeNnbF+MFxjzRsfQDofDMRmAjRFdCEnwnXfekYMHDwbiNfbBGwE2ZT7Y3r9/f3geciB2dfTs9YBNko+fIQ0yh7B37973HcRD4sUHnAbsuHx8xn3mKMwvtlI+vkafT1bQjrFggH1ATl7hfM7b4bg5RExhDofD4XB8ADpVdF6ZQHdy4fjDJn4x4uA4n6iTvw5HtsB0InWRwSnEDNuqgPOqqirhS3y+8qbeok95BiISZB2+FkS3smJc1BCQqSDuDLr5ShMSIelkVWPIVKSftNjKcEamJB/QX9nafhjxjvTgAAQyyjxb02RtuzlLH8YqiKFcQx4hG2J04hxDmvUD7HnyZizygHfwPvoiFmfiA7mV+BIfZA05pGwwrFGnjNyF/FH/qHd8jUqaMOzxjJH1DaQpPX943sqbvCJc5Hq4lekMEImXLFkSVoCkrkffk40gDyBxbt++XZ544gl54hOfkHsf/LCs3LJJZq5cLiXz50msXPXAVWISBL9+zb9AMlSwxfIVlaUzWm4H687L3jO1sv/8GbnU1poiEhYVypnGy7L/7Bk5dvGi1F5ukLP6++SlS3Ks/qI0qL9pJaUytbgkrG4Y1/cQds9AvzSpHLCVck4sHu5BbuS1bFncp++dXlYmU4tKwhbICS0/Vk+MqYeE/u4fTJEf8Vvf2iK7Tp2UYxfq5Iq+j7ImTFZSXDm3WmZPnSoN6ufk+XNyUmWsWesMzyZhMalMFBQUSZGmpUP1InJ2I4biTIDVEerDRCYX3gooWqouZDTNovD7RoFfnlHRCGHczLNDwcIiLiNRRIQDIfBGw8MPccBNNBGhfKycbgeqDuTo0Zi8+GJcnnsuLmfOsL12avtptl6m2bhdOXA4HA6Hw+EYKzCeYczN2JtzxgqMHRwOh2OyAVshNnA+yoYYiO0beynXbwTYF/GP3ZznCcfszDcC7FM8Txyw13K0XUJtFXuz0XKfj15xvIff5rBVEZfJCtow7MO2KxHlh415LOzbDsdEgluOHQ6HwzEkMBzQccXR0fJO1viCyV6b/OXoBh2HY/xBXYTUxCp9OIiEEIm4ZnoTItKUKVPCymqco1MhN2FEgKzEYJbnrkdWygTQLmDAgFi4e/fuYEwh3hibcaSXtDBQx4Bx8uTJYOQgvdkK0kxZYcTBAEPaKE90cTbC2nZkD7IoacPAZAYyrpM+K2sj1JkjDywMzke7b0D41CVkiDIgzrwXI9qRI0fC178Y0fhNfLlP2RixFfA86cSIRzhs9ct2xmZEIi3A3kUYlm7uEw5yzn3ehWM1RAiE12qLuYej/b6Wv2wC6WDFClZfve++++T3/+AP5N/93/+3/MVf/IX8+f/z/8hDH/lI0H99g6rnevsCaQ9iIc+lVjRMyPkrTfL8wf3y0z075Z2Tx2XW1KnyyLoNsn3xCjnHvQP75Bf79sjLhw7Kq0cOy0uHD8oLB9+TU42XZVV1tdRMrwwrFFIeyEKHlun55iZp6mgPWyYXadnn5rBCp5Y9skqZDqjMqxvQMk1gwA3lKlKm5VqjOmvzosXy4Jq1smT2HDl08bzsrj0txy5ckLau7kBiZOXDharj19YskFVzqqXucqO8dmC/nIJkrXowSXjqTzRMSIuDgymi6mjXj5FAKBvNL6s3Ti50OG4dqpLk3LnUtsiXLqEvRVavTm2NvHp1MhA6HQ6Hw+FwOLIJjGkYL2DTwlYwUca2DofDcTOI2nfs/GZtPviPulsBz6GHsd1wzq5KtsqshcvqhVwzm6br7V8Hdl7aNey/2MWx4TkcjhuHW48dDofDMSSsQ2rOMT6wiV8nFzocmQeINhAK2R7YVjCEdLN58+ZAwmJwz2Cee0YyhNjEMvyQcNg2de3atbJ+/fpAWMoGkC50EGlgW1rSQRvBwJzVzdgWduXKlbJx48ZAwDKSTba2I8QbohlpJj2kmbIk3ayGl01kHNJCeeBIA6toQryz1fyQZ+QTueQeK3BSlsg4QJbJg+XLlwe/yMJYlC3vgUhGG0j+c+Qa7SF1jrhA/CQeXF+3bp2sWrUqlI+BcqKOUVeJP6sKQgqOliHPG2nSSMPkDeEj20Yy5J2sTEo+TUYyFnWBPECfzZ49O5ANbTt48iy3IF+aOjqlubNTevr61fVJn+Zrbk48kAlXVc+T7UuXyodWr5EH166Xu1esko2LFqtbJPfp+bqaBVKued4/MBhWQJxSVCyr59XIVvWzaOYsKSssCsRBVg7s7O0JKxvuOHVCzjY1yYyyKVKBLlYZicdzpKSgUPK0jGobLwfXDkm1q1sGr5JKIT0Wqt9SDIv6nprKGXLv8pXSruH+fN+esIpir8af5RKLi4tk1rQKWab1YoPqubVzq2Wm6kEIjbFYXJKDSens7pHW7i7J1/DQEZkuH5Sl1SOOk1GeHY6RhKoTbTcT8vjjSfn85xPye783KJ/9bEKvpbZj1qbW4XA4HA6HIyuBzQDnNmmHw+EYP6CDmWfgA3/soxAM2cUluoIhKxdCMIQ8Z/Z7bD6OD0A+0qaZzdnyzuFw3BjcguxwOByOXwMdKpyRBxzjAzq6TPYyWHByocOReYCcBOEK0hLELEhbd911l2zYsCHUVwaowAiF3IeAV11dHQhLbJ+6Zs2aQHjCT6YD/QMRhTQTd0hWEIogbkE0grSFW716tWzZsiUQjiAhZbvuIv6QzEgn6TFCG3mRjemiPWEVP8qHc9oZ5JNypTyRTchzyOWdd94ZCGSkFX9c37RpUyAgjoVxyvogkPuM/EhdIT7EwcqEc47cg2C4bNmy9wmGxBv/pA2ZJT2Eg8ySbgxKUfAceYMsk0YMd5AtyR/qMEfqMcRLJ2SlQD0gr8ibkilT5WRTY9juuKu3R7p7eqWnt0/7lEmpKCmVFVVzA5GQVQsfXneHrF+0WGo0fxeoTN6/dr3cv2adrKpZINVapnOmVcpyvXfX0mWyZdESmVZaJnGJSW9fv3R090h9S6scu3hB9p6tlY6eblk2e47M1DKCzMgqh+WFxVKisnOptVXOaJwutbSElTB7ens1jIGwuiFERgiQff39MkXr9j3LVoStlN86eVRONtRLc2dHWIVREyjFKi9zNF4bFi+RTRqn6pmzwrWkylCfhnm55YpcbG2R0inlIS/Goo7cKigzZJ84ogfS64HD4bh5aFMj69cn5VOfGpQvfCFFLnzooaS2F0ltL656cjgcDofD4cgCMBa3lf3NFsS4weFwOBzjC2yf2DSxT2PjglDIKnzoa3S3rWDIb+ym2EPR4Y6hwYf4tmOOw+G4MbiJy+FwOBy/AjqhdKbogPrXG+MLJxc6HJkN6ijEJwb1S5cuDaQmiFsM3iEuQXqChMTAn3rMEdIWBDyOEJ0goURXUctkoIMgW0GQJA2svAhBklXuSLvlAeQrvqSExEV+kEfZqr/QvRDXKFPSwW/KCpeNacKgBFEPgiir/EGORU6RTdLIEdmFULdixYpQzpwbsQ7SHeUOUQ//o902kc/UD+oYceb9xJevb4kXcYDsibEMUiAySJ3jmsXLwiBdttIoZclvrhuxCv9cJ3zehSxDtrR8QZ4Jn/dwLROMc8T5Vt1Ig/wgf+YvWyJ76s/LjtqTgdjX2t0t7biu7kA2ZFVC0a4lcYhr3scLCiVeXCK5KmOlqjfmqM5YpeFsWbVa7ly2PKwYCDExpuXIaoLt3V1ypaNDGtva5b1zZ+XVo4elWX/XVE6Xu5Yul7kVlRo2NMSYlBQVyPSycpmq8sDqgqcaGkKcWjo6QxhX2tXpsUVdW2eXdGn82N44X+NF/E42XJLT+gxkRlZNjOXmSbxU44pT+YkXF8FIld7koDQ0Ncqx82flRGODTFN5RPdRP24E0XK5GXer4FnknjLDZUP743BkA6hKqsq0fUo5baq0/0edu+rB4XA4HA6HI0sA4aKlpUXa2trCb+wiOB87OBwOx/gCeya2T2y12HcgF16+fPn9rX4hGLKqIefY57FtYv90fICobQ1yYUNDQyBrOhyOG4P3Bh0Oh8PxK4BQSOcT5ysYjg9s4teIhZxzzeFwZBasrvLFIAQkjK2cW92FrMRvI6VxHT/RbXazidxBekkD5C3SAMkKR1pIl+WBpQt/OCNwZSMszaQn23UxcTeyHWVFGXJETi2NHEkn5Ua54jjnuj1HmRtRbyxkl3dYHbP4mnxZuXCNe8gf59Gy4shv6iHO8oFrHNP9RcOyd5B+5JprpJ3f9tx4IJom4nKrzvJgJEA+Qcq8c9s2mV5TLafaW+Qnu3fKjtMn5WxTo7R0dkpnb28g8XX29EhnV7d06LWOtg7pbG2XrrZ26e7okEH1k6v9z0KNVyG6U2KhT9qt1zv0uYa2Njl6sU5eOPyevKju1OVLsnjmLNmycInMnz49PNPfPxC2UGblQbZb3lCzQEpUfg/WnZOdtSfDqofN7R0hPLZwZkVE/PcNqNNnCzUtRbl5gcC4U+Nf19Qkbeq/p7tbBviqeTAhA4lkivCocT6r4b1yYL8cOH9OBnJzZOWaNSEvyJPhQN4PVSY346xtuRkgOzxncj1S5e9wOKhfKUKhNiPBaVVzOBwOh8PhyEowH2ArGAIbP4znONjhcDgcqZ1psFHysTV2WwhyFy5ckLq6ukAyhCzH1snYnSAYQkbkGccHID/Mts0ceE9Pz/vtncPhuD5i2lEcN+ZIbW2t7NixIzgq8Yc//OGwIgQK0eFwXB90HPgy4d1335V9+/aF1VbYyo6VZZgQHQ40lCdPngzPvffee2GlIZ674447hp0Ig73Plw8//OEPw9cPbD/HNpCsVMIklWPigElcZAs5oXOVaYgaMjLZqGHN6802s6SJyV46udQt3Eilk/KkXCljh8PhcDgc4wsjmd0syYu+xUh8BEL/wvodtwuLE2OMn/zkJ/LKyy/L8YOHZOPcGlkzu0rmV04PKxEW5xdIIX2csI1xylk8NBQWNwxhhfCC077LwID0aP8FgmJ9W4scv3QxEAWv6PhkRmm5PLJmg2xZvFiqp1VKnvabyBb6OjzXO9AvLV1d8uaJo7K79rTk6HsWzZgpK2bPleLCAvWv+a+vDm+/+r69tafk3RPH5UxzkyycNVs+ouGvqp4n0yFQFxVJXNNAD7m7q1MaW1rk4Pmz8ux7e6Whr0fmLFksf/Knfypbt24N47Hh8pa8ut1+tq04TlpvRBbIY2RuPMmFrIJiNiCM3th/NmzQ/F21KsTN4XDcOrAJvfLKK1JfXx9Wl/6t3/qtMJk0GkDvHD16VH75y1/KkSNHwgrDX/7yl8Nqxw6Hw+FwXA+sisccA31CVpBnXvDee++9OiZwZAIYZ5w5c0befvtt2bNnT9hBgDkn5oScqOJwOCYbTp06Jf/wD//w/vgHm8+jjz4qn/70p8MOFtcDY6enn35afvazn8nBgwfDLil/9Ed/JL/zO79z3dUFOzo65IUXXgi2tpdffjm8jx1nGJPBs2lsbJT77rtP7rnnnrBj0l/+5V/Ks88+G/T29u3bA5Huqaeekq6urnD/3/ybfxPa3MkMtpWmbWMMje3ukUceCVwHCJkOh+P6cIKhw5HFcIKhY6QRJmNVPvhCcSQmrkcaNgHNhGjUZSKY9LUtpm8mH6Mr2nA+UsY14kA9ZjK3u7s7lLPD4XA4HI7xAW08Rj62f2bVxJtt70eqjzbSk3gYLDHUvfHGG/LCL38ptUePSXtjkxTGc2RaSYnUTJsui2fOlilFRVKSXyD59HlycyQnlhO2V9DepwwmE9LLKoTaj+ru7ZGG9jY509woJy7Vh5UQBxKDUqJ5tnbuPNm6aGkgDEJeLIA0p+kJfS8Ni22N6YuxTTOkxD21p2Xv2VMS1+szNe876fcmkpKjfUmyIU//K9G4TM/LlQIN4cSVK9LQ1y/FJWWyoWahrKiaK1UVlWHs06/PXmq9InvPnJb9589KfUe73HnP3fL4xz4m99x7bzD4Xoswd7vlx/PWb8ddj6xIOROf8SQXAicYOhyjBycYOhzDY6T6TQ6H49dxK+MJJxhmPhhjOMHQ4XA4Usg0giF2lMrKSjlx4kSY42c8tnHjRlmxYoV861vfCmNDSIjM+WMv4lnmBp1gmAJj5r179wbHrjlOMHQ4bg5OMHQ4shhOMHSMJGgOkA3IhUaMyxQw4YjD0IQzYqH9zkRYXt4oUdPShZFmpFcuNDAYQWdAMKSMHQ6Hw+FwjA9o8yEYQv7AmJWp/ZlbBWMNxicYTo8dOyYX6uqku61dNKUys7hE8mJxYQSRw5F+XejTpfqjrFwI8Q+y4YD2o5o62qWuuVHONV6W5OCAFOfly7TyKXLH/EWBYDidrbP1GmHwbJ/2cTp7e8LKhR093doXG5Q+HTe1d7ZL85VGkcF+KcrJlQ7tp/Vq+KFPqXHJ0yMEwwVTymWmlsmFzi45euWKnO/qluKScqkomyJlRcUSi8dCvNpYUbGrQwYKC2TpyhXvT4xCGqVMRxPkU/qHQcMB2bK+pX3AMl5wgqHDMXpwgqHDMTRoM6PO4XCMDOhjRt3NwAmGmQ8nGA4PttLEts7R7euOTAY6taKiImyn67g9ZBrBcMuWLWHMd/jwYXnuuecCH2DZsmXBvfjii2EOkHYVwiHbJdPmOsHwAzjB0OG4PTjB0OHIYjjB0DGSoDlApnDXWwVlLGAGKiZ9kUsj3GWLsYl8vFGCoaXTJn4trSMN6r6RC92w7nA4HA7H6CG9nU1v1/lNe0+f3T6imEigD8SECysaMnZgrHLk8GGpv3BB2tvapLOjU3r0nvVJUmsOpqC9vQ/6f/l50qHjkCvNzdLW1CTT8nJlakGBsA7ztLIpUj1thkwtK5OywiIpzM0j4wO5sLG9Vc41N8qVjnYp177VNH2murRYVk6dIhV6nqPZnQiv1HeR9+rYKjlH/yvgwxZ9f2dvn9R1dMjxlhY50dohDZqedu1LDeg7BmNxySktlbmMo7Ztk8c/9ngg2GDLoC832uVJnt0IwZB8tLwkXpyPJ5xg6HCMHpxg6HAMDdpM2knk1uFwjBzo7+JuZSzjBMPMhxMMhwZtygUd09bV1YWxDfZ/hyNTwfifVeyqq6snpN1pLJFpBEPm5mk3Dxw4EFYsbG9vDx+7LliwIMQPPf3xj39campq5PTp0/L973/fCYYROMHQ4bg9OMHQ4chiOMHQMZLA2GqkuHFsGgLMQIVs4WwAlE2DICbVb4RgSJoY7JFO6t9oDvaIB+XMcbzL2OFwOByOiQ4jfQ3Vh7FrE93IS3+DcURjY6M0NzcHo6f1NxmTDNdPsryBgMi4Zcc778jbr78ma3SMs3X2LCkrKJDC/Dzp00cPXWkNpMSa8jIpycsLKyMK2yz39Uq/Pl+o4ZSoX4iJM4uLpCgvV1izkP5XXjxH8q72N+NxQoF4mJQB7S+1a1+uradXWjW+LXps0zh39vdJR/+AtGlY/fPmy4a775btOiZiHMaKlGNF4LM+3bUIhuQh6cKwnAnkQuAEQ4dj9OAEQ4djaNBmGsHQ7SAOx8iBvib9y1sZzzjBMPPhBMOhQXvy2muvya5du4Ltn7bF4chUoKPvueceWbt2rZSVlU3qunu7yDSC4X333SePP/54WMHwb//2b8OcPXOLlDNt7MKFC+V3f/d3w3gQHf6d73zHCYYROMHQ4bg9OMHQ4chiOMHQMVKgKWAC1yZ7x9PoysDHSIUMerJ1svFGCIZmjCOdpBnnxjSHI/tAHcdR34ciedh9kG6Ez+Y6H00XRlWcpY2j67PMg5WZyelQk0HctwlY7pv/qGxzjTI2l+2yPNIgf+g7s3ofbXthYWFw5NVkBPlhx+j5jYD+FOOWZ599Vv73V74icwf65N7Zs2XDzBlSWVQoV3Q89Mvas9KrMruiYqpUFhZJeUGBunzJQS71Pcgm5EFWKkxtxXz1XMsjF4KhyjkkwxTBMBZWUxxUeWerZY4QDqkTvf0D0ql9uyMtLdJcVi6z7rlPtj74oKxavTqMocayDpB/xGk4giFxsb4lccsU2XOCocMxenCCocMxNGgzaSetf+twOEYGqT62EwwnKpxgODRoSyD50OdijA+Zh3m4yTrWd2QemOPjw05sUtgKHnzwQdm8eXMgTl2PyOYYHplGMHzggQfkU5/6VFid8Ktf/WoIExuk2YI2btwof/InfxI+hIUU/e1vf9sJhhE4wdDhuD04wdDhyGI4wdAxUsDYahOU42l0ZTCOkcImQrN5cH4jBEOMcKTXiJRuSHM4shPUcQw41HvaZuq91WfuRfUAeg0dhyGSYzYTKmwVMnQY52zBjlGEgTnHiaTThtPj2ZRGk0XKDAcoJ2uDQFSW8UsfjyN9g2gfATnmHs8jy5yPVl5YvK0MeA/O+ghcNz+A6xaXm41Teliky66ZA/aOocI3f+fPnw8GK4x5GKgqKyuzur6PFygL5PH111+X//Kf/7PUHjkiVTlx+eTiRbKqskIGNa936hilV+V2QWmZzCopkUodB5WpXA4mBsMKhioJ72/BTIkF+eEsWnyUp/4hVXENP1fLqljlmyMkwy6NQ6vquMsdnfLTM2elc/YcefL3fl+23H23zJkzZ8zLFhmjPg5FMCR9xIc2hvptdSUT4ARDh2P04ARDh2No0GbSTo6nrcvhmIgIfWrtZ9KHG2pcdC04wTDz4QTDoUFb8uMf/1jeeOMNmTVrVpiPYwvS4ebUHI6xBmS048ePh/rb1NQU9Ctzv04wvD2kEwwhbX7kIx+RJ554ItiE0hFtI9GZlEmUYAhJ8Pd+7/fCmG2ocuF5HHZJbO4vvfTSrxEMf/M3fzPsFAJ5EJ3E/D193qlTp4aVK//0T/80vP+5554L2yg7wfADOMHQ4bg9OMHQ4chiOMHQMVKg4wkxBFJBdIJyLEFnF1miwz2aRIWxwvUIhqTRBhgcM2ny1+Fw3BwwMGK0gVBEW8kXg8DIW+gz2mx0AWQj6j/HBQsWhH4vOiDbgH7bv39/MFjR70DXQRbBiMFk9kQjU9E+0k5Sduhr0s+RflO2tFnE+cqVK6HMkFP6gxABkEMM4qSB+xcvXgx+jbhk7ZWVMQQhJoF4FvmdPXt2kOfRKG/Ld96LkZR3lJeXBzkrKSl5v24Rb+7jn+vEi5UEbiZO1hciLPrXlC19a+ot/eDW1tZwpNyRcd5B3qQDfYAOwPhYV1cX6gLGPyZkhvLvuDbIf9zZs2eDIfdnP/6x7N/xrswvLJSFZaUyp6Q4bIHc3N0jl7X81mh+r55eKTNVDuhZQUDUUIKs5FBntQxYtTBXz1PV9gPyIf+zWiGywDEeS/kZTCSld6Bfjjc1yzv1l+TEYFKqdNz0h3/8x7J6zZow7hprHUCeIGvpBMOQTs0P5Jf2J9P6l04wdDhGD04wdDiGBm0m7SRyy7nD4RgZ0O+kr0kf7mb7wk4wzHw4wXBo0JYYwbC6uvr9sQwEFYcjE4BNa+fOnaGPzpjbCYYjgyjBkG2JIRhjS2UchB0yHbSNtG+My5h3x6b4zDPPBIIh9nTsqKtXrw6Ev6FsIbSv2DV5nvJDD6cTDJ988slgB33qqaeCYzVD+rzYMin3z3/+8+E+O4J885vfdIJhBE4wdDhuDzl/rrh6PubAuHzhwoXgUKDsCV9RURFWwXA4HNeHTWAyeclEMcZcOhdM9F5roEcng44mzxkZgOeutfoGg0o6IHSeMALQgWIZZyZNM23iynHzsAlKjuMBZAiZzSaixvVA530oA7YZ4EgvjvR6HXI4shvUdQhJEPA50sbyBSHGA7alwEHa4howMhSDVgwK6IBs0nu0F42NjWGLBSYE6MNDqoYABDCsYAQxAky2pW8oQGKjPI3MAxGN9Bn5LhvSRxlBhDXiG8YUPviCHIVRjP4h144dO/Z+GvGH/NKm4bjHl7aUsa3CSVlzPhptGXGmvhBPCLyUATJF+0kciDP9UmSPtNFXRTYpGyMA4ody42htMufRNpry4xrlTN+Y/i7v4iMc4kC95jphU3ejz9EX5xr5xfP85lnrZ+OHPKK/PVw/2zE8yD8cBj8mcPq0LC+pLj3T0irnW9uktadXcvX+ufYO2XnpsgxQNvqv/SqpO1/zPCcnHo4FKjeFBQVSmJ8XziEbsjVyQV5uOC9QF+q0hgcGVEZ6+gekTWWgTuVsd8Nlef1Sg8xYtkzuvPde2bJ1a9DjxG88QPqiskw8kDHrU2di/5L6ZDYg6iv2H8auGN69P+xw3B6wCUEEoE2iTq1Zs2bIya6RADrH+hS0jdiimDjzyXVHpgKZxTkcjpGD9dNvZbzPuIkxF31C2ipsCoy9xqtf7fh1MM5gXGsr8zOmpd/OnNBkHtfSlkDcwv7HGB+55aNNbHvYRdy5G2+HfQB7FHYsxtzoV+Z+sf1h73DcGrBN8kGXjX/IW9owbKSQ/9IdJELGZ5QJJETatxMnTgS7KnMEtIOUE2EO9zyEQUihEBGZU+ADL+yjfHiN3lm7dm3QzdxDL1Hm6KgVK1aELZI5Yt9lsSEWGsI2ig0LciG6azKDMTNtGw4bGqRMuA7XWrjJ4XB8ALfgOhwOh2NcQefayHYTgYRyPZBGW1WGQZ0bzxyOiQEMrEyuskXC9u3bwxfMGF8Z9DPYp87jBwMk5xgSIGxl20QX8YXQhVEDAwUGFtIBMGJhuIBAgqEFfwzYuZ7tMPIZRhsMREYwzLZ2C8MWH4hs2rQpfJlJGWKcwsiE4wMw0oic4o/JHuSVI8YnZJnyxiAFKZGPU0Zz9TbkCYKj1RcjHJrccZ04UybEAbISE1UHDhwI9wDpIgyeQSYJw34jp9wH1E/yh4kBCxfZ5RnC55z04wcj36FDh4IhCuMi9QFn5E1IF0zG4DDwcZwI9WA8Qd8JY9/HP/5x+bM/+zN5/PHHZd7y5XKloFBeb7oie5qvSIuW5eHWdnn6fJ1849gxef3CRanv6pIerasxyIPoYUhs+putk/uC65e+/gEZ0DrOqoWsbAj5kO2RISMmVLTrVE5e0DDfRu6mTJV7H3oovJ/4ZAKQfXQRMkw+ZSq50OFwOBwOR/aBMY+NhegX04dmvOBu4jvGQ4znGZM5HA6HwzGZgb2Fj6mw82N7NIf9dDiHX54xew32ROyKN/Iszp7nWbPNcp0wuM41Pq7GPgv528LFfovjffZe/NmzTjR1OBy3C98i2eHIYmDcYdLSt0h23A4wFCITyNNYT35HJ0PpEE8ksh0EBoxw5C9NrU3+ks6wOo6m2yd/HY6JAeo5ky2sXAY5CXIRXyWi26j7GOYhH+GHLwzRtQzu77777kDiyhb9hy5Dt7EqG+QqtoXgq8s/+IM/CF9UkmaMHeQBvzFqQERjFZ1s34YDQiGTLEyoodvNaENas6UfFCV/kh7IcE8//XQgxX7yk58M/QC+vIWgt2XLlkCYhahHH4FyRHZffPHFsBUQ/vlSlnGbkZlGQ4apU+Q5XwUja9Qh4kVc+AqbOLN6IfGG7Aip9xvf+EaQuc997nMh3hARIRRC/ON5SGHUQeTZtjqmPgKu09997rnnQro//elPBxIhhEJkG+Me+Ui+YJjbtm1b+E08SD91mfadsFlFij4z+cPXyqwkZe9x3DrIW2SCsQ/lwooafN19/MgR6brSIpsXLJTivHzZd+aUTFGRnFNcJBVFRTKrpFhqyspkfnmZlGk5xbW8IBPaaoUi2k/Tc8pxIJmQbpWbi1pXjjXpu3S8dbilTaZrOd7/yCPyyKOPhrHQeH4oQj4gr8gpMm7yZ33MTAXtodmAaEt8i2SHY+TgWyQ7HEODNpPxmvX/HDcO8ot+BuMH5nF27tyhY93mMKZwTHxAQmDVK/q9tCvpoP9pdt2b7RMzLvMtkjMbjDN8i+RfB22JbZEMqQfbia/i7MgkYJN+/fXXg90K+5ZvkTwywJZhKxiSx9cD7SNjI2yX2Dv4bSsYUi7XA+0hdkjGWdhMsEnzLP0x2mfsotgZOcdeylw/9jH6vLyPrZAZC/7/7L0HlF3Hdab7d9/bOUegG42ccwYI5pzEqGwry5LT8yxP8nvrreWZ0YyfPbPGM56R08iicqBEiWJOAAkmEAARiZxz6pxzfvVVo8BWCxndQF9g/2Dx3D6nTp3KtWvXX7uQ4YhzsGCITpT1CPqvGxnMme2IZIPh8mEEQ4MhhmEEQ8NggPrA4jhKQwTQqwnqDnVuuC+GXg76EwwBk4iw8IszpZnBcH2Btk6bh7iFEpbJO2Mkk9T9+/d7hSykmHAULUqCcCQBCp5Y6BNQpKLQYBIOqeudd97xMsEXv/hFL3cgWzCW4I80ouhAoRHrBEPSQrmiTMIFhQwEQ9JN/x4LCAuEkPYoQ8oO4h5HZqCsgoQHUQ+F1dLTR79CNqRek16UV2+++aZXpH/yk5/07zBvG0o5kG8TX5R3xBdlGfFizgg5l3KhLqI45fhcSIZPPfWUr3Nf/vKX/Q5egD+OF2H+SZkxHhN3FHUo/IL8S1kj7y5fvtyXMwRDvgmRDf+UOXFChiZsrNiR/jClDu2Yv1Hw4QDxIb7nkrMNlwb6GMqBMqp1fdIHrk6ueOMN7d64SQ/PmK1Rrpw2Hz6k0tpqVWOtsqlRyerVGNfvzszPVV5yct+RyREnk7n2S7FRhJRbT2+P2ru61ejCPuHq3uG2DlU5vykuzLvuuUdf+tKXVFBY6OvRte63yYfQ5yJHU7+G+1hiBEODYehgBEOD4ezw47uT8ai3QWYzXBjIWVjhZpPOqVMn/QLs66+/4ecP6IgN1z+Y77BW8JnPfEYPP/zw6bsfA7nTCIbXL+gDjGD4u2AsMYKhYTjDCIZDA2RJ1uPpG+kHLoQwRtJfBl0g7+LQ41wIvI/jfcqNb4b3+4fLb8IjblyRdfHPc8Zn/uYdnvObe/RXQ6nLjQUYwdBguDKY6SSDwWC4wYFwjLsWitYgDHO9nhHSiTNyocFwfYJ2DuElHJEKIBHiaPvsKET5iOKRxVcWayBaQHyKlYUu4slCAKQQiIYQ0jgyib8hT0IEg4iFEpq8wLofhKxYVlqQ5kDKI40oZQKhLBYXKUkLZRh226IMR4ESxmHSRNkFZRnjFekNSquAMI4N9XhGvldVVfn4oCDDsaBJ/aMsyP9QBv1/E68QN+JN/CFJ0g5ZFOX9QMi6EKmpfxrDN7jHe+QN7ZtwcGGs5zfWFCFN4ViM4b5hcBAUoliULB41SnfddZcWLl6s7mhEO04eV3N7uz5/0y369594Qn94zwOaMWa8GhWntWXlenrPfn1n+079/Ufb9O2Ptnj3jx9t1T9v3aZ/dO7b7vc/uOv3du7S8pOlanP99V2f+IT+7V/8hb74pS9pVEnJsCAXglAPg/J4OMTJYDAYDAZD7AN5l7kexGU27/zt3/6tfvrTn/nNSGygYV5k7vp36CvsiGSDwWAwGPp0i0EPFU61OZ8beMQxjlNN0Euezf9Ax/tsdEb/xLvoGVljGBgueiD0Qejgeca7YWM1cQ7fJSye8/1Y1tMbDIbhAbNgaDDEMMyCoWEwgKW9sIPlag0JCL5hwZ9F0etxQZR8pa0B2ggOgb4/QcNgMFwfoO+EuMX4iIU32vqCBQs8oZC+AFmXI4UBYzTkLsbVZcuWedmXyX0s9A2QvCATYtENEiFpxdodVtxQYATiIelnxx9yCXIC+YASJBb7P9LM4hqkPEihjFkocRi/kLWCoicWQF1kQRCZEat/bC7gyCss61Fe1EnkQ0iiHH8MEZaypmyxDEhaX331Va1Zs0aPPfaYlx0h0Z1LdhwMEF/mjLQvFreoX4ynKM5wxJF0YD2JdoSyDMuapOvRRx/1zylD0kb7hPzLb8IIlhBJH+VKOOQRdfuNN97wY/jjjz/uSY7U7SC7EBfaMPWaNmxy8LUF/S9lTL381S9/qX0fbdXM7Fz98d33qjAzS1WNTXp/7249v2mddldXahHWOQsK1O7KusLNbSpd2dadJqxSx/Pcs0JXr6kf1Kmxp4+dgShKXRlO/ViQ22NJjjYLhgbD0MEsGBoMZwfjJXIe9fZq6bxiEeQPukFkb6weITdDKER3zIYf2jfjNVcWqg3XH2gfYd7EKQzImOg1vvCFL+iJJ5447etjhPkRMtylyqNmwXD4g/mRWTD8XdBXmgVDw3CGWTA0GC4Ms2BoMFwZjGBoMMQwjGBoGAxgPYsF9asJFFDUG+rb9aqUoH3S1lC0kcbLUbgZDIbYAOI0fSkkwp///Of+KNlvfvObvo+DoIVih/GWfoFnkFawpsa4C3GLPiIW+gfSifUCCCIHDhzQyy+/7CfiyPDsnmSRgHSQHtJOH0j6br31Vn+8UiySR0gvpDbIbYxdlB0yFouUIZ3DiXB0LlB2lBskgBUrVvh6SRqI/5QpUzwRAUU5i4c4yot0Iv9xzDUkRPLgtdde83O3e++91x+jTH1mgXGo6i9ESAgMfBuiH3WKuSIkP9JAvJFjsRxKWSFbUF7El/hBbKVtomBFfiU9LIzQFpF9qJ8ojyArkn5IpHzv7bff9uHdc889PoxQ73lOXkLYIO3jx4+PifK/EUC93bRpk/7pH/5BkfIq/YfHP6lxBYWKxMWrpqVZP3hvpTbU1ej//g9/6Qlt9FebN2/Wlo8+8u2i0dUx6j3PFi1a5BfQqRex0j/HCoxgaDAMHYxgaDCcHchuRjA8N8gXZGzkZGRm5grMaZGd2WiFrM8mi5tuuklf+vKXNHXKVGvr1yloJxBLmQv95je/OaP/N4LhjQn6BSMY/i7oM41gaBjOMIKhwXBhGMHQYLgyRL7lcPr3VQfKZY6owjERYYGGCavtgjMYLg5MaFhsZfKPsIiCB4stWJM530QPhQFHHfAeCztYe+E9FlnPtbATlE27d+/2SgAW4Fi8hSBhC6uxDRbpcVcT1BkmNBABrmflEekkjVytnRgM1zdo4xCyIDtBlEBJzj3GY8ZKJqkoYnnGIiyKSMZt+sJY6R/CAgJpgmgHwQoiDlYN2OSAg6xGWvnNc/KDyTlEtljs75GZGCPpyyFRhiOvcchM5EcspIs4El8IU8hv1EXKDkU41gopJ+THkpISX24QEqjD3GchIRy/gfyHNbeZM2d6P9T5oay/5DOEQuaI1CNkVdoOjnjiaEfEDz/85hlppI4SNxztjL8Jg/ZImPjlytwzlCPljD/ep62G8MMxIrxPfpBPhBOr9fp6BOUAcXTd+vWqOHFC+WnpykpNU5Zrs11uznSgvEzVXR2aOmumRri6TrlShhNcf0wfdsstt/iFc9pGqD9BhjMMHiD2Bh0Qc0vaLX1PGDMNBsPlA50QRABI+bQpxmvklqEAJC0WLyH4B8tmtrhuGM6gzhq58GME0iXjMgusEIggzDz//POelMBGHuTcadOmeYven/zkJ/0GI/5GLg76PHPXl2O+iHyGUZD++n/kY8p+IJC/cWEudSlAbucbyISMVawLMve0udXwAX0EG+zoD+gnmB8ht1MnzrV+dCOA/pPNFRBw0SeENTV0FwbDcACbbNmsy7ovfTr9K/UU/R19vcFgkJ8zM7bh6L9Zx0BHSDsxGAwXhmlwDQaDwXBVEZRPgZhxvYK0BSWdLZgaDNc36Ndo6xAlsMQEyS60fZTlKMpRyrPwyjMsNqHc4Vms9Q/EF0IWRCvSdMcdd3gLd5AmWXTAkcZANoSow2JzrC4UhPSiTIecx0Jb6NsZx2IpXcQdZQnkQHYv4yAZQghloQClOIQ6yo06Sx3lCmmAMiQPeAYJC38o04d6YQHFDnEjLoFUyG8IftQ7nhEv4s59yH8oTyE/UkaAOBJ/6iz+AxmRvKANBn+UZcgjyIUQMyBokP7wLvWZeLCoQn04X/mHhVucLWgPPVjopuyos0lZmdp+8oRO1daIEkpwdaA4O0djc3LVXFfvrfFQt6hHEGyxyn777bd7q5fUccqbumDym8FgMBgMhusNbJ5iUZUjcLHA9frrr+ull17ylsqxgspJOcwPmOc99thjevTRR/XQQw95K13IwMhQyNfmrl9nMrDBYDAYDAaDwWA4F2y2YDAYDDc4rvaiN4oqFFaxSja5WJA+U8pdv6DdYEUWB3lkuCAQWi63XfMuR4FiyQDLtZeatvB+OCKceOBYxOAeLsTvbHEkP/t/P5YQ0kPbD/1b/3SGe6Q/WI4dbvXnUjCwDyedoWwBz8MCxUC/sQTiDzkNR58ey2kBxB8X6ia/Q7qCC2kOi4eAsg31NbjwdyjzoQJxgjwGGZA4sbOUewHEn3vEORAG+z8fDPCNkCfB4uGFQH8XjnamXxvqfLrRQZlT/pC8J0+fruP1tapuavTPoq68xubna2ZxieK7utXqysRgMBgMwx+Mn1hP4gQOc+au1HGS0lA46igyH7LfcJX3kNlDe8IaGccmvvHGG/rhD3+on/70p95qIVaO2HwBsfDLX/6y/uiP/kif/exn/eYkNhaFTTkGg8FgMBgMBoPBYLhxEecmvtds5ou59Q0bNniHhYC7777bW3Rh0mowGC4MlFfsLF2/fr22bt3qrQVhjWbu3Ll+AfRcgLRx8OBB/9727du9pQ/ew7ISbfFsQNHEkTvPPfecNwGP1SKsfbCr1ZRMsQuUjJhNR9F4tQBZgXpmlmEMsQzaTFhAoA+EeDLYhJZLBXEJJBbINoE0dLGgPyBNLL5A8MM6F5bBSNvFgO/yPsekMc7wHscnEQ/GEMLlN5bFwtEh/YlAfB9/LHrwfYgiWEiIhX6CtEO2wpGmEOdA0qTf4x7PSSP3SDf3IU7hrnX9uRwEghkIbYKyJT0DSWCG4YFQVyG4grP1FZQp5Yk/nvM3v0N9xi/hUH95znhusuBvg/zhmA36Q2RyLCZeDYuPNzoYO/bt26d3lq/Q8t/8Rp+eM1+fXrxM3a7eltbVqsp1zclzZ6lo2hSVjB5tfdQ1AESMoAPiyCb0P5BCsRpq7cNguDKgE8L6GOPP9OnT9fnPf97L3UMB5IK9e/fqrbfe8scEYsn6a1/7mrf6O5jg6MydO3d6sr7BcLlALuvvBhvIwcx7saiNO5de9VoCGQm5lCNpt23b5tsVFgxLS0u9PIQlcKw6o+vFmjMWwWnPzMlNXrpxwFwPAuqbb76pZ555xh+xSb34whe+oCeeeOK0r49B3WAuiQx3qfWE45dZY0AmxGo864K33Xab1bdhBOb/R48e9Ueof/TRR/60ANac6CfQA9yoQAbiSHkswHK6AdZdWVO7WN2pwTDUqK6u9hsH2EzAei79K2u/nNSAvtZgMMjPmbds2eId/fcDDzzguQ60E4PBcGEYwdBgiGEYwdBwJaD7Z1JMPbqaBEPqC/UskBoMhlgD7QblPMp4FKkcyQgR7loujIf2TLy4IksxObqUOAVyIQt5/EZ5GI4QvRglL3HAcgPjC8RljiHlOFHaO+Ey3qCwZvzAAhhhQmAMY0iI/8mTJ/0iIt9mcWM4Ew6IM/1nsFxBmlhUQmHDIg6TVe6TRu7T76GghdxF/8e7EI9Y0AmW12IFpIEFB8qMcka2oJwhprIYRR28nvp4ypo0BjIef3OlzMK94Q7KDHmOMqOsQl+B8oT2Bmh71GfqL88DoRBH+lkIon1TztRZ2jDljdw5FOXN92knfJd+iW/Qt/G9oBglXsSbOOOf+/i5WEuDAfRhhEUecYQufQ99O/f5NuFzJS8C+Ro/PA/gGXHA36FDh3x/Rv7Q/jliOZbaeCyCOk79ff+tt/SLf/muHpk6Q08uXOoJhmX1dWpISVLRnbcpf8I4pbr6a7j6MIKhwTB0uJ4IhmEsZWH0nXfe8fK0wXAl6C+vDTaY4yHnzZo1y7vz6WOvJpCLkNshGqDHZRMGi6i7du3y8zjizRyA9ku8maczHnOPOXyszHEMgwf6XiMYGgKYGxvB8HeBDGQEQ8NwhhEMDYYLwwiGBsOVIfIth9O/rzpQLrOIjWMiwu44FllZDDIYDBcGExoWVFm8RFhEmQtJAcXW+SZ6KAxYWOY9FnbYact7LH6ea2EnLLiy2xUlAAuuEApQPA3FgrLh6gAlK/UId7XA4jr105SVhlgFinpIdCjZ6EuRW1i8u5YL44EYc+DAAdXU1HjiTyDXXEw7433GE5TJjCf0CYwJkIguhRADAYcwIOfwbeQ62jthr1mzxi9sMEYRV/wSzzBeMTYF2ZDxhsndcLdgSDrIb6xAoHCFdEpdIM/4TR1BqcMCLIRL6gj3SCPPN2/e7BdMySfy4lwk/+EIyo9F5eXLl/u6QpnRLqg7lB0ErOtJPghEUtJHXYVMS/lTpqQzFtLKAiN1b926db49QvpFocIiKGVIX0G9RAmJfMhv6i6/STtKSuo5G1Qof/pC8oN+gjIfijwg32kjyJ/Ehf6JfKff4Jv0XZQFCx8Q+uh/iBttkDZ1Kf0y5YqMyyY40tmfPMuGHsLH0gtxCCRGvkMceDcAQgT5Rf7RPxBX2kPoAwxDC/K4uqxcB7Zu1eTcfE0eWeTLp7KxQQ2uio6cMV1ZhQXXVf8US6B9Bh0Q4wbjH3IBC8tWJgbDlYHxifGQcZE2BWGIsXAowNiHXMDYyHiJLmowF9eRMQiXsZ9FH2QRxmhz5i7XIU+e7f5gOMYzgH4U3epwWLynjTLmIo9CgnnppZfOkLloT8w7IQk99NBD/hjkO++8029aRz9s5MIbF9Qb6jR9O1YuaTfoZKgr06ZNO+3rY1BHcJdTX5gzMcdDJmSsYl0QAwhW74YPmENRB5hjozdgTovcfq03WF9r0E7YXIF+kw2bYU0t6DYNhmsNdH8QxBnvkVHoX6mn6P4uRcdvMFzPYM7M2Iaj/0YORpYfLhuFDIbhDrNgaDDEMJiMmwVDw+UCRQGKexbvqRNXC9QxFK4oI0xxZIg10FboA9n1D5kPYg0WQuhDqdu0KSYoOBT6KFoh1/H3uHHjfN2n34Z8EkgygZiCUhWFHeGwoEY/HhYoUPJCCgf0ubRb7hM+4fEuSj7iRBxQ+kHy4xtB+cd7LP4RfiBLsTAISRJlAyIhRDkUEbwzc+ZMv0hIOphscSW+EBe58m3uEX/uEV/+3rRp0xnSAOGwSEHYK1as8N+86aab/G4w/LP4SRxQepAG0s0iCPFnUke8IezxLe6Ho6eGy7gTyvvw4cO+TpD+O+64w6eLMkNZzn3G3Ntvv92PtyzwkB7SRnmRPvKEMTxWZGDqFnF/+eWX/fUP/uAPfJ1gIYL0UNfZ3U65089Tj2JdAU2dpn5S5oAypP2HNsvfwx2kgbZGfaV+8huSK5YiPvWpT3k/LOTTD7ATnz6Gukva6Dtok5ATkR2p58iAtHPqO+13KMqYeNL/ULfoK/gGcSkpKTnTb5EO4k08qWvUSfoJ5FTKh36Qfo2+ivdJV5B/qLf8TZ/Cc/zR10LAJLzHH3/c1/dAXOS9MH2mP6KPo49HJqeuUxe44pe5bui3pk6d6i3C8C3D0IGyoRxPHj2qnes2aExGpibkF6inu0e1rc1qS0rQiBnTle7qkMmg1wbIAUEHRNs1C4YGw+DherJgyBiN/gmd1bvvvuvHV2RLxncbSw3DBbQDNp8gLyMTLlq0yLsga/I8ALmScQ6H/IlD/gzyyGDIJchBYZ6PvM+mIfoF5GjkW2Ra5Hnk0gULFnhZnnkbcjUys43DBuRo5j1mwdAAmEebBcPfBX27WTA0DGegOzMLhgbD+cGc2SwYGgyXDyMYGgwxDCMYGq4ETIipC7hAmBhqoCgKi/mXo4AyGK4laCeQySDQIcNARIFYR9+7bNkyX68hlUAogVjGTl/6VAh8TFroZ5FxUPaj4KcPR5EfCBEQdFg0g7DDQgV+UfxzH//IS4Aw6ZODNSwW9yDk4ZfvAggt9PMsLkBSJCwcv4lz2IUc4hLiD3mI77EoyfHExIv0MNkKpEFIVaENs0hBOhhziAPK5VWrVvmFjUA64rs8Zwc88cEiGOkk/oCxBeIWcebKuLZw4UKfNyyGsGBJv8H7KO8gIgwXhQhlRx+K8oY8Iu6MjSheQ/+KQgfFDkpHFnAYe8kn+j+IUCzEkl4sApAHwxmkl3ZAmdEOVq5c6dP8R3/0R75MqLekoT9BlPoWiK2xjEDOYyGEcqVeky7qcawo1+mf6MNot9Q/+rEf//jHnuD6pS99yZct9RXHPcoQ+ZL0QlCA0EeZQ7578sknvWUk2mnoD4ZiTKfPoP+kH8IaIP0NfRALobQZ+ppA+qbesbj7y1/+0vc/n/vc53waeIc+jzKkT+N93gvlyN/UU8Bz6jeWOXkO8TKQMunH8Mt9yBT8zYIbdQKSIeknL6jr/GZBjv4VRRV9Kn2XLTpcHXR3dqnT1ff4nm556m9Pr7rlxlpXTxNduUVs7nLNYARDg2HocL0SDJGjSQd9BeMu8wuDYTgA2RnZmI10yJTIysz3mDNRh3nOb+RC5n/MGYI+DJkwyJbUaf5GhmQsvNTxEPkV+RQZn/aPHgAyEKQg5qh8n7ZJ3JDfly5d6olczNtiZR5juDqgLhnB0BBAv2IEw9+FEQwNwx1GMDQYLgwjGBoMVwY7g8ZgMBgMVxVBAWVKo+GBQG5DcYRDUcLfht8G+QQxBwIfDqIKCwEsAoQ8xLGIgGIf0hWEGCb1kGL4G4IKyjmILuQ1bYBFhUDAwy/fQFFHGyE8yiOEGb7NgjyOiRCO3xDzCBfSI/55PxAQ+S7kFxYQIIQHohTf435IG/Fk4T+8SzwgJBE2RD++zUIJ7/MuSgreIax9+/b5uPAMhTT3CQdAsEQxDRGJxUHiSX6w8PHaa6/5sFBSQn4kP0kD3+Hb4Tt8gziGRZrhgtCfBRdAvoe8xvE3JCfKkbSRP9QPnpHOUG7DHeQ9aWAxgDQFpTLxJ32kjfKCDApZlTpDHR5OZXY5IP6UdVj0C79DW40VEGfaIX0B9Q2HIhwFCukIYwBpDHU6pDPcC2M3V+p4/zwZCtA3QhqkfyDe9IvEh/6Xb/bve0M5UR/p28I94omyiL6IY7ion8EiIum9UNzD+BjCIr18g/cgS2MFhkUWHIQOSOeQpshXiNps5CHusVRXYh3x0YgSXZlHXT2J4NLTlJCeriRXn+Jd+RkMBoPBYDAMFpA5mdu98cYb+q//9b/qv/yX/+Kv//2//3f97d/+rb/+t//23/TXf/3X+s//+T/rL//yL73D3/e//32/sQWCMLoC5t5hznihORRyLHNJZFw2rr3zzjt66qmn9Hd/93ee/MJmIhZJsarIxps///M/11e+8hW/IQ4dBHKtwWAwGAwGg8FgMBgMlwJb5TAYDAbDVQUL8hdazDdcPaCUhriFlSVIYBC5IE5A2DD0gbyAXAIZBfIcxBZ27kIYgVyD4j+QqHCBuMN7kK6o7xD2yFfIdSjysbQVrAlCVuF9FhKCFSwINDj8QkphgYHvEy6EG56FBQcWBwgnEGrwz45E7hEW5cs94kB4IR6BMMM7/YEVBdIWSEOBFEN68B8IRYQT0sqiRiA4ssiBP76P4/uQefg2BEPizjukl/zkN/kB8SmQmfDL9wJhEfIi+U7chhtJhzwKLoA0UWbkE+nCURcgFpIu8gVrkNQj8or8IT+GO0KZQESlrEkjV9JF/Cl7yorFLBbIqGOkdbiV2aWA8qF9Un6BkEsdjcU0EWfqKeUGSZS/IRhidYi6GtJK/aWsAfdJ78A0hzofwhwqEFfqHN+hX6NvoF5R70IciXdwgDSEZ4A08C5X6igWDxnrCIdwuX8hhLADCJ/36JfoL7GUiGUYftMn4+jjIRiSv1jlIO6GqwNfN6MR56KKw1F/cfwewvpqMBgMBoPhxgNyIXMh9ClYQcHSF4RBTowJbtu2bf7Z5s2btXHjRm8RDIujkBKx4vbzn/9cP/zhD/WLX/zCW49jfogc3F+m7Y/wTTbPvPrqq/rJT36iZ599Vm+//ba/hzzM5heIhbiHHnrIWy7EAjhzb+bdJhMZDAaDwWAwGAwGg+FSEburfQaDwWAwGK4IECZQTEMAw8odu97ZeQ+JxvAxIKFAqMIyG5b3AmkEIglKeZ7jgvKfe1jLggwH+I2Vq0Csg2QCsQyrVpBeIC4FS3aUBb95FwIain/8U1Z8AzILYWGJgN+ETXzwywJEILYRDtewaACJhnD4Gz8hziGukMC4QkIkXXwf8gyOeENq5DnxgUiDH94HPCccFjG4xzuB4Eh4xB0CK/EhzjieEyfyLKSf58QRMh7f4jnPIKxhyYHv4rg/nNCffIYjLZC3IN2RH+QZaQaUMY48Im8oR9JM/SKMWAFlSj0iztQ1ygeSKaQvFrrIA/IlpJffAwlasQLqKGkgTaQvkMqGWz28GFAfqWv09ThIdqFP4H4oO9LLb+oxv2njtOfQv+ACwTT0e0MF6heWMCFYhzZCnKhzEAWJI2mgXEgf8ae/oh/hHs+oe9RB2iLPKDvuhzIMdZMrYRA+jjQSHmkkDwgj9NHUAb5xLhA+z+nrQx9NfAwGg8FgMBgM1x+Q89hsgp4AYh9HeuPYzBNcuMdzLF6zAYVNeps2bfIWB3/0ox+dIRly/DhkRE4KQAeBjIqsijyMHMx9SIpYP3z66af1y1/+0oeDfMvpAPfcc48+/elPe4uFjzzyiD/mFtk0FucwBoPBYDAYDAaDwWAYPrBZpcFgMBgMNzAgQUCegBwEgQPyBCQK7hs+ttiGRTbIlxBsINexGADhBgIKBBf8BKU/CAQX7uECuQ/SCkQe8hgSHfdZJAgEM8oB0g/hl5SUeFIL3wSQ/nAQWyCnBUINVxYKIPsQPhb/sJoAwY3weZ/4hbINhEAWJnh/3LhxPkz8YnWL34GYQ/pY9OBd8oJ0ETeupBtCHf4C+ZDf+OM7AFINccYf/gkn5AfxIM2QD7GyQP3j3UCqBPwdvs83AzlouIDyJt+Je7ACiqMM+Hvv3r3+KGjykXpDnaB89+/f79PMwhBpZaEJ8uRwB3lPOiBNQY4MBNlARqOcKb+QVtK5YcMGX78py1gE8ab90LZIL22ExcPQZqjzsYLQl2EthUVLrKP8+te/PnMs2549ezyRmnLEAiX1l/pKOkkvZUy/QX2H+Bv6stDvDQVoX/QBfI+4kQZA/0YaqGPUS8qGeggZEUupo0eP9vcon9C3Eg59G0caU4eJO+HxDUBZk3a+g+Ob5APv0/fQjmnbjAW0V8I4X3/Et+jncPw2GAwGg8FgMFx/QB5kzrtkyRL92Z/9mf7Df/gP/ijkv/qrv/LHIHMNv7n/rW99S//23/5bfelLX9JNN93k59fMkZGt2fi5Zs0a/eAHP/BHKnPk8dq1a70ci6yKLL5+/Xp997vf9ccvQy5k7o88ywbGT33qU/qLv/gLfeMb39Add9zh5VXmZrFuVd5gMBgMBoPBYDAYDMMDETep/dbp31cdTJxZBMKx6DJ+/Hi/EM2k12AwXBgsjLIQComBhVAWvLGSwnEXLGaeCyilWGjlPRaOUTbxHlaxzrUAirKLRVgIESwwjxgxwi/eDkdrUoaLA4QA6kJwVwMs9ENUiDVSxvUM6gFtG1IUSulgVQ0yCYQaCBZhxzxtnbLjnUA8ww/1h/KEdEKfEgggAAU49+g3eBf/9FeQ3ggPx7uBoML3ILVAGsEf/VwgnF2rOkMcIJhAVqGfhfjHAgLxoa/lHn0i8kvoQyGjkJfkI/0khBdkHPpbHP4hKhEm7+EXP/TD9OEsBPCc8MgbHH7pd3mX70Pgwg/h4wKpi2/i8MdiBffo4/FDXoZ8J0+5xzPCJ428x/cJCz+QaVjkII5jxozxR32GY45JP7/5DteweEE8CZMFDu7zHe5NmDDBx4e4U8dIM+GSJr4ZyI3cxx/vUU8gLxJPrDyQ1+HbwwXUf9oOeUgaqB/kOXnNPeJLeiFykj/cIx2kkfvkKZYuqB+keTiDfCeOpCk4ynjWrFm+DCkn0k/ZU66UHeVJveb3cCq3iwXlS79FuVGu1N9ADib9oQ+LBdCeSAuOdFAHqXeUD+2YdNEGqafU5VB+tGfqLOkkDJ5RZ3mPPBnKehsIjHyDfoT4UNe4AuJAOwrP6VNIC/1VuE8ZEm/iT3zpq0g/fvmbK2UJ+F7wS92mHpNe/BMWjneo53yH/IrFem0wXCvQ/wQdEPIifRBtmv6I/tRgMFw+mDtBjGdjDm0K+YxxaijA2Mzcj2Ncme8hJ2ChDdlhMMB4TB8RNvCEOQpjLzK2wTAcgIzJXJl2gHxIm7v11ls1Y8YMv8bBnAiZ9Gwu6GCp18ib+J88ebKXMZFN0cGwUY32FTb5YIEcciGbhT744AO/cQj5d9r0abr9tts9oRC3YMGCM3K6ja2GSwF9O/WNOr1z506v86Pfxeomx2sPBPMg3OXMidE/scaATEgfH9qMza2GD+jjqAPoBdHV0qeEsfhG3kBHO2FzJicqoCsI/XnQKRgM1xqs2SA/hzUa+lfqKWsV6LQMBoP8nJmxDUf/zboT6xq0E4PBcGHEOYHomp1XxkQYqyY4FER33323X3xGMDMYDBcGk3GUTiiYsDzDILh48WJ/HMb5BkKIQiiqeG/79u1+As97KITPpaxFGEVh/dxzz/kJFMqFm2++2SvATDCNTaC0py7gWEy/GkBRFAgApjQaHqAeMOFkVzwTUJTdEEcQqLEQtW3bNr94FJR9LFbxDv0BikDaPyQOFpUgBqJ4CmQyFo137NjhleEsNtE3sbCMIgalFJNb3g1ELJRX9Esoy1lM4v1Fixb5vulaKrBC3MLvEI/+98M96jX3gngVrtzvf6+/op+/wzOA36CgDe2EPCfc8Oxs7wSEZ+F93uOd/t8McWQcgaiOTEbZUP6UCWMI765evdrXAcYIyhSFImXOuyHtASEOxBWEb+KXe/weGM/+aQqgP4Kwx7gTjmwlfwNxb7iNOSF9pCXkdUgbectv6j/tgrTS5wJ+h7QHFysIaaas+H22uHMvjC2kPZbS1x+kkzpIebLwQV0kLaS7f32OBQwst3CPNPXv13gerJaSZsqPdsffvI8L75AXQ5kPIc60JfoFvkV7YtwI7Q3Ql/Ecv/Rfoc7hv39a+4O/w/shDdwL6eReeM496gF5g+KJOHAdyrQbDNcj2GQadEDIn8gWWPGFjBH6IYPBcHlAJwTxiEWS6dOn6/Of/7wnyw8FGCeZ+2FNmLkdG2a+9rWv+TnhYIAxnflmmBuSDvoKjnkdKtKkwXCpQC5cuXKlbwfoSbBgCMGQOSsYKCf2l0X5jaMtEQ5zcea+EAJoy4S5bt06r6NBDke+Rf6kbUAAQw6F0LJw4ULdf//9fr7OPB5dG/5NRjVcDpjzoNPD0v0zzzzj6yP97he+8AU98cQTp319jDBfQoa71DpHPWaNAZmQ9sO64G233WZ1dxgB3RUbFziOHUupbNRDr8ua0I1MpqPf5lh79KXoUOn7B3OThcFwpWAdB/mZNRnkafrXICegKzMYDH1GTrZs2eId/fcDDzzguQ60E4PBcGHE5kqfwWAwGAyGIUVQePcHf6NwRMmNEL5r1y4vhLO7GXIHikUIGNxnJzI77FGO8zwcocp7HAvLsT8oqLgXvsMVUiHCfLCIhrIdpdbAuFxNoOAMZJqgrA+KVP7ufw8EBWvwjwt/8wx/5BeLCPzmOco5XAgrkGX65w15gSKL3/2/3T/88M0QFn/zm2uII473AbuRKSfKAcJOIBPhJ4B3sd4VLHiF98O3g8Mf93Ehvly5T9jBT3D8zf0Qv+B4B2UIx5By5R4W1Vgs4fdwA/lBvEgHeceVdPGbxSUsrYW447gfXMiXUB6xgpBm0kDaSMdAR7p4hp9YS19/EHcUDbSN/mXVv43ECgaWG460UV7cJ33UX+6z4QvHc+6Hdh+ec+Xvoc6HEGcWVWlLtCniT5xCPeM5caKP6t/eQty4hvj3d8FP8Af4zX3CJo38xvEd6gDhExee9X/vbAhERfpCfhsMBoPBYDAYrk8EWTPIlmeTE/s/C7IoMifyNjImRNqpU6d6IsAnPvEJ3XXXXX7jJ3oB5u1sMGfejiz60EMP6U//9E/9MctLly71uhPuB7ndYDAYDAaDwWAwGAyGoYARDA0Gg8FguMEBoQvXH0GJHYiDOIh+EAW5jwUayF/sZmWHc7AyhzIbxTekNYiEHMdOGDgsIqIQJxwsoeKHe/2JdBBDIJNh/QKyCGHGOkgbJBMscJF3WHRkJzhWGiGeBOCPfCJfec4uw0BMCURA8jYQ964ElAFlQ1woAxY2IM9AeiQOhI+D8MmOXIhGF0OoIW6EC5GU+BNWf5CWUB/OlY7+iy4skASS3sV8/1ohxLm/I/7kKy60jf5+Yx0hHcFRlpRvIFKF++RDLIM0UPeGc/27FIRyOZsL4Df19lxEvf7uaoDvBEJhaE/9wXPuUUY8H4o6xzdC+BdbF+jzw7F29K1X2m8bDAaDwWAwGK4/IFfimB+jV2F+juyITIuMi14EayoQEfkbxxwZC/9Tpk7xlgzDs4uRUQ0Gg8FgMBgMBoPBYLhcGMHQYDAYDAaDByQ4yH4otSGyQXKD2AZhKBDSIMVBHONvlNoosCEdogTnfXbNQ1SDUAHJjF344VhlwuQ6btw4T1iDZAb5gqM4IV4QFtYRucc3iEewTjVciBnEIxCpwu8LuZB/kDI5VprjiDmmAAJmIJ0Ef+Qb+c7x9fgjj3iX9zjegOPtAzEvuIEY+Kz/38HxXcqSvIZExKIEZcJxhZQz5QRGjx6tBQsWeKIh5dM/jLOB+BLX/fv36/Dhw2eOWQ35xXepK3wD8s3AfASQd7DAwLGJHLdGHRquZFPiTF4F8ihlTBpD/pE+8oR84BqIu/jDkQ88GwzS6NUG8aW9kh7SRh6QJtptf9Lw9QjSRrsknbFUdiHelBF1D8dv7gVQd6nDlCd1E4cfHH0970CCpqz5mzow1HkQ4k28yPPQvgDPQpyJI/HlN3G6HNBmeZcwcIQfvkE9J70h/HOlOfhnzMOCL30ieUXYBoPBYDAYLg1GmDLcCEC+ZK6/YsUKfec739EvfvELf0Qy9X/mzJn+6GWOQ+aIUuRhjixdvny59uze4+VyZM9zyaYGg8FgMBgMBoPBYDAMFoxgaDAYDAbDDY6waANxAtIZ5DYIcJDPIFGgwOZ+sFbI0bUor9kxj3U7CGqQCSGOscue++PHj/dXiGk8R+ENYYWjLSGNQR7DSiH3AsEF8gZh8K1AXsENF7IScYAgEognxIt4k0fc5zeO36SX57j+pBLC4B7v4zeEye/gn2cQDSHKBD/kA6Q98gUCHuAbZyO5cJ+8DIsM4RvhGt6DGEb+Ex5lFKyV4Sc4ygsiafgmYYR0Dvwu/gmXekSZBuIRLuQX6YI4CYkykFJ5j2f44zd1iO+G406HszUG4kyZbNu2Ta+//rpee+01fzw46SQt5ANESxaASDdEo7feesv7feONN/Tcc89p7dq1Pi8IK5ZAfHfu3OkXwegbSB/HnmNpExIa6b9eQfuiz6Ovouyov7EA2i39+saNG/Xyyy/rxRdf1ObNmz0RLvQV1F1Icdyn3uLwj4Pk/NJLL/kFT+o6R93v2bPnDBF9KBD6B/KbtoT1W/rG0P/wnL9ZkCWOxDfUQfqXSwFhUrYQrUkXYfJt8i2Qv3ft2qW9e/f6NJ8rfO4TJ+oGeRtIuAP7TIPBYDAYDAaDASBrvvvuu/rNb37jZWzkRzZmPvnkk/rqV7/qj0L+5je/qccff9zrU5B1V69ZrVdeecXPRcP802AwGAwGg8FgMBgMhqGEEQwNBoPBYLiBAWmLIx9HjBjhLcVhgRDiEAQJyIFYtuM+hC9IZvjFD0f0QCKcNWuWv0ICg6DG/WnTpmnOnDl+p/3YsWM1atQov9OecHBYxZsxY4amTJnij0PGSiFAIU74fBf/uIs9mncoQbwgmKDkh1CE5UFIZOEKmQWLi/wNMRMSGURM7kPUgRADyQRiD2Q0CCvB0iMkPPzjFzIL5M1AqmTRANIWZBe+jcM/DrINhEPegcQW4sg7hAcRhm/zHAcxh7+JL2Qw4hGIneQtFslIGwsThM07hIH/rVu3+rgTL+LHPcKDNAhhhvoCmQY/vIuDuAj4Dt+FtEqekE/8Tbr4G8fv8L3+RDviNdyPeSKu5CP5EtIB+Ygy4BnpoWwha3GPMuQ55CXqC/fJT/IuVkhqIKRty5Ytev/9932ZU8fJA8qYekTdwtGXXE/EKtof7Yx2GdpzrKSP8qAdU170S/QhEFypt5QpZRXqLOVIvaS94mirlDH1NVhT5W/KmjwYygVN4kZeh76NbwbwXfob+i/6JeLJc94J5RL6KeJJHvAOfw+sn8Ef74f+Gz+0cdJKnpB3tHe+F/pF6gO/ceQff9On4vgbF/ppg8FgMBgMBoNhIJhjsHlr06ZNXtZED3PPPfd4guFDDz2k22+/XQ888IAefvhh3X333V5XUllRqffee89vYGOjF/JyfxnYYDAYDIazgbEi6HjRe6DfQZ/H30HHczXHEnTKQad6NoeuBx0MY+X5gP4GnRAbQ9G/ngukEf09fskLg8FgMBgMl4bItxxO/77qCAtFOBaQISgES0cGg+HCYFE0LKqy6Ik1MIg8HC0ZrE2dDSxwsvjJeyyQQ/DhPRRY5zqCMiywsrCMEA4ZCZJQXl6etzZliD0wUaQu4PgNiWeoHYBMhgt/G64tKAdIfRADIQMyFuPYLY91QqwQBkuFOPoKHH0AfQ7v0Xfg6HtwPONYZJ5BKMQ/79Jn8DeW6Xg3kBqxUkcc6Le4z/chH06aNMm/Rx91LUmG9LX0fyg0IFSh9EdZwd8oJOhLiRvKfaxn0Z4g74RjjiGm0OdC5kFZw3PSSboIl7BYECC8QM7iPuQUyDQodnif3+Qh+UfYfIt3KB/IisGKF8Q1yDGQ2Oiz+RtSId/nmCXCpQ3yHdo/chd+ccQV5RLvMz5gRYF0UVbkA9YRIJThLxBLUfCglOEbfAs/pJHxgXiTtlWrVvk8Ii3kCXFl/CI/eAfH9xhPyBvqQyyAsZH0c0WGpUwhIJEO6j31gHyljtC+qO/kCcRZ/KPIgmzKbxz5OdxB2aKIpK5RByn7RYsWeRIyCjzqInWUNkudAddLn097oY1Snlikoxzp6yi3WEgfcaYNUj/pW6l7kAW5zp4925cX/Qx9wOTJk70f2irlRzohj1P29CE333yzf6c/EXwo5EHqG/lOPJg70m/RVugnyHPaEM/okwDjDuMX/U8YN0gX7RQHMZp4kg9BOY0f5N/wLdIIEZjnU6dOPSMz8zxYeqXvor7zLr95DsGQe7yPoy+gXyOOjHP0o8TJYLiRQRsMOiBkHdozsiN9zFD0IQbDjQRkFORxxkXaFBvBkMuGAoyJyAPIDIx/jMvz588fNFk2zL8YX5GnSUeYZ8bKPMFw/QO5kfk47YA6GvQmzIsuFWxIYd6NbgDZ8cEHH/TkQqwVhk2ZyKDI3cwzaR/Iv2EDDjIoMjDPaSNDMTdxzV49Xl7mam64OFfY/DcooG9nvkWdhvBKvaLfZQMxc8GBoJ7hkOEutc4xN0VfRR2m/aBfQmcyFHXXcHmgj6MOoPNhbsvpJmEsPtf60Y0A2gl6PuST0CejE4+VuT7xR9dB/NHXsvEZ/QfljEwHGEdwl9O2LwfIrytXrvRjIPph8pc48RtdNs/RraDnIc/PBtLF2Pjmm2/6E2PoT6irZwNhfu973/N6IdYegu7oegHli2xAnpEn9K/UU3R/yBMGg0F+zszYhqMPoC+gj6GdGAyGC8M0uAaDwXCDgkkikwqUnyyYXy13vU3argdQDyAEobgORCd+Q4jAMXkPf6NQwqEApDy5hnuEwd84hHEWmLgXwuDKfeoAz0I4oU5QJ3mOMI9joYp3hkpBfrFgMZzJBoQRlGhcUUKQTyzeBbIg+QhpDr8QUgDxJw94Rvp4j/fD35C0WJwjjeQnSgD+JjyIO+QTYaLYg1xIPkBmYQGCfINMQxvmPRYVQxzJb0hvKANZoON9vo2ymPTw/f7lgHKX+6SJMiFuxId3SQth813ixn388T4KHL6JIoo4hLhwj/CIA795n7BIFwrJkDbChDREnHkeaztHyQPyj3IgPaQXBNITf5OXlG9QwJL3pJV8It/xS/nEAoIiknpEvSQdpC+03/4KaEiqLArzd8iXWAPpJU2UEWVF2lnIQ+lK26afotxJeyyAekjbDWRX0oDCNShoKSf6CUC6uE9/QJunbw79A/Wed0PfR7sfqjygbtF2iAPxp85RLgE8p00RP+LGb/op+q3QrvDPeyhXww54iM701aEegxBWWBQObZO+DKUsIF94zjdoB+RDINLTr5Gf/M0CQ7hHPsVSPTEYDAaDwWAwXFsE2RsZElkYORWHvAtRIFgzhFTIfBuSyPLlyz1hC3m+v7w8WGho6dG2wx1at6dNq3eZGw5u7e42HavsVLdnGhoMBsPFIei5IROjl0XHgy4EPQqbyt955x3/DH3J1UDQraKDYsxDh4xeEZ0LuiD0LjwPetVzAd0OaSBN6O/OBdJP+OjWY0UfazAYDAbDcIKtchgMBsMNirCQziSOxfKr5fimwRArQDEfFC8o7mkvKOypx5BHwo5ISITBAg/+UIJAhoFoArkE/2FxAKJKIBqyIxMCFgsIKFIgtEBcQXmC9QNIKgH8pt1CbkJpwt9YeuTbKE54DxIU7QzSD45wWJiACMXfxIMwglKGK+8TH8hF+CM9EOZITyDOEVe+iz/SzaJGICAG6wl8m3uEQ/pJF/7xwzUojIgPeUKaUVaRx8SBv8kz4hcrID9D2fGbciBNkCYD4ZMyJY08I6/4mzzCQTgK+cV1uAPFG2lCCdc/baSL9FEHaCuQC6nb1MngJxYR6jfpQPmIhRJ+g0Cuu5CCcziB9kVbo1+gTIj73LlzfXumLlJWpJk2yN885x36FNovf4f2yTXID6FfGQoQbogH7mwkPe7RT7HgCgKBkH4HEAaOssRCBrvVeU49pt2FuIdvkS7SFECe4I88C8+o79ynHkD+xrrMhAkTfL9NH4kFG/7GAiL5iz/CNhgMBoPBYDAYLhfIvcw/Fy5c6EmGWBVnzs7GLo5K5sQC5F3mZYONxtZu7TnervX72rRqR4u5a+xW72rVBlcWp6rcHM74MQaD4RIQ9JDobdGDoLtAb4FuE90Xp/cEfTV6D8aUoBcLpzeg+0RHiG4EUh/6FsIN9wifjddBL0wY6BHRlw7Uf6KjwkrqzJkzvZ4d3Qn6F/RX/fUqQY/M5lHiwniHXge9PeAdvk/4xIFnkA2JH34CmTD444oDxJP0otMM7xDn8NxgMBgMBsPHMIKhwWAw3OAIC+9XyxkMsQQUCSgmAmEMBQbKhqA8waGw4LgGFCIoPFCg4A+FB7/xiwIGxQQLAig1uIfCgvc4tiCEGRx+A3mN7/MOxBbig9KDZyhaIOzxOyhQULSg9MGx0MAxp/iD3AL5EQIR4aMMIt7ED0UPcSUuvMc3AtmPK6SdEDbf4W/CJC2ER1xxhElaCIOwCZN0BWUPyiX8kO5AvuMbfI98DCSiWOoniDuOMiB/IZxhNWLDhg2eZMeOX/KV+gDpifwhD/EHEWnGjBk+Hznymnwb7iCtlBtlyHEqlCNlGqwVBgIX5C38kC+klbobayCt1GvqMceis2D36quv+nqMwpP2TvpibVwLbRVH3DnmmPQA6mZo04EYih/Kr3/b5H7A1Uw/3+3v6JdwgRgZSIYQCDlKh3QA+hbIz9RF/FBv6Qe5f7aND6QppDFcwz3yCEe75X2uZ3M8g7DNERs4yObcMxgMBoPBYDAYrgTIpciwyJif+MQn/PG13GPuGSxPIesHOXawQHAQ2eqaenSyqtvcNXYVtd1qae81cqHBYLhsMJagt2DDJGPJkiVLtHjxYk80DJsy0QWj3/vwww/10ksv6ZVXXtGaNWv8BlxIfDzjmGXGIAh66H/RfaLn5KjjoBNGP4y+dOvWrd5ff6DbZkxjEz16dfTZbE5nYz1x41k41Qcd63vvvacXX3zR6+jWrl3r44CeJgBdEDpKdLMcl8wVvSz3++t6wm/eZdzctm2btwaMQw9IuMR9sMdTg8FgMBhiHUYwNBgMBoPBYDgPINYEi35YpmJHJTsr+RtFDIoXCHdYrJo+fbpXiEDsY5clyhD84IJFK5Qj4RhN/KEogeCDsoS/CRsiDASmcMQm7/IefngXMhtxCpbzIM0Eghvv8T3C5B3eJYxAaENZw3PiTxxQ5OCXb/M34RNvlDnEDQUT7+A/OL7DdyHMQOjhHu8Q31mzZp3ZdUp8SQ8KKp4RN75BXPHPLlS+G/xChBxI9hnOQDmFsgxFFSRU6gp5Q9mQjzj+Jp8CGQkFXHC8g6KKvEaxN9yBMo+6ThmG8gT9FXPUJ+oj6SNfcPyORYUcZUa8IaTt3LlTGzdu9GmhvpNG8iOWADkZa6M7duzw6UEBjNI4EJ3DTnRIpDjIlFxDOycvCAOHkhVy31CXK+FDHkSpTVy48n0IuRwFR9wpE+4FwmF/xTLgffygxOZK+6Tu4jeQKQPwS9rwx7e4AkiWhBvyhvfpr85XB2jTtAccBMdYqy8Gg8FgMBgMhuEJ5ErkUebad999t7dkyFxz165dngQSrBgOhaze1S21tveau8aurZPNT26+dLpcDAaD4VLAOIKuJ+gt0F2iY0b3gY4EHQv6EUh2b7/9ttcdBZ0GG6gZa9CHojdizOEeV3QobPpcvXq1J/ZxDx0KhEQcOifC7g++SRwY19A5oj/BoXcLOhXiiQ4LEiBkRYDuBiJgOEElxDmQItlYyj30X1j4ZaM0G6P7A70QejLGz/Xr1/u4Mn6ia+JbpGtgfA0Gg8FguNFhBEODwWAwGAyGcwDlCcQaCHZY+lq0aJHuu+8+3XHHHV6ZHwiDPB83bpwny6Hcv+uuu7Rs2TJPoIOkB7EPx45QjjPiXf6+9957vd+lS5dq3rx5uuWWW/wCAWFBwuOKXwh7gdjIN7gPQS8of1C2QNCDRBiOj4C0BwkMvxAMeX7rrbfqpptu8nEiPMKCbEhc77nnnjNp4l1IZKSZ+BC3QAbkGxDiUETxbX5DqoRYGI5qIixIhfxNXj366KM+LOKAP9LOt+bPn+/9ED5hk+ZYIxhCEgxWKMkT0nPbbbf5o2cpb0in5CdET4hNLPygBEPRhRKLsiMfKIfhDtJHGqgLpI0rdQzCHfUAhSTtAcffpA9lHAq8gaSv4Q7aPgpMFJxcWZxD8YiSMxB7Yw3UV+pdsKqJchWFKbvI+RtrjSiDKedgnRVFKn0g+UAZ8gzLpvwmP4aaYAj4FvEKO8dRGKM8RkkcdqpTXqQPP9RJ2hv9E0A5joI5EHrpf+jHwjtBWcwz/BIGoK3yHmVNmYd84Tl9Kn0wfgwGg8FgMBgMhqsNZFPmX7fffrufbyOfIuuHzURY9IbMASkkEDuCYz7A/BViyNWS6Q0Gg8EwfEC/jy6EjZjhJBL0d1zReaCjRBeEHoVxBd0HOkD034w1kPHQx6BDQSeDXpSxhZNyCAOrhmwKDWMNYxJ6HPRrl6NHQQ+EDovwICGih0aHTJyxlMj30RsFXRUOvSS6ZtKA5USIiPzuD94JxEXiyDvoeiAaclQ099EbGQwGg8Fg+BiRbzmc/n3VwWCO8IJjUoxwwgDOwp3BYLgwwo4cLM5gXQdLKmGhPyyqng0I2kwEeI/FZBRSvIfwTFs8GxDKWVRlEZpJAVaZIIow2bDFVYPBcL0CAgp9HLJJOC6YPhN5hd/cgyDWf4clv/EDIYfn7Ljkyv3+V+5DWqHv5kp4hEXYvB/CxF/4Fn/zm7DDDk76beJJv4+f/vHBol6IF365H74V4oUf/iYeIc7hWyE8nod3w3dD3oTv8hx/+CENXLmHf9LDNaQFxzshXK58j7BiaUxBIReUcpCQyMNAOCVdjNOkh/xg3AzpxB9/o+iC7MR4Sv7EQtqJI3IECjbqQSBzUXdCXeYeBFKUfcgJXEOdiSWEOk5dJY2QRSHIQrKMtbQAyg1QVymTQJKDbMdvyg8ZkrSSbuo29ZTypD5Tz7kP+Rg/tGvy5lyy42CAOAR5l28TR/I/9B3EjfbFM0DaaH8QpamT4T7lRd0kncSf36Gfpk2SBr6FvEs6uYe/0NfiF+CPe6SfvOl/dLTBYLgwWKwKOiDmlrRf+h36pNBeDQbD5QGdUFjYpU0hszBeDgUYM1kkZkEWSzSMz2yYQdYfDDD200egs2IxmXTQV4Sx12AYDkC2hqxHO6COIh8iVzPvuVSgo121apXXuULWYFMgG9eQQ8+n32Xs5Hu8Q1uhPbLJCwIGxIgPPvhAK1eu9A4LVByfzDHKWJZigxGyLzI1bZewzifXNrT06GhFl6rqe9TofhuuLaLROGWkxGvcyASV5EcVib/yOQl9Ozp/6jQkVYhH9LtsLmR+PxDUF9yF6s7ZgD6B+o5MSPthDsfGTJtbDR/Qx1EH6CvoU+iPwlg8lDqA4Q7aCYQ2+tyg/0Kvcr6+erghkAA5xpi2fuDAAT8uMA6xZs9GdTaio6skjbRNdCm0dTaiQurDH/fRj1AfsFrImEIdITzCpw6hI0VvxFHGjDfIi0FHeDZQ53gfvQz6RMZV3ufbfCvorggb2Zf6iR4IHQ5zS75LfWXzO0YC8Et/E3Q96JMYMxkfSSdpgAhJeSJjUr+JG/PWkAbiTBxiBZAmIVyy7os8Tf9KPSUN5JXBYOjrB+krcPTfGOOgz4mltm4wXEuYBtdgMBgMBoPhPAhKe5QVOJRqKEBRPDDp4DfPmaTjuIfShCsTlEDgww8O5T33+I1yBL+Ey9+8Ewgt3ENJE94hbN7jN8+5BqUeceQ53+EZ7/AsvE88cP3jFb6JH+7z3XAvvMOV73A/xI3fQYGMC2kO7/L9EL+B74X4B7/9f/M9wo0lkB7SFhaTAlmLCWkoD5RfkAjxw30mrBALgxVJFD2Ql/AfK6DOUK6kgSO0UTqSTpRzpB8FHWnEocSijGN1oYC6SfpQKGKdlHKM1bTQxqhrwQIlVjaxLAoZDyUqimDKjp3ggYhHeaKkDe2VMl6wYIEvV+r3ULdZ8pr6RrzJe75PfGh3KKqJM/0XSuKQBtoUcQ9tijhSX3lOvOmfUJLzDu+SLyD0ZzwL7ZTv4Gi7hEkbD3nDuxeqCyw+oJDGGQwGg8FgMBgMgw3kdORX5FTk3GB1CTLhihUr9Oabb3rH7+CWL1+uV155Rc8995yef/55Tz6EMMNiK/KrwWAwGK5vBP0HuhJ0LOjtAgmPcYUxBZ0HugwIaxDhIRf2P/GB+/hFP8JvSHpsDkHHgt4EPQr+g5ETdDuEy7cuB8SFzaeQ6YkP8SUsCIOQo9mgQrq4R5xwpA09EHodSJG83x+kl7gxdhI/roTFWEg46MEupPcxGAwGg+FGgxEMDQaDwWAwGAyGy0AgLqEgg/zU31IfzyBgQWLiOc9w/EbRBtkpkJtiTVkVSFgo60gLpC7SFkhegTCJAo/fsZjGAMoRhSJpYefz5SpChwNQllIn+9dDFK1hh2Yg/OKH+gzZkPKjTlOegcjHu1zxR5hDCeoN30YpTN0i7iycEl/iA8mX+kX58Jw0DVQAU4bEk2u4z+/wd7jHNZCG+Q71O+QN9/kd2vLF1AMU1ewWR9GOwtsWaw0Gg8FgMBgMgwnIFsibkCEgWiBvBgugWIHCQZbgbxwEQvxCEsFyFSTDf/mXf9EPfvADTzrEAlUgVlwUnL/enm71dHequ6tdPV0d/m/3oO+5wWAwGIYl0LOg28DK3+///u/rS1/6kj7xiU+csfiHPgM9Blb8IKNv2LDBk/rQhaAjCbog/kY/iH4Fi4IcQ4xOhk2tbF5FH8LYQpj4RZ/Dty8VjHGMW1jh5dhixjO+Q1wGhhfGwv4bPc92DwSdEPdJM9/BHzogNriyIZffhthCKFdzw8cZDIbrC0YwNBgMBoPBYDAYLgNMkFGioWyChDWQSMczFF3cx/Ebvzj+RiEXi5Ns4kzcQ7pwpLW/oi74idU09gdpIx2UH79jFZQD8ae8Qj3EhXT1d9wPxELeG1jmVzMv+n+b7/Kbb4dr/+fE+2zxCmnoj3PdIyy+0z+d3Oc34XN/4HtnA8fToUjn2C8Wdgcqsg0Gg8FgMBgMhisBhA0IF2+99ZbWrVvn5U5kThwEif6u/31II5A+IBpytDrvvvzyy/r1r3/tf0OwuLDsiqXuLnV2NKmloUzNdSfc9ZS63N/qde/ijGhoMBgMwxLoNNBthI3RkAE5uYO/GRuwRsgVa4EciQyJEDIiJz2wETWMKYSD7giyIgQ9jhtGdzJv3jx/tDphcBw6xELIi+hbLkafMhCMWxydTHwg/UFg5MQNvhUAiZA4EQ8IiGGzJxYKIc+zaZR49AekR8Jh4zRkReJ80003+dNaOPWDE036f8MwvEHdCvpCys3c8HGUicFguH5gBEODwWAwGAwGg+EywIIOO3TZycsRVDiOn0KhVl1d7Xf67ty5Uzt27PD+uLd161Zt2rRJmzdv1tq1a/3uXshHLPTEEojvsWPHfFpR1pWVlXllH4taKPIuvCAVm0BhyYIbO7cpT8oOhWUsgDJDqUo5rV+/3i8eoijGsgnpwnFcDGV56NAhXzdx+Mft2rXLv8eOceowx6idOHHCW0MZqvImTuQv+U0bQpmMkjiA7/J3aWmpjyPx5ffl1MFQtuQHR/gQDt8m3/gGhMFw5M/ANIf8wwX/xBW/XMlXnhkMBoPBYDAYDFcK5ErkXeRfZPOVK1d6Agcy86XIwIQT5rQQFZnLEh4yNfOGcwGLhS31pao+sUWle9/Ryd3LdWIXboVO7l2pU/veUdmBVWqsOuwtG5ocbDAYDMMbEAs5sQGyHeMI+h90fYFAiI6Dvxkvgg6T8QMdCkRFiHicPMExxFgWxPofp2KgU0HHwukZnDpxJQgEJfQxxAVdC0cwEzeuYfwjXpDnt2zZ4vVejJXEl/hAcuxPcIRUCLmSdBM+Yx9ho++DqBg2nBpiB5QXZUm9NDc8HOVhbclguL5gBEODwWAwGAwGg+EywKIOiy8/+9nP9L//9//W3/3d3+lv/uZv9Mwzz3gF1ocffuiPnXrppZc80ZB7zz77rJ5++mnvvve97+mNN97whC4UV7ECFohQ3q1Zs8anA6IZbvXq1V4JiZIP5R3KvettMYk0ocxEWXn48GFPOouVskPJCmmORUPq7I9+9CO/GElaQlmxu5zjZijLt99+2y8yvvvuu3rvvff8sWk//vGP9e1vf9uX+4oVK7zClnco76EA8aKuQWaFqEt7Y+E01Cu+iwKbBVEIvlhvoa1RB/sTP/GPC+ns7wL4jWIaUuDGjRt9PpBnfJ/w9u3b54mVpBmlOs8Ijzj0d9wnjijRqSuESR3p/y2DwWAwGAwGg+FygQzKHHLVqlVeBmYT25VYzEZOZW6LvMscjzkA84azobe3R53tTao6tlGHN/9au1b9H+1d8wMdWPdT7fvwR9r13j9p25t/qx3vfNuTDDvbG91Llxcvg8FgMAw+BhKwwj3IdlgxxKIhuhd0LfzGqh9/o0PC2i06JN5HV4L+gzAg6E2cONGTCCHx4XgXa4dYDoTEx7MLEYwgIYV4YfUs+OfvqVOneiIkOqpf/epXXreFziXobRjLsEhIGJwm8frrr3v9LJu/scBI/IgXzyFBEiZX7hN/3mdMJZ28h+7nUon7huEB6k44tcXctXW0M/oLg8FwfcEIhgaDwWAwGAwGw2UApVRmZqZmzJih2267zTsUUyjlUHAxkWYHLwo1FmzYBcvO36BsQ/EFISmWrOABrLGhUMTKHWQsdiyjLEDxh+INxR7kKtIFuYo0Xg9A2cguZqz2kS7Kl+NVUFzFAog/cUWpi9KYY2Ug0aEopv5BgoMsCHkORXCoy5Qtytjp06f7d/mbnd8od9mljoKXtjAUoE4RNvEmjsSPvA8IBL+wa572Rtxpl0GBxfNQJ3G0TeowO9O5379+8i2+yXcgj/JNlOqBBIzymXAhl5JvtGnIj1iCpD3wG7+8z3tc+ZvvXi/twGAwGAwGg8Fw7YBMj4yKzM7mF+RSZNsrJUDwPnIyRAzIG8zzkF/5Xn90tjaoseqgyg+uVl3ZLicbJyq3eJZGTLhZBWMXKSExTW1NVWqpO6nWxnJ1tkF8NDnYYDAYhgvQY6HHvOuuu/xxwOhPADqu8ePH64477tDDDz/srRBCvOP3448/rk984hN66KGH9OlPf