LangGraph_07_CheckPoints_Breakpoints.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyMmSozPms1wnWeoEJe3jQU4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aritrasen87/LLM_RAG_Model_Deployment/blob/main/LangGraph_07_CheckPoints_Breakpoints.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Source: https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/breakpoints/\n",
        "\n",
        "**Breakpoints** are a common HIL interaction pattern, allowing the graph to\n",
        "stop at specific steps and seek human approval before proceeding.\n",
        "\n",
        "Breakpoints are built on top of LangGraph **checkpoints**, which save the graph's state after each node execution.\n",
        "\n",
        "Checkpoints are saved in **threads** that preserve graph state and can be accessed after a graph has finished execution.\n",
        "\n",
        "Benefits:\n",
        "\n",
        "- Graph execution to pause at specific points\n",
        "- Await human approval\n",
        "- Resume execution from the last checkpoint."
      ],
      "metadata": {
        "id": "mgs9EN3SRzFX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3pODaDlxRMlP",
        "outputId": "96469602-5bd3-41f8-b2db-4408cdfcc570",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.1/97.1 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m33.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.9/318.9 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "! pip install langgraph langchain_community --q\n",
        "! pip install langchain_openai --q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import TypedDict, Annotated, Sequence\n",
        "from langgraph.graph import StateGraph, START, END\n",
        "from langgraph.checkpoint.memory import MemorySaver\n",
        "from IPython.display import Image, display\n",
        "from langgraph.graph.message import add_messages\n",
        "import operator\n",
        "\n",
        "\n",
        "\n",
        "class State(TypedDict):\n",
        "    messages: Annotated[list, operator.add]"
      ],
      "metadata": {
        "id": "MXPnHpN5TpVn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def function_1(state):\n",
        "    return {'messages' :  [\" First Function \"]}\n",
        "\n",
        "def function_2(state):\n",
        "    return {'messages' : [\" 2nd Function \"]}\n",
        "\n",
        "def function_3(state):\n",
        "    return {'messages' : [\" 3rd Function \"]}"
      ],
      "metadata": {
        "id": "TpIUro39T6Qy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph = StateGraph(State)\n",
        "graph.add_node(\"step_1\", function_1)\n",
        "graph.add_node(\"step_2\", function_2)\n",
        "graph.add_node(\"step_3\", function_3)\n",
        "graph.add_edge(START, \"step_1\")\n",
        "graph.add_edge(\"step_1\", \"step_2\")\n",
        "graph.add_edge(\"step_2\", \"step_3\")\n",
        "graph.add_edge(\"step_3\", END)\n",
        "\n",
        "# Set up memory\n",
        "memory = MemorySaver()\n",
        "\n",
        "# Add\n",
        "graph = graph.compile(checkpointer=memory, interrupt_before=[\"step_3\"])"
      ],
      "metadata": {
        "id": "JVNVPre8UZnb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View\n",
        "display(Image(graph.get_graph().draw_mermaid_png()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "8MiWQiXxU19z",
        "outputId": "ea2584fa-dbd9-40da-9d99-d750be5c4be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Input\n",
        "initial_input = {\"messages\": [\"hello langgraph\"]}\n",
        "\n",
        "# Thread\n",
        "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
        "\n",
        "# Run the graph until the first interruption\n",
        "for event in graph.stream(initial_input, thread, stream_mode=\"values\"):\n",
        "    print(event)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0sy7bcHVENy",
        "outputId": "be108ecb-8645-4be6-8529-3f683e09cd43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'messages': ['hello langgraph']}\n",
            "{'messages': ['hello langgraph', ' First Function ']}\n",
            "{'messages': ['hello langgraph', ' First Function ', ' 2nd Function ']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "snapshot = graph.get_state(thread)\n",
        "snapshot.next"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqnLcPcVZMPn",
        "outputId": "8cb66bc9-5cf0-4255-fa33-f35ecd72f9b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('step_3',)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thread = {\"configurable\": {\"thread_id\": \"11\"}}\n",
        "\n",
        "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
        "        print(event)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "QDLlUhdNVYuA",
        "outputId": "6809e4f0-9aa5-457e-c301-3a59662223e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "EmptyInputError",
