mirror of
https://github.com/langchain-ai/langgraph.git
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langgraph checkpoint: new library for checkpoint interfaces (#1163)
--------- Co-authored-by: Nuno Campos <nuno@langchain.dev>
This commit is contained in:
Vadym Barda
co-authored by
Nuno Campos
Nuno Campos
parent
913a2d975b
commit
ae74825ea7
@@ -1,255 +1,255 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
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"metadata": {},
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"source": [
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"# How to add memory to the prebuilt ReAct agent\n",
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"\n",
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"This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
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"\n",
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"All we need to do to enable memory is pass in a checkpointer to `create_react_agents`"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
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||||
"metadata": {},
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||||
"source": [
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"## Setup"
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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": "a213e11a-5c62-4ddb-a707-490d91add383",
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||||
"metadata": {},
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"outputs": [],
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"source": [
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"%%capture --no-stderr\n",
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"%pip install -U langgraph langchain-openai"
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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": "23a1885c-04ab-4750-aefa-105891fddf3e",
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"metadata": {},
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||||
"outputs": [
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"cells": [
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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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"OPENAI_API_KEY: ········\n"
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]
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}
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],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"\n",
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"def _set_env(var: str):\n",
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
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"\n",
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"\n",
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"_set_env(\"OPENAI_API_KEY\")\n",
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"\n",
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"# Recommended\n",
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"_set_env(\"LANGCHAIN_API_KEY\")\n",
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
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]
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},
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{
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"cell_type": "markdown",
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||||
"id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
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"metadata": {},
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"source": [
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"## Code"
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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": "7a154152-973e-4b5d-aa13-48c617744a4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# First we initialize the model we want to use.\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
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"\n",
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"\n",
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"# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
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"\n",
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"from typing import Literal\n",
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"\n",
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"from langchain_core.tools import tool\n",
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"\n",
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"\n",
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"@tool\n",
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"def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
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" \"\"\"Use this to get weather information.\"\"\"\n",
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" if city == \"nyc\":\n",
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" return \"It might be cloudy in nyc\"\n",
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" elif city == \"sf\":\n",
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" return \"It's always sunny in sf\"\n",
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" else:\n",
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" raise AssertionError(\"Unknown city\")\n",
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"\n",
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"\n",
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"tools = [get_weather]\n",
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"\n",
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"# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n",
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"# to retain the chat context between interactions\n",
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"from langgraph.checkpoint import MemorySaver\n",
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"\n",
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"memory = MemorySaver()\n",
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"\n",
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"# Define the graph\n",
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"\n",
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"from langgraph.prebuilt import create_react_agent\n",
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"\n",
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"graph = create_react_agent(model, tools=tools, checkpointer=memory)"
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]
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},
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{
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"cell_type": "markdown",
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||||
"id": "00407425-506d-4ffd-9c86-987921d8c844",
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||||
"metadata": {},
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||||
"source": [
|
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"## Usage\n",
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"\n",
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"Let's interact with it multiple times to show that it can remember"
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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": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
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||||
"metadata": {},
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"outputs": [],
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"source": [
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"def print_stream(stream):\n",
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" for s in stream:\n",
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" message = s[\"messages\"][-1]\n",
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" if isinstance(message, tuple):\n",
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" print(message)\n",
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" else:\n",
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" message.pretty_print()"
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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": 5,
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||||
"id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
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||||
"metadata": {},
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||||
"outputs": [
|
||||
"cell_type": "markdown",
|
||||
"id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to add memory to the prebuilt ReAct agent\n",
|
||||
"\n",
|
||||
"This tutorial will show how to add memory to the prebuilt ReAct agent. Please see [this tutorial](./create-react-agent.ipynb) for how to get started with the prebuilt ReAct agent\n",
|
||||
"\n",
|
||||
"All we need to do to enable memory is pass in a checkpointer to `create_react_agents`"
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]
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},
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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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"================================\u001b[1m Human Message \u001b[0m=================================\n",
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"\n",
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"What's the weather in NYC?\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"Tool Calls:\n",
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" get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n",
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" Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n",
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" Args:\n",
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" city: nyc\n",
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"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
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"Name: get_weather\n",
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"\n",
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"It might be cloudy in nyc\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"\n",
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"The weather in NYC might be cloudy.\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\"}}\n",
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"inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
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"\n",
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"print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
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]
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},
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{
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"cell_type": "markdown",
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||||
"id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"Notice that when we pass the same the same thread ID, the chat history is preserved"
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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": 6,
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||||
"id": "187479f9-32fa-4611-9487-cf816ba2e147",
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||||
"metadata": {},
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||||
"outputs": [
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||||
"cell_type": "markdown",
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||||
"id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup"
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]
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},
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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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"================================\u001b[1m Human Message \u001b[0m=================================\n",
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"\n",