1qf//zntWzZMm/1MFgsvPvuu/XZz37WkwnRm6Snp3s9KfcWLFjgdTXnIxjyDJ0Ocbr99tt9HNE3cR+dEDqp++67T0888YQeeeQRHy/i8uSTT3r/WEm8+eab9fWvf10PPvigTx9xfuyxx3Trrbd6fRZpLCkp0Re/+EXdcsstPnziiX7r/vvv9+kjXK6Ehd4MXZMhtkCdMTd8nMFguP5gBEODwWAYBHT3dqu1q1W17TWqbKvwrqqt8qJdeOeCrvU87rf8lP+uc8/4Vl1HnTq629Xr/hkMBoPh8gFBEGUaSql7773XK85QxE2aNMkv+qAYQxmFog1yEjtf2blbXFyskSNHeuUaiioIUxCkhjtYXCKeEAdZ0II8RTpR9KFQJD/4GzIVpCrIeIOx2HUtQZr7k9iwZIc1D9JM+aKIRUEZC4A8hyIZZS8K25kzZ/q6F8hv1FGIsJQZdRRlbmFhoSfIUl/nzp3r6zP1FsXu7Nmz/d+EOZQEQxTTYXc5i57Usf4LnfiBCEm8aI8DCY9hsTSQ/ShDyIP8TfoJDxAO5Uo9pqzZCU++UN/xS5iUOellwRWSLXlFnkFWxB/vhJ3z+McRvlkwNBgMBoPBYDAMBpApkWGZk2BxENl2MOVMwmMTEpuqkG8HbpLpaK1VY+VB1ZbtVndnm3JL5mjUjAc1du4TGjPrUeWNXqCUTKxURdy7nerpcnJwT6fammvUWHVI9RX71Vh9xJMQOT652z1vb65WU81R92yv6sv3qbn+lDrbGjw5sbWxQg1VB/2zBvduQ+UBT2ysc99vcuEQZl35HtWW7vDPPKmxvcnFs84/r6/Y59/z/tw7taU7nb+DLg6HvX/e82HVHPPvYaHR5bK/dnU0q7nuhI8z79Wc3KbaU+477u/m2hPqaKlzcw3mEiH/e9XV2erSWuXDry93cXbfamko9enwaSH+pbv8s+a6k+poq3f50+3zAL998XV5QHxc+nt7u/3ztqbK0/m3zzued7g86u4a/noEg8EwfIDeA50JupxFixZpzpw5Xp8C0F+gs5w3b54n57GhFJ3Q0qVLPWEPkuEDDzzgSXv8jY6I52yk5srf99xzjyclAnRl4R4bWNGlXAj4mTVrlickEkd0QQC9EATCJUuW6NFHH/UEQ+JAfCA2khbigJ4KAiL3cPiFlEg6w0ZUiIaQCEkncUcPFMiJIZ2kEb0ZaeG5wWAwGAyGj2EEQ4PBYBgEtHa3egLfrrqd2lS1QZurNvqrd9V9141V67WxErdOG5xb792HWlf1Yd/Vu7X6sOJjty44d//M9Tzuw8o1px2/B7o1Lg4btKdul2o6Pj5e0GAwGAyXB5RlWHYrKCjwJCIWYCBlQTCEZIQSiuc4CEsQDLlCNuI573CFoBRITsMZjBsQqUI6SB+kL+IeSFSnTp3yR5Fg0Q1SFvcgeMXimBNId5DGSDOkSkiT3AtWKGNpJ3OorxDxAIplLBlSXwMRjjRTrvyN4hVlK++RTv7mPu9x5V5QxnJvKEC4fBfSH98a+B3+Jn4oisOuexZaseRCHQX4wdHOjh8/7o/xpn4OPN4aP6SF8PqniXyBoEgdDs+oE9yn7bPDn93ukydP9r9RQEMq5h6Kce6j6OY9g8FgMBgMNw6QHYKMjMwPKYwrfyNzXU35mG/xTWQa4nE21z9u50IIB39stAjy1kAEP3yP69VM6/UO5lbMSSAYciWPBxPUWea1WDLEOvfA8Ds7WtTWUusqQ4/SckarYOwS5Y2areyRU5XjrsVT71bR5DuVmjNKSSlZiiameCJi9fHNOrDh59r7wXd1aNMvVXHkQ0+qg7DIs6NbnnPPntLuD76jE7veUH3lAU8sLD/4gfav+4l2r/qO9qz+rvZ/+GPtfOfvtfPdf9RBF86B9T/T7vf/j3a8/b/8Ec2ndr+lxupDnjh46KNfa+/qp/reW/9T7Xrvn7Vj5f9yYfxIB9f/XPvW/tC9923tev+f/XHPje6bPd0QInt8nCFCHt/xmvP3A/9Nf/Tzu3+vvWu/r6Pbnlf1iS3eoiP+AfUcImD1sc0ujb/SHpfW/R/+0IdRuv9dndq70v39Exf3v3dxesrdf9WTJbs6m1V5bKOL4098XPeu+Z6ObHtRTTWHfTxwFUfW68DGp33+8e7hrc+rtuyAOtqaznzfYDAYhgroZXBBT3KtwPf7u/OB50GHdTHA33BJp8FgMBgMwxlGMDQYDIZBQCQuoqRIktIT0pWRkKHUaKr/OxoP8SBePex87elSe0+7Wrpb1dTVpIaOem9NsLa9WlXtVao6bYGwoq1c5W1l7oord+5j64R9Fg+rVO3eqTnjavpcW40Lq1a1Hc5x7e9O36vvrFNzV7O6e7vMgqHBYDBcIVA6ARbkWGBDccVOWCy+sfCDA/iDmBTISezAhZzGMbMorQ4cOOCJe8MdpId4spiFRQusW3AMLJbcOCqWPOAZ5C0IXixOhSN2Y005x+IQi6GQ0Hbu3KnVq1f7o8IgTULQo4wpR8o8VhDKgUVCCHIQBMNRyTwjzZQxC8L8xm8g3OG3f1p5xt9Xo2zDt3D9vzUwvoGECMEQkmtYDOW90P5YBIdkCFmU+kn8+6crfKf/twgbB7iH45uAb3KcTiA48js4rD6ySx/SIXWGbxkMBoPBYLhxgNyB9WRkyQ8//NC7jRs3au/evV52Zg4R5gtDDeRayGjbt2/X2rVrz+qIG/MSNtYEWWcguI/8j7z13nvveZn/bMAP6d6yZYuXy0irYXBAGTAnYZ4CKfRcZXW5oE5SX6ijyMwDSaSRaLISkzOcTBxRR2u9mqqPeut/bU017mmvMvLHe4Lh2DmPKW/0fCUk920C6jztt/rER6o5sVXNNcfV1dGinu4uF06Dt+aHJcKqoxu8hT7Ih53tjWppKFPdqZ2qPLJBlUc3qubUjj7rgBV7VXZwtcoPrfHWBZvrSr11wVP731Fj1WG11Jd5S4NVxzap4sg69/56TzzEIiG/S/e/58NrbSxTY+UhVRz+0IfdUl/qSYZYV4QsSNhYVcQKIfHpaKn16S0/tNZ/G4uE3HcTBp/O3q5Ony98B5Ijfk7uflPHd76mEztfV83JLZ64yHuVR9f7vIBAyLewbFh1dJOqj+PnsAu3+fScp1vtTVVqKN+vSpeeaucaKg66+Y4dP20wGK4Ogi5kOOBS4nKpcQ5hX+p7BoPBYDDcSDCCocFgMFwEIONBEvSLzKf/9UdSfJJyknI0Nn28JmdO1cTMSRqfMVHj0sdpTNpYlaSN0ai00SpOHaWi1CKNTBmpkakjVZgyQgXJhSpIKlC+c3lJ+c7lnb72ufzgkp2fFHd1Li85T7nOX05SrrKTspWZmKWMhExPbkyPpistmqrkSLKi8VFF4uMVjYsoIS6hz7l78db9GwwGwxWDxRcWyyDVQdiCTAfpLBAJIdyxkMcRtBxBghUziEsQjSAgQVLDH5bVWMSJFTAWEl8WtEg3i08sIrL4BGkrpBvLF/gjnwJBK5ZAvEnDnj17tGbNGr3zzjs+vVioG3gMbyyAsqEuYo0Ekh1po65SNv2P+A0WbIIVG8iF4Vga8gRHWIO9mHk29C2o9fi6RVzCdwOZlQXwEFf8UibUxXDsc0CofyiJA4EwoL/iOKQtOL7Lc9opYfAdiIu0derAucA7PIdYiJVD2nus1ReDwWAwGAxXBuSGQ4cOafPmzVq3bp0n2+3evdtbU+YIWkh6zAOQP4YSyDDINch7yIHEAaLhqlWrPEmQ+O3YscPH5+TJk14mPFeckK+QtSANsvnmfARDvvPRRx8ZwXCQEWTSoSaoBhl74DcSU7OVnjdOaTkl6upsUfnhD3Vyz0qV7nvHk/Qg90WiiSoYu0hZhVMVTUhVfDRJSen5Ssse5cLrUntrvSfPQS6MnH6W6p4lpub4+1gFxB9kxmhiqpOt49V1+vji1Kwi//2klGy1NVZ4ch5HMucUz1R8JHqabHjSH80cSUj2RzBDZIx3ccrIn6DMgknqaG9UW3O1v5c7ao4Lc6Q/WhmCI0c18w6IjyQoKS3bxa1Ime7d7JHT/ftJKTlqbahQzant/nvtLbVeTxzn/kUSUpSc4eT/3DE+7hyLjL+60t3+d3JGofvmbGWNcHmTlO7SyZwlTokpWS4f8lyeuDl1J+3FhRcfda5vQ1d8JNHfIx+4n+jSn5jk5qQujs4D0TUYDAaDwTAMgKyGPhEdK/pxTvvBKjRyM46N+twLOs2BshbyNjpQ3kcuZ76AP+T5oN80GAyGawlb5TAYDIYLACUR5MLu3u4+y3/9iIYB8XHxSohP9AQ/SH/5yYWeRFicWqKx6WM1PmOCJmVO1tTsaZqePUuzcuZqTu58zc9doIV5i7W4YKmWFi7TTYU36+bCW8+4ZYW3+HtLT7slBcu0MH+x5rn3ZuXO0fScGZ7QOMGFz3eKU0r8d/OS85WRmKFIPNZyUDQFZVPfbxNDDQaD4crBxJ7JPpb8mPhDKIKwhRU1CEgsrGEFBIt+WDorKSnx/rA4gQVAFt0AVuTCsbXDGRAjscrGsa/Lli3zR79y5C7EyYyMDJ9GjoaFgAcpDessLCpC9oo1BQiLOJQj6UKxg4UQlEEofXJycnz6Yg0ooyDlUSbBrVy50ltnxMIMdRUrOyivqKMQR1F2kVbKmXyAXEcdhuDH76Fc1ATUm7AgTlxQqrGYiiIOwifkzxAP7tMesQ5Ke4IUCFDqUQfDgvm0adM0atQo/15Q0oH+CsDg8A9oz3yDONCuqfNFRUXnJQ3yjDgMtJJoMBgMBoPhxgAyE/Iji4jIKVg7Ro5Ennjrrbf0/PPP+00fyB9DDWQR5BlkGOYkeXl53jodcWP+woYI5CPus7Gk/waM/giyGelBTkKeOhuQG/GDzHi2hVPD5SPMU5h7DZWMyTeoF7iBdQGLhJn5k1Q05S5lj5ypjtY6lR1c5Y893v3+d7TrvX/UgQ1Pe0Idlv3ikIkTUjyRr3javUpJL3QVqQdNq69PiS68nKIZPrzCcUv9kcroXbGQmJyer/TcsUrLGaNIYqon+U1a/HsaPfNBT/ZLSM5Q1shp7u+HNGbWJ5RZMFFd7c3eyiDkwcyCyUpKzVZqxkiVTL9fY2Y/qpGTbvXkxNTMkRoxfpnGzn5M+WMWKT4h0RMWW+vL/PcTktKVPWKaxs37lAv7YeWNXqCMvHEuPuM8UTExJVOdLu191hb7jox2kfYkQdJaMuNBl65ZPh3dXZAgR6p46j2auOj3NfWWb2jqsq9r2s3fcP4e8nmaO2qei+MDzt8ITyaMRFM8WRGyJiTDhOQ0T0iErJlTNFPFk29XVt5Yl39pv1NGBoPBYDAYrh2Qe5GTkfPRvb755pte7v/Vr36lZ555Rs8995xWrFjhNxyhe+0/F0A2Yg7BfIG1AzYrhZOD0FOaTG0wGIYDbKXDYDAYLoReqb6jTnvrd2tX/U7ndmm3u+6s3a6DDQfU2tXqlU/t3W3aXbdLG6rWaUv1Jm2r3aY9zt+Bhn062nxEp1pOqKK1QnUdtWrqbFSbe49jk0E0LuqPVE6LpntrhLmepFiggpTCPoeVQywYOlfofuNGJI/UyJQiFacWa1RqibeQONq7sRqbPs4/S3DhorQLYDctZMj4OH4ZDAaD4UoB+YoFueLiYn9lEQZrZfzNAh73IOXxm+OTR48e7a/c5/jUmTNnavr06X7BcbiDhQsWHFkYJT1z587Vbbfd5o96hmwFuZDjdknTokWL/OIl/mP1aFjIYZQLRMqlS5fqzjvv9OljQS8WCWNhMRLyHQvI1FHKkvoKORYSIWVJ+bFgyeIxdZWyDtb6eG/evHmexHc1yzWUBfWMuPA3FkDDscSkgzjjZs+erSlTpvi0AtJNOyXdHFdMGZJO2iUL7SEdKPFw+OU59Zm/Q3smf6jPfDPUeSMOGgwGg8FgOBdYAIRgB9kOWQU5BLkf2RJ5IlgUZCMLftm0xAYkjivGejaWDllcDJsieAZhMSxCYhGFe2wQgcTHfcJiEZJNJbyDLBNkIb5JHGbMmOHnIchKyEHId8hOOGQe5jPEBYuLH3zwgd+MMvDoZMINm3CIw6ZNm/zmIjZiIEMGP8EFsDBKWBzHzJHRbMTiHVssvXhQZsi1yLJBLh5sUGeQdakb1If+iPfW87KUVwIh7j6NmfmwiiffoewiN4dIz/cWCOvKdqn0wHuqPblN7c01vg54ol9WkSKJWEbv2/zsw4smesuFWBHE8l98fN9GLuKAdcOExPQ+Yl0kwfuDUJiaPVoJLg48T07LP2NRMSE5y7/b3d3pSXmQAPHD+xl5450b6y0IYtmQNGBREfJiUnqu/y7HIkMG7O3t9lYEWxrKVV+2WzUntzu3zR9nXHtyq+rLd3urhZ0dzZ5g2dPdfmYjt7fI6OJJ2MQ3mpSmjNzxyh+7SEVT71ZuyVxlFkxRZuFkT5KEQMk7KRkj/N/ZWH1MTPNHRHe4vCM/u4lLfaknTian5fX5K5ruwnd5EBn88jcYDAaDwXDxCIRCZFysg0Mi/OmPf6Jf/uzneu2FF7X27Xe0bc1a7d6w0but7vcqNhv9+tf6xdNP67XXXtP69eu97P3qq6/qpz/9qX74gx/qFz/7mX79i1/olz9/Wj/54Y9cmD/Wiy+84P0hl7M5nM3UyOBYJ2fzOJbDmRswDwgyucFgMAw2It9yOP37qgMFAtYncEyOUXKExSGDwXBhoMxDWYkQwc5jFntR/qBkQnl4LiDwIIDwHjsfWKDlPZRH51osDhZdUByiBA0kCXY33wiLqyeaj2t95Yc63nxMpS2ndLL5hI40HVZTZ5NGpBQpJZrifjdo+cnX9WHFGh1uPOSeH9LRpiM62XJCZe6d8tYyVbZVqLq9SrXtNarvrFOje6e5q1mt3S2eoNjR3aHOni5vKbGnF8uJ3eryrsu7PiuK7PRFSSxPFuQY5MRIklIiyUpLSFd2YraynOMY5LK2Mk9k9KRC9y8xPkEZzg/HK0NmRGFnMBgMhstHID5BwmI8ZQGGMRhSEjIt4yRkJJ5DTuI+C0Is3mEJkAVGxu/zjdvDDSgoWOgk3pC1SHewZEheIFNAUuNv/HCPfIrFMYd4U16TJk3yxDWsM7IYG4tpIc7UT+ZbkD+pl6QHIh0yIOnkPveQFZHv8IPMx3uUO2XJPeo1C5rU8aGUA8OiNDJvsLpD/LEsSRsKC6u0H+JB2UydOtXXwdCmQrp5l/u8G0iJhMkz/ITvANJH2Dyjbgd5l/CDvE29j8V6YDBcS0CACTog5pa0Q9oapPsbYU5pMAwlgjU6FpNoU8iZjJFDAcZMyG1Y1ICIxtiIRW5k3cEA4zF9BDorLH+QDvqKIJPECigLFvzo+9CfIR8jp1A+3EO3hnyFbIIcxhHFEPUg37FAyUIlsgr5Sljvv/++1wEiAyHnsIAIEZH+kzziCmEPh6yOnIbMg7zCs/5yDe9DYKQsb775Zi/rIg/yLSwP8n2IhVhdIV7Ek/CID3HAogp+CBf/HLGMnjHMd4g7ZEjiQfqYM/B9dJAsoJJOwkXXSLixOF9AXuaYO9oB+R/k6LDJ5VJA3nFkNTpX8vqmm27SwoULfd4NnCeSR3yDNsgCM4vL/S3fXCkoU8r5s5/9rG655ZYz8n5DS4+OVnSpvKZVDU0d3sJfRsFEfxRyXslcZRVO9iQ56lRrQ7kaKg54Il9azmhPLsQqX3trrcr2v++tDGbkj3fvTHHvFLhOpccT+ppqjqjq6EZPVMSqIeF1tjeooXK/GquPKD17tLcC2NFa749ibuJe7mhlj5ju41N7ytXDU9uVPXKGt1DIMcn15Xs9iRHriElpuepsa1Dl0fUubknKGTnNWz1srDmq6pNblJCY1kdEzJ+g9qYqlR1crYMbn1bZgdU+nKaaY94v1w6XFkiMxJH0p2WVeGuNfYjzRzdXn9yqZuc326WlePKdKpp02+kjn+NOu3jvAHnMVnCsPrbUnXB5WKrU7GJ/FHJPV5uO73zdxWGPi69rqxNvU8HoWcpKT9K4kQkqyY8qEn/lbYeyo1+iTkNSwAo8/e6cOXO8FfiBCOnwcXfXSwF9A/UdmZD2w7og88xLDccwdKCPow7Q30NWoT8KY/G51o9uBNBOOEkB+YQxNaypDeyrDYZrBeQyZLBg8Y7+lXrK2D4UmxKGE8K4RHscqn6KPqA/6CvJc/rKjRs26M3ly/Xi88/rnRVvaeuG9ao4clTtNbWKb25RtK1dPa5MmqprdPzwES9nb3Qy8Qk356lxsuApd33pxRf16gsvatW772qne75323Zt37RZH65d6/y6OYKT80ljput/kAWPOFkUmX7vnr2u3PssHba5MZZ4Mj6HMTq4awHyiPkd1+EC5laMbTjqC3Mh5ki0E4PBcGGYBtdgMBgugCB4QfjrEyCdMBbvBNX4BE/uC3JZnOtSed5HBuz0ZMHmzmbVtdeqvLW8j5TYeFj76vdqZ91Obal2AmTVBq2tWK1VZe/q7VNvavnJ1/TKiRf1wtHn9OyRZ/TM4af1y0M/0y8O/lQ/P/hj/ezgj9z1J/7vXxz6uZ459LSePfyM8/8bvXTsea0ue181HTXq4agRv382uNMgrqarMRgMhkEBk3QWylC0spgWlBeMGyhtmJiiaOTKcxzKWBQ7XMOC0bWa4F8uiHMgFAZlKr9xKJwhjJB+FgohYcXaYmF/UMZh0ZPFc37HKqifLN6EOsgiKC4oUKifpC8skEI8pCyp45Qhi8UsiPMuV/4e6oUF6g31jXgQb74b2hvfZ3GV+FE+1L2gNO1f3/hNPPsvfPGbe/3jH9ozdZvvUJ9Z7Cc/uB/aM8/Ip1it0waDwWAwGK4u0BOxqAZxDGIhG+5ZzIJUhtyCDMLfEO8gimLlEDIPi5UcpwaRgXcheuAP4iUEPqwfYiVl3bp1nugGMQjSHsQgcDlEN45k27p1qycXIgNBHMUyOeG+++67nlBKXMJCKiShICNBFHrllVc8wXEg4Q1/LH5CooO8hGyHxUTSgCVD0kj8DRdGkI+RfdkARd4PlkxO2ISL9XaIrNQh6md/YM2PI5APbnxGFYfXqau9RdHEdKVlj1H+2IUqmfmgiibfroQkrPDVqLWhTD3dAwiQ6FZ7ut2VY5J71ON+93R3eH9el+n1r316TdqP18X6/z6+fwY893+fvucvff78scX+XdxFvMc957o7WlR+eK1O7X1bPV2dyhuzQJOWfFGz7vnXmnPvv9OUW/5AuaPm+qONIRX6+IXTY3wYfeny33fomzW4+y6dfY7n/b7tEeetHeYUz1LmiCn+SdXRTTq5+02V7V+llrpTSkzJVcHYxUrLHWNzEYPBYDAYrhK8bOBkeeRkSJveemB1td/kgfz78ssv67v/9E9a/tzzUnmlHpk8Xf/PQ4/obz77+/rLT35G//oTj+mb9z+kr959n752x736k3se0L9/+FH9O+fn0TlzVXHsuN5evkKrV7yl3I4ufXbBIv3Vpz6nv/nMF/T/feb39P999vf0V5/+nL667FYVONHivTeW6z/9x/+o//ytb+mf/+Ef9Mqvn9UHy5fr3Vdf10vP/Fr/59vf1l/9p/+kv/6rv/IWErc52Z74Io/3yR8Gg8FwZTCCocFguCGBEgkSHv8uBunRdI1JH6Nx6ePddazGpo1z19PHEMf37aDHSmByNEXJkRSxAxVrg+097WrualJDZ71qOqpV1V6hirZylbWeUmnrSZ1oOa7jTce8pcNDjYd0oGG/9tXv0e66viOYd9Ru+9jVbNPOmgH3vNvu/W53v/c37ldLV/N5BMU4969vV6zBYDAYrgwo9VnMgXzUnygY7rNgCPGJxTme4yBHcY8rCzaDtRh0NcEiEwQrCF4hLfwmnYH8Rbrwg4vFNAZQlpQb6aA8+R2roNyIP+VEueEg04V0BRdIdiz8hrLkXeprKGOu+BvqhS3C59vEI9Qz4kE8uUJ8DPWR56SJZ/3jFcLAhfvhHtf+fklrSCffCnnD/dCeuc93DQaDwWAwGC4G4cg0LBNC0oMUiGUZ5Bc2RwSryMgfwdI5chjvYaEQIh+yCDIJ1jawmgTJkMVN/oZciAuWOCApho0j/eWci0XYWAJxDUf8CBOLiRyJHBYnCRvZCKt9HLnM94grcYMs2F8vxYIs72OpBQuIpBV5E6IiljfJHyMYXjyoC5QNlg6xVsoGGO5dCZCNkYOxNnnPPff4DUdn2yjWXHtM5Qc/0Kl9K1V24H3VnNqu5rpT6mxr9BYDIdAF4hxHDfu/T9cFr5GMJKinp0ttzdVqaSxz4R1XQ+UBfwRxffk+fxxwV2eLJyd2tTWoq71Rne1N7p1ObxWwvbnKXwEkPp5h0ZAwz9xzf7c3Vfpv+GOPO9vV2uTe62jFh48PYRDnLvfck/7ce11d7l57s9pb69RYdUjNNUdcpOOVnjNaOcWzPfkPy4scU4xFRt6DYIm1Q45M7nVx6MACYf0pb12RNPS48L1VwvpS1VfsU33lfjW5POS4426XXz4+pxEfdfOQnBJlFUxWUmquGqsPq/zAKpUfWuvTmZZd3HcUdUah833pbdtgMBgMBsOlAXkWORZZmE09b7z+ul588UW94BzXV19+WatXvq0TO3Ypq7lVMzKydduESbp/zjw9uGip7l64RLfOXaCls+Zo0YxZzs3U0mkzdOvUGVo8fqLG5eSpx8nGcQ2NKlK8FhaV6J5Zc/Xw0mV60LkHlrjr4qV6aOFi3TdzjpaMGqOUphbt2bRZG1av0fFdu5Xm/i7qkYqdK+zoVEpNvVoOH9Xudev17oo39eorr+iVV1/Vtm3b/HxhMC1fGwyGGxN2RLLBEMNg14QdkXzpgFzY2dOp9u52tXa1qqOn3blOd6+j79rbqa4ejiju8Yo0XFIkSXnJ+RqVVqJRqSUqSi3WiJSR/l5aNE2RuIgnK9Z11Dr/8scVJ8YnevJhYiRRSfFJPgyOMg6Oe95PJMG/7xe/Tx+NgdKNcCAq+uON3XOsJUbjE5QQh3/3m3e863sHy4lpCamanDlFGQmZLm0tOtV6Um3dbd4fYRKXjIQM5STlKjX62xZ+DNc/aPtMiGjP5syZM2fOnLlr6xiXgxxtMpnhRoAdkWwwDB3QCdkRycMLpIEjkrHoR9mgf4Nkx/HAyAAzZszQ4sWLvS6OvGMzA8S+oOvjHRYyOSoXS3Xo4iAdok/nOX0qhDDKHCIYeUT4EABvu+02T1Y8V9/KO8SDMKgrweI6/tHLoyckbAiAlDUEQIAekG8RD6y2YN0QkhtEQ9JLHCkvrBziB/0Dm0CI2+bNm711RcIgfNJJ+FwBREXiESugDK/FEcnA6ynddyBpAsox5DfvXyoo00AWvf322/Xkk096PXH/NZpwRPK+XVt1eM8Gfwxye3O12ltq/LW57rjqynar8sg61Zza4S0SQobLH+36hqwRnpAHSa6udIdaG8v8MclY/+NdrCJCoqs58ZHamqoUcX4jCSlKTM7wZL2aE1vU6q5YRUzOKPDEPPw1VOz3JL+klCx/JDIEvurjHynq3oVA2NpYrqaa494yIvd4F3Ih1gk72+qV4N7jSOTmuhP+G8Q5MTnTH9vc7L5H3IgLRytzbWusVG3pLlW7eDZWHvTERsoikkhcM5WcWeiPT645ucX52eKuW70FR4iHkBeJc2PVQRdulU97NCndHyMd5gFc+Uc8Otoa3TcOuPCO+qOlOTY6b8xCFYxb4gmOCdGIMlLj7Yhkw5CBPs6OSP5d0E7siGTDcAaylR2RPDhHJAcL4vSDbJJ58bnn9M6KFfpo7YfatmmzNm1Yr63u2lFdo5mFI/X4goWaO3a8RubkKdX1mRE3vsU7+TqODdrRBMUluPw/LZt3OtntYHmZ9paeUreTZW6fMUufWXaL5kyYqJEjRjrZKbvvXSfvxfn0RJXhfo91snK6k89Yu25zct+M4hJ9evFNumP6TC2ZOEmLJk7WkslTNXVUiVgRrqwo165du73c39zcoiwngwPqAu5qjLuMJ8j7XIcLwsYsHPXFjkg2GC4NcU4g+nib1FUGxyBs2LDBOxREd999t98lFybHBoPh/GAyzu5hjlHhCBMGQZSTc+fOPe9AyIImCk7e2759u5/A8x4K4XMpaxFGUVg/54QoJlAoF26++WZNnjw55gRTLAs2dTaqsq1S5a1lThjr9iQ9lDh9RL6IEuOTlJWYfYaIh/DT1t2q5GiyJw16hY8T4iAresHV/YOUSHgNnQ3uG13q9CRFdsH2HY/R7f7hJzzjd6sLEwuHxKepq9n93eIJgRyvDAmS9wk7Eh/1JERPRDwd199Grzqd39FpJXp09JOeCFnTVq0N1R/6I5ojcQmKd/9SE9I0MmWkxmdMUF5S/hlyouHGAIIzk0sWIy5H6WwwGAwGg2FwwEIcizQs0LB4a+Qqw40ASDFBB4RMiv5n3rx5nmRzIy9UGgyDAXRCWMZjkYQjdj//+c/74/6HAiwQQZx76623/CI71uu+9rWveaLhYCBYCUFnxVG9ECbpKyDZDRVpcihAGrBsQh5B3iOf0B+xQIkeDR3c/fff7xezIIctX77clx/lhn/0dT/5yU/0F3/xF/riF7/ojykmTyARovOD0EaeQ0KkD12yZIl/Br785S97wsO5wHe++93vemIbdYXjiiF9o2fcsWOHd+iykFEI++233/Zxv++++/w3WbimbB5++GE99thjnuBGPDjumPTwHmlC94BFRtKKPvH999/35DlIKsg+PAeQIW+55Ra/CB4rQKeycuVK3w4oM44VJg3Id5cKdLR//dd/7fOIvPzX//pf65vf/KYvw3Ppdykf/LK+wqIx8cBKJqTHQNq8GFC+lBlj8R133KFbb71VCxYs8ATG/vre45Wdem97q9549TmteetZtTacUldHiycOQgZkgzTEuK6OZkWiycoqmq7iKXercPxSJaVi8S/qCXsndq/QqX1vq650lyfl8S7kOwiBXe1Nzk+tElMylVEwUUUTb/OkvNID73kLgOnZJSqc4PK4YIK7X63jO17x5L2CcUs1du7jKt33jvat/bEnCCYkprs86vbvx7lvp+eN0/j5n1ZyWq52vvsP3upi7qg5mrT491VfeUBHPvq1OjtalVs0U6NW+kvCAAD/9ElEQVSm3aOuznZvWbGubJfX6UYiST48LCKSbm+B0PmBXEhcS6bfrwnzP6PyQ6v90coNlQfVXHvCWy+MRJOUkJyhaFKGy4eI0rJHa8SEmzVioktL3njFxX8sA0GYbGuuUuVR19bX/tCTL+Ndfo6Z/YhKZjzoCZsczZycFKfi3KjunJuim6YlKzF65QQBdN+QKN58800988wzvp3T737hC1/QE088cdrXx6BPoB1Thy6VoACRkfqOTEgfz7ogxOhLDccwdGD9CKI7/TrjFn05a06sCd3IZDr61xdeeMGPgZCyGXsHc5OFwXClYGPIBx984GU5ZFH6V9Z+kbUuZxNELCGMS7THwein2EzDBhDGqgNOnm8oLdPIpBTNGFEkVoarGhu059RJVTc1aVbJaP1f9z6o0YUjFElKVhx9AnKUG9edUIHgxhKuH+d7O9mI06C3tm/RVtfPThs1SvMnTdWksePcGO/KCLnApcWPiegK3bUX2c7NkXrbWlVaWaGPDuzXTz54X3lpGfr8Tbdq1ujRyspIV7yTwXvdd5uc32MVZTpeWqr9J49r27GjaktKVOaoIi12/RZy61BuSAtAXmU8CZtgriEl6bfAXGjLli3eUV8eeOABz3WgnRgMhgvDCIYGQwzDCIaXByz9NXTU61jTUe1r2OvJfPEIbIpXJC5e0bioEqNJKkguVHHqKOUnF3gS4HHnH4UZz33H6bpPLBFCRMxMyPDWCCvaytXuwsNaIQ5rg1gddCKgFzrZjcL3u3o71dndqZauFjV2Nqihs9F9o9H/3eoJhlhV7OgjKLpPQSxEkOzu6VJzZ5MnJhJO6MIhOkJYLEkbrcfGPOGtLNa012hD9brTBMOoJximJaSpKLVI49InKDcpzwiGNxCoKyzqomxGkYdQbzAYDAaD4doA+ZkFeua/KPSMXGW4EWAEQ4Nh6GAEw+GHQDAsLS31xDn0bujq1q5d6/MP/fejjz7qiX34+dWvfuUJdxDwSDOksX/+53/Wv//3/15f//rXfdmuWLHCH7MMli1b5h0Ln+jpyH/6UiwEskh2vvI4G8EQq4LoCt544w1PKEHfR7whzAXi27333uvJaOj033nnHT3yyCP65Cc/6Z+xmM17EC4oJxa40SVCSsEa3+uvv+7Lk7IkjoQLoYk4kxfUWzZexAquNcEQkH+0F+oP5EKO1GahlL+DpUt0wP2Xf1isRg6FBIrVQuoaZU39hGAYynwg0SsQDFetXq9tH633xwJ3tNapo73JWyPkCGMXuhISU5SaWaTc0fOUWzxHaTmjPbmQZxx/3Fh1WJXHNqjyyAb3boNLRK/io4mebAgRsLO1wVv3S0rNUXbRLHW1NaqufI+3fpiYkqX0nDHKLJio7q5OF8aH/jjljPwJKpp8p7egeHLPSkVcePHx7psu3d3dHV6Xm5CcrqKpdys5Ld8TE1saypWWPcqTIFubKrzlRX8McdYo5Y1e4L6f7Y9Yrjq2WS31J30aycWoS19CIhvRu4VlRLKWuOePWaDRMx5U1fFNqji8zlsu7Oxocc/7TsdBp4veGTJhSkahJzfmj1no4lDy2wRDF2BPd7tqT23Xznf/XtUntvnjkqcs+6pGTbvPWzKE1GkEQ8NQwwiGZ4cRDA3DHUYwvHKCIWMx+gry8MXnn9d+d03p7NakwhGaP26ClkyY5OSQLlXW1WrDwQN6b/dONw5H9Qd33qPpY8Y6uTZbcYnkda96O5x81NXBINsXuEOne7e2sUnrDuxVaX2d7pk1R6OLRynRyc/IQMhGEAr9iIieJOrGWScreGnO9c3dTr4+fPyo/tsrL6rX+X1iwRItdPJbfm6OElPdXCkJkmHEr0W3t7aqzKVly6GD2nLkoPaVlymnpESzFi3QHXfe6S0UMxcZqvGXvDSCocFw/cGOSDYYYhhMaFAW2RHJlwosCvaquatZlW0V3qIhyiYACbDL/Y31QPwlxCf444bL28q0qvw9ba3eoq01H2mbc9trtup483FPHkyNpnnLhhur1utg4361dbX6972iJS7qlUiQBCEhJkdS/LHK6dEMZSVlqyCl0FscHJc+XpMyp2ha9nTNyJmlWTmznZujme46PXuGJmZMUkFygSceQjDkCrEwAApjVkKWpmROVUZSpreEeKolHJHcZ/UwOZJ82jKj62tdPExxc2MBUjJWBIL1Qtq7OXPmzJkzZ+7qOhZ1cSwYM/9Fbo89edpguHRAmrAjkg2GoQE6ITsieXiBNEAk5MpiFZt7IWgw/nOf/GOjMAtu/KYMsUqIBTkIa6Qf8iCEQxaG0d9wVDFkUo5r412eoa+DFLRr1y7/HmXBd86XV2c7Ihl5BH0fhB++zUIbluzQGXK8Me9ADCf+bHZmwzLkUt4nHP4mXZDGSS91hLRBVoOEwfvoIvgNaYl4oovk2xwNzZgQSwvf6Fav1RHJAdQJZEr8jRs3zpcH8eA++h90xoRHXAPwT1vFH+Xw0EMP6fHHH9edd97p753LsnY4IrmxM0uRzCnKHzNfBeNuUuG4pcobPd+T5UZOWKbiqfdo5OTblDliqifGxUeIf5/uMS4+6ol7mfkTlT92oXKKZ6tg7GKNmnq3iqbepaJJd7j371bxlLs0YsItyima4fzM8r+Lp7lwJ93mLfhl5k9SZuEEb7mQd/JHL1BqVpEyCyapaIoLw71fNPkO/xtSYbELe+REFyf3HH+QAYum3KnC8TcpPXe0C2+CTwfvcAQxf6dmFSs9b6zySuY5N0e5JXO9NUbChuhX7MN33yHsSbcqe+R0f+Rxigs/z/kdOel2H+dR0+51V+dcvoS0FY5fpmyXP4mpOafJlx8DNS3WjVobKlRx+ENv2RG/xZPvct+Y5srG+fflHqeMFDsi2TB0oN+wI5J/F7QTOyLZMJxhRyRf+RHJtHPI1S++8ILWrvpARU6W+fTipbp/wRLNmDhJ2U52TUuIKiclVYUZmWpycnCLG9dSneydmZyiPDaHdLT3Odbhup0c5sIMrt3J7nWubNKczDXR9anFeQVKpg/pdn5dWL1tvNshdTrnwsX1uj4Z8mEc/JneHjW78frDg/tdX92rCYUjVJiZqdSERJ3Zb4Du0fmFcJiZmeXkhSJNysegTro+2rtbW10/1tzaouLTfIKh1McwnjC/6y+PXmswL2Fsw1Ff7Ihkg+HSYBYMDYYYhlkwvDzQ7WHt71jzUX1UvekMAW8gOBq5MGWEpmZNU3V7tVaVvauTzSf8ccbdTogDOUnZmpI1XXNy52pkSpFeOPob7W/Y5y0X8n5KNEUJkSSv6OH4Za906Y1TxP1LiU9RSkKaJxumOb9p0XRPVIQEmBSf5N5L8OHwHh01RyvXttdoe+1W7a/fp4rWcm/FEEA05HlJ6mh9YvTjKkkvcX5rtaHqty0YZiRkqDitRGPTxyo7MccsGN5gCARDrgj1BoPBYDAYrj6C0jNYjkGZZYtphhsBZsHQYBg6mAXD4Qf6OawckRbyCAt3LPJCJsNC2LZt27w+Dl0e5LpXXnnFP6NvRD4gj9HB/emf/qm++tWvel0WJDTehTgGIQwLiBB0uIc1PY4w5sjiC5ExqSdPPfWU/97nPve5MxYMIZNwVDOW8AgjhMPRxnz/9ttv94uQkFlfe+01339TNhBNsZqHTPPggw/6d+jrCY9FbUiT+MESDP54TllCfqN8+T7kvFhaEyDu19qCYX9QPuhu0ROz5kI+Q/KkDKgvLOqikyQ84kidgwgKqRBCKuV/Pv1usGC4+1inTlb16SJZJMeSX3dXmziq2Em5iiQke1Jh2Mh9NmDVD/9dHa3+N0cIY3UQAuKwAzrkrlZ/HHJf+pIUdWnk92Cio7XeW4QkT9qaKlRzaruObn3RzxFGTr5DY2c/6gmZAWbB0DDUMAuGZwcykFkwNAxnmAXDK7NgiHwHSfP73/++XvjVrzU2JVXLxk3QLdNmaGRBoZLc33HRCAuy3ppgU22Nth4+pD0nT6qxrVVLJk3RTZOnOCmhz8hNd4+TI7p7TpPr+ug4Xe53u/sOQx6WD1OTk50c5GQg5x/rhQ0tzapqaFQUuc2VWYp7jv4w4pw/Arm9TSdPndTfL39Nbe3tunPaLM0ZM1YjnSyXmpzk4ufSztic6K6Ei6Vk59paW1RVU6P3t2/R5qOHVO/SMe+mpbrNzSnY2DIUxr/IA7NgaDBcfzCCocEQwzCC4eWDru9o01Gtr1rrjyOGxDcQWC/E0t/s3Llq727T5qpNOtiw3x89HCwHpiekqzilWPPzF2pM2ng9ffgn2lC5TvUddZ7EiL7JK0f47/RuUr6d4P6lRzI8yS87MVvZSc6533wvLZqh1IRUTzZMiU9WonNJ8YnKSMzw7+6q2+HcTh//zp4OF7QXVy+OYOjC4PjkMUYwNBgMBoPBYDAYDFcRRjA0GIYORjAcfqipqfHHCLPQywkgxB8yFwQyLHex8ItOD/0algJ/9atfeR0dOiTIX/jDYuGTTz6phx9+2C8K79u3z5c1+sCZM2d6Uhu/IQRytPE999yj+++/3+v2zkfUgez3m9/8xm9AZEENvSCkRsIifMLbv3+/X6SFeIY/9PWkgXrF+5QN32FRjj6d9yknSHboHSGk8B7+Fy1a5HWH6CIhK2IhkcVbdFxYRoMsiQU+wogVDDeC4UCQt5QDZYBOl/hSBrQh8pnfl0LmOivB0DAI6PVWiWpLd6qufLc6WhvUUndS9RX7VFe6S8nphd4S4uiZD3uLjv7oRMUZwdAw5DCC4dlhBEPDcIcRDK+MYIjcxFj499/+tla9+rr+6O57deeMWSpy85zEpGS5D7DQqzjksx4na9XVqLa2TrtOHNdbO7dr8cRJemDuPJaDvaXCRifXNbW2qasbQqGLn3s3Gh9RUmKCkhOi3nJhcmKSopG+8R35raK+XkfcHIH+JsXJ2nnpGcpOT/MyISTDOHe/orpSP139vuqamjW7ZIx3RTm5Sk9xz913/Gga764RJ+8lJSo+OUW97jdjfVVFuT7ctV3Ld2xVk4vHkjtu11fdXK7A1RFPdBxEBHnUCIYGw/UFY5YYDIYbE15h0etkwB5/LPLZ0NXbqfaedu8nOT5FuUl5SookezJfACTCpq4mtUNSdAJbRjRT6dF0T+bzcF690IQV7C73Zrf73e3ecwIllhDLWk/5I5U5enlNxQdacfINvXz8eT17+Jf6+cEf6Qf7n9IPD3xXzx59RttrtnmLhdH4qDhu+XJULuwgjncC7Pl2EhsMBoPBYDAYDAaDwWAwGC4fEM0gnGHRD4Idx24BSHkQrFnwhTwIKQ+/kAj/+I//2BPLIPT8wR/8gf7yL//S+4MQxuIXG4uxUohfjiZm8ZTFRqyO/OEf/qEnOnDvQiQdwuNo3E9+8pOe2BdIbLw7depUf2Qu4RGXL33pS/43cSI9fJcNx9/4xjf0la98Rb/3e7/nn+MPghDpxPLhsmXLPOGRuPE9iG3EH6LS1772Nf/On/zJn3iLi/3jYBgcUAcgEVK3IHBheRIiJH9fKrmwP3iNNXBzg+Pi41AYt6v25GYd2/KsTu56VVVH1qq17pjnMHR31Kv2xCa1N57w/iJxvWfeNdVubAOSB+SR4UK2MBgMBoM8KZMNJBXHT6goK1sl2XnKSkpxA3KPeto73FDcd2QxRxlzhHGc68MTo31E+4a2VlU01OtUTY1Ka2u199QprdqzWyt2bNWbO7dp1d7dWrN/r9Yd3K+Nhw5q69GjOuC+V9/Sos4uNx6477ORIDs9XWOd7NbZ26Nj1VU6XFmhyoYGvwmlp71NvV2dSk5I1KySsSrOyVVtU7OqGhvV4MKpb25RU2urP7K53cW307nullZ1u/d7m5sU19PtNw3NmzJV98+ao6S2du3dudNvHsMqOhsA+I6NT5cO8svyzHCjwKYhBoPhhoRXo/n/nXvA5xjkrp5OP+FPiiQpNzHXX6EkBnT3dqs5EAwV8UcQpyWk/7ZFRPeJIFz09vRdIS129HSopcsJfR31qm6vUllLqY43HdPhhkM60LBfe+v2aHfdTud26VDDAdW0V7t3ezx58bItD/aye+XylIgGg8FgMBgMBoPBYDAYDIYLA7IepC4sHEHwCtaNsKzCwh5HB3NkMpYy8Is/yHtYDIPkh2U/yHlYPYSUCCmM97CGiMVBLEYSFvcJH2uwEPu4dyEQHt+D6BgIZyDEDcIfccFxfDFhc8WiHt8lXVjKJI5Y+8Afv3nO8WqkFT+kkbjxPdJI2KSZd0P4wXJhiINh8EB5BiuTEDgpG/6+XHIhSEuOU0FWxNxguWxcokpGFWn8pOkaN2GaJkyZoymzlmnmwvs0Y8HdmjxjgUYVF6owO0H5Wa69u/dy0iNKSYxT5PRpOYbYA+QNLDoZicNgMBiGB+iLK8rLvQXy9to6zSwuUUZSsrq6utXS2u5cqzra2tWDJb72du/iXPcdjTgZ3clZEwoKVe/8vLNrhz7Yu0dbjx1VeV2dEt3z/PRM5aZnKCct3ftNdfIYRyfXNjXpVE2t6lpa+ladk5KU5GTznDwnbzuZOxqN6FBluScschxyV0enurFKHYloQuEIFTrZura1WXtKT2rnyeOejHiiplrl9fWeeNjY3Kym5ha1uO90uG/0trUp0cmBI7NzNWfsBC0cPVaJjc164dln9dOf/EQvPPe8t2aOlfXhZHFwuIN8Yjy3Md1wo8AIhgaD4YYFRD0sAcafg3AHma/HCQP8Szx9RDHWAyEHcg9gUbC5q8VbOiSsjAQsGGb431cEgj/tEEg+/iZmtLFeeHndNyntoxiaAspgMBgMBoPBYDAYDAaDYbgA4heksCshgA0GQjwuhqwILjXOIexrnU7DxSMhGq+cjIjGjYhq5vhEc4PkZo1P0dxJ6brzjlv12Gf+QI9+6qt65JNfOeMe/fTX3PVLunnJbM2ZlKFZE5L8e9NGJ2h0QVSZqWxCP11IhpgCJASOjYTAYYQEg8FguHbAwAxW+0pLS70lv61btqi1oVHpycmerLfj+DHtLz2liro6f9xxR6frt7FieNqCIWS/gowsLR4/UVEn2354YL+2HjuiqsYGTypcOG6Cbp86Q8smTdWSiZM0f9x4zRw1WqNz85SamKSWjnZvcbDHjedxSUmKS0tVYnq6xowY6Y9HPu7isOfUSe0vK9ORygodq6pSWW2dWlycW9radaquVpuOHtIH+/Zo/cH92nDooDYdPqSPjh7WFhePPSdPqLyuXk2NTWpvbFR3W6vY1lOYk+uPfx6Xlq7XX35ZLz7zK7394kta+8672rp5s44cOaIWSIk2Pl0UGNNx1CfLM8P1DiMYGgyGGxZYAUyMT1Yk7ly7pD/W0ETiI86vE+5ctwnZz0kI/j6/sV7Y2dPlw8tKzPJWDCODeE4FRMfuXie09na7v9idGvHuchSxvBNIlUYyNBgMBoPBYDAYDAaDwWAwDMRQk/+MXBhbyE6L18JJSbp9VqrunmNuUN3cND20rFhP3jtTn35wnj7z8MIz7tMPzNUTd03VA0sKvb/wzl3O3TYzRWMLo2bFMEYB+QBiIVYMISQYydBgMBiuDeiHy8rK9Oabb+qN5ctVXV2tg2Wlemf3Tj394Qf64Qfv6MWPNniCX11zs5r9kcZd/rQ6J9AqASvjaakanZevwswsZSSnaERmtqYWjdKCCRM0obhII3JzVJCVpQL3vCAzU/nu95jCAk0uKtLEESOUl5HhxvN4H55Y+41ElZSYoEgkXjXNTXp37y79/MNV+s2GdXplyya95txzG9fprZ3btP34Ue04cUwfHtyn17d/pBc2r9cz61frR6vf1XffXalfbVjrCYpVDfVqbGpWRwvHO3cq2X2jJDdPRS5O3W0dmpaTpwW5+TqxdZteffY3evH553XyxAmzZHgRIH8gFjKeU5/4bTBczzCCocFguGGBNcLUaKoS4hPOQ7YL9+O8f/z1F6bCUcfdpwmG3oKhPyJ5EAmG7h/kwh7nkC8T4pxgeUELiX0pGpgq7sZ7Je7AJwaDwWAwGAwGg8FgMBgMBoPhWoLFydbWVk84Gi5ITozTyJyoRuWbGwo3ZkSyxhWlaUJJliaOzj7jxo/K1Fh3f3Rh0u+8U5zXZ8HQuLqxDbNkaDAYDNcG9L2HDh3Se++9p988+6zeXr5craXlenj2PN0/Z64Wjh+vGaNGaeLIkW68TdX+ilLtLTulNvfemf7aOdZbEyIRpSYlebJhYjRB4wsKNW1UicYUjHDvpikxgfsR9xwXVZL7GwuJOenpynUuNTlJ8S4M4YAbEziKGSJjR1enUhITlJ+WocykFKUmJCklIVFZySnuOwVaMmGibps6XTdPmar54ydozpixmlY0SuPyCjUqJ0fR+Ii2Hj+ijYcP6XBFhWob6tXe1ib3ASW7OI/KL9AiF8bs0WM0pXCkpuQWKL29U6d279X7b7+tnTt3+rwynB+QCnGM5+SXjemG6xlGMDQYDDcsonFRpSWkKjGSePrObwMyXpwb//ssBzrZzvlHZ9N3aHIfEBC6nCCGH0h/adE0pTrHMcaDBh8HJ5y4H8SJeHuCIZE4h3zyW/TCoGhy1zNKp3A1GAwGg8FgMBgMBoPBYDAYDNcEWHOMRqPegbq6Op06dUq1tbX+aDqzHGMwXN+AhIDFI9q6WT0yGAyGoQf9LXLWmjVr9POf/kw/+d73dGDzR5qenaO/ePhR/cdPflb/7+Of0v/z6JP6Nw8+oofnLVBFY4N2njyuprZ2dbp+eyCQ1SADdnR3amROjifupaalKj4S5/v59s5OTxj0RDTnFxfWbOOwXhic89vt5L9W55ra2rwxm6UTJutzS27WI3MX6J5ps3TntJl6fMESffnWu/Rn9z2sf/PQY/q3Dz+uv/jE4/p3Dz2uf/PAI/pX9z6oP7n7Ad01fYaOVFVo7cF92nbimEqdnNnS0qqejk7/3VH5hT59o3PzlZKcrLsWLta9s+cpvzdOK199Te++/Y6XTUmD4fygDoQxHWfyu+F6hREMDQbDDYtofILSoxlKjD87wRBAJcRyIMcURzla2AlzcPPO8PSCAOj+x7+USLKSI0ne32DCUxq9MOK+4sLm38ex+Bj4wKoi8fUgcqf9BVok5Me+VBgMBoPBYDAYDAaDwWAwGAyGa4WkpCSVlJRo9OjR/vf69ev13e9+V9/5znf06quv6uDBg2pubvYL0rZQaTBcn6B9B0KCkYoNBoNh6ED/iuXC1157Ta++/IqqjxzRfZOn6Wu33qlH5i9SUX6BMnJylJabq7S0NG9pcFROnh6YM0+j8vK0cvd2ldXX9a0J+/VXqdv14S2u/2YJNt3JcilJiUpMSVa8c3HRBNW2tGj3yRM6WlmpqoYG1Tu5rs357+52st3po5F7IR42Namjvl51zu04dkwHysuUnJCg3LR05WakqyA7U4W52RqZm6ORebkaWVCgwpEjlTtihLLy85Wela309DR/ZHNBdpZG5mRrWnGJHpu/WCU5udpx4qjK6urU2NaqlrY2dbe1K8fFd/7YccrPyPDjT5KLz0T3zsKp01WUnKryg4f0wapV/hhpw4VB/QqWDNvb2/3VYLjeYARDg8Fww4KjkdMTMjzR8GObhL+LLo4ndv/YzRHvHAS/j8GRw9D1+v5F4xP9EcZesBwkDl8fybHPgiF/YXL7bOH3PQ3/P1t6+iwghvgaDAaDwWAwGAwGg8FgMBgMhmuH1NRUzZs3T/fee6+WLVvmdY8fffSR3njjDb300ktasWKF1q5dq/3796u+vt6OqTMYrkNASMDqkR2XbDAYDEMHiF+tra3au3evVr75lsoOHlRxQpLunj5T986dr9kTJiolJVVxkajEOjBrqc5lpqRo3thxmjhihFo729Xt+mdvdTCa4I81prfuOm3hL+r+jrCOzHHH7nlcQqJ63N8t7R2qcHLcgbJyTx7cc+qkjldXq7a5WU2tbWpqalJlZYUOHj+mLYcPaePhgyp3/kty81SUk6PU5BQXt2SlZqQrLSdbadnOZWYq2cmRkUQXD/eN7h43lrg0gsRo1B/bPML5WzpxsiYVjvRxLK+vVWldrYuPS4cbb1Kdv1HZOcpyaYxz6Yp39yAzTh49RjOKRqmjtk4frlmj8vJyH67hwrAx3XC9wwiGBoPhhkU0niOS0zzR0Fv9G0DKO7P7pLfbkwx5HIftQC9YfkzQwyIghD3e99Q9L3SefjgIINzu3i5vSZGAgwXC83/i3E/9E/43iHE0GAwGg8FgMBgMBoPBYDAYDJeGjIwM3XrrrfrKV76iP/mTP9EjjzyiKVOm+MXIjRs36umnn9ZTTz2l559/Xlu2bNHJkyfV2NjoLZ2xUG4wGK4fBEuGEBL4bYQEg8FgGDy0tbXp+PHj2rF9u/bt2KEZ+YW6f/ZcTS0epazsbMUnJas3Lk69zl9vc7N6XV8M4qNRJbtn4wpH6rbpMzUiJ0dKTFBcSrLknrGWnBAfOWPJsIM+nHfpw5OSlJmdq7EjRvj13cOVFfrw4D69v3uXPty/V3uOHdeR0jIdKi3VjqNHtWbvHr2za7sOlpcpNTFRC8dP0IQRIz25MN79HQcBMj1DcWlp6nHf7mjvUFNdvWqqqlTlXF19gz9aGTIhS8CJ0YjS3bvjCwo1e/RYVTU1+rBb29v9GMNKM2vaKQmJSnfhR9x7Tgh176Rq3oRJyoxEtWfXLlVXV9uYdAkgr8KYHmR2yz/D9QIjGBoMhhsW0TgnFManQBn0FgrPBob79q4259r9X1EnJEZOEwoBV97vjetVV0+nJ+3Fx7v/DSLDEKGju6db3b09/qt8D4Hv7FYKzw8EXf++D8lgMBgMBoPBYDAYDAaDwWAwXCtEo1Hl5uZq0qRJuummm/TEE0/oy1/+sh599FFNmzbNL4Zv27bNWzP8/ve/r2eeeUarVq3yC+RY4TEYDNcP/DpAd5/VIyMkGAwGw+CitrZWK1eu1NaNG5Xa06t5o8Zoztjxys7KVjQp2a/r9kDyxnV2+GOLIRDGp6Qomp6unLx8TXb+s3JyFZfcRy7EciDWp5MSEtTm+m6sDjY5+ayzrd2HpfiIklJTlZedrdEFBZpSVKTJI4v8eu/Ok8e1+cghrT+4z7n92nH8qGqbmpSbnqGF4yfq5ilTnd9iZWdkKJKUJEEudN+E+NjV3KLW+gZPKtyJxcN9e7Xp4AEdLC9VVWOj2l1cMKtD3BITot6S4ZQRRep0Y0xNc7O/ssTMCMO6cXZamgozs5TI2rbLmyT3zqj8fGW670JgbHJhIpMyLhkuDoFkyJiOY3w3GK4HGMHQYDDcsIBkhxVDqHbeguGAyTrP3fCvlm4nqHW3+ueJ8UlKjiR7gcv7cReOLMbKYUePExb9PQiA7u3TfgYD/oBkF0ekvcs94tgLiu5f3zHPgxc3g8FgMBgMBoPBYDAYDAaDwXD5SE5OVmFhoebPn6+HHnpITz75pCcZ3nLLLSopKVFNTY3efvttvfjii3rhhRf01ltveQuHhw8fVkNDg1+4NCKSwXB9AAuGgWRoRysaDAbDlQOiFwTDNWvWqOzwUU0fMdKT/Ubk5ikhOcX76XZ9bntLi9pa29TW0ake+t5IvJSQ4AmFSRkZyikoUGJ6et8xypDtnONI5OSkRE/kY625rK5OFbU16mQjiPubI4yT3btF+fma4WS6+ePGKy8j3R9T3Oq+2dHNCXa93oogRyIvGj9Rt02drgXjJig/M1OJSUn+yGXWdXvc2NDV1KyGmmqdOHlC+44d1fHyctU0Nrg4t6uLOMX1jRmsArMmzHupLv65aRn+TvheAP7SkpKVnZqqqEsLFgzju3uU6tKdkZik5LiI2ppb1OLyxsajSwP5Rd1jPLfjkg3XC4xgaDAYblggVEHWYyj35L2zgB0ezV0tanXOvaDUSKrSomne+mEfvHjmhLYutXa3ecHAEwAHmcCHvEE8+wuEfX9dLgY3fgaDwWAwGAwGg8FgMBgMBoPh8oG+D4uGHJuM9UIIhn/8x3+sb37zm7r99tuVlJSkQ4cO6dVXX9U//uM/6l/+5V/08ssva9++faqrqztzrKrBYIh9BEuGweqRERIMBoPh8kD/SV/a2NiosrIypcbFa8n4SRqRmaX4hATFJyapt6tbXS0tampuUU1To7ck2Mkxx91OrnJ9MDJaHH6xJBiJqpd+GQJhR4c41C7JyW+jc/M1sWCkDlWUa9eJ456Q18NRxC4O8enpiqamKjklRVlp6cpJTVdRdo4Wjhuve2bM1qPzF+qxhYt175w5mj9+vIpzc5XivhWJcqKeC6G7S70uXj3OtTl3qqJSHx05rM1HDyk5MUHzx43TnTNnau7YcRqdl6+05OTT69+9nkzIONLe1en/xmgO6fGjyumxhXvBuI0/GrqtTXHtHUqLRJSfmqYu9zf5RziGS0N/kiHOxnNDrMMIhgaD4YZGH1nv3HS7nt5utXY1ewuGbPpIT0hXZkKmPya5PyAYtnW3eUJiXK8TwLyAMDhCAgJf33HIfeF5QZYYXzD4036CP3+1I5INBoPBYDAYDAaDwWAwGAyG4YpIJKK0tDTl5eWpqKhIBQUFSklJUXt7u5qamlRdXa0jR45o/fr1/ujk733ve/rlL3+pDz74QKdOnfJH2NnipcEQ++hPMoScYO3aYDAYLh30nRwljNXn+tpapSUkaGxBodLTsEQY6Vsg5pQ71+d2u752+/FjemvnNp2qq1VnR4d6OBq4pVm9zU3qcc5fW1v9Ecjd3iqdP4NORRxDPLJINe754cpy1UNSbG72JESOUo5LTFAkMVFJ7pqXkans1DSV1tep2vlLjCb4Y4q5l5qYRKTV4cKG5NjT1S33Q73Otbu4VNbWaf2h/Spz745z6ZjkvjkqJ0+5Lj2ZTl4kLNDl0tPW3qHGlhYdKi/TR8cOu7GkVxnJqZ5Q2N3T7fz0+PifGV/clXR1YsmxrdVlS68i8XF+HEIOtXHo8kC+MY6zGShYM7S8NMQqjGBoMBhuWPSRC+M8WTA+HvPSpx/0AwM+5MJ25xAy0xMylJmY9VsEQ8Lp7nWCnbdg2OP+ild8r+teewePxNcbFyiGPtb+GwNxMV/zb7q4czUYDAaDwWAwGAwGg8FgMBgMww/oJCETYmmHBfGTJ0+qubnZ38fCYXFxsbd2uH//fn9k8i9+8Qt/XbVqlXbu3KnS0lK1svjt/BsM1yNYmIeAF4vuUtol/o1kOPxAWUC24Yj6+vp6c+aGhcPCHFbz6C8MH4N+9Pjx414+ampsUnpikvIzMpWckuyPQO6lT3YO0l1CJKJj1ZVavX+vth87opNVFWpy7bytrl7tdXVqr61Vm8vrVpfXDU4uK3V/Hygv055TJ1XZ2OBJexXueqymWqU1NWpz33OdhQ+fY5XjEhO9/DYiM1OFWdmqdGHvPnXCfeuodpw4pl0nT/iwjlRWqsqF09HR2WctkfHDhdHS1u7eqXdxrHLhxHuLhRMKRyg9JcWvd0NIbG5r88c0E68dLt1bjhzWmv379MG+PapqcnWko92TE6td3JqdrNje0aF2xhn3LqTEJpeuMhf+cReHcuevqb1NbS4N1KvrYQwi/zNd/o8YMcJv4klOTvZ513+cvlR3MQhySyAYXosxnW9SjvQTwTFfuF7K1nB1EOcqyzWrLeyy27Bhg3eJrkO9++67NWHCBGVlZZ32YTAYzgcmMJVugGe36tatWzVp0iQtXrxYc+fOVWpq6mlfvwsGioMHD/r3tm/frrFjx/r35s+f79vi2YACq7y8XM8995wXxObMmaObb75ZkydP9oNxrKKjp0NbqjfrQON+2Hu/A2h9Pb09Gp8xXnNzF6iyrVy7andqe+02NXY2+Od0o9OzZ2hh/mKNSCnSjtqteu7Ir3Wi5bi3bHilgPiYkpiqh0se02MlT6qju10fVW/Susq1autq84IP8eh23xqVVqKHSh7VmPSxauis1/qqD1XbXquI+wdGpoz0cc1LzldCfKIRDQ0Gg8FgMBgMBsNVAUc3Bh1QRUWF1//MmzdPM2bM8JaaDAbD5QOd0HvvveeJSNOnT9fnP/95FRYWnn46uGBRZO/evXrrrbe0Z88eTZw4UV/72teUm5t72seVgQUX9E/orFavXu0XfegrZs+e7S26GQzDASwKrly50rcD2trSpUt1yy23eOLfYIDFP3SxtLX3339fK1as0K5du1RTU+MXQWfOnOnXUmiPEAw3b97sFwfRB2dnZ/uxddmyZV53i96Xo5XR36JDNBjOBurciRMn9Oabb+qZZ57RsWPHfL/7hS98QU888cRpXx+DuhQfH+9luEutVxCyWGNAJqSPpy7fdtttlxxOsAJ0LRborxTEGXexCPmdkJDg3eXk+6WA9aOjR4/qww8/1EcffeQJzaw5sSbE929U0OdC5F6zZo1Gjhyp0aNHe4LKjZwnhuEF+tewyYB2fO+992rRokXKz8/3ssD1jNBPYvF5YJvEsjPjzs9/+jPt2bFDD02bqX/z0KPKKyxQxMlVnmDo/HS3QqTr0KtbNuud3Tu85b7ZJWO0dMJkb12w78jhj4lidS0t2n7imHaXnlB1c5Oa29vU5MKAxDc2r0D3z56re2bN1thRJYrLzOxb7O3sUHd9g5fzKuvr/XHKW44e1u6yU57cF0caEhK9JcT5Y8dpzphxynLyHd/EomFZba2OVVWpvKFOI3NyNM/5yUjuIxfip76lVSedvLj9xFHtKT3pfze0tXgribXum+muHnA09NSiYi1x6ZpUOFKp7l5iJOotKyZEIzpWU+XePeUJjluOHtERd330s5/RfQ8+oClTpigtPf2idDjEh3o43Cz2ER/yH0edyXH5iHx9uX056boUWaT/mA4n42rpw4gjhEL6CeYU1GHAHAGr6fQTQy1fGK4PGMHQYIhhGMHwytHZ06kdtdt1sPGAutxvyIT9wd+dPR0alzFBiwuWqKWzWbvrd2tdxRrVdtR6gh7kvokZkzQ7b67Gpo3TgYb9eunoczrcdLjPqqEXOS8fjOWpyal6aNRjenzUk560uLl6o9ZWrFZbV6t7jlB7mmCYWqKHRj+iMenjzkowLEop0vScmcpNylNCfIIRDA0Gg8FgMBgMBsNVgREMDYahgxEMDYari6EkGBI2x/jRBtatW6dNmzZp27Zt3ioR1lYgDt55551asmSJX8hET7t7925PQNy3b58nhrG+Ql8wa9Ysf506dapvq7RTFjQNhoFg0TnWCIaMF6yPMC5dw2XOqwrWYSAk4Mj/oSIBGMHw7KCuLV++XGvXrvV1nzU4yEzWrxqGC+gTkaNZz6V/ePTRR728kJ6eHtPruBeDMC4NJBgyPkCq+u53v6vvP/WUWmrr9KkFi/V/3f+wckYUKj4xUb0dnZ5g2Nnaqsa2Vq1xc51Nhw+prbND6cnJykxJ9eS/rtPEcKwcci/XzU3qmltU3liv1s52dXR2qdmVwcnaGkWdn1mjR+vJRUs0Y9wExZ22kuc6EvW6b/U4ea/Nfbe6scGTFHeePO7C6PR+kl38x+QVaFLhCI3LL1CKi2N3Tx/BsLyuVqfq6tTR3eWtII53cmhyYpK3vohrdWNjlZMZD1aW6Xh1tScWkg7i3qNeRePileTqAqTEwswsv0KMFUWIhpNGjFRqcpJqW5r98dBYQuQI54rWFuWNH6v8cWNVMGa0ZrvxYPz48T4vzjcOkffDkWA4HBDqK5wM6uvV0InRLzCPhxfCPJ5yAZCPFyxY4DkfkAzPxRMxGAIi33I4/fuqA+XyqVOnvKPh0BkFlrDBYLgwmNAgGHFEBkIjSqJRo0b53VPnm+ihMKitrfXvsbDDzlbeKyoqOucghhDA4IPCCiUAO7PYoQWrPZYnUBxpXNdZp6bORnX1dv0OwbDXiVwdPZ3KSsxWSdoYT8pr7KzX0aYjau1u8QQ9/qUmpCkzIdNbBmzpataR5sOq66jxFhKvFMhnCdEETc6cqmmZM7wQVtZaquPNRz3ZMAhwpIU4TMqcrGwX3/aedp1qOeHi2cahzd4PzwtTRiol6ia+TpA0gqHBYDAYDAaDwWC4GsBqQNABMbdE/8PclYVlW5QzGK4M6IQgAnCcKm0KUtFQkfHQSVRXV+vQoUOeBIUuig2rLOYNBtB10Uegs4I4RTroK9BD2WKHYbgA3SrHFtMOIJigVx0zZsxlWwcKC7C0YdozhMKnn37ak73YJM6iLAt+kAW/+MUv6pFHHvHfg/TDhnPaIG2E8ZTxFjIibQjLhrzPAiJrLrQh4o6/651oYLg0UAfR+VOnsX7FUZvUKQhl06ZNO+3rY6CPDovj5yMXnA0QYFhjQCakj2ddEAMIlxoO48WNRC4EtF/SG/I/uMEG36EOQDpl8wLk6TAWXw0SxHAG62r01dRjLMfikFvMmRsODhmAfpkxn3bLJp1x48bdEO029IdnI2vRXtmMtXrVKiW6IWPO6DFaNGGSkl0exSMPubHECVv+yOPDFeWqcW08NTFJ04qKXcDSrlMntPXYEW/VD4uDpxpq/dpsUXaOJ+XNGT1Wi8ZO0PxxE/w7Pa6fhtRHWUweWawR6RnqdXJet4tHt5PJ/JHHLl6RSLwn9JXk5mm2C2PB+Ala6MbE+WPcuOjkPuLQ5MqU+FQ3NforVgWxkFjb0uJJjwTU4cLudL8hGEbjI0pzYRbn5Gqak08Xjp+opRMna9mUqbp5ynQtmTRF89x3xhYW+ngeqCjTmgN7/VpxelKKez9OmW5eNyonT5OLijTL5RXWDuvdvG/7rl3aunevsnJyNMrJoFGX16TxfGA8YazmavhdhHwJ8tRQjOkBjF/vvvuu3+TxyiuvnNnIhNzH90tKSvx8w3hahgvBLBgaDDEMhCKzYHhlgFR4tPGIjjYfVk3b7xICIRy2dbdqfMYELSu8VYmRRO2t36PXjr+sqrZKReL6BNX85EJNyJyo+bkLVNNerTdPLdfO2u2q7ai5ciWHkyewYPjwqEf1+KhPqbu3Wx9Vb9Tq8lW/e0RyaokeLPmExmLBsKtBG05bMIwPFgxTizUrZ45yEnOcoGiKRIPBYDAYDAaDwXB1YBYMDYahg1kwNBiuLoIFQwiAEOZvuukm3XHHHd7C4KUCvSEOshWEQCyGbdy4UQcOHPAkXnS16G6x8Hbfffd5vS9EH8bOsAjJ4iRHnREGxMItW7acsTzG5nQ2lbNoyJiLJaOFCxf6tjuUi5iG2AJ1yCwYxgZC3kOioX8IpITBhFkwPDuoZxA0mMuwLgeZy2AYjmB9GPmEtouBmRsBoW88mwVDxp3/+T//p576zneUHU3UZ5cs09fvulfZyFOJSertaFdPa6u2Hz6op9es0vjcAs0fM045bu7R2dOtmuZm1Tt5qrO7y4UYp+TEBGWlpGpkVrYyXV4nRRO8VUMIe/UtzVq1d492nTzh4iTdN3uOphWV+HggP+InLh4ioOvHo1F/jUb7xtLOrm7X/3apnflQY4OOVVfpUGW5t0gI0RDSYltXp7dS2Oj+znZpHV9QqOKcPHct8Mcqp7j0JSZEfdiJLh+wpBiPvoXx2t3rTXLp5Vvu/cb6WpVXVbpvVGjbkaNqdHlwy+SpnjSZl5Hh05bgvtHl3q1w/tbu26OVB/Zp0sL5uv2+ez2v4Hz1izSbBcPzg3JHloJnwZg+VLoxZCV4Wf/0T//kZTDmC5QJgFDI/OBTn/qUl/nYxGQwnA9GMDQYYhhGMLxyQNYra8Ea4DGdbDnpjzTuDwiGkA7HZ4zX0oKblRpN1Z663Xr+6LOqbq86c8xwZmKWStJG6+bCW9Xa1aJ3y1Zqc/UmT0IcaBXxkuGE0NQkjkh+VI+XfNILYRAMPyh//5IIhvgpThul2dlzlJOUo0icEQwNBoPBYDAYDAbD1YERDA2GoYMRDA2Gq4tAMHz99df9IjY61bvuustbMrwYqx/o9iD+QQqk3WIVFF0tVkQg87Bugo4Wa2Ecb0z952hkFv9oB+fSxdI+0RdDToSkSFi0V4iHkGJYd0FvDMGQMCEdQlZkcdjG4hsbRjCMPZD39D+4yymH88EIhucH+UM95mowDEdAsmPTw2D2C8MdYVw6L8Hw/3xHWdGEfgTDIkWTIRh2qMfJZe9t36q/fvFZ3Tt9tj696CZlp6f544njXLjdLvye0/kJaS8Sj+PsuF7INopz4yjyYXVTs97bvdMfedzu/p5ePEolObnesiBjbXdvn4NMyLHF0Ui8slLTvNVAjkCG5FfX3KwWF6emNve7rcWH0+2+0e2GOr8W7N7n+OKE+DhlJ6coNZFjnFOUm5quJJf2XBdeSV6ectLTleziT37EQ2KMOPkR7gA8ABderwu/w32rqbFRK3ds07ZjR5WXlq7pRaN8vDOdzJnqXKJzfHfv8WN6c+tmHe1oU/GM6frc539PEyZO8DLr2eoaeW8Ewwsj1F3y0ZeV+z2YbTe0Acbzf/iHf9Abb7zhre+G8mBewYmVzGX+1b/6V36sJw4Gw7lgRyQbDDEMJs92RPKVo6un01sprGuvVWdvpycMnoH7yTgOIa8opdgJe1FPGtxZt10tXS3eCiD+ESOTIskalz7OHz0MabG05ZSauhq94HVFcN/vOyJ5iqZlTvffKms9pWP+iGQX39OCxsdHJE/5rSOS27rbfBwBz0ekjlRypO+IZIPBYDAYDAaDwWC4GrAjkg2GoQM6ITsi2WC4ekC3yhHJkPc4RpR6S/3kOELaQiD74A+dKiQodLi0UY4vZl2ENstC34oVKzxR8f3339euXbt8/ScciPh33nmnJ3dBwJoyZYq/fy7dLWA85TlEQtZaIPFDImThkHGYuNK2IAcTd+KE/7AwjDtf+IbrF2Hx2Y5Ijh2Q7pD2UB6XmofnAn2XHZF8blDvORIfIx/mzA1HR/280ebYoQ9kbXxgPwXBbe3atX7zBWTAacWjNG/seKWmpyniN230qsfJa/udjPTG1o80rqDQH3ucnpx8hqAXJV+dS4TA6O5Hk5IVn5hAh6DeHjcWMYfBKJDrO7cfP6qy+jp1ufsQBetaW1Tf1qpad61uafLPDlSW+6OXd7r5zpGqSpXW1nqLhQfcvO5gZYW6XDyzM9I1fmSRZrm4Lpg4WYumTNXCSVPcb9wkzXJxLMnLV6qLS2N7mw5WlOpkXY3qmltOW0fsI0FiXdHXBsYMnLsXz7yKvCLPXJ8PsTDV3dtx8rgfWzj++cz7LqwIdcv5yXHf2nXooA6Vl2nM+HHKcXNB6ty56psnVbq84Wo4P0IeBdlqMMd05H82DaxZs8bPsblHO8HxG9mM+QLzergi1PmL+T515Wz+CBNSaUjTpaSHMHkvyHn968+FwjlXfAaiv/x0MbiYcPHTP74XiutADPwGfxMOLjy7lPCGEqbBNRgMNzToihPjE5UUn+Q6Zvaa/C7i47D+54TQng51O4HwbOCoZUiKWCtMiiQpPSFdCS5c192f9jE4IB4IF4EceLb4ng34g+h4sf4NBoPBYDAYDAaDwWAwGAwGw7nBYg8LchD2IAlikQ0yTlgMwjpIaWmptm3b5q1+/upXv9J3v/td/d3f/Z3+1//6X/rBD36g5cuX+6OReS8cUfbFL35Rf/iHf6jPfOYzWrRokd/kDcHnYsgC+IHkmJ+f70mKt9xyiz73uc/pG9/4hp588klP5oJYyDdffPFFH4ef/OQn+uCDD84sOBoMhtgAi9iQmHH8pk8yDD1Y4IeUARnDnLnh6IwI/NugzYZ8wUogpL+Ozi51d/Wt93rLfpDpIvHeAiB9aRtW97p7/KoqnJ6Ik4/ie7oVD9EHWamrE+aid90dXWpubdeJ6mrtOnVS5Y31ynCy2MIJE3XHrFm6ffYcLZs5SzfNmKll02foNufud/fumj5TYwoKVN3cpI+OHVZjW6vGjijUvfPn6+6Fi7Rs3gLNnzFL0ydP1oSx4zS2ZLTGjCpx1xJNGjdBUydP0YxpM7Rg1mzdOmeu7pu/QHPGjvey4NoD+7Th0AGV1dWr1cmq3S4tTsjDmpCLc7snRGKZMe40ITPfyZnFOblKdn83tbf5Y5lb2zvUg+VBl1byJCUlVUUjRnp/ETYL7NjhieiMP4YrA/mLDA4pD0LsYOYp4WJsivkIGxODrM8mDzb+0jbYiMSJmcxpmA+c7/shrvjhiusvf/j248Ljm4TJhgXklIsB7xIuG654l7hQxwiLjVqE0/9b/RHiEuJ1Ln/cD36DO5vfi/UXQNxIK3lMfJkHXkw5nusb/CY8DIaRH9SN4QKzYGgwxDDomMyC4RXCCYaQAlu6W1XeWq6OnvbfIgXyG+EzNZqmrIQsJUeTVd9Rp30N+zyhEPIhfhAyEyKJmpI11R+jzPHJR5uOqLajxj+7IrjoJEQSNCljsqZlzlDEfbOs5ZSONB1WV0+Xjx84Y8HQxSE76bQFw+YTZ45RBt6CYYpZMDQYDAaDwWAwGAxXF2bB0GAYOpgFQ4Ph6gLdKhYM9+3b5xePaIMcawzZMFgK5JjiHTt2eCIfxMPg+BvLhfv37/dtiXpNHcdK3OLFi3XHHXd4t2DBAm/VLVgtDLq9i0VYTE9PT/ftB90vV8LDslHQD3MULgt3/EbPzKImemC+yfs2Rt8YoG83C4axCdKPC2US3JUgLGqbBUODwRArCH3fQAuG3IOYA7lqp5PLmtw8Y0J+gRaMG6+0lGRFWUt3DtJgfWOTKupqlRAfUZcbX1KdvOR/90Dm7lCv8xOJd99xz9wApF4nL3U5uamuqVlHqyp1qLJCpe79lOQkTSop0fxJUzXNfWdskZPB3DcL83I1IjtHBW7sS3fyH6S/8vo6dbjwc9IzNGvcOM2dOEmzJ0zUmMIRKsjMUkZqilJc/IhH1I25WBwkDlgXTEyIKiUp2R9lnJ+VpeLcfGXAsXFjQkVDvWpbmn06slJT/VHPWDJkNdtliuIifeTCuN4eqa3VWySDeHnEpaHNpSshElWOC5fwEp2/ePcuxyy7DHN52KT6hkadcnEvLC7yG1rOZfGO8YSxmqvhwggyTajPwV0uCA8dwdatW/1mKOQ8ygOrk1hHh+dRU1Pj5zOUYZg3jHN18Ww8E8ox6AIIE+vrzIGII3MI5AZkvC1btvj5DnMl/DJX6m85faAswbvwVZg/ES7tFcdcCqvrBw8e9N9h4xbyKnMU+GQhb0jDpk2btH37dh8GegTmM/BfwlyGfiB8g7gRT+KGdXnCIbz+8cIf4ZFGiJcQHskf4k+Y5C1pIk48J824EGfygmfIU+RlmFeFOIe5GOkiPsjf6EAoH/KLvGPeSHikh3gCZLIrqRODASMYGgwxDDoZIxgOBuLU2t2i8tZStfe0ub8GpIeBJT7ZEwfTnONo5CNNBz0p0Q3t3kuP+5cQF9XUrOnKTMjwxMLDjQdV01GlntMCwWXDfcITDP0RyTMUdd/hiGQIjGc7InlyFkck56i9u0OnWk56IiR+IDpmJmRpREqRkiPJRjA0GAwGg8FgMBgMVw1GMDQYhg5GMDQYri7QrQaCIeMbi1osIrEQxeJQWMTDrVq1yi8a8YwxkEUm9KwsULEesnTpUj3wwAN65JFH9OCDD2ru3LleR0vdD4u1V7KIxLvoe1lILCws9GQxFhQ5Opn2FtZoIDyyGMaV9OGfds1i2JXGwTD8Qd9uBMPYBnkRyiW4ywV9gBEMDQZDLCH0e8hOA/upILcxxlVUVqokK1vzxozzJL8EyE5JSYpz7yS4Ma0wNU17S096C4DZKWmeyNfS3qFmJ+/BzUtPSPTHLLtA1eP63RY3ph13c6J1B/arrKFeqclJum3GTC2cOl2FRcVKdd+KOnkqAinJvesiqbbWVh0oPaXNhw9pX1mpphWX6NPLbtHcKdNUVDhCKRxxjbWydvfNjg7FuW844bHPYqKLS6/7u8c96+F+V7fiXZgR0p2c7NKUpBHpGcp3cubBinLtLTupEZnZynV/Ey4LzowOPr/gEbik9LqwSBNHPHNMc42bU0JMLM7O9SRDTzDs7VGcy4t4lz7O7mtoqNM6JzcWjhqlGTNneJnxbOMOec/4xNVwcUCuCfmFnHUl+jLCgeTG8chYU4cTQlkVFxfrrrvu0syZM73sz1wGIKPxbN68eWed30MEhPD20ksv6amnntKzzz7rj13Oysryc/j3339fzzzzjL/P99577z0/F4Ioh1yRmZnpeSX9yYEQ/+CsrF+/Xs8//7x+/OMf69VXX9U777yj1atXe0cY7777rg8H+ZEwkEuYp5BGCH7/43/8D/30pz/VihUr/HHoPEOOZWMVgE/D+88995z+5V/+xVtyJ1zSjpxDuoNfwvzlL395Jo2vvPKK100gE6P/QC8RCJWE8bOf/Uy/+MUv9MYbb/g0kw/EF9IjchT6T9JOukN58j7zQ97553/+Z/8dSJXEG6Ii8XvhhRfOWMlH50OZMI+71rKYaXANBsMNDUQpLAJG4qL+77PqI9y9zp5ONXc1qbO30wmUCUqLZniSXp8o1jfYYE0QEh9hJcUn+2t4fsXw8eJ/fea4+/4aaBvR7z3xFhn7K1bO/PI/3Pvu/2cT9AwGg8FgMBgMBoPBYDAYDAbDxQMdHI7FMch6WKHA2gWLdRBlWUxjIQlyH4t49913n77+9a/rz//8z/Vnf/Zn/vhiFvggBrOwxQLdQAsaVwr0gITHohQLcixM3Xbbbfq93/s9feUrX9FDDz3kN5JDMGNB7je/+Y0/OvnXv/71mQUtSJFG5DIYhif690O0VdYqrL0aDAZDH5DDkHNmODksLT3NHxlc39yippYWtTvX3dqGsKRMJ4NNGzNWN02ZppmjSnSoslzv7t2trceOqLmtXX3n2TnQ33b3qMndq3ByXmltrepbWzTSvb900hSNLS5RZna2kpzsBTmwt7FRPY0N/trk/B48dULbXJhVTQ1aNmWqbps5W2OKnAyYlKRIR4fa8Ydralari2dnh5PBurq9a29vU717VlpZqdKKSk/Mwn93c7Pk/CZ0dyvDhTMmN09zSsZofP4I1bc0q9LJeJ1ujMAKo/ufC8v9dn77/u4bLxg3IAN2dHf5o6RZawY+zaePWI6PjygvM0sjnIPwWFNV6S27QZYyDA4oB8ZxxnPylTK5XBAGG6LYRIQFPMLF6BQbBJcsWeIJePn5+Z4wB7kQEh1zGUhxWDUcCOLG5qpgxApL6FgXXLlypZ8/vPzyy34uAaEXIiDkRsLECiDEP0hzEH0JAxAf5hmQ8SAtEg5x5R3us4GS+Qnx4R4bVCD0QcqDNMlz2jfEYtJAnIkTJEi+EzZ2Aa48Iy7Ej99c2VhD2MEfshTxYy4H4Y+NnMF6IBuw+A7P2VBGnAPJErIgeUKacYRP2G+//ba+973v6bXXXvPPyWdAXvKb+WPIS9JHPr355ptat26dz1vykbwgr4bLhksjGBoMhhseWPJjh4cbsp0728S7V509HWrubFJnd4cS4hOUkdBHMAxWAHm3q9cJZE7gwsIgRylH4yEtDh6Rj8GG7/SF2Ucm/C303Xb3+/71/RqAsyXPYDAYDAaDwWAwGAwGg8FgMFwyIO+x2AMpEIsTLE5hCQMrGSx4YeUCaxe33HKLt1T46U9/Wl/+8pc9sY/fEP0mT57s/QVrgUMJwsdKx8SJE3XPPff4OHzmM5/xcZsxY4a33MHCG5Y6WDBjMYwFLhbNWNxikc/c9edYOGWRlgVTswoYm6DMAsEQZ+VoMBgMfWCTBQRDNnqkOxmotbNDNc1NamptU0drqzrdb447TkxMUl5evpZOn6G75mDBLUkNbS1qcf6TEjmOuI/c093To+a2Vh2vrtKOE8d1qKrCHz881r07Y9QoZaSlKc51v70dHep1Y2tvU2MfydBd69w7248c0anaGmWlpOr2aTM0a+xYL0u2t7aorKxU2w8e0EdYxT5+zH2j2hME21xYbR3tOuVkzN0cvVpbp/qmZrVAQHRp6HXvuj/8N+Nd35+RnKKZo0ZrRnGJJ0GerKn2ZLUzBPSe7j6LiC7d/OnHkO4eNbqwurq6lZQQVcTJsV4uRTR1z/HDinhKQqIykpKV5J431tV78pcRDAcX5DVlRb4ypp8pt0sE70Naw8JfIPWx2WjRokWaPXu2tyLNCZdscOIbkN0g1EFKhJx3MYDUB6kQkh3EOqz1Mfdh8xQbrJgPIWdC+sOyH3HhO4BvQqCDYIjlP0h9pJP2Shyx9A4RkrA4qYH0QArEqiHzE4iO+GduwwYqrMAD4s68BReIkrxL/CDzBTIh9wKBj/whLK5YeqRec3ID90gH1rYhZ9KfED5xYL5EvAmDOdSYMWP8fIq+BmvxgLyEWAkpEsIhcve5QLhYPQyniVIupCkcW81v8vNaw45INhhiGEwS7YjkwUFTV5NOtBxXe3f72Y8ORoZyglRBcoES4hNV3VGjho56tXa1eFoi1gshFM7InqX85Hwf3p763SprKz2zy+Oy4b7dd0TyZE3Lmq7ESJLKWkt1qOGAJzUGxaMfRBMyvD+OSO7o6dDJfkckc4wzRyiPTC1ScjTFBdv3nsFgMBgMBoPBYDAMNVDSBR2QHZFsMAwu0AnZEckGw9UDulUWtyDd8Zu6yZHHEARpI5B8uGLlAiIf5ELIfCySsfjFIhXPrvX4h76QxTAslxCvSZMmef0w99E5Q5QkjSyM0SZZ9GI8D5Y5zF1fDos1LPpS3izG0vfaEcmxCfolEMrnUvOW92nvdkSywWCIFYS+7mxHJIf7yDZr16xRV1OzirKyVZCZobTEJMX19PojguPj3bjm5jSJzuVmZWpifoFmjy7R9OISjczK6dtI0t3lrRUedfOgjYcOaM2BfSqrr9OEwhEa4fwkOT/xLryI8wd5L47+2I9Tcepx1+M11Xpv9y7nN0t3TJ+lopxcJcRH1AmpqabWH7X84uYNLty9OlRZoQ4XRiQuXt093apubPTP1x0+oJLcPBW7eVi6i2uyk0OjLs2s+X78j3XliI/rahcWw8DY/EIluPhBHPRxQl7t6osj/X5Zfa0+2LeHqGrqyGL3jXxlpKQqyeVdHOMJvAPnIGWeKi/T9uPHlOjyqaikRKPHjPay7UAQLmN1GJcMlw7kHNyljunMR+CCYFkQMl6wwjd9+nR99rOf9XMUwgocE+b2lBWyGeM+8hlzlv7fI0zm6MiLyHIhTMqXOcS9997rNy898sgjfv4TCHxwTHiXsOGWIE/AS+E+pDuOAmbOQdtF3vjkJz+pb37zm34DFHOouXPn+jwIJEnCIW5Tp071JDxAXIJVQr7FNyD8ccU/6ePYZo5iRm8S8hVMmDDBf4Ow8Ic1ekiPWBBEz4F+hfQgFxM2uhAsvZOvyMzUffw8+eST3t19990+HfQ58HB4h3ykDyHfiTt5jYyFxUasIRJ/4kOeoGOBpLhs2TIvhyMn33777f4eZXOxdWCo4PrL0zl3DcCEBRP7OCbhZDYFGCqCwWA4P5iMY3qYzpDOByXQ4sWLfSd4toE8gM6JTpH36CTpmHgPhfC5lLUodxlgOJuewYNO9Oabb/Y7bOkQYx3lrWVaX7lW9R31io87+yQZi4UL8hcqMyFbu+t3akfNNp34/9n7D/i6juvaH18A7kXvvQMsYO9dosQiierNsmxLtiPHTpzYz0lsJ5+XZuf9nMRx+j+x/Zzy4thxUbMSqzdSEqvYe+8gUYjey0W5AP6zNnBoiCIpFoAEyPWlRvfi3HNm5rSZfWbW2butFD19Pejs6XC/R+GTY5/ExITJKG8vw3PFT2NH7TbzenhVuH4i2h+Fu3Pux6O5H0dseCy212zDqvK3TDzI+tJXIUWO2dE5uC/3fhTEjkFLdwu21m5BQ2e9iSZ73O+5MXmYlTIHiRFJCJUTWyGEEEIIIcQ1gi+ZemNAHGDj+M+sWbPs7V5NVApxdXBMiG/Oc4CaEwZPPPGEDVoPBxwI5yA4B+c5scAB8s9//vM2CD4UcBKC408cs6JXAAom2VbQwwInBoQYCXCSiCG86ImC46K8TvkSNvs6TpJt2rTJvnMikJNo3jgqvRXyb85/XO+JofNBQSEnGenBkJOG/OR9zgkvvhhATxycpBM3Hpwm5CQoJ4E5b0fbbM6cOfjMZz6DRx99dGCtX8Hrl9c317vca5nOCzjHQJuQ9w7nBXlvXG4+7C84PyKR4fnhufFCBl6urc35I764wPCDu3btsvDtnHNiW3YxxxZCCHG98PolioHO106xr9i9eze+8ad/irLDRzEzJw+Pzp5nXv4ououMiEBUTDTC42ItLwsf3NHpNgwOCASBMmcn7S8rQXlDPZpdnxl069S3MvJdD5Ldc0p0eASSYmIxq6AQBalpiKV364F+kv0UPSfuLTltAsJZ+YW4f+YcREdGgBH2Ol27S6+ERyvO4FBFuVvXle2KDQ/zI9bVjXlQLMh1k10Zi4omIN/1oX5Xps/tdyhX7v1VX8hvvb09lt9zmzYiztVl4dgiFLhtklxdmY/1E74wmz9ubGvD7tOn8NyW95EZn4gVU2e4fUhFUmysq2MkQqkDcPXoC/cj0NiEbQcP4L82rUfqxCLc9cD9WO76cr6wMhjuM/sT9te0ndVXXxnetc3rmulSbS++FEh7/jvf+Y6F6eXxj4+Px/33348/+7M/s2cS2vj87bnnnsPbb79ttiBtfXoP/OpXv4ply5bZs45XHgV+9FT44osvmi1Hu5G/U1y4dOlSEy7y2Z0iOJ73559/3oR4fD7icwbLfPjhh/HII49gxYoVJs5788038ed//ue2jvfiE0WKFOrR/uAzCNej3UgPhxQHclyAY4qsJ+vL64tjFD/60Y/w9NNPm61Jm4Vl0VM7vf/Rg+L3vvc9C+NMzYsH9TQPPfQQfuu3fstsX9o/r7/+ugkzd+7caeMctIWffPJJe1mML+XQE+O///u/2+90nMe6UAjJsijM5DFhfbjvFE9yfIMvo/HY/MEf/IEdK55TL8zyT3/6U3sG4zJuu3z5cjtGrDf/ZvtFMSVFmSPhpUt5MBRiFMMGhY29py6XB8MrJ9DTjqqOanT29se+PxfPC2F2VDbiwxNM2FfTWY36rnrrlCkyNA+GSdORHpVh+R1o3Isz7eX221Xh+m16MBwXRw+GUxARFoEKl++JFs+DYf/xZx3j/PEouoAHQ4ZvjnN1z5IHQyGEEEIIIcQ1Rh4MhRg+OCYkD4ZCXDs4tkrvGEwcG+U9wAk4TmRRPMjxWk6q8VrmOCononi/eJNz3uQRx28vZYLwWsF7jP0zJ7M4V8P7mxN9rCP3h5OQHEtmm6N0YyWeV7btnMDltck2l5OlFHdTSH4uvCaYeC1f7jUsD4bXDh5Tns/LtbXZxsmDoRBiNOH1S54A61z4G9s1vjxR5vqfuvp6jElNR3p8Qn8oYNeX9PX0IpSCwiBTD0L7ei3UcY9b3uRsuz0lp/Hm3l0oqat17WQfxmZkYkJWNpJjY9HQ7p5fGhrMQyHbUOYZFR5hHg3DQkNM8NfW2YGq5kZUuPUo3hufmeXa6H7vgHDrcCY5OT4eU/ILMC0vH1kJSQg4++tkdRX2l5fgaFUFJmRk4/5Zc5Dlto+OjoHPH+627xcw9ro+ket3dHebKJKeD4OuLl1ufyiCrHN2aZ+rd1ewG53dAx7VKFZz2x93bT3FjzUtzSik58bcPCS6PjqCInW3DyEu9bluOuj68FZnD56orMCOklNIK8jHjNmzkJeff159D48F+2p+iquD54v9+aUKDPm8zhf2KAjkszVtetp29IrHFzsoeGM+vHY4lkDbjON2tPm5jDYg7TNu59kRFPKd68GQzw98cZjPQhTGUWvCsQFeD9SC0b5k/nwmYpnMk57TmZgfX2zhCw10qOUJUlkebVLesyyDtiO3pc1IESCdZrFMPrd4L1Lw3qcAkY61+CxG+4f14PMZRYR79+61clge95vbeTYSRY30dkjdC20fCgzpUZFl81mPgkAeM461cN9Xr15tgkXuG/PmS55z5861cRgeL17vLIPPgHxe5HHgcyHLosiQZbG+tL8HezDkMopA77vvPvzar/2aORbj8WLivo6UZ0cJDIUYxUhgOHTQA2F9Zz26ejrRc56Qxs40M0FedkwOkiOS0d0bNJFfdaDKQg/zX0RYOCYnTEVqVBrag23Y37DPBH5DITD0mcBwvIVIpifFisCZQSGS+4//pQgM48PjkRWdg0hfpASGQgghhBBCiGuGBIZCDB8cE5LAUIhrB8dWPYEhhTcU5HGSjPcDvYdOmjTRrmWOv3IclYn3DSeqeF1zsoz3KtfnmN1ImCgi7I850caJMe4Xx4o5YcY2xRMdMjGijdKNl3gNMwwbw69xMpafXMZr9Vy865bXzOVevxIYXhs4z8P7WQJDIcTNgNcvcW78fO0Uf2OfQfFSdU0NTrhnmZToGPMGGOeeY7wwxMGg61d6KCzssxlUdk30HLin9BS2nTyBwxXlWDppClbMmIkpubkoTM9AQVo6xrn2MTUu3uZp95eVoqalBcku/wQLMdz/DEMvhTXOJix1z1CZiUkmUPQxRDMFXK4OEW7dmIQEJLjf4miLuWefiDAfyurrzHtiuNuv2c4emzduAiLcemFuHVMu9gTR4/LmPH6Jy7vS2Z+c//W7baMjwpHm6tXm+t4jFWXmfbHate9dbj/pcTGGZYeGYe3hg9hefAK5ickWEjovJcWOS3/oZU5T97kygmhvC6CmoRGHykuxs7wERTOmYfGA8Op8ugT2Jzzu/BRXjndte336pdhM9EBMb31Hjx61Pp19OT3+3XbbbWbv8dmaefF+YWhhz2s57xEu8zyXU3znndvzCQw5DsDxAHr3o+jPEy4yb2pQvLz5XES7j+I6ls9nDNpwFBbyd35yHIBlUEPGSJz0EMgwxNwXlstxRerIvOco1pNleeWxLD5vsSzqZ8iiRYusXObD/PhMxmPBMUku533DfaCdQ3uY4yrPPvvs2ZfDaKNSOMljx/UpYFy/fr0JFbn/rI/naZHHjvYt68H6cszEe9GMdacYkTY28+Rx4j4PFhjyd688CjZZL54nT0R5Kef9WqAQyUKMYhQieeho6mrEseajqGyvQGt3qwkGB+N5KJyfuhCFsWNQ21mLdZWrsbtupwn3KEqM8cfgobxHMDlpKmo7avFc8c+xtWYzOoIdA7lcOVH+KKzIvheP5j+GpPBkbKvdirfLXkegp8MZvpcTIjkXs1PmISEiUSGShRBCCCGEENcMDhB6Y0AcZOT4j0IkCzE03Kghkhl6iJMZFDaxnKESMQpxtXCSlF4wmDgZxQm1xYsX22QVf+N1zGuYXjLo3YL3KCeruJz3Jsdw6T2E/SCvbY7Nck7kckVAww2njpgoXuZ9yfFh7oO48WHby0lUTiqfr333JnKvZLKTgluFSB4+vHPDc8h0JeeI80dssxQiWQgxWvDaPj4vXKidoh1DwQ/Dlj739NPIjozGwsJxWDJhMrISkxDh95nQkCGT/T7Xx4W69jM0xAR+L+/cjrrWFuQkJmP51GmYlJcPX2QkQsN8pvHr6wmiur4ex8tKsPbwIdS1tKAgJRXLp0zFxMxsq1OH67cOuN9f370TRZlZ7rfpSHHlhkdHISTcb/mA87Zssjs60BMIoK6pCav278WW48fQ1tWJB2bNwccW3ILQ+DjbZ67T7VKtW6+0rgYtgU7bj7HpGUhNSER4RLjle7yqEgfLShGgQMv1CwyDnJ+SguTYOCvvP9a8Y2XcO20WZhcUIispycSFnW6/uk2MGI4eZ+NWuXJ2nS7GrvJSVPQF8eDjH8djH/+42Qvnjuuwb2Z/wv6aojH11VcGjyuTJzL7qOcF2kW0j5555hn84z/+o724R/EaBW30Ssj+nM8inh6E6+7YsQMbNmywsTqeLz7T0DZ77LHHLFSxZwtSJHduiGQ+w3jrcRs6tfKgqI/eACnYo/CPz00Uzn3cXTMMg8y6Ui/21ltvWYhmCgD5zMHlhNc47x3apNyWokeODXAckTYJ94fCPh4XXmsco2C451WrVtm+0H755je/aQ6zGPJ4zZo1JrZkHqw3y6FQkpqaJUuW4DOf+YyNdXzjG9+wfeW+cJ+eeuop3HXXXbYewz7/67/+q4V0pnCQAkOKJlkW1/fuA+bN33n8KTJkflznN37jNyz8Mc8Byx4cIplOvXh+fv3Xf908GI5UJDAUYhQjgeHQ0RpsRUnraZS3laGus/ZsSGSPXhMY+jEvdT4K48aisbMB71W8g5112+0NEQr8Yv2xuCfnfkxOnILGrkb84tSz2FqzCe3d/Sr5q4ECw7uy78GjeR9HSmSKCQffKn0NHT2dFxQYtg4SGNLLoQSGQgghhBBCiOuFBIZCDB83qsDw3XfftbaDY1yDPScIMRLgtUnhHaPCLFiwwMZJORnnwWkXrsP7kxPZnDSjxwxe4xQhcgKcHuIeeOAB3H333TbGynFZjrOqXxQjHU/IwWv1csVrEhgOLzwvbEvYZ17pvI0EhkKI0YbXL11MYMi+gn3GSy+9hB/84Ac47p5pxiUk4eNzF2F6Tp6FOmZoYw/mSVHd0aoz+NG61ShMS8f/fuhRxERGIdw9n4TExCDU58py3VefW6+vrQ3tjQ3YV3oab+3ZhUPl5fjkgltMZJgQHWNhkCnye27jBitr0fiJmJCVhbj4eIRybt6VZ1PTrp4+V0/09qClvR07T57AlhPHXD0qcd/MWXh80WIrm793OVu0uaUNh8+U2zpZ7plsfEYmJuXmIj4+ASF+H/poV7o8e4M9CLg60tshj5B/wN4MdHXiX955G7tPn8Lnb1uOmQWFiIoIR0tHO6qamtHU2oakmFjz8Eih4pv7dqOksw23LFmKRz/+mL1kc77opDzeEhheHbwG2ad7YYovxVaikI3Ctn//93/Hd77zHTvug4/9hfIYvA7LZN//6KOP4g/+4A/seYfbnU9gSFHdpz71KRMY0lvgYJ0Xbb0333wTP//5zy188bkCQ8JrhPlQaPfaa6+Z3eF5MuQzE9PgfeD9zbrdeeed+PKXv2zjHxQg8t6m5uWNN97AT37yE/PcyDGKr3zlKyZC5H3PF78otOQ1y/ESejnkuEZ9fb2JFZ988knzPvj3f//3VhbHLVlPHgeOX/I8MO9//ud/Np0bn/V4XGgPM32U+JNl8lg99NBD9hzIcZXBAkN6jORLaBQ6esdnJKIQyUKMYthYKkTy0EBBYXdvN1qDLeb5j4K9wfAvvrmSGZWFhPBEdLl1S1qLUdF+xn6nh8Dw0HALP5zofg/2deNI02ELo9zVc/Vv9tKV9VgLkTwF0b5oC318vPmoK6fHyibsXOP9cRjnhUju+2CIZO5jgoVIzlaIZCGEEEIIIcQ1hQOR3hgQny05/sNnV04s3wjPlEJcTzgmRCHAjRIimWNdfMufg/uckPKWsx0JBAJKSleUeF2db/mVJgqbOP5KjxWc1+C46uDxWG+yiZNYHEPli+GcVOI9xHuHeOO69IBBwS6XUxBEoaI3icZ8mIQYSXjX5aVOdg+G945CJA89PH5sczxxIb9f7jH14PyRQiQLIUYTbO+YvPbvfHjrmK3l7DOGSj7j2rjGlhaEuuVRrv2M8veHjSXdro8pra/FwfIyNDnbb0JWNhaNL4LfrRfKMrq70Of6tL5gECHcxq0f4tpPf1goGtvbUOvyTYmNR4prQ+Ojo0y8yHlZX2gYGtxz24nqKjS2tdt31qG2oQFt7ntfdxCUh/vc+kGXZ01Ls4VILq6twpj0DMzMLzRxYZBOiBobsbP4JKqaGpGZlIiJOTkoyM5GXEoKwqi38fX3BaxfiPseFh4Bf4RL3Ae33DX4tp87Tp20cuaNGYsct220e7aj90Pux5HKM9h5uhhbi49jT1mJ+7vcQjTfededmDlr1kXHdNifsK/mp7g8zu3TCc/lR0GxHIV9DONLQR3tJG7Hc+TZbRdKhJ/chroQakD4UjBFg9RvnS9EMpdTnEehHz0MDtZ50daj50J6fWe9+FzkeSBk8mw43pN8oXDixIk2tsDPwR4Bef0wcX2vboR2JOvIa9DbB46JcP9pv3Bb5kFnXRQfsg58Hlu6dKk9m3EMg89hHE8hFBDS9uH+8Vixrvfee6/tH/PhMv7G/eGYCJ8JuU98CYOhp+kFkSJLvnzGROdeDK3MTyZ6vecnhYvx8fEfCpFMe4tl8oUOHs+RigSGQoxivEFXCQyvHgoKKcBjqOSGroaBpYPpc8ZWGNKj0k281+uMt9NtxTjTXm7iQ4r8GEI5PbL/dy471nzE/X4GXT2dlsPV4AvzYVzceExMmIIoX7SVe67AkMSFx2Nc/HgTOXb1duFMYEBg6P7RC2N8eIIEhkIIIYQQQohrjgSGQgwfN5LAkPlzvMsTFnJ/2FZwwoFjUEpKl5N4fTKxz/G+D1XitclJNE5OcYzUmwAbDP/mvchxVK7LOvFe4SQaPzkpxX6xuLjYxmk56cX7mIn3GftOTpRdbLJciOuBN4nrTVRfDhIYDj3euWBb4bUXl3s8B8P5IwkMhRCjCbZ5TB9lM3Edekenbdji7K2aunqUVlehzdlk3T1B9JqACa5NDUF3MIiTNVU4VlmBCF84JmfnYlpuHkKYP9frds8rwW4TFdp0a7AHoW55uGuPm9vbTWAY7my++MgopCck2DwvncnEREag0+Xd5uw8hmFubGtFdWMDWOsonx+Rfp9LfhMYUvxX3dyEkroaFNfUoMi1xXMKxqDPLad3w1L3PLa/tNTmuGcVFiIvM8vtWzLC3PY2Uc26sa/scZ+ubhQVWuhnHi8eELdOj/ttf1mJCR2n5OQh3R2bmJho89LY4erZ3NaGFldXHh/ORze1tyE8NhYz3DMg+3DaxRc65uxPJDC8fNin83mBAkN+etf3pUD7auXKlRYRgM8X3jVPYR2fXwY/Iw1OFLyxLJ4rCgn5PM5lfJmK/T/XuRSBIUV6HqzLxQSGzI/PQ9S6EP7OevLZiutRZMjEevM32nwcT6Qtye+0IynW43Xo2UHMb/v27VY28+d6HC+hTcNnK+b74IMPnvUeTzEif+O6zJe6GY5z8Flt0qRJ5m2ez3vcLx5L7g/3n3nymY114PgLvdnTO6N3LCiS5H54z4HMg8tYV+4P68o8zhUY8ncKDFn2SEUhkoUYxbChU4jkoYHiwvaeNhxqPGieB2laDZhXBptKCginJk3DmLixFo54TeV72FazGT1uW64bFRaFKYlTMcWtE+mLwptlr2FLzSY0dNSbcXc1UBBoIZLzP46UiFRsq9uCN0tfQ2ePM0BDePz7rB5ZUdlYkXMvCuPGoC3Yim1eiGSEujp3IzcmD3NS55kAkcuEEEIIIYQQ4lrAl0y9MSCFSBZiaLmRQiRz/IVleBMN/C7E1eBNbA7HNAgnsjipxgknb9LpfLBsjsfymmYfyLHV3bt3Y+fOnTh48KC9CM46Mj9O5FFQy/Fahs7iJBUnmjhn4omHvMnGqxURCXGl8Lrj9Xol16BCJA89PA9e+zAU7QLbK4VIFkKMJrx+iaKgj2qnaBu2t7XhyNGjWLtmDd5+6y1UONssGqEoSEjClKwcMBxymMvzcMUZnKyuQn5qGm6dMBFLJ0+hxxmXi0uu76EzGvZA9Ghoc8puWYfrn3adOon3jx5BZ1c3JufmYPmUaUiIdiWEhKLL9Vut7lmnOdCOtkAHthafQLEr465pMzAlJxdRzqakwJD71OLWo4fC948exvZTJ/DI3AX49duXWbji07W12FtyGiXuM8/Zjne7Njo6KQU+t32fy7s3GESQYZHpadHtM/ML9/X3FaFhPoSEhZoAsduV8er2rZbXtLwCzHB255icHITHxbt+tgNtTU3ocP1tMNiNptZW/HLbFuytq8bU+fPx4KOPmOe28zkP8+xfhUi+dHiOmLw+3dNfcNlHwePL47xjxw4L8ctQxhwfYD58lpg7d+5ZYdv5oA6E2i2GFuazCs8dt7vnnnvw6U9/2s4z7a7zhUhmaGGGSKaHPj7LeNDWu1CIZCa+zEARJEV7rCuvFU/Yx3EG2ngsk/oU5rVq1SqsXr3anqk43sFwwl/4whdMBEj7h/c2y/nud79rIktGRPB0L96LW/Re+Bd/8Rc2Jsn9/OY3v2ljKSyL17G1Dy5/igNXrFiBr33tax8Yt1zj2owXXngBb7l2gy9d8hmNXgrvv/9+C33McRGuy+PJcRPWh3nyONGeopaHz3pcxnGcwSGS+TuPD8dzmNdIRR4MhRjFsLFjI8dGh40rGy15MLxCXN9MkWF1R5UlMtgzIKEHw5TIFMT7E8xgLG07bSGSuR0dW/P3GH8skiOSTWx4qq0YFYEzaO9u68/gKqC4cWzcOExMmIxoX4x5MKSHxA+ESHb/4ixE8jgkRiRayGeu9ysPhr2/8mAYJg+GQgghhBBCiGuHPBgKMXxwTOhG8WDIQX+2CRRr0UsB3+JXUrrSxGuIiffDcFxPzJNzGZ5nkQvB3zjmynW5HSfEOLZKjyAcX+UkE697vkhO7x68pz0hIl8S50QfPxk6nN4tOLbLMj2RoRDXGl7TXnt9sWv/fHCiWB4MhwavbfGECPx+ucfxfLCNkQdDIcRogm0fk9cWXgyu53PrUQjFZ6d017aFur8bnI11vLQEp2urcayqEnvLSnCgvBQtHQFMyc7FxMwspLltrCxQXNiL1kAH2l2/ZiMarj/qccvovXB/WSk2HD2E4y6f8DAfJmXmINo93/hpv7m+MzLcj+jwCNvmhLP7yhrrMTPf9YfOJoww+y4Ufb19aO3scL9X4oSzC1mPqbl5mO7sx87uII6cKTdRYISr+9iMTEt+Oh9y+fe6enW6VN3YiH3uObHYteX8TqGhs1oRFur6cB4LVz69Loa4rrTXlXeqrsZ+y0529aBY09mu0c7ejU9IQEJsHKJc3crr61DmUnNXJ8aMG2de2S6kSeAxYl/NT/HR0K7y+nTv+YLpUuBx5nMERWsU9XF8gMedfTjFhZ/85CfNcRRfXuJLTOcmCt84TscxBW5LXQhtLObBlxgnTJhgf3shhIfCgyGfd/jCFQWLGzZssHDFFORRt0KBoedtkceDz0kcg+DvrBvvXzrB4lgE1/VsUtqZtF2YPK+FXJ+/cxvWlR4MqYmhoJHls57U23jrEh4TChgpHuRx8eA2TAw/zWczlsnjQpuWWhuuS1Ej81y3bp0JEbkuPdZTG8fnPz4Lchs+8w32YMj6UfjI8Rwe75GKRnCFEGIAE9wNdJbn0t+Ju46jtwsdPR0m5vOFuE4+NLx/O0e/F8R2BHoC9jdFhpGhUZfc+X8UrmrW4bj/D1rQ//XyYH2Gpk5CCCGEEEIIIYQQQohLgxNknOjjhBg9gdA7BT1+PPzww+Z948477zTvH0UTihAXH2eifE5eUWzISTJ+52QUBb58gZwTVZyQotdPTnZpAldcKzhO7SVx7eGcw2AhwlCJC4UQ4maA7ScFQbTH6KHt0UcfxYr77sXMRQuRMXECfDkZ6E1LRmu4Dy3BboS59Zk6u7vN1uK8cK/r/vh3dVMT9pWcxq7Txdh9qhibjx/FkYozaO/qtLDC1c3N2Fd6GqV1teaRMNDZifaOTtRTyOXsOXo8TI2NQ1xUpIVUZt049xvs63XrdqHC2Xp1rS1IjI5BXGT/S11Bl29DWyuqmhst7HJ8ZKSJBBkKmeGTmQEFg0H3vbO7C22uLk2BdqvXwbJSlNfWmI3ZFwza3Dc9IE7OyUF0eLgJEfefPok9bj9KqyptH62nd/UKcf1NTFQUwkPDUFdbZw6JZHtePV6fTlHhlfbp9F5Ix1IUrPGFQD4XMB8K6Sjmo1COiZFEzpcoQlyyZIkJ63hvcFteI/QCSM+GFOx54ruhgvtMUR+fcygupPfFTZs2WbSE1157DW+//bald99914SA3D9ebzxOFB5ShEex3uBjxRcfKXrlyytcz4PfKSiml0AKHXmcua4Xxph/D4ZCSOZ/bsRQlkePzl7ZrA+fz1h31pnp9ddft096XHz//ffN6yNFmXxu43ka7UhgKIQQDhqD/R0Q0/kHRSgqpLgw0NOOPmfYhYf6EREacdaDYK/7Fwg649Ctw7xifLEuxVjeQwHLdyahS/3f+Y3/Lp3+/Rua2gghhBBCCCGEEEIIIS4HbwKRk1gUGnLyi95EPvvZz4LBpr7zne9Y+uY3vonf//rv47d/+7fxqU99ykJ0MUSpN5FFjx/0LsJJP05qcSKRk4D0XiLEcOMJC+VF8PrhtSNMnpcjIYQQlw7bUXoao5e0xYsX44tf/CK+8zd/jW//9XfwZ84m+4M//EOsuOceJCQlmtivo7sb7V1dJtpjr8dmNzQs1ER+L2zdhJ+uW4Ofvb8WP16/GkerKjA9rwBLJk5FpCvj5V3bsfX4cZypq0ddUwtqmppx9MwZrNy/F3XtrZicnYvkmFirU3977vrYYA/aAgGcqqlGRWMDMuITkRIba3Xvdf1ul/u929XFFxpiXhJdZ0w3dkB3kCsg3O9DRmIiFo2fgNsnTsLE7BzzzLjqwF7sKj6J2qYm2xdC74qF6RluvcnurxC8vHULfrrqbazfvhUN5eXorq8DOgIWCprHzOfqGGhvM29xsgGGBvbn9P7HPp3XweVCQSGFgPSWR+EnzwvzpGdCCu7oHY/n7kKJv1M0R2+A9MLHbSmG44tM9BpIj4X08jdU8Dqn4I9lUgBJwSAFjPSgSIHhX/3VX+GP/uiP8Pu///v4xje+gf/6r//Crl27bD95nOgtkR4Gz/WCTUGgJzAc7M2Ux5ViSwoKub/chvnQoyC9NA8WI/I3bn8+gSGPE71vs2x6GmRePC48PgwFzfDLrDfDNFMsyecz7hfFzPfdd58JF73yRysh7uK6bnc9L3LGy2bigbzjjjvM7SNjVQshPhp23HQJu3XrVnOfyk5i/vz5Nth0boM3GDZkDKvB7fbt22eNJLejG1nei+eDDSDfjmVMfSrJqc7m4BcbxMGN7mimq7cT++v3Yn/jfoTy3zkhksNCw5AUnoycmBxkRmXhSONh7KrbieqOSgR7u53JFYoYfwxmJM/C7JS5qApUYnfdLuyp34mmria097Q5IzSInr4eEyPSAjWB4CW0wpG+SNyRtQKP5H8MaZEZ2Fa7BW+UvoLOni74Q6mqd8amM3CzorKxIuceFMaNQVt3m61X31lv+xLsDSI3Jg9zU+chITxRUkMhhBBCCCHENYNv6npjQBRFcPyHb0lzIHHwoJ8Q4vLhmNDatWvNqwBDEz3xxBMmQhoOKCShVwQO+vMtfE5AfP7zn7cJASFGGpz6oBhvNAmgOG7LiTMvcWLvfPXnpBQnHzkuy8RJQI7rMqlfFdcK7zrkNXepE6Wc9OYcA21CTixzXvD222+/7IlW3h+cH7nZBI7eMec9z3Q5x/5SYTtEAfPmzZttMp/efjjnxDkhlnmzwuuM83EMe8jnGXqOFWKkwnaBz9sUz9wMeG0jPZJdaTvl2Yye/cVQ8Qzd+taLL2F8ZAxuKRiHOWPGIjoywkSDDHVMgd+ZxnocKC1FU0e7az+D6OwJIiU2DnnJKTb/e6ahAceqKkwEGOUPR6irq3/AVusIdiHXrTcpOxc57nmKQj9Gy+ty+dS4/nK/y/cXWzeipqUZTyxcjFsnTMDYjAw0twew+tB+rDl0EPnJqZg/djxmFhQiKjqqf87e1Z/hkNk7su1i6GZ6VNx28jh2nSpGk9t++eSpWOC2i3PbhPh96HHrNbe0oqSmGqX1tSao5D5Mys5BbHQMfK5urd1deG3rZryxZydq3H5+9je+YC/J8KWZc2G5nl3L43kz9dWXA/txz5bn9ysRF/K6pYbj2WefNZHb0aNHrY/iefmt3/ot89DJfpye+y4GzxE9Bv7Hf/yH2QB8iYkhkOfMmWNhhT/xiU/Yy028L2jLsT9kOOMnn3zSyqAXdgrwPGjrMVzz008/bSJFegpcvnw5Pv7xj1viPcd+ld7/OJ6xZcsWG2PgvcfrxtOfeLYe86ZAkG0bvS3eddddZqMM1rZwXWpaeCz+/u//3rwe0l6k/uzee+/F448/bi9uMS+KJ+lh8Pnnn8crr7xi3uF5HujB8dvf/raNq/AYntum0OsiBYU8RgyDvHv3biuHQsPB1zkFjLR1GZaZnupZPkWOPKbMg9ux3J/97Ge2PfeFdjHDWfN4j1QkMBRiFCOB4dDS3duFA40HcMglGnAfEOC5rxTpRYVGIyc6xwR8pW2l2Fu/G6dbi82zIdf3hfowM3k2bs9cZuufajmJfQ17Ud1RhabuRmdYdrpyuhHsc6k3iC5+7+myZf3LnZE18M8svwE8geHD+Y8i3QSGW/FG6avocvn5ziswHIu27lZsrd2CBgkMhRBCCCGEENcZCQyFGD4kMBTi/HDqY7QJDIUYTXiCDgkMrw08Rkycw/GECJd73C4FCQzPD/sT2lwUQnB+jXNmeo4RIwm2hWwTea2ybaZIZdGiRTaHOxxtxUiC+3e1AsNz4T3+6quv4sXnn0dPdR0W5BTg3hmzEOnyp8AwKiIcPtcG8Lh39/agOdCONtcvxURGIiYi0tYJ9vSipqkJJ6uqcLC8DKX1NegOBhEdHoHsxCRMysrG2IxMZCQkWv0ZPY9eBVsDAZysrsaGY4ex+tABl18Evnr3/ZiWl2+hlNs7u7DlxDGsPXQQPb19mJabhyUTJyM+OhoRfp+JGG1i+yz9c8i1rg9+e98erHTptqJJuGvadBS4vjg8Jgah7jrpc+1/r0sUSlJgyDqZmJIhmN33JndMnt+wFisP7kNociI+/dRTJtiiIOtc7Li4PCQwvDA8vrxevReErvQ+5fGlGPAXv/gFXn75ZRPosR2g5urLX/6y2VqpqamXdG/QAyJD+65evRrFxcXWfnBbeux7yp3v+vp6C1nM8QCO7VGAd//99+POO+80Id1gESPDHq9fv97CBdOuYD7Uo1Dgx+RBr4XUq2zYsMEEfxQu0l5ke0Z4nFgPivNYBstiSGd6H72QrmXlypX40Y9+ZOUyfwoh6UGQIdEZBpoiP9qRJ0+ePBvamPvGNoTr0nMi173QOWHdKAqk1obHivvK7Zknjz3ry+PP8RIKISm+pI7Hqy/X47gKt2U7Qw0OBZg8PhQiLl261NYbiUhgKMQoRgLDoYUCv2PNR3Ck6TA6g64DoJfBc6C3wKzobExKmIy6zjocaNhngsS2YFu/KNClaUkzsSzrDsT74y1kcmuwxfKm8cbAxvRgGOgJoLmzGfVddajvqLW8mJq6GtDp/rHsPmcUshOi2JHl3paxDA/kPWgiwh112/F6ySvo6u33YMiymW92VA5W5N6Dwth+geEHPRh2IzcmXwJDIYQQQgghxDVHAkMhhg8JDIU4P5z64NjazSRAEuJa4gk6rqfAkPf4aLu/r6S+3rGmOIBpuMSFRALD88O+hIIFiiV47XEOjh6QeF6EGAnw3qUHLi+MKcU0nPulWOZGv3e9NnIoBYb0MEax0ysvvohdGzdjSmIynly4GHGRkYiOiESEK4diPr+Pgr5QBBm9zrXvYRER5u0vzOfv90bY2YXW9gCa21os5HFvbw98CEGU2z4uMgrR4eHwh/nMG2JXsBsBt35dayt2nS7Gyv27Ud/Wium5+fjNZXeiMC3d7Z9bNxjEqeoa80i41aX0+AQ8PGsukmJi+4WPoa6PCOUscEj/zDXtYZdaOwK2/jsH9pkXxbmFY3HP9FnuWS4R4e7YhbhlfbSbu/s9aNtxdfUMcfvD66ui4gz+871V2Fh6ClPnzsHjn/qUib0o1joX9nUjUWDIetjcu0uE/SmvnavpU69k31gmNRnUWHg6iyutA8vn9UphHrUc3vFmvtSAUNjHsi6lv6JXP2pP6urqrC1hnbgtwyYzLDHPaW1trSX2hSyDYw8sg33i4PE95kXRHetEj4rMh+0R7T+u78H6cl2WyfU5fkh7kdoU7gevLwoZqSFjPbgtv19sn1g/eltkucyf7QLLZV2ZF7fjNcB9ZP2YuG9cznU5ZnmxMRXvmLOuLIuiTu4D//ZsBNaRdWU+3G8Kcb36smwKH7nPLJvb0OOhd3wonhypSGAoxCiGjY0EhkMHvQfS4+Dx5uNo7mqyv8+FQr6s6CzMSJpl4sFDTQewo3YbWrpb7Df+Y/jkovgJJjCM9EUj1hdj32P8cYjyRVn4ZQoO24NtaO5utrLo3ZBhlFtdPl3oRLcru7sniA4KFLuc0enWnZI4FbdlLEFOTC62uzJfLXnR6hgeGmF1o6FK8eNdOfdgTGx/iOTtAwLDEGfcssw8Exi6B4rwRNtGCCGEEEIIIa4FEhgKMXxIYCjE+eHUBydvJDAUYnjwBB3XQ2DIyWKKFkbjvc02iely4DHmhDfT5RzvK4HzRxIYfhies5deesnCIVJEMWbMGJtbu9CcmhDXGopbGIaUIiOKZij84twvBS4UrtzIeP3RUAoMvbZw7Zo1+MWzzwENjViQW4AZeQUoTE0zL4UUGfp8YRYuOSw0DKFhof2CPNaBc+cUE7l+qq+nF709QfQGnU1Ku9R9d0Zq/2+9fQj29bp+rQctHQFUNTXhQHkpdpw6iSOVZzAuPQPLp0zDimkzkBwbZ65jKBZsbG3DkYozWLV/D1o7O1GUkYkp2bnIS0k1L4thA0Im9pLsKyky7HJln6iqxPvHj2Bv6Wm3bgqeunUZ8jPSzfMcPRWGuv2xjWw7pn5xYlV9HXYeOYznNq3HmZ5uPPaJT+C+++83j3LnawdZJo/hSBMYUhRGIS7HqNifZmZmmuDsSu8R7hf7B0+weCmwXGoreK3yczj79NEGjyf1LxQG8lzxb15f1L3wHPHYjUR4/llv6ml4zbOurDPFkTfa+ZXAUIhRjASGQws9AJ5pL8ep1pOoCdRY2OPBUDzIMMOZ0VmYmzLPlh1pOoT1VevQ1NVob4IQdhRh7l9EWATiwxOQEpGKtMh0pESmIM4fbx4HbT33j2+1hIW4tV3id7qtZqPc09tj3glbuptR11mLqvYqJEckm3AxLSrDPCe+V/GOWy/o7NMwMLwzwy+nRqbhjuwVKIwdg/butrMeDFkn1l0CQyGEEEIIIcT1QAJDIYYPCQyFOD+c+pDAUIjhwxN0XI7gbagEhry3vcn80XZ/c+KZ6VLwjvG1EhcSCQzPjycwpEczHhOGZ+QxoaBJiJEAPX9t2rQJhw4dMo9abF8lMLxy2LdwHp7PPs8+8wx2uDaxobwCS4omYmZOvoU1jnflRfrDEe7z9SeGJ3b1CKEHQc6dc6wjlG22SwN2ab+HwG4Egy51B9EVpLOZbrR2dJi48ERNFbadPIayxgakxMTizinTsWTSZOSn9Ie4ZZfX6/LodHlUu/V3ni7G4YpynHHrT8/Jw4TMbKTExiLanXN6Mgxl+W4bm+Pu6UV1cxN2lxTjnQN7LcTzx+YuxIz8QuS5PjnaleePijSBJB3XsN3r6gigyeW9t/gkVu3bg72V5UgpLMBvf+lLFvaVYV3PN6bD4zcSBYa8T6iRYOJ9MW/ePOTk5Jhw/ErgflEIx2P1UfA6ZRrcp/N6ER+Ex9TulYFrxjtuXhqpDK4362ltwQiu75US9i3HwPdrDgeXqaJn4g3Etz3o1vJ8blSFEB+GnRXfQmGMd4r/OJjLTpBq+4sZUGzcqM7ndpzYobtVbsfY9eczAgiNAIoMaZhyEIAGQ15enrlovZE6v36hXgeaupvQ1dP1oYafIr1oXzRyYnIQFRZtnguLW08g0NN+VmBoRho9EPZ1m+ivNdhqIsHy9lIcbz6Kgw37cajxIE60HENlewWaXVn9YZDDEemLQrTLN8ofjVh/HBL8CUiJTDXPhNnROSYMjAgLR7Q/BtmuDgWxhUiNSEVkaKSFVY5wn1yWEJ5gIZHL2krQ7urWL1zss+2zY7IRGaZ2VgghhBBCCHHt8MK1MPHZkuM/fHblxLIGVIW4OjgmRCEAQ+zwnpo2bZqF3xkOOFjOMD4nT560iUuORfGFVU2ui5EKr1lvckoIMbR4E72XM4FKsQbnGGgTsq/yvMBd7gTs4LJHW+K8DsUWHwX3j/M1g4UIl3ucrgTOHzHUX1lZmb28QM9OtNsvJCK5WWBfwpcr6ICDzzK8bvPz883Zh3eOlJSuZ+I8Lud9aaPzmZvtK+d+eY3eKI5iLoTXJ/A4DFU75bXBfM7hy1vdPb3Ye+ggTtdU40TFGTQH2k0caFDAR5uT3ggH2vhgVxe6mTo60d3Z0f/Jv10/2OXOVaf73uqW17W2oLi2BhuPH8Wawwex6cRR1LS0ICchCXdNmYaZeQXmuZCFdHW57Sjac4nlcO6XIkeGWG7uaMeBM2UmHmzp6EDArRNwZbS48ljXRpeqmxtxsroKR9x6VY11qGtpxhH3LOlz2ydFRbMDQI8ro8ut29HWjrbmZtTU15u4cJ2r25ZTJ5GSl4Pbly3D0qVLTWx9MQ987E/Y5/FzpECNA9txPs/SJmE7zmdaL3zt5SbuO8/3pewj1+c1SmdPPG78W3wYHtPBx5iJyy50nY0Uzq33SK/vlSIPhkKMYuTBcGihyI+CwYrAGRxtOozmrmbzKuhBgR7FhxlRmViYdouJ+I41H8UvT72Ahs56EwhyfQoNua4HOxD+zfwp+mOoYv7qC/UjzheLpPAkJEemINF9UrzIkMe+EJ/7vT8xX4oOWR5DLfNv5kmPi/Sy2OLqSQFjrUssfWx8EVIiUqyux5qPWOjlHld2INiO9Oh0TEucYZ4UhRBCCCGEEOJaIQ+GQgwf8mAoxPnh1Ic3sXkdp0FuCHj8KGJm+EWOLfOYihsfToLTuw/n7M7n/cqbSL0c4dtQeTAczXhh/y6Gd2w94dDlHOOrhfe4PBh+GLZ7ngdDClIWLFiglyzEiIIvAW3YsAH79++3+Vx5MBwaeO9zPp7t4cqVK3H40CFUlpYhtD2A5IhIpMbEIi6Sngxdn+nzmdiPnwyfTK+GrvW2OeFuZ5N2BbvR4drY7mDQwhV3ur/bOjtMUHi8utK8GDKvgpRUzMjNx/yx45EWF2/5eHC+2YSMvT1o7+pCtetXD1eUWUjlM7XV8PX1YkZ2LhJcvXpCwtDnug4KERnGOdYXhp7uTjS2NKGtvR2nmxqxp7YBk3MLMC0v3zwfJkbHIjrcb4JKhmz2BJBVgTb0xcVi6R3LseLuu61fuJjXP9qO7E9GmgdDPjPv3r3bEq+Xe+65x7QOvE+uBD5rUENxsRcHeH0yUU9BHQb7dF6vQoxGJDAUYhQjgeHQwuaQ3v6qA1XY37gPTZ2NHxIYdvV0Ij06AwtTb3GGYwqOtxzFcyeeRmNXI2J8MWYgUEjI5IkMKTj8Fd7SfkL5b6AMbkHRYJ/bllBcGBkWhTh/HNIjMyw88vj4IsSFx1ue9KZohqSXo6s/86JwkZ/MKxAMmLCwLdiGuq5axITFoCBujAkWhRBCCCGEEOJaIYGhEMOHBIZCnB+O9UlgeOV4x49jyRRElZSU2JwOx4m5TNz40JsPRYBTp04978S7J+iQwPDyuBSBIY8r52oolLnW8y8SGJ4fCQzFSEcCw+ERGBLaRBSQse3m3Dqfvda89x7Ky8rR1dlp7UOwJ4jeYA/8oWGIi4hEoutD492n3/WRvW779m56EgyYJ0GGRO6hjeqW99D7XU8vfKEhmJiZjduKJmNKTi6yGfXT3y9G8+aZbUbYbdPtyqInw1rXp+4rO429JafMK+HExARMSUlGbmwsqtrbcayxCY1dXQhzece5vKa6Z7bxbp2MqEj43DHb766ZXx4vxpn2AAJuH/x+H9LiEpAQHY02t1917W1o6gjA5/qj8UVFuOPOO3H/Aw9Y28fjfLG+2+rp6iiBYf/1yT7dExfeTDaPuPGQwFCIUYwEhkMLDTOGRa7pqMbu+l1o7Gz4kMCwpzdoAsN5KfORFJFiHg9PNB9Fa3cbunq7UBk4g6pAJRq6Giw8Mo0Ef4jPjL+PamyZ/7nCRHoy9If6EeOPwfj4CZieNAPZ0bnOIGzAyZYTJiKkx8Nohlb2xSI+PN7CIIeH9p/Hnt4eWyfY123eDsNcfnH+WPsUQgghhBBCiGuFBIZCDB8SGApxfjj1IYHhlcHjxrFnihUYzpaCGoaS41g0J1D5u7hx4f3CxJC4tNU++clPYtKkSQO//gpP0CGB4eXxUQJDHk8KN5iuhxBBAsPzI4GhGOlIYDh8AkMP9o20hRiKmi9eMJw858/Ztx07dsyek2qqqhFoa0UfRYOuXt78sDmncX/7IyOQ5p7V4uLjbduKM2fQWV+PWzLSkB4Xh3aEIeC26UEoXQ/a9l4uzMfsW5eXz3UN9EiY5vch0v3dE+xGWlQkUt0xiPb50O7a8kZny3W5enCqOzw0DMnu92T3e3w4I/KFoLa9HUfrG1Hv+iQKEo82NqGmswvtbpt2l1+0q2P+2LG4fckSa+8YdruAIYVTUux4XAzWUwLD/hcGBvfp/FuI0UzYtxwD3685HFw+4xpNJt5QbJSSqMaOjBxYQwhxMfhA0+46fBoyNBY5mJuTk4PMzMyLGlDs7BoaGmw7TuwkJibadllZWXYvng8aAewgOaBEQ4mDC3l5eUhxRsSN0hnSPON/FAZWBMrN+x+NUltuv/f/P9IXiXh/gon4In1RyIrKQmZ0pnk0jAiLdCkCUb7+kMYU+9HjYBjdULt/FPv10kthiCciHPx/ZwA7K29wItymu88Zci7PFAulnGjhkA81HkRdZx3agq3O0GtDh6svjcoYX6zVzcuDnhD5N7ePdPXz8hVCCCGEEEKIawUnUb0xID5bcvyHz66cWNYAqxBXB8eEKARg+FLeU9OmTTOvU8MBJ4Y4eUmhUW1trY1FaXJdiBsH3uMUP3Hy9cCBA9i5cyfWrVuHN954AwcPHrT2xhM7mbeeYFDpBkoUAXC+gXMGx48ft5DYnK+bO3euiczOxcbOXaIt510XHwWFq5xjoE3IvsrECgUFl7z9jYB3vM/FO5ae58LrIS4knD+iaKasrMzagri4OLPbOSd0ofmjmwG2j3y5gg446CjHm1MbTjGTEJeD522YbTifudm+8jqlQ5obxVHMhWBbyeS1ncMFy2DfxTaRTn/4chcF+BSo8TmM4xwZmRnIdH1mVm4OMnNzkZmXiyyXcgsLMGbcOEyZOhVz580z0TZfCmt3z3Bt7rlqdkoKcmOi0NfTjfZAO+paGlHVUI8GhjNua0Z7W4ulgEsdgVb0dQUQh15MiIvFlOQkTE9LQUF8AtLc+U6IjECq+8yLj0NhYgIKEhORGx9vy2IjIsyrIgWGke66yIyOwhjXpuXGc245FHSZg94eBJxNUDi+CCvuvRePPPoobr3lFmTzerqM50z2J7QXR9KLKXxmZt/GxOuF55Fah4s5broYnpDyfPvIPp1lsF/nPaixL3EjIA+GQoxi5MFw6KEIsL6jDltqN6HOfVKM5wkMPaJ8UUiPTEd6VCaSwpNMUMh1uG13bxDBPr6J0W9IBHra0dDZgLK2UpxpL0d9Zz06e/l2Iptel69rgrkmPSNSHGjLaQifU2ZYaBjGxY3HrJTZ7rMIlYEK7KrbaV4TQ90/Chj5mRSRhMlJ05AakWqiwusxACGEEEIIIYQQ5yIPhkIMH/JgKMSF4fQH00ib3Bxp8BhRmEBBWUVFhXktW7lypY0h82Vzjidz0pxeuziOzIl1cePB+4SOCSguZTsfGxuLefPm4etf/7qJDM+FY8+cLL8cIZw8GJ7fg6F3LClE8AQy1+uY8H6XB8MPw/tDHgzFSEYeDIffg+FgPBvTE9GxbadI3/PY59mdXIewjkxs3zkXz3Uo5H7+uefwhusXC3xhmJ+RjrlZGUiMikZDZycO19aio4sOaHxIiYxAZJgPPrefoaEh8LvPSLdNYmQk4lyKYjhlt5zCwcHl8W/77v5xHjro6trZHUSXK59R8Fi/SFefPrdOdUsr6t0+nGpuwdozFZhy11349Bd/C2OLikxsfjkCOebL/uRm9GDoHXtPXHg9+3QhhhoJDIUYxUhgODzUd9ZhU/X7qOusBUMJ0+gaDMV89AToeQRkGOPQ0DBnzPkQ64tHvD8esf4483RIY6092I6mrgaXmszTIEWB3b3dCPQEnIFYb+Xws839Zm6tXT6+EL+V6jXQXFYYNxYzk2ehKH4iqgOV2FO/Cx0MwzxQP26bFJGM6ckzkB6ZgYjQCBksQgghhBBCiBGBBIZCDB8SGApxcTgFMniiV3wQ3tccZ2Zb8t5771nUG44d04MdJ8o5drx06VLru3Nzcy2qTXx8/MDW4kaCIgDOAaxfvx4//vGPzUaTwHDoOVdgyH3nMeQ8y/UWFxIJDM+PBIZipCOB4bUVGF4tbGsp6n/xxRfx/DPPoOL4MeS7ui/Lz8VC1+7Gu3N2srEB+6pr0B3swbz0NCRHRpqw0PoL11cwhYW5ftgt4/6z5/hV70GRW/+x8RLpoyDSJc4/2/xyqEtmK/egKdCOsqYWHGxqwtaWVtz66KP4za/8jnn4u9xxm5tZYMhzweuQiefqevbpQgw1CpEsxCiGDzQKkTz0UPhHj4OBYPt5PRj2eyp0xyPYhsbuBjR01aOxswHNXc3o6OlAV1+/gLDTfae3QoY4jvRFIzUyDTkxOS7lIjM6CykRqYj194czDg/jmydRiPXFumXxJlykadfbR4+IPWZ8JPgTkOby4HYULVJk2ON+98xF1ivKF4mMqEwLk0zho4wWIYQQQgghxEhAIZKFGD44JqQQyUJcHI6HjpSJzZGAN+nLl9d5P1MwTOHwCy+8gL1799r9TaHyhAkTTPz1iU98AosXLzYRM8eFOZ5MRxFKN1aix0KOJ9NeowCQ3ykuu4VhEd3nufB3JhM1XOI4tEIkfzBEsnf8PCHC9RYXEraXCpH8YdhuKkSyGMkoRPLwh0geStj2U/jJuX6K8Mqra1DV0Iim9oD7td9mDQS7cbi+HqXNLYhy55DeC31u/yLc90i3rz5ff5/R69on5tPb22ff+1O/kJAp2NN71hbmb9yG+fj9/UJFejVspVMjd90cqK3DYVeHsOwcTF+40J71rlS7wzL76zVyXvIZzhDJPK48r7zfeG55LWq8S9xo6IoWQojz0G+MXryJpPDQmQqW+Fd3X9DEhiWtp3CgcS+21mzGpuqN2F67BUebD6OqoxIdQRqGIYgIjURyRArGx0/ALRm34f68h/D4mE/hY4WP486cFZiRMhPp0RkmPKSI0NxWu8+u3m5nVjojhaWG0Ei+0GCDBkyFEEIIIYQQQgghhBC/gpOg3kQ2xcL0VPeDH/wAf/mXf4nnnnvOJls5GUpPZV/4whfwx3/8x+aldOLEiea10Ju452Sp0o2Z+sfF+5MYXjwhAu8r797ScRdCiJsDtvd+nw9Tp07Fo48+imV33IGkvDzsb2rG04eO4D/37MOa06WoaGnDmdZ2vHSyGKtKynC8oQkNHR3oCHajw9lz7Z2dCLjU2d2Nnj4K+foFhHSgczaF9vfrFCBSbNjVHXTbdaE1EHCpA+0un2aXx/GGRuyorUVxsAczFy0ye/BCkQ/Fh7Fz6vpziQvFjYyuaiGEOAcKC8NDI+A/T3jkc+Hvv1rHGWa9Pejs6URbsB2twTa0dLegqbsJNYFqlLSexuGmQ9hXvwf7G/biePNRW97jtonxxQx4OMxFYcxYTEqYivmpC7EkcxnuzL4byzLvwLTE6UiJSHH1Cnd1HCiRb6D09aDX/etHAxBCCCGEEEIIIYQQQogPQmEhvbYw5Onzzz+P//iP/8AvfvELC/d59OhR8173wAMP4KmnnsJnP/tZLFu2zCaWGcWGXko5USqEGBpM/CFxoRBC3Ny4dp9eYseNH4/7778fjz3+OG694w4kFBSi0vURexqbcLSpCWcC7ajo6MSuunq8eeoUVpWUYFtFJcpaWtDV22v9SHREBKLCwxHhUrjPB78v7Gzi3+b1MNyPqIhwSxF+n4VWpofDCpfPzspqbKyqRmtcPKbMn4/5ixZh7NixN7z3y6GA/Tf7ca9P917YEOJGJKSPr6xdJ065BpBu1pmofr7DNZhsqOhaWgjx0TCcAMNYbN26FXv27DE3vvNdpz9z5syLuvKlq94TJ07Ydvv27bMQBNyObo4v9CYC3Wkz5M4vf/lLcwHPwaVbb70VRUVFN5xx0dLdjIMNB1AZqLQwyfQcSPiWx5XSb0jQTXUPgr1B+zsqLBpJEUlIjUxFnD8e0b5oC5ccSq+IrqywkLCBFIpe9KGrp9PEhLH+OFQHqnC0+QjaultNoEiBIesZ7/KZmjQNaZHp5iVRBowQQgghhBBiJNDY2Hh2DIghmzj+M2vWLEyZMkWCBSGuEo4JrV271jyPMXzpE088YaFNhwMKlI4cOWKhVBkmcNy4cebdjKGSh5L29nZrN7wQkkJcDZ7XvqEOz8bJQwrvmOipZCR6KeE+cyy4paUF9fX11k6sWbMG69ats9Dq9GTIMLUMebxw4UKb3OZ9zVConvcVjS/ePFiIxvJyrFy5Et/97nft3pk7dy6+/vWv2+e58NrgdX8510lzc7PNMdAmZFh/zgsyDPfNdJ11dHTYsR6p4kK2GWwfNm/ebIJkhsfmnBPnhFjfmxX2Iy+99JKJsvPz87FgwQKbU2MfIMRIgJ6JN2zYgP3799t8LttXzv2mpqZan34j4/VHvB9HYzvF/pbPPQxxvXPnTmt7Dx86ZPPxTN2BAHKcrWbzxa4tikIv8mKiMTklGeOSEpERG4vEiEjEhPsRwT7F5Tm4VwkdOD6ce2Z3Q1Fhl8un2fVHlW1t2FtVgz3u+ilxW01w/f199z9gL5rQHrxS+5b7xP6E/R337TpKkj4AbeHdu3db4vVyzz33mNaB98mVQFub/Tr3jxoLaiZuJptG3HxIYCjEKEYCw+Ghs8cZVIFKlLaVoKL9jIkMaYr5w/qN0qtpNvv4z1xUhyAsNAz+UD/8IX5n3IWZgJHGIZfRo2FSZIp5LKRokKJDlsrtfSE+dPV2orW7FZ3uszPovgdbUddRYxZjUfwkpLhtIyUwFEIIIYQQQowQJDAUYvi40QSGHHc5dOiQlcG2Q2Mb4mrxxvKGeiokMjLSxmMZPphik5E0ec995YRnoCOA6qpqbN++3fpgig4oIGtoaLAx3UmTJtkYL8d62Tfn5OTYuDLHiEeiYFIMLxIYXht4nNmfjkRxIZHA8PxIYChGOhIYjl6BoUcgELB+ssk9A/EcHnbPXS+++CJKDh3GwsIxmJCZjUifH+/s24XTVZUIcbZeRFgocuNiMCUxCWMTE5ARE+PWCTOhod8dE4oLzQJ2/+Mcc5CCuGAQra6tP1Rfj9Wl5ajq7EKke55bsGQplt55p7VvFBdeSDNwKdCGuBkEhtwv76U49umyn8WNjgSGQoxiJDAcHuglsD3YjvrOWtR11jlDK4Du3m509nY5g6sFgZ72AUMoxIzWX4VIvjL6RYf2f8uJXgsjfVEmLGSiZ0O+VRIVFoU4fwLiw+PN0yHXZ127XL0Cro5NXY32N0Mtx/pi4Qv96BDPQgghhBBCCHEtkMBQiOHjRhIYUhBF74VbtmzBK6+8YuFcOVZ1MwlPxMiH43gcl+V1SXGhNx5LT4AjAY798t7hGO7Jkyct/DEnUXnvVlRUICUlxcaDOZbMMV5PXBgfH3/DjfOKy0MCw2sD+1Ie25EoLiQSGJ4fCQzFSEcCw9EvMBwMxYa05Sgw3LxmLfwtrbhr4hQsHFeEDUcP4XhVJc40NGB/WQkC7a1Ic89M2bExSI6MNHFhRJgP4e7TFzrQz7h+h+JCei7scCkQDKK0tQ37Gpswdc5sLL7tdtxyyy2YPn26tftXaxOyn7sZBIbE27ebyZYRNy8SGAoxipHAcPigl0ELPOyMLYr22oKtqO2sRWV7BRq66i3UMZvPXpf4u/vmUj9DIerrly26fNx//QLEXiRFJCMvJh8FsYUWUtmj/3fWoJd/mCdEGjFDUQ8hhBBCCCGEGAokMBRi+LiRBIYcs2IbwTGr1atX25hTUlKSjVfJG4QYKXC8kGOynMjnBOy8efNMfMVJSk6g8j7hOhyv86ZfvIlvT4zFxOvb+850NZOSLIf3D0O0MRTy6ZLT2LRpk/W7Rw4fsRDJvI94r7K+ixcvxpw5c6z+FB1wMl73mJDAUBAJDM+PBIZipCOB4Y0lMGQfzBevKIR764038MtfvIAHJk7BF5ffifAwn4kFT1ZX4fktG01wWObOf1JCgukD+lx7xdRLYV9P0BmvnDx2xyk0DHDbhvpdov3p8yMiJgZf+I0v4LHHPm4aHR5D9utXi2eb3gwCQyFuJiQwFGIUI4HhtYHNZLAvaKGTAz0B8xhIUWFLV4t5OKztrEagO2DrUNxnAr8hEvdRPMj/87+48HhkR+WiKKEICeGJ/SsIIYQQQgghxChAAkMhho8bSWDICSiOP3HMav369SYunDBhAgoLC2/4iVExeqB4kP0Zww7TGyDHR/Py8kzEx7FaCvw4IcyxW44rcsKbk90MqRwXF2eeAnlt877hZCbziI2NtXHZKxFZsQyWxf71wIED2LFjh4mCKBBiXZgn7yGO/S5atMi+UzDEOrBOEnYJDwkMBZHA8PxIYChGOhIY3lgCQ8J2h+f1Xffs9dd//dfICvPjwRmzsHzKNOQmpaCutQWbTxzDqkP7serwQTzw0EPWXjc0NKDizBmUlJSg5NQps0t5fLJzcpBfUGC2IJ8Xk5wtSnuQHrlpy3K+f6j6Y9oQEhgKceMhgaEQoxgJDK8fvX29aO1uRb0JDGvR0dOOrt5u9PQFEQh2uNRmQkTXzFrI46uFBl1MWCwyozIxIXEiEsOTBn4RQgghhBBCiJGPBIZCDB83qsCQE/gUn7CtYKiukRJ+VghOkr777rt46623zgoHCcV8nARmn+cJDAnH9Th+yklMXscUGSYmJp4VGVJgyHs2MzPTrnku43rM+0KTvJzWodCRY7a1tbUoLi7GoUOHTFRAkSEnlCkmoDCIk8ZTp0618VzeTyxffa84HxIYCiKB4fmRwFCMdCQwvPEEhuyH2fbs3LkT//f//l+cOngIqSGh+MqK+zCnYAy6e4KobGrEm/v24j82rsXvfu1ruP+BB8w+5LgL5/PLXGrzBIauPaeQkG0YrwtqcmgX8reh7odZdwkMhbjxkM97IYS4AuilMMYfg+yYHExJnIqZybMxy6UJ8ZOQ45bFh8cjPDQCvhCf+wwfJDKkAXVlRhS9GVoYZCGEEEIIIYQQQgghxHWFk6WeIJaik1WrVpn3wJMnT9rypqYmE1PxkyJATvJSBLhlyxYTcL3wwgv44Q9/iH/6p3/C9773Pfz85z838e7evXvPeh9kyGMKCT04OctyOXFcUVGBgwcP4r333sPTTz+NH/3oR7Y9J5SzsrKwcOFCPP744/jt3/5tfOITnzCBmDeJLIQQQgghRjYU/VHEn5OTg/vvvx/p+Xk4UnEGta0t6HT2oC80DBnxiUh39p2/pw++kFDzis0XOm+55RY8+uijZgd+7Wtfw+/93u/hySefxPLly+2lNNqKwyUuFELcuOhJUgghrhCKBikejPZFI9YfZ2GL0yMzUBA7BlMSp2N2yhxMTZ6GvNgCxLnfaZ4xtDITpYKXS7+8sNeEhkIIIYQQQgghhBBCiOsHxX70ykJvhUz8m14J6QWF4r4777wT9957L+6++24sXboU8+bNM6+f9F5Ij4b0cEjx4ZkzZ8zrIIWCzz77rHmo+f73v49XXnnFxIoUEzJvT1xID0X0XPr888/jBz/4gW2zadMmEzEyb5b1W7/1W/jc5z5nZTPMeEZGxlnPhZpEFkIIIYQYHdBui4+PN4/u6ZmZaHX2Y3tnB7qC3fZbpN+P5JgYFKak0AWtvdxCO5N2H71kZ2ZlmedCJnoOpq3IKIiMaCi7UAhxuUhgKIQQQ0DIQCjkSF8kUsJTkBeTj7Hx4zE2bhzy3ffcmDxkR+dYSotMR7w/AeFhftuO4ZYvRTjIQUSGYOanEEIIIYQQQgghhBDi+sEJWU7Qjh8/3sIQ0rPMgw8+iEceecTSxz72sQ8kepF5+OGHbZ0HHnjAEgWIFCIyhDEng48fP27hl19++WXzivj666+b8JCeEfnbxo0b8cYbb5j4kInrnjhxwoSNDDPLOjz00EOW9+LFi03QyHDL8loohBBCCDE6iYyMNI+DycnJJgoM9vSgm2GH3T8TIEZFozA1HTHh4SMqHLEQ4sZDT5VCCDGEUDBIY44hlBkeOTosxgSFExImYV7aAsxPXYgZybNQEFuIeF+8iRK9sMe2rftHzic2NBlinzwYCiGEEEIIIYQQQghxveEELyd6GWruj//4j/F//s//wVe/+lV89rOfNRHhsmXLTORH4R9FhBT+/dqv/Zqt8yd/8if41re+hW9/+9v4y7/8SwtbR7EhBYH0KEOPiAy9/B//8R/427/9Wwul/Oabb+K73/0u/vqv/xrPPfccjhw5goiICPNQ+MlPfhJ/9Ed/hC996UtWbmpqqk1GyzONEEIIIcTohvYcXxihJ0N6JkRoKHp6e+HpCCP94chITEB6Sqq9dMKXVoQQYjiQwFAIIYYRExqG+hAZFokYXyziwuORHJGC3Og8TEiYjOnJMzAzeTYmuu9Z0Tm2HkMod/d2f1BIyK/OUuzru/zQykIIIYQQQgghhBBCiKGFXgE50cvww7m5ucjJyUFmZqaFo2P4OU4Ax8bGWuJ6XEbhH9dhmDpuk5+fbx4QFyxYYB4Ov/zlL+MP//AP7fPWW2+1coqLi7F+/Xq8+OKL2LlzJxoaGkzYSEHiV37nK/jNL/4m7rrrLsuHeSsUshBCCCHEjQNtOooGaevddttipKakItQt8yw9n7P7oqNjkJSUiISEBAkMhRDDhgSGQghxDaG5RxFhcmQKCuPGYFLiFExNmoaJCZNQEFOAtMg0JIQnIM4fh1hfLMJDw81wpNiw10Ik85s8GAohhBBCCCGEEEIIcT2hiI9eAq/GUyBFivRCSMEhBYX0ckhvhAy5XFRUZCGYOzo6LAzypk2bUFVVZcv4G9f9/K9/Ho997DFMnTbVlisUshBCCCHEjQVtTNp49Fp9x/I7kZ2VBZ/fT0OSP8IfGYH4pCTEJySYp0PapUIIMRzoaVMIIa4jFBwyTHJ0WDQyo7MwNWk6FqbdggWpizAxcTLSozIRGRZl6/BVFA5ThvRd/mClEEIIIYQQQgghhBBiZNLT04NAIIBTp05h3bp1ePrpp/Hee++htrYWfr//rDdEihH5t/dJDzWcdP6VDxshhBBCCHEjkp2VjZmzZiEjPw+RyYkIi4kCoiIQl5aKMVMnIy45WU5qhBDDigSGQghxneEAIMMoR/uikRSRjIyoTBMbZkfloCC2EOPji1AUP8G+p0SmwR/mH9hSCCGEEEIIIYQQQggx2qmsrMSqVassDPJLL71k3gqbmposFN4DDzyAT33qU3j44Yft776+PpSWlmLHjh3YvXs3WltbbZkQQgghhLgx4Qsl8fHxyM7PQ8rkiYidOhnhkya4VISk6VNQNH8eEtJS9dKJEGJYkcBQCCFGIBQcxvpjkR2djQnxEzA5cSqKEia6v3MQERY5sJYQQgghhBBCCCGEEGI0Q3FgeXm5iQt/+ctf4v3337fl06dPx/3334/Pfe5z+OIXv4innnoKy5cvR2Zmpq3/zjvv4LXXXsO+fftQV1eHYDAooaEQQgghxA1KmC8MUfFxiCksQPSUiYh0KcKlhKmTkTt5EuKSkgbWFEKI4UECQyGEGIHwDZPQkFD4Q/0mKIzyRSHeH4/E8ESEh4YPrCWEEEIIIYQQQgghhBjt0Avh8ePHUVFRYaGPly5diieeeAKPPPIIZs+ejQkTJmDmzJn4+Mc/jgcffBDJycnmxfDdd9/Fs88+i+3bt1mIZQkMhRBCCCFuTOjFMCQ0FKER4QiNjERIFFMUwtz3cJdCw8IG1hRCiOFBAkMhhBglhIWEmeBQ7q2FEEIIIYQQQgghhLhx6OnpQUdHB7q7uxEVFWWCwqlTp6KwsNDC4XFZQkICJk+ejGXLluHee+/FmDFjUF9fb+GU165diz179qClpWUgx6Gnz9Wxr6tLaSQld72gt3fgDAkhxOXT2NhoAnf2Idu2bcPOnTtx6NAh85Tb3t5u/dO1gOU0Nzfj5MmTVgfW5dzE5UeOHEFVVZVr+tT2CSGEENcaCQyFEEIIIYQQQgghhBBCCCGuI573QZ/PZ6LC2NhYhIWFmbcaEhoaasvp0fAzn/mMeTmMi4tDSUkJ1q1bj9WrV5voYrjEIBSz9ba3K42k1NGBPolshBBXAfuNrVu3YuXKlXj11Vfx9ttvW6h+igxramqumXdchvmnaJ5CR9bh9ddfxyuvvIIXX3zRPl977TVbvnnzZpw+fdoE+VcD94l5MLHflAdgIYQQ4qORwFAIIYQQQgghhBBCCCGEEGKEQ7EhQyinp6ebJ8P7778fqampFi55w4YNJgqh96erFV6cj75gED2BAIJNTQjW1ytdz9TQgGBzM3rd+ZAHQyHE1UARIT0Y0lshw+/n5uZaP7Nv3z7867/+K7Zs2WIedukxkCI8flKQx+Qt8+Dfg5d565+7bHDyoIg+OjoaOTk5mDRpEqZMmWKCewodKbYvKChAUVGRefZNSkqy9Znv+erCzwuV6y2vrq420eKbb75pXhHpAdhbVwghhBDnRwJDIYQQQgghhBBCCCGEEEKIUQCFFvRcOH36dBMZ0qNheHg4jh49ijVr1mD37t0WZpLeoIYUenjq6jKveRQaKl3H5Hkv7O6WB0MhxFVBYWFdXZ2J+TIzM03ERzEfBe2HDx828SHFeJ2dnebNkB4PKcij8I+Cdnod5G9drn+oqKgwwTv/Jvw8c+aMiRgpUqQQkHnU1tZamW1tbWdFfRQMxsTEnBUYTp061bz2cj32eWPHjsXEiROtbhQYeiJB1u/YsWMW0pn5sQyWxTJZ14aGBhPdcxnzotffU6dOWZ+5fv166zcZepn1ZN2Yrxi9eAJSpZGRJNoV4sZDAkMhhBBCCCGEEEIIIYQQQohRAoUYiYmJJsB45JFHMG/ePJvIZehIejEsLi42kSEFFa2trfadiR6aKCahEIQiDE38CiGE8PqU7OxsjBs3DjNnzsSMGTNMzEfRHUMSNzU1mYiPYrx169aZMG/jxo04ePCgCf3Yt+zdu9f6IYr6KHKnoI/r7Nq1y9ZhXhT+7d+/30SB/J19l1eHyMhIpKWlmZfCMWPG2PeIiAj7zMvLQ35+PrKyskxUX1lZaV4WKRKkB1/Wi4JHr89jP8hyKYTk36w71+d6FOJTPMllFBseOHAAZWVl1keybxSjE9o0PH9e6Gul65vYBnj3txDixkECQyGEEEIIIYQQQgghhBBCiFHC4FDJCxcuNIEhvT5RHEExxdatW3HixAkTWOzZs8f+puhjx44d5rWJwgyKMDgBLA8zQghxc+P1KRT4RUVFWeKywf1EY2OjCQgp0ON6FCRSxM5+hYI9rsu+hSI/frI/opjQC9/PvodCP/5GgeFgcSFhefTQS/Egy2e4ZH6n8JCf/JuJgkN6QGRoY/Z3FJTxd4og165da14UuQ33h8LBbdu2nRVGsh+kV8WEhASkpKQgNjbWvqemppq3RObNbcXog3YME0VtfIlCaWQkth96oUWIGwv1kkIIIYQQQgghhBBCCCGEEKMMiiHozYmepmbNmmXiC4os6Fnq7bffxuuvv45XX30VL7/8sqVXXnkFr732mgkzVq5caWIMChAZ4nLIQyoLIYQYFbD9Z4hgeiOkIJ19A8V5hEI8ehCkYI/iO4ry6E2QfQ//pliQ61JMxDDLzIveAJkoMKQwkWGJKQakF0R6N+RvFBcyJDKFhZcKt6FgiXVlHRkCmUJHhkxmeGSGbmaIZtaB9aZgkYLD7du3mziSfR3DQI8fP968M3K/+De9JWZkZFh9JDAcvXgeDHn+lUZG4vkYLCQWQox+1EsKIYQQQgghhBBCCCGEEEKMMijMoOemCRMmYMGCBeaJiZ6aVq9ejX//93/H9773Pfz0pz/FCy+8gP/5n//Bs88+i3/7t3/D3/zN3+Cb3/wmvvWtb+FHP/qReZZi6EohhBA3H/RESGHhM888g3/+53/Gt7/9baxatQpjx47FtGnTTJBHUeGyZcssLD9FfRT6MSwyBX0U/FHcRdFhcnKy9UMUFFLQxzzYT7GfodiQ2zBRzMc+63IEfRQq0VMihYssl6JGejukkMnzgEhBIctlHefMmYO4uDi88cYbtj49/bI+DAXN3ynKp2CSAkXmQxHl5QgehRBCiJsNCQyFEEIIIYQQQgghhBBCCCFGGfQMQ7EFvUcx9CM9Q1H04YWmrK6uNqEFRR1M/J2hJSnAoNiC3p7Wr1+PH/7wh3juueewa9cuW/9SoNCjo6sL9U1NKDlzBqUuzxq3badbxt96FQ5PCCFGBRTWUUR42223Yfny5Zg6dap5AKSXXIoDmSjmo2hw3bp1FoKfonSK8ggFfsyDIkSG7qfAkP0S+6GJEyfacq5Pz4Xss5g3hX8UBF4OFDGyHhREsv9jnuzX2L+xfM8LIfPl3xQ8UkjIfo3L+Dd/528UNlJMyE8vCSGEEOLiqLcUQowqevqCaO5uRF1n7YdSU1cTAsF2t07PwNofps/9C7o82oJtaOisR01Hjdu2Ds1dzejq7UJv34VdNXPbzp5ONHU39Zfptm3qakRHT8Dq5a3T0dNhywPBwIfy4+8sp6W7xcpvd/UYjFe/drcf/L3WlVHrymp2ZXa6fPsuUD/L19Wtpbv5bL59+PC6LLvZHad67rNb92L7y/3iPjZ0Ndjx4nHl+qxHvZXRPrCmEEIIIYQQQgghhBDiWkJxBcNNHjt2zEIiv/POOyYevFQ8T1AUGT7//PP4+c9/bnmcPHnSQk0y//NBgUdHZydqGxpwoqQEew4dwuY9eyztOHAAh932x0+fRllFBdo7OiQ0FEKIEQ6FhPRUeO+99+ITn/gEHnjgAfOMyz6C3gY7XZtPkd6OHTvw3nvvmXidIj16IKT4kFDAx3DD9GDI39k31dXVWfhhJq7H/oWCQK5HgeGVeAtkH8TtWL4XEpfiQJZbWFhoYY/pmZDrsJ/jbxQVRkZG2ncTwLtEvPKZjxBCCCE+GgkMhRCjitbuVmyu3oQ3y17F22VvYGX5m5b4fWPVOhxsPIC6jloT+Z0rnqNAjgLE6kAl9jbswZrK9/CGy2eV235LzSaUtJ42IR9FeBTsDYaiP5Z9suU4tlRvdOW96erwBja4Mk80HzeRIsWHTKdai7GxegOKW0+YMG9wXsHeIKpc+Xvqd1n5R5oOn/2d9aNorzpQhUNuP1ZXvOvKeA1vlb3u9nkjTrScMFEg63Iu3LY8UIbttVvwXsU7OOzy7XB1GVw2j0dNoNr29W23z1vdJwWJnjjSg9t093ajuOUkXi99DWsr3sPhxoO2LzyuJa0lroxVON589AP5CyGEEEIIIYQQQgghhh8KKuixaefOnRYKmaEsKeSgF6nLxRNoMHzlL37xC7z55pvYs2ePCUvOFV3wr67ubpwqL8Ob69biX372M/zzf/0XfvzCC/iRS//685/jb/7t3/Ct730P//bMMzhZUmIeDSXeEEKI0QHFeTNmzDCvgxSgFxcXo7Ky0rwP0kMuwwtTjDhu3DjEx8ebsJCCPbbz9GhIMR/7FPYh9DRID4Jcl+I/ig7pPZeeBrnt5QoMWRbDGdNLIsWNrCPrwlDI06dPNyEjwyAz7DE9+W7atMnKW7RoEXw+n/WZ9K5I0aQnUiSsJz0jesJDIYQQQpwfCQyFEKMKetU72nwYu+t34VDTARxvPmZCOIr6djfswoaatVhfvRbHWo6gJUjxXP+btvyk177DTYewoXodNlWvx776PTjttjvUeBDbajdjfdUabK/davlRiOhBEV1F+xmsq1yNtdWrsat+hyuPZZ7EgYZ9rkzm9z72N+wd8IpYjSMth1HVUWmCw8H0uno0djWg2G27z61f1lZqD14U/7F+FB6+7+q2qbY/v5MtJ1x9TmB73Vasq1rtlm80r4befnkEe7txsvU4Nta9b+sebzpqQksu9+B+0Pvi0eaj2FW3A3vd/jN/en4cDOtcGahwx/egHQ/uY7mrJz0Xdvd2obazxupZ3l6mwUEhhBBCCCGEEEIIIa4xFHmcOnUKW7duxcaNG80rFENFXqk4gmN89Cp1+PBhy2/79u1nBYuDx/+CPT2oa2zE3iNHsGbzFvtsbG5GbEwM4qKj0eXWpwfDTbt2mTfDytpaE25oBFEIIUYH9DRID4O5ubnmZZAh9SlAp2iQv9Gb4fHjx3Hw4EGUUETu/mY/wbaeoj0KDOlNkMJCbk/BIvNiCGaK/rgeQxVficCQHgjpbZH1o6iQokGKFimEZJ3OnDlzVtzIv1k/rjNv3jwUFRXZb/v27bPlzItCRfanXMY+lcL9KxHqCyGEEDcLEhgKIUYVwb4eNHTVm6fBEPcv3h+PlIhUpEWmoTekFyXtp/F+zTpsrtmI4pbis94I6XnvRMsxbK3d7NIWVAQq4A/zIyMqE7HhsWgNtmBf417zPLizbrsJBSnio6CuJlCFPXU7sbL8LRxpOoRu9y/VlZcWme4eQsJQ1l6KI82HzaNfa3cLAr3taOiuszDFPfjgw0ivqwuFehT1MV96JGT96LmQwsaN1euxs367iRMjwiKtDJbFelBAuK9htwkRKVT0vAfSA2FrsBUn207iUPMB27ak/ZQJE9uDAVvHo7O3w/aNXhL5O/eVHhUHQ0+FFBcebDhgQkMKGhnSmd4XKYQM9LS7MqrsmAkhhBBCCHEuDNVHLwccoOcAf3Nzsw3aXwqcFOfkNr0KcNCf23OQnxMBlwI9EdCzwokTJ2wShJMN/M58OPExGE6CcBm9MXDygeszcX1OonBSQh4MhBBCCDHSoA1DMeGBAwdMYEgh4KXaWh8FbS4KNXbv3m32E8sZLDDsDgZxpqoK+484u8nZeilJSVi6cAE+ef99+IRL99x+GyYUFiImKsrWZSjlTlc3flbW1OCUs9OYqtz3lrY2Eyy2uzJq6utx2tl/J0tLcdqVW+/syTZn1zU7e+xMdbVtw9/4Wew+j7myuX65qwuXHXd/cxnr1tTSYnlWD87TfZ5yievQq2Kps/WY+NtR2q0uD4Z8Zp25v0z8TvFkmbMVGQr6iFuP4skTp0/btrUuf+7X4OPT42zHVlfvqtpaK5N1LXfbU5RZ19iACrffLJMhpEsG9tOOUVeXbcM6Fru68JPHhOeV9qh3/AbvD+vL5bJXhRBXA739UfBHUSA9ENJLIIWE9AR46623mkCQz+Spqam2jL+xn6DAkP0PxX5czraQfQiFg1OnTjUBIEV93J55M+QyPQwWFBSYF0QvjPHFoJdCbkOxIoWCHhQG3nXXXSZgPHTokHndZZ/oPfczzH+ta1NZ7uTJkzFp0iQsXLjQ6sXxBoZ7Zshkih6ZN0M617g2VmMAQgghxMUJ+5Zj4Ps1hwYJO3smGiw0EujamJ26EOKjobHOCTROvlVVVZmxTEOeBr3n2vt8DJ60o+FMA5rb8SGC9+L54GAGjXIa65wgpAtzuh9PSUmxN32uFS3dTdhRv83EhdOTZmJOylxMSZyGsfHjMTZuLBL8iagKVKGsrczEdGPjxiEqLBoNXQ1YXfmuieooSFyYdgsWuTQhYRLGxRchOzrbBHv0PkgBY35MARLDEy0sMkMOM6xwW08bFqXfiqWZd2Ki245lFsaNsXULXMqMzkFyRDIqOyrMu2F2VC5yonMR64+1+hKGN6agr6StxER6uTF5rv5TzRvg/sY9ONFyHOPixuOWtMWYmTzLyhkXV2T7kR6V4fJPQWHsWBNWhob0H3cKGctaS80jY2X7GSSHp6Crt9ulzv51wxNsPe4fBYOHm92DX0eN7Q+FkHkx+Za/Bz0Urqtcg8NNBxDoDbj6x9nx4f6Ghfhwpr3cPBiOdfWcmjj9st8yE0IIIYQQNzb0ePPCCy9g165dZ5/3OWnBZ/2Psh05GbBt2zasXbvWJsxPnz5tHgTS0tLMU8FHQWHjyy+/jHfffRfr1q3D5s2brR6cLODzDicovDowX4oL3377bdtmw4YNFkKJ6/N5ieVxjOJiz1bi0qGnCm8MiM+WPLZ8duW5vZbPlELciHBMiO0lJ0V5TzFUHL3HDAecPOZkMr2leRO3s2fPtgnpoYBjXWwjOGbFkHbcD7YVHIfihLYQIwGOrdLm4H3Aa5SemfLz8y/JVrkQzIv2C+dMKJi45ZZbMHbsWPt+bj/J8nkfrly5Eu+9957ZLZf6MsZHYcK67m6z3RjSkvcfv7MOfRRtNDXh0JEj2LFvL+rqG7B04UKsWHwbpk+chMKcHKSnpiLSHQeK3yi74+8ZKSlocNu96+ys95xttvvQQRPcMU8KESng27ZnD95xv292dtjhEydsW4oPy1z7ttbZhEzb9u7FvqNHsdV98u/jJadRXFaK93fsxJotW7DRbUvBHuG2zOvdjRuxefduHDx2DLsOHsQ77u99rv4U/u13eW1wdifrdcy1oRQUZrj6R7n6UyhIceNut43V223Hz7WuHNaBAr+WtlbExcSa98awgXNEYSMFgPTg+B73x5XN/WvvCJgokXV4e916bHD2MkWNPN4R4X50dHXjnfffx2p3fLZzP10daTvxePp9PhNZcl/WbduKLe5YsQ70FpmQlIjYpGT4h2hOjdcRPYzxhZstbl8JhUC8Hvl5LrSrmXguL3WMmPY+5xhoE/L+8QRHl7q9GH7YxvClMb64xecltkNeX3yh+aObAd6v9PJK+4TPld6c2mh/XvQ8DLLPoXCPNh3vaT6/85zz/mQfx2uAIkPesxQLUrg3ZcoUzJo1CzNnzrTjwW3YFzIfiveYJ5/7uIz3O+cUKfJje8Lj9lH3Pa895sPyOI/pbcNP/s3jzzIoZPSEhKwHRY4sl2GZ2ZexPlyf+8D94f7yHDINzoPznbQ3z+13Rwt8KYAvanLel/Y0zxWPB8/xYIGmEDczfGZm38bEtoRtDO993idCiI9GI7hCiFFHKEIQ5Ysy7375sWMwJm5cv9gtYQbmpSzE7OS5CA8Nt/DDdR11JhosaytBeaDUHoBnJs3G3JQFmJw4zcR8kxImY1byHMxJnmcCwerOSgu/fLr1tHn7Y7hlCu3mpS7A/JRFmJY43bZjmpzgHqCS5mBa0kz39zjE+GNsAOxyCPZ143jrUZxuP+X2KwZF8RMxM3m2y3sqxsdPcKnIxJSs8/TEGUgKT0JoyK8e5OtdHQ807kdbdxvyogtt2zh/HCo76CGxybwfDsYX4rP9pHCwvqveHZdyVLRXINATMO+FFBhWd1VaDzEmfpwJJM+icR4hhBBCCPERcIL8tddew+uvv26iPU7Ac3CbEzIfBSe06UWQAkMK/7g9/z7X++CF4GQ7hYmrVq2y8r3E/OiV0MuHdeHLWhx8pwjxzTffPLsut6XIkIONCo8khBBiqGBfSNHO3r17zTscve3Q0y77G/ZJ18pjDgVEFI5QlMq6sM87X6J3X4rduro+OK4krj+0Z2jzUKDFlyiG2l6hPUbRIsVFvFYGX5sUXUSEh8PvD0ePs6cowqt310ljSzN63d9pycmYP2MG7l+2DHfecgtyM/tfhG/v6DBBHcVza7dsxc6DB3Gmuso899GTYWllBfYePmzCOxMPuuuztqEeNW4/KbZbv20b3nF2IT+37duHHfv3Y93WbVi5foOJCxmO+QBtyAEhIr0D0uMgxXir3n/fxIHv79yJfa4Miv7eXr/eEn+nV8L3d+ywdNzZhtwn7jPrRe+GW906u9125umwvNzle9K26xdLHjLPg57A0+6vlhaUuPX2uH1k3d5atw6vvfceXnWJIkIegz1uu6379trfJ0pKzVvjIVcP7j9Fhptd/ifd8epwx4fHuZV1OVWMjW4fKHbkJz0xtrUHTAwphBBXCoU1EydOPCu680SkFNtQWEjhHYV6FPtRrEZB4dy5c01USLHg/PnzTWjI37ktt6OIj6Idrk9xIZfz9+nTp5tYkCLGSxEVU+DIsigMZD7eNuxXKCCkeJD1obdEvvBCgSGFhNwP1p1lUdRI4SOXsV7cVwoOmR/3nfvHMrj/FEHezCJaIYQQ4qOQwFAIMWrpAwdP+s56B6RHP4Y8vi1jKYoSJrhfe1HbWY2TLSdwrOWoCeiyonMwM2W2hVT2tuMnBXn0Fpgdk4OOnk7sqtuJg4370NjdgI7eDvM0eH/uQ+bpz9uOhIWEmdgx1heDGF+sefi7XLp7u3G09QgqOiuQFZWFzMgMRIdFn/VQSPg9wZ+ATPc7Qyd7mNdFt4+763dYOGZ6Pbw1/XbkxRagu6/LBIStwTZbz8Mf6rfjkBddgDhfHKraK3CwaT+auhpR31lr3hS7Q7qRGZuNmUlzzGuiEEIIIYQQlwrDDTFkEhPFC5ygvlRxAidl6RGLokSKLhhmmX9f6sQ5J9y5PsUaFBRy4p2Cx8ECDkKBIb2zDA7lzPWZ6AmMdebb/9dK7CGEEOLGh33Lxo0b8dZbb5mInoJ2es6lJy+vj/JESsMJ+1T2dfv27TOPeazLG2+8gVdeecVeEPDqt2PHDluPXtSuFPa33CfmoX51aOAx5bGkdyLaMjyfl/ISx+XgvYjB888yBudPb3pZ6enIcYnndv327fgfd71QKLdz/34T26UkJuLBO+7AUx/7GApzc80jYLjfj7iY/hezuU5dQ6OJ4yhKpAc/ejKMjow0IWIF9621xfIPDQszb4SNrh7l1VUWfjiCnqvi4lweDThIIaz7LSE2FjmZmebR0BP9dXV3I+BsQ+bH0MQMJ5yZlmb7Q7EgwyUz/6LCAsuXXgH3um0pbKRoj9u3uOUU+SXQi1ZhIWZNnozcjEwTBG7bu89EixT+0fshMa9fbn8ppKF4hR4V6QXxjTVr3THa6Mo9hSh6BUtN5cAudh44YPWjaCbE1avNndsat18mOnZ/0zNiKH8bENU0t7Ra6Gful1sIn6v/pYh0hBDiYlxOO8J1KfCjEM/aroF0KVzOuh4ftT7bXXrn8+rzUZy7Dre7nO2FEEKImxkJDIUQow4ORHnpXCj4iwgLR3iY37Vwfejo6TAvhDWBatsgOjQaEaGRtt5gQlxz6A8NR4IvAfG+eLR0NbntGmz73pBe+MJ8JiT0hX5YQOgeidyDBwd76FvxUh9AflV7iv+CvUFXrwgTSMb54z+UT29fL2o7anC8+TgauxrR3deNnr4etAfbzUshPSwy1DNDP6dEprh98aG9uw376ndbWOjBA4EsjyGUY3wxJiAMBAPYVbcDtZ21JsY83HQQmRFZyInMs/DLrJsQQgghhBCXCsUDnOxm4sTw5YoJuD638/K4nElzruttz8S/+ckQKBQSUsDBZUzNzc22jB6AvHW9uvOTfwshhBBDBT3BUUxIIb4X1o/9z/79+03ct3r1auubPLz+yuuTBvdL5/5NLnUZJ+LpXYhhDundxwtp6HksZL3oqYheg+jJh5PuXj7ny+/cZYPXo/CfXvAopqR4kfs+eF1xZfAY8rpgGi5YBq/Pc20iXg8Uxy2YORN33XorkhPiceL0abz67nv40X//N/75xz/G937yE7yxZg1OnSlHV3eXCVEYJvlOt/5Ct114RLiNdVJAR8FiXlY2Fs2ejQeWL8fEsWOtXFcoYt11mpuZgaKCAmSlpdr47NIFC/CZRx7BkgXzTSxIj4l333YbPnbPPVjhPpPi4tHc0oK2QDsKcrIxwV3LFI3MmDgRj919Nx664w5MHDMGcbGxmO6W3Xv77XjwjjsxJjfXvCmWVVWiubXNBJHMe/nChXjywQdwx6KFmDJ+HHJcfcbm59kn8y121zc9EHoCQ4oLGSqa3hvvdnmnunvd8zA4a/IkfOLee/HFJ57Alz7zGXzZpd/93OfM42NaUhKWu23mTZtmQkzmQ8ElxYg85qxPfGwcwsP9SHZ5Mv/506cjjeE83W9CCCGuHAkLhRBCiEtDAkMhxA0J/RpS+ue+9A+69XEgx6SA9suFoEiQ4Ye5PbfrHz7r3+JShz9/lf/5t2Cu/QNzH/ydNWP4YooVPyww7MGZQDn2N+5DXUctgj1BBHu7URmoQHl7OTp7O83rYbQvBq3d/W8W8+1Wem4sbTtt++/tDQfvGDaZAkOGhvaH+VHcdsLlU4ri1pOo7qhGflQh8qI8gWG3bSeEEEIIIcRogpOxFFBQNEGBA70V0kOh2fkuUcTheVik2MLzNCOEEEIMB57XOU5ie2H7KO4LDw/HgQMHLGwy+yQK3bkuRfEU/R0+fPhsf8X+jIlCQHrf9TzzMqQtvc2Vl5ef9RrM9ZgHPfvyuydGY58XHx9vZTOUIBNDBdJTHftOhjRkyEOGEWTfSLEXxfjsRxnimXmyfiyTnglZD+4Xy2X/SlE/68v1+Bs9Ja5duxbvvfeeCSy5H/3jYuJK4Xmi+JPXznCJIjw7iuLAwWVwvDE+NhZTisabYPC2efPMsx+9FDY624rhhtdt3YqVGzZg085d5jmQ1zS3mVZUhPHu2h8siKOHPno8HF9QiJmTJyPbXYuGKyciPALJ8QnITEtFkrtmKUac6vJYMn8+Jo8bh6SEeJcSMMddrxQu8jcKBzu7u+z6pGgvMz3NhIBj8vJwy+zZmD1linlgjIuJdmXmY/bUKeaVMD0lxV2XvRbeuKOrs/8adYl1ZT2Cwf7Qx9V19Whoakago9P2q7a+P4yzd3+xjhQmTnH3EUWNrE+sO1cUO97u6v3A8mUmiKRAcMXixXhw+XITQdJDIuvBfcnJyECfy49eEnlf2b3mPlvb29zxCkN+VhYWz5ljZXA77p8QQgghhBBCDDcSGAohRh0c0upPA4Nbg8bRevqCCATb0dnTiZC+EESFRSElMhUZ0RkICQ1Be28bAj3t6DnHKx8FeNymsbsRLT3NSI5MQlpUen+o4r5QdAW70NLVjK6eD4Z2o2iv/43b/sQlrBeXMz+mwYOm/M6QyMyH3zlAx38RoeFnBYPNXU3ocf88QSDpcXlXdVTiaPMhNHTVI8j97OnAocaDONJ4CIHuAErbSrC9dgv21O0yr4Tx/gQL8cwQyrbPff1hdrxjFxceh/FxRUiPyrDyjjcftfDIEWERKIwttHDNlDp+4AALIYQQQggxSuCke2ZmJpKSkj4kMOQkMD1JlZaW2idFiLm5uUhISBjYWgghhBh6KASi90IKDIuKijBv3jwsWbLEBH8UvrNPamtrM3Heli1b8M4771jI4vXr1+PIkSMm9ON6FB5yGdejAJDLtm3bhnXr1pmAj8uY1+bNm03gxzzNK5yDAkOK0ygqHDNmjCWKDWNjY5GSkmJ/jxs3zjwZsn+keJDiR4oEmRg6uaSkxMqk0JHl7tq1CzU1NSZ45G/0xshyjx49ip07d1p96cmQ3/m7Vxdx+XAskcI/epikF0p+5zkdSpgfrwfaUbw2B+dPGyrQ0eGWhWF8YQE+9cAD+NrnP4+vf+EL+Pzjj+O+pUtNzHfGXZtr3DV88Nhx1LnrhC88n+WK9KX945MU8DHxBW0uCw2lEDHcQgVfCG4Z7rbhetwXG49129NWtLz6V/sADJl84NgxC//8/557zj7f27QZm921/r67B/hbg7vHTFjrjsnFdokCyYWzZmH+jJkYV1Bo3gkJ62EhkAcSw0SPLyjA1PHjLTTzEWe7Hj992kSaJWfOYP/RY7YNxZWF7hgnuHPDv4UQQgghhBDiWiCBoRBiFNIvyrOQxIMGUThRSO9+O+t24HRLsQkMkyOSkR+bj7Gx4y10clWgEoeaDqK+q35gq34YavhU60lUtleYF8GpidMxIX4i4v3xFrq4ov0MVle+g5K20x8QDFK01xpsRUt3i+XB30Ldv87uThxrPoYTzcfPCvsIRYjNXc2o76yzffCH+OEP9aMgegySw1NQETiDyo4KtHW3faAcjrxRrNjW02bhjZlPV28nygIltj69LjKE8oGG/bZ/rG97dzsCwQ63z1UmHmztahnIqx+fKzvWH4vc6HykR2SgtLXEwkmnhKciKTzZHa9Iq6MQQgghhBCjEU80yMl3en45efLkWYEhhRb06EQxhHnViY9Hdna2eWoSQgghhguOY9EjXFRU1Fkvu1xGT4AUw7NPYl9FT38Mncw+Ky0tzUILcxkFe1yPwkKKB/nJPo3ive3bt2PTpk0m4KM3Qor/9uzZg1OnTlkZnkiM3yl0pNde1oNiQ9aDHusG143rMG8KHZk368bf6SmRokcKHgk9FlLET+EhPTHS4yLrynqyD6aQkQJ+Ctb4nX3t4PE8cfnwXFGoOnbsWBOD8vwNFTw3zI9iUwphWc7g89UWCGDr3r1YtWEDdh88BL/PbyGH6XmPngXvWXK7hSrOcNdtdV0tiktLTWBIEd4HsHHPQWOf7m8T6n1gPPTDnL2WWSWXbIzY/f1R11TIgIhv8HoX245eCd9199NWdw/Rc2FBTg5unzfPwjjfc/vtmDVlioUqprDRanyRelNQyHWT4uNMROjdi2Rw+RQ8FjrblXnHxcagzLUF63lf79qJ/e5+o7fEfGevzp0xw7w+9gstL77fQgghhBBCCDFUSGAohBh19KHXhHM1HTU43XIKJ0zIdwxHmg9hd8Nu7Kjbhs6eDuRG5yElMg3pkZnIiy1AdlQuunq7sKd+F/a69Y41HcHJlhM47rY90LgPu+p2oL6zHhlufQoMC+IKkRiehMKYsSY63Fy70eW9FYebDuFY8xET7R1uOoh9DXvss6ytBB2u3Dh/PBL9STjTXo69jbtxxK1PoSHL4nan2k6iracVuTF5yIzKhD8kHEVxE5AfXYCW7ma3zlGrz1G37smW4yi27Y65/a02sSJDkbCcmkANqjuq0BfSh/HxRS6vbET5+sMkx4cnICkiGYm+RDR3NmG322d6PvTg0FN/CnH7V4iJcZNcnp0ID43A2Nhxbh8Szj9AdfExPiGEEEIIIUYMFE5QNEhhBqEIg4lCCHpj8r5zEp0CiOTk5CGdoBdCCCHOhSI9itspwtu7dy+2bt1qHgAJvcVR7EdRPMV5FBuxH8vLyzPRFYWC3I6/M1GISI+G7NMYuph/M2+ux9DEFCXyb4YzZl6XG0aV3gi9EMcMeUxhIPtL5kfviRTuU3DoLaMI0VtOkSL7X9adoaDpDZHr8Ts97w0WWInLh8ePItApU6Zg7ty5QyraZD48RxQu8vwxb+98cViQAsPt7tp9bfVqvEGPlvv3o8zZVO0dHXaNJcTGmfgt2l0Dvb19bnmg38uf5eDq7tbhely/vrEJNQ0NqHDX8KmyMguvzL97evvDAzMkMLdvaw+gcyAEN8tvaWtF74AXzG53T1EASK+KhOvw/mhubUNDczNa29rM6yK3a25tQXBgO1vmyuB2XMbt6Fmzo6PTXc8dqKytxa4DB+yTYZjnTp+GZYsWYvktt2DejBkozMlBtLMb6ZmReVNEGRgQCbM+lbU1qHb3pxfiuMXZnJXufqQnwrKqKlufx2CwoJLeDBmqeSKFo/n5tu0Gd1+9u3ET9h454o5bqP3G0MsUKgohhBBCCCHEtURP8kKIUQfDBVcGzmBz9ft4ueSXePbk03jm5M/xk2M/wqslL6K6qwrTUmZgedadSApPgi/Uh0R/IuYlLTBPgSfbTuKV0pfw8xM/wX+fet5t/zP3+Ry21m52jWIYpsZPR0ZUJqLCok2ktyLnXtyWtRQ9oT1YXfUufnzs/+Fpt+3TJ3+KZ078DM8XP4O3yl83YWNLsAUFsWOwIG0R4sPjcbBxv1v3p26dp/FC8bP4xalnsKthB2IiYnBXzt2YnTIXvjAfcmPyMS62CDFhMdhbvwsvnHrW7dNP3TbP4b+Ln8dzbv+YfxDdtj/0gHigYR/q2+uRF5mPh/M+hk+OfRKPj/nU2fRA7sOYlTzHlIT7G/eisavBjt+5XgnzYwoxM3k2MiIzTOg4J2WeeX609ey/oRmgFEIIIYQQ4lpCMQXFDEwUOlAoQa+FFGB4oRo5uUxBB0NDcn0JHoQQQgwn9FRIId5PfvITfPe738U//dM/4ac//SkKCgqwfPlyExQyRPGiRYvw4IMPmtCLoY4pWmLoY/ZdFB/S8y5/o4dB9mn0IEhBH0VhXIfeBdnn0Qsd+8HLFRcSlkePiawz+0iKr9hvsi70kMhEMdrkyZOtbAoLGRqZwsa7777bvOux/2VdKYaj8J+eDPldXteuHr4UMX36dNx66612/IfCjuF5oWiU+c6fP99sJJ63weeL1wHFcSdLSrBt9278z5tv4n/eegsbtm/Dmq1b8Pqa1Xhx1SocOnHCPOylp6QiLjbWXph2GSHS1TPK5VldW4d9Rw5j+769WLNlM15y2/zkl7/Ezv370RXsxukzFThZWoryqmqUOtut1l2PQXf9lVW65SWlJuYjFAkeLT6JitoaE+tR8NfY3GKhhfe5++B0+Rm7Zs9UVeKYW8b1uU6H276ssgpnqmtMIEiRYaCjE9Xu+mVI4np333E91pdCQn+Yz4SDh9z9tmbzZvNsSKEg60XRIIWWVW67dpfXEVefdVu34v1dO0zk2ODqc+RksdvPrXj1vXexcv067Dp40ASHg8OF8ziHu+NPkeG8adOQmZ7m9q0YG3fswAlX98zUNExgSPP0dDtPQgghhBBCCHEt0ci5EGKUEWphhTlg1NzdbCGF6TmQqauny7z43Zp+G+anLDTRXnhYhAnkItzn2LhxWJi2CAtTFpngsK6zFiebj6O8rczWmZk0C7dl3I5ZKXOQGJ5oIZgZvjg9Mh2zkmdjRea9KIgZY6GKy9vLXZmlrsxO8/g3NXEaxsdPMO+FFDVOTpiKJRnLMClhMpq6m1DSehqnWooRCAYwPq4It6UvdetPRALLcf+iw6IxJm6sK38p5qbMR0pEKmoC1eb18HTbaQT7ghayeUn6HciOzrGQzCVtpxDti0Zh7FjkROciOyrnA2mM299F6bea4JHhm5vdNqw794nhj33ukwN7Ub4o86a4JHMZ5qUuQJrbX64TFhKG8NBwO3Z+98ljxH9h7vjbshDfwDkRQgghhBBi5MFJWk6KU1jBCXKGE6SnJ4ouGLqRISQ9gSFDKWuiVgghxHDDvoii9jlz5pgwjIJCigTpJY5iQP5Ob2cMQ0whIoWD9A7I5ezXKC5k30Wvu4WFheaJlyJAehqkt7mpU6eakJ6eDunZkJ4D2c9difCMwkKKBT2PiQy5zL85JkcRJEPnsi4UEPI769jc3Gy/UzDJ5SyX4kYmfuc6EvMPDTymvGZmz56NT3/60yZQ5bmm0PBK4PXFa5P53HXXXeYZkef1XHEqz19sbAwSYmPNKyFFe+9t3oxfrlyJl1etwjvvb8SREyeQ6K7p+TNnYOr48Uhz1yvzZ8pOT8ecadNMfLjvyFG8+u57Jkhct30bSukJ0V3vXV3d5tHw4PHjJrArdt9rBq7FY6dO48CxYyYUJPRmaEJCtw6FhPRs2Oiuw2OnTv1quVtGEeBBt11jS0u/p0J3L5WUl9tyigTpIZHLqmtrbVlnVyemT5yA7MwMEwm+v2MHXl+9Bm+uXYudBw6YCJEeBilOZBhoehqk4LDZ3ZO7Dx608Mqbd+6yujS5dNzVZ/22bXhjzVqs3LABm3btsv3q7gnafnjwGPHYzp02HZPGjrP7icJG2qkMQz3W3ecMuWyCTSGEEEIIIYS4huhpXggxqqDgjaGOx8SOQ35MAXKic5AVnYWsqGxMSZyK29Jvx12Z92BSwhQT31EQRyiWo2hvRtIsrMi6FwtSFrk8xiI1MhXZ0bmYljQDd2Xfg9sylmBc3HjzXuhBoWFedAHuzrwPS9KXWfhkCvpyXD0oJLwz627ckbnCPP9RXEjxHUWAi9OXYEnGcvMKyFDIaVHpmBA/2dVxGW5LW4Lk8OSBEvrLyIzKMnHkXdn34ta021AYM8bqR8EfBYxLM+5w5dxl++oL7Rf/jYsvQlHCBFfmhwcPY30xdkymu33Li8k3QSBDPWe4clh/eikMc+WSBH+ieV2c7NZn/QmFh1yX+5IamWbl0Xtigj8eY2LGmnfHgcMrhBBCCCHEiIST4BQY5ufnm8ckemM6cOAADh48iNLSUhNGUHzBCXWKHoQQQojhhMJ3hrV96KGHTBS2YsUKjB8/3vojeiqkmJACQYZPXrdunYXzpxfA2NjYD3iSS0pKMpEfRX/Hjh0zb4Lsy+g1kKJDChO5LQVn7OeuRNTHbShWo8CJgiyWTbEZxY2zZs0ycSR/o8iKvzG8Mz0Uchvuh+eZzauzGFp4XHmsec6feOIJPProo+Z10BN38lx91LHn7xSucX0KVBcsWIDHHnsMS5YsQVFRkV17g2FuERHhGO/KmDd9OmZNnozMtDQT9pW565beABlyOC0lBYvcNXLXrYstpG/igNiU5eVlZ2HpwgWY4vJn/Rlemdv19fZZWOCpbjnzT4iLs2UMgRwVGWle+ya6az7CbdPZ3YUYZ9fluWs7NzPLosO4S9Hy47I8d23Guusx3B+ORHdN0utfUnyC5UdhHgWPY9z+JlNA6erV5+6/DGcvMuwxf2Mo4ji3/eK58zB36jQTS9LbID0qVtXVWR1YxmR37453dWZdW9vbTaRIMWNrWztaWtvcAQtFvluvwKUEdwx4vzAsMkM+94dnDtiyc+H+8lgU5uZaqGmeh+yMDBNm5rpP3VNCCCGEEEKI60HYtxwD3685HADh25VMfODloAgHR+jeXwjx0XCgjmG+ysvL7c1mDvBxMJGDhxfzvsFBS06scTu+zcy3UbkdBxzPfSvVg2+I8o1qevrg28gMw8KBJ74peyWDlFdKv0fBTExOmoIZyTMxPclLMzApcQpyY/PM+6DncW8wHHwJDQkDvfdRbEiPhlOTp2OG23ZCwiRkRDMschTCQj88AOd5M6QQLz8235U1FdNd+RMTJpsAL8Yfa79zO/7z1o/zx7n1CzAlaZqtTwFffkyeeTqk6HEw3NYX4kekq0NyZLJ5NJzm9o37yfplx+QgzhfrtvOZeDLP5TshYQJyovNc+TFW5mCYH8tgWfRmyHpQKEhBJfOmyJD15nbeuvZ94LhFhEaYKJIhlOnhkCGf/WHhiHXb0CtiQVwhYlx9hBBCCCGEGMymTZvwzjvvmI3JZwyG2eMkNZ/3P+rZgd6StmzZYs8dFEtwm4kTJ5qnJ4orPopT9A6zfr2JKijEmDlzpj278NnpxIkT9lzDZx6KMRhCkvWhBylOznMbejWkwIMhJukFip8aoxgaeG69MSA+W/Lc8tmVYR2v5TOlEDciHBM6ffq0Cct4T02bNs3EXsMBxTD0BstwuAzBy7EoelA7V4h0pbC9ZhvBMSsKwbkfbCvYllM8NJph+7dr166z58gbU2O/xP6H7WR6err1ZeyjKPqikI99FMfieJ55/L2+jeN77DN5LniMeB64Db0X8vjxdwrGvFDF7JfPB8Vh7AN37Nhh+bBM73xyfJEhmPm5dOlS6xtZR/aNFBiyPJ4n9tsURnKfKDJkH85PemZkv0ovixzD9LziXaguowUeW4o4eex5jdITMl9moO1xpTCvd9991+ZMeNxuueUWO3f8fqF+0sbz3PXD88Fjz/FdnhPeR8yH9fTWOzcxT75cwXuY55Uhue+77z4Lz+2dY643mL7OToR2B5EaF49JY8eYl72F7nqZP2MGZk6ejEXu2rx9/nzc6WwrCgyLBoSCvoHxZuZHcV5qYhKKxhRi1qTJmOG2u81dF3e4/WVa6q7Z5a4OyxcuxAKX35Tx403IyHzvcPneNm+eK2sSxhcUut/G4ZZZs+03rpPj7ouiwkJbZ5nbfqlL/GR9mCfrSvEixYULXJ25bL6zUynk43aL3D20xJVPT4FjcvNMzMf1p0+ahGkTJrj9nYZbnU161+LFuNPVlXmzzsyHy5lHkjv+FAPOdnksHTgWtr77ZP25HevFY8a842Niz55fT2zI89flbFZ6cGQo5lRnM1HQuczVjUJIb/w+xH2GuuMZyrDjQ/SiDMvm/Uu7me0L4b3O65Gf5zL4ejr3erkQ9MTKNoNtIu8fzgtSGHup24vhh20H2272Rewf2A55ffGF5o9uBniPso9lv8c+zptTu9hcnBDXEr7gQZuS8760p9m+8jrly5Z6oVKIfvjMzL6Nie03X/aiHc37RAjx0WgEVwgxqqAXPRP4JUw2cd/ZlDgZeW55ckSKiQsvRIj7xzzSo9IxLn48JidMMfEeBXT0WhhyjkhvMBTgJUUkufILXZmTLFG0R8EdPfudC8V68eEJJmRkHVnnQrdtYniS5XU+bKAtLMIEkAy5PDnxV/WL9ye4PPu3Y2hl5psXU2B1ulB+hJ4Ki1xezJNl58bkmkfDlMjUi24X5Ys2r4o5bn0KE3lc6QGRZY+PL0JqRNrAmkIIIYQQQoxMONlJD4Z8OYqDhRTF7N+/38QbFGtQCMBJdCYNuAshhLjWUJhAYSBFCxQsUHBDwR8FHOyn2F954j0KxijSo/CDfRr7N8J12c9RuE2xDr9TlE+hEEUh/H4lwh1uS9Ec82N/ybrQCzAFhxQ7si6cvKa3RfavfCHgtttus3rt3LnzbHhnCtU4eUexCpdTDMu6iaGB55biW4ocKSi99957zQvhxz/+cfOQSWEYRamTJ0/GpEmTzIMmX9xYvHixCQq9dfmdHhApIruYQDnc2UsU8jF870yXH4Vyi+fONbHdskWLsMTlsdCVN2nsWPMGyFDIg6HAMN1dIzMmTjJhIMV53JZ5zJs2zUSF/E4xIL0Z0pPfLFfnW1hnt3y2+15UUIjczExMHjfe1uM2FA3SU+J0dx1S7HeLu68ocuQnt+P3aS6/9JQUy9P7jd4NWc/J48ZZvSk8ZP4Zbj0upwhwwfTpJjz06srtuB7zYFn8ZL24Pj0OchvuC3+jeHJw8urCfctMTTsr1qLnQ4ZTPlNdjZNlpdhz+BBOlJSYB8csZ6eOdeeX+ychkxBCCCGEEOJ6IYGhEEIIIYQQQgghbkgoMKRnHk66c7KcHlnovZBetyjmoOcJepLip+c9RgghhBgOKNJjn0QPcxS1UxjG/ofeZSjio0iM/RQ9RFF4SBHf9u3bsXHjxrPeC7kOBUncngJAeqWhsI/CMS9vCsiY6BGM61yKtynWjcJAbu/1hxQy0dsg82LeFAe+9dZb2LBhg3k3q6+vt/pSGEkRIT0cLlu2DIWFhSY85O8UGFLg6Hmr3bNnjwkVJTAcenieeZ4oHqRg8Hd+53fwm7/5m/jUpz6Fhx9+2ISHTPfffz8+9rGP4bOf/Sy+9KUv4ctf/jI++clPmgiR21+OPcRrmCGGo9z1k+KuFYYcjnfnm6LCi4la+QvryzDHFOXR6x/zGKm2GO831jF1oK7h7t64EtHuheC9zZDJh0+exBZ3j6zduhVvrFmDHfv3o6OzE5HuvmfYZG9dIYQQQgghhLgeaPRcCCGEEEIIIYQQNyScqKa4gmINenbiBDHDslHY4IU05PJLEV8IIYQQVwNFhE888QSWL19uYj72PeynKMCjp7lHH33UxGEM07Vw4UIThz311FN4/PHHTTD2hS98wURhFBVS3MR+jF7nnnzySfOC6AkMp0+fjkceecTyY0jLiwq93G8ULVLk+JnPfMYEgp73Ov5GkSHzoyiNQjWWxbo89NBDJjykEJLiNf4+btw4qwNDLLPu9JzHcGPsZ5csWYJf//Vft+24f+p3hx6eL15PFIvymFNgypDUvLbopfDTn/60JV6DPGc81xQV8rqk0JXbXel58cqm2JCfF7vmPLhGqFvvcra5XlzJ/l0ODIdcWlmJF958E//+7LN4+pVXsOr993Hs1Cm0BQLYe/gwtu7di+r6eltXCCGEEEIIIa4HEhgKIYQQQgghhBBiyGEIR4ZM3L17N958803893//93kTfzt48KCFfhzslYUiQHo/YkjjV1999bzbMtGbEj0iNTQ0nNerC4UTFFww5B8n3Aknhik85KQ6l3GyWAghhBhO6A1wxowZJsRjiGOv76GIj6K7adOmmfCdIj32TxToMc2bN89Eewx/SwEixWCE/duECRMsTwoJ6UWQyxj23wuFS/HiR0FRGcukSJF1GxyClf0lvQ8yvC7LX7RokdXHK5N14Xf+zu/clsspkKQnQ4oVWS8KGLk9y6AoUf3u8MJzGhUVZeeO54FeLqdOnWrXGK+LoqIiC3/N8871JPi8/vjcOYh19ws9QCYnJKIgJxcz3X3FMMuTXPvghZseyUJMIYQQQgghxI2NnuSFEEIIIYQQQggx5FAgWFJSgldeeQX/8A//gD/90z/FH//xH38o/d3f/R3effddlJWVmSjRo7u725atXLkSf/VXf3XebZmYN8tgiMYLhY2juIGefCh6IJycpeiQk+sUfEjoIIQQYiTC/mo4PKZdCV5drqTP9LalAJFitpGwP+IK4Lnn+aPQTWnIUnhUFPJyc/GZj30MX//N38Tv/vqv4yuf+5wlfv+aW/aJhx+2dcKdTWvbuXuJn3ZOdD8JIYQQQgghrgEaQRdCCCGEEEIIIcSwwHDEtbW1Jv4rLi7GyZMnP5QoQqT3Qa47GIoFuYy/nT59+rzbMjFveko8d/vBDBYYUtTARA+GEhgKIYQYyXh91kjhaurjbTuS9kdcHqEREfDFx8NHb3ppaUpDlCLS05GQl4eiWbMwZ/FiLFi6FAsHEr/PvvVWjJs+HXE5OQh369p2qanwJSUhzNmzJjQUQgghhBBCiGFGI+hCCHGTwglbeogJBoPmHeZaJXqyEUIIIYQQNy4MxXfPPffg3nvvxX333WeJ3y+Uli5daqEh6VHQCxO5ePFi3H333We3v1geS5YsORuaMSUlBfPnz8ddd92FZcuWWQhA5ksxIcNFMl8vr9tuu+1sudyWeXDZihUrLBQlxYg+TdgKIYQQ4hrjeWS+kGfm6wWFbGExMfA5u8qEhkpDlvyJiYhJS0NSdjZS8/KQlp9vid8Ts7LsN67zge3ceQiLikKIXpQRQgghhBBCXAP05CGEEDcpFBdS8EdPL+3t7dckBQIBK3OkDZAKIYQQQoih484778T3v/99/OAHP7ik9Od//ucm+qM3wZiYGPv+jW98A//0T/903vXPTf/f//f/4fHHHzcPhRMnTsTv/d7vWdjlv/iLvzibb1JSEmbOnInPf/7zVrfvfe97+N3f/V0TFUZHRyM3NxePPfaYhXH++7//e/zGb/wGZs+ejaioqIG9EkIIIYS4NnDcbPCLuiNmHI1CtoEwyUpDm0K85PMh9DzJ+/1820JeQYUQQgghhBDXAAkMhRDiJoYDlBQaXsskcaEQQgghxI0NvQGOGTPmklNeXp4JACMiIhAWFmbfc3JyzBPi+dY/N1EcSM+F3J6CwKysLBMbDs6XnggpJExPTz+7HT0Ucn2GRw4PD7c8WC635XoUOyp0shBCCCGuNQ0NDXjnnXewcuVKHDlyBG1tbQO/jAAoZlMaOUkIIYQQQgghrhEaKRdCCCGEEEIIIYQQQgghhLhO8MUGvlzBlx3Ihg0b8Nprr2HNmjXYu3cvSktL0djYiK6uLvtdCCGEEEIIIYS4lkhgKIQQQgghhBBCCCGEEEIIcR0ICQnBhAkT8LWvfQ1PPfUUbrvtNiQmJpr3wmeeeQZ/8zd/g3/7t38z0WF5eTkCgYCFT2akECGEEEIIIYQQ4loggaEQQgghhBBCCCGEEEIIIcR1IikpCXPnzsXdd9+Nxx57DI8++ihmzpyJnp4e7NmzB2+99Rb+53/+B//93/+NVatW4ejRo2hubkZfX99ADkIIIYQQQgghxPAhgaEQQgghhBBCCCGEEEIIIcR1IiwsDJGRkSgqKsKKFSvw2c9+Fp/+9Kdx1113YdKkSeaxcOvWrXjhhRfw9NNP4+2338aOHTtQXFyM+vp6dHR0SGwohBBCCCGEEGLYkMBQCCGEEEIIIYQQQgghhBDiOsJQyRQZJicnY+zYsbj99tvx5JNP4itf+Qoef/xxjBs3Dk1NTXj//fdNZMiwyT/72c+wbds21NTUmLdDIYQQQgghhBBiOJDAUAghhBBCCCGEENcUetjhJHhXVxfa2trQ0NCA2tpaSy0tLejs7LTfz+eJp7e31xJ/977LY48QQgghbhQ8oWFaWpp5L7z11ltxzz33WPjkwsJC82Z44sQJbNq0yTwZ/vKXv8SLL76I9957z0InNzY2XtCOEkIIIYQQQgghrgQJDIUQQgghhBBCCDHseKJCigfb29tNSMiQfpWVlTh16hSOHz9uqays7KzQkAJET0jIyfRAIHB2Wy/xb4YFDAaDH5pI59+aXBdCCCHEaIRCw/DwcKSkpGDatGm44447MGXKFCQlJZl9U1dXhz179uDZZ5/Fv/zLv+A///M/sX79epw+fdrsI9pGehFDCCGEEEIIIcRQIIGhEEIIIYQQQgghhg1PHFhVVWUh/P79X/8Vf/D1r+PrX/0q/vfv/z7+7E//FH/z7b/CP/7t3+H/97d/iz//P/8Hf/i//zf+5I/+CD/+0Y+wZcsW89Lz8ksv4c+/9S18/Wtfw+/9zu/gq7/7u/bJfL797W9j9erVqK6uNhEikydgpHfE5uZmW0bBojfZLoQQQggxkqEwkDYUwyJv3rwZP3J2ET/5IgZJT0+3UMr5+flm2+zduxc///nP8f3vfx8//OEPsXHjRpw5c8ZexJDQUAghhg62p2xX+TKcktJISurvhRBCDCchrpO5br0MPRRwcoGJb+LxDTw+ECckJAysIYS4GPT8UVNTg61bt9rbquPHj8f8+fMxc+ZMREdHD6z1YbwwGtxu3759KCgosO1mz55t9+L5YNgyTggy5EZpaSlmzJhh4TmKiorg8/kG1hKjCT5s8Fpg4iTrtYBvXkdERFiYF34XQgghhBA3NrQzOSnO54/jR4+i7NQpnNi3H5XFp9BLG7Svz9mFoQgLDe23D/ucndrXgw7aqO7vjHFjUDBxIrIyMnDy8BGc2L8fvYEOhAwIBDmg0dXXC39MLGYsWogJU6cgMTHRfmPZXZ1dtpbP74ffJdqhMTEx9hzDSXk+y8guvTYwXKM3BkQhKMd/Zs2aZZ6YwsLCBtYSQlwJHBNau3ateYSdPHkynnjiCWvjhgOOJRw5cgTvvPMODh8+jHHjxuHzn/88kpOTB9a4OigE5/gTx6zef/99CxHLtmL69OnWfgsxEqCN8e6772LVqlVITU3FokWLcNtttyE+Pn5gjauH0zYc++X9cPDgQSvvrbfesvuc5fPe4BjwmDFjbH2O1548eRIVFRU2vpuXl4d58+bZGC7tHv7N+5S2UKizu4Q4H2yDy8vLsXLlSnz3u9+163Du3Ln4+te/bp/nQjua1xNtuUu1qfniD+cYaBPyOua84O233y6bfATB+QJ6QqWgedeuXcjOzrb2hu0Jn6luVmgDvfTSS+Yplm3/hAkT7JmG8x1CjATYvtJmKCkpsXEYtq+c++X1qutUiH5oS+/evdtSVFQU7rnnHrOVeZ8IIT4aCQyFGMVIYCiuhuspMGTSYKYQQgghxI0D35Ln8wm9BtK+5FADEz3mnCouxsrX30DxoUOI9/kxNycPkzOyEOkPh8+bjOR/ff2T6b29PWhsbcWhyjNYe/woihvqkJyQiAmpaZianoWJWVlIjKbIhOv24UxDPfa6Z5QDNZUoa25Cuys/yj3XxLjEvDlVyYEPfvdHRiAhORmPfuxjmD1vHuLi4kwMwEFF2afDiwSGQgwfEhgKcW3xBIZvv/222RELFy7E8uXLLXTx1YqkaAvxPuNYLL0P7ty5E6+88ord5/RcyD4zIyMDc+bMwb333muftGE410Ih0KZNm0wY1OpsKS7nugyvTBHk1KlTkeXsKNo/ntDwausrbiwkMBREAsPzw7b5tddew7p16+yaZzsq0bYYSdA+aW9vt7EZjtHceeed9rIB7fQLzf0KcbMhgaEQV0fYtxwD3685HFzmQzITjTG+bceHcBpkQoiPhg80NBb50M/BVxqJOTk5yMzMvOiDHg1LhgnjdpzYoYcPbscBpgtN7PChkgNbhw4dskEADk7xzdeUlBQ9QI1i+idwey1dCzhQxGvMG3TSwJEQQgghxOiHtiSfFY4ePYqXX37ZJiQ3bNhgwhCmvTt2wt/QiAmx8ZiZmY1ZuQUo4nNLVjbSMzJtYjEtNQ2pySlISUpCSnQM+FRS3dSEk5VnENIdxNKx47Fo/ATMGleEosIxyM7MQlpyKtISEpAUE4OkyEjEOBuzubER+48fQ0FMHG7NL8TklHRMcPmOT0zC2IREhPf0oramFqfKy7B7/3578YovZ/G5hs9Qsk+HD4pNvTEgXi8c/+GzK8+/nimFuDo4JuQJinhPUUw0XGI8jiPU1dWZpzSKnTgWxRdWOTkzFHjCKo5Z8QVX7gfbCo5DaWJUjBRo+xQXF5vtw/6NY6u5ublmU3hjXlcKxQGcN+Gk53vvvWd21Y4dO+w+50veEydOxJIlS0xcuGDBAntxnPeh16/yb94vXJcTqOx3uS29GZWVldn9y9/o5IGf6oPFYNgG84Uh2shbtmyxZRSX3XLLLfZ5Lt74Lq+jS73uKXzhHAOvTbbxnBfkdSs7fOTANo7ez9hmsB2hKNnriy80f3SzUF9fb3NyvF49QTjbbSWlkZB47/IepYMP2iR8mY/tK21o9fdC9MNnZvZtTBwHpPMmjglezHGTEOJXyIOhEKMYeTAUV4P3AMzriG+nXit4jfEB52oHXIUQQgghxPWHE5AUtTAd2r8fm1avQWt9PSKcrUfvgvWt/R4N75s2A3dOmYbx9FzonlV8UdEIjYpCiFuvr6+338UgX3oJ9iDY1opjpSVYc2g/KhobkJ2cgvvmzEd6air8kZEIi4hE6MAzSJ/Lu5f2bHs7mpobsfbAPvzXutWYnT8G90yfhbEZmYhz5YWEhaI3NBTHq6uw/eRxHCorQ1ljHcJiY3HL0iW4felSTJw0ySbnh0okIz6IPBgKMXzIg6EQ1xZO4tODIT1ZcZKSL2FTgMXxVYqwLneslGN0FKxQuEJxLcdtKSpkn0kRI8fueE/z/qaokB4TOf5Lu8Uri6IC3p+8hygK2u/sso0bN9r9ynuKfS8nUXnP0pshvdGxrnzpXC9aCA95MBSEbRxtCrYhFJqyjWBfzGvgZhb7837gvLYXfpbPuUKMVNimcs63sLBQ7asQg6CdvH37drOV+ZLQww8/bPeJ9ElCXBoSGAoxipHAUFwNbP4lMBRCCCGEEFcK7UmKS372s5+Zx8LupmbMzcrBnIJCTHKfHd1d2HL8KNYcPIAp2bm4c+p0LBw/ARFJyQjl80pvD/q6OtFLW7S3jwMULtNedHV2YUfxCazcvwfTc/OweOoMpGZnwx8ewULR52xYsyP5HBLaL0zrc2X1NDVhryvvl9s2o7OrG2PSMnDvrNnIo6eNyCggKgqdbtv29jY0uOeofSeOY9WBvThcV42CSZPw2V/7NQszSJGAGHokMBRi+JDAUIhriycwfPXVVy1KDEWGvAd4L1AoxXHZSx3z8sbn+LIGhTxvvvmmfTJPig75W2xsLO677z587nOfM29vLIsvRFyo/+TYbyAQsDyOHz9udhrrSq9x3Ib1oycyhk1kWDjWmWJF9cdCAkNBeN7Z/rDt4LXA+R/2xcuWLbvpI9CxfeV9wraZx0mIkQxthZtZFCzEubDdpif+9evX49ixY6aPeOqpp8wulpdPIS4NhUgWYhTDhxiFSBZXgzeIyXSt4DXGQQleNxo4EkIIIYQYnXBinaH7LBzymrWI7+zG4rHjcfu0mZjmPvOzspESHY34iEhEuGeTKvcMEXQ25yT33MG/TUzY2Yk+iguD3e7hphch7jmlt9c947jlAbc8OiICE3PykJuaBh+fU9wzia3P7dx3246fDnpCDEGf+7PLJnwaOCnv1hufkYkkhlwOC0Wosz194eGIjolFXGws4uPikORSemQ0Qrq6sf/4cQTcNlGu3hyX4EsxYuhQiGQhhg+OCSlEshDXjsEhkjk2yxfA2b9R1MexLgp8OTZ7vnFW9occl+UY6+bNm00cvGbNGksU81AwTM8q7BsZDvmuu+7CihUr7JMvQnAslvfbxfpOlks7hsLE+Ph4u08ZwpljuRwH9kKfcn6G7Qe/l58pN/uO+XMfNGZ3c8I2WCGSBc8F2zm2DRRgsO9nP8wXhG52ZxNsXz0HCnxmVFIayelC871C3MzwWZZ2PD2H0+7lCxR6kU2IS0cjuEIIcRNzPQZuOCDBJIQQQgghRi+cGNy0aRPefuNNtFfX4JaCQnz61ttx+4xZKMgrQFR8AmJi4zA5Lx/LJk+zbU7UVKOtoxPBQDv6WlvQGwiY98LeYA96gkGb1A5296CzuxspMbG4pWgiCtMzEOZM1r62dvS6bYLNTWhpaECbS11NTQg2NaKntRV9bluE+RAfF29hmGMiI9Ha2WGCwe7OLvS5z972NvR1dNAghT8qCrm5eVgxfxE+d+fdWFwwFse27cDale/g/Q0bzAsYhYqyW4UQQghxITiu5oltKNSj98GXX34ZBw4cMPEUJzBpU1CkQxEwJzPp+XPnzp1466238POf/xzf//738Y//+I/48Y9/bAJDihTz8/PNGymFhV/4whfwla98xTwNUih4sZfKz4X14zacOP3sZz+L//W//hc+9alPmViMYiG+RE5h4w9/+EP89Cc/Nc+kFBNVVFTYb7TNhBBCCCGEuFHgOB9tXArpNeYnxOUjgaEQQtykcJDxengRpNHmGW9CCCGEEGL0QeEdPd8cP3YMnXX1eHTmbCydPBWJkZEI7QgAFPJ1dSIkItxCIUdFRiAzIQHxUVGoaWlCa0cHent60OnyaXbr1jY3o86l+uYWNLW2ud964ff5EO5s1dDePiA44N3QLW9qb8e+0tM4UFqKqvpGtLYH0O2W9ba2Aj1B+P3hiI2KNju3k4JFt01vXx/6LJ8e9HHdhgaX6tHX1mrrRScmYdbkKfiDhx5FJkLwzhtvYsfWreY9a6SKDFknepjRYKgQQghx/aBnIHrkTXB2Dr/TPqLXt+9973t4+umn8eKLL+L//b//h3/4h3/AX/7lX+JP//RP8Wd/9mf467/+awsRu2/fPtuG9gi9Hs6fPx+f/vSn8Sd/8ie2HsOdT5gwwbyqXM34nSeEpKhw8eLF+OIXv4hvfvObePLJJzFz5kyrO0Mps05/8Rd/gf/8z//EunXrTCRJD42MbCObQ4ibD2/+gLANUDsghBBitMMXpunFm/YtbeBrPUcuxGhHAkMhhLiJoeF0rY0nCgtvhslQ7p9ElEIIIYS4EamurraQfuWnTiMWIZiSlYO85BT4Q0MRwgnozk5LztpEaGgYoiMiMTEzBxlxCThy5gwqGhrQ2N6OIxXl2Hj0CNYfPoSD5WUoqa1FeUMdTlRV4rD7+3hlJRpaWsy7Yb/t2D/BFeHzo6UjgBPVVahzv3d1dKK3s8PCJ/vdanGuPIZjbmhrM2+IXS61u3UCHR3ocOV2um2CFEEyjKGrJ/0OZSQn45bpMzAnrwApXUHs3LQZO7Zvt0n/kWjTebYmB0TlXUgIIYS4PtAuYQhChiKOjo628S56K6QnwjfeeAMvvfQSXn/9dfNWuHLlSvMQuH79euzatctsjNTUVPMueO+99+Kxxx7D448/joceegh33303br/9dvNiSAEjvRZe7fgd60qhYk5OjoVapnfEBx98EI888gjuuOMOC1HLOlFYyPp6dafHanpflGdnIW4u2OYwDDBDsvN5g95V1QYIIYQY7dBeZ7/GF4RoiyuUuBCXhwSGQgghrineZOiNPiDhCSlvhn0VQgghxM1FSUkJ3nj9dbTV1iE/ORURYX70BHsQ7O5Gd3cQPZyAZursQoizh6L94ZiUnYPcpGScrqnGofIy7Hd57CwuxraTJ7GvrAQVjY1oCrSjqaMDZQ11OFpZgSMVZ1DV1IgO5uX+hfp8iIuLR1FOLuJjYlDR1ICyujrzgNjR0YXu9oCVGR4WhkhXJm2wSpfvMZfX0TPlOFFZhTP19Whsa0Mg0OnW70A3BYytrQhx9Y+IjsHCCZOwYvJUlB8+gvfXrsX+PXtw+NAhm1jnpDsn10cCnq1JgSHTzfACjxBCCDHSYN/rCf0pAuQEJfvlFmdfMEwyX8g4evSovZzB9SgWLCwsxLRp00xASIHfZz7zGfz2b/82fuM3fgOPPvooZs+ejbS0NBP2DNdLweHh4RY6edasWSYwfOqpp0zguGjRImRlZaHe2Uvvv/8+nnnmGTz//PN47733cMjZQwyd3OrsJr3cIMSND0XJcXFx5l2Vzx5s1/gsxOcOIYQQYrRBu51ieSba7PTszRdvaMMLIS4dCQyFEEJcFzgwwXSjMnjCVyJDIYQQQtxIcNL5wP4DiOrtw5jUdLN7mtsDaG5jakdHZyd6g0H0mVfBTvhCQ5AYHY20uASkxMRh56li/M/2LRbueFJWNpZOmoLZhWMwITsb4zMyMDO/ELMLxmBMWrrbNhRtLr+gs6dCIiPhS0hATFISMlJTkRIfj6NVFdhTeto8Gba0tqPN1YPhl+fkj8G8wnFYf/QQ/nP9ajyz+X28vmcHthefRHldPeoYjrmtFS0trehoa0PvgDfDZFfPwrQMpIRHoGT/QTz7nz/CP/7d3+FnP/0pdu/ejYaGhoGjcP3w7Ep+ejYnQ7xosk8IIYS4trDvZYi12trasy8ieP0zJy8pxuMyegCbNGkSPvWpT+FrX/savvGNb+BLX/qSeSxcsmSJCQ7z8vKQkpJinhAZztgLSzocULjIMujRkJOr06dPx/33349f+7VfM7EjwyjHOzurtLQUq1c7O+qZZ/Av//IvePXVV01oSKGREOLGhm0QPTuNHTvWvLR2dHTYcyA/hRBCiNEGX5ApLy+3xHE0Cgv50s21jvInxGgnxD3wXjfFw6lTp7Bt2zZLvIHpip/GKl2SCiE+Gk4i1dTUYOvWrdizZw/Gjx+P+fPnY+bMmTYYdSHYcZ44ccK227dvn4XA4HZ8Q5b34vloa2tDVVUVfvnLX9rg0owZM3DrrbeiqKjIBqTE6IWDAt7AwLXqEjhA4RlvN+r1w/uTg8jcV+4jk1xtCyGEEGI0QhuxubnZvNYw7N+qlSvxwnPPYVFuIW4rmoi02Hh0UVDY24ssiv8SE5ESG4cI2nphoejt7UFHVxDl9XXYX1qCTSeOWvjiFVNnYkZhITLcNtHONgwNCUGIM0d7XXldPUEEKJpzefqdHRUfEw1/YhJCGX6wPYCG2locPX0Kb+zeAX+YD4vGjkeIs7uCriyKEuHyqWlpwWZXFj0jxkVGITkm1kI557rU2hGwcpJjY1GQmobMpGT3DBWF0HA/GgMBrN6zE+sO7EdxbQ3SkpOR6+qZOW4sps2ZjYlTp9pbzgyJeD3whAt8rvOEDBwQpW3NRPtzJA6QUoDhjQHRkxPHf+g5acqUKbKThbhKOCa0du1aVFZWWkjVJ554wjwODQdsf44cOWKhXg8fPoxx48bh85//vHlDGwrYrnH8iWNW9KBGT25sKyiAohhKiJEAJyjfffddvPbaazZmSjEhr116HWT/XFdXZ5OXFOMQivV4bzL0McdTOa7KsVvPY8pI6LdpT3BfWHe+VMF25eDBgzh58uTZFywmTpxo96I3h8N6a1L2xoLXL883zz9DZdPe5bzB17/+dQvnfS48/7Q9actd6rXA5wrOMdAmZBvPeUF69NS1NLLgC/MUTrOvZ4h3tnPz5s0z252eToUQQojRBOfB9+/fbzYO7XTatAsXLjSdw4W0EUKIDyOBoRCjGAkMxVDA64jpWnrZ8wafeL0NZ8iX6wmNVW/Cd7Cgkt+FEEIIIUYLtBFp11BMsn7dOqx5bzWOHDqI9qYm3DFlOuYXjgMtuQNlpahvbcW8MWMxNScPBampSIyNRaSzf2gPUcxHgeHuU8Uoa6iHLywMSydPRUFGFsIjwhHCUHu9tEVdYobuw/6ifer+pngwND4eIRGR6HXPJg119Th4uhjPbFyPTvd8My03D/VtrWjt7EBMeCRSXNlJ0TGIDo8wAWJoqLM/aYO61N3Tg3cP7UdlUwPGpqZjcdEk85qYlpiACJYR7keNe/Z5cfP72Hj0CB5bdKvZcu8ePoCEwgLMWLQQd61YgczMzOsijOMxGSww9LyCsy58NqN9PRIFexIYCjF8SGAoxLXFExi++eabNqaVm5tr4jt6IaTXQo63rlq1Crt27UJ7e7utT++EHLO999578cADD9jfvKZH0osBg20MiszM/lu/Hps2bTLPhd4LyhSd0eZgvW/EMb2bGdqVvAZ4rjkfkJ2djQULFuCrX/2qBIY3GbwWeK7YDlBgyJfNKCxctmyZeV0VQgghRhO0bfjczMQXalasWGFeu+lpXDaIEJdO2LccA9+vORxcPnPmjCU+gIwZMwZJSUnXzQuAEKMNPuxzkIpKew6+cjCXnjQ40eW9AXs++HDIQSJux4mdxMRE244PiBea2OHAEgcVOJjEB0uGz/BCd0gwdWPA68ImcK8xNNy8dCPBwWPeozym5x5X3TNCCCGEGA3QhuFzA72XvPryy9i9ZSsyQ0KxuGAs7p42E7eOn4Ai9wyR7Z7j6RUwMSYaZfX1JiZMi01AVEQEIgaeS+iJsLSuDhuPHUFydCym5uYhLyPTwm1RPNjjnjd6nP0UDPa4v/snKkN9PoRGRiI0OgohEVHOiHLPKt1unUA7quvrcNI9AzE0MsteOL4I41x+4zOyMCErG2Pd80phajoK0lKR655ZWMdM99yTGp+AlLg4JMXEWshmekdsbG+zMMxJMTGIcM9DfmeW9jk7rrS2FvXut7ljx2FCbj7S0zJQ31CP6rpaRLp9jXH5eMKAaw3PjTcBPNjW9L6PRBubg7neGBCfLTn+w2dXTizLPhbi6uCY0OnTp03YxHuKE//DJcZjO8MJGXo1Y1hYjkXxhVVOzAwFbNfYRnDMii+4cj/YVnAcSp4lxEiBfXBxcbElOktg+GOKr/gSN0OKUpTFMVPaObwvOX5L8SxDC/Nl8ZKSEsuH9433YsBI6LNZB/bJHFdmveh5kWPGfCmAL7azvvRiRnEh12O9zWZTumESz6n3wgrbX84XcN6OzgnYDp+LZ29y20u9hvmiO+cYaBOyDObPe2ck3APig/B6YL/MdovzuWzPeL7YLgghhBCjif8/e//hHdmVpQe+X0TAe++BRALpvU8mPZlksUiWYXnTRl0aSUstLY2WpBlJo5k1Iy39ARq99+Y99Ziu7lZXG1WXZbGq6IrepTdIA++9SXgT9u3vBG5WEIX0yEwg+f2ShxGIuHHj2nPPvXfHPmzD8sc/zc3N7m/+MK+2tlZJlERukTIYiqxhymAoK4EXRRkMxxt+vGBwL/Hik3fRarVcTF0pXJ5eRhkeajlvnFcW7mf8WxfOREREZLViG4Y3kdg13p/+6Z+i+ew5lCSl4IVdu3Fo42ZUFBUjiakFY1H4FjMPMoDw1fNnkJ6cip0V1agpKXZBh3yb5yDH21vxX99/B49v2oond+xCYVER0tLTXRt0fnISEyyzcyjIzkRWegaSUpLht3aiNZ6s3ZSEWDiE8PwcpiYncK6jA+e6OhGw9uTWikocrK9HJn+syCBEtivtdU4efz7Fbpdthty0Rm1iItEIZueDGJqYwMW+Hpyw6Uq1NtoLu/dhfUkJcm2a521637hwHqc72/HCPjtX2rwFyMzCrz/+AOfsM7l1tXjs2WfcedS9/pEk25ZcZkszGBLbl2xjJ2bPXi1tTmUwFLl7lMFQ5N7yMhhyP+C+xu7VmAElOzvbvc9jdX9/n7vu+tprr+PEiRPuXgi7TOZ1Ie4vzJpy5MgRF5zIa7P8ATivj63Ga0W8LsxjN/dL3pi9H9cQ5f7gD0J48/3o0aNuO13Ka3veynVdZTBcW/hjgrfeesvVY3zODKysu7xzDRERkdWObVdm4n3llVfcD+WYfZzHs3379l0zLkJElqcAQ5E1TAGGshLuZ4AhLxyx8AIqb4Kuphugd2ppgCFx3ri/cF75qBupIiIishqx7cJzBt5Eeu3Xr+Lt199AXXoGvrLvADZXVCIvLx9pWVkum2AsGIIvEuanXFDe6PQUJmdmEbT3akuKkZuZ6YL6gvbeJ63N+LN33nJdKx/dtRuFxcVIzcyw7wpjYmQEzd1daO7vR21RMUpz85CSnBTPYhiItxEjEXbXvICB8TGcbG9Fx8gwjtq49q2vQ2lengtItA+5bpR9/qR4F8s2bRFrk4Xtc7FQ0AUcBmx8MZumOXu9Y3gIr5w9jYnZGTxj4+L8leXlImjf9UHzZTT0dLvp3bZhI5JtmgZs+GNNl/Cb1iY8+vnn8KWvvOSCBO5lu47r51oBhsRltRqDDBVgKHL3PKgBhu+//74bLzMycr6ud61L5F7ifvDuu++6wqxuSwMMidsyr6cySxu35d/85jduX2WQIY/hDChk7zA8Fj7xxBNuO+d+y+2cx+/VdH3Ma3cwexnL0raHPLh4/ZLXbb0A2KW8dqcCDB9czFrMrtJ5bsj6jHUdu83multumxAREVltGFx44cIF1/bg/XC2vxksz6y8inEQuTUKMBRZwxRgKCvBu0HJgDg2rO7HYYEXoVZjlpU7sVyAIXHevPl90IIqRURE5MHAtiFv/P3whz/ET//2b1EZSMFjdRvw2NbtyC8shD8pGYhGEF0IIWbtR78vBp+9FrM2DbMMzszOYWZhHvm8EZmS6oYJh4I43hIPMDzAcW3ZhuqKCmTl5yNqn5u9cgV9A/3oHBpywYjssnhsZhoha0uxqcQyHwwhbNOWZG2pgL2Qk5mJh+s3YV1pGdIy0uBLS4cvOcnaXtbGdd8ZwryNZ2hsDMM2/lBwASXZOagpKnLBi2yhjU1N41hLC/rGr6A0J8d1r7yuuNi1z5oH+9E9NoIdlTWoqqhEkr0ftPGesfn40Sfvo3bvbhx98UVs3bbtnga9eO33awUYEqffCzJkWQ3nbAowFLl7HtQAw7ffftvVZwxiYNH1J1kteCxmN8c9PT2uC9nlAgw93KbZhXlDQ4PbZz7++GNX2E0br4Px87ymy32X2Qx5bOT1Vr6n46Osdl6bUwGGDy527c4urbm+Tp065drwvP/EoGjdyxURkdXOa7efOXMG586dc1nxH3/8cZchn+fMbMeIyM1TgKHIGqYAQ1kpvCnJgDhuG/frV8i8EMXt70EJurtWgCFx3rjfeDd7FWQoIiIiqwkz07S0tODPvv99vPvKL/GPnziKJ7ZuR3FhIZKzcxCNRBCZnEQkHOaVunj7LT0dPmvLxaw9GbP32f7xp6XCZ/+idt4SDS7gQmcnfnL8E+TaucrGsgrst3OJ0vIK+NLSELW20/zUJCbHx9E6MOC6P27o7XbjyUhNhd/aSvPhkBtfZX4BtlVWYVtVFUrz8pGRnuG+m+NhkGPYxjVr0zdp4xufmsbQ5ASu2PmMTSxqiopdl8ppNjzbnwvWXusbvYKu0REMTFzBhtIybLH3k5OTMMXumK0UZeUgKycHgawsA2e2ZQAAnz5JREFURPw+NPV049c2H7HiAux85GE8dfSoy+pyr3CZ3CjAkNi+ZGEbm+V+tzkVYChy9zxIAYb84SOzJXnzxOes97w6TeR2LL0usxK4L/CaDgMEDxw44IIMs5jheRkcdnZ21m3P7Gr05ZdfdplUeK2Vx0e+z0ArBhgym+H+/fvdPRK+Vmjtr3RrZ+nmp6xGrJe5bSrA8MHF8w1mXnXZ7V97za1rHvtZV5WVlenekIiIrFpsYzO+ob293cVDMBMvj11PPvmky17IHwep/SFyaxRgKLKGKcBQVspqCDD0Lkg9KEGG1wswJM4bL8hwXll0U1VERERWC/6y92c/+xne+NWvMdXVjf/x+S/i8NbtSFo8x2AA3/zUlLtQ57c2TEZaKgJZ2fDzfa9NY+0fdkNsJy2IzUy7jIKDV67gvI37dGc7FiIhfPXwEWyqrUMKb8bbOUV0dg7hyQlMzc6iobsLH7c0oSIvHxtKSpGamuKCDAM+PzLT0pCdnu5KMoMP7bM+azvy+5i1kDfwmQmxbXgQwxMTKM7JQX1pGQrse7Lss5kuYDHe1gzZdI1MTqFjaBCNA33YVF6JndU1LgCRTdGIteVcxkT7jAuEtBc77bzo3YazGE72o2bPLnzppZfczdF7hW1LLvsbBRiS18bmORu7MLufbU4FGIrcPQ9SgCHrONZvzPjGYAZmiGPg+328hC0PCG5DK70d8RoWAwx5LGO53vVYHq8ZQHvF2kMMLuQ1WXY7ykydvOZKDCTkjxa4P1VUVLjsKkePHkVNTY0bN4/r3rUy71HkfvLammzL3ew2qQDDtYX1Jusudi35yiuvuLYG7+E+9dRTrg2QY+daWnciIrIa8TyyubnZZRJnkGF1dbWLb2A8BNvcuhYlcusUYCiyhinAUFYKL3Jyu2C5X90kEy9GsEHHbYrFu0C1Ft0owJA4v5xPL5OhGrMiIiJyI1674k7ba95NoKU3g9h2Ybch//k//ScMNLVgS24Bfv/Rx7Glvh6+9AzErI2zMDWFSSttQ4MI23Rsr6628/g8+FJTYQ0bN043fWxXhoIuyBDRGKbn5jE0MY5fnD6J9pEhvLBnP3bX1aOwuBjJ2dnxzIfT0y6A8XJ3Fz5sakRBZhY2lZe7rIUM+nPBfgE//LDptu/x23NrNLppj4QjmLTzlr6xMbQPDbkulotyslFTWITKggJkp6W7z3KOIzY9nNepuTk09fehe3QUIfv+zeUVrqSlxr/rKvsOn7XXIva0f2wUHzU3oiMaQsm2LfjGt76F0tLS+HA3gcvmTtcfA3zYbr9RgCFxfbBdnfjDlqXr/V5QgKHI3XM/Agxff/1197jSAYYeBqAwwwSDsXh+LXIneNy93vWZ28XjK4NrioqKXKAUj7M3wmng9bfe3l4XpHv69GkXUMvs0bz5yR9KEG96MqMhr8FWVla67+B+XVtb6zKvMOOKjp9yv3ntzFtpXyrAcG3iPSGus5MnT7p1yHY870OxTlJdJCIiqxFjKHiefPHiRXdO+eyzz7rM42xLXyseQkSuTwGGImuYAgxlJXk3KLl93Ogm5d3GC1Ne4J13gcora8XNBBgS54nzysL9b63Np8gNcR+IRNwjYndYt/j8LjOWjxcuP6v7iVueDNgJW8UdcgE78NuysDrEl5RsxZaN/zoXdlkfWWEmMUQZJmOf5bJcDJx5IJcr55nLjfPsbYPalmQNY7tiJX4QwvaWVzwcJ9uCzKbzv/4v/wsK58P46oHDeHTLFpRWVsYDDKemMD85hbGpSbzecA5j01N4ZucurCsuQVpqGvy8sW77VYz7m00ng/5CVl/N23hn7fxl1tpIr54/i8v9/di3bj0ObdiILbW1SCssdPtkzIaJTE+js68XJ9va7HNBFGRlo7a4OJ6xkPuujT/J6roUq7fSGXRodR+zFy7Y93WNDON0R7t9Fz+XhQP19SjJyUWA02TzGLI6dMGmZc7aaSwjk5P4uKUZIzZfdaWl2FJWYfMS/y6On9/FzIlRLnebJ362d2wMJ7s70B2LoGzHNnz7u991FydvFoNzWO4E25hekOHNtN3dfNi6ZvuabU4+v9dtTgUYitw9D2KAIes2r9zHy9fyALnZY+at8o6pt3Is4zbNfYnXjXh8ZPZoXqf91a9+5Y6RU2xvLV5X4rgzMzNdpkTub7yGy/2cP25IS0u7emxnpmIWPr+VYC+RO+G1MW9lm1OA4drEeolZhdlN8qVLl1wddOjQIdeeZ/ZVrT8REVlN2I7mj3hefvll98Metjm++c1vuuOWrkGJ3L7AvzeLz+85njzzl6gs3JHZ13l+fr47MRaRG+OFKP6qlQdGBv/xYi5/0cqbW9f7xSwvpvEX4PwcL1rxF7H8HC9UXeugyhuNDDLkySMvAvAEkqmECwsLP3VTUtY27wLnauBNCwuf8yKFV9YCXrj2pv1GOEziPGqfkgdJbGEekYlxREdHEBkdRdTaf7dVJiYQm1/gGOFjMO5q3U+4zyfu9ytRZ7nxWT3BOmV+zpblKMK93Qi3tiHU1YHI0CBic7PxeoTLxbuwv9x38ybtwgKiw8OIDA/Fl+uMfdZeZ+YxF3D3gGFgIbtnjYzYNmjl6rbE5WXtpQdxnuXBxrY82+Zsa3iPt1q89p7X7khsX83NzaG1pQVvvfkbrMvIxjM7dqLEzjNSedOIgXwLQRfgHLZxnO/pQvNAP2bsNQbwpds+5QIKgwvux1Bzc/MYm5xy2QTPdna4rpHPdXWi044JU1afLdh48jMzUVVYFP+hBfdHBjsHbXw2n8n2/Mr0jMuUeLm/F62DA2i3uqvVzn1GpibdsshMS0Vqkn2vPZ+Zn8dFqx9fbziLajtPeWzLVhRmZdv7SS5AkEGOA1fGXcbC8zYdZ7utWD36SVsLusdG3LLgfAVtGdlScdVv2OaH9essgxHtO5sHBtw8XLDpmbblVVxZge07diArK+tTy/FaOC6uA17oXLpebqVwHd5O0I03/P1oczJQwrsGxHNLXv/huSsv8qr9K3JneE2IN0/YBRT3qR1WLzEg6W5gPTJq7dG2tjb3yGtR/MEqgwtWkldP8TqV96M8FZXbLV7wk7dNrWThOG/1OOZNC6eN+w6PibzOyn2JP+pmED7bFmwvTNj5C/dtPrJrZWY9ZAYxBmedOnXKXatl0A+v17KNsHSeRe4mbmPe9nyz2xvPE7jdsk3IYxXvCzIBgrbX1Y11Cu/d8n7SyMiIuy/FeortANZjfF9ERGQ14PUy/viuqanJnbcyKdP27dvdj3RW+odxIp81CjAUWcN4U0kBhrKSvBuOt3OzcqXx+69Vlr6/GnH/vNkAQ/KG8y7MeUVkrYsMDSN04TxC504jfOEcIi1Nt1c6Wl2QIgPhAtZedN1wrjKxYBARO75GRuLBe76UZJvOO2/XMkgz3NWF8KUGhM+cQKjhDMIN5xC+eB6R1iZEO9oQ7elCtLsT0cF+RKemXKChLzMzXo8k1CXRuTmbNlsnp44hdPITRNqaEenvc9MeKC6B7wFsh8emJhHq6nTzHD53ypaZzfPoKA92COTZtqRzD1lj2GZg2zwxwOx2inczPPHmM1/nDWxegPvwvfewPjsXj23ZhtzsbCTZvsK6hV0eR8NhBCNhXOrrRc/YqOtueILnJVfG0Dk8iNaBfhcM2GF1IbP9MUiQmQg5/gCzrhpmG3RdGNu4N5SVu0yEPnufBTYsc6SnJae4zzA4kJ9iwGFKUrIbNjc9A3kZGciy6QrYdDEocHZ+AY1Wp33Q0oitFVU4sL7eZR/kKLmsQjbM9Pw8JudmEIpE3Xyn2HlThh1TSnPyUGXnU+wWmcGPAxMTGLX6gxkLA/6Am46gLXOWqP0L+KyeZffMNnzUHlNsHOwi8WbOjXjzn+uQ03Qn65Dldnif824E36s2pwIM7wOu63u0fuX+ehADDEVW2mq41rUUj8FsizHzYOL1WR4fee2Vpay8DGWl8S6Rvf2Pvctwn2fmQwYXsvA1PrI+4HGVdYECfuRu89qSXrvyZijAcG3iOmbwMu9HMZshgwzZvudrrG90X1dERFYLXjNlT47sxZE9QTKe4ciRI6ioqNDxSuQOKcBQZA1TgKGsJO+CEC9W3kpg3L3g3Xz1pst7zuJdIPZeXy0lcdpuljesty60b8mDINzehoV33kLow7cQPvEBIpcbbq+0NCI6NgakpiO5tg7+rOzFb1gFWDe5wL0hhJoaEWy8jEhXBwJFRQjk3/4v4lzGQjvOR3p6MP/xB1h4/VcIvvlrhE8fQ6TxAqIdLYh1dyDa2WrL5zLCF88i3NKM8PAIkJSMQE5ePENfMsN04hfqo3YMd8F2r/0cwd/8CpFmm9a+XsCfhOSt2+DPXkXLdYWEGVB5/iyCb/zStsO3EWm6iOgV25aSU5Fk5x/+7JzFIUXWBrYXGKB2q+2MRGxnsN3vFf5NHCfPE5oaG/HJxx+jxuraQ/WbkJWTgwCznLKtGAy5LIfDVp/0jI64ALyctHQMTIzHMxoODqBlaMA99li9PbOwgOz0NNSVlGJLeSW2VFS6gL6ZYNAFJxbn5GIbX0uyuorzZa/HuzSPBxQWZGahqqAQ64tLsK6oGDWFRTauEpTbOUxGapoLLGRA4MQsyyzahwbR0NPthqsqKHLTx+yG7EKZ/9i+yrLpqbY6eoOd/zAQcd/6Ouyx+qCutMwFHF6x8TQP9qPP6oqFcAQptozSUlJcMGNZbi7W2/evKyqBPxLFoNX9F9takWTvl9n4vG4Jr8drx95P3rbj3Qz2toG7aVUEGNr2EJueBmamrNjj7MzNFdsufQyO5XTe7WXFdZNY6Ga/k9u6LWc3zdOcx4T5tP2W888mgct4fA/Wudw7CjAUubE7aTvdCzwW83jIYEPuV7W1tW5f3r1nNzZv3uyuw/J13jthW4PzwvYEj6m8ccp9kt2WM9CQ13r5WbZLRO4mrx3ptSlvhgIM1zaua9anXH/MDsVzQ95b4rpMPLcUERG5H9g+5nnxhQsXXOExi9kLH3nkEfeDHR2nRO6MAgxF1jAeJBVgKHeDd9F1NV549S6gJhZOr9dd3GopnK7bWYaJn/Eu0ImsZRFr54Uunke0qw2x0SF3g/62iu1Tvsxs+KvWIWXzltUTFMZ66MoYgufOYOHN1xD64C1EujtsZ/YjeX09Ana8vB2xKIMLZxD85EPM//JnCJ/4GNH2JsTGx4B5BgjYMgmHflv498JCvKvkK6OI9vciMjwIf3EJ/Ln5VwMJonZyze6Rw2eOu+yFVmG5QLtAeSVSdu+FP+fBC7aLjl9BuLMdkYtnEe3rcfPsy8qFv6IayVu22jznLg4psjawncC2htdeux1sY7Dd7xXv4pp3nsAMhiePHY8HGNZtREZuDpIYYGjDxOy8oLm3G6+dP4v05CRst31pY1mFC7rbUFqGeiubysqxtbwKO6pqsK2K75ejurAIRVbH5GRkIj0l1X1Xj9VXJbYPbiotR5LVUwsMXpyft2o/iJBr4zHjoR+p9j0MJkxLSUayTS+DBa/YuQkzJF624wwDCs91d+Jibw8abNra7ByHn2PGwpGpKffoZ3CYLS9mHky3eWGwYFZaOjLT05GVnY0sqyuz7XyK2RrLcvNQlZdv85eCibkZFzzJjIfFduxhACJv/GdmZrnunZNt2XX39GLC6ux5q48Li4rcBcvr8dqw99u9bnPe9wBD2+Z4fIy+9w5i77+N2MfvI3byEyvHrl9OHUfMti1k2jHS1jmD93lMvSu4ThgEacd0BK3YNPO7bjYgkFl7w5cvInr84/g8fvIBYic+Quz0CUQvXUDU9o2Y7X/MBO2yQd+t+ZB7TgGGIjd2J22ne4nHRLbPGBzI/Srb2mPcz6qqqrBx40bs3r0b+/fvx549e7Bp0yaXiYXHVO7zbIMwMJHDbNmyxWUWE7mb2I702pLeOcWNKMBwbeN9J9ZRvC/FdgezGfLeEc+RWBfxPRERkftlYmICZ86cweXLl921J7YxeH5cV1entrHIClCAocgaxptSCjCUlZR4IYfbCctqw4vBS4s3raupcLpul/dZ7yKdV0TWoujkBKJjo/EAuNR0+AqLF0sRfAVF8YHmZuOPxpeSCn9xGXylFfAVlV4d3l9cikCNnQRu2ISk+g3wZ9ydG6a3igEA4Z5uBI99iOB7b7nseAwI8OUXI3nrViSVlS8OeWtik1MItTQj+PabCNl4o/3dLgORLynZlpstj7JKBKrWxZcPszn67FgcjcSXsw3H7qQZWOeWHbsBtuXls2N81I7l0dFRRBrOINrTCfgD9l42AtXrkLJn34MZYDgxjkhPFyLNlxAbGXbZp3z5RTbPtfGsjQowlDWG7YS7HWDY0tKC08dPoDozBwfqNyIzKyseYGjDMcCwoaMdPzt5DOsKSrBvfb3rWrjK6vWa4mJUlRSjpqQM6+2cpNbOL6rstSI732DWwJRAkuuCmOcx47MzaBrsB88kMpJTXMDgoO2vg+Pj6Lf6q+fKGLpHh9E3PuYyCoYiYVyZnkafTR8DC7usnmM3xlML85i35RFiykPbvxkAmJeZ5QIXU5KSMBsMYnJ+DuM2/tHpKZfNMDs1zaYjyQUrBmwYlqTUFCSnZyAzIwMFVheWWN2aYXXujI2/c2TEvj+EInuN059sr6empSInO9sFK87NzKJvxKbJpqtm3Tp3jsSLltdqv62WAEPiOud0Jpa75b4HGMZsn+ntQeSD9xA7exKxlsuIdXVY6XQZgWNd7fG/u+1vO0a6R5bernhWzdr6ePvlbgbm2f4V7e1GrNGOWa1NiNk2bjs6kJJibaTrZKGy7SnGY799LnrsI9gOjNjFs0BnG2DzB84PMxbb/sO2QozjYtvAtmXuN7L2KcBQ5Mbu9FrN/cDjMtsUGdY+4fVbXo+tqalx91EYSMhrsww8ZOHNUwYg7tq1C1vtXLSkpOSa13lFVorXflSA4WcD1xHvO3lBhjwvZduD7QH+zXqKgYYK4BARkXuNbX0GvXd1dbkAQwYasj3MH+WwjcxrUIpnELlz2otERORTeKFg6c1mube8wAGXvYc3Ga1hLLJW+QsKkbx7L1KeeBYpz33pt+VzVp79AgL1mxeHXJSZicCu/Ug++sKnhk9+9otIfuRJJG3eCl96xuLAi3ijyPYZVxiwca9uGtn3xIILCA/0I8Lgg7FBIDTvven+uy02P5HBASx8+B7Cl84hNjkGRMLwpaXDxyxfuw8ghcvnK99Byhe/geQnn0Ng+174i8uBlLR40EM4iOjYMELHPkKo4bzrwvmmcfkxUGFxmXJ6Vp3EabyX63ypxOmwOpuBV256VG/LGsb2n9/nR8yqEqvl4u0Qb7vmhTh7fc6296GJCcxYHejnTaakZKSnpiArIx35eXkoKCxEXkEBsnJzkZadjaR0q78CSYjaPrNgn2VWwaHJSTfuYXt+uqsDxzracLKzHae6O3C8sw3vN1/Gr86dxk9OHsPPrbx1oQHvNl7Eeyz2XuPQACJ+H6pLS3F48xY8v/8gvnLkEfz+U8/gn77wRfd8T/0GF9w4MT+Ps1ZPf9zahLNdnegcGcaYfe/cwoJVryGXKZfdysYWrA7nzVE7zjDYsKqkFLtqalGSm4O5UBAd/Nz0tLspGmV2ORu2sLAIT+zahSKrf9sbm9BihYE+bMutdmxzsrC9yXbnWgy8uDW28dp2iKxMIDcPvvwC+K0wUN+XnGLvxuCP2vHWBvNlZbn34qXQBeC7AD9mwrT/HC4rt3/Yur7WscgbxiuJlr5nhcfraMM5RF/+MWJ/82eIvvYLhG3bj02OLz9+D7fJxkuIvPUGfMffg6+3I37BLy8fsO0YOTa/nLeuVuCdV11mw6jtb6475aXTsfR7Et/n88Sy3OtcFsu1H7z3rrWsyBtf4jj5nONb7nPeOL3hr2dxXFfbNtcafnG4qyXxc953LX0/0XKfFxFZQWyrMbshg3s3bNiAw4cP4/nnn8c3v/lNfPe738Vzzz3nbqIqwEdE7hYGhrKe4Q8MmEmV5xEM6GC2KAYb8vziwT6vEBGR1YTHIXbZ7x2LRkZGXFuZwYXM7M0kSwouFFkZ2pNERORTEgMMb+XXp7KyeBGGmW14QYY3qFdLlhuRW8Ub8sn1G5G6/xDSHn0yoTyB1IcfR6Bm/eKQcS6oY/M2pB157NPDP/IYUvfsRVJ1dTxzkMdOHqPz84iMjSIyPOyy1bkbwNe6kLn0pm9iuZblhuV4WCJRl8XQ3aBn9sCr38v3E4a9WTY8uzFmxr3wuVOI9nXDdYHMOrl+M1KeeQFpn/8i0p48itQDh5D60MNIe/pzSH/xy0g+/Bj8VbUAsxHxO2dnEGltQoTdU9tzd1P8Ztg0RKamEBkdRXRszC3f684D3/OWi1dudfillg6zZHwM5otMTiIyMoLolSsusO+633mzlpu2Zb7fsde96QgPDSLc3YXIQP/iMptDjAEnImsM2328GZ2ckgwfw62iMSu2rXMfY5ZUxla5tqIfKVbXMBvgvNV9Eb5nGJiYxHHYcHwM2Pt+1smuPRPCtNUnzFDILo1bbb9ht8mHNm7Eoc2bcXjLVhzeth1HrDy8eSset78ftcfK/AKMz87ieHsLhqcnUVFY6IZ5ct9+PLR7N3bZcJvq6lFXsw7rqqrtscb9vWnDBmzetAV7t+/EkZ278fDW7dhRU4vp4DxePnPKdad8ZXoGwWDIZs2mn3Utu6Dn9Pqtzk1LR3pmBsrz83HYjmNlefmu++Vh1j1W94dDYUTtM8ySWFxcgvVl5ShLTUfzpUvuYiYD9tYCtjl5Idb7ccsD3ea0bdLHbE6PPYnA176NwHf/Pvy/99/BZ8fW2OYdADMCM7NvUSn8T38e/u9+z73vHp/7Ivy2jTHY3+0IxH3DtmlX74/bscjaA+6Y4bFl6doItv3GZmbibQVv+fK4Ysvcvc/3OMwch5sGbHyx4UFgcMAehxC7Yn9b24DHnGWPR3yNAa/9vfC1XAJGhlxAYezAEfi+8DX4v/NH8H/lW8DWXbZjJgFXRhFrbkS08bJrK7jpsGljWyHenuH+/tv54I8ponyP08h55Dxw3m164tNuZXHe+Ri1NgQzKXP4q+NZnN8oA4vt/aXf4XB5cTo4Tv4wgt+z+FrUloE73nMZJIzTvWfju+7yIb7OcTGAk5keOQ0cz9Lh+drV5RGfX/fjAc4X20VczzzGe+vUxud+WODhNPHzfH1xvXJ815wuEZHbxOt1zCDGzKE5ds7N7MnMbsjCm6l8XdfzRORu4X0DdsnO7JPbt293dQ8zGV68eNGdC/EHV7qWLSIi9wp7yWhvb8eFCxfcI9vJ7BJ527ZtLsM/s+uqbSyyMtRFssgaxpM0dZEsdwMbWrzZmFjk/khc9lwvXhFZK9gtr9/adv7sbASsnXe15Oa5rmkjjRcQOnVscWi4jIepjz6F5F17kFRS+tvh7Vjlz86BPzXN7QNRa0eya+JIRysizZcRbm5EuLMNkf5exEaGEB3jDeD5eJeCvJm+uN/wJnCMAWHtLQi3NiLS3YlITyeiw4OILQRdIJ+Px1Ab3t20Hr+CSGuzDdvkhnPDTk4AyamIMqiM712+gEhbE6K8oc9Mgyl2wmonrX52E7d4U56Zu26mS0XexGagWujMSYSPv4/Y+Jh9xg9fbj6SDz6M1KPPIWX7DgRKyxCw5eEKj8XFJTavqfGb/TbNzATpLyyB35Zh8obNSNqw0XUrHXU31n+3i2R/WQUCNkxswpbr4vKMtDUvLpsBt8xi4YjL/OQtHze9Nr9ueXJdtNg66Gq35TJgy2jKDedLbNdz2c/PuXGGLjXExz3Q54INmBXKZ+vWLXdr20SGh9z64Xpy43M38ePBD5GOtvj6tu+LtLXYeLoRGx12wQUMDHDjYoaqRTfTRbILHLD3Il0dNt7Lbtr4Ga5jBlW5zFaLbSsOG+nvs+2t3QVwctsIt8QfI722HAZ649NsyxnzC/Cl2zabZO2rG6x7kZvFtgGDwe4k2xzrUe8HJSxe24LjY3a+7u5ufPTRhyiyum6ntfkzbf9MZr1m+wL3s/GpKYxNTbrshQwuTOd+Z//C9txl7rPx8D3Wia5es7ppenYOfWNjuMxzl6kJd76yrbYWezZswpbaunh3ynaOUVFUhIrcXBRYnRWw/XXC6i1mSkxJTcGmyhrstbpqR+161JdXoDS/AHlW32XYtDHQLyngt88EXDfG7Co5w+qgvKwclNkxhIGChVlZmLX567A6fz4cQtQWX25Ghpt+Tq/P29/5t303jxlJNg956Rnot7rkrUsXsK6wGNVW77rulVmSkxCw93m+tDA3i2arH5Iy0t1FzGvd3Od53Gq84eZtT96PjJab9jtx37tI5jzZtsxjo796HXzrN8BXtwFRf1L8uDXUD59tb6iogv9zX4D/yGPw1W+MD1dZDV92lmvX2MQjym6V7TgAa4fE7FiEvh6AgYD2nstgzGMGA9N43OHxh90vj47E2wi2fbn2AT9vn3XdM9v+xAA6dNi4rF0BO5745m1cNmyM2ySP1wz8zcyKBwkuxSC/82esnLLPzQJVtYjtewj+gw8hsHMPfOWVtj1bnWHHctceycoFCmy5r7Ph5uyzdhyLddp02DzwxxOu/cR5YD1jx93Y5YuI2TGbQbi2d9u2YsOwzXXxPGJ8ZEAh59/aVbBjd8yWC8ZGgOkp+05bDmOjtpya49/DH09weAYF2n7gpseWa8zaV1FrH/D7YMdR974dh2McHz9ry9g3wR8VWL3CdoEtI7Tb8uc6sGOv+z6uPy4vLiNuU5x+BiD29tg6sPFyeDtec7zgNNl+zfYJbD/mMmaQZ3RgwNaDzbO1K2PD1r5goKe1C9znFoMjue45vtjYsJt+n9VXrPcYIBob6AeaLtuysGG4PK3ewWL7ldvg3aIukkVu7E7aTiKyPK/N6LUfb4a6SH4wcH0xYIP3c3luw/b9sLWd2OZnXcuu3ZltVdlURUTkbmH7nt0i9/T0oKGhAc3Nze44tGXLFpe5kG0MBReKrCwFGIqsYQowlLuJDS4Wbi8scv/wokziDV/tc7Km8OSNhdttYlk8qWMgXejUJ+45+fMKkHzgIQRq6+IBZ0s/w/3BjkmhCw2Ye/WXCL76cwTfes2NI3zmOMKnjiF0/GOEmpoQnZpBgEELWYsBAfZ5Bq+Fuzow/5O/xcLPfojQe28h9OE7bnzRhVA8iNHao7wxzSA3vj7/o7/Cwss/suHeRejj9xG11/0VVQge/wQLr76C8OlPEOvtct0TuqgCZhG6MoJI06V48OGVCfgKCpFk0+Lm4zp4Yzt46qSN+0NEu9ptnPNAWjoCVeuRfOhhpOw7gEBO7tX5ubps7Ljvt+O5Lz0d0YlJ+KvrkLT3IFIeftx1UR0oLnWBAlGb/98NMLTlkxvvIjL4yQeY/7u/svl815alLdNTtkxPfIRwW6tLXubPzXXBnt58MGAw1NKChTd+jfkf/zVC77yOYMM5hAeHEGCAaFm5G46iDMyzZbfw2iuY//M/Qeijd21eTyDSP+CGCzBI0sYb7utF8OQxLPzwL239vuzGFxmfcNmGIkMDWPjFT7Dwq58hdOwDW/bH3HoPnbRpte9kMGagyNY5b/QvbmM3DDC0dR4dG8XCMa7PX2DhJ38T3yY+/sC2o4uIpaTZ+rPhs7NccEbExjH/7ltY+PUvEHzjFRvuPZuO44icP40wiz1nCV266KY5wKAUmx4X7CCyAtgmuFsBhsRzDJ6jv/vOO8i00ddZ/ZFt+0Eq66L0NPitLmFAXmlmBk61t+FkeyuyrL6ORmOYnV+w84YwAja+dAYk22vMDjhjrw/Y+T+zBn7Qehkb7Lzj6V27sWl9PYpt/0/PyUFSWhoCySnw277CgKTekREcb21Bj+2fxVb3fPHgYezfvBVlRSXIsGkI2P7oY8ayUBA+ZpC1Y4Pr6tjqGmZejdl38njht3o5YNPCAMlM+45S+65aq5M/aW1G+/AgqvMLXZAiAxR9VocwOMkFh7Gei4TcdzAb4yU7d/r1uTPYZvX/hhI737Ll5gIaOZ8ZGTaMH6G5OXxw4bz9nY59+/cji93sLtaXiVZrgCF57U5uFyvd5rzvAYbEbZ3fZfPnlSiDyLq74evrgM+OkygsgW/fQRdU6IL5vGGZ1ZPbWGcHIq/8DLG3XwPseIlzp4CG04hdOBcfl20TfjsWucx3DMz72X8DPnjLBbfBtjd2z2wn1Yja8TD6+ivACTvm27E71tMD2LERLZfhmxq3A1gEPjvO+oYHXDZD92OFdetdtuffYdterNG+y45bPrZDbB5gbQd/VbVr17jsxna8Q0k5YmUVQI2Np2adHatLELPjVeQXP4XP5sUFO9pe4yssgo/D274YsemM/fRvbR6t7cAsi/x+qxti1lbBD/8CMTv2xRovIdrSBBz/yMr7wPlTiDWcRczqCBfYeLEBMRsPjn8AnD2J6LnTiPb322h88JeWufYLg/qif/sXwLtv2vBnEbHthMP53n8DsGM9+D2X2V6zfduO+dF3bLiP3rbltzhOm39mk4Qtex/bSqxLOGxHOyJvvorYW7+Ory+uK46LmaK7ulzGQZ+1P7kfM2NkxNphsbdeBd5/y3U7HTtj6/fEx/CdPR4PILS6BW/z/TddgKjP6kiUV8IXs/NmW4dRGxa/+inwiS0Hq/MYxOqzesvH7S6hrl1pCjAUubE7aTuJyPK868dsyyWeU1yPAgwfHFxnDNzgfSXWrwwwZJuEwR7cJng+xOINKyIislJ43GHsQkdHhwsuZBZdxj4wfuHzn/+868KfMUc6/oisrHt0BVdERNYaNrp4Y5G/Mlx641nuLTaUeROawQQsq/WGtMi9EBkdwcKbr7lAMGb4izDbXF83XCa8wX5E+3tdVsLw5QaEPnwb8z//EUKnTyLGQD3bl1xWKgYRlpQD6ZkuYM11R2zjCR3/EOGmSy47osu019yIhdfsey6ejweoMcthIBm+gmIE8gtcMAuz3rmMPXOzNv7FYGxm7JqdcVkRmVGR3Te7rvbi716f7d/MAuQCFMIh95IvNR3+6loEyiriWRGXCcLgTWvOV/KmLUh7/otIe+4FpD7yBFJ27nafc9mSlqvHGUA+M4VoRzOCv/klQp/YMu1ss+XYE1+eg30uK1/40nmE3v8NgmdPIdTfF++G0H0+5gJ5oldGXXfOLuufve+WC5d5It7QY7DF+Fh8eTLrYZ99D4fl8lm82cdholOTiNg6deNjtslzpxF8z77/1ZcRPn/K1nGHW9e/XecdCJ89juCH7yB4sQFhW35u3m6EQT5DQy6IkZ8Nnz1h39kZz4Rp0xNYV48AM1nxgjiDh1qaMf/2my44MnzhNCLM2MiMhcyaadsN54XTFOm26W6+5AJf599+A2Fmt7qZ6RG5z9jeY5YJ7wJc3/iVeLfAtk8uzM4iwuAa2++zc3JRX1OLI1u2YkdVtcsy2Gl115i97/f7kMwbjFbrcT8KR6K4Mj2Ns50dNr4x7LbPba2uQXlRMXIyMmG1kwuiik3PuKxkoakpdA8Motn2wzH7u7ak1H1PvdVl+cw+Z3XE7NQ0JiYm3I2r2ZlZBOeDiNrrwYUgRscn0NjdjZaeHgxaXTA3