mirror of
https://github.com/langchain-ai/langgraph.git
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186 lines
5.7 KiB
Plaintext
186 lines
5.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3631f2b9-aa79-472e-a9d6-9125a90ee704",
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"metadata": {},
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"source": [
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"# How to stream state updates of your 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": "858c7499-0c92-40a9-bd95-e5a5a5817e92",
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"metadata": {},
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"source": [
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"LangGraph supports multiple streaming modes. The main ones are:\n",
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"\n",
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"- `values`: This streaming mode streams back values of the graph. This is the **full state of the graph** after each node is called.\n",
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"- `updates`: This streaming mode streams back updates to the graph. This is the **update to the state of the graph** after each node is called.\n",
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"\n",
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"This guide covers `stream_mode=\"updates\"`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7c2f84f1-0751-4779-97d4-5cbb286093b7",
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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": "markdown",
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"id": "323db423-b644-40bd-9c2d-976a53f602f7",
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"metadata": {},
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"source": [
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"We'll be using a simple ReAct agent for this guide."
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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": "6b4285e4-7434-4971-bde0-aabceef8ee7e",
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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": "f7f9f24a-e3d0-422b-8924-47950b2facd6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdin",
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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\")"
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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": "85cf2e23-29f2-40cc-b302-5377b3b49da9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Literal\n",
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"from langchain_community.tools.tavily_search import TavilySearchResults\n",
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"from langchain_core.runnables import ConfigurableField\n",
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"from langchain_core.tools import tool\n",
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"from langchain_openai import ChatOpenAI\n",
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"from langgraph.prebuilt import create_react_agent\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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"model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n",
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"graph = create_react_agent(model, 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": "956db549-5207-4be1-a823-78311738e3f8",
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"metadata": {},
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"source": [
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"## Stream updates"
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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": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1",
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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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"Receiving update from node: 'agent'\n",
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"{'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn', 'function': {'arguments': '{\"city\":\"sf\"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd68b3a0-86c3-4afa-9649-1b962a0dd062-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]}\n",
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"\n",
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"\n",
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"\n",
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"Receiving update from node: 'tools'\n",
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"{'messages': [ToolMessage(content=\"It's always sunny in sf\", name='get_weather', tool_call_id='call_kc6cvcEkTAUGRlSHrP4PK9fn')]}\n",
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"\n",
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"\n",
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"\n",
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"Receiving update from node: 'agent'\n",
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"{'messages': [AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'stop', 'logprobs': None}, id='run-009d83c4-b874-4acc-9494-20aba43132b9-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}\n",
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"\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
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"async for chunk in graph.astream(inputs, stream_mode=\"updates\"):\n",
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" for node, values in chunk.items():\n",
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" print(f\"Receiving update from node: '{node}'\")\n",
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" print(values)\n",
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" print(\"\\n\\n\")"
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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": null,
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"id": "8cc57240-243c-4d9a-a845-cb55ef973a59",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "langgraph",
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"language": "python",
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"name": "langgraph"
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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": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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