Prateek_Majumder_EX_4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Class Exercise 4\n",
        "# Prateek Majumder\n",
        "# Adaptive RAG with the Wikipedia search tool and tracing"
      ],
      "metadata": {
        "id": "OnsclM-p8_v8"
      },
      "id": "OnsclM-p8_v8"
    },
    {
      "cell_type": "markdown",
      "id": "0da6a885",
      "metadata": {
        "id": "0da6a885"
      },
      "source": [
        "## Environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "5fae88dd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "5fae88dd",
        "outputId": "e8272a39-a936-4b2d-9bdd-c0329a796114"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.3.17'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "#Using latest version of LangChain\n",
        "import langchain\n",
        "langchain.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4143d95a",
      "metadata": {
        "id": "4143d95a"
      },
      "source": [
        "### Install Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "c21c82a5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c21c82a5",
        "outputId": "2d322ce9-f13f-4137-d20b-57e8ed2d5a42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install langchain_community tiktoken langchain-groq langchainhub chromadb langchain\n",
        "!pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_huggingface"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LaGj_YdQ85X0",
        "outputId": "7368352c-eba6-4458-e8fd-4ec8f11c5908"
      },
      "id": "LaGj_YdQ85X0",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain_huggingface\n",
            "  Downloading langchain_huggingface-0.1.2-py3-none-any.whl.metadata (1.3 kB)\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface) (2.21.5)\n",
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            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain_huggingface)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
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            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m52.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, langchain_huggingface\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed langchain_huggingface-0.1.2 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_chroma"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZF0j_HsH9r8j",
        "outputId": "3d0048f5-aa6e-48e0-dfbf-3983e2483f77"
      },
      "id": "ZF0j_HsH9r8j",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain_chroma\n",
            "  Downloading langchain_chroma-0.2.1-py3-none-any.whl.metadata (1.7 kB)\n",
            "Requirement already satisfied: chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from langchain_chroma) (0.6.3)\n",
            "Requirement already satisfied: fastapi<1,>=0.95.2 in /usr/local/lib/python3.11/dist-packages (from langchain_chroma) (0.115.8)\n",
            "Requirement already satisfied: langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43 in /usr/local/lib/python3.11/dist-packages (from langchain_chroma) (0.3.34)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.22.4 in /usr/local/lib/python3.11/dist-packages (from langchain_chroma) (1.26.4)\n",
            "Requirement already satisfied: build>=1.0.3 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.2.2.post1)\n",
            "Requirement already satisfied: pydantic>=1.9 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.10.6)\n",
            "Requirement already satisfied: chroma-hnswlib==0.7.6 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.7.6)\n",
            "Requirement already satisfied: uvicorn>=0.18.3 in /usr/local/lib/python3.11/dist-packages (from uvicorn[standard]>=0.18.3->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.34.0)\n",
            "Requirement already satisfied: posthog>=2.4.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.11.0)\n",
            "Requirement already satisfied: typing_extensions>=4.5.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (4.12.2)\n",
            "Requirement already satisfied: onnxruntime>=1.14.1 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.20.1)\n",
            "Requirement already satisfied: opentelemetry-api>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.30.0)\n",
            "Requirement already satisfied: opentelemetry-exporter-otlp-proto-grpc>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.30.0)\n",
            "Requirement already satisfied: opentelemetry-instrumentation-fastapi>=0.41b0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.51b0)\n",
            "Requirement already satisfied: opentelemetry-sdk>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.30.0)\n",
            "Requirement already satisfied: tokenizers>=0.13.2 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.21.0)\n",
            "Requirement already satisfied: pypika>=0.48.9 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.48.9)\n",
            "Requirement already satisfied: tqdm>=4.65.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (4.67.1)\n",
            "Requirement already satisfied: overrides>=7.3.1 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (7.7.0)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (6.5.2)\n",
            "Requirement already satisfied: grpcio>=1.58.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.70.0)\n",
            "Requirement already satisfied: bcrypt>=4.0.1 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (4.2.1)\n",
            "Requirement already satisfied: typer>=0.9.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.15.1)\n",
            "Requirement already satisfied: kubernetes>=28.1.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (32.0.0)\n",
            "Requirement already satisfied: tenacity>=8.2.3 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (9.0.0)\n",
            "Requirement already satisfied: PyYAML>=6.0.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (6.0.2)\n",
            "Requirement already satisfied: mmh3>=4.0.1 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (5.1.0)\n",
            "Requirement already satisfied: orjson>=3.9.12 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.10.15)\n",
            "Requirement already satisfied: httpx>=0.27.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.28.1)\n",
            "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.11/dist-packages (from chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (13.9.4)\n",
            "Requirement already satisfied: starlette<0.46.0,>=0.40.0 in /usr/local/lib/python3.11/dist-packages (from fastapi<1,>=0.95.2->langchain_chroma) (0.45.3)\n",
            "Requirement already satisfied: langsmith<0.4,>=0.1.125 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langchain_chroma) (0.3.4)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langchain_chroma) (1.33)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.11/dist-packages (from langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langchain_chroma) (24.2)\n",