OYWI1dE+q5OSbFrybN5qi0uwq2odqqwun7T3Jm3ewt4+ymxrDKJivR605za/rJ6YnXHW6r6Q1w5jlco3Fut+Bi8WZGXb/PgwY9M2NDiIOWZTW0O4vhh8wfZm0OaVj17dLMa2odjlC4i++yZgxwAwGI8B8DHb2kN2/Lb6HxfOInbmOKKtzfEfHtjxI5aWGQ9K47HrxCeIHvsYEQbctTUBvV0u87D7ccP6esSKSl37JOZnQKOtE3+Sfd7GkZOPWFaOtQHir/8OBhDmFdjny1xGYwwPwHfqI8Te+CXCdtyKMLgxOcVlKw7sOYDArj0IrK9zP2LArO1/zCjIQEaXWXDK7QcO17/t/7Ghfiv2vh3rMMdulK0dYPsfbJ59tq+CP4pgdkB7LRq07YbZGJlV0I6FsPYYgw2ZJZK7WdS+D9Zm8F0+D1/zRZe50Da2+Dj5/VyOdvxHY4NbPm58nEa2p2wZ4oTN1yfvAcwwODNny98f/z5Og7XnopwOGw/HGbXjdoQBfxfPunHyxyTRwhLEbHnF7PjP4WMnP0HklK0zfjfbKcycOGrtCE5jZyt8Xbbs+pkteTS+PDJt/bB+YBZEtkW6Ou3YPxBvywzYcrLvBNtRVn9Ffba+bB3e7eBCERERkfuF9w7YVTuDRDdu3OgSXwwMDLhADwaSMskFA0BERERWEpMhtbS04Pz58y5zIRMy1dbWYv/+/aioqLhmryIicmcUYCgiIstKDDBkUUPs/nI36MNhd0GGj8oqKZ85tg+4rmm7uzD32i+x8O4biLY3uRu8zMDnLymDv6LaBQ66zD7TE4g0nsfCqy9j4dgHiI7Ebxq7AMPiEiTtewhJuw+4DDfs9hcz04i2XHTBhCF2e9zVhVDDWYSYvWZk0N3Q92XnImnbbiTxxnx5hcuU58vLj2cE4s18r57kzX92hZdXEO+mmN1B2/feVC1q+3ZsehKxqQkX/Eauu+WKKpdN6JqBBfbdzNTDrpPTHnsCaUceQeq2bUgqK4c/w5bHMkGJTjSC2OwUor2dCJ/8xHX15+dnqtbZcioDuCzts7Ero4icP4nQmRMIt7bacr93QTMM9mTXktEGZgg8yVfgr6yyUm3rbzErE5cbAxzPn0Lo7BlE+npdt4vXZfMenZ5GqK0FwROfuABFBlZyfv0FRUjavgspTxxF8radCHA92+uhyxex8OariFw4E+8WMTnZllclAus3ImnDFnvcEN8GbX275dregoV33nIZNbn9usAEkVWON4QYYJhidc/g9BTOWb17xerIoO2LwSmrK21bTrJ6k9nnHt+xG8/s3ouczExEbPtm25GBduyeOBiOYHx2Bv3jV9A6NIiTne0YnprCvto61FjdmMyAbQZVM/Oave66iLUSnJhAU3cnWgcGkJGcgl3rarG3th4ZVq/OzsxgYKAfbT3daO7tQYs97x0dc9M3szCPwYlxl5WwfXDIvnccE4vT7QJ/+Bich9/qiwzbRw/WbcCW8koMTU7a5yawYMcIl9mIGeoYJD47Gz/u2DRO2Wfn7PU0totZD7POXSzO4ryn2XgzrYSZsdGmn1n61hovyJABhmx3KtvTIlsGDICNnj+H6IfvugAy2D6C2g3Atj2I1W8BsnLgGxsCLp2PBxDa8L7ScmDrLqC0Mr6tn7NjlH0+YsdTDPbZscWHmB1vfVu2wX/gEHz7DiNWvR5IYyZeO/7aON3f23fbd9XHv3Mpboe23bFrZ2zcilhhKXwzU/Cds+Pa679E5NVfuMBGF5wXXIA/39ol1o5he8j9AOFOxaI2qXZszrI2A6fRlkXMHTet7TR5Bb7Gi/DZvu2zfT9my4rdNzNbqG/UlpW1P1xQ4eIPQa6ycfpDC/DlWjtt8w7EauqA3Hz47DzI13oZvo5m+Jidr34zYrZ8Y3bcdqmWRwddl9URdnNs+27U2gOuK2oGh+YW2PC2fA49buO0dcKshdbmQtMFxM5Y+4JBiYnTELHvCs3Dl2ZtOGtfMesj1tXDV1XjMlHGUtPjQYx9PYhZneS6Z2YQJrtuDoZsGJsmfobdWl+rHSYi95R37FZRUVn5IlJUVIStW7de7Y6yv7/fBRg2NTW5jFI8v9B5hYiI3Cl3nW5qCt3d3bhw4QJaW1tdJv+SkhKXWZ8Bhl72XBFZebrCJSIi1+UFGPKm6T3rukyW5QUZ8oIMizIZymcJgwNDPV0IXjjvguEwM+luvLNr38D2vUh56TtI//v/FKnf+AMEdh1wWQZd0NnsNMKtLZg/ddJ1lUs+Br9s2oiU3fsQqNsC5OS513lzO9xwFvM//wlmf/JDhNglXmgx82FGFgLV65G87wBStm13gWWBjTaOJ44iafdB+Mur4oFuxO5wK2qR/OhRpDz/ElIOPwx/WdnN3VxenGaXPYg3yolZZLOz4t0H+u/ChXv7TgYmsFvllGe+gIx//C+Q+U/+FVJf+hYCG7bZ/GS7wdiFYKSrE+HmRheoc88wI+TctAvaDGzebdP1HZvGf2nr+4+R/PAT8DFbkx2niEFB4dYml9XQzdeyYm6dMrMQg0mDH72H8PEPXAYpBGybYiDpw08i9ennkFy/cTF7oX2M4xseQKyzJR5gmZIG/7p6pH3995D5z/5HZP2Lf4PM//5fI+0bv2/bxlbbNgvj3azOTMWzNk5MuO1YZDXjzUG2+ZiBona91XkZGei5MoaZhQUXgMeAuQWrS6O2r3HYnMICbKirx1M7d+HpHTuwo7oaOVZXzYWC6BodxkdWX/zizEm8ceEc+m0/mF6YQ8vAAHqHhzBp+1yQmcIYfBux/Yv3mqwshENoGRxwQX37atejpsD2JasHIvPz6LLXP2puwmsN5/CyjZfjfq/xIi70dKOlfwBvXWzA6Y4O5Nt+u664GOX5BciwOj9g9a9v8R93aL8dPwqzrW6zeWjo60azjffK9AyCVs+F7fuZDdF1d29trWmbDmZfHJ6cxPbKahTZ5zi+JLaN3c1UK6xTGIxnJdVeZwbaifFx115bi9jmZGGAodqci2w7jbH73bEh+KatPcGgum074P/KNxB48UvwP/40Yjv2xYPYJu19Zu4btWFzc+E/cBC+DRvdsco3NQZ/8wX4z50EbJ9AaQVgxxxU1rhulQO7dsPPTIaZtn0ymJUZk+s3IWD7WKC29toBgTyOb9qKpKc/Bzz0OKK1GxFLy4DP9jm/Hbd8b/0asR/8P4j81fcR/uh9xPr74lkDbT3fMdvmsX4TfF/7DgLf+n34n/k8Ylv3IMYAO5sHLhPf3gNIsmWV/M3vwv/QI4hl5brsjLD9Ltrb4zJBfmpaGFi5/2H4XvgKAl/6GnyfexHYtjuecdH2O/7IwP/SN5Bk40t67kX4tu8BCkpsniKI9VkbYHjQDeerqIDP2nuxx47aOL5o0/Yckmx9+K0954I2s3Pgs2O6b8LWBYMcE9l+HsvMBQ4/Dv8f/TEC/+RfIGDL119eDtTUIcZ1x2nu7UWsm21TqxevjMDHH6YwOLGs3AWOIivejhKR+4vtFl7TYjtHRUVl5YrXNbKCDIXdXdfV1eELX/gCnn/+edTU1LjsUsxkyAAQdp3Mcwv9gElERG4Hjx+8PjU+Po6TJ0/iww8/dBkMGdS+b98+fO1rX8ORI0dcwDt/PC0id4ciRURE5Lp4gYgBhmyQeReO5P7hBRg2or1MhnyuizLymWDbfKSzE+GL51zmOHaj52NXf+s3IvnAQ0h5+DGkHLHCboEfP4pATa27sYzQAqJdbQifO4WYF2DIuiw3D0kbN7vh3bD2Gm9KR/p7bNgT8a6CO5pdcAnf85dXInn/YSRv2Y6AyyToR1JFVbwLYhuPr7A4Pg6OPy3DZbFL2r0PqYePIHnzlngGvJu54M79mTe4F+bsrHlx3w74XRbCa3ZzvAL8+YW2DB9H6hNHkXLoYStH3GPS7v3wM7iHeBF4/IrrDthl5rlXuBiiEQSqa5B69DmkPPpkfBoP2vQdsLJ5G3ypafFBbbtgt4auu8VrBRguLmNmHAoxuPDUMde9M7cVt9722/Z0+FGXwZDzzmDS+DQsBn9O2XbEriM5Hn4FAyRse/LVrEfyjt1uOaY8+wLSvvAVpL3wEtKefhZJdRvu6voTWUls6xUWFuLwoUOoWFeD8blZXJmZwczcPOYXgi6L4PzUlAv4S45EkZuaisr8Aldyra5asPbJ+e5OvHzqBC719bqgveLcXGyrqkZhVjYu9nbjw8bLONbciIHREdsdbX9c3F/ZTTEzBTKoj3tLWV6eC1h0e469l2b7Y2F2DmqKS1zXyUU5uRidmca5rk6c7GjD2PQ0Mmx6yvLzUFyQj2z73uS0NFfvc0d27SgbD78v3fbJ3MwMZKSkugyI3TYtEzOzmJmdx+zsnO3mdtyJhF33yWds/COTk9hZVYNS+85kq5eZydDP8do4GSQZtvkIstjn2GVuILC2M4BzWXE5JbY5P7MW13F02I5/V8bgC/LHB7bN2r7ALnHR0QrYMQWua+EwwO5zBwdcdmQenxgMh7pNQHWd6yrXBbOxLWPbCbMOBvYdtONPqfsBhI/H3JyceIA6MUMyjzE8HjFj8mJb43fY6z52s7xhE/wPPQr/U5+D78nPAVt3ABlZwIh9X9MlwI55sQ/eQfTcaUT7+xBjIO2d4g8+rJ3j37QNgc1b4V+33nbeysXMfQHE+P3M7mvHQv+GjfBXVNpr2W6a+eOOmC3T2NyMLY/F8RHnm5mK+Zl6O4bWbQSYRdAtFx9itiz81v7yb7Kyrhax0vJ4F9K2zXIdue6bbf/zFdtyrVoHZOe57KRcT76GMwC7p74yautyAT47prMLddfmS5gIFwDJca6z9bZrL/w7rVg9xuzX7M7ax/Vq0+Ibsm2gq8NtH+7HCnPWVuB32zoNcDhmkhaRVYFtHBUVlZUvCi4U4r0D90O12lrs2bMHBw8edEGGPI9gFsNz5865LixHRkYwb+dNIiIiN4vHEmYp7OnpuRq4Pjw87I4727dvx6FDh1wW3dLSUtc1MtsnInJ3aO8SEZEbYmOMFwlYdOHo/vOCDPmrT3VdJ58VMWaxG+xDpJ3d887GX0xOgr+qBv6aWviZNZDd6dkJZNLGja7bP3cTPhpDdHgQ0bYmxGamuQPFP2sCJaVIffgxFxTmKymP37Sen0FsbAix/i7EGADAC+aFJUjavhMpDz2MQGXV1Zv7PmaxspNWf1EJfJlZLmjASUp2N58ZgJhUsy7elXJamrvRfWMMWotaCcefO/Y53rx341/h+pfTFLDlaPPglsX2HQjk5rr5YVBl0tbt8DN7khOLByLMz9tivIeBJpzvlDQE6jYi9fEnkby+DoGcHBe06YI3N2112Q0dZkHjheoQs4Zdo17kNjE9GQ88PfkRIh0ti8PbV1XVIvnxZ5G8ZZsLJGW30x5Xz7I77eTU+HKzz7D77ODJY1j44F0ETx1HuKUJVjm7QNSUp59D6pe/jvSvfRsp+w7El2vC+ERWs4KCAjx05Ag2btqEhWgErVaPdo+NuiyGLshwdhYLk1MITk0iMjNrVZbV0baPTNv+d6mvB8fbWl1QXsAfwA6rB5/dtQef37sfO+152MbXaPX5R7a/XOzpxiAz/dl4XfvG3gtafe/3+6yKT7LPx7MNMUAwkJKCCqtzD23ZiucOHMSXDj6EZ6xuZsAfu2E+09XhggUZyFiSn48Mq8cCVjdwv2P44nwojNHpKRdMOG/fx924wuqRPetqXeAiAwzbhgYwcOUKpmbnbJqYzTDq5onZGBk4udXqxWLW7zZOxoaxWohamyw0PYOZiQmMTU1h0ob3p6agqLjIXdR8EHDdfNbbmzz+uUC4GQbCxZdDtLMD0bdeR+S1XyL60XuIdba5TLUxHieIw3H7ZbfGxWWuq2NmFeTrPgbJ2fbqyy+ydsw6+O4kyx3XDTMssqtxbtvl5fAffgT+L34NvqeeBw48DGzY4rr1xdwMfBfPIsbg+ssXAdt/75zNDdtG1i5zbSQWbvv8m80WHvtc24l/2OwzMyOD7nl8t/3HZTG1/c3ece873EHZLuNn+ZzLkIXPOR5+lu/z0dpI7j1+H5ct2yqLx3W2DX2zts7YPfTp44i+8yYib76K6ImP4ePxn+9xPTFglN9/dRLsO2yaY2np8KVbGzOxHcf1Zm1PdpUc43zxhwe9nYi2NAJWVzgMoLT2I4MM3foXkVWD17NUVFRWvoh4uD0UFxe7LioPHz7sukxmJnx2l3zixAn32NfX5wJF+EMmERGR5fA6FO+BMhsus+CyK+SzZ8+6gHV2vc8fR7NLZB5rGFyYxvN2Ebnr/IuPIiIi18WLA8xgyBuqunB0/7mb8LyhHQpdDTIUeaDx5vnMNGITV+LZgdxrMcRmZxHp6cHC6VOY/+QjLJw8jtDly4jaiacLBiMGBfCzfEzYV3xpqQiUliH54MNIfugx+NlVMt9ndjremLZ9zGVJ3L3fhnkEgep1LpOgVYLxz1ud6GO3yLyhzRvcHr7PrDcpyS5rnQsqS3z/uuyznO6AjXfxRryb9/l5RJl5hzfBVxKnNTnVBTb4CwrgZ7YhzhcLf+3HLD3ejXF+N5cLb9x72RXvBR53MnNclsVAfkH8Jj9v6DPoPTcX/ry8xexkhgEC4aDrzvmaOMz0BGKjg67rYrC7Zzc/PjeupPX1LsOlW2/e8c4e3TLJtuWRV2zfnWKfibhshpELpxF6/RdY+Ks/xez/839g9i//FPNvv4lQUyOizKDE4FNuN5xGb3wiqxwD49atW4eKigqE/T6823QJxztaXVfJzAC4EAxhmtn+ZuYwZ/VTyPY5vt5vdfRPTx7DlelpfHHvfjy5bTv21tWhrqoam6wOPbx5K75y8JALNAzaPvRRa7MLDJycm3NtGXZdnGz7iquKwxGEIzFEbd/0paRafZSD9MIi5JSWIq+4BIWFBaiwemtndQ3yszLRN37FfbbU9t802+f8DD7y2edt2oJWbw1PTOBEW5sLapycs2OHfUlRdo593ubT6pYJe+29psu41N+LKZsnZlMMR8IuqDJg08XsjOxyOT8zw3WPHAxF3HII2TKZs/ntHxlF+/AQxu27MnKyUVlVhSx2sb6GJbb/+fiZPgeI2LHYjoH8wYMLQmNQm22bttXaIcSKHbdjdqyKFZYhVlaFGH+4kJEZr/dZ+EMJBtJx4yYei2zbcm0NFyR3+8vWBTWOjCDS1ITI2TMI2/GHXfn7yisR2H8Agee/gMDXvgPsOeS6JvbxuNfWjNilCy7L4orx5oGPV+fJytVH964T4/Jzr9vC5DJZ2r5xH+FnFj/kjW/xT/ck8X3vuQsWXCy270cvNriMjb7zp4C+Lvdd0YoaxNjFcXm1CyB0lnz9VRyn++7friNmNvaz+2NmVOQPHGwdYqAPMav7Yr3dLgAxVl4FFBbH21A33QYUEREReTDw/CE3NxebNm1y2Qzr6+uteRZzXVmyS0sGGvI5Aw9FRESWw+DCsbExl7Hw3XffdeXUqVMYGhpCSUkJdu3a5QIMKysrXXChshaK3Bva00RE5KbwhiIbaLxAwKLG2v23NMiQzz/LmWXkAWbbtbuhz8C20G9vzsfC7Da5FeGTHyL03psIvvUagm+/jtCH7yDa0/npm/iREKJzcy673NXXffGsQkkbNiF55274cvPjwX0e3khOTkFS3SY3jAu+u9t1H8efngEfgxK877J9Ozo95YIF3LxcR3RmBuH2NoR7ehAZH3dBB9dl88hl4LLzJAbUGdeNowvQ+e1r7ib6p7IrXsOyb9uLtuxvuZ5ipiNbJu4mfiAhSM+tn2QXBGp/xF/jqCP2P37H9b6GgZKuG2qvO8R4iV65gkhrs1ven8LvsuWRtHGL647bX1kDMAsVA0bGRxHtbkf4cgNCp08gdMy2x4/fQ/Bd2ybf+DUWXv8Vwk2XXTeQV7c9kVWOwWS8IcSLdQ8/9hgmYhGc6GjDqc52jExNIWR1gctmaHXq3ELQdZ88NDGO7pERDIxPINfqsCMbN2NTdQ2KCouQmZmJzJxslJaUYmttPQ7UbcSW8krM2ufbhgfROTLsghcDtp+l2T7NYD6r+dE+NOgyCs7b/hNhW8eGZ0Bf2EosHEFqUrLrmrnA6md2xcz2agrrBQYD2TRGbNqmbH9u7utDQ083wvZaRlo6Uqw+CaSnIy0vF6Xl5dhQV4eN69ejurwCxXl5yEq39wN+N5/RaAwlObn2PfnIs+lKTUnBvNUhrUMDaOzrQWNvL852tOP9xos41tWOvLIybNy8GUVFRUhhlrY1KDGwkBnM+fwz3/a3ZeGOlVyndkjw2XGGAWb+PfsReOQxBB5/GoFHn4Rv3yH49h6E/9ARF+DnjnsMNh/sh48BbjwWsNtgK+yaF6NDiDQ3I2b7z295xzn+z44bPHZc75hm22lscgKxFjvWfPQuYG2imG2P/HECbNv1166Hb8s2l3UPOfn23bYu+UOMsdF4kGOSHVtTUu377HUG6DMQkhmBOV4+575l3++77kRcx21+7Nrix+wbjtimH8wqeemczesQfPxBwvZd8S6kd+0Hqm15sH1xq9g+srrAV1CEWHG5tQfS4ZuegK+rDeAPF+w9WB3nKymzhba4LkVEREQ+Y3j+wK4r+cM1nlcyyJDnmAvWvuzq6sLp06dx/Phxl4mq186p5ubmdF1bRETsVD7iMhS2tbW5YwWzFrKLfb6WYefbGzZscIGFzFrIjLmp6jVA5J76jF8hFhGRW+EFGPLXILzZKPcfL7ywOwkGGPJRF2LkgcTtOmTbt8sqmBDcZn9H2y67AMPwsXd/W068j2h/N1wgXMCauwycM8x2yCDDT+0nrNfsRDS5sga+rFwbloED3s1ge0xKhr+oEP7CQrhMhXcbgx4zs+PTwmA6w2x8sYmJeDeG18scaPMVHRvD/CcfY+HMKYS7OhGdmoxn87tW3cBggpR0+Bgs54t/31V+m3+WpTfHrzMJDr/LBUMsGZAZJzktDFq4BQx0uZoJMhEnjcGHDIz41CTa995oGonzxSALFo7ApjfS1oz5V19BtK932XlI3rcfaS99HUlbd8OXX2zLLs0+z+Vmn+fwzJ44OYZoeyPCH72F+R//ADP/5T9j4b23EWXwh9t+RVY/7nds7z366KP4wz/8Q5RWVrrMfr86fxqtwwMumC9q+wcD9hhkOD4zg+b+fnQMDaEwKwu1xSUoLyhARnYO/OymmMFUySnueVJ6BrZU1+CRTZtQlpvrAhYv2T7HLIasctJtf6+2Ojc5kITTHe1o6unC+OgopoeHMD3Qj+nBAcyNDCNk3+m36cy06Uxnl6WccO6yrGIYGBVcsGFmMXxlHMdbW3G6sx21/IXz+jrXBXRqbp7Lgppux4ANmzbjqcNH8M0nn8bD27ejwup9jpNZDDneyrwCVBcUISst1Q4pAUzMz+FcVwc+aWnGR81NeOtCA162evf9jlbU7djmugPLzs52bee1yAswZIAk2/z6YZEtk1Q7Dtl26boy5rEzZttGZRX8jz6OpBe/jOSvfgNJX3oJ/kcehf/ww0h68qjr+pjHvahtu+hshb+zGb6FecTs2MGukpn/MNbXjdBHbLf0//aYw42OOwMxSI4/jmAwIp8vh59bWHABbv7j78H/7uvA2ROI2f7iugD2jrvM1sfgN65PfoY/WuDXcP/hjyh4nOWPOdh2mJt1P2yIjY8Dc3caIM/5Wpy3e4nriIGFIwO2ymxG6zfB99AjSDp0GP6Nm4DCIpv32wwCtn0kxh+mrKtzQZxcbr6JUfjmZ92y9FmdgrJyN5yIiIjIZxXPK/Ly8rDdzrEOHDiAnTt3oqamxgUZnjlzBr/4xS/w61//GufPn3dZqrxeeryi69wiIg821vNeYWAh73HOzs6is7MTJ0+exFtvveUyGE5NTblreQwsfOaZZ3DkyBHXBT+vW/FYIyL3jq4Si4jILeNNRt505A1HNd7uP60DeeBxG2fmIAY2JwbBpaUhsPsQkj/3ZaQ8/1WkvPC1qyWZ5UWWr7uS9OTn4WcmITeO3+4zzIIYvHAB88c+QnR0MJ7Z7uoFTHucn0Xwg/cR/OgDxJjt56Yvbt7mRdCAHz52z5tXYM8XA+pmZxBtaUKkv/vaGQl58XV6GuGOVoQ++A2CP/1rzP3p/4GZ/+9/wvyrv0BkeCge3LccF0hopwV3VJUkfJgBC8wMuDTbov0dC96g++LlcH0xmGNF6zobF7uF3rgd/nWb4MuJL+8Yu07utmVoyzvU1/s7y9t1mV1RhfQvfQWZ/+CfIP0P/qFtf19CYNcB+MprgMwcF5TqppXbSnDBtpsrCDacxfyH77sAUJG1hEFy7Nbqy1/+Mnbt34/uqQn86MQnePnMCfSMjWKaXQlHoi6j3+X+PjQO9qG2sBjrCoviFxu4DzBIad6Ky8Q66+oA7s256ZnYYvsTsxWO2HinbBiOK8X2RXZb/NCGTa4L4ws9PfjhJx/ih1YPv3b+HM53dmJiZtYFOLKmTSxRq2ei7L7cviO0EHTT+M7liy646KntO1FVUoJkdv/OXzfbNLNej45fcUFUvtlZJNk4A2n2PgOEkpOQYsceFmYyDNl4XSbv9AwUF5fg0KbN2FJZifzsTORkZbhumnPts8yyOM9u7e8oIOv+8TIX8iItn4uxOt3H7YIBYxXViOWXxOv6S+cR+9EPEPmbv0D4L/8Mkf/6p4j9+K8Re8OOu8etXTE4gBiPvx+8i1jTZfeZaFUtYgcfge/hJ4DiMpfxzn/hNGI9nbaPsLti25IZuGb7UcyOOT5mPjzxISJvvorImVOIMWBwCWYi9tu2jZJyxHLy7Jhu7YfLDYj99fcR+fP/C+Hv/wkif2nT9t6b8PV2wMcA3NIyxGw/i9n+4ALt1te7Lv0xOW6fPYfoKz9F5C/+b0T/4v8ELp6Bzx3XF79wrbC2QyyvCLGCEsQYZNl8GbG330D4Fz9D9LVfwPfJe8DYiM0X2y3WNvH22ZuZT24ThYXwWz3gy7f1FX/RreMYA6srKuHPs3Wxom0XERERkbWJ5xYMLHzooYfw3HPP4YknnsCOHTuQlZXlAguZnYrBhr/61a+uZqvyulBODDZcroiIyOq0XJ3tFdbrDDYfHR3FwMCAy2R77NgxvPLKK/jpT3/qukPu6+tDvp1v80e8L774Ip5//nkcOnQIFRUVSE9Pt9NtnW+L3A8KMBQRkVvCRhuL12Wa122aGnP3D5c9gz69dSPywOG2zSx1vJnPAMHFTEq+pBQEauuRfPBhpDz1OaQefd7K593z5P0PuSxzSbsPIPnQI0g+cBiBkjL4E37VxuCx6MQEQg1nETz5CWJXRuM3mHmDmAFtHCa4gPCFcwidPIZId7fLgmhnwe49h4O6/y3d927vIqfP5jFQUYlAdU28G0bDrnUjvR2IdLQjMjQY764wEU/MbZhwd6eb1khjA8JnjruuooPvv41w42UX3POp6f6U5ab/JvGjXC8ui+DiOHiDnoGaS7ItMuCH3US6LhtvFcd9u9P4O2w8qWnwl1badnIESQeOwM+uDNlNIjNLjQ0jfNGWY3OjC4ZyeDGbwZEzM26b8BeXILB5G5L2HUbKY08j5YlnkPLIU0h56FEk79wLf+U6+DKy4ss8aOuvs93WTQOi7CZTZI1gXckbQaWlpXj66afx5DNHUbGhHm3Tk3jX9o/3mi7jk7ZmnOnqQGN/Py719aB3bMx1J1ycnWPbv43E6lnuM9GFIKJz84hy/2cmNtunkv0B5DObmhmZnsKMC1aMuG6SqwoKXPBeXWkJUpOTMDQ5aePvQ//4eDyroNXRzF7IfYyf4a+cGVzoqjk+RqKYs7qS3TY3D/ZbFeXHtupqd2HSn2J1lg0YtfeD09OYG5/ArA23YNMQZkAk6y7XrvLb55KsBDBjdQMzLIZYv1kbODs3F/XV67DBxllbWoaN5RXYZKUqLx8zNq3stmWtBRh67UkvcyEfvfalGGt/+G279G3YDGzdiVhxmbUbxgAGEr7zJqJvvYboe28B1qZAbw9itl0xcDXWY22H86eB/l7E0tLg27wdPmuX+PYfQqyiymUl9PV2ItbSiGhXp+0z1g6xYwyq1gGZ2QCDDtubED19HLHmy8Byx1DbJn227fk3bAK273HjZUAvTn2M6Ls2bW++atP2G6DpAnyzdhwrKIRv4xb47Tjmy8qBr6jYPrsxHvBo69s3YNNq7aLoB28jdvoEYiMj9voavHzILNZcjnW2XNKzEGMb6uxJxDhfDecA+9tlbOS+ygzDtp+7bJE304bjcrJ6wHU7bcve/SjE9pcYf2hQUGLbSpHrRllERERE2Ezyu3MxZptiNkNmoGJGQ3adXF5e7oJMGhsbXbYqZjZkgCG7Tr506ZILNOzu7sbg4CCGh4ddMAozWTHDFX/YxWxXIiKyurBuZh3NujqxTE9Pu3q8v78f7e3trp5vaGhwmWxZ/7NcvnwZIyMjLoiQwegHDx7E4cOH3TGjtrbWZcbl/WkRuT8C/94sPr/nxsfHXfQxCy9es3HJRia7YhKRG+ONNB6QGdnPEyymB66srERZWZkL+roW3uziTS9+bmhoyB2M+TmezF0rSwXT0/MXYzzYT05OuhuN1dXVKOSv9u0EUT57vJuQbMglbgP89YncO9464D6/dF2IrGqsK+x4FDpzAqFTnyy+aNt0Xj6S9x9GYN36qwF2xKC6SHsbwu2t8Zvm0Yh7P7B+IwJ1G5BSV49ARQUCvGmemo7o0CAigwPu5rI/K9O9HuANdGbqWQyWiE6MI9TUiND7v0GEXQkyCC8p2QWauWAzDsdAmHDQBRGyO0N/fgH82dnwLR4vY/MLCPf1uK51Y73dLrCOXRz7C0uQvHkrAgwS4I1rGxWDVW6IN6yZWWthDuG2JsRmpuM3vd382uspafBnZtqCCsQD9uxEndmMOK+hD99H8GN2s9gT/4zNi7+kHMk7drviz8pC1NoNUTuJjzScQbSn043Hl5GNQPU6pOzZB39OzuKE2LwxAHN0BGFbR25YLg/7/kBlDVIOHnbritMV5fe3tyDSctl10YiALUNbVsnbdtp4q+PLjgFAI8MIXjiPCLMqsQtrN75U+Iut3bJrrwus5HKNjI0h0tMVH87WEbMG+rLzkGTLM3n7zqvL3gUR2fKPdHcgfPLj+LJy85OFpK07bJ53uayXXM9ufM2XELNpsIoSvvwiF4Ca9rnPI4nZh2w4DuNtW7G5ebe8k2rX23LLjk8/g4862hG6eAHhpsuIMIAkEkWyDZOyZZubNi7nQPV6xDjPk+NWJtx0cptksEHKLnvftkORO8G2Fi/UeVkcbgfbcWz3e+V6QWR8P8fqBp5jlFqZnZvDoJ1LtA4P4mJ/L1qGBjBg2zr/jtg0HVhfj5rCImSxvuW+YNPI85Z5q1OCVqfGbJiwPR+fnsZF248a+/swG1zA+qISFNj+lpoSz5CdzO9NS0eRvZZpdc+c1a/bqqqwb30d8qwe5PsRG/eUTc95238v2Lh2VtVgS3klUm2fvmLj77H6btCmrdKOARsqq5Cam8sF6IKFw8EgZq0OZffO41aPMRtjcCHeFX8Ss7Ux45ntvwwsZHbGYCSEfKv/U6yOTrb6IWB1U5pNV4EdY8rz89w0MxBxyL43t7jIXfjkxdDlcHmwrBacdrfMbbl5bUrvtZXEi8veNSCeW/L6D7er4mI7Pt/HNmx0bBQYGYJ/fIybLFBmx6Ode+ArKY0fqzw8fjB4NjfPDhWupo/X8QFbVixpaYiVVsC/fQ/8+w644WO2bfram+Czbd9XZu89+Sz8O+34ZNtS7IodJ+y4yUTCseRUd+z0WVvGfQfbDraf8dEbt6+iCv6NdsxaLnCNy8+my2U+9CchxjYH1yGn2cbvfqjB7dGmL8bpO/wwAjym2r7t2lu23l0GxcUbtG59ZGYhVlLhMv760qwNwvZHzXortfFpHLZ2Vq/NH99fvwG+HXvj82XjYTCfb2wEvpC1C+w4z+BHvx0vedyPDfYjZu05H5cd2xLr6uHnOG15xhovumUFBugxGNPm2Wffy/aOj+06q3OsUoJvXR18ew/Gu6xmW8j2UWYj9LH9kxsPtmQAJb/P/UCFn7f3bI3Axx+T5BW6bJQ+n61D3phgFscNWxBLtWVrdYbvyuK4bNp91v7g9LnhPG7Z2nTYfhxrtPaFtQGZKTVWZcNts2O9tQs43fcSrwmxGynesOE+xRsxmXdpGliv86ZQW1ube/S6qrpWnSciIpKIwWS8x8A2IY9VvC+4bt06O7yubNtTVieuZ94DLioqcuue5wOpdo7Fc1yWoJ2n8d4xgwkZWOgFF/J+Fl/j/SzeE+P5BAMNvXMrnmuoqKioqKyO4tXjrLsnJiZc4WsMHOzo6EBra6sLLG9ubnaxCnyddTp/8MpYBS8gncGFjEXgdUler1JbQeT+88Vu947ECmAFcvz4cVdYYTArQl1dHXJ50V9Ebogn4zxAM20wf9W1YcMGd7DdvXs3Mq7za3kGC/Lgzc/xVwE8gefneEGY++JyeMLGE7kf//jH7qSON8wefvhhbNy40d2Aks823uDmBQBuW6vthumDTMGFsqZZvcEAudnv/xfM/l//r8UXgcC6OmT8w3+GlMeecl1UeqIz01j44D0s/PoXCJ/5xAVvMQtdYOvueAbDhx5BUk2NuwEdutCA4LEPEWlrcoGC/pIyJG3cgrQnnkagvCIeXGLfH7pwHvOv/Ayh4x8i2tUaDy4sr4K/sgY+++5oTweijRfiN5FzC5C0bRdSX3wJKcyGmF/gbuYzA+L88U8QfN2m6+N3XZCbL7cIvtp6pH/560jeucfdkA7k5bkAP47rujhtwSCCDecw9+O/dYGP0YEe912+olIXUJm0eTsCVetcl3yu69GxEUR7uxC+3OAy5bGbXxeQkJ2LpIeeQNozn0fK4SMuUC48NOSCKhf+5vsI2fS6eS4qR8qRx5H5R/8Agcqqq9PBwMFQ4yXMff//h9BHNqwLNshFyqFHkPnH/z2SauvcOmT3y/M//yEWfviXrqtRMCAwvxhpX/8OUhlIYcuKmR+DF84h+NoriFw+b/PUa8vCxpeVY8t1DzJ+/3u2XA+5QL9gSzMWPnoPwZ/8ta2Xdhcw6KuoRdqXvoqMb3zHDeMmMRK275uw7eJtzP3J/47oYL/NTwr8RWVI++q3kfHt33dBGKHODhvf+wi+8iP7blufflufVfVunjO+/i34CwsRsuU9+1d/jsjFs65LY6RmIHn3fqR+6etI2bsf7BozbPMZfOcNhE587LYzrksGZqY+9RySt+1AoMDmMxREqK0N82+97gJXo22NLkjJV1GDpH0PIfO7f4DkTVvc9IvcLu/miRdkeDt4Qc4LJrvZC3RzVt/w3OPChQvu18T8pXFLUxPGh0eQYp/vs/qlwOrlf/rU53CwfgOK7bw+wMgpMzu/gHbbh+YWgihkXRSNoPfKKH5zsQGdoyPIsn1s37r12LuuFhvKypHKgCDE3PD9Vq+027jbR4dxoK7OjTvJH3BdJM9bfcmsiT8/dRyvnj+L3zvyKL5g+2xWWhrahgZxqbcXw1OT2Gp128EtW5FRVOQCqCLj465L5mmbp3Grn9iV8tj0lBsnAxQ3V1QgjXWZTf7Y9AwaurvQMTKEqYV57Fxfj41V1ci3+iudQewcyB5Pt7bgrfNn8GFPFw4dfRr//J//cxfksxQvwfA8joXP7+Mlmau8NmVi5sK7gReTvWtAvDHI6z979uzBtm3b3PfeL9GRYcT6el3mPvDHBnbM923dDh9/JLB037D9j8HjUTu2YHQIPgaTz9kxgceW9EzEmBHQ2hq+KmuTMBve0ADQ1RbPPMhAvO277Lhb4jJ7MsguNtAP38wUYpn2Xmm5tR/q4sF+nKa2FvuOYdv5ZoCMTBdU59+81T1fFren6Wlrv3QBtr+57o6ZiTfMrHw2fQy2y8l12Rd9tk/4GVTHdc1t0PaDaEebTU+fzZO1I5iJmMGA1pZg0K2Pmf4YUFnCbqIrAZtX8McCLXacYxvF2k++rTvcPDJbcdQtT2u/TNg02DiY6c9nx0zbyNyyjjKQcHY6HgDIz/JHBjyu2z6EKft+/tDD2jxumdixnD+QYDAjOqy9ZvuOr9TmYdc+FwTJZRvt7kSs377T9veYjdNXXeu6L3Y/xOCy7OL6smUZnI/Xd7aOYxm2zCdG3WtuPPWb3XzFrM5x28L0pM2PTbvt877qGhfYuBQDKV3309Y+8MHatocfg++JZxHYtccFW95LvCb0zjvvuO6ltm7dim9/+9soKbFt+C7gcYg3g15//XX3WF9fj+9973su0FBERORGmLyA9xjYJmR7mfcFH3vMjqFL213ywOO5ELcHnhvw3hMDB3mO29PT47JbMdiE577EHzLwPIWF9754HsvnDFa81j0tERG5Pxgszvp76XVLnksy3oD1Pe8n81oQz1uZBIntgGw7j2aCI8YK8UepfE8xCCKriwIMRdYwBRjKasKGIhuH3L7uNKuOXB8b2ryAwsa3ggtlzWIdcQsBhrwRH7Jj18IH7yL465+6gDp3wzsnH4G6TUjaewBJ62rjgWxnTyNy6Tyi/d0u+12gfguSjzyO9M+/iEBVtbvhz4x/C++/jfkf/Y0bDqEF101gErvM3f8Q/Hl5LpAs9NovXPfDzFDkLy5F8uPPuKC5lN17XabB6PQ0gufPYuHXP0fo3dfjGfBSmemwEEmHH0FS/Sb48gqQsm0bkmrWgRn2bsjqznB/HxaO2fe/+ybCZ4/Hs/Mx0w+zGxWWws8b7kXF8SxBo0OIDvYBvKnOIAJbtpwXvy3L1OdfQsohmyebb2YoWukAQzecTcP8yz/C3F99H1EGUth0IjXddU2dtHsfAkUlbhhm/WOmweiIDcMLxLx5cD8CDBkQVLsZyQ8/iYyvfh1JFZUI93Rh7le/cN1KRy6ecfPqr6ix7eYJpH3ueQSqaxGdnHDby8Lrtk3YsmGQib/EtglbdslbdiBQWuqyPnLdhU6fROTCGZumXreMAjsPuG0n/eizbl2I3In7FWDIdp3X1mPGKmbKYrdV7W3tVv1M4PSZM/BNTOEfPfoU9q+vQ1F2DtJs3AHb56bn59DU34+RyUmkWL0zHw5hxOosZj9k1sMsdlvu86Mgk9kA85HOACH7TnZzPDk37zIfplkdtqOmBpvLK1zgYigcwYTV+Rd7uvFawzmctvriDx55HJ/ftdc+n4zLfb1o7GV2xCA2VVRgd109Mqxu9/H4Y+c2k3OzmF0IYtrq+I6RYbTYcaF3dMQFIzLYsaKgANnp6QhHoi4QkVkSP2ppcl0hbyyrQKmNKzc9AylpqfDb49nWFrx5+gTebL6Mg0efxr/5N/9m2eAeLsfVEmDI9c7CtuS9aFOu1gBDHjcZeMp6nXW2O97Z8lguoMy5OnzYtWXixzR/PMsfz43ZXa73WY6TGfmujtfOt90ytr+973TdctsWz8/ws9wfmUUzZOO37ZNZDl1woI3fPV5vHXHaOE433oj7rPse4zIk83yfbRH3XQnzx+njdLKLZntk5l6XFfjq9QF7n/hZZkPkjwRs/q1CsBcXp53dj/P1T02DPee8ecuF88bX+Tm2F/hZjo/vcRoY2MjPcDzuM/Ye59cbp7cs3ffZ/HB8btoXl+XVcdpnuQ753uLnXMAnn9v4XFuC38nlzGH4fXzNLfvF7+K4vOlg8dh7bJe5LMXdXYj88mfAmRNAehp8L7wE/9Ofh7+sPL6s7yEFGIqIyFqhAENJ5N1T4KN3P4E/ZuM9qLGxMZf9ygs0ZMAKi3ceQ3zk57zzKxERuf9YH7N+9q41EeMPvOe8BsQ4BsYE1dTUuK6POTxfY6+LHJbXp1i8+l5EVgcFGIqsYQowlNWGhxQvkyHL7d70lutjo5r7ndcgVwNb1iReNLT64mYDDDl8dG4unmnuB3/mgrdik+zK0E4yU9PgYzYf3jiPxrvshQ3rAhAzc5D63BeR+pVvIpnZc9LTXZa9ubd+g+DbryPCbIh2jONNcX9ZFdK+9DWkPfuC6zJw/o1XMfdXf47YUK8NM+Vu6vtKKpD6xDPI+KN/CH9unrtZHepow8LLP8bCr34az6zobkYHXFfJvoJiBDZuQ/qLX0bKoYfgu8ljJscbHRvD/G9ec9MRa7uMGDPpcHcPJMdvgifbuHgDnDfVvRvyi/xbdiH58GM2L88huW5D/Ea61RUrHmDIQW1a51/9Jeb+5i8Q6WwB5mfddyEjC36uF84zgxzY3agvCTFmYpqxeaH7EmAYgH/9YoDhV77mAv5cACSH+8WPMf+jH8SXpW1PgdoNSP/WHyDlkSfduOZ+bu//wtZzN7vqtnmwaWG2SzeNDHTgds2sVLPTLrukWzc2/6lf/DrSXvqWCzJ1mSxF7sD9CjBMxGnguQh/ccxHdmXy/e9/H2ffeQ/f3LUfu6vXoTg7G9mZmUizusplG7S2ITMFNti5xKTVEww+ZLAeg/RGJ6dxyurSs90daB8Zcu8x4JBty5rCYhfw9/iWrSjLz0eGtX9isajLbtg3dgVvXWrAR61NGJ+bxfcefwrPbN9lVbAfLf39LvMgMydurqjCI5u3ID8nG0msy2y5hbm/RmOum+WFcMhlPHzn0gWbtyhKc/LwyKbNqC4sgt/GFbN/07NzGJ6cwgLrW8OgSHaZnJ1tdZ3t16esbn3j1HG83daMw88cxb/+1/961QcY3us25aoNMLwTXH/cD7nsuG2tNG/8HPftrJ/EOoKfv5lx8DtZbnb4tcKbL7qDdcXje6S5CdEL54CGM/AxWzF/5GHtj8A3vgv/058DMx/f62WnAEMREVkrFGAoN8JzJQYSevcZeN7J+2C8P8Ufuy3Fe1d8n8OLiMjqwOuNPEfMyspyx/jCwkIXPMhrUV6GQl6PYjf5LMT3eH1IbQKR1UsBhiJrGE+0FGAoq433a0Oe0Hu/PuTj/bx5+qBgo9prYHNf5SP/FlmTrG64pQBDYn3CbmpPnUDo5CcInTuN2HC/C/771E1jnoCmpIFdCift3Iu0J4+6LpQZ7MZAslBrM+Zf/jFCxz5AjNn0rI7yl1Qg5fGjSH36OSTv3A1fWhpCFxsw/947CP3mV667ZTfe5FQk7diD1C99Eym7diOpogKR0RHXJe7Cr36GSKsNxwA6bzqy85C0dTfS2V3wo49fDY67IZsXlw2vvQ3BC+cRvnAWkaZLrttDBq+5rEFeNiFiUCMDIAuLkbR+g8vomLznIJLXr3eBkJ5lAwyLy235XCfA8M/+i8vs527GLxNgyODBUNNlLLz/LoLvv41IR4s1HLhOFoMa2E7IykWgpg7Jew8i0nwxHrDIaEkXYGjtlusGGGYvBhh+5doBhv/n/47oQL+tHwYYliPtq99CxreWBhj+GJHGBpuP3w0w5LbI7Jfzr/0Ss//tvwKDtl3ZcvYVFCH5iWeR+pSV/QcQbrX1ceak2/4izbY+RoasYbXglpdbB952yKxPtr35yyoR2LzNtucnkXrgIfh4QUXtJrlDqyHAcCne5PmTP/kTvPmzn2NXQTEeqt2AndU1SE9NccGCzFrIdsvE/Cx6bNgFqzfYlqkpLkaG7beT0zNoHxpE2+AAusdGEbDpYdfD/ExZTh7qS0qxsbwcqTYuzn/Q6sfJ2Tm02L76t8c+wsDEOLaUV+ArBw9jb63Ve/bZ3tExnLI69NWGs9haUYkX9+xHUXY2Um0fTE5OshooPs9sozLIcHhiwmU9/MDqs77xK/jWoYexx8aVnZWJpNRUt6xD8wuYs2UfsmXPc6D0zAykpqfbfh3Am6dP4hcnjqE7OI/HP/8c/viP/9hdPF2K33e/AwzvV5vygQwwlM+cmNUVYWsfxo5/CJ+1aVxX1EUlwOHHEHj8afi3bHM/ZrjXFGAoIiJrhQIM5Vbxftb09LTLZMjHpXhuxfd0/0FEZPXgdZ7MzEx33YnXnBi3wMLjPbu997q+F5G1RQGGImuYAgxlNfMCC1l4A9z7BaFO9G8fG9sMAuA+txLBACL3FQMMGSTzdz/A3N/++eKLtp1XVCP923+EFAZjLXcsWwzGYJDh/FtvINp8EbHhAcTYZV44Eh+G2QOz8xCo34yUo59H8rbtSCoqdgFy4YF+16Vx8LWXEW25bONjcI4f/vpNSPvad5G8fScC2Tlu2OjsrMuKN/sX/zfCZ0+4QDr7gMv459+2F2lPH0Xqnn2IsZuW8+ew8N5bCJ85htjYsAta5M1lX3YuAhu2Iu2FLyPl4OHbCi5jUGSwsRGhTz5w08Hxu26b7XjusC5gnZCVg0DdZiQdeQwpO3YhuaYm/n6CyNgoQgxa/Pnf2biO2TTa5/KLkbT3EDK+/i0ESsviA3I5M2NkewsWbB2FTtuwDKBLz0by7v3I+L2/h0DVb8fPYMhIbzfmfvFzN2xsdNCmj1m+bNp4saCkEkl7DiDt+S8g9OHbmP9rW+eswmx8gY1bkf7lryFl1x4XjMiAwOCp4wi9buuorxtIy4SvtBKpti7TX/ji1WXI7Sc6NYngiU9cwKQL9ktKcfOT9tyLSPvCSy6DULi3B8HTJxF869eIukBRP3zVdUjefwTpn3sOAXZjuGjh5AnMvf4rxJou2HK2eUhNt2HrkXL4UfvuL8Bv22T0yhjmP3gPweMfI9p6GbGpCVuwti4idnzjuuCFkZRU+AtLENi6CylPPeMCPQN5+fH3Re7QagwwnJiYwN/93d/hlZ/8BLN9A3ikth7P7djjggszUlNdYVBfwPYPH/dBdonKID/Wk/b5mM1HlO3FUNiqjpDLLuiz+pnZBpNsGAYMEgP7mAlxanYOvbYvnu7qwH879iFybd/87kOPuiyFlS7AxYeJ6Vmc6mjF961urswvwBes7qouKHTdHmewq1kbJ8caicbs8BFG0L57wb77T95+Ha9fPIc/fOQJPLV1B6pLS5GRmxPPqBhkvWb7OpuznCY7d3IZEGdn8MMP38fPra7Jr63B577wIr7xjW8se22Dx7D7HWDINiXbkzz34+O9alMqwFAeBDGre8Kv/hLRk58gMNjDliF823bB9+JX4V9fB19m5n053ivAUERE1goFGIqIiIiIrE0KMBRZwxRgKKuZd8OUJTHAUBkNb48XXMhyr7LMiNx1rA/6ehDp6Vp8wRqn6ekIVNbAn19w7ewvVn9Ex8ddNsPYzCRi09OITk+5ro5ZtwSys11gH8fhLymFPzsHPh7ffD4XDBidnEB0aMA+N7U4QnsrM8u+t9o+Z8PyuMZhGSS4MI9IVweiE+Pue92wqWkAAweLi+PZARkYY+OMDA0iatPEALTozLR9bzb8OZyOQgTKK+LzdBsXzDkdsakpG++o696Z0xTl3zZN7N7Tn5riptufmenm21dYhIB9L5flUq7r5VlbTv298Xmy6fElp8KXl2/zXxmfN499b9SWKYP83Pdy0gNJNs82bFXNp8dvyyA2P4fIwACiYyNu2XKZcJlx/t305dvnSssRHR3+7Tq38fmysm35VNp47RzApscFdtr3xbiObJwuUDM1Hf7iUgTKSm2YxfrPxs3Mg9FxW97M7GjtIvhtIm1+GCjJ7+Jn2bV2zOY1OjwYz/7IQIB0W1acZ9s+mK3S47Yr+15wewra+AL++LA2D0llZW563TIctXVh64NdYnPbYxAoMyD6UpJtfBk2v7bubZvisvIVl7jARLcNiqwAtqN4HnCnAYbeDxZWIsCQ3Vd1dnbinbfewg/+7M9RHPPhyY1bUGf7bYnVR+kMZLP9kSWFj8lJ8cA21rcMNHT1rt/NTzRixwbOW8j2tYWgza89t9cYdMgAwxnbNwdtX/2gpQnvNV3CjNWJD2/cjO8ceQR1tk9npKS6+D9mOey2ffWNC+fQZucxmbavP7ZpC9YVFiPD6k22pWwPd8Py2BHhd1v5q4/fx6/PncaWiko8tnkbHtm0FaWFBUi1/djPem+xDcagyIhNyxWbltaebvzdJx/hk94uF1z4/Isv4qGHHlr2nIzfxeXFdXi7689zO+3ppW1KF2h5h+v/ZjHA8MSJE+4aEM8tFWAoa5K1JSOdHYhZG5YZm31sc5WUwWd1BrMXW+W2OOC9pQBDERFZKxRgKCIiIiKyNinAUGQNU4ChrBW8AcEbqO7mrT3nNuj9LTfm3Qjmvsai4EKRBFaPMHses+1F52btb9tnMjNc5rp7HtDFeo312+xsPNgsM9MFo3jBjSvGvofzyiBDzr+P3QIzuJDfsxoCNLgcGIRnbQdmiPTn5LhpXNFlsFowCGp+3m1/LFZZu4BFF1Boz71AJJGVxDbUSrSlGNDllTu9mcfpYMDjpUuX8IMf/ACtZ84hMjqGdQVF2FBahg0lZSjMykJ6SqrrojgpKYAkv9VXDOLluURS4v5i9TrbjjaPETvfCTGzoI17zp53jo2gbWjQZS+83N+Hwclx7K1Zjye3bMNDGzchh3WuzQsXS9Tqn2nbP1vtHOZMZzs6RoaRnZ6Gapum2sJim55sZKXGMxkSMxGGwiH8+OQx/OrsKaTYPrx/PTMx7sbWyirk5eUhicFDnE77COd3dnoKLT3dePPsGZzu70EoOxP/3T/4B3jyySdRWVnp2m/L4frj5+9k/XH9e+Vmx+Otb57z8fFetynZbRnPPz/++GN3Trl+/XoFGMraw32Ox32rXxAJA/xBAa+/3Od2jgIMRURkrVCAoYiIiIjI2qQ7XiIictfx5mVilhwvY4p3Y5MXkLwin+YtOwUXilwD6w6rU/xZWQgwc19Rkcsc54K77jXWZykpLqAuUFLiMhiueHAh2ff40zNsXotd8eflwZea6l5fFbgcbHo4Xcxa+MAGFxKPY8xqxuyMZWXx7Y8BlVzvqq/lLmFbgAFiqbafpaWl3XbxgsxWov3FcbCdUlNT47oG3vvIwxj0RfFWRyt+deEcjtvjxb4eNPb34lJfLy70dON8dxcudXejqasLLR3taG1vQ0tbK5paW3HJygX7+6K9f9mG5+caervx9qUGvHzmJD5qbUIwHMLemlo8u30ndlRWu0A7dp08OTOLydlZ95zdHjOIsDwv33XX/H7jRbx5/jQud3eg1cbZNjiAtqEBdAwPosfK4OgIYsE5ZDPWLTiPLnvvo5YmHLfpOW/TdqnpMhqbGnHp0mWcbWjAJxcu4L1LF/FOSyOi2Zk4cuSI+7FXRUWFWx7XwvfudP3x8xzPzaw/DpPYHr8fwYXE78+xOjKTAfiqI2Wtsm2XWZz9ebnwFxTGM5s+qO0cERERERERERGRRYF/bxaf33PsHqevr88VXuDmr9fz8/PdxXIRuTH+anx2dha9vb0uuyB/Lc5MGWVlZdfMlkG8+XblyhX3uaGhIZeNg58rLy93++JymGWDWQyZlYS/MiwtLUV1dTUKCwt1c0huiDc1vULczniDkY/eDc6buTn6WePdCGYAAB+1r4lcA+sP1iNWn7DwuXvtfmBd503L3ZyO5b7nfs3zcrzpu5vLYDVYXO5X18XdXu8iixLbVndaVgrHxfYKeyQor6xA3YYNuDI1iY7+PjT19uAMezDoaMUnbc34pKUFJzvbcKmvB82D/WgdHEDLQD8u9nThdGc7Pmy6jPesvN/ciI9bmvBxWwtO2Gcv2HiCkTAeqtuIRzdtxf7aOtQWlyAzLZVTgGgstpjVL4ZwNIKp+TmX8fCYff5UR5udz0yhIj0VdRlpaBros2lox/muDjT2dWPsyggywvMoRAwbsjNRlZWJ2YV5nOnrw5meTly072YwYlN/r81LG35z8Tzet2lsHh2GPzsLTz37LL729a+7rPIMoLvesk1c/rdTiFkLOa88J7xRBsPV0qbktE5MTLjshSMjI+48lOeuzFyjdq6sKdwPuc2uomM+rwmxq/rp6Wm3T+3YscPVRXcD65zR0VG0tbW5R16LYo8Y6Qy2FBERuQH2ysR7DLwvyGMV7wuyhyWvnSsiIiIiIquTruCKiMg9xZuHXoBhYkZDr/DGJx+94MPPagCidyOYy+J+3ggWERERuVlss2RnZ7sub1944QV86ctfxjPPP4/tjzyMmgN7kbtpA4IFeeiNLODcYD8+6WzD+y1NeLf5spVL7vG9lkYc6+7ApbER9IUX0Lkwi6YrI2js70Gy/b0xOxM5SX7ML8yhY3QEH9hnfn3+rJUzVk7j1w1n8KqVNy6cxUeNF9E30APMTKA8CThYXITd+fn23Iccm4a0hWmkLswgdX7Gns8gIzSH9VnpOFJRjofKSrErPw8VydYGnZ1Ct33/J/Zd7zdewgc2zeeGBhDOy8HWwwfxwpe+iGeffdZlL2SA5WrB9nNim9JrV68G3o/l5tnN/E128Swi18YfhXKf4vk0A/10/igiIiIiIiIiIitJGQxF1jBlMJS1zrvpycJtLzGgLvG9xOBCPl+uPEg4z1wOXrDlgzZ/IiIi8mBj24XtmLq6Otdt8DPPPOMC8A4cOODOOZLtvQU7vwja+cxcOIRZe5yO2KOdp4T9PmTb+cn6DfXYs3evO+9IYVtxbhZHSoqxtzAfFwf68EFri8twyEyHHzEosa0Jx1qb8YmV41bOdLZicGQQ1cl+7M7PwZGyEuwpLkZtTjZy01JRzUBIO3/aVVSInVY22HcWZaQjLz0dGex+GD6UpKdhS0E+6nJz4EcMHePjGLfzr7lYFOVVVfjWd76Nf/iP/hGeeuopdz2D83yv2m03k8GQbUq2q702Jf++n8LhsDuX7OrqcteBOP1FRUWua+1rnYeKyM1pbW1FQ0ODC/Kur6935W5lFFQGQxERuRPKYCgiIiIisjYpwFBkDVOAoTwoeAFpaeF2xcJtkoXbNIsXhOg994ZLHHY1FE7P7eBnvfm7k/GIiIiI3E9sz7E9w4CTjIwMd/MwKyvLdd3JboQZjPLoo4/iyaeewtNHn8ZTTz+No88cxdGjR/G0PX/88cexb98+d54SCYfR2NSE1GgEZZkZ2FxYgL2lJdhUkI+F0AKKU1PwmJ0D7S0qxK7CfOy011l2WNmUn4fq3ByU5+aiICsTuTYtWTZNfCywacpzJQPZ6WlIS06xKY8haN8XsXOmtJRk95mSbJvu9AyU2ufyUlNRVFKMHQcO4ZEnnnDdkObk5Lh55TzfK9cLMOR0sB3pBReuljYlp4vTzGtA7CaZ01dRUYHa2lo3jSJy+9g9cmNjo6trWW8ywPtuXV9VgKGIiNwJBRiKiIiIiKxNiloQEZFVJ/GmaGJAIW+QpqamXi3828vIstoKp/9WL4zxxq83z6vlRrCIiIjInWKbiO0aBuIxuPCRRx7BSy+9hO985zv4gz/4A/zhH/4h/t7f+3vukX9/61vfcl0sP/zww67s2b8f+eUVGIpE0To5hby0dDxcVYkvbazHw5UV2F1chD1WnqiqwPPravDC+nX4wvr1eHrdOmwsLEBxVhZyFgMLM9PTkJX225Jt48q21/lelr2XwnacTW9qShIyrb2ZY68XZ2ZiY34+DpeVojonC5UlJdi/b6+7Gco26Wq7Geq1pdkmZbtytbQpOU28iazuW0VERERERERERETWFl3RFRGRNYs3Jr2bp6ut3OpNUw6fGFCpm64iIiLyWcc2UUlJCXbu3InHHn8caeXlODU2jlc7OnGifwAzoTCqcnJc++nM8Ag6JycxF464ALuM9DQUZGchNzMTqTYeZvoLBkNYWAhhLhh0ZdY9LmDeHsP2PsME01NSkJeVhaLsHGSnZ7iLJtOzcxienkbfzDSapmYwYt9Xs2EDSktLXFt0tfACObnc+CMcLpfVGPzoFWZBW5p9UURuDfch9jjBOo68c2QREREREREREZGVpOgFERFZ07wblLyRsprKrfCCJFfrjWARERGR+4FtInbxyS7TmNHwyBNPIteeN0xO4SdNzfjLhgt4r7sHpweH8fHAAF7v6sGxwUFMh8Lwu/ahD8FQCNPz85icm8PU3KwVe5yNFwYOen9PzsxiwsqMDcsukqOxmBuHTQVC0ShaJybwencvoiUl2Lx7Dyqrq12Xz6up3cY2qPdjFbYvb7VNei9weXldZs/Zsp+dnVWQocgdYDeTvb29GBkZcftXdna2q5tW4/4vIiIiIiIiIiJrl642iYiI3EfejWCv6EaQiIiIyKcVFBTg8OHD+PznP4+nnn0W5Vu3YSAtA2+PXsFbvX04NTiElskpnBm7gmPDozg7MoILVhpHRjE4NYmJ2RnMLSwgFIkgEotezZzHIMJolCXqMhgysHAhxBLCrA0/MTeHgelpNI+P46KN/3IkhvV79rpsisXFxa7tthowqIhtSO9HK6s1uJA4bfn5+SgtLXUZ16amplyAlJd9TURuDfefgYEBjI6Ouv2e+1ZZWZmrC0RERERERERERFaKohhERETuE94A4o0fr6ymDDgiIiIiqwXbTOnp6di3bx/+8A//EP/63/5b/P1//I9x6LHHkJKbh/loDKnJqSiw5+M2/A8am/GfT57BDxouonnsChYiURcMmJ2R4bpNLsxhyf5UKcrNQXFuLvKzMl03yfOhEFpGR/BGWzv+/MIlnAuGsOfIETx59Cj279/vsoStFmxDcv7WQjZsBhgy+GnDhg0uyxozGA4PD2N+fn5xCBG5FQyWZoAuA6V5Trl+/XpXUlNTF4cQERERERERERG5cwowFBERuQ+84ELeBOaNVgUXioiIiFwb2065ubmora11gYbPPPMMXvrqV/GVr30N+w8eRE5ONnatq8XTO/dg/+btSMrMRsPYOH7V0Y2/a27Bj5pa8LPmVrzb1YOm0TGMTM+47pCvlrl5TM3PocXee6ezGz9vacVPbfiP7O9YRSX2PP4EvvzSS9i5c6fLwLdashd6WQsZXOhlLlzN7UpOGwOf2EUyp3d8fBytra2YnJxcHEJEbgUzGPb19bkuksnrgpx1gYiIiIiIiIiIyErR1SYREZF7iDdVE4MLWXTzR0REROTmsO2Uk5ODjRs34ujTT+P3fv/38dQzR1FUVIyqwmI8vHELfu/hx/DEtp3IyytE48w8XunswQ8uN+EvL13Gjxub8UZLGz7q6sbp3j5XTvX2W+nDyZ5evNnajp82t+C1gSGcDoYQLCnFQ08/hS986Ut4+uhRVFZW3vcAPn6/16bk8vDalWuhTcnpZmAhAyIZaMgukpubmxVgKHKLmLkwGAy6IN2uri5MTEy44ELWByIiIiIiIiIiIitNEQ0iIiL3kBdcuJZuBIuIiIisJgxSYzsqKzsb9fX12LVrF6rr1qNhaAAnOtpQmpuHr+w/hH/23Iv4vUefwJaqdVjw+TGdnIKm+Xn8rL0Df3rxEv7f5xrw/3Hl/OJjA35i7zXNL6Bix0589fd+H//kn/9zfOOb38LevXtdl7783tUgsU25lrJhczq5HEtKSlBeXu6mn10kz83NuYApEbk57BaZ+05nZ6cL1GVm1S1btrhMryIiIiIiIiIiIitNUQ0iIiL3SGKWGa8LOxERERG5PWxPMVht06ZN2H/4ECZ9MTT297lAtfXFJXh44yY8sWUb6kvLUFhUiEeefBLPvPQVPPTFL2Hr0WdQ88ijKD/8EMoOHUblkSNY/9jjOPD88/jcV7+KF156CS984Qt40j7DoJ3CwsJV1XbzMhiutTYlp5ttYWahZJAh19/s7CyGhoYwOjqKcDi8OKSIXA/rOe47zFzI/YbZC1lPMTOoiIiIiIiIiIjISlNkg4iIyD3Am7+8mcoAQ2UuFBEREVkZDFirqqrCww8/jLziYozPzmB4agKzoSBSre1VlpeH4pwc1FRW4atf/Sr+1b/6V/iP//E/4n/93/43/Nv/6X/Cv/of/gf8i3/5L93jv/uf/2f8h//wH/Dv/t2/w9e+9jXs3LnTZQVbLVkLHyTsIrmsrAwFBQUuExu7eG1vb8f8/PziECJyLdFoFAsLC25/YXAhzzPT0tJc4XmniIiIiIiIiIjISlN0g4iIyF3GYEIvcyEf10oXdiIiIiJrQWZmJkrLmKWwCNOxKD5saUbnyDD81uZikGFlfgFqC4sQWVhwwzOwbd26ddi4cSO2b9/uAgm3bt3quluurq52mfWys7NdJjAG66jttvKYbY2ZJ2tqalwmNi/AkEFTInJ97FK8o6MDFy9exMDAACorK7F7925Xj6mLZBERERERERERuRsUYCgiInKX8Ga0MheKiIiI3F1sY7HLXQYM5peW4tJAH/rHr7j3kv0BVBUUYmNpGaLzC5idmXHtMwYPsnteBuPk5eW5Rwa9sd3G9pqCCu8uLueioiKXfZIBnczENjg4iJGRERc8xaBDEfldXtfILS0trvA5g6O3bduGiooKpKenLw4pIiIiIiIiIiKychTlICIichfwpjSLMheKiIiI3H0MDty/fz927tmDyVAI0/PxTHgBvx9luXnYVFaOvKwsBKxNJqtHcXGxy7zGYMOpqSkXMDU0NOS6TRaR38UukScnJ13WzytXrrhAaQZXM4uhfswmIiIiIiIiIiJ3i648iYiI3AXMjJOSkuKCC/lcN3tERERE7h62u+rq6rC+br1re0VjUfc6f+CRkZqCotw8VFVWoiA/370uqwMDC/fs2eO6rGZmtra2Nhc4NTExoSBDkWUMDw+77pEZkMvMq7W1tSgsLHRZWfWDNhERERERERERuVsU7SAiInIXMKCQN7cVXCgiIiJy9zFbdEFBASrLy1FSWIiMtDRgMdYmGrO2WbK9b6/n5OYoCGcVyc7ORk1NjQsO5fobHR11wVP9/f0IBoOLQ4nIwsKC2z9aW1vR3Nzs9g92ibxp0yaXxVBERERERERERORuUsSDiIjIXeAFGOoGtoiIiMjdxzZXWloaSouLsbV+A4oKi6xBFkDU78NcLILpaIQpptVF8irD9cbMa+vXr3dBhmxD9/b2or29HfPz8y6rochnHfcDZvW8ePEizp0757oSD4VCrltkBhhmZmYuDikiIiIiIiIiInJ3KMBQRERERERERNY0Bqrxxx0V1TV4/MUXsOHJx5G6Zwcy9uxE2ZHDqD14AFkFefD7dBlktWFQYXFxMerr612goddV8qlTp1ywYTQa7+5a5LOIXYVPT0+7fYHBhX19fUhOTsa2bdtccCGzgLLuExERERERERERuZt0ZV1EbpqXFYS/jucFbd4IUnY2ERERERFZDXh+UlRRjgNHn8K6x44gdfd2pO3bhYpHHkL94YPIyi/Q+csqxPWWm5uLdevWYevWrVe7SmaAIbuDnZ2ddUFWIp9FzFQ4NDTkgm6ZuZCZPcvKyrBv3z4XkMtrMyIiIiIiIiIiInebAgxF5KYlJSVhy5Yt2LNnD8rLy5Genr74joiIiIiIyCrBIEIW/iBqsbi/ZVVjkOH27dtx4MAB110yAwsZYHj58mVMTk4qk6F8pjCTJwNrue0zc+GFCxfcawzE3b17N6qrq5GVlaWgaRERERERERERuScUYCgiN4W/imfXOxs3bsSuXbvcDZ+cnByXbUJERERERGRV8YIMvSKrGoOkUlNTXVfJ/FEbMxny/HN4eNgFVnV0dGB8fFxBhvKZwEBCdovc39+P5uZml71wbm7OBRXu3LkTO3bsQFFREVJSUty+IyIiIiIiIiIicrcpMkhEbooXYMjMheyGhxe2+bcuZouIiIiIiMhKYNb80tJSbN682ZWMjAzXPeylS5dc97DsOjkYDC4OLfLgYdZCZu/s6enB+fPnXeHfvBazf/9+F1xYU1ODtLS0xU+IiIiIiIiIiIjcfQowFJGbwkDCQCDgfiHPzBJ85N8iIiIiIiIiK4HnnfxxW1lZGR599FEcOnTIBRz29vbi+PHjOHnyJK5cubI4tMiDh8GEAwMDOH36NE6cOOGyGBYUFLjMhew+vKqqyl2TUW8SIiIiIiIiIiJyL+lqlIiIiIiIiIiIrBrMzsas+du3b3cZ2woLCzE1NYWLFy+6oKtz5865DG8MxmJ3siJr3cLCAgYHB9HU1IRTp06htbUVMzMzritk7gcsDLxNT09f/ISIiIiIiIiIiMi9owBDERERERERERFZNbwM+szWtm/fPuzZs8cFGTKT4WuvvYZXXnkFZ8+exdjYmOtSNhqNKtBQ1iRutywMoG1sbMRHH32E9957z23bDC5kgC27Rq6vr3fZPblviIiIiIiIiIiI3GsKMBQRERERERERkVWHwVSZmZnYsmWLC7JiFresrCyMj4/j8uXLLpPh+fPn0dDQgOHh4avBWiKrmbedelkLz5w5gw8++MB1i8zukdkFMrf3p59++mpwLQNuRURERERERERE7hcFGIqIiIiIiIiIyKqUkpKC8vJybN26FXv37sXmzZtRUFCAYDCIjo4Ol8mQgYZ8zqxv7FY2FAop0FBWpXA47Lr2HhoaQltbm+v2mwGGly5dwsTEBPLz813WwgMHDrjgwpqaGhdkq8yFIiIiIiIiIiJyPynAUEREREREREREVi0GVzGL265du/DEE0/gqaeecs8ZRNjZ2YmWlhZcuHDBZTJk0BazwDFYa25uDvPz8y5THIMO2Z2yyL3AbrsZTMjC7Y+Br5OTky7TJrfZEydO4N1338Xx48fR1dWFpKQkl6Hz2Wefxde//nVs27YNOTk5ylwoIiIiIiIiIiKrggIMRURERERERERk1WKAIbuNZebC+vp6F1zI7G58zsBDZjlkUOGpU6fw0Ucf4Z133sF7773ngg4ZcMjCrminp6cVZCj3BANbr1y54rJqMqDw5MmTbrt8++238eGHH7quvXt6elwAYkVFhdueDx486IIMq6qqkJubi+TkZGUuFBERERERERGRVcEXu499xrD7Gv5Sl4UXg59++mnU1dW5i2gicmP8FTx//X7s2DHXLdSGDRvcBendu3cjIyNjcajfxcwNvMHCbqSY6aG0tBQ7d+50n+O+KCIiIiIiIrIaMTMcL2UxgKu9vR3d3d0YGRlBb2+vOz9mQBbf42NlZSWys7Ph9/uRl5fnCv9m4BZf4/mvArjkTnGbZJfd3iVWPjKYdWpqyj3n9slgQu9vbn/MWMhtkd1/r1+/3nWFzGszaWlpqz5rIYN0Gxsb8frrr7tHBvp+73vfcwHAIiIiN8KMvj/+8Y/dfcHi4mJ3X/Cxxx5Tm0xEREREZJVTgKHIGnYnAYa8EcMbMOw2ihev+Qt5/lJeAYYiIiIiIiKy2vFyFs+JvXL58mX3QzpmhGPQIQtvVHvBWgzoYmFgIV9PT0932Q/5msidYFfczFTIay0eBh0yEI/bKbdJPrLLYwZSlJSUuFJWVuYKX2eGzrWSsVABhiIicicUYCgiIiIisjYpwFBkDbvdAENeDGZXPTMzM+4COKuBzMxMd4FbN1dERERERERkLWEw19DQEEZHR91zPrJLZN7AZtY4nvvyvJfn0AwG8wK/RFbS0sAIXutkVkJen/GeFxUVuYBCZi/kY1ZW1pq7DqMAQxERuRMKMBQRERERWZsUYCiyht1ugKGIiIiIiIjIg8w7X2agIR95+YuBhuyulj+043O+nph1TuR28AebDJBgBsJEDB7ctm2bC7zjdU8GF/JazVoPoFCAoYiI3AkFGIqIiIiIrE0KMBRZwxRgKCIiIiIiIvK7mMmQ58zz8/MIBoPuNXZVy+d8j8/5/n28LCYPCHbDzS6O2f12ImYmZKZCXvPke3x8EHqNUIChiIjcCQUYioiIiIisTQowFFnDFGAoIiIiIiIiIiL3igIMRUTkTijAUERERERkbfr0T2tFRERERERERERERERERERERERERIwCDEVERERERERERERERERERERERETkdyjAUERERERERERERERERERERERERER+x6oJMIzFYohGo4hEIgiHwyoqKjdRuL+wcN8RERERERERERERERERERERERFZSb4YI/vuk46ODhw/ftwVTsa+fftQVFSElJSUxSFE5HqCwSCGh4fR0tKCgYEBbNmyBQcPHsTu3buRkZGxOJSIiIiIiIiIiMid4w9dGxsb8frrr7vH+vp6fO9730NBQcHiECIiItc2OTmJH//4x+6+YHFxMZ5++mk89thj8Pl8i0OIiIiIiMhqdF8DDHt6enD69Gl8+OGH7qRi3bp1SEtLW3xXRG4kFAphfHwcY2NjLqPh9u3bFWAoIiIiIiIiIiJ3hQIMRUTkTijAUERERERkbbqvAYYMimLmtd/85jdob293wYV+/6rptVlk1fO6FWegYWpqKnbs2OEygTLQMD09fXEoERERERERERGRO6cAQxERuRMKMBQRERERWZvua4DhwsICRkZGcO7cOfT29i6+KiK3gwGGdXV1qK2tRWlpKZKSkhbfERERERERERERuXMKMBQRkTuhAEMRERERkbXpvgYY8qvZrevw8DCmp6cXXxWR28GAwqKiImRlZSkTqIiIiIiIiIiIrDgFGIqIyJ1QgKGIiIiIyNp0XwMMiV/P7l15cUpEbh9PwJOTkxEIBBZfERERERERERERWTm8htvU1ITXXntNAYYiInLLEgMMmTCBAYaPP/64AgxFRERERFa5+x5gKCIiIiIiIiIiIiKrHwMM29vb8c477+D8+fMoKyvDt7/9bZSXlyM1NXVxKBERkd/FZCPs0ezll1/GhQsXUFNT47IXHjp0SAGGIiIiIiKrnPpRFREREREREREREZEbYgAIAwnz8vKQnp7ugkVGR0cxMzOzOISIiMjygsEgrly5gvn5edcbU2FhITIzMxVcKCIiIiKyBijAUERERERERERERERuCgMMc3NzkZaW5oJFRkZGMDs7u/iuiIjI8hYWFlyAIY8ZSUlJLlidAYYiIiIiIrL6KcBQRERERERERERERG7Iy2CYk5PjHpnBkAGGymAoIiI34mUwnJubuxpgmJGRsfiuiIiIiIisZgowFBEREREREREREZGbkpKS4gIMGRQSDoddgOHU1BQikQhisdjiUCIiInE8NkSjUZe5cGxszHWR7GXDZXf7IiIiIiKy+inAUERERERERERERERuiBkMGWDIrFNFRUXueU9PDwYGBjAxMeECDkVERBIxwHB6ehpDQ0Po6+tzf/MYUlhYqABDEREREZE1QgGGIiIiIiIiIiIiInJTAoEAMjMzUVVV5QJEGFjY1dXlCru9FBERScTu9Pv7+9HR0YHR0VGXBbe6uto9JicnLw4lIiIiIiKrmQIMRUREREREREREROSmsWvLdevWoa6uDmlpaS4jVUNDg+v6klkM1VWyiIgQu8+fmZlBS0uLKzw+1NTUoL6+XtkLRURERETWEAUYioiIiIiIiIiIiMhNS0pKctkLGWC4efNmFzDS3NzsCrvAZECJiIh8tvHYwCy3ra2t7vgwOTnpst8yuLC8vFzZC0VERERE1hAFGIqIiIiIiIiIiIjITfP7/cjIyEBFRQX27t2L4uJiF0TS2NjoMlSNj4+7TIYiIvLZFI1GMT09je7ubpw/fx6Dg4Oue/2tW7e6DLjZ2dmuy30REREREVkbFGAoIiIiIiIiIiIiIrfE5/MhLy8Pu3fvxsaNG13AYXt7uwsk6ejocF1iqqtkEZHPpmAw6LrPv3DhgisMOKytrcX27dtRVlam4EIRERERkTUm8O/N4nMRERERERERERERkZvCTIYpKSkucITBhOz+kpkM5+bmrr7HwEMGI4qIyGfDlStX0NbWhtOnT7vukZnRloHou3btQk1NDdLS0nRcEBERERFZY5TBUERERERERERERERuW1VVFfbt24f169e7QJJLly7h1KlTaG5udkGHoVBocUgREXlQRSIRl722q6sLZ86cwdmzZzE2NuaOEV62WwYXioiIiIjI2uOLqZ8KEREREREREREREblNDCqcnZ1FT0+P6wqzsbERU1NTKC0txY4dO7B161aUl5e7rIYsIiLy4OBtRmayHR8fd/W/11U+63sGnu/du9dlLmS3+klJScpeKCIiIiKyBinAUERERERERERERETuCINLEoMMGxoa3N8FBQWuW8xNmzahqKjIdZmcnJy8+CkREVnLmLWQ3eIzU2FnZ6fLWtjf3++CDjdv3ozt27djw4YNyM7ORiAQWPyUiIiIiIisNQowFBEREREREREREZE7xiBDBpoMDAzg3LlzLpPV0NAQSkpKUFtb67rHLCsrc1ms2E2mgk1ERNYm3lqcn5/H9PQ0BgcH0dbW5kpvby+ysrJQV1d3NXMhA8tZ3ytzoYiIiIjI2qUAQxERERERERERERFZEV7QycjICC5fvoxTp06558TgQgadMNCQ3WYy6EQBJyIiawvreXaN39XVhZaWFjQ3N6Ovrw8LCwvIyclxWQt3797t6vzMzEx1jS8iIiIi8gBQgKGIiIiIiIiIiIiIrBhecma3mewms6mpyQWfMKsVAw8ZfFJdXX01m2F+fr7rOlPdJouIrG6s12dmZjA+Pu4Cxxlc2NHRgdHRURdEyGy19fX1rmtkZq1NSUlRELmIiIiIyANCAYYiIiIiIiIiIiIisuJ46TkYDKK9vR0XL15EQ0ODCzpkV8rFxcXYsmWLy3TFrIYMPFQgiojI6sT6nF3gd3d349KlS64+Z3f4zFrI+pxBhTt27HDB4+wiWVkLRUREREQeLAowFBEREREREREREZG7gpefp6amXIYrdqHZ2tqKzs5Ol/0qLS0NpaWlLsBw3bp1qKioQG5uLlJTU91nFXAoInJ/eLcOmbVwYmICQ0NDrkvktrY2l5GWr+Xl5aGystIFFfKRgYasw5OSktxnRURERETkwaEAQxERERERERERERG5q5i1kF1rMvvV5cuXXUbD4eFhhMNhFBYWum6T169f74JU2G1yRkYG0tPT1XWyiMg9xqBCdmk/OzvrAsQZUMjAcBYGGvJ9BhIyC+3WrVuvZqFVYKGIiIiIyINLAYYiIiIiIiIiIiIiclfxMjSDDBm0MjY25gINmc2QGbHGx8fdewwoLC8vR1VVlctm6GU0ZJAhA1cCgcDi2EREZKWwfmZhwDfL9PQ0BgcHXWBhT0+P69qeGQspMzPTBYJv2LABNTU1KCkpcXU362hlnRUREREReXApwFBERERERERERERE7plQKOSyYjGDIQNX2tvbXffJDDwkZi9kVkMvyJDdbrKwO06/3++GERGRlcEAb2YrZJ3MMjAw4ILA2ZX95OSkGyYrKwtlZWWora11QeAMLGQAOLu6FxERERGRB58CDEVERERERERERETknuElaRZ2s8mgFmbIYmG2LAYcMqMhs2gxcMULNGSmLGY3ZJALAxBTU1NdxiwFHIqI3BrWv6xjg8Eg5ubmXMZCBhMyoyzrYQYZMrCQWWO9wELWwwwsZHf2DPbme6x/lbVQREREROSzQQGGIiIiIiIiIiIiInJfMHMWg1wWFhZcVkNmzero6HCFWbQYgJiSkoKCggKUlpZi/fr1LtiQAS8McmH3ySIicvMYXDgzM4OhoSFX5zKLLAMLmUWWdS7r1aKiIqxbt+5qncs6mHUxA7/VXb2IiIiIyGePAgxFRERERERERERE5L5j0AuzF46OjmJwcNAFvjCjIYNeGAzDYER2lcyshgx+YRed+fn5yMnJcYWZthgAo6yGIiK/5WUqZFZClitXrrgshcxayMI6d35+HpmZmS5wmwHczFTIoG6ve3p1hSwiIiIi8tmmAEMRERERERERERERWVUYbMigF3bZ2dLS4gozbPF1dq3M7pEZZFhZWXm1MOAwNzfXdZ/MDFteF8rqwlNEPit4y4/B2KwnWVhnMqiQgYSsQ73C4G1mjvXqSmYo3LhxIzZs2OAyF7JLZGWIFRERERERjwIMRURERERERERERGRV4WVrBr8w6xa7TmZXngyIYdYtFmbg4nsMIGRmLRZmMWTGLQYaMusWAxCzs7ORnp7uggy9YEMFHIrIg4J1ZWLxuptn5lcvSyG7m2fANt9jpkIvSJsB2awrWWey7mRhtsKMjAxXpyobrIiIiIiIeBRgKCIiIiIiIiIiIiKrFi9hz87OYmJiwgXJsDBwhkGH7FKZwTTsQplBM+wmmd0msxtlZuRikCGf83V2/8lgQxYG14iIrGXMVMiAQdaPLKwHve6PGWDIupKPrCOZyZD1IAuDCL0gbBbWlaw3WUeqbhQRERERkeUowFBEREREREREREREVjWvy0+vi2RmL2TgDIMMmZ2Lj8zUNT097YbhZW8WL5CGGbq8QBoGHDJDFwNpEou6UxaR1Yp1IOu2xMLgQgYUsi5k/ecFXvM1vu/VaV5QoZelkIX1YmI9yK6SVQeKiIiIiMi1KMBQRERERERERERERNYUL3MXAw29zF3MZshulL1gQwbbBINBFzDDQBpmLmSGLmbqYmGwoRdwyEe+zyAbEZHVhPUd6zIGDrJu84IKvUdmKGRdyMLAQgYMso5jECFLWVnZ1YBCr6i+ExERERGRW6EAQxERERERERERERFZk7zL23xkwCG7BPWCC71HBuHwPRYG6TBLFwNsvCBDBhiy5ObmukxfDLzxAnBSU1MVhCMi94wXTMj6KrHrYwYRepkKveBCvhYKhVxAYVpamquzcnJyXLZWBhR6j6zn+LqXnVBZCkVERERE5FYpwFBERERERERERERE1jxe6mb3yV53yizMZtjX14fBwUFXvOyGDN4hBtow4DAlJeVqF6LM9lVZWYny8nIXmMPAHQ63XBERuR2JwdGJhcGFzMbK+qq3t9dlZeVz1lsLCwuufmPhsMnJyS44mkGEXtfHrLf4yHqLwdFe18csIiIiIiIit0sBhiIiIiIiIiIiIiLyQGL2r+np6atZwPicwTte16ITExOu8HUGJDLQkN0oZ2dnu8KsX8xsyMLn3usszBamQEMRuVW8LcdgQWYgZGH9Mzk5ebU+Ykl8j8PyM6ybWBcxGJrF6+6d9RHf8wozsCrzqoiIiIiIrCQFGIqIiIiIiIiIiIjIZwIzfzF7IQMM2X2y15UyCwMPGYTIYB6WcDjsggfZbTK7GmU2Qy+gxwvqYUAiCzOJeYV/s8tSZQwT+ezirTfWIezCmFkJ+egV/s26hvUQ6x2vy2PWQwwu5OdYh7AuYTftDBhkYCGzFbIu8ro+5t98j8OKiIiIiIjcTQowFBEREREREREREZHPBF4OZ5ChF/jjBfuwMIPY6Ojo1aBDPvJvvud1Nep1N8pHdkHKrIaJ2cS853zd61rZ657Uy3boPReRtY31iVenLH1k3cIMhAwg9LKmsngZCufm5lw9xGG9Lt1ZWD+wLvECCUtKSlwgIesV1ilLA5lZF6k+ERERERGRu00BhiIiIiIiIiIiIiLymcbL5MxayKxiDDT0itdNKV/3ulj2nnN4YtAPs4gldlGaWJgBkcX7m8PyMwwkEpG1iXUGg4+97te9+iGxjliuMIMqAwkZJLi0nvAeve7Y+cjC19glu+oMERERERG5XxRgKCIiIiIiIiIiIiKfebxUnli8bGQMIErMQOY9ZxYyBh963SknZiHj55hhjMFBXmZDr2tldnXKgCF2fcogI2Yh8wqzkSkrmcjqwH3Z27f56BUv8ynrBtYDXt3g1Q98jYGErAe4Lyfu16wXuP+zHmBZWj/wPW//T8x8qvpARERERETuJwUYioiIiIiIiIiIiIhcg9fdqVe8ICOva2UGE42NjX0qwIiPzGTG4MPEAEKva1NmI/OCiwoKClyAETOWJWYtW9qVsgKMRO6epbfKuN8z4yCDiL2MponBhNzP+b4XYOzVDQxGZIAgM5V6gYN85H7uPef+73V17AUZe49ecKGIiIiIiMhqogBDEREREREREREREZFbxEvrDEKam5tzQUgMKPQeE7tKZZYzFg7HR2Y2YzASg4nYVTIzlnldJ3uFAUh8j4WZDlm854mvMyhJ3aaK3Dzus16AMPdFBgjyMfG59+jts97+y/2Z+zX/5ji473Ff5P7q7bd89Lo6TiwMHvb2de77CiIUEREREZG1RAGGIiIiIiIiIiIiIiK3yetK2Xv0njMAiYFKid0rJ2Y6ZLASg5gSP5s4DgYueYFKzGjoZTdMfGQwE4MMmfWMwU6Jj95zFgYzKaBJHnTePsQsgt6j99z7m8G93O8YDOxlJ1z6nPsmAwo5Pu43ifsQnzMLKfe9xK6NvcLXvP1y6f6nfVFERERERNYqBRiKiIiIiIiIiIiIiKwwL9jJ60qZwYbecxYvMxq7XWVQ09IMiAxwYjAUg5EYLOh1ocrgJq9LVWYx9IIQvUcvU1pixjR+hsFNpOAmedBwX2PhfsX9xtuHEvcnr3iBvRzW69rYK/yb4+H+5gX4Lt2f2LU5H70so9y3lhZlKBQRERERkQeNAgxFRERERERERERERO4xBjN5mdS8wKfEwtcYLOUFQyUGKHrPGcDIYCgGNTHgiQFRS4vXdSuDEb3gJxZ+LvHRe+4FMvK5l4FN5F7hLSsv8yAL9xOveH8vfd3bL7i/sCzdj7xgXu43/CyD/7idJwYFevsHH71ujpcriZlDFUQoIiIiIiKfFQowFBERERERERERERG5x3hpnsXrvnW55wye8jKysftWFmY89Lpy5SODqDgMh10Og6ASg6b4yMxrXgY2ryT+nZidjcGGHMftFvns8W47edvycoX4uHQY/s1gQQbWsnhBtsv97T3nPsD9gZ9jAOFyGCjLbdoLEmQ340uLFzzI7Z7brhdgm/jc+1vbtoiIiIiIfJYowFBEREREREREREREZBXi5XsGDrL7VgZPLZfFkIVBVszQxkAr79Er/NsLQOT4GCDlZSz0MhVe67mX5S0xu9vSx6XPE//m5/l98tnBbYzbq7d9Xu/xWu953RWz3EwWQwb7cXvlNnet7IMMmmVJ3H6XPmfhtq9tVkRERERE5NMUYCgiIiIiIiIiIiIiskZ5QYgMIvQyuTHjIUvicxYGInrBWYmFWeOWvsZCDN5ioGBil7IMyvIyHPLR+zvxOR9ZErtaXlq8wDA+Lve+V5YbzvubRW4etxcWb517z5crfO96w3jvL/3bCxbk9uYFEnL79J4vfc8LKmTheMhbx0vLcq9ze2NgYVZWlstCyMLniYXvK+BVRERERETk9ijAUERERERERERERERkDfMCwLxgLz56xfvby/bmBXd5Zbm/E19j8JdX+Hl+V2Jgn/d8ub9ZmBGOgV1LHxOLlzku8e/lnicW7zMMMPO+y/v+xMdrudH7iVZqXDdzO+ZWbtlcb1jvvcRHFm4PXI9eVkwvsM8ry72WWLz3vXF4xfvb20auV7zp8TDoj+uTAaneoxeomhi0uvS59ze3B29bYFkuCJGv3co6FxERERERkd9SgKGIiIiIiIiIiIiIyAPOC+5KDARb+uiV5V7ncxYvwIyPXln6t/caCwPavO/2CjHYa2kQ2O0WBpd5xfubj/wOr3jfufT5jf5OfH6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}
