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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U tavily-python langchain_community"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"model = os.getenv(\"OPENAI_MODEL\", \"gpt-4o\")\n",
"temperature = float(os.getenv(\"OPENAI_TEMPERATURE\", 0))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"\n",
"tool = TavilySearchResults(max_results=2)\n",
"tools = [tool]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x10cdb52b0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import ChatOpenAI as Chat\n",
"from typing import Annotated\n",
"from typing_extensions import TypedDict\n",
"\n",
"from langgraph.graph import StateGraph, START, END\n",
"from langgraph.graph.message import add_messages\n",
"\n",
"\n",
"class State(TypedDict):\n",
" messages: Annotated[list, add_messages]\n",
"\n",
"\n",
"graph_builder = StateGraph(State)\n",
"\n",
"\n",
"llm = Chat(\n",
" openai_api_key=openai_api_key,\n",
" model=model,\n",
" temperature=temperature\n",
")\n",
"# Modification: tell the LLM which tools it can call\n",
"llm_with_tools = llm.bind_tools(tools)\n",
"\n",
"\n",
"def chatbot(state: State):\n",
" return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n",
"\n",
"\n",
"graph_builder.add_node(\"chatbot\", chatbot)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x10cdb52b0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import json\n",
"\n",
"from langchain_core.messages import ToolMessage\n",
"\n",
"\n",
"class BasicToolNode:\n",
" \"\"\"A node that runs the tools requested in the last AIMessage.\"\"\"\n",
"\n",
" def __init__(self, tools: list) -> None:\n",
" self.tools_by_name = {tool.name: tool for tool in tools}\n",
"\n",
" def __call__(self, inputs: dict):\n",
" if messages := inputs.get(\"messages\", []):\n",
" message = messages[-1]\n",
" else:\n",
" raise ValueError(\"No message found in input\")\n",
" outputs = []\n",
" for tool_call in message.tool_calls:\n",
" tool_result = self.tools_by_name[tool_call[\"name\"]].invoke(\n",
" tool_call[\"args\"]\n",
" )\n",
" outputs.append(\n",
" ToolMessage(\n",
" content=json.dumps(tool_result),\n",
" name=tool_call[\"name\"],\n",
" tool_call_id=tool_call[\"id\"],\n",
" )\n",
" )\n",
" return {\"messages\": outputs}\n",
"\n",
"\n",
"tool_node = BasicToolNode(tools=[tool])\n",
"graph_builder.add_node(\"tools\", tool_node)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<langgraph.graph.state.StateGraph at 0x10cdb52b0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from typing import Literal\n",
"\n",
"\n",
"def route_tools(\n",
" state: State,\n",
"):\n",
" \"\"\"\n",
" Use in the conditional_edge to route to the ToolNode if the last message\n",
" has tool calls. Otherwise, route to the end.\n",
" \"\"\"\n",
" if isinstance(state, list):\n",
" ai_message = state[-1]\n",
" elif messages := state.get(\"messages\", []):\n",
" ai_message = messages[-1]\n",
" else:\n",
" raise ValueError(\n",
" f\"No messages found in input state to tool_edge: {state}\")\n",
" if hasattr(ai_message, \"tool_calls\") and len(ai_message.tool_calls) > 0:\n",
" return \"tools\"\n",
" return END\n",
"\n",
"\n",
"# The `tools_condition` function returns \"tools\" if the chatbot asks to use a tool, and \"END\" if\n",
"# it is fine directly responding. This conditional routing defines the main agent loop.\n",
"graph_builder.add_conditional_edges(\n",
" \"chatbot\",\n",
" route_tools,\n",
" # The following dictionary lets you tell the graph to interpret the condition's outputs as a specific node\n",
" # It defaults to the identity function, but if you\n",
" # want to use a node named something else apart from \"tools\",\n",
" # You can update the value of the dictionary to something else\n",
" # e.g., \"tools\": \"my_tools\"\n",
" {\"tools\": \"tools\", END: END},\n",
")\n",
"# Any time a tool is called, we return to the chatbot to decide the next step\n",
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
"graph_builder.add_edge(START, \"chatbot\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph().draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from langgraph.checkpoint.memory import MemorySaver\n",
"\n",
"memory = MemorySaver()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"graph = graph_builder.compile(checkpointer=memory)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph().draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"config = {\"configurable\": {\"thread_id\": \"1\"}}"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Hi there! My name is Akilesh.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"Hello Akilesh! How can I assist you today?\n"