          "evalue": "Received no input for __start__",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mEmptyInputError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-9da6ec9f624a>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mthread\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"configurable\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"thread_id\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"11\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langgraph/pregel/__init__.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, input, config, stream_mode, output_keys, interrupt_before, interrupt_after, debug, subgraphs)\u001b[0m\n\u001b[1;32m   1213\u001b[0m                 \u001b[0;31m# channels are guaranteed to be immutable for the duration of the step,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1214\u001b[0m                 \u001b[0;31m# with channel updates applied only at the transition between steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1215\u001b[0;31m                 while loop.tick(\n\u001b[0m\u001b[1;32m   1216\u001b[0m                     \u001b[0minput_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_channels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1217\u001b[0m                     \u001b[0minterrupt_before\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterrupt_before\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langgraph/pregel/loop.py\u001b[0m in \u001b[0;36mtick\u001b[0;34m(self, input_keys, interrupt_after, interrupt_before, manager)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mINPUT_DONE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mINPUT_RESUMING\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_keys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    247\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrites\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtask\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0mwrites\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mw\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrites\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langgraph/pregel/loop.py\u001b[0m in \u001b[0;36m_first\u001b[0;34m(self, input_keys)\u001b[0m\n\u001b[1;32m    425\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_put_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"source\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"input\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"writes\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_writes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mEmptyInputError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Received no input for {input_keys}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m         \u001b[0;31m# done with input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mINPUT_RESUMING\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_resuming\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mINPUT_DONE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mEmptyInputError\u001b[0m: Received no input for __start__"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
        "\n",
        "for event in graph.stream(None, thread, stream_mode=\"values\"):\n",
        "        print(event)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7F41fysAZAJO",
        "outputId": "c779a382-5ad4-439b-e891-71183fd5251b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'messages': ['hello langgraph', ' First Function ', ' 2nd Function ']}\n",
            "{'messages': ['hello langgraph', ' First Function ', ' 2nd Function ', ' 3rd Function ']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "snapshot = graph.get_state(thread)\n",
        "snapshot.next"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uKtWuzKZETP",
        "outputId": "911df9b7-3d76-46f3-b29c-de341cf9b0b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "()"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CheckwithPoint + Breakpoint with Toolcalling"
      ],
      "metadata": {
        "id": "QGCuhGa7bWi_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install langgraph-checkpoint-sqlite --q"
      ],
      "metadata": {
        "id": "7tyiUjV_c8sW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "# Now you can access your environment variables using os.environ\n",
        "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_KEY')\n",
        "os.environ['TAVILY_API_KEY'] = userdata.get('TAVILY')\n",
        "\n",
        "llm = ChatOpenAI(model=\"gpt-4o-mini\")"
      ],
      "metadata": {
        "id": "l59NOkmibzvH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class State(TypedDict):\n",
        "    messages: Annotated[list, add_messages]"
      ],
      "metadata": {
        "id": "qVyRnIvtZypJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_community.tools.tavily_search import TavilySearchResults\n",
        "from langchain_openai import ChatOpenAI\n",
        "from typing_extensions import TypedDict\n",
        "\n",