},
"cell_type": "markdown",
"id": "b6d466cc-aa8b-4baf-a80a-fef01921ca8d",
"metadata": {},
"source": [
"## Docugami RAG over XML Knowledge Graphs (KG-RAG)\n",
"\n",
"Many documents contain a mixture of content types, including text and tables. \n",
"\n",
"Semi-structured data can be challenging for conventional RAG for a few reasons since semantics may be lost by text-only chunking techniques, e.g.: \n",
"\n",
"* Text splitting may break up tables, corrupting the data in retrieval\n",
"* Embedding tables may pose challenges for semantic similarity search \n",
"\n",
"Docugami deconstructs documents into XML Knowledge Graphs consisting of hierarchical semantic chunks using the XML data model. This cookbook shows how to perform RAG using XML Knowledge Graphs as input (**KG-RAG**):\n",
"\n",
"* We will use [Docugami](http://docugami.com/) to segment out text and table chunks from documents (PDF \\[scanned or digital\\], DOC or DOCX) including semantic XML markup in the chunks.\n",
"* We will use the [multi-vector retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) to store raw tables and text (including semantic XML markup) along with table summaries better suited for retrieval.\n",
"* We will use [LCEL](https://python.langchain.com/docs/expression_language/) to implement the chains used.\n",
"\n",
"The overall flow is here:\n",
"\n",
"\n",
"\n",
"## Packages"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5740fc70-c513-4ff4-9d72-cfc098f85fef",
"metadata": {},
"outputs": [],
"source": [
"! pip install langchain docugami==0.0.8 dgml-utils==0.3.0 pydantic langchainhub chromadb hnswlib --upgrade --quiet"
]
},
{
"cell_type": "markdown",
"id": "44349a83-e1dc-4eed-ba75-587f309d8c88",
"metadata": {},
"source": [
"Docugami processes documents in the cloud, so you don't need to install any additional local dependencies. "
]
},
{
"cell_type": "markdown",
"id": "c6fb4903-f845-4907-ae14-df305891b0ff",
"metadata": {},
"source": [
"## Data Loading\n",
"\n",
"Let's use Docugami to process some documents. Here's what you need to get started:\n",
"\n",
"1. Create a [Docugami workspace](http://www.docugami.com) (free trials available)\n",
"1. Create an access token via the Developer Playground for your workspace. [Detailed instructions](https://help.docugami.com/home/docugami-api).\n",
"1. Add your documents (PDF \\[scanned or digital\\], DOC or DOCX) to Docugami for processing. There are two ways to do this:\n",
" 1. Use the simple Docugami web experience. [Detailed instructions](https://help.docugami.com/home/adding-documents).\n",
" 1. Use the [Docugami API](https://api-docs.docugami.com), specifically the [documents](https://api-docs.docugami.com/#tag/documents/operation/upload-document) endpoint. You can also use the [docugami python library](https://pypi.org/project/docugami/) as a convenient wrapper.\n",
"\n",
"Once your documents are in Docugami, they are processed and organized into sets of similar documents, e.g. NDAs, Lease Agreements, and Service Agreements. Docugami is not limited to any particular types of documents, and the clusters created depend on your particular documents. You can [change the docset assignments](https://help.docugami.com/home/working-with-the-doc-sets-view) later if you wish. You can monitor file status in the simple Docugami webapp, or use a [webhook](https://api-docs.docugami.com/#tag/webhooks) to be informed when your documents are done processing.\n",
"\n",
"You can also use the [Docugami API](https://api-docs.docugami.com) or the [docugami](https://pypi.org/project/docugami/) python library to do all the file processing without visiting the Docugami webapp except to get the API key.\n",
"\n",
"> You can get an API key as documented here: https://help.docugami.com/home/docugami-api. This following code assumes you have set the `DOCUGAMI_API_TOKEN` environment variable.\n",
"\n",
"First, let's define two simple helper methods to upload files and wait for them to finish processing."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ce0b2b21-7623-46e7-ae2c-3a9f67e8b9b9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Report_CEN23LA277_192541.pdf': '/tmp/tmpa0c77x46',\n",
" 'Report_CEN23LA338_192753.pdf': '/tmp/tmpaftfld2w',\n",
" 'Report_CEN23LA363_192876.pdf': '/tmp/tmpn7gp6be2',\n",
" 'Report_CEN23LA394_192995.pdf': '/tmp/tmp9udymprf',\n",
" 'Report_ERA23LA114_106615.pdf': '/tmp/tmpxdjbh4r_',\n",
" 'Report_WPR23LA254_192532.pdf': '/tmp/tmpz6h75a0h'}\n"
]
}
],
"source": [
"from pprint import pprint\n",
"\n",
"from docugami import Docugami\n",
"from docugami.lib.upload import upload_to_named_docset, wait_for_dgml\n",
"\n",
"#### START DOCSET INFO (please change this values as needed)\n",
"DOCSET_NAME = \"NTSB Aviation Incident Reports\"\n",
"FILE_PATHS = [\n",
" \"/Users/tjaffri/ntsb/Report_CEN23LA277_192541.pdf\",\n",
" \"/Users/tjaffri/ntsb/Report_CEN23LA338_192753.pdf\",\n",
" \"/Users/tjaffri/ntsb/Report_CEN23LA363_192876.pdf\",\n",
" \"/Users/tjaffri/ntsb/Report_CEN23LA394_192995.pdf\",\n",
" \"/Users/tjaffri/ntsb/Report_ERA23LA114_106615.pdf\",\n",
" \"/Users/tjaffri/ntsb/Report_WPR23LA254_192532.pdf\",\n",
"]\n",
"\n",
"# Note: Please specify ~6 (or more!) similar files to process together as a document set\n",
"# This is currently a requirement for Docugami to automatically detect motifs\n",
"# across the document set to generate a semantic XML Knowledge Graph.\n",
"assert len(FILE_PATHS) > 5, \"Please provide at least 6 files\"\n",
"#### END DOCSET INFO\n",
"\n",
"dg_client = Docugami()\n",
"dg_docs = upload_to_named_docset(dg_client, FILE_PATHS, DOCSET_NAME)\n",
"dgml_paths = wait_for_dgml(dg_client, dg_docs)\n",
"\n",
"pprint(dgml_paths)"
]
},
{
"cell_type": "markdown",
"id": "01f035e5-c3f8-4d23-9d1b-8d2babdea8e9",
"metadata": {},
"source": [
"If you are on the free Docugami tier, your files should be done in ~15 minutes or less depending on the number of pages uploaded and available resources (please contact Docugami for paid plans for faster processing). You can re-run the code above without reprocessing your files to continue waiting if your notebook is not continuously running (it does not re-upload)."
]
},
{
"cell_type": "markdown",
"id": "7c24efa9-b6f6-4dc2-bfe3-70819ba3ef75",
"metadata": {},
"source": [
"### Partition PDF tables and text\n",
"\n",
"You can use the [Docugami Loader](https://python.langchain.com/docs/integrations/document_loaders/docugami) to very easily get chunks for your documents, including semantic and structural metadata. This is the simpler and recommended approach for most use cases but in this notebook let's explore using the `dgml-utils` library to explore the segmented output for this file in more detail by processing the XML we just downloaded above."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "05fcdd57-090f-44bf-a1fb-2c3609c80e34",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"found 30 chunks, here are the first few\n",
"<AviationInvestigationFinalReport-section>Aviation </AviationInvestigationFinalReport-section>Investigation Final Report\n",
"<table><tbody><tr><td>Location: </td> <td><Location><TownName>Elbert</TownName>, <USState>Colorado </USState></Location></td> <td>Accident Number: </td> <td><AccidentNumber>CEN23LA277 </AccidentNumber></td></tr> <tr><td><LocationDateTime>Date & Time: </LocationDateTime></td> <td><DateTime><EventDate>June 26, 2023</EventDate>, <EventTime>11:00 Local </EventTime></DateTime></td> <td><DateTimeAccidentNumber>Registration: </DateTimeAccidentNumber></td> <td><Registration>N23161 </Registration></td></tr> <tr><td><LocationAircraft>Aircraft: </LocationAircraft></td> <td><AircraftType>Piper <AircraftType>J3C-50 </AircraftType></AircraftType></td> <td><AircraftAccidentNumber>Aircraft Damage: </AircraftAccidentNumber></td> <td><AircraftDamage>Substantial </AircraftDamage></td></tr> <tr><td><LocationDefiningEvent>Defining Event: </LocationDefiningEvent></td> <td><DefiningEvent>Nose over/nose down </DefiningEvent></td> <td><DefiningEventAccidentNumber>Injuries: </DefiningEventAccidentNumber></td> <td><Injuries><Minor>1 </Minor>Minor </Injuries></td></tr> <tr><td><LocationFlightConductedUnder>Flight Conducted Under: </LocationFlightConductedUnder></td> <td><FlightConductedUnder><Part91-cell>Part <RegulationPart>91</RegulationPart>: General aviation - Personal </Part91-cell></FlightConductedUnder></td><td/><td><FlightConductedUnderCEN23LA277/></td></tr></tbody></table>\n",
"Analysis\n",
"<TakeoffAccident> <Analysis>The pilot reported that, as the tail lifted during takeoff, the airplane veered left. He attempted to correct with full right rudder and full brakes. However, the airplane subsequently nosed over resulting in substantial damage to the fuselage, lift struts, rudder, and vertical stabilizer. </Analysis></TakeoffAccident>\n",
"<AircraftCondition> The pilot reported that there were no preaccident mechanical malfunctions or anomalies with the airplane that would have precluded normal operation. </AircraftCondition>\n",
"<WindConditions> At about the time of the accident, wind was from <WindDirection>180</WindDirection>° at <WindConditions>5 </WindConditions>knots. The pilot decided to depart on runway <Runway>35 </Runway>due to the prevailing airport traffic. He stated that departing with “more favorable wind conditions” may have prevented the accident. </WindConditions>\n",
"<ProbableCauseAndFindings-section>Probable Cause and Findings </ProbableCauseAndFindings-section>\n",
"<ProbableCause> The <ProbableCause>National Transportation Safety Board </ProbableCause>determines the probable cause(s) of this accident to be: </ProbableCause>\n",
"<AccidentCause> The pilot's loss of directional control during takeoff and subsequent excessive use of brakes which resulted in a nose-over. Contributing to the accident was his decision to takeoff downwind. </AccidentCause>\n",
"Page 1 of <PageNumber>5 </PageNumber>\n"
]
}
],
"source": [
"from pathlib import Path\n",
"\n",
"from dgml_utils.segmentation import get_chunks_str\n",
"\n",
"# Here we just read the first file, you can do the same for others\n",
"dgml_path = dgml_paths[Path(FILE_PATHS[0]).name]\n",
"\n",
"with open(dgml_path, \"r\") as file:\n",
" contents = file.read().encode(\"utf-8\")\n",
"\n",
" chunks = get_chunks_str(\n",
" contents,\n",
" include_xml_tags=True, # Ensures Docugami XML semantic tags are included in the chunked output (set to False for text-only chunks and tables as Markdown)\n",
" max_text_length=1024 * 8, # 8k chars are ~2k tokens for OpenAI.\n",
" # Ref: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them\n",
" )\n",
"\n",
" print(f\"found {len(chunks)} chunks, here are the first few\")\n",
" for chunk in chunks[:10]:\n",
" print(chunk.text)"
]
},
{
"cell_type": "markdown",
"id": "bfc1f2c9-e6d4-4d98-a799-6bc30bc61661",
"metadata": {},
"source": [
"The file processed by Docugami in the example above was [this one](https://data.ntsb.gov/carol-repgen/api/Aviation/ReportMain/GenerateNewestReport/192541/pdf) from the NTSB and you can look at the PDF side by side to compare the XML chunks above. \n",
"\n",
"If you want text based chunks instead, Docugami also supports those and renders tables as markdown:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8a4b49e0-de78-4790-a930-ad7cf324697a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"found 30 chunks, here are the first few\n",
"Aviation Investigation Final Report\n",
"+-------------------------+---------------------------------------+-------------------+-------------+\n",
"| Location: | Elbert , Colorado | Accident Number: | CEN23LA277 |\n",
"+-------------------------+---------------------------------------+-------------------+-------------+\n",
"| Date & Time: | June 26, 2023 , 11:00 Local | Registration: | N23161 |\n",
"+-------------------------+---------------------------------------+-------------------+-------------+\n",
"| Aircraft: | Piper J3C-50 | Aircraft Damage : | Substantial |\n",
"+-------------------------+---------------------------------------+-------------------+-------------+\n",
"| Defining Event: | Nose over/nose down | Injuries: | 1 Minor |\n",
"+-------------------------+---------------------------------------+-------------------+-------------+\n",
"| Flight Conducted Under: | Part 91 : General aviation - Personal | | |\n",
"+-------------------------+---------------------------------------+-------------------+-------------+\n",
"Analysis\n",
"The pilot reported that, as the tail lifted during takeoff, the airplane veered left. He attempted to correct with full right rudder and full brakes. However, the airplane subsequently nosed over resulting in substantial damage to the fuselage, lift struts, rudder, and vertical stabilizer.\n",
"The pilot reported that there were no preaccident mechanical malfunctions or anomalies with the airplane that would have precluded normal operation.\n",
"At about the time of the accident, wind was from 180 ° at 5 knots. The pilot decided to depart on runway 35 due to the prevailing airport traffic. He stated that departing with “more favorable wind conditions” may have prevented the accident.\n",
"Probable Cause and Findings\n",
"The National Transportation Safety Board determines the probable cause(s) of this accident to be:\n",
"The pilot's loss of directional control during takeoff and subsequent excessive use of brakes which resulted in a nose-over. Contributing to the accident was his decision to takeoff downwind.\n",
"Page 1 of 5\n"
]
}
],
"source": [
"with open(dgml_path, \"r\") as file:\n",
" contents = file.read().encode(\"utf-8\")\n",
"\n",
" chunks = get_chunks_str(\n",
" contents,\n",
" include_xml_tags=False, # text-only chunks and tables as Markdown\n",
" max_text_length=1024\n",
" * 8, # 8k chars are ~2k tokens for OpenAI. Ref: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them\n",
" )\n",
"\n",
" print(f\"found {len(chunks)} chunks, here are the first few\")\n",
" for chunk in chunks[:10]:\n",
" print(chunk.text)"
]
},
{
"cell_type": "markdown",
"id": "1cfc06bc-67d2-46dd-b04d-95efa3619d0a",
"metadata": {},
"source": [
"## Docugami XML Deep Dive: Jane Doe NDA Example\n",
"\n",
"Let's explore the Docugami XML output for a different example PDF file (a long form contract): [Jane Doe NDA](https://github.com/docugami/dgml-utils/blob/main/python/tests/test_data/article/Jane%20Doe%20NDA.pdf). We have provided processed Docugami XML output for this PDF here: https://github.com/docugami/dgml-utils/blob/main/python/tests/test_data/article/Jane%20Doe.xml so you can follow along without processing your own documents."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7b697d30-1e94-47f0-87e8-f81d4b180da2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"39"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests\n",
"\n",
"# Download XML from known URL\n",
"dgml = requests.get(\n",
" \"https://raw.githubusercontent.com/docugami/dgml-utils/main/python/tests/test_data/article/Jane%20Doe.xml\"\n",
").text\n",
"chunks = get_chunks_str(dgml, include_xml_tags=True)\n",
"len(chunks)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "14714576-6e1d-499b-bcc8-39140bb2fd78",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'h1': 9, 'div': 12, 'p': 3, 'lim h1': 9, 'lim': 1, 'table': 1, 'h1 div': 4}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Count all the different structure categories\n",
"category_counts = {}\n",
"\n",
"for element in chunks:\n",
" category = element.structure\n",
" if category in category_counts:\n",
" category_counts[category] += 1\n",
" else:\n",
" category_counts[category] = 1\n",
"\n",
"category_counts"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5462f29e-fd59-4e0e-9493-ea3b560e523e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 1 tables\n",
"There are 38 text elements\n"
]
}
],
"source": [
"# Tables\n",
"table_elements = [c for c in chunks if \"table\" in c.structure.split()]\n",
"print(f\"There are {len(table_elements)} tables\")\n",
"\n",
"# Text\n",
"text_elements = [c for c in chunks if \"table\" not in c.structure.split()]\n",
"print(f\"There are {len(text_elements)} text elements\")"
]
},
{
"cell_type": "markdown",
"id": "dc09ba64-4973-4471-9501-54294c1143fc",
"metadata": {},
"source": [
"The Docugami XML contains extremely detailed semantics and visual bounding boxes for all elements. The `dgml-utils` library parses text and non-text elements into formats appropriate to pass into LLMs (chunked text with XML semantic labels)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2b4ece00-2e43-4254-adc9-66dbb79139a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NON-DISCLOSURE AGREEMENT\n",
"<MUTUALNON-DISCLOSUREAGREEMENT> This Non-Disclosure Agreement (\"Agreement\") is entered into as of <EffectiveDate>November 4, 2023 </EffectiveDate>(\"Effective Date\"), by and between: </MUTUALNON-DISCLOSUREAGREEMENT>\n",
"Disclosing Party:\n",
"<DisclosingParty><PrincipalPlaceofBusiness>Widget Corp.</PrincipalPlaceofBusiness>, a <USState>Delaware </USState>corporation with its principal place of business at <PrincipalPlaceofBusiness><PrincipalPlaceofBusiness> <WidgetCorpAddress>123 </WidgetCorpAddress> <PrincipalPlaceofBusiness>Innovation Drive</PrincipalPlaceofBusiness> </PrincipalPlaceofBusiness> , <PrincipalPlaceofBusiness>Techville</PrincipalPlaceofBusiness>, <USState> Delaware</USState>, <PrincipalPlaceofBusiness>12345 </PrincipalPlaceofBusiness></PrincipalPlaceofBusiness> (\"<Org> <CompanyName>Widget </CompanyName> <CorporateName>Corp.</CorporateName> </Org>\") </DisclosingParty>\n",
"Receiving Party:\n",
"<RecipientName>Jane Doe</RecipientName>, an individual residing at <RecipientAddress><RecipientAddress> <RecipientAddress>456 </RecipientAddress> <RecipientAddress>Privacy Lane</RecipientAddress> </RecipientAddress> , <RecipientAddress>Safetown</RecipientAddress>, <USState> California</USState>, <RecipientAddress>67890 </RecipientAddress></RecipientAddress> (\"Recipient\")\n",
"(collectively referred to as the \"Parties\").\n",
"1. Definition of Confidential Information\n",
"<DefinitionofConfidentialInformation>For purposes of this Agreement, \"Confidential Information\" shall include all information or material that has or could have commercial value or other utility in the business in which Disclosing Party is engaged. If Confidential Information is in written form, the Disclosing Party shall label or stamp the materials with the word \"Confidential\" or some similar warning. If Confidential Information is transmitted orally, the Disclosing Party shall promptly provide writing indicating that such oral communication constituted Confidential Information . </DefinitionofConfidentialInformation>\n",
"2. Exclusions from Confidential Information\n",
"<ExclusionsFromConfidentialInformation>Recipient's obligations under this Agreement do not extend to information that is: (a) publicly known at the time of disclosure or subsequently becomes publicly known through no fault of the Recipient; (b) discovered or created by the Recipient before disclosure by Disclosing Party; (c) learned by the Recipient through legitimate means other than from the Disclosing Party or Disclosing Party's representatives; or (d) is disclosed by Recipient with Disclosing Party's prior written approval. </ExclusionsFromConfidentialInformation>\n",
"3. Obligations of Receiving Party\n",
"<ObligationsofReceivingParty>Recipient shall hold and maintain the Confidential Information in strictest confidence for the sole and exclusive benefit of the Disclosing Party. Recipient shall carefully restrict access to Confidential Information to employees, contractors, and third parties as is reasonably required and shall require those persons to sign nondisclosure restrictions at least as protective as those in this Agreement. </ObligationsofReceivingParty>\n",
"4. Time Periods\n",
"<TimePeriods>The nondisclosure provisions of this Agreement shall survive the termination of this Agreement and Recipient's duty to hold Confidential Information in confidence shall remain in effect until the Confidential Information no longer qualifies as a trade secret or until Disclosing Party sends Recipient written notice releasing Recipient from this Agreement, whichever occurs first. </TimePeriods>\n",
"5. Relationships\n",
"<Relationships>Nothing contained in this Agreement shall be deemed to constitute either party a partner, joint venture, or employee of the other party for any purpose. </Relationships>\n",
"6. Severability\n",
"<Severability>If a court finds any provision of this Agreement invalid or unenforceable, the remainder of this Agreement shall be interpreted so as best to effect the intent of the parties. </Severability>\n",
"7. Integration\n"
]
}
],
"source": [
"for element in text_elements[:20]:\n",
" print(element.text)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "08350119-aa22-4ec1-8f65-b1316a0d4123",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<table> <tbody> <tr> <td> Authorized Individual </td> <td> Role </td> <td>Purpose of Disclosure </td> </tr> <tr> <td> <AuthorizedIndividualJohnSmith> <Name>John Smith </Name> </AuthorizedIndividualJohnSmith> </td> <td> <JohnSmithRole> <ProjectManagerName>Project Manager </ProjectManagerName> </JohnSmithRole> </td> <td> <JohnSmithPurposeofDisclosure> Oversee project to which the NDA relates </JohnSmithPurposeofDisclosure> </td> </tr> <tr> <td> <AuthorizedIndividualLisaWhite> <Author>Lisa White </Author> </AuthorizedIndividualLisaWhite> </td> <td> <LisaWhiteRole> Lead Developer </LisaWhiteRole> </td> <td> <LisaWhitePurposeofDisclosure>Software development and analysis </LisaWhitePurposeofDisclosure> </td> </tr> <tr> <td> <AuthorizedIndividualMichaelBrown> <Name>Michael Brown </Name> </AuthorizedIndividualMichaelBrown> </td> <td> <MichaelBrownRole> Financial <FinancialAnalyst> Analyst </FinancialAnalyst> </MichaelBrownRole> </td> <td> <MichaelBrownPurposeofDisclosure>Financial analysis and reporting </MichaelBrownPurposeofDisclosure> </td> </tr> </tbody> </table>\n"
]
}
],
"source": [
"print(table_elements[0].text)"
]
},
{
"cell_type": "markdown",
"id": "dca87b46-c0c2-4973-94ec-689c18075653",
"metadata": {},
"source": [
"The XML markup contains structural as well as semantic tags, which provide additional semantics to the LLM for improved retrieval and generation.\n",
"\n",
"If you prefer, you can set `include_xml_tags=False` in the `get_chunks_str` call above to not include XML markup. The text-only Docugami chunks are still very good since they follow the structural and semantic contours of the document rather than whitespace-only chunking. Tables are rendered as markdown in this case, so that some structural context is maintained even without the XML markup."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bcac8294-c54a-4b6e-af9d-3911a69620b2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-----------------------+-------------------+------------------------------------------+\n",
"| Authorized Individual | Role | Purpose of Disclosure |\n",
"+-----------------------+-------------------+------------------------------------------+\n",
"| John Smith | Project Manager | Oversee project to which the NDA relates |\n",
"+-----------------------+-------------------+------------------------------------------+\n",
"| Lisa White | Lead Developer | Software development and analysis |\n",
"+-----------------------+-------------------+------------------------------------------+\n",
"| Michael Brown | Financial Analyst | Financial analysis and reporting |\n",
"+-----------------------+-------------------+------------------------------------------+\n"
]
}
],
"source": [
"chunks_as_text = get_chunks_str(dgml, include_xml_tags=False)\n",
"table_elements_as_text = [c for c in chunks_as_text if \"table\" in c.structure.split()]\n",
"\n",
"print(table_elements_as_text[0].text)"
]
},
{
"cell_type": "markdown",
"id": "731b3dfc-7ddf-4a11-9a30-9a79b7c66e16",
"metadata": {},
"source": [
"## Multi-vector retriever\n",
"\n",
"Use [multi-vector-retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary) to produce summaries of tables and, optionally, text. \n",
"\n",
"With the summary, we will also store the raw table elements.\n",
"\n",
"The summaries are used to improve the quality of retrieval, [as explained in the multi vector retriever docs](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector).\n",
"\n",
"The raw tables are passed to the LLM, providing the full table context for the LLM to generate the answer. \n",
"\n",
"### Summaries"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8e275736-3408-4d7a-990e-4362c88e81f8",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import (\n",
" ChatPromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
")\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "markdown",
"id": "37b65677-aeb4-44fd-b06d-4539341ede97",
"metadata": {},
"source": [
"We create a simple summarize chain for each element.\n",
"\n",
"You can also see, re-use, or modify the prompt in the Hub [here](https://smith.langchain.com/hub/rlm/multi-vector-retriever-summarization).\n",
"\n",
"```\n",
"from langchain import hub\n",
"obj = hub.pull(\"rlm/multi-vector-retriever-summarization\")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "1b12536a-1303-41ad-9948-4eb5a5f32614",
"metadata": {},
"outputs": [],
"source": [
"# Prompt\n",
"prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text. \\ \n",
"Give a concise summary of the table or text. Table or text chunk: {element} \"\"\"\n",
"prompt = ChatPromptTemplate.from_template(prompt_text)\n",
"\n",
"# Summary chain\n",
"model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8d8b567c-b442-4bf0-b639-04bd89effc62",
"metadata": {},
"outputs": [],
"source": [
"# Apply summarizer to tables\n",
"tables = [i.text for i in table_elements]\n",
"table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "markdown",
"id": "60524010-754f-4924-ad75-78cb54ca7257",
"metadata": {},
"source": [
"### Add to vectorstore\n",
"\n",
"Use [Multi Vector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary) with summaries: \n",
"\n",
"* `InMemoryStore` stores the raw text, tables\n",
"* `vectorstore` stores the embedded summaries"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "346c3a02-8fea-4f75-a69e-fc9542b99dbc",
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"\n",
"from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
"from langchain.storage import InMemoryStore\n",
"from langchain_community.vectorstores.chroma import Chroma\n",
"from langchain_core.documents import Document\n",
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"\n",
"def build_retriever(text_elements, tables, table_summaries):\n",
" # The vectorstore to use to index the child chunks\n",
" vectorstore = Chroma(\n",
" collection_name=\"summaries\", embedding_function=OpenAIEmbeddings()\n",
" )\n",
"\n",
" # The storage layer for the parent documents\n",
" store = InMemoryStore()\n",
" id_key = \"doc_id\"\n",
"\n",
" # The retriever (empty to start)\n",
" retriever = MultiVectorRetriever(\n",
" vectorstore=vectorstore,\n",
" docstore=store,\n",
" id_key=id_key,\n",
" )\n",
"\n",
" # Add texts\n",
" texts = [i.text for i in text_elements]\n",
" doc_ids = [str(uuid.uuid4()) for _ in texts]\n",
" retriever.docstore.mset(list(zip(doc_ids, texts)))\n",
"\n",
" # Add tables and summaries\n",
" table_ids = [str(uuid.uuid4()) for _ in tables]\n",
" summary_tables = [\n",
" Document(page_content=s, metadata={id_key: table_ids[i]})\n",
" for i, s in enumerate(table_summaries)\n",
" ]\n",
" retriever.vectorstore.add_documents(summary_tables)\n",
" retriever.docstore.mset(list(zip(table_ids, tables)))\n",
" return retriever\n",
"\n",
"\n",
"retriever = build_retriever(text_elements, tables, table_summaries)"
]
},
{
"cell_type": "markdown",
"id": "1d8bbbd9-009b-4b34-a206-5874a60adbda",
"metadata": {},
"source": [
"## RAG\n",
"\n",
"Run [RAG pipeline](https://python.langchain.com/docs/expression_language/cookbook/retrieval)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f2489de4-51e3-48b4-bbcd-ed9171deadf3",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"system_prompt = SystemMessagePromptTemplate.from_template(\n",
" \"You are a helpful assistant that answers questions based on provided context. Your provided context can include text or tables, \"\n",
" \"and may also contain semantic XML markup. Pay attention the semantic XML markup to understand more about the context semantics as \"\n",
" \"well as structure (e.g. lists and tabular layouts expressed with HTML-like tags)\"\n",
")\n",
"\n",
"human_prompt = HumanMessagePromptTemplate.from_template(\n",
" \"\"\"Context:\n",
"\n",
" {context}\n",
"\n",
" Question: {question}\"\"\"\n",
")\n",
"\n",
"\n",
"def build_chain(retriever, model):\n",
" prompt = ChatPromptTemplate.from_messages([system_prompt, human_prompt])\n",
"\n",
" # LLM\n",
" model = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
"\n",
" # RAG pipeline\n",
" chain = (\n",
" {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | model\n",
" | StrOutputParser()\n",
" )\n",
"\n",
" return chain\n",
"\n",
"\n",
"chain = build_chain(retriever, model)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "636e992f-823b-496b-a082-8b4fcd479de5",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The people authorized to receive confidential information and their roles are:\n",
"\n",
"1. John Smith - Project Manager\n",
"2. Lisa White - Lead Developer\n",
"3. Michael Brown - Financial Analyst\n"
]
}
],
"source": [
"result = chain.invoke(\n",
" \"Name all the people authorized to receive confidential information, and their roles\"\n",
")\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"id": "37f46054-e239-4ba8-af81-22d0d6a9bc32",
"metadata": {},
"source": [
"We can check the [trace](https://smith.langchain.com/public/21b3aa16-4ef3-40c3-92f6-3f0ceab2aedb/r) to see what chunks were retrieved.\n",
"\n",
"This includes Table 1 in the doc, showing the disclosures table as XML markup (same one as above)"
]
},
{
"cell_type": "markdown",
"id": "86cad5db-81fe-4ae6-a20e-550b85fcbe96",
"metadata": {},
"source": [
"# RAG on Llama2 paper\n",
"\n",
"Let's run the same Llama2 paper example from the [Semi_Structured_RAG.ipynb](./Semi_Structured_RAG.ipynb) notebook to see if we get the same results, and to contrast the table chunk returned by Docugami with the ones returned from Unstructured."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "0e4a2f43-dd48-4ae3-8e27-7e87d169965f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"669"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dgml = requests.get(\n",
" \"https://raw.githubusercontent.com/docugami/dgml-utils/main/python/tests/test_data/arxiv/2307.09288.xml\"\n",
").text\n",
"llama2_chunks = get_chunks_str(dgml, include_xml_tags=True)\n",
"len(llama2_chunks)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "56b78fb3-603d-4343-ae72-be54a3c5dd72",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 33 tables\n",
"There are 636 text elements\n"
]
}
],
"source": [
"# Tables\n",
"llama2_table_elements = [c for c in llama2_chunks if \"table\" in c.structure.split()]\n",
"print(f\"There are {len(llama2_table_elements)} tables\")\n",
"\n",
"# Text\n",
"llama2_text_elements = [c for c in llama2_chunks if \"table\" not in c.structure.split()]\n",
"print(f\"There are {len(llama2_text_elements)} text elements\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d3cc5ba9-8553-4eda-a5d1-b799751186af",
"metadata": {},
"outputs": [],
"source": [
"# Apply summarizer to tables\n",
"llama2_tables = [i.text for i in llama2_table_elements]\n",
"llama2_table_summaries = summarize_chain.batch(llama2_tables, {\"max_concurrency\": 5})"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d7c73faf-74cb-400d-8059-b69e2493de38",
"metadata": {},
"outputs": [],
"source": [
"llama2_retriever = build_retriever(\n",
" llama2_text_elements, llama2_tables, llama2_table_summaries\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "4c553722-be42-42ce-83b8-76a17f323f1c",
"metadata": {},
"outputs": [],
"source": [
"llama2_chain = build_chain(llama2_retriever, model)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "65dce40b-f1c3-494a-949e-69a9c9544ddb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The number of training tokens for LLaMA2 is 2.0T for all parameter sizes.'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llama2_chain.invoke(\"What is the number of training tokens for LLaMA2?\")"
]
},
{
"cell_type": "markdown",
"id": "59877edf-9a02-45db-95cb-b7f4234abfa3",
"metadata": {},
"source": [
"We can check the [trace](https://smith.langchain.com/public/5de100c3-bb40-4234-bf02-64bc708686a1/r) to see what chunks were retrieved.\n",
"\n",
"This includes Table 1 in the doc, showing the tokens used for training table as semantic XML markup:\n",
"\n",
"```xml\n",
"<table>\n",
" <tbody>\n",
" <tr>\n",
" <td />\n",
" <td>Training Data </td>\n",
" <td>Params </td>\n",
" <td>Context Length </td>\n",
" <td>\n",
" <Org>GQA </Org>\n",
" </td>\n",
" <td>Tokens </td>\n",
" <td>LR </td>\n",
" </tr>\n",
" <tr>\n",
" <td>Llama <Number>1 </Number></td>\n",
" <td>\n",
" <Llama1TrainingData>See <Person>Touvron </Person>et al. (<Number>2023</Number>) </Llama1TrainingData>\n",
" </td>\n",
" <td>\n",
" <Llama1Params>\n",
" <Number>7B </Number>\n",
" <Number>13B </Number>\n",
" <Number>33B </Number>\n",
" <Number>65B </Number>\n",
" </Llama1Params>\n",
" </td>\n",
" <td>\n",
" <Llama1ContextLength>\n",
" <Number>2k </Number>\n",
" <Number>2k </Number>\n",
" <Number>2k </Number>\n",
" <Number>2k </Number>\n",
" </Llama1ContextLength>\n",
" </td>\n",
" <td>\n",
" <Llama1GQA>✗ ✗ ✗ ✗ </Llama1GQA>\n",
" </td>\n",
" <td>\n",
" <Llama1Tokens><Number>1.0</Number>T <Number>1.0</Number>T <Number>1.4</Number>T <Number>\n",
" 1.4</Number>T </Llama1Tokens>\n",
" </td>\n",
" <td>\n",
" <Llama1LR> 3.0 × <Number>10−4 </Number> 3.0 × <Number>10−4 </Number> 1.5 × <Number>\n",
" 10−4 </Number> 1.5 × <Number>10−4 </Number></Llama1LR>\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>Llama <Number>2 </Number></td>\n",
" <td>\n",
" <Llama2TrainingData>A new mix of publicly available online data </Llama2TrainingData>\n",
" </td>\n",
" <td>\n",
" <Llama2Params><Number>7B </Number>13B <Number>34B </Number><Number>70B </Number></Llama2Params>\n",
" </td>\n",
" <td>\n",
" <Llama2ContextLength>\n",
" <Number>4k </Number>\n",
" <Number>4k </Number>\n",
" <Number>4k </Number>\n",
" <Number>4k </Number>\n",
" </Llama2ContextLength>\n",
" </td>\n",
" <td>\n",
" <Llama2GQA>✗ ✗ ✓ ✓ </Llama2GQA>\n",
" </td>\n",
" <td>\n",
" <Llama2Tokens><Number>2.0</Number>T <Number>2.0</Number>T <Number>2.0</Number>T <Number>\n",
" 2.0</Number>T </Llama2Tokens>\n",
" </td>\n",
" <td>\n",
" <Llama2LR> 3.0 × <Number>10−4 </Number> 3.0 × <Number>10−4 </Number> 1.5 × <Number>\n",
" 10−4 </Number> 1.5 × <Number>10−4 </Number></Llama2LR>\n",
" </td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "867f8e11-384c-4aa1-8b3e-c59fb8d5fd7d",
"metadata": {},
"source": [
"Finally, you can ask other questions that rely on more subtle parsing of the table, e.g.:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "d38f1459-7d2b-40df-8dcd-e747f85eb144",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The learning rate for LLaMA2 was 3.0 × 10−4 for the 7B and 13B models, and 1.5 × 10−4 for the 34B and 70B models.'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llama2_chain.invoke(\"What was the learning rate for LLaMA2?\")"
]
},
{
"cell_type": "markdown",
"id": "94826165",
"metadata": {},
"source": [
"## Docugami KG-RAG Template\n",
"\n",
"Docugami also provides a [langchain template](https://github.com/docugami/langchain-template-docugami-kg-rag) that you can integrate into your langchain projects.\n",
"\n",
"Here's a walkthrough of how you can do this.\n",
"\n",
"[](https://www.youtube.com/watch?v=xOHOmL1NFMg)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/elasticsearch_db_qa.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Elasticsearch\n",
"\n",
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/qa_structured/integrations/elasticsearch.ipynb)\n",
"\n",
"We can use LLMs to interact with Elasticsearch analytics databases in natural language.\n",
"\n",
"This chain builds search queries via the Elasticsearch DSL API (filters and aggregations).\n",
"\n",
"The Elasticsearch client must have permissions for index listing, mapping description and search queries.\n",
"\n",
"See [here](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html) for instructions on how to run Elasticsearch locally."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"! pip install langchain langchain-experimental openai elasticsearch\n",
