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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U tavily-python langchain_community"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"\n",
"load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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": 4,
"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": 6,
"metadata": {},
"outputs": [],
"source": [
"from typing import Annotated\n",
"from langchain_openai import ChatOpenAI as Chat\n",
"\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from typing_extensions import TypedDict\n",
"\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.graph import StateGraph, START\n",
"from langgraph.graph.message import add_messages\n",
"from langgraph.prebuilt import ToolNode, tools_condition\n",
"\n",
"memory = MemorySaver()\n",
"\n",
"\n",
"class State(TypedDict):\n",
" messages: Annotated[list, add_messages]\n",
"\n",
"\n",
"graph_builder = StateGraph(State)\n",
"\n",
"\n",
"tool = TavilySearchResults(max_results=2)\n",
"tools = [tool]\n",
"llm = Chat(\n",
" openai_api_key=openai_api_key,\n",
" model=model,\n",
" temperature=temperature\n",
")\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)\n",
"\n",
"tool_node = ToolNode(tools=[tool])\n",
"graph_builder.add_node(\"tools\", tool_node)\n",
"\n",
"graph_builder.add_conditional_edges(\n",
" \"chatbot\",\n",
" tools_condition,\n",
")\n",
"graph_builder.add_edge(\"tools\", \"chatbot\")\n",
"graph_builder.add_edge(START, \"chatbot\")\n",
"memory = MemorySaver()\n",
"graph = graph_builder.compile(\n",
" checkpointer=memory,\n",
" # This is new!\n",
" interrupt_before=[\"tools\"],\n",
" # Note: can also interrupt **after** actions, if desired.\n",
" # interrupt_after=[\"tools\"]\n",
")\n",
"\n",
"user_input = \"I'm learning LangGraph. Could you do some research on it for me?\"\n",
"config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
"# The config is the **second positional argument** to stream() or invoke()!\n",
"events = graph.stream({\"messages\": [(\"user\", user_input)]}, config)\n",
"for event in events:\n",
" if \"messages\" in event:\n",
" event[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" tavily_search_results_json (call_LGHiKqqnTR0xCTMQj4iKZ2Uy)\n",
" Call ID: call_LGHiKqqnTR0xCTMQj4iKZ2Uy\n",
" Args:\n",
" query: LangGraph programming language\n"
]
}
],
"source": [
"snapshot = graph.get_state(config)\n",
"existing_message = snapshot.values[\"messages\"][-1]\n",
"existing_message.pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"LangGraph is a library for building stateful, multi-actor applications with LLMs.\n",
"\n",
"\n",
"Last 2 messages;\n",
"[ToolMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', id='c7ee8f20-19bc-430f-a7ad-4219c25fc6ec', tool_call_id='call_LGHiKqqnTR0xCTMQj4iKZ2Uy'), AIMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', additional_kwargs={}, response_metadata={}, id='a7973d5c-e60b-4c97-8876-d32b9e280acf')]\n"
]
}
],
"source": [
"from langchain_core.messages import AIMessage, ToolMessage\n",
"\n",
"answer = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs.\"\n",
")\n",
"new_messages = [\n",
" # The LLM API expects some ToolMessage to match its tool call. We'll satisfy that here.\n",
" ToolMessage(content=answer,\n",
" tool_call_id=existing_message.tool_calls[0][\"id\"]),\n",
" # And then directly \"put words in the LLM's mouth\" by populating its response.\n",
" AIMessage(content=answer),\n",
"]\n",
"\n",
"new_messages[-1].pretty_print()\n",
"graph.update_state(\n",
" # Which state to update\n",
" config,\n",
" # The updated values to provide. The messages in our `State` are \"append-only\", meaning this will be appended\n",
" # to the existing state. We will review how to update existing messages in the next section!\n",
" {\"messages\": new_messages},\n",
")\n",
"\n",
"print(\"\\n\\nLast 2 messages;\")\n",
"print(graph.get_state(config).values[\"messages\"][-2:])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"class State(TypedDict):\n",
" messages: Annotated[list, add_messages]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'configurable': {'thread_id': '1',\n",
" 'checkpoint_ns': '',\n",
" 'checkpoint_id': '1ef97736-c6dd-6e14-8003-b885bde8bfbe'}}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph.update_state(\n",
" config,\n",
" {\"messages\": [AIMessage(content=\"I'm an AI expert!\")]},\n",
" # Which node for this function to act as. It will automatically continue\n",
" # processing as if this node just ran.\n",
" as_node=\"chatbot\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ToolMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', id='c7ee8f20-19bc-430f-a7ad-4219c25fc6ec', tool_call_id='call_LGHiKqqnTR0xCTMQj4iKZ2Uy'), AIMessage(content='LangGraph is a library for building stateful, multi-actor applications with LLMs.', additional_kwargs={}, response_metadata={}, id='a7973d5c-e60b-4c97-8876-d32b9e280acf'), AIMessage(content=\"I'm an AI expert!\", additional_kwargs={}, response_metadata={}, id='e70e7694-b6f1-44bb-bb03-01e6140d8452')]\n",
"()\n"
]
}
],
"source": [
"snapshot = graph.get_state(config)\n",
"print(snapshot.values[\"messages\"][-3:])\n",