        "from langgraph.checkpoint.sqlite import SqliteSaver\n",
        "from langgraph.graph import StateGraph\n",
        "from langgraph.graph.message import add_messages\n",
        "from langgraph.prebuilt import ToolNode, tools_condition\n",
        "\n",
        "# memory = SqliteSaver.from_conn_string(\":memory:\")\n",
        "# Set up memory\n",
        "memory = MemorySaver()\n",
        "\n",
        "graph = StateGraph(State)\n",
        "\n",
        "tool = TavilySearchResults(max_results=2)\n",
        "tools = [tool]\n",
        "llm_with_tools = llm.bind_tools(tools)"
      ],
      "metadata": {
        "id": "YNx-v1JTbeMr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm_with_tools.invoke('Hi')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6oX45zVjvoK",
        "outputId": "b9f5fc77-6015-4e8f-ceff-d549968dd48e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AIMessage(content='Hello! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 80, 'total_tokens': 90}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_483d39d857', 'finish_reason': 'stop', 'logprobs': None}, id='run-9c243ba8-2f59-413d-9807-f9bdbd2a5a69-0', usage_metadata={'input_tokens': 80, 'output_tokens': 10, 'total_tokens': 90})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Agent(state: State):\n",
        "    print('---> Starting Agent----->')\n",
        "    return {\"messages\": [llm_with_tools.invoke(state[\"messages\"])]}\n",
        "\n",
        "tool_node = ToolNode(tools=[tool])"
      ],
      "metadata": {
        "id": "5ukwumm9dHib"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph.add_node(\"Agent\", Agent)\n",
        "graph.add_node(\"tools\", tool_node)\n",
        "\n",
        "graph.add_conditional_edges(\n",
        "    \"Agent\",\n",
        "    tools_condition,\n",
        ")\n",
        "graph.add_edge(\"tools\", \"Agent\")\n",
        "graph.set_entry_point(\"Agent\")"
      ],
      "metadata": {
        "id": "Xz1FlZ7QdPSe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = graph.compile(checkpointer=memory,interrupt_before=[\"tools\"])"
      ],
      "metadata": {
        "id": "pFXQdKy-doXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View\n",
        "display(Image(app.get_graph().draw_mermaid_png()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "HGAzneBZeN_U",
        "outputId": "94880606-71fb-4178-e546-9e0e0d2d530b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.messages import HumanMessage\n",
        "# Input\n",
        "input_message = {'messages':HumanMessage(content=\"Hello, I am Aritra , from Kolkata\")}\n",
        "\n",
        "# Thread\n",
        "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
        "\n",
        "# Run the graph until the first interruption\n",
        "for event in app.stream(input_message, thread, stream_mode=\"values\"):\n",
        "    print(event[\"messages\"][-1].pretty_print())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BoQqnivrfZZr",
        "outputId": "7dd5fa9d-e2db-4d38-e44b-372601f640eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================\u001b[1m Human Message \u001b[0m=================================\n",
            "\n",
            "Hello, I am Aritra , from Kolkata\n",
            "None\n",
            "---> Starting Agent----->\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "Hello Aritra! How can I assist you today?\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Input\n",
        "input_message = {'messages':HumanMessage(content=\"Sorry where I am from?\")}\n",
        "\n",
        "# Thread\n",
        "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
        "\n",
        "# Run the graph until the first interruption\n",
        "for event in app.stream(input_message, thread, stream_mode=\"values\"):\n",
        "    print(event[\"messages\"][-1].pretty_print())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSyhuRDAfx7l",
        "outputId": "e4cee630-dbbc-45e8-ced9-b48f4a3ca2c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================\u001b[1m Human Message \u001b[0m=================================\n",
            "\n",
            "Sorry where I am from?\n",
            "None\n",
            "---> Starting Agent----->\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "You're from Kolkata. If you have any specific questions or topics you'd like to discuss about Kolkata or anything else, feel free to let me know!\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Input\n",
        "input_message = {'messages':HumanMessage(content=\"What is there weather there?\")}\n",
        "\n",
        "# Thread\n",
        "thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
        "\n",
        "# Run the graph until the first interruption\n",
        "for event in app.stream(input_message, thread, stream_mode=\"values\"):\n",
        "    print(event[\"messages\"][-1].pretty_print())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "91xCCfojkUGR",