"\n",
"# Set env var OPENAI_API_KEY or load from a .env file\n",
"# import dotenv\n",
"\n",
"# dotenv.load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Initialize Elasticsearch python client.\n",
"# See https://elasticsearch-py.readthedocs.io/en/v8.8.2/api.html#elasticsearch.Elasticsearch\n",
"ELASTIC_SEARCH_SERVER = \"https://elastic:pass@localhost:9200\"\n",
"db = Elasticsearch(ELASTIC_SEARCH_SERVER)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uncomment the next cell to initially populate your db."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# customers = [\n",
"# {\"firstname\": \"Jennifer\", \"lastname\": \"Walters\"},\n",
"# {\"firstname\": \"Monica\",\"lastname\":\"Rambeau\"},\n",
"# {\"firstname\": \"Carol\",\"lastname\":\"Danvers\"},\n",
"# {\"firstname\": \"Wanda\",\"lastname\":\"Maximoff\"},\n",
"# {\"firstname\": \"Jennifer\",\"lastname\":\"Takeda\"},\n",
"# ]\n",
"# for i, customer in enumerate(customers):\n",
"# db.create(index=\"customers\", document=customer, id=i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n",
"chain = ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What are the first names of all the customers?\"\n",
"chain.run(question)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can customize the prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.prompt import PromptTemplate\n",
"\n",
"PROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n",
"\n",
"Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.\n",
"\n",
"Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.\n",
"\n",
"Use the following format:\n",
"\n",
"Question: Question here\n",
"ESQuery: Elasticsearch Query formatted as json\n",
"\"\"\"\n",
"\n",
"PROMPT = PromptTemplate.from_template(\n",
" PROMPT_TEMPLATE,\n",
")\n",
"chain = ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, query_prompt=PROMPT)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/extraction_openai_tools.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "2def22ea",
"metadata": {},
"source": [
"# Extraction with OpenAI Tools\n",
"\n",
"Performing extraction has never been easier! OpenAI's tool calling ability is the perfect thing to use as it allows for extracting multiple different elements from text that are different types. \n",
"\n",
"Models after 1106 use tools and support \"parallel function calling\" which makes this super easy."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5c628496",
"metadata": {},
"outputs": [],
"source": [
"from typing import List, Optional\n",
"\n",
"from langchain.chains.openai_tools import create_extraction_chain_pydantic\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "afe9657b",
"metadata": {},
"outputs": [],
"source": [
"# Make sure to use a recent model that supports tools\n",
"model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bc0ca3b6",
"metadata": {},
"outputs": [],
"source": [
"# Pydantic is an easy way to define a schema\n",
"class Person(BaseModel):\n",
" \"\"\"Information about people to extract.\"\"\"\n",
"\n",
" name: str\n",
" age: Optional[int] = None"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2036af68",
"metadata": {},
"outputs": [],
"source": [
"chain = create_extraction_chain_pydantic(Person, model)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1748ad21",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Person(name='jane', age=2), Person(name='bob', age=3)]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke({\"input\": \"jane is 2 and bob is 3\"})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c8262ce5",
"metadata": {},
"outputs": [],
"source": [
"# Let's define another element\n",
"class Class(BaseModel):\n",
" \"\"\"Information about classes to extract.\"\"\"\n",
"\n",
" teacher: str\n",
" students: List[str]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4973c104",
"metadata": {},
"outputs": [],
"source": [
"chain = create_extraction_chain_pydantic([Person, Class], model)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e976a15e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Person(name='jane', age=2),\n",
" Person(name='bob', age=3),\n",
" Class(teacher='Mrs Sampson', students=['jane', 'bob'])]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke({\"input\": \"jane is 2 and bob is 3 and they are in Mrs Sampson's class\"})"
]
},
{
"cell_type": "markdown",
"id": "6575a7d6",
"metadata": {},
"source": [
"## Under the hood\n",
"\n",
"Under the hood, this is a simple chain:"
]
},
{
"cell_type": "markdown",
"id": "b8ba83e5",
"metadata": {},
"source": [
"```python\n",
"from typing import Union, List, Type, Optional\n",
"\n",
"from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
"from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
"from langchain_core.runnables import Runnable\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.messages import SystemMessage\n",
"from langchain_core.language_models import BaseLanguageModel\n",
"\n",
"_EXTRACTION_TEMPLATE = \"\"\"Extract and save the relevant entities mentioned \\\n",
"in the following passage together with their properties.\n",
"\n",
"If a property is not present and is not required in the function parameters, do not include it in the output.\"\"\" # noqa: E501\n",
"\n",
"\n",
"def create_extraction_chain_pydantic(\n",
" pydantic_schemas: Union[List[Type[BaseModel]], Type[BaseModel]],\n",
" llm: BaseLanguageModel,\n",
" system_message: str = _EXTRACTION_TEMPLATE,\n",
") -> Runnable:\n",
" if not isinstance(pydantic_schemas, list):\n",
" pydantic_schemas = [pydantic_schemas]\n",
" prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", system_message),\n",
" (\"user\", \"{input}\")\n",
" ])\n",
" tools = [convert_pydantic_to_openai_tool(p) for p in pydantic_schemas]\n",
" model = llm.bind(tools=tools)\n",
" chain = prompt | model | PydanticToolsParser(tools=pydantic_schemas)\n",
" return chain\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2eac6b68",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/fake_llm.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "052dfe58",
"metadata": {},
"source": [
"# Fake LLM\n",
"LangChain provides a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.\n",
"\n",
"In this notebook we go over how to use this.\n",
"\n",
"We start this with using the FakeLLM in an agent."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ef97ac4d",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.llms.fake import FakeListLLM"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9a0a160f",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import AgentType, initialize_agent, load_tools"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b272258c",
"metadata": {},
"outputs": [],
"source": [
"tools = load_tools([\"python_repl\"])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "94096c4c",
"metadata": {},
"outputs": [],
"source": [
"responses = [\"Action: Python REPL\\nAction Input: print(2 + 2)\", \"Final Answer: 4\"]\n",
"llm = FakeListLLM(responses=responses)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "da226d02",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(\n",
" tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "44c13426",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: Python REPL\n",
"Action Input: print(2 + 2)\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m4\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mFinal Answer: 4\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'4'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.invoke(\"whats 2 + 2\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "814c2858",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/fireworks_rag.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "0fc0309d-4d49-4bb5-bec0-bd92c6fddb28",
"metadata": {},
"source": [
"## Fireworks.AI + LangChain + RAG\n",
" \n",
"[Fireworks AI](https://python.langchain.com/docs/integrations/llms/fireworks) wants to provide the best experience when working with LangChain, and here is an example of Fireworks + LangChain doing RAG\n",
"\n",
"See [our models page](https://fireworks.ai/models) for the full list of models. We use `accounts/fireworks/models/mixtral-8x7b-instruct` for RAG In this tutorial.\n",
"\n",
"For the RAG target, we will use the Gemma technical report https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d12fb75a-f707-48d5-82a5-efe2d041813c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n",
"Found existing installation: langchain-fireworks 0.0.1\n",
"Uninstalling langchain-fireworks-0.0.1:\n",
" Successfully uninstalled langchain-fireworks-0.0.1\n",
"Note: you may need to restart the kernel to use updated packages.\n",
"Obtaining file:///mnt/disks/data/langchain/libs/partners/fireworks\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Checking if build backend supports build_editable ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build editable ... \u001b[?25ldone\n",
"\u001b[?25h Preparing editable metadata (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25hRequirement already satisfied: aiohttp<4.0.0,>=3.9.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (3.9.3)\n",
"Requirement already satisfied: fireworks-ai<0.13.0,>=0.12.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (0.12.0)\n",
"Requirement already satisfied: langchain-core<0.2,>=0.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (0.1.23)\n",
"Requirement already satisfied: requests<3,>=2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-fireworks==0.0.1) (2.31.0)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (1.3.1)\n",
"Requirement already satisfied: attrs>=17.3.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (23.1.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (1.4.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (6.0.4)\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (1.9.2)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from aiohttp<4.0.0,>=3.9.1->langchain-fireworks==0.0.1) (4.0.3)\n",
"Requirement already satisfied: httpx in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.26.0)\n",
"Requirement already satisfied: httpx-sse in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.4.0)\n",
"Requirement already satisfied: pydantic in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (2.4.2)\n",
"Requirement already satisfied: Pillow in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (10.2.0)\n",
"Requirement already satisfied: PyYAML>=5.3 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (6.0.1)\n",
"Requirement already satisfied: anyio<5,>=3 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (3.7.1)\n",
"Requirement already satisfied: jsonpatch<2.0,>=1.33 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (1.33)\n",
"Requirement already satisfied: langsmith<0.2.0,>=0.1.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (0.1.5)\n",
"Requirement already satisfied: packaging<24.0,>=23.2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (23.2)\n",
"Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (8.2.3)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (3.3.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (2.0.6)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from requests<3,>=2->langchain-fireworks==0.0.1) (2023.7.22)\n",
"Requirement already satisfied: sniffio>=1.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (1.3.0)\n",
"Requirement already satisfied: exceptiongroup in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (1.1.3)\n",
"Requirement already satisfied: jsonpointer>=1.9 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.2,>=0.1->langchain-fireworks==0.0.1) (2.4)\n",
"Requirement already satisfied: annotated-types>=0.4.0 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from pydantic->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.5.0)\n",
"Requirement already satisfied: pydantic-core==2.10.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from pydantic->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (2.10.1)\n",
"Requirement already satisfied: typing-extensions>=4.6.1 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from pydantic->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (4.8.0)\n",
"Requirement already satisfied: httpcore==1.* in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from httpx->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (1.0.2)\n",
"Requirement already satisfied: h11<0.15,>=0.13 in /mnt/disks/data/langchain/.venv/lib/python3.9/site-packages (from httpcore==1.*->httpx->fireworks-ai<0.13.0,>=0.12.0->langchain-fireworks==0.0.1) (0.14.0)\n",
"Building wheels for collected packages: langchain-fireworks\n",
" Building editable for langchain-fireworks (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for langchain-fireworks: filename=langchain_fireworks-0.0.1-py3-none-any.whl size=2228 sha256=564071b120b09ec31f2dc737733448a33bbb26e40b49fcde0c129ad26045259d\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-oz368vdk/wheels/e0/ad/31/d7e76dd73d61905ff7f369f5b0d21a4b5e7af4d3cb7487aece\n",
"Successfully built langchain-fireworks\n",
"Installing collected packages: langchain-fireworks\n",
"Successfully installed langchain-fireworks-0.0.1\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --quiet pypdf chromadb tiktoken openai \n",
"%pip uninstall -y langchain-fireworks\n",
"%pip install --editable /mnt/disks/data/langchain/libs/partners/fireworks"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cf719376",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<module 'fireworks' from '/mnt/disks/data/langchain/.venv/lib/python3.9/site-packages/fireworks/__init__.py'>\n"
]
}
],
"source": [
"import fireworks\n",
"\n",
"print(fireworks)\n",
"import fireworks.client"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ab49327-0532-4480-804c-d066c302a322",
"metadata": {},
"outputs": [],
"source": [
"# Load\n",
"import requests\n",
"from langchain_community.document_loaders import PyPDFLoader\n",
"\n",
"# Download the PDF from a URL and save it to a temporary location\n",
"url = \"https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf\"\n",
"response = requests.get(url, stream=True)\n",
"file_name = \"temp_file.pdf\"\n",
"with open(file_name, \"wb\") as pdf:\n",
" pdf.write(response.content)\n",
"\n",
"loader = PyPDFLoader(file_name)\n",
"data = loader.load()\n",
"\n",
"# Split\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)\n",
"all_splits = text_splitter.split_documents(data)\n",
"\n",
"# Add to vectorDB\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_fireworks.embeddings import FireworksEmbeddings\n",
"\n",
"vectorstore = Chroma.from_documents(\n",
" documents=all_splits,\n",
" collection_name=\"rag-chroma\",\n",
" embedding=FireworksEmbeddings(),\n",
")\n",
"\n",
"retriever = vectorstore.as_retriever()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4efaddd9-3dbb-455c-ba54-0ad7f2d2ce0f",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
"\n",
"# RAG prompt\n",
"template = \"\"\"Answer the question based only on the following context:\n",
"{context}\n",
"\n",
"Question: {question}\n",
"\"\"\"\n",
"prompt = ChatPromptTemplate.from_template(template)\n",
"\n",
"# LLM\n",
"from langchain_together import Together\n",
"\n",
"llm = Together(\n",
" model=\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
" temperature=0.0,\n",
" max_tokens=2000,\n",
" top_k=1,\n",
")\n",
"\n",
"# RAG chain\n",
"chain = (\n",
" RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "88b1ee51-1b0f-4ebf-bb32-e50e843f0eeb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nAnswer: The architectural details of Mixtral are as follows:\\n- Dimension (dim): 4096\\n- Number of layers (n\\\\_layers): 32\\n- Dimension of each head (head\\\\_dim): 128\\n- Hidden dimension (hidden\\\\_dim): 14336\\n- Number of heads (n\\\\_heads): 32\\n- Number of kv heads (n\\\\_kv\\\\_heads): 8\\n- Context length (context\\\\_len): 32768\\n- Vocabulary size (vocab\\\\_size): 32000\\n- Number of experts (num\\\\_experts): 8\\n- Number of top k experts (top\\\\_k\\\\_experts): 2\\n\\nMixtral is based on a transformer architecture and uses the same modifications as described in [18], with the notable exceptions that Mixtral supports a fully dense context length of 32k tokens, and the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. This technique increases the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token. Mixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"What are the Architectural details of Mixtral?\")"
]
},
{
"cell_type": "markdown",
"id": "755cf871-26b7-4e30-8b91-9ffd698470f4",
"metadata": {},
"source": [
"Trace: \n",
"\n",
"https://smith.langchain.com/public/935fd642-06a6-4b42-98e3-6074f93115cd/r"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/forward_looking_retrieval_augmented_generation.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "0f0b9afa",
"metadata": {},
"source": [
"# Retrieve as you generate with FLARE\n",
"\n",
"This notebook is an implementation of Forward-Looking Active REtrieval augmented generation (FLARE).\n",
"\n",
"Please see the original repo [here](https://github.com/jzbjyb/FLARE/tree/main).\n",
"\n",
"The basic idea is:\n",
"\n",
"- Start answering a question\n",
"- If you start generating tokens the model is uncertain about, look up relevant documents\n",
"- Use those documents to continue generating\n",
"- Repeat until finished\n",
"\n",
"There is a lot of cool detail in how the lookup of relevant documents is done.\n",
"Basically, the tokens that model is uncertain about are highlighted, and then an LLM is called to generate a question that would lead to that answer. For example, if the generated text is `Joe Biden went to Harvard`, and the tokens the model was uncertain about was `Harvard`, then a good generated question would be `where did Joe Biden go to college`. This generated question is then used in a retrieval step to fetch relevant documents.\n",
"\n",
"In order to set up this chain, we will need three things:\n",
"\n",
"- An LLM to generate the answer\n",
"- An LLM to generate hypothetical questions to use in retrieval\n",
"- A retriever to use to look up answers for\n",
"\n",
"The LLM that we use to generate the answer needs to return logprobs so we can identify uncertain tokens. For that reason, we HIGHLY recommend that you use the OpenAI wrapper (NB: not the ChatOpenAI wrapper, as that does not return logprobs).\n",
"\n",
"The LLM we use to generate hypothetical questions to use in retrieval can be anything. In this notebook we will use ChatOpenAI because it is fast and cheap.\n",
"\n",
"The retriever can be anything. In this notebook we will use [SERPER](https://serper.dev/) search engine, because it is cheap.\n",
"\n",
"Other important parameters to understand:\n",
"\n",
"- `max_generation_len`: The maximum number of tokens to generate before stopping to check if any are uncertain\n",
"- `min_prob`: Any tokens generated with probability below this will be considered uncertain"
]
},
{
"cell_type": "markdown",
"id": "a7e4b63d",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "042bb161",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"SERPER_API_KEY\"] = \"\"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a7888f4a",
"metadata": {},
"outputs": [],
"source": [
"from typing import Any, List\n",
"\n",
"from langchain.callbacks.manager import (\n",
" AsyncCallbackManagerForRetrieverRun,\n",
" CallbackManagerForRetrieverRun,\n",
")\n",
"from langchain_community.utilities import GoogleSerperAPIWrapper\n",
"from langchain_core.documents import Document\n",
"from langchain_core.retrievers import BaseRetriever\n",
"from langchain_openai import ChatOpenAI, OpenAI"
]
},
{
"cell_type": "markdown",
"id": "5f552dce",
"metadata": {},
"source": [
"## Retriever"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "59c7d875",
"metadata": {},
"outputs": [],
"source": [
"class SerperSearchRetriever(BaseRetriever):\n",
" search: GoogleSerperAPIWrapper = None\n",
"\n",
" def _get_relevant_documents(\n",
" self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any\n",
" ) -> List[Document]:\n",
" return [Document(page_content=self.search.run(query))]\n",
"\n",
" async def _aget_relevant_documents(\n",
" self,\n",
" query: str,\n",
" *,\n",
" run_manager: AsyncCallbackManagerForRetrieverRun,\n",
" **kwargs: Any,\n",
" ) -> List[Document]:\n",
" raise NotImplementedError()\n",
"\n",
"\n",
"retriever = SerperSearchRetriever(search=GoogleSerperAPIWrapper())"
]
},
{
"cell_type": "markdown",
"id": "92478194",
"metadata": {},
"source": [
"## FLARE Chain"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "577e7c2c",
"metadata": {},
"outputs": [],
"source": [
"# We set this so we can see what exactly is going on\n",
"from langchain.globals import set_verbose\n",
"\n",
"set_verbose(True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "300d783e",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import FlareChain\n",
"\n",
"flare = FlareChain.from_llm(\n",
" ChatOpenAI(temperature=0),\n",
" retriever=retriever,\n",
" max_generation_len=164,\n",
" min_prob=0.3,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1f3d5e90",
"metadata": {},
"outputs": [],
"source": [
"query = \"explain in great detail the difference between the langchain framework and baby agi\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4b1bfa8c",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new FlareChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3mCurrent Response: \u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
"\n",
">>> CONTEXT: \n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> RESPONSE: \u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Entering new QuestionGeneratorChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" decentralized platform for natural language processing\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" uses a blockchain\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" distributed ledger to\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" process data, allowing for secure and transparent data sharing.\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" set of tools\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" help developers create\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" create an AI system\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications.\n",
"\n",
"Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing.\n",
"\n",
"In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for\n",
"\n",
"The question to which the answer is the term/entity/phrase \" NLP applications\" is:\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\u001b[33;1m\u001b[1;3mGenerated Questions: ['What is the Langchain Framework?', 'What technology does the Langchain Framework use to store and process data for secure and transparent data sharing?', 'What technology does the Langchain Framework use to store and process data?', 'What does the Langchain Framework use a blockchain-based distributed ledger for?', 'What does the Langchain Framework provide in addition to a decentralized platform for natural language processing applications?', 'What set of tools and services does the Langchain Framework provide?', 'What is the purpose of Baby AGI?', 'What type of applications is the Langchain Framework designed for?']\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Entering new _OpenAIResponseChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
"\n",
">>> CONTEXT: LangChain: Software. LangChain is a software development framework designed to simplify the creation of applications using large language models. LangChain Initial release date: October 2022. LangChain Programming languages: Python and JavaScript. LangChain Developer(s): Harrison Chase. LangChain License: MIT License. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... Type: Software framework. At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. LangChain is a powerful tool that can be used to work with Large Language Models (LLMs). LLMs are very general in nature, which means that while they can ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. LangChain is a software development framework designed to simplify the creation of applications using large language models (LLMs). Written in: Python and JavaScript. Initial release: October 2022. LangChain - The A.I-native developer toolkit We started LangChain with the intent to build a modular and flexible framework for developing A.I- ... LangChain explained in 3 minutes - LangChain is a ... Duration: 3:03. Posted: Apr 13, 2023. LangChain is a framework built to help you build LLM-powered applications more easily by providing you with the following:. LangChain is a framework that enables quick and easy development of applications that make use of Large Language Models, for example, GPT-3. LangChain is a powerful open-source framework for developing applications powered by language models. It connects to the AI models you want to ...\n",
"\n",
"LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Missing: secure | Must include:secure. Blockchain is the best way to secure the data of the shared community. Utilizing the capabilities of the blockchain nobody can read or interfere ... This modern technology consists of a chain of blocks that allows to securely store all committed transactions using shared and distributed ... A Blockchain network is used in the healthcare system to preserve and exchange patient data through hospitals, diagnostic laboratories, pharmacy firms, and ... In this article, I will walk you through the process of using the LangChain.js library with Google Cloud Functions, helping you leverage the ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: transparent | Must include:transparent. This technology keeps a distributed ledger on each blockchain node, making it more secure and transparent. The blockchain network can operate smart ... blockchain technology can offer a highly secured health data ledger to ... framework can be employed to store encrypted healthcare data in a ... In a simplified way, Blockchain is a data structure that stores transactions in an ordered way and linked to the previous block, serving as a ... Blockchain technology is a decentralized, distributed ledger that stores the record of ownership of digital assets. Missing: Langchain | Must include:Langchain.\n",
"\n",
"LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. This documentation covers the steps to integrate Pinecone, a high-performance vector database, with LangChain, a framework for building applications powered ... The ability to connect to any model, ingest any custom database, and build upon a framework that can take action provides numerous use cases for ... With LangChain, developers can use a framework that abstracts the core building blocks of LLM applications. LangChain empowers developers to ... Build a question-answering tool based on financial data with LangChain & Deep Lake's unified & streamable data store. Browse applications built on LangChain technology. Explore PoC and MVP applications created by our community and discover innovative use cases for LangChain ... LangChain is a great framework that can be used for developing applications powered by LLMs. When you intend to enhance your application ... In this blog, we'll introduce you to LangChain and Ray Serve and how to use them to build a search engine using LLM embeddings and a vector ... The LinkChain Framework simplifies embedding creation and storage using Pinecone and Chroma, with code that loads files, splits documents, and creates embedding ... Missing: technology | Must include:technology.\n",
"\n",
"Blockchain is one type of a distributed ledger. Distributed ledgers use independent computers (referred to as nodes) to record, share and ... Missing: Langchain | Must include:Langchain. Blockchain is used in distributed storage software where huge data is broken down into chunks. This is available in encrypted data across a ... People sometimes use the terms 'Blockchain' and 'Distributed Ledger' interchangeably. This post aims to analyze the features of each. A distributed ledger ... Missing: Framework | Must include:Framework. Think of a “distributed ledger” that uses cryptography to allow each participant in the transaction to add to the ledger in a secure way without ... In this paper, we provide an overview of the history of trade settlement and discuss this nascent technology that may now transform traditional ... Missing: Langchain | Must include:Langchain. LangChain is a blockchain-based language education platform that aims to revolutionize the way people learn languages. Missing: Framework | Must include:Framework. It uses the distributed ledger technology framework and Smart contract engine for building scalable Business Blockchain applications. The fabric ... It looks at the assets the use case is handling, the different parties conducting transactions, and the smart contract, distributed ... Are you curious to know how Blockchain and Distributed ... Duration: 44:31. Posted: May 4, 2021. A blockchain is a distributed and immutable ledger to transfer ownership, record transactions, track assets, and ensure transparency, security, trust and value ... Missing: Langchain | Must include:Langchain.\n",
"\n",
"LangChain is an intuitive framework created to assist in developing applications driven by a language model, such as OpenAI or Hugging Face. Missing: decentralized | Must include:decentralized. LangChain, created by Harrison Chase, is a Python library that provides out-of-the-box support to build NLP applications using LLMs. Missing: decentralized | Must include:decentralized. LangChain provides a standard interface for chains, enabling developers to create sequences of calls that go beyond a single LLM call. Chains ... Missing: decentralized platform natural. LangChain is a powerful framework that simplifies the process of building advanced language model applications. Missing: platform | Must include:platform. Are your language models ignoring previous instructions ... Duration: 32:23. Posted: Feb 21, 2023. LangChain is a framework that enables quick and easy development of applications ... Prompting is the new way of programming NLP models. Missing: decentralized platform. It then uses natural language processing and machine learning algorithms to search ... Summarization is handled via cohere, QnA is handled via langchain, ... LangChain is a framework for developing applications powered by language models. ... There are several main modules that LangChain provides support for. Missing: decentralized platform. In the healthcare-chain system, blockchain provides an appreciated secure ... The entire process of adding new and previous block data is performed based on ... ChatGPT is a large language model developed by OpenAI, ... tool for a wide range of applications, including natural language processing, ...\n",
"\n",
"LangChain is a powerful tool that can be used to work with Large Language ... If an API key has been provided, create an OpenAI language model instance At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. A tutorial of the six core modules of the LangChain Python package covering models, prompts, chains, agents, indexes, and memory with OpenAI ... LangChain's collection of tools refers to a set of tools provided by the LangChain framework for developing applications powered by language models. LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only ... LangChain is an open-source library that provides developers with the tools to build applications powered by large language models (LLMs). LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you ... Plan-and-Execute Agents · Feature Stores and LLMs · Structured Tools · Auto-Evaluator Opportunities · Callbacks Improvements · Unleashing the power ... Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. · LLM: The language model ... LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\n",
"\n",
"Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. This system is exploring and demonstrating to us the potential of large language models, such as GPT and how it can autonomously perform tasks. Apr 17, 2023\n",
"\n",
"At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.\n",
">>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi\n",
">>> RESPONSE: \u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' LangChain is a framework for developing applications powered by language models. It provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. On the other hand, Baby AGI is an AI system that is exploring and demonstrating the potential of large language models, such as GPT, and how it can autonomously perform tasks. Baby AGI has the ability to complete tasks, generate new tasks based on previous results, and prioritize tasks in real-time. '"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flare.run(query)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7bed8944",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\n\\nThe Langchain framework and Baby AGI are both artificial intelligence (AI) frameworks that are used to create intelligent agents. The Langchain framework is a supervised learning system that is based on the concept of “language chains”. It uses a set of rules to map natural language inputs to specific outputs. It is a general-purpose AI framework and can be used to build applications such as natural language processing (NLP), chatbots, and more.\\n\\nBaby AGI, on the other hand, is an unsupervised learning system that uses neural networks and reinforcement learning to learn from its environment. It is used to create intelligent agents that can adapt to changing environments. It is a more advanced AI system and can be used to build more complex applications such as game playing, robotic vision, and more.\\n\\nThe main difference between the two is that the Langchain framework uses supervised learning while Baby AGI uses unsupervised learning. The Langchain framework is a general-purpose AI framework that can be used for various applications, while Baby AGI is a more advanced AI system that can be used to create more complex applications.'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm = OpenAI()\n",
"llm.invoke(query)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8fb76286",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new FlareChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3mCurrent Response: \u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
"\n",
">>> CONTEXT: \n",
">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
">>> RESPONSE: \u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Entering new QuestionGeneratorChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"\n",
"Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications. \n",
"\n",
"FINISHED\n",
"\n",
"The question to which the answer is the term/entity/phrase \" very different origin\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"\n",
"Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications. \n",
"\n",
"FINISHED\n",
"\n",
"The question to which the answer is the term/entity/phrase \" 2020 by a\" is:\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mGiven a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n",
"\n",
">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
">>> EXISTING PARTIAL RESPONSE: \n",
"\n",
"Langchain and Bitcoin have very different origin stories. Bitcoin was created by the mysterious Satoshi Nakamoto in 2008 as a decentralized digital currency. Langchain, on the other hand, was created in 2020 by a team of developers as a platform for creating and managing decentralized language learning applications. \n",
"\n",
"FINISHED\n",
"\n",
"The question to which the answer is the term/entity/phrase \" developers as a platform for creating and managing decentralized language learning applications.\" is:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\u001b[33;1m\u001b[1;3mGenerated Questions: ['How would you describe the origin stories of Langchain and Bitcoin in terms of their similarities or differences?', 'When was Langchain created and by whom?', 'What was the purpose of creating Langchain?']\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Entering new _OpenAIResponseChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mRespond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED.\n",
"\n",
">>> CONTEXT: Bitcoin and Ethereum have many similarities but different long-term visions and limitations. Ethereum changed from proof of work to proof of ... Bitcoin will be around for many years and examining its white paper origins is a great exercise in understanding why. Satoshi Nakamoto's blueprint describes ... Bitcoin is a new currency that was created in 2009 by an unknown person using the alias Satoshi Nakamoto. Transactions are made with no middle men – meaning, no ... Missing: Langchain | Must include:Langchain. By comparison, Bitcoin transaction speeds are tremendously lower. ... learn about its history and its role in the emergence of the Bitcoin ... LangChain is a powerful framework that simplifies the process of ... tasks like document retrieval, clustering, and similarity comparisons. Key terms: Bitcoin System, Blockchain Technology, ... Furthermore, the research paper will discuss and compare the five payment. Blockchain first appeared in Nakamoto's Bitcoin white paper that describes a new decentralized cryptocurrency [1]. Bitcoin takes the blockchain technology ... Missing: stories | Must include:stories. A score of 0 means there were not enough data for this term. Google trends was accessed on 5 November 2018 with searches for bitcoin, euro, gold ... Contracts, transactions, and records of them provide critical structure in our economic system, but they haven't kept up with the world's digital ... Missing: Langchain | Must include:Langchain. Of course, traders try to make a profit on their portfolio in this way.The difference between investing and trading is the regularity with which ...\n",