"print(snapshot.next)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"I'm learning LangGraph. Could you do some research on it for me?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" tavily_search_results_json (call_a5QcJ8OTfJ81NCohydwHBe43)\n",
" Call ID: call_a5QcJ8OTfJ81NCohydwHBe43\n",
" Args:\n",
" query: LangGraph programming language\n"
]
}
],
"source": [
"user_input = \"I'm learning LangGraph. Could you do some research on it for me?\"\n",
"config = {\"configurable\": {\"thread_id\": \"2\"}} # we'll use thread_id = 2 here\n",
"events = graph.stream(\n",
" {\"messages\": [(\"user\", user_input)]}, config, stream_mode=\"values\"\n",
")\n",
"for event in events:\n",
" if \"messages\" in event:\n",
" event[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original\n",
"Message ID run-2f27ab36-7466-40c5-adcf-18972d1bc9fa-0\n",
"{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph programming language'}, 'id': 'call_a5QcJ8OTfJ81NCohydwHBe43', 'type': 'tool_call'}\n",
"Updated\n",
"{'name': 'tavily_search_results_json', 'args': {'query': 'LangGraph human-in-the-loop workflow'}, 'id': 'call_a5QcJ8OTfJ81NCohydwHBe43', 'type': 'tool_call'}\n",
"Message ID run-2f27ab36-7466-40c5-adcf-18972d1bc9fa-0\n",
"\n",
"\n",
"Tool calls\n"
]
},
{
"data": {
"text/plain": [
"[{'name': 'tavily_search_results_json',\n",
" 'args': {'query': 'LangGraph human-in-the-loop workflow'},\n",
" 'id': 'call_a5QcJ8OTfJ81NCohydwHBe43',\n",
" 'type': 'tool_call'}]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_core.messages import AIMessage\n",
"\n",
"snapshot = graph.get_state(config)\n",
"existing_message = snapshot.values[\"messages\"][-1]\n",
"print(\"Original\")\n",
"print(\"Message ID\", existing_message.id)\n",
"print(existing_message.tool_calls[0])\n",
"new_tool_call = existing_message.tool_calls[0].copy()\n",
"new_tool_call[\"args\"][\"query\"] = \"LangGraph human-in-the-loop workflow\"\n",
"new_message = AIMessage(\n",
" content=existing_message.content,\n",
" tool_calls=[new_tool_call],\n",
" # Important! The ID is how LangGraph knows to REPLACE the message in the state rather than APPEND this messages\n",
" id=existing_message.id,\n",
")\n",
"\n",
"print(\"Updated\")\n",
"print(new_message.tool_calls[0])\n",
"print(\"Message ID\", new_message.id)\n",
"graph.update_state(config, {\"messages\": [new_message]})\n",
"\n",
"print(\"\\n\\nTool calls\")\n",
"graph.get_state(config).values[\"messages\"][-1].tool_calls"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" tavily_search_results_json (call_a5QcJ8OTfJ81NCohydwHBe43)\n",
" Call ID: call_a5QcJ8OTfJ81NCohydwHBe43\n",
" Args:\n",
" query: LangGraph human-in-the-loop workflow\n",
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
"Name: tavily_search_results_json\n",
"\n",
"[{\"url\": \"https://www.youtube.com/watch?v=9BPCV5TYPmg\", \"content\": \"In this video, I'll show you how to handle persistence with LangGraph, enabling a unique Human-in-the-Loop workflow. This approach allows a human to grant an\"}, {\"url\": \"https://medium.com/@kbdhunga/implementing-human-in-the-loop-with-langgraph-ccfde023385c\", \"content\": \"Implementing a Human-in-the-Loop (HIL) framework in LangGraph with the Streamlit app provides a robust mechanism for user engagement and decision-making. By incorporating breakpoints and\"}]\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"LangGraph is a tool that supports a Human-in-the-Loop (HIL) workflow, which is a process that allows human intervention in automated systems to improve decision-making and outcomes. Here are some resources that might help you understand LangGraph better:\n",
"\n",
"1. **YouTube Video**: [Handling Persistence with LangGraph](https://www.youtube.com/watch?v=9BPCV5TYPmg) - This video demonstrates how to manage persistence in LangGraph, enabling a unique Human-in-the-Loop workflow. It shows how a human can intervene in the process to grant permissions or make decisions.\n",
"\n",
"2. **Medium Article**: [Implementing Human-in-the-Loop with LangGraph](https://medium.com/@kbdhunga/implementing-human-in-the-loop-with-langgraph-ccfde023385c) - This article discusses implementing a Human-in-the-Loop framework in LangGraph using a Streamlit app. It provides insights into creating a robust mechanism for user engagement and decision-making by incorporating breakpoints and other interactive elements.\n",
"\n",
"These resources should give you a good starting point to understand how LangGraph can be used to integrate human decision-making into automated workflows.\n"
]
}
],
"source": [
"events = graph.stream(None, config, stream_mode=\"values\")\n",
"for event in events:\n",
" if \"messages\" in event:\n",
" event[\"messages\"][-1].pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"Remember what I'm learning about?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"You're learning about LangGraph, specifically focusing on its Human-in-the-Loop workflow capabilities.\n"
]
}
],
"source": [
"events = graph.stream(\n",
" {\n",
" \"messages\": (\n",
" \"user\",\n",
" \"Remember what I'm learning about?\",\n",
" )\n",
" },\n",
" config,\n",
" stream_mode=\"values\",\n",
")\n",
"for event in events:\n",
" if \"messages\" in event:\n",
" event[\"messages\"][-1].pretty_print()"
]
},
{
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|