            "Requirement already satisfied: pyproject_hooks in /usr/local/lib/python3.11/dist-packages (from build>=1.0.3->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.2.0)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.11/dist-packages (from httpx>=0.27.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.7.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.11/dist-packages (from httpx>=0.27.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2025.1.31)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.11/dist-packages (from httpx>=0.27.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.0.7)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.11/dist-packages (from httpx>=0.27.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.10)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.11/dist-packages (from httpcore==1.*->httpx>=0.27.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.14.0)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.11/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langchain_chroma) (3.0.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.17.0)\n",
            "Requirement already satisfied: python-dateutil>=2.5.3 in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.8.2)\n",
            "Requirement already satisfied: google-auth>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.27.0)\n",
            "Requirement already satisfied: websocket-client!=0.40.0,!=0.41.*,!=0.42.*,>=0.32.0 in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.8.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.32.3)\n",
            "Requirement already satisfied: requests-oauthlib in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.0.0)\n",
            "Requirement already satisfied: oauthlib>=3.2.2 in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.2.2)\n",
            "Requirement already satisfied: urllib3>=1.24.2 in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.3.0)\n",
            "Requirement already satisfied: durationpy>=0.7 in /usr/local/lib/python3.11/dist-packages (from kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.9)\n",
            "Requirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langchain_chroma) (1.0.0)\n",
            "Requirement already satisfied: zstandard<0.24.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from langsmith<0.4,>=0.1.125->langchain-core!=0.3.0,!=0.3.1,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,<0.4.0,>=0.2.43->langchain_chroma) (0.23.0)\n",
            "Requirement already satisfied: coloredlogs in /usr/local/lib/python3.11/dist-packages (from onnxruntime>=1.14.1->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (15.0.1)\n",
            "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.11/dist-packages (from onnxruntime>=1.14.1->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (25.1.24)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.11/dist-packages (from onnxruntime>=1.14.1->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (5.29.3)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.11/dist-packages (from onnxruntime>=1.14.1->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.13.1)\n",
            "Requirement already satisfied: deprecated>=1.2.6 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-api>=1.2.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.2.18)\n",
            "Requirement already satisfied: importlib-metadata<=8.5.0,>=6.0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-api>=1.2.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (8.5.0)\n",
            "Requirement already satisfied: googleapis-common-protos~=1.52 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.66.0)\n",
            "Requirement already satisfied: opentelemetry-exporter-otlp-proto-common==1.30.0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.30.0)\n",
            "Requirement already satisfied: opentelemetry-proto==1.30.0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-exporter-otlp-proto-grpc>=1.2.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.30.0)\n",
            "Requirement already satisfied: opentelemetry-instrumentation-asgi==0.51b0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.51b0)\n",
            "Requirement already satisfied: opentelemetry-instrumentation==0.51b0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.51b0)\n",
            "Requirement already satisfied: opentelemetry-semantic-conventions==0.51b0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.51b0)\n",
            "Requirement already satisfied: opentelemetry-util-http==0.51b0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-instrumentation-fastapi>=0.41b0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.51b0)\n",
            "Requirement already satisfied: wrapt<2.0.0,>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-instrumentation==0.51b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.17.2)\n",
            "Requirement already satisfied: asgiref~=3.0 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-instrumentation-asgi==0.51b0->opentelemetry-instrumentation-fastapi>=0.41b0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.8.1)\n",
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            "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.11/dist-packages (from posthog>=2.4.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.2.1)\n",
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            "Requirement already satisfied: pydantic-core==2.27.2 in /usr/local/lib/python3.11/dist-packages (from pydantic>=1.9->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2.27.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.0.0)\n",
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            "Requirement already satisfied: watchfiles>=0.13 in /usr/local/lib/python3.11/dist-packages (from uvicorn[standard]>=0.18.3->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.0.4)\n",
            "Requirement already satisfied: websockets>=10.4 in /usr/local/lib/python3.11/dist-packages (from uvicorn[standard]>=0.18.3->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (14.2)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio->httpx>=0.27.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.3.1)\n",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth>=1.0.1->kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth>=1.0.1->kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (4.9)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.2->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.17.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.2->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (2024.10.0)\n",