        "outputId": "ab9957b4-ca05-4022-b4a3-7b9a69ae5ce1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================\u001b[1m Human Message \u001b[0m=================================\n",
            "\n",
            "What is there weather there?\n",
            "None\n",
            "---> Starting Agent----->\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "Tool Calls:\n",
            "  tavily_search_results_json (call_p5UbmJU1mFB0kUoDpb3kO2JT)\n",
            " Call ID: call_p5UbmJU1mFB0kUoDpb3kO2JT\n",
            "  Args:\n",
            "    query: Kolkata current weather\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the graph until the first interruption\n",
        "for event in app.stream(None, thread, stream_mode=\"values\"):\n",
        "    print(event[\"messages\"][-1].pretty_print())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GJJNl4WlSkL",
        "outputId": "9c45e86c-29cf-44d7-d188-db45af3f7ded"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "Tool Calls:\n",
            "  tavily_search_results_json (call_p5UbmJU1mFB0kUoDpb3kO2JT)\n",
            " Call ID: call_p5UbmJU1mFB0kUoDpb3kO2JT\n",
            "  Args:\n",
            "    query: Kolkata current weather\n",
            "None\n",
            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
            "Name: tavily_search_results_json\n",
            "\n",
            "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'Kolkata', 'region': 'West Bengal', 'country': 'India', 'lat': 22.57, 'lon': 88.37, 'tz_id': 'Asia/Kolkata', 'localtime_epoch': 1725888381, 'localtime': '2024-09-09 18:56'}, 'current': {'last_updated_epoch': 1725887700, 'last_updated': '2024-09-09 18:45', 'temp_c': 29.2, 'temp_f': 84.6, 'is_day': 0, 'condition': {'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}, 'wind_mph': 6.9, 'wind_kph': 11.2, 'wind_degree': 120, 'wind_dir': 'ESE', 'pressure_mb': 999.0, 'pressure_in': 29.5, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 89, 'cloud': 50, 'feelslike_c': 35.5, 'feelslike_f': 95.9, 'windchill_c': 28.8, 'windchill_f': 83.8, 'heatindex_c': 34.4, 'heatindex_f': 93.9, 'dewpoint_c': 25.3, 'dewpoint_f': 77.6, 'vis_km': 3.5, 'vis_miles': 2.0, 'uv': 1.0, 'gust_mph': 18.5, 'gust_kph': 29.7}}\"}, {\"url\": \"https://www.msn.com/en-in/news/India/kolkata-weather-and-aqi-today-warm-start-at-27-32-c-check-weather-forecast-for-september-9-2024/ar-AA1qdRci\", \"content\": \"The temperature in Kolkata today, on September 9, 2024, is 30.96 \\u00b0C. The day's forecast indicates a minimum and maximum temperature of 27.32 \\u00b0C and 33.79 \\u00b0C, respectively. The relative humidity ...\"}]\n",
            "None\n",
            "---> Starting Agent----->\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "As of now, the weather in Kolkata is as follows:\n",
            "\n",
            "- **Temperature**: Approximately 29.2°C (84.6°F)\n",
            "- **Condition**: Mist\n",
            "- **Humidity**: 89%\n",
            "- **Wind**: ESE at 11.2 km/h (6.9 mph)\n",
            "- **Feels Like**: 35.5°C (95.9°F)\n",
            "\n",
            "The forecast for today, September 9, 2024, indicates:\n",
            "- **Minimum Temperature**: 27.32°C\n",
            "- **Maximum Temperature**: 33.79°C\n",
            "\n",
            "For more detailed weather updates, you can check [Weather API](https://www.weatherapi.com/) or [MSN Weather](https://www.msn.com/en-in/news/India/kolkata-weather-and-aqi-today-warm-start-at-27-32-c-check-weather-forecast-for-september-9-2024/ar-AA1qdRci).\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Input\n",
        "input_message = {'messages':HumanMessage(content=\"What are the best foods to explore there?\")}\n",
        "\n",
        "# Run the graph until the first interruption\n",
        "for event in app.stream(input_message, thread, stream_mode=\"values\"):\n",
        "    print(event[\"messages\"][-1].pretty_print())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UOUnN4rOlZi4",
        "outputId": "b85f22a1-a8ad-4b2f-bebc-4c477be05300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================\u001b[1m Human Message \u001b[0m=================================\n",
            "\n",
            "What are the best foods to explore there?\n",
            "None\n",
            "---> Starting Agent----->\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "Kolkata is famous for its rich culinary heritage and diverse food offerings. Here are some of the best foods to explore in Kolkata:\n",
            "\n",
            "1. **Puchka (Pani Puri)**: A popular street snack consisting of hollow puris filled with spicy tamarind water, chickpeas, and potatoes.\n",
            "\n",
            "2. **Kathi Rolls**: A delicious wrap made with paratha (flatbread) filled with spicy grilled meat or vegetables, often served with chutney.\n",
            "\n",
            "3. **Macher Jhol**: A traditional Bengali fish curry made with fresh fish, vegetables, and a variety of spices, usually served with rice.\n",
            "\n",
            "4. **Shorshe Ilish**: Hilsa fish cooked in mustard sauce, a quintessential Bengali dish enjoyed with steamed rice.\n",
            "\n",
            "5. **Chingri Malai Curry**: A creamy prawn curry made with coconut milk and aromatic spices, served with rice.\n",
            "\n",
            "6. **Biryani**: Kolkata-style biryani is known for its unique flavor, made with marinated meat, rice, and boiled potatoes, often served with raita.\n",
            "\n",
            "7. **Mishti (Sweets)**: Kolkata is famous for its sweets, particularly Rasgulla, Sandesh, and Mishti Doi (sweetened yogurt).\n",
            "\n",
            "8. **Telebhaja**: A variety of deep-fried snacks, including onion fritters, potato fritters, and eggplant fritters, often enjoyed with chutney.\n",
            "\n",
            "9. **Luchi and Cholar Dal**: Fluffy deep-fried bread (luchi) served with spiced Bengal gram dal (cholar dal).\n",
            "\n",
            "10. **Bhelpuri**: A savory snack made with puffed rice, vegetables, and tangy tamarind sauce, offering a delightful mix of flavors.\n",
            "\n",
            "Exploring these dishes will give you a taste of the vibrant food culture in Kolkata!\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Input\n",
        "input_message = {'messages':HumanMessage(content=\"Tell me about the recent protest happening there?\")}\n",
        "\n",
        "# Run the graph until the first interruption\n",
        "for event in app.stream(input_message, thread, stream_mode=\"values\"):\n",
        "    print(event[\"messages\"][-1].pretty_print())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mr1SQuQLqCog",
        "outputId": "5558ad1f-a5cf-4a36-e4fe-dd644a6f8aec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================\u001b[1m Human Message \u001b[0m=================================\n",
            "\n",
            "Tell me about the recent protest happening there?\n",
            "None\n",
            "---> Starting Agent----->\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "Tool Calls:\n",
            "  tavily_search_results_json (call_wCXMvCYl0uLpi6NXZoWfuSpQ)\n",
            " Call ID: call_wCXMvCYl0uLpi6NXZoWfuSpQ\n",
            "  Args:\n",
            "    query: recent protests in Kolkata September 2024\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "snapshot = app.get_state(thread)\n",
        "current_message = snapshot.values[\"messages\"][-1]\n",
        "current_message.pretty_print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QKd81aIqcDp",
        "outputId": "7c22d41d-9d5c-4b16-b407-ab7bae557eb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "Tool Calls:\n",
            "  tavily_search_results_json (call_wCXMvCYl0uLpi6NXZoWfuSpQ)\n",
            " Call ID: call_wCXMvCYl0uLpi6NXZoWfuSpQ\n",
            "  Args:\n",
            "    query: recent protests in Kolkata September 2024\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tool_call_id = current_message.tool_calls[0][\"id\"]\n",
        "tool_call_id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "q9IZh9Fnrwwo",
        "outputId": "b611703d-827a-4963-a933-69de4cac7303"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'call_wCXMvCYl0uLpi6NXZoWfuSpQ'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.messages import AIMessage, ToolMessage\n",
        "\n",
        "answer = \"\"\"The protest is about an unfortune incident with a lady Doctor in R.G.Kar hospital in Kolkata, as a protest against the adiministration.\n",
        "People are using a slogan - \"#WeWantJustice\"! \"\"\"\n",
        "new_messages = [\n",
        "    ToolMessage(content=answer, tool_call_id=tool_call_id),\n",
        "    AIMessage(content=answer),\n",
        "]"
      ],
      "metadata": {
        "id": "UQ9sijmgqsCK"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app.update_state(\n",
        "    thread,\n",
        "    {\"messages\": new_messages},\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXQB-Li5sCHY",
        "outputId": "c2a1fabb-58ac-4460-8c86-af22b78a0aed"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'configurable': {'thread_id': '1',\n",
              "  'checkpoint_ns': '',\n",
              "  'checkpoint_id': '1ef6eb0c-dd6d-68a6-8012-b6d0136bdbd4'}}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(app.get_state(thread).values[\"messages\"][-1:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjb4T23VsJRr",
        "outputId": "b9a6080c-2b07-4f60-bc47-63a23fa5ea62"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[AIMessage(content='The protest is about an unfortune incident with a lady Doctor in R.G.Kar hospital in Kolkata, as a protest against the adiministration.\\nPeople are using a slogan - \"#WeWantJustice\"! ', id='169d57de-7452-4398-8833-a387b7e53e97')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xdIfpaOLs0Hc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}