mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-07-19 14:33:37 -04:00
Harrison Chase
275 lines
20 KiB
Plaintext
275 lines
20 KiB
Plaintext
{
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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 use the prebuilt ReAct agent"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e0fcced0-9767-412f-90f9-7f3cd618ff90",
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"metadata": {},
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"source": [
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"In this how-to we'll create a simple [ReAct](https://arxiv.org/abs/2210.03629) agent app that can check the weather. The app consists of an agent (LLM) and tools. As we interact with the app, we will first call the agent (LLM) to decide if we should use tools. Then we will run a loop: \n",
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"\n",
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"1. If the agent said to take an action (i.e. call tool), we'll run the tools and pass the results back to the agent\n",
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"2. If the agent did not ask to run tools, we will finish (respond to the user)\n",
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"\n",
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"<div class=\"admonition warning\">\n",
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" <p class=\"admonition-title\">Prebuilt Agent</p>\n",
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" <p>\n",
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"Please note that here will we use a prebuilt agent. One of the big benefits of LangGraph is that you can easily create your own agent architectures. So while it's fine to start here to build an agent quickly, we would strongly recommend learning how to build your own agent so that you can take full advantage of LangGraph.\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": "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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{
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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": 1,
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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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"\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)"
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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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"First, let's visualize the graph we just created"
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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": "fa16de4c-aac0-4ff4-ab69-60d399f75423",
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"metadata": {},
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"outputs": [
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{
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"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",
|
|
"display(Image(graph.get_graph().draw_mermaid_png()))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def print_stream(stream):\n",
|
|
" for s in stream:\n",
|
|
" message = s[\"messages\"][-1]\n",
|
|
" if isinstance(message, tuple):\n",
|
|
" print(message)\n",
|
|
" else:\n",
|
|
" message.pretty_print()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9d187d6b-0fb6-4860-8771-160c3cf403c6",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's run the app with an input that needs a tool call"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
|
"\n",
|
|
"what is the weather in sf\n",
|
|
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
|
"Tool Calls:\n",
|
|
" get_weather (call_jgO5OOUnugRkhRi3wAOHl8Et)\n",
|
|
" Call ID: call_jgO5OOUnugRkhRi3wAOHl8Et\n",
|
|
" Args:\n",
|
|
" city: sf\n",
|
|
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
|
|
"Name: get_weather\n",
|
|
"\n",
|
|
"It's always sunny in sf\n",
|
|
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
|
"\n",
|
|
"The weather in San Francisco is currently sunny.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inputs = {\"messages\": [(\"user\", \"what is the weather in sf\")]}\n",
|
|
"print_stream(graph.stream(inputs, stream_mode=\"values\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "838a043f-90ad-4e69-9d1d-6e22db2c346c",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now let's try a question that doesn't need tools"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "187479f9-32fa-4611-9487-cf816ba2e147",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"================================\u001b[1m Human Message \u001b[0m=================================\n",
|
|
"\n",
|
|
"who built you?\n",
|
|
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
|
|
"\n",
|
|
"I was created by OpenAI, a research organization focused on developing and advancing artificial intelligence technology.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"inputs = {\"messages\": [(\"user\", \"who built you?\")]}\n",
|
|
"print_stream(graph.stream(inputs, stream_mode=\"values\"))"
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|