learn/function-calling/notebooks_langchain/fireworks_langgraph_tool_usage.ipynb

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        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1iRf6_ZXi2yPemZSJr2bMi4qQ7PM851ok?usp=sharing)"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSmb-rejDOnT"
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      "source": [
        "# Introduction - Fireworks x LangGraph\n",
        "\n",
        "In this notebook, we demonstrate how to use the Fireworks function-calling model as a router across multiple models with specialized capabilities. Function-calling models have seen a rapid rise in usage due to their ability to easily utilize external tools. One such powerful tool is other LLMs. We have a variety of specialized OSS LLMs for [coding](https://www.deepseek.com/), [chatting in certain languages](https://github.com/QwenLM/Qwen), or just plain [HF Assistants](https://huggingface.co/chat/assistants).\n",
        "\n",
        "The function-calling model allows us to:\n",
        "1. Analyze the user query for intent.\n",
        "2. Find the best model to answer the request, which could even be the function-calling model itself!\n",
        "3. Construct the right query for the chosen LLM.\n",
        "4. Profit!\n",
        "\n",
        "This notebook is a sister notebook to LangChain, though we will use the newer and more controllable[LangGraph](https://www.langchain.com/langgraph) framework to construct an agent graph capable of chit-chatting and solving math equations using a calculator tool.\n",
        "\n",
        "This agent chain will utilize [custom-defined tools](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/) capable of executing a math query using an LLM. The main routing LLM will be the Fireworks function-calling model.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
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            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!pip3 install langgraph langchain-fireworks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYRSabqIL35i"
      },
      "source": [
        "## Setup Dependencies\n",
        "\n",
        "To accomplish the task in this notebook, we need to import some dependencies from LangChain to interface with the model. Specifically, we will use the [ChatFireworks](https://python.langchain.com/v0.2/docs/integrations/chat/fireworks/) implementation.\n",
        "\n",
        "For solving our math equations, we will use the recently released [Firefunction V2](https://fireworks.ai/blog/firefunction-v2-launch-post) and interface with it using the [Fireworks Inference Service](https://fireworks.ai/models).\n",
        "\n",
        "To use the Fireworks AI function-calling model, you must first obtain Fireworks API keys. If you don't already have one, you can get one by following the instructions [here](https://readme.fireworks.ai/docs/quickstart). When prompted below paste in your `FIREWORKS_API_KEY`.\n",
        "\n",
        "**NOTE:** It's important to set the temperature to 0.0 for the function-calling model because we want reliable behavior in routing.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "rVndeZE_eMFi"
      },
      "outputs": [],
      "source": [
        "import getpass\n",
        "import os\n",
        "\n",
        "from langchain_fireworks import ChatFireworks\n",
        "\n",
        "# Replace 'YOUR_API_KEY_HERE' with your actual API key\n",
        "FIREWORKS_API_KEY = \"<YOUR_FIREWORKS_API_KEY>\"\n",
        "\n",
        "# Initialize a Fireworks chat model\n",
        "llm = ChatFireworks(\n",
        "  model=\"accounts/fireworks/models/firefunction-v2\",\n",
        "  temperature=0.0,\n",
        "  max_tokens=256\n",
        "  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RoiXlEo0SxUR"
      },
      "source": [
        "## Base LangGraph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "qF1Iqw7dSwp4"
      },
      "outputs": [],
      "source": [
        "from typing import Annotated, TypedDict\n",
        "\n",
        "from langchain_core.messages import BaseMessage, HumanMessage\n",
        "from langgraph.graph import START, END, StateGraph\n",
        "from langgraph.graph.message import AnyMessage, add_messages\n",
        "\n",
        "# This is the default state  same as \"MessageState\" TypedDict but allows us accessibility to\n",
        "# custom keys to our state like user's details\n",
        "class GraphsState(TypedDict):\n",
        "    messages: Annotated[list[AnyMessage], add_messages]\n",
        "    # user_id: int\n",
        "\n",
        "graph = StateGraph(GraphsState)\n",
        "\n",
        "def _call_model(state: GraphsState):\n",
        "    messages = state[\"messages\"]\n",
        "    response = llm.invoke(messages)\n",
        "    return {\"messages\": [response]}\n",
        "\n",
        "graph.add_edge(START, \"modelNode\")\n",
        "graph.add_node(\"modelNode\", _call_model)\n",
        "graph.add_edge(\"modelNode\", END)\n",
        "\n",
        "graph_runnable = graph.compile()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "uzu0szKEYnnS",
        "outputId": "8d060e8f-c327-4933-ffce-90fab3d0764e"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#We can visualize it using Mermaid\n",
        "from IPython.display import Image, display\n",
        "\n",
        "try:\n",
        "    display(Image(graph_runnable.get_graph().draw_mermaid_png()))\n",