|
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"What's it known for?\n",
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
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"\n",
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"New York City (NYC) is known for many things, including:\n",
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"\n",
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"1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n",
|
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"2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n",
|
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"3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n",
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"4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n",
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"5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n",
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"6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n",
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"7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n",
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"8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n",
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"9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n",
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"10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n"
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]
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||||
"cell_type": "code",
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||||
"execution_count": 1,
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||||
"id": "a213e11a-5c62-4ddb-a707-490d91add383",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"%%capture --no-stderr\n",
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"%pip install -U langgraph langchain-openai"
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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": "23a1885c-04ab-4750-aefa-105891fddf3e",
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||||
"metadata": {},
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||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
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||||
"output_type": "stream",
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||||
"text": [
|
||||
"OPENAI_API_KEY: ········\n"
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]
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}
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],
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"source": [
|
||||
"import getpass\n",
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"import os\n",
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"\n",
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"\n",
|
||||
"def _set_env(var: str):\n",
|
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" if not os.environ.get(var):\n",
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" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
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"\n",
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"\n",
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"_set_env(\"OPENAI_API_KEY\")\n",
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"\n",
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"# Recommended\n",
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"_set_env(\"LANGCHAIN_API_KEY\")\n",
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"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"os.environ[\"LANGCHAIN_PROJECT\"] = \"Create ReAct Agent Tutorial\""
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]
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||||
},
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{
|
||||
"cell_type": "markdown",
|
||||
"id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Code"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "7a154152-973e-4b5d-aa13-48c617744a4c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# First we initialize the model we want to use.\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
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"\n",
|
||||
"model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
|
||||
"\n",
|
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"\n",
|
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"# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
|
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"\n",
|
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"from typing import Literal\n",
|
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"\n",
|
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"from langchain_core.tools import tool\n",
|
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"\n",
|
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"\n",
|
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"@tool\n",
|
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"def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
|
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" \"\"\"Use this to get weather information.\"\"\"\n",
|
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" if city == \"nyc\":\n",
|
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" return \"It might be cloudy in nyc\"\n",
|
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" elif city == \"sf\":\n",
|
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" return \"It's always sunny in sf\"\n",
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" else:\n",
|
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" raise AssertionError(\"Unknown city\")\n",
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"\n",
|
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"\n",
|
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"tools = [get_weather]\n",
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"\n",
|
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"# We can add \"chat memory\" to the graph with LangGraph's checkpointer\n",
|
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"# to retain the chat context between interactions\n",
|
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"from langgraph.checkpoint.memory import MemorySaver\n",
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"\n",
|
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"memory = MemorySaver()\n",
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"\n",
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"# Define the graph\n",
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"\n",
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"from langgraph.prebuilt import create_react_agent\n",
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"\n",
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"graph = create_react_agent(model, tools=tools, checkpointer=memory)"
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]
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},
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{
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"cell_type": "markdown",
|
||||
"id": "00407425-506d-4ffd-9c86-987921d8c844",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Usage\n",
|
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"\n",
|
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"Let's interact with it multiple times to show that it can remember"
|
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]
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},
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{
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"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
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"def print_stream(stream):\n",
|
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" for s in stream:\n",
|
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" message = s[\"messages\"][-1]\n",
|
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" if isinstance(message, tuple):\n",
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" print(message)\n",
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" else:\n",
|
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" message.pretty_print()"
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]
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},
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{
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||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
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"\n",
|
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"What's the weather in NYC?\n",
|
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
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"Tool Calls:\n",
|
||||
" get_weather (call_mdovy4yXSSYrmSlnlVSUacVn)\n",
|
||||
" Call ID: call_mdovy4yXSSYrmSlnlVSUacVn\n",
|
||||
" Args:\n",
|
||||
" city: nyc\n",
|
||||
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
|
||||
"Name: get_weather\n",
|
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"\n",
|
||||
"It might be cloudy in nyc\n",
|
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"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
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"\n",
|
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"The weather in NYC might be cloudy.\n"
|
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]
|
||||
}
|
||||
],
|
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"source": [
|
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"config = {\"configurable\": {\"thread_id\": \"1\"}}\n",
|
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"inputs = {\"messages\": [(\"user\", \"What's the weather in NYC?\")]}\n",
|
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"\n",
|
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"print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
|
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Notice that when we pass the same the same thread ID, the chat history is preserved"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "187479f9-32fa-4611-9487-cf816ba2e147",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
||||
"\n",
|
||||
"What's it known for?\n",
|
||||
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
||||
"\n",
|
||||
"New York City (NYC) is known for many things, including:\n",
|
||||
"\n",
|
||||
"1. **Landmarks and Attractions**: The Statue of Liberty, Times Square, Central Park, Empire State Building, and Brooklyn Bridge.\n",
|
||||
"2. **Cultural Institutions**: Broadway theaters, Metropolitan Museum of Art, Museum of Modern Art (MoMA), and the American Museum of Natural History.\n",
|
||||
"3. **Diverse Neighborhoods**: Areas like Chinatown, Little Italy, Harlem, and Greenwich Village.\n",
|
||||
"4. **Financial Hub**: Wall Street and the New York Stock Exchange.\n",
|
||||
"5. **Cuisine**: A melting pot of global cuisines, famous for its pizza, bagels, and street food.\n",
|
||||
"6. **Media and Entertainment**: Home to major media companies, TV networks, and film studios.\n",
|
||||
"7. **Fashion**: A global fashion capital, hosting New York Fashion Week.\n",
|
||||
"8. **Sports**: Teams like the New York Yankees, New York Mets, New York Knicks, and New York Rangers.\n",
|
||||
"9. **Public Transportation**: An extensive subway system and iconic yellow taxis.\n",
|
||||
"10. **Events**: New Year's Eve celebration in Times Square, Macy's Thanksgiving Day Parade, and various cultural festivals.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n",
|
||||
"print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
|
||||
"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": "3.11.1"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"inputs = {\"messages\": [(\"user\", \"What's it known for?\")]}\n",
|
||||
"print_stream(graph.stream(inputs, config=config, stream_mode=\"values\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
|
||||
"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": "3.11.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user