]
}
],
"source": [
"user_input = \"Hi there! My name is Akilesh.\"\n",
"\n",
"# The config is the **second positional argument** to stream() or invoke()!\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" event[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Remember my name?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"Yes, your name is Akilesh. How can I assist you today?\n"
]
}
],
"source": [
"user_input = \"Remember my name?\"\n",
"\n",
"# The config is the **second positional argument** to stream() or invoke()!\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" event[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Remember my name?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"I don't have access to personal data about individuals unless it has been shared with me in the course of our conversation. Therefore, I don't know your name. If you'd like, you can tell me your name!\n"
]
}
],
"source": [
"# The only difference is we change the `thread_id` here to \"2\" instead of \"1\"\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", user_input)]},\n",
" {\"configurable\": {\"thread_id\": \"2\"}},\n",
" stream_mode=\"values\",\n",
")\n",
"for event in events:\n",
" event[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StateSnapshot(values={'messages': [HumanMessage(content='Hi there! My name is Will.', additional_kwargs={}, response_metadata={}, id='7d987ada-9d45-40c7-bed5-87954770d924'), AIMessage(content='Hello Will! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 87, 'total_tokens': 98, 'completion_tokens_details': {'audio_tokens': None, 'reasoning_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_159d8341cc', 'finish_reason': 'stop', 'logprobs': None}, id='run-4be6cc28-59ae-47fa-a6f2-db0fc95f8ebb-0', usage_metadata={'input_tokens': 87, 'output_tokens': 11, 'total_tokens': 98, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 0}}), HumanMessage(content='Hi there! My name is Akilesh.', additional_kwargs={}, response_metadata={}, id='4892fff9-4a37-4124-8f00-e3da884b9f76'), AIMessage(content='Hello Akilesh! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 115, 'total_tokens': 128, 'completion_tokens_details': {'audio_tokens': None, 'reasoning_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_159d8341cc', 'finish_reason': 'stop', 'logprobs': None}, id='run-5a2cca31-601a-4c7d-85ac-69c2431d89d8-0', usage_metadata={'input_tokens': 115, 'output_tokens': 13, 'total_tokens': 128, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 0}}), HumanMessage(content='Remember my name?', additional_kwargs={}, response_metadata={}, id='90b50933-01b5-4432-8d43-a30a88c3e076'), AIMessage(content='Yes, your name is Akilesh. How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 139, 'total_tokens': 156, 'completion_tokens_details': {'audio_tokens': None, 'reasoning_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_90354628f2', 'finish_reason': 'stop', 'logprobs': None}, id='run-20f062ea-5018-4654-b42e-3ae33d695af6-0', usage_metadata={'input_tokens': 139, 'output_tokens': 17, 'total_tokens': 156, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 0}})]}, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef976e5-63e2-6734-8007-34e57952e482'}}, metadata={'source': 'loop', 'writes': {'chatbot': {'messages': [AIMessage(content='Yes, your name is Akilesh. How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 139, 'total_tokens': 156, 'completion_tokens_details': {'audio_tokens': None, 'reasoning_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_90354628f2', 'finish_reason': 'stop', 'logprobs': None}, id='run-20f062ea-5018-4654-b42e-3ae33d695af6-0', usage_metadata={'input_tokens': 139, 'output_tokens': 17, 'total_tokens': 156, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 0}})]}}, 'step': 7, 'parents': {}}, created_at='2024-10-31T09:56:02.088510+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef976e5-5c40-6a76-8006-154293b894d0'}}, tasks=())"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"snapshot = graph.get_state(config)\n",
"snapshot"
]
}
],
"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": "3.12.7"
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|