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 "cells": [
  {
   "cell_type": "markdown",
   "id": "3631f2b9-aa79-472e-a9d6-9125a90ee704",
   "metadata": {},
   "source": [
    "# How to stream full state of your graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858c7499-0c92-40a9-bd95-e5a5a5817e92",
   "metadata": {},
   "source": [
    "LangGraph supports multiple streaming modes. The main ones are:\n",
    "\n",
    "- `values`: This streaming mode streams back values of the graph. This is the **full state of the graph** after each node is called.\n",
    "- `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",
    "\n",
    "This guide covers `stream_mode=\"values\"`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c2f84f1-0751-4779-97d4-5cbb286093b7",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First, let's install the required packages and set our API keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b4285e4-7434-4971-bde0-aabceef8ee7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install -U langgraph langchain-openai langchain-community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f9f24a-e3d0-422b-8924-47950b2facd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "\n",
    "def _set_env(var: str):\n",
    "    if not os.environ.get(var):\n",
    "        os.environ[var] = getpass.getpass(f\"{var}: \")\n",
    "\n",
    "\n",
    "_set_env(\"OPENAI_API_KEY\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaaab1fc",
   "metadata": {},
   "source": [
    "<div class=\"admonition tip\">\n",
    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
    "    <p style=\"padding-top: 5px;\">\n",
    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
    "    </p>\n",
    "</div>    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7939a3c5",
   "metadata": {},
   "source": [
    "## Define the graph\n",
    "\n",
    "We'll be using a simple ReAct agent for this guide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef5a3ec6-0cd0-4541-ab1b-d63ede22720e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Literal\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "from langchain_core.runnables import ConfigurableField\n",
    "from langchain_core.tools import tool\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langgraph.prebuilt import create_react_agent\n",
    "\n",
    "\n",
    "@tool\n",
    "def get_weather(city: Literal[\"nyc\", \"sf\"]):\n",
    "    \"\"\"Use this to get weather information.\"\"\"\n",
    "    if city == \"nyc\":\n",
    "        return \"It might be cloudy in nyc\"\n",
    "    elif city == \"sf\":\n",
    "        return \"It's always sunny in sf\"\n",
    "    else:\n",
    "        raise AssertionError(\"Unknown city\")\n",
    "\n",
    "\n",
    "tools = [get_weather]\n",
    "\n",
    "model = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n",
    "graph = create_react_agent(model, tools)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "002a715b-e0be-4e89-8d42-f0098882586b",
   "metadata": {},
   "source": [
    "## Stream values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9e9ffb0-2cd5-466f-b70b-b6ed51b852d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "what's the weather in sf\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  get_weather (call_61VvIzqVGtyxcXi0z6knZkjZ)\n",
      " Call ID: call_61VvIzqVGtyxcXi0z6knZkjZ\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\": [(\"human\", \"what's the weather in sf\")]}\n",
    "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n",
    "    chunk[\"messages\"][-1].pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d73de237-bf45-4fa7-93ef-6dae7eacffc0",
   "metadata": {},
   "source": [
    "If we want to just get the final result, we can use the same method and just keep track of the last value we received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c122bf15-a489-47bf-b482-a744a54e2cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = {\"messages\": [(\"human\", \"what's the weather in sf\")]}\n",
    "async for chunk in graph.astream(inputs, stream_mode=\"values\"):\n",
    "    final_result = chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "316022e5-4c65-48e4-9878-8d94a2425ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'messages': [HumanMessage(content=\"what's the weather in sf\", id='54b39b6f-054b-4306-980b-86905e48a6bc'),\n",
       "  AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_avoKnK8reERzTUSxrN9cgFxY', '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_5e6c71d4a8', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-f2f43c89-2c96-45f4-975c-2d0f22d0d2d1-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_avoKnK8reERzTUSxrN9cgFxY'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71}),\n",
       "  ToolMessage(content=\"It's always sunny in sf\", name='get_weather', id='fc18a798-c7b2-4f73-84fa-8ffdffb6ddcb', tool_call_id='call_avoKnK8reERzTUSxrN9cgFxY'),\n",
       "  AIMessage(content='The weather in San Francisco is currently sunny. Enjoy the sunshine!', response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 84, 'total_tokens': 98}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_5e6c71d4a8', 'finish_reason': 'stop', 'logprobs': None}, id='run-21418147-da8e-4738-a076-239377397c40-0', usage_metadata={'input_tokens': 84, 'output_tokens': 14, 'total_tokens': 98})]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f64ebbe-535c-4b35-a95f-0a7490cfed90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "The weather in San Francisco is currently sunny. Enjoy the sunshine!\n"
     ]
    }
   ],
   "source": [
    "final_result[\"messages\"][-1].pretty_print()"
   ]
  }
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