            "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.11/dist-packages (from importlib-metadata<=8.5.0,>=6.0->opentelemetry-api>=1.2.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.21.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.1.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (3.4.1)\n",
            "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.11/dist-packages (from coloredlogs->onnxruntime>=1.14.1->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (10.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy->onnxruntime>=1.14.1->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (1.3.0)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.0.1->kubernetes>=28.1.0->chromadb!=0.5.10,!=0.5.11,!=0.5.12,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,<0.7.0,>=0.4.0->langchain_chroma) (0.6.1)\n",
            "Downloading langchain_chroma-0.2.1-py3-none-any.whl (11 kB)\n",
            "Installing collected packages: langchain_chroma\n",
            "Successfully installed langchain_chroma-0.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wikipedia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JyQeIdke-e2K",
        "outputId": "5b2fdae7-2bd3-4b14-e188-e4893f5dc3ab"
      },
      "id": "JyQeIdke-e2K",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wikipedia\n",
            "  Downloading wikipedia-1.4.0.tar.gz (27 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.11/dist-packages (from wikipedia) (4.13.1)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from wikipedia) (2.32.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2025.1.31)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4->wikipedia) (2.6)\n",
            "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4->wikipedia) (4.12.2)\n",
            "Building wheels for collected packages: wikipedia\n",
            "  Building wheel for wikipedia (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11679 sha256=880f6b3b0f50b503627387e587b88aa46b5e2ef1831f948537ecb648b95bfcef\n",
            "  Stored in directory: /root/.cache/pip/wheels/8f/ab/cb/45ccc40522d3a1c41e1d2ad53b8f33a62f394011ec38cd71c6\n",
            "Successfully built wikipedia\n",
            "Installing collected packages: wikipedia\n",
            "Successfully installed wikipedia-1.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b489bbe3",
      "metadata": {
        "id": "b489bbe3"
      },
      "source": [
        "### API Keys"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "6145c445",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6145c445",
        "outputId": "c432da53-641e-4fb0-fe6a-1cde574d2e07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import os, json, re, getpass\n",
        "from dotenv import load_dotenv\n",
        "\n",
        "load_dotenv(\".env\", override=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "b8e1fc61",
      "metadata": {
        "id": "b8e1fc61"
      },
      "outputs": [],
      "source": [
        "def _set_env(var: str):\n",
        "    if not os.environ.get(var):\n",
        "        os.environ[var] = getpass.getpass(f\"{var}: \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "a51029f6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a51029f6",
        "outputId": "b5f62053-e1c9-4876-d757-22613dcebe54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GROQ_API_KEY: ··········\n"
          ]
        }
      ],
      "source": [
        "#Check for Groq API Key\n",
        "_set_env(\"GROQ_API_KEY\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "5cba33cc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cba33cc",
        "outputId": "ed97d593-94ee-486c-cd3e-c6b307ba0c05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TAVILY_API_KEY: ··········\n"
          ]
        }
      ],
      "source": [
        "#Check for Tavily API Key\n",
        "_set_env(\"TAVILY_API_KEY\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "53e42ada",
      "metadata": {
        "id": "53e42ada"
      },
      "source": [
        "### Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "ec60ade4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ec60ade4",
        "outputId": "d1c5d378-c698-42ea-d08f-b2763709ba1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:langchain_community.utils.user_agent:USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
          ]
        }
      ],
      "source": [
        "from langchain_groq import ChatGroq\n",
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "from langchain_chroma import Chroma\n",
        "\n",
        "from typing import Literal\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_community.document_loaders import WebBaseLoader\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "from pydantic import BaseModel, Field\n",
        "\n",
        "from pprint import pprint"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cddefc68",
      "metadata": {
        "id": "cddefc68"
      },
      "source": [
        "## Selecting Components"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e4534b6",
      "metadata": {
        "id": "0e4534b6"
      },
      "source": [
        "### Chat Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "486ea89e",
      "metadata": {
        "id": "486ea89e"
      },
      "outputs": [],
      "source": [
        "llm = ChatGroq(model=\"llama3-8b-8192\") #llama3-8b-8192, llama-3.1-8b-instant, llama3-groq-8b-8192-tool-use-preview, llama3-groq-70b-8192-tool-use-preview"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c312c922",
      "metadata": {
        "id": "c312c922"
      },
      "source": [
        "### Embedding Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "6e774117",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493,
          "referenced_widgets": [
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        "id": "6e774117",
        "outputId": "b64318da-e5d5-413f-baa9-39694016b8a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "version_major": 2,
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              "model_id": "4d3c5f9b66b34fbeb9d613865f0f9509"
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          "metadata": {}
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              "model_id": "7ddac3debf234aa1b5471638c168cb73"
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          "metadata": {}
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      ],
      "source": [
        "embed_model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "261d6aa7",
      "metadata": {
        "id": "261d6aa7"
      },
      "source": [
        "#### Flow 4 - Adaptive RAG with the Wikipedia search tool"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Import Dependencies ###"
      ],
      "metadata": {
        "id": "n82XpX7AB9oP"
      },
      "id": "n82XpX7AB9oP"
    },
    {
      "cell_type": "code",
      "source": [
        "### 1. Import Dependencies ