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6134b0e995
* add shared value * changes * z * spelling * z * vadym comments * spelling * cloud compatibility * spelling --------- Co-authored-by: isaac hershenson <ihershenson@hmc.edu> Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
286 lines
14 KiB
Plaintext
286 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7240d5b5-9dac-4070-8a9e-2350fb01e0be",
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"metadata": {},
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"source": [
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"# How to share state between threads\n",
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"\n",
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"By default, state in a graph is scoped to that thread.\n",
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"LangGraph also allows you to specify a \"scope\" for a given key/value pair that exists between threads. This can be useful for storing information that is shared between threads. For instance, you may want to store information about a user's preferences expressed in one thread, and then use that information in another thread.\n",
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"\n",
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"In this notebook we will go through an example of how to construct and use such a graph."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c4c550b5-1954-496b-8b9d-800361af17dc",
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"metadata": {},
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"source": [
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"## Create graph\n",
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"\n",
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"In this example we will create a graph that will let us store information about a user's preferences. We will do so by defining a state key that will be scoped to a user_id, and allowing the model to populate this field as it deems fit (by providing the model with a tool to save information about the user).\n",
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"\n",
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" \n",
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"<div class=\"admonition note\">\n",
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" <p class=\"admonition-title\">Typing shared state keys</p>\n",
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" <p style=\"margin-top: 5px;\">\n",
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" Shared state channels (keys) MUST be dictionaries (see `info` channel in the AgentState example below)\n",
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" </p>\n",
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"</div>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a7f303d6-612e-4e34-bf36-29d4ed25d802",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langgraph.graph.graph import START, END\n",
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"from langgraph.graph.message import MessagesState\n",
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"from langgraph.graph.state import StateGraph\n",
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"from langgraph.store.memory import MemoryStore\n",
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"from langgraph.managed.shared_value import SharedValue\n",
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"from typing import TypedDict, Annotated, Any\n",
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"import uuid\n",
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"from langchain_openai import ChatOpenAI\n",
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"from langgraph.checkpoint.memory import MemorySaver\n",
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"\n",
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"\n",
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"class AgentState(MessagesState):\n",
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" # We use an info key to track information\n",
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" # This is scoped to a user_id, so it will be information specific to each user\n",
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" info: Annotated[dict, SharedValue.on(\"user_id\")]\n",
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"\n",
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"\n",
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"# We will give this as a tool to the agent\n",
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"# This will let the agent call this tool to save a fact\n",
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"class Info(TypedDict):\n",
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" \"\"\"This tool should be called when you want to save a new fact about the user.\n",
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" \n",
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" Attributes:\n",
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" fact (str): A fact about the user.\n",
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" topic (str): The topic related the fact is about, i.e. Food, Location, Movies, etc.\n",
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" \"\"\"\n",
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" fact: str\n",
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" topic: str\n",
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"\n",
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"\n",
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"# This is the prompt we give the agent\n",
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"# We will pass known info into the prompt\n",
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"# We will tell it to use the Info tool to save more\n",
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"prompt = \"\"\"You are helpful assistant.\n",
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"\n",
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"Here is what you know about the user:\n",
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"\n",
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"<info>\n",
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"{info}\n",
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"</info>\n",
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"\n",
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"Help out the user. If the user tells you any information about themselves, save the information using the `Info` tool.\n",
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"\n",
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"This means if the user provides any sort of fact about themselves, be it an opinion they have, a fact about themselves, etc. SAVE IT!\n",
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"\"\"\"\n",
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"\n",
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"\n",
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"# We give the model access to the Info tool\n",
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"model = ChatOpenAI().bind_tools([Info])\n",
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"\n",
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"\n",
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"# Our first node - this will call the model\n",
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"def call_model(state):\n",
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" # We get all facts and assemble them into a string\n",
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" facts = [d['fact'] for d in state['info'].values()]\n",
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" info = \"\\n\".join(facts)\n",
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" # Format system prompt\n",
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" system_msg = prompt.format(info=info)\n",
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" # Call model\n",
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" response = model.invoke([{\"role\": \"system\", \"content\": system_msg}] + state['messages'])\n",
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
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"# Routing function to decide what to do next\n",
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"# If no tool calls, then we end\n",
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"# If tool calls, then we update memory\n",
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"def route(state):\n",
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" if len(state['messages'][-1].tool_calls) == 0:\n",
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" return END\n",
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" else:\n",
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" return \"update_memory\"\n",
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"\n",
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"\n",
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"# This function is responsible for updating the memory\n",
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"def update_memory(state):\n",
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" tool_calls = []\n",
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" memories = {}\n",
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" # Each tool call is a new memory to save\n",
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" for tc in state['messages'][-1].tool_calls:\n",
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" # We append ToolMessages (to pass back to the LLM)\n",
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" # This is needed because OpenAI requires each tool call be followed by a ToolMessage\n",
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" tool_calls.append({\"role\": \"tool\", \"content\": \"Saved!\", \"tool_call_id\": tc['id']})\n",
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" # We create a new memory from this tool call\n",
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" memories[str(uuid.uuid4())] = {\"fact\": tc['args']['fact'], \"topic\": tc['args']['topic']}\n",
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" # Return the messages and memories to update the state with\n",
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" return {\"messages\": tool_calls, \"info\": memories}\n",
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"\n",
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"\n",
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"# This is the in memory checkpointer we will use\n",
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"# We need this because we want to enable threads (conversations)\n",
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"memory = MemorySaver()\n",
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"\n",
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"# This is the in memory Key Value store\n",
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"# This is needed to save the memories\n",
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"kv = MemoryStore()\n",
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"\n",
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"# Construct this relatively simple graph\n",
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"graph = StateGraph(AgentState)\n",
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"graph.add_node(call_model)\n",
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"graph.add_node(update_memory)\n",
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"graph.add_edge(\"update_memory\", END)\n",
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"graph.add_edge(START, \"call_model\")\n",
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"graph.add_conditional_edges(\"call_model\", route)\n",
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"graph = graph.compile(checkpointer=memory, store=kv)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "552d4e33-556d-4fa5-8094-2a076bc21529",
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"metadata": {},
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"source": [
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"## Run graph on one thread\n",
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"\n",
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"We can now run the graph on one thread and give it some information"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "18bd8679-3a73-4033-bfb4-5093ac1f5d7f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'call_model': {'messages': [AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 171, 'total_tokens': 181}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-fbbb73a4-7c94-4db1-8761-44ea2fe9feaf-0', usage_metadata={'input_tokens': 171, 'output_tokens': 10, 'total_tokens': 181})]}}\n",
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"{'call_model': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_zMUXZfhOCFYvZg5TwXyBzw16', 'function': {'arguments': '{\"fact\":\"I like pepperoni pizza\",\"topic\":\"Food\"}', 'name': 'Info'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 193, 'total_tokens': 214}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7297f9fb-1d3e-480e-b125-ab269f648158-0', tool_calls=[{'name': 'Info', 'args': {'fact': 'I like pepperoni pizza', 'topic': 'Food'}, 'id': 'call_zMUXZfhOCFYvZg5TwXyBzw16', 'type': 'tool_call'}], usage_metadata={'input_tokens': 193, 'output_tokens': 21, 'total_tokens': 214})]}}\n",
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"{'update_memory': {'messages': [{'role': 'tool', 'content': 'Saved!', 'tool_call_id': 'call_zMUXZfhOCFYvZg5TwXyBzw16'}]}}\n",
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"{'call_model': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_GjshujJAeqoTuuBeHCD5YTPQ', 'function': {'arguments': '{\"fact\":\"I just moved to SF\",\"topic\":\"Location\"}', 'name': 'Info'}, 'type': 'function'}], 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 239, 'total_tokens': 260}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-4abea1d6-7ccb-49b4-b805-0e04ebb542e3-0', tool_calls=[{'name': 'Info', 'args': {'fact': 'I just moved to SF', 'topic': 'Location'}, 'id': 'call_GjshujJAeqoTuuBeHCD5YTPQ', 'type': 'tool_call'}], usage_metadata={'input_tokens': 239, 'output_tokens': 21, 'total_tokens': 260})]}}\n",
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"{'update_memory': {'messages': [{'role': 'tool', 'content': 'Saved!', 'tool_call_id': 'call_GjshujJAeqoTuuBeHCD5YTPQ'}]}}\n"
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]
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}
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],
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"source": [
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"config = {\"configurable\": {\"thread_id\": \"1\", \"user_id\": \"1\"}}\n",
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"\n",
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"# First let's just say hi to the AI\n",
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"for update in graph.stream({\"messages\": [{\"role\": \"user\", \"content\": \"hi\"}]}, config, stream_mode=\"updates\"):\n",
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" print(update)\n",
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"\n",
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"# Let's continue the conversation (by passing the same config) and tell the AI we like pepperoni pizza\n",
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"for update in graph.stream({\"messages\": [{\"role\": \"user\", \"content\": \"i like pepperoni pizza\"}]}, config, stream_mode=\"updates\"):\n",
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" print(update)\n",
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"\n",
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"# Let's continue the conversation even further (by passing the same config) and tell the AI we live in SF\n",
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"for update in graph.stream({\"messages\": [{\"role\": \"user\", \"content\": \"i also just moved to SF\"}]}, config, stream_mode=\"updates\"):\n",
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" print(update)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b8c416fa-086a-491d-a7d3-57091f6413e3",
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"metadata": {},
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"source": [
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"## Run graph on a different thread\n",
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"\n",
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"We can now run the graph on a different thread and see that it remembers facts about the user (specifically that the user likes pepperoni pizza and lives in SF):"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "e240f025-ff8b-4d17-beb7-2420c0575dd9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'call_model': {'messages': [AIMessage(content=\"Sure! Since you just moved to San Francisco, how about trying some popular local spots? Here are a few restaurant recommendations in SF:\\n\\n1. Tony's Pizza Napoletana - Known for their delicious pepperoni pizza!\\n2. The Slanted Door - A popular Vietnamese restaurant in the city.\\n3. Zuni Cafe - A classic American restaurant with a great ambiance.\\n4. Tartine Bakery - Perfect for a casual dinner with amazing baked goods.\\n5. State Bird Provisions - A unique dining experience with small plates and a lively atmosphere.\\n\\nFeel free to explore these options and enjoy your dinner! If you need more recommendations or information about a specific cuisine, let me know!\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 138, 'prompt_tokens': 197, 'total_tokens': 335}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-de8ad08c-0810-4bb5-b2e8-d3dc89522f8e-0', usage_metadata={'input_tokens': 197, 'output_tokens': 138, 'total_tokens': 335})]}}\n"
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]
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}
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],
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"source": [
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"config = {\"configurable\": {\"thread_id\": \"2\", \"user_id\": \"1\"}}\n",
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"\n",
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"for update in graph.stream({\"messages\": [{\"role\": \"user\", \"content\": \"where and what should i eat for dinner? Can you list some restaurants?\"}]}, config, stream_mode=\"updates\"):\n",
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" print(update)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "091995d3",
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"metadata": {},
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"source": [
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"Perfect! The AI recommended restaurants in SF, and included a pizza restaurant at the top of it's list.\n",
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"\n",
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"Notice that the `messages` in this new thread do NOT contain the messages from the previous thread since we didn't store them as shared values across the `user_id`. However, the `info` we saved in the previous thread was saved since we passed in the same `user_id` in this new thread.\n",
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"\n",
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"Let's now run the graph for another user to verify that the preferences of the first user are self contained:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f9bf2c15",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'call_model': {'messages': [AIMessage(content='I can definitely help you with that! To provide you with personalized restaurant recommendations, could you please let me know your location or any specific preferences you have for dinner?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 34, 'prompt_tokens': 185, 'total_tokens': 219}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5a483acf-1289-4d7f-b707-97760a8c3620-0', usage_metadata={'input_tokens': 185, 'output_tokens': 34, 'total_tokens': 219})]}}\n"
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]
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}
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],
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"source": [
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"config = {\"configurable\": {\"thread_id\": \"3\", \"user_id\": \"2\"}}\n",
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"\n",
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"for update in graph.stream({\"messages\": [{\"role\": \"user\", \"content\": \"where and what should i eat for dinner? Can you list some restaurants?\"}]}, config, stream_mode=\"updates\"):\n",
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" print(update)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7086cea",
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"metadata": {},
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"source": [
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"Perfect! The graph has forgotten all of the previous preferences and has to ask the user for it's location and dietary preferences."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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"nbformat": 4,
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"nbformat_minor": 5
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}
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