        "except Exception:\n",
        "    # This requires some extra dependencies and is optional\n",
        "    pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEg4XDmkT8X8",
        "outputId": "f95dd47f-3843-40b8-be67-ae5b0d919f28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "I'm an AI assistant, and I don't have a personal name. I'm here to help you with any questions or tasks you may have.\n"
          ]
        }
      ],
      "source": [
        "# simple Fireworks x LangGraph implementation\n",
        "resp = graph_runnable.invoke({\"messages\": HumanMessage(\"What is your name?\")})\n",
        "resp[\"messages\"][-1].pretty_print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGjCfWemNsse"
      },
      "source": [
        "## Custom Tools\n",
        "\n",
        "To seamlessly use the function-calling ability of the models, we can utilize the [Tool Node](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/) functionality built into LangGraph. This allows the model to select the appropriate tool based on the given options.\n",
        "\n",
        "For this notebook, we will construct an `area_of_circle` function, which will use an LLM to calculate and return the area of a circle given a radius `r`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "50g_NBE_Atn5"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "\n",
        "from langchain_core.messages import AIMessage\n",
        "from langchain_core.tools import tool\n",
        "from langgraph.prebuilt import ToolNode\n",
        "\n",
        "@tool\n",
        "def get_weather(location: str):\n",
        "    \"\"\"Call to get the fake current weather\"\"\"\n",
        "    if location.lower() in [\"sf\", \"san francisco\"]:\n",
        "        return \"It's 60 degrees and foggy.\"\n",
        "    else:\n",
        "        return \"It's 90 degrees and sunny.\"\n",
        "\n",
        "@tool\n",
        "def area_of_circle(r: float):\n",
        "    \"\"\"Call to get the area of a circle in units squared\"\"\"\n",
        "    return math.pi * r * r\n",
        "\n",
        "\n",
        "tools = [get_weather, area_of_circle]\n",
        "tool_node = ToolNode(tools)\n",
        "\n",
        "model_with_tools = llm.bind_tools(tools)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iS_NBVgJVKEa"
      },
      "source": [
        "now let's adjust the graph to include the ToolNode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "xGMAnlxDT6T-"
      },
      "outputs": [],
      "source": [
        "from typing import Literal\n",
        "from langgraph.graph import START, END, StateGraph, MessagesState\n",
        "\n",
        "#note for clarity, I am treating this cell as if `Base LangGraph` was not instantiated, but `Setup Dep` was.\n",
        "\n",
        "def call_model(state: MessagesState):\n",
        "    messages = state[\"messages\"]\n",
        "    response = model_with_tools.invoke(messages)\n",
        "    return {\"messages\": [response]}\n",
        "\n",
        "def tool_handler(state: MessagesState) -> Literal[\"tools\", \"__end__\"]:\n",
        "    messages = state[\"messages\"]\n",
        "    last_message = messages[-1]\n",
        "    if last_message.tool_calls:\n",
        "        return \"tools\"\n",
        "    return END\n",
        "\n",
        "workflow = StateGraph(MessagesState)\n",
        "\n",
        "workflow.add_edge(START, \"modelNode\")\n",
        "workflow.add_node(\"modelNode\", call_model)\n",
        "workflow.add_conditional_edges(\n",
        "    \"modelNode\",\n",
        "    tool_handler,\n",
        ")\n",
        "workflow.add_node(\"tools\", tool_node)\n",
        "workflow.add_edge(\"tools\", \"modelNode\")\n",
        "\n",
        "app = workflow.compile()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "q7say9gHYkNm",
        "outputId": "e500406c-6612-4a58-a3ff-8567ba199e49"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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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",
        "try:\n",
        "    display(Image(app.get_graph().draw_mermaid_png()))\n",
        "except Exception:\n",
        "    # This requires some extra dependencies and is optional\n",
        "    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2FKMb9gORy-"
      },
      "source": [
        "### Test Chit Chat Ability\n",
        "\n",
        "As we outlined in the beginning, the model should be able to both chit-chat, route queries to external tools when necessary or answer from internal knowledge.\n",
        "\n",
        "Let's first start with a question that can be answered from internal knowledge."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A18JJlKaOgLL",
        "outputId": "24c51a31-b2a9-4548-f383-2c7beb301142"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "George Washington was the first President of the United States.\n"
          ]
        }
      ],
      "source": [
        "from langchain_core.messages import HumanMessage\n",
        "app.invoke({\"messages\": HumanMessage(\"Who was the first President of the USA?\")})[\"messages\"][-1].pretty_print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jL0hyeYCOikE"
      },
      "source": [
        "## Test Area of Circle and Weather in SF\n",
        "\n",
        "Now let's test it's ability to detect a area of circle or weather questions & route the query accordingly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qu0HFN35OuRo",
        "outputId": "aadbf697-ff2f-4ad3-f188-9d2f8338db37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "I'm here to help! What would you like to know?\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "Tool Calls:\n",
            "  get_weather (call_Vee0KBC1mhiypoeH6sWoyUYg)\n",
            " Call ID: call_Vee0KBC1mhiypoeH6sWoyUYg\n",
            "  Args:\n",
            "    location: SF\n",
            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
            "Name: get_weather\n",
            "\n",
            "It's 60 degrees and foggy.\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "It's 60 degrees and foggy.\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "The weather in Berkeley in the spring can be quite pleasant, with average highs in the mid-60s to low 70s Fahrenheit (18-22°C). However, it can still be a bit chilly in the mornings and evenings, so it's a good idea to pack layers.\n",
            "\n",
            "For a wedding, you'll likely want to dress up a bit, so consider packing:\n",
            "\n",
            "* A dress or suit in a lightweight, breathable fabric (such as cotton or linen)\n",
            "* A light jacket or sweater for cooler moments\n",
            "* Comfortable shoes (you'll likely be standing and dancing for several hours)\n",
            "* A hat or fascinator to add a touch of elegance\n",
            "* A small umbrella or raincoat (spring showers are always a possibility)\n",
            "\n",
            "Of course, the specific dress code and weather forecast will depend on the wedding and the time of year, so be sure to check with the wedding party or the weather forecast before you pack.\n",
            "Tool Calls:\n",
            "  get_weather (call_oB7GEbauiTaujQwReBZZQqvN)\n",
            " Call ID: call_oB7GEbauiTaujQwReBZZQqvN\n",
            "  Args:\n",
            "    location: Berkeley\n",
            "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
            "Name: get_weather\n",
            "\n",
            "It's 90 degrees and sunny.\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "In that case, you may want to pack lighter, breathable clothing that will keep you cool in the warm weather. A lightweight dress or suit, a pair of sunglasses, and a hat to protect your face and head from the sun would be good choices. You may also want to consider packing a light scarf or shawl to add a touch of elegance to your outfit.\n",
            "\n",
            "Remember to stay hydrated by bringing a refillable water bottle with you, and don't forget to apply sunscreen to protect your skin from the sun's strong rays.\n",
            "\n",
            "I hope this helps, and I wish you a wonderful time at the wedding!\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "I'm here to help! What would you like to know?\n",
            "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
            "\n",
            "I'm here to help! What would you like to know?\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "Interrupted by user",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlangchain_core\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmessages\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HumanMessage\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m----> 3\u001b[0m   user \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mUser (q/Q to quit): \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m user \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQ\u001b[39m\u001b[38;5;124m\"\u001b[39m}:\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAI: Byebye\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ipykernel/kernelbase.py:1282\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1280\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1281\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1283\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1284\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1285\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1286\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1287\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ipykernel/kernelbase.py:1325\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1322\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1323\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m   1324\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1325\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1326\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1327\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ],
      "source": [
        "from langchain_core.messages import HumanMessage\n",
        "while True:\n",
        "  user = input(\"User (q/Q to quit): \")\n",
        "  if user in {\"q\", \"Q\"}:\n",
        "    print(\"AI: Byebye\")\n",
        "    break\n",
        "  for output in app.stream({\"messages\": HumanMessage(user)}, stream_mode=\"updates\"):\n",
        "    last_message = next(iter(output.values()))['messages'][-1]\n",
        "    last_message.pretty_print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFrp6ckCOyYt"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "The fireworks function calling model can route request to external tools or internal knowledge appropriately - thus helping developers build co-operative agents."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.12.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}