"\n",
"After all these giant leaps forward in the LLM space, OpenAI released ChatGPT — thrusting LLMs into the spotlight. LangChain appeared around the same time. Its creator, Harrison Chase, made the first commit in late October 2022. Leaving a short couple of months of development before getting caught in the LLM wave.\n",
"\n",
"At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.\n",
">>> USER INPUT: how are the origin stories of langchain and bitcoin similar or different?\n",
">>> RESPONSE: \u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' The origin stories of LangChain and Bitcoin are quite different. Bitcoin was created in 2009 by an unknown person using the alias Satoshi Nakamoto. LangChain was created in late October 2022 by Harrison Chase. Bitcoin is a decentralized cryptocurrency, while LangChain is a framework built around LLMs. '"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flare.run(\"how are the origin stories of langchain and bitcoin similar or different?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fbadd022",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "e9732067-71c7-46f7-ad09-381b3bf21a27",
"metadata": {},
"source": [
"# Generative Agents in LangChain\n",
"\n",
"This notebook implements a generative agent based on the paper [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442) by Park, et. al.\n",
"\n",
"In it, we leverage a time-weighted Memory object backed by a LangChain Retriever."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "53f81c37-db45-4fdc-843c-aa8fd2a9e99d",
"metadata": {},
"outputs": [],
"source": [
"# Use termcolor to make it easy to colorize the outputs.\n",
"!pip install termcolor > /dev/null"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3128fc21",
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"logging.basicConfig(level=logging.ERROR)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8851c370-b395-4b80-a79d-486a38ffc244",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from datetime import datetime, timedelta\n",
"from typing import List\n",
"\n",
"from langchain.docstore import InMemoryDocstore\n",
"from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
"from langchain_community.vectorstores import FAISS\n",
"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
"from termcolor import colored"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "81824e76",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"USER_NAME = \"Person A\" # The name you want to use when interviewing the agent.\n",
"LLM = ChatOpenAI(max_tokens=1500) # Can be any LLM you want."
]
},
{
"cell_type": "markdown",
"id": "c3da1649-d88f-4973-b655-7042975cde7e",
"metadata": {},
"source": [
"### Generative Agent Memory Components\n",
"\n",
"This tutorial highlights the memory of generative agents and its impact on their behavior. The memory varies from standard LangChain Chat memory in two aspects:\n",
"\n",
"1. **Memory Formation**\n",
"\n",
" Generative Agents have extended memories, stored in a single stream:\n",
" 1. Observations - from dialogues or interactions with the virtual world, about self or others\n",
" 2. Reflections - resurfaced and summarized core memories\n",
"\n",
"\n",
"2. **Memory Recall**\n",
"\n",
" Memories are retrieved using a weighted sum of salience, recency, and importance.\n",
"\n",
"You can review the definitions of the `GenerativeAgent` and `GenerativeAgentMemory` in the [reference documentation](\"https://api.python.langchain.com/en/latest/modules/experimental.html\") for the following imports, focusing on `add_memory` and `summarize_related_memories` methods."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "043e5203-6a41-431c-9efa-3e1743d7d25a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_experimental.generative_agents import (\n",
" GenerativeAgent,\n",
" GenerativeAgentMemory,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "361bd49e",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
},
"source": [
"## Memory Lifecycle\n",
"\n",
"Summarizing the key methods in the above: `add_memory` and `summarize_related_memories`.\n",
"\n",
"When an agent makes an observation, it stores the memory:\n",
" \n",
"1. Language model scores the memory's importance (1 for mundane, 10 for poignant)\n",
"2. Observation and importance are stored within a document by TimeWeightedVectorStoreRetriever, with a `last_accessed_time`.\n",
"\n",
"When an agent responds to an observation:\n",
"\n",
"1. Generates query(s) for retriever, which fetches documents based on salience, recency, and importance.\n",
"2. Summarizes the retrieved information\n",
"3. Updates the `last_accessed_time` for the used documents.\n"
]
},
{
"cell_type": "markdown",
"id": "2fa3ca02",
"metadata": {},
"source": [
"## Create a Generative Character\n",
"\n",
"\n",
"\n",
"Now that we've walked through the definition, we will create two characters named \"Tommie\" and \"Eve\"."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ee9c1a1d-c311-4f1c-8131-75fccd9025b1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import math\n",
"\n",
"import faiss\n",
"\n",
"\n",
"def relevance_score_fn(score: float) -> float:\n",
" \"\"\"Return a similarity score on a scale [0, 1].\"\"\"\n",
" # This will differ depending on a few things:\n",
" # - the distance / similarity metric used by the VectorStore\n",
" # - the scale of your embeddings (OpenAI's are unit norm. Many others are not!)\n",
" # This function converts the euclidean norm of normalized embeddings\n",
" # (0 is most similar, sqrt(2) most dissimilar)\n",
" # to a similarity function (0 to 1)\n",
" return 1.0 - score / math.sqrt(2)\n",
"\n",
"\n",
"def create_new_memory_retriever():\n",
" \"\"\"Create a new vector store retriever unique to the agent.\"\"\"\n",
" # Define your embedding model\n",
" embeddings_model = OpenAIEmbeddings()\n",
" # Initialize the vectorstore as empty\n",
" embedding_size = 1536\n",
" index = faiss.IndexFlatL2(embedding_size)\n",
" vectorstore = FAISS(\n",
" embeddings_model.embed_query,\n",
" index,\n",
" InMemoryDocstore({}),\n",
" {},\n",
" relevance_score_fn=relevance_score_fn,\n",
" )\n",
" return TimeWeightedVectorStoreRetriever(\n",
" vectorstore=vectorstore, other_score_keys=[\"importance\"], k=15\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7884f9dd-c597-4c27-8c77-1402c71bc2f8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"tommies_memory = GenerativeAgentMemory(\n",
" llm=LLM,\n",
" memory_retriever=create_new_memory_retriever(),\n",
" verbose=False,\n",
" reflection_threshold=8, # we will give this a relatively low number to show how reflection works\n",
")\n",
"\n",
"tommie = GenerativeAgent(\n",
" name=\"Tommie\",\n",
" age=25,\n",
" traits=\"anxious, likes design, talkative\", # You can add more persistent traits here\n",
" status=\"looking for a job\", # When connected to a virtual world, we can have the characters update their status\n",
" memory_retriever=create_new_memory_retriever(),\n",
" llm=LLM,\n",
" memory=tommies_memory,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c524d529",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: Tommie (age: 25)\n",
"Innate traits: anxious, likes design, talkative\n",
"No information about Tommie's core characteristics is provided in the given statements.\n"
]
}
],
"source": [
"# The current \"Summary\" of a character can't be made because the agent hasn't made\n",
"# any observations yet.\n",
"print(tommie.get_summary())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4be60979-d56e-4abf-a636-b34ffa8b7fba",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# We can add memories directly to the memory object\n",
"tommie_observations = [\n",
" \"Tommie remembers his dog, Bruno, from when he was a kid\",\n",
" \"Tommie feels tired from driving so far\",\n",
" \"Tommie sees the new home\",\n",
" \"The new neighbors have a cat\",\n",
" \"The road is noisy at night\",\n",
" \"Tommie is hungry\",\n",
" \"Tommie tries to get some rest.\",\n",
"]\n",
"for observation in tommie_observations:\n",
" tommie.memory.add_memory(observation)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6992b48b-697f-4973-9560-142ef85357d7",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: Tommie (age: 25)\n",
"Innate traits: anxious, likes design, talkative\n",
"Tommie is a person who is observant of his surroundings, has a sentimental side, and experiences basic human needs such as hunger and the need for rest. He also tends to get tired easily and is affected by external factors such as noise from the road or a neighbor's pet.\n"
]
}
],
"source": [
"# Now that Tommie has 'memories', their self-summary is more descriptive, though still rudimentary.\n",
"# We will see how this summary updates after more observations to create a more rich description.\n",
"print(tommie.get_summary(force_refresh=True))"
]
},
{
"cell_type": "markdown",
"id": "40d39a32-838c-4a03-8b27-a52c76c402e7",
"metadata": {
"tags": []
},
"source": [
"## Pre-Interview with Character\n",
"\n",
"Before sending our character on their way, let's ask them a few questions."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "eaf125d8-f54c-4c5f-b6af-32789b1f7d3a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def interview_agent(agent: GenerativeAgent, message: str) -> str:\n",
" \"\"\"Help the notebook user interact with the agent.\"\"\"\n",
" new_message = f\"{USER_NAME} says {message}\"\n",
" return agent.generate_dialogue_response(new_message)[1]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "54024d41-6e83-4914-91e5-73140e2dd9c8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Tommie said \"I really enjoy design and being creative. I\\'ve been working on some personal projects lately. What about you, Person A? What do you like to do?\"'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(tommie, \"What do you like to do?\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "71e2e8cc-921e-4816-82f1-66962b2c1055",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Tommie said \"Well, I\\'m actually looking for a job right now, so hopefully I can find some job postings online and start applying. How about you, Person A? What\\'s on your schedule for today?\"'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(tommie, \"What are you looking forward to doing today?\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a2521ffc-7050-4ac3-9a18-4cccfc798c31",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Tommie said \"Honestly, I\\'m feeling pretty anxious about finding a job. It\\'s been a bit of a struggle lately, but I\\'m trying to stay positive and keep searching. How about you, Person A? What worries you?\"'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(tommie, \"What are you most worried about today?\")"
]
},
{
"cell_type": "markdown",
"id": "e509c468-f7cd-4d72-9f3a-f4aba28b1eea",
"metadata": {},
"source": [
"## Step through the day's observations."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "154dee3d-bfe0-4828-b963-ed7e885799b3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Let's have Tommie start going through a day in the life.\n",
"observations = [\n",
" \"Tommie wakes up to the sound of a noisy construction site outside his window.\",\n",
" \"Tommie gets out of bed and heads to the kitchen to make himself some coffee.\",\n",
" \"Tommie realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.\",\n",
" \"Tommie finally finds the filters and makes himself a cup of coffee.\",\n",
" \"The coffee tastes bitter, and Tommie regrets not buying a better brand.\",\n",
" \"Tommie checks his email and sees that he has no job offers yet.\",\n",
" \"Tommie spends some time updating his resume and cover letter.\",\n",
" \"Tommie heads out to explore the city and look for job openings.\",\n",
" \"Tommie sees a sign for a job fair and decides to attend.\",\n",
" \"The line to get in is long, and Tommie has to wait for an hour.\",\n",
" \"Tommie meets several potential employers at the job fair but doesn't receive any offers.\",\n",
" \"Tommie leaves the job fair feeling disappointed.\",\n",
" \"Tommie stops by a local diner to grab some lunch.\",\n",
" \"The service is slow, and Tommie has to wait for 30 minutes to get his food.\",\n",
" \"Tommie overhears a conversation at the next table about a job opening.\",\n",
" \"Tommie asks the diners about the job opening and gets some information about the company.\",\n",
" \"Tommie decides to apply for the job and sends his resume and cover letter.\",\n",
" \"Tommie continues his search for job openings and drops off his resume at several local businesses.\",\n",
" \"Tommie takes a break from his job search to go for a walk in a nearby park.\",\n",
" \"A dog approaches and licks Tommie's feet, and he pets it for a few minutes.\",\n",
" \"Tommie sees a group of people playing frisbee and decides to join in.\",\n",
" \"Tommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.\",\n",
" \"Tommie goes back to his apartment to rest for a bit.\",\n",
" \"A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.\",\n",
" \"Tommie starts to feel frustrated with his job search.\",\n",
" \"Tommie calls his best friend to vent about his struggles.\",\n",
" \"Tommie's friend offers some words of encouragement and tells him to keep trying.\",\n",
" \"Tommie feels slightly better after talking to his friend.\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "238be49c-edb3-4e26-a2b6-98777ba8de86",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32mTommie wakes up to the sound of a noisy construction site outside his window.\u001b[0m Tommie groans and covers his head with a pillow, trying to block out the noise.\n",
"\u001b[32mTommie gets out of bed and heads to the kitchen to make himself some coffee.\u001b[0m Tommie stretches his arms and yawns before starting to make the coffee.\n",
"\u001b[32mTommie realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.\u001b[0m Tommie sighs in frustration and continues searching through the boxes.\n",
"\u001b[32mTommie finally finds the filters and makes himself a cup of coffee.\u001b[0m Tommie takes a deep breath and enjoys the aroma of the fresh coffee.\n",
"\u001b[32mThe coffee tastes bitter, and Tommie regrets not buying a better brand.\u001b[0m Tommie grimaces and sets the coffee mug aside.\n",
"\u001b[32mTommie checks his email and sees that he has no job offers yet.\u001b[0m Tommie sighs and closes his laptop, feeling discouraged.\n",
"\u001b[32mTommie spends some time updating his resume and cover letter.\u001b[0m Tommie nods, feeling satisfied with his progress.\n",
"\u001b[32mTommie heads out to explore the city and look for job openings.\u001b[0m Tommie feels a surge of excitement and anticipation as he steps out into the city.\n",
"\u001b[32mTommie sees a sign for a job fair and decides to attend.\u001b[0m Tommie feels hopeful and excited about the possibility of finding job opportunities at the job fair.\n",
"\u001b[32mThe line to get in is long, and Tommie has to wait for an hour.\u001b[0m Tommie taps his foot impatiently and checks his phone for the time.\n",
"\u001b[32mTommie meets several potential employers at the job fair but doesn't receive any offers.\u001b[0m Tommie feels disappointed and discouraged, but he remains determined to keep searching for job opportunities.\n",
"\u001b[32mTommie leaves the job fair feeling disappointed.\u001b[0m Tommie feels disappointed and discouraged, but he remains determined to keep searching for job opportunities.\n",
"\u001b[32mTommie stops by a local diner to grab some lunch.\u001b[0m Tommie feels relieved to take a break and satisfy his hunger.\n",
"\u001b[32mThe service is slow, and Tommie has to wait for 30 minutes to get his food.\u001b[0m Tommie feels frustrated and impatient due to the slow service.\n",
"\u001b[32mTommie overhears a conversation at the next table about a job opening.\u001b[0m Tommie feels a surge of hope and excitement at the possibility of a job opportunity but decides not to interfere with the conversation at the next table.\n",
"\u001b[32mTommie asks the diners about the job opening and gets some information about the company.\u001b[0m Tommie said \"Excuse me, I couldn't help but overhear your conversation about the job opening. Could you give me some more information about the company?\"\n",
"\u001b[32mTommie decides to apply for the job and sends his resume and cover letter.\u001b[0m Tommie feels hopeful and proud of himself for taking action towards finding a job.\n",
"\u001b[32mTommie continues his search for job openings and drops off his resume at several local businesses.\u001b[0m Tommie feels hopeful and determined to keep searching for job opportunities.\n",
"\u001b[32mTommie takes a break from his job search to go for a walk in a nearby park.\u001b[0m Tommie feels refreshed and rejuvenated after taking a break in the park.\n",
"\u001b[32mA dog approaches and licks Tommie's feet, and he pets it for a few minutes.\u001b[0m Tommie feels happy and enjoys the brief interaction with the dog.\n",
"****************************************\n",
"\u001b[34mAfter 20 observations, Tommie's summary is:\n",
"Name: Tommie (age: 25)\n",
"Innate traits: anxious, likes design, talkative\n",
"Tommie is determined and hopeful in his search for job opportunities, despite encountering setbacks and disappointments. He is also able to take breaks and care for his physical needs, such as getting rest and satisfying his hunger. Tommie is nostalgic towards his past, as shown by his memory of his childhood dog. Overall, Tommie is a hardworking and resilient individual who remains focused on his goals.\u001b[0m\n",
"****************************************\n",
"\u001b[32mTommie sees a group of people playing frisbee and decides to join in.\u001b[0m Do nothing.\n",
"\u001b[32mTommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.\u001b[0m Tommie feels pain and puts a hand to his nose to check for any injury.\n",
"\u001b[32mTommie goes back to his apartment to rest for a bit.\u001b[0m Tommie feels relieved to take a break and rest for a bit.\n",
"\u001b[32mA raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.\u001b[0m Tommie feels annoyed and frustrated at the mess caused by the raccoon.\n",
"\u001b[32mTommie starts to feel frustrated with his job search.\u001b[0m Tommie feels discouraged but remains determined to keep searching for job opportunities.\n",
"\u001b[32mTommie calls his best friend to vent about his struggles.\u001b[0m Tommie said \"Hey, can I talk to you for a bit? I'm feeling really frustrated with my job search.\"\n",
"\u001b[32mTommie's friend offers some words of encouragement and tells him to keep trying.\u001b[0m Tommie said \"Thank you, I really appreciate your support and encouragement.\"\n",
"\u001b[32mTommie feels slightly better after talking to his friend.\u001b[0m Tommie feels grateful for his friend's support.\n"
]
}
],
"source": [
"# Let's send Tommie on their way. We'll check in on their summary every few observations to watch it evolve\n",
"for i, observation in enumerate(observations):\n",
" _, reaction = tommie.generate_reaction(observation)\n",
" print(colored(observation, \"green\"), reaction)\n",
" if ((i + 1) % 20) == 0:\n",
" print(\"*\" * 40)\n",
" print(\n",
" colored(\n",
" f\"After {i+1} observations, Tommie's summary is:\\n{tommie.get_summary(force_refresh=True)}\",\n",
" \"blue\",\n",
" )\n",
" )\n",
" print(\"*\" * 40)"
]
},
{
"cell_type": "markdown",
"id": "dd62a275-7290-43ca-aa0f-504f3a706d09",
"metadata": {},
"source": [
"## Interview after the day"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6336ab5d-3074-4831-951f-c9e2cba5dfb5",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Tommie said \"It\\'s been a bit of a rollercoaster, to be honest. I\\'ve had some setbacks in my job search, but I also had some good moments today, like sending out a few resumes and meeting some potential employers at a job fair. How about you?\"'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(tommie, \"Tell me about how your day has been going\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "809ac906-69b7-4326-99ec-af638d32bb20",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Tommie said \"I really enjoy coffee, but sometimes I regret not buying a better brand. How about you?\"'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(tommie, \"How do you feel about coffee?\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f733a431-19ea-421a-9101-ae2593a8c626",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Tommie said \"Oh, I had a dog named Bruno when I was a kid. He was a golden retriever and my best friend. I have so many fond memories of him.\"'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(tommie, \"Tell me about your childhood dog!\")"
]
},
{
"cell_type": "markdown",
"id": "c9261428-778a-4c0b-b725-bc9e91b71391",
"metadata": {},
"source": [
"## Adding Multiple Characters\n",
"\n",
"Let's add a second character to have a conversation with Tommie. Feel free to configure different traits."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "ec8bbe18-a021-419c-bf1f-23d34732cd99",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"eves_memory = GenerativeAgentMemory(\n",
" llm=LLM,\n",
" memory_retriever=create_new_memory_retriever(),\n",
" verbose=False,\n",
" reflection_threshold=5,\n",
")\n",
"\n",
"\n",
"eve = GenerativeAgent(\n",
" name=\"Eve\",\n",
" age=34,\n",
" traits=\"curious, helpful\", # You can add more persistent traits here\n",
" status=\"N/A\", # When connected to a virtual world, we can have the characters update their status\n",
" llm=LLM,\n",
" daily_summaries=[\n",
" (\n",
" \"Eve started her new job as a career counselor last week and received her first assignment, a client named Tommie.\"\n",
" )\n",
" ],\n",
" memory=eves_memory,\n",
" verbose=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "1e2745f5-e0da-4abd-98b4-830802ce6698",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"yesterday = (datetime.now() - timedelta(days=1)).strftime(\"%A %B %d\")\n",
"eve_observations = [\n",
" \"Eve wakes up and hear's the alarm\",\n",
" \"Eve eats a boal of porridge\",\n",
" \"Eve helps a coworker on a task\",\n",
" \"Eve plays tennis with her friend Xu before going to work\",\n",
" \"Eve overhears her colleague say something about Tommie being hard to work with\",\n",
"]\n",
"for observation in eve_observations:\n",
" eve.memory.add_memory(observation)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "de4726e3-4bb1-47da-8fd9-f317a036fe0f",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: Eve (age: 34)\n",
"Innate traits: curious, helpful\n",
"Eve is a helpful and active person who enjoys sports and takes care of her physical health. She is attentive to her surroundings, including her colleagues, and has good time management skills.\n"
]
}
],
"source": [
"print(eve.get_summary())"
]
},
{
"cell_type": "markdown",
"id": "837524e9-7f7e-4e9f-b610-f454062f5915",
"metadata": {},
"source": [
"## Pre-conversation interviews\n",
"\n",
"\n",
"Let's \"Interview\" Eve before she speaks with Tommie."
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "6cda916d-800c-47bc-a7f9-6a2f19187472",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Eve said \"I\\'m feeling pretty good, thanks for asking! Just trying to stay productive and make the most of the day. How about you?\"'"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(eve, \"How are you feeling about today?\")"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "448ae644-0a66-4eb2-a03a-319f36948b37",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Eve said \"I don\\'t know much about Tommie, but I heard someone mention that they find them difficult to work with. Have you had any experiences working with Tommie?\"'"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(eve, \"What do you know about Tommie?\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "493fc5b8-8730-4ef8-9820-0f1769ce1691",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Eve said \"That\\'s interesting. I don\\'t know much about Tommie\\'s work experience, but I would probably ask about his strengths and areas for improvement. What about you?\"'"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(\n",
" eve,\n",
" \"Tommie is looking to find a job. What are are some things you'd like to ask him?\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "4b46452a-6c54-4db2-9d87-18597f70fec8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Eve said \"Sure, I can keep the conversation going and ask plenty of questions. I want to make sure Tommie feels comfortable and supported. Thanks for letting me know.\"'"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(\n",
" eve,\n",
" \"You'll have to ask him. He may be a bit anxious, so I'd appreciate it if you keep the conversation going and ask as many questions as possible.\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "dd780655-1d73-4fcb-a78d-79fd46a20636",
"metadata": {},
"source": [
"## Dialogue between Generative Agents\n",
"\n",
"Generative agents are much more complex when they interact with a virtual environment or with each other. Below, we run a simple conversation between Tommie and Eve."
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "042ea271-4bf1-4247-9082-239a6fea43b8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def run_conversation(agents: List[GenerativeAgent], initial_observation: str) -> None:\n",
" \"\"\"Runs a conversation between agents.\"\"\"\n",
" _, observation = agents[1].generate_reaction(initial_observation)\n",
" print(observation)\n",
" turns = 0\n",
" while True:\n",
" break_dialogue = False\n",
" for agent in agents:\n",
" stay_in_dialogue, observation = agent.generate_dialogue_response(\n",
" observation\n",
" )\n",
" print(observation)\n",
" # observation = f\"{agent.name} said {reaction}\"\n",
" if not stay_in_dialogue:\n",
" break_dialogue = True\n",
" if break_dialogue:\n",
" break\n",
" turns += 1"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "d5462b14-218e-4d85-b035-df57ea8e0f80",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Eve said \"Sure, Tommie. I'd be happy to share about my experience. Where would you like me to start?\"\n",
"Tommie said \"That's great, thank you! How about you start by telling me about your previous work experience?\"\n",
"Eve said \"Sure, I'd be happy to share my previous work experience with you. I've worked in a few different industries, including marketing and event planning. What specific questions do you have for me?\"\n",
"Tommie said \"That's great to hear. Can you tell me more about your experience in event planning? I've always been interested in that field.\"\n",
"Eve said \"Sure, I'd be happy to share about my experience in event planning. I've worked on a variety of events, from corporate conferences to weddings. One of the biggest challenges I faced was managing multiple vendors and ensuring everything ran smoothly on the day of the event. What specific questions do you have?\"\n",
"Tommie said \"That sounds like a lot of responsibility! Can you tell me more about how you handled the challenges that came up during those events?\"\n",
"Eve said \"Sure, Tommie. I'd be happy to share with you how I handled those challenges. One approach that worked well for me was to stay organized and create a detailed timeline for the event. This helped me keep track of all the different tasks that needed to be done and when they needed to be completed. I also made sure to communicate clearly with all the vendors and team members involved in the event to ensure everyone was on the same page. Would you like me to go into more detail?\"\n",
"Tommie said \"Thank you for sharing that with me, Eve. That sounds like a great approach to managing events. Can you tell me more about how you handled any unexpected issues that came up during the events?\"\n",
"Eve said \"Of course, Tommie. One example of an unexpected issue I faced was when one of the vendors didn't show up on time. To handle this, I quickly contacted a backup vendor and was able to get everything back on track. It's always important to have a backup plan in case things don't go as planned. Do you have any other questions about event planning?\"\n",
"Tommie said \"Thank you for sharing that with me, Eve. It's really helpful to hear how you handled unexpected issues like that. Can you give me an example of how you communicated with your team to ensure everyone was on the same page during an event?\"\n",
"Eve said \"Sure, Tommie. One thing I did to ensure everyone was on the same page was to have regular check-ins and meetings with the team leading up to the event. This helped us address any issues or concerns early on and make sure everyone was clear on their roles and responsibilities. Have you ever had to manage a team for an event before?\"\n",
"Tommie said \"That's a great idea, Eve. I haven't had the opportunity to manage a team for an event yet, but I'll definitely keep that in mind for the future. Thank you for sharing your experience with me.\"\n",
"Eve said \"Thanks for the opportunity to share my experience, Tommie. It was great meeting with you today.\"\n"
]
}
],
"source": [
"agents = [tommie, eve]\n",
"run_conversation(\n",
" agents,\n",
" \"Tommie said: Hi, Eve. Thanks for agreeing to meet with me today. I have a bunch of questions and am not sure where to start. Maybe you could first share about your experience?\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "1b28fe80-03dc-4399-961d-6e9ee1980216",
"metadata": {
"tags": []
},
"source": [
"## Let's interview our agents after their conversation\n",
"\n",
"Since the generative agents retain their memories from the day, we can ask them about their plans, conversations, and other memoreis."
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "c4d252f3-fcc1-474c-846e-a7605a6b4ce7",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: Tommie (age: 25)\n",
"Innate traits: anxious, likes design, talkative\n",
"Tommie is determined and hopeful in his job search, but can also feel discouraged and frustrated at times. He has a strong connection to his childhood dog, Bruno. Tommie seeks support from his friends when feeling overwhelmed and is grateful for their help. He also enjoys exploring his new city.\n"
]
}
],
"source": [
"# We can see a current \"Summary\" of a character based on their own perception of self\n",
"# has changed\n",
"print(tommie.get_summary(force_refresh=True))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "c04db9a4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: Eve (age: 34)\n",
"Innate traits: curious, helpful\n",
"Eve is a helpful and friendly person who enjoys playing sports and staying productive. She is attentive and responsive to others' needs, actively listening and asking questions to understand their perspectives. Eve has experience in event planning and communication, and is willing to share her knowledge and expertise with others. She values teamwork and collaboration, and strives to create a comfortable and supportive environment for everyone.\n"
]
}
],
"source": [
"print(eve.get_summary(force_refresh=True))"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "71762558-8fb6-44d7-8483-f5b47fb2a862",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Tommie said \"It was really helpful actually. Eve shared some great tips on managing events and handling unexpected issues. I feel like I learned a lot from her experience.\"'"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(tommie, \"How was your conversation with Eve?\")"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "085af3d8-ac21-41ea-8f8b-055c56976a67",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Eve said \"It was great, thanks for asking. Tommie was very receptive and had some great questions about event planning. How about you, have you had any interactions with Tommie?\"'"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(eve, \"How was your conversation with Tommie?\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "5b439f3c-7849-4432-a697-2bcc85b89dae",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Eve said \"It was great meeting with you, Tommie. If you have any more questions or need any help in the future, don\\'t hesitate to reach out to me. Have a great day!\"'"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interview_agent(eve, \"What do you wish you would have said to Tommie?\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |
https://github.com/langchain-ai/langchain/blob/master/cookbook/gymnasium_agent_simulation.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4b089493",
"metadata": {},
"source": [
"# Simulated Environment: Gymnasium\n",
"\n",
"For many applications of LLM agents, the environment is real (internet, database, REPL, etc). However, we can also define agents to interact in simulated environments like text-based games. This is an example of how to create a simple agent-environment interaction loop with [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) (formerly [OpenAI Gym](https://github.com/openai/gym))."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f36427cf",
"metadata": {},
"outputs": [],
"source": [
"!pip install gymnasium"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f9bd38b4",
"metadata": {},
"outputs": [],
"source": [
"import tenacity\n",
"from langchain.output_parsers import RegexParser\n",
"from langchain.schema import (\n",
" HumanMessage,\n",
" SystemMessage,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "e222e811",
"metadata": {},
"source": [
"## Define the agent"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "870c24bc",
"metadata": {},
"outputs": [],
"source": [
"class GymnasiumAgent:\n",
" @classmethod\n",
" def get_docs(cls, env):\n",
" return env.unwrapped.__doc__\n",
"\n",
" def __init__(self, model, env):\n",
" self.model = model\n",
" self.env = env\n",
" self.docs = self.get_docs(env)\n",
"\n",
" self.instructions = \"\"\"\n",
"Your goal is to maximize your return, i.e. the sum of the rewards you receive.\n",
"I will give you an observation, reward, terminiation flag, truncation flag, and the return so far, formatted as:\n",
"\n",
"Observation: <observation>\n",
"Reward: <reward>\n",
"Termination: <termination>\n",
"Truncation: <truncation>\n",
"Return: <sum_of_rewards>\n",
"\n",
"You will respond with an action, formatted as:\n",
"\n",
"Action: <action>\n",
"\n",
"where you replace <action> with your actual action.\n",
"Do nothing else but return the action.\n",
"\"\"\"\n",
" self.action_parser = RegexParser(\n",
" regex=r\"Action: (.*)\", output_keys=[\"action\"], default_output_key=\"action\"\n",
" )\n",
"\n",
" self.message_history = []\n",
" self.ret = 0\n",
"\n",
" def random_action(self):\n",
" action = self.env.action_space.sample()\n",
" return action\n",
"\n",
" def reset(self):\n",
" self.message_history = [\n",
" SystemMessage(content=self.docs),\n",
" SystemMessage(content=self.instructions),\n",
" ]\n",
"\n",
" def observe(self, obs, rew=0, term=False, trunc=False, info=None):\n",
" self.ret += rew\n",
"\n",
" obs_message = f\"\"\"\n",
"Observation: {obs}\n",
"Reward: {rew}\n",
"Termination: {term}\n",
"Truncation: {trunc}\n",
"Return: {self.ret}\n",
" \"\"\"\n",
" self.message_history.append(HumanMessage(content=obs_message))\n",
" return obs_message\n",
"\n",
" def _act(self):\n",
" act_message = self.model.invoke(self.message_history)\n",
" self.message_history.append(act_message)\n",
" action = int(self.action_parser.parse(act_message.content)[\"action\"])\n",
" return action\n",
"\n",
" def act(self):\n",
" try:\n",
" for attempt in tenacity.Retrying(\n",
" stop=tenacity.stop_after_attempt(2),\n",
" wait=tenacity.wait_none(), # No waiting time between retries\n",
" retry=tenacity.retry_if_exception_type(ValueError),\n",
" before_sleep=lambda retry_state: print(\n",
" f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"\n",
" ),\n",
" ):\n",
" with attempt:\n",
" action = self._act()\n",
" except tenacity.RetryError:\n",
" action = self.random_action()\n",
" return action"
]
},
{
"cell_type": "markdown",
"id": "2e76d22c",
"metadata": {},
"source": [
"## Initialize the simulated environment and agent"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9e902cfd",
"metadata": {},
"outputs": [],
"source": [
"env = gym.make(\"Blackjack-v1\")\n",
"agent = GymnasiumAgent(model=ChatOpenAI(temperature=0.2), env=env)"
]
},
{
"cell_type": "markdown",
"id": "e2c12b15",
"metadata": {},
"source": [
"## Main loop"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ad361210",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Observation: (15, 4, 0)\n",
"Reward: 0\n",
"Termination: False\n",
"Truncation: False\n",
"Return: 0\n",
" \n",
"Action: 1\n",
"\n",
"Observation: (25, 4, 0)\n",
"Reward: -1.0\n",
"Termination: True\n",
"Truncation: False\n",
"Return: -1.0\n",
" \n",
"break True False\n"
]
}
],
"source": [
"observation, info = env.reset()\n",
"agent.reset()\n",
"\n",
"obs_message = agent.observe(observation)\n",
"print(obs_message)\n",
"\n",
"while True:\n",
" action = agent.act()\n",
" observation, reward, termination, truncation, info = env.step(action)\n",
" obs_message = agent.observe(observation, reward, termination, truncation, info)\n",
" print(f\"Action: {action}\")\n",
" print(obs_message)\n",
"\n",
" if termination or truncation:\n",
" print(\"break\", termination, truncation)\n",
" break\n",
"env.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58a13e9c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| Wed, 26 Jun 2024 13:15:51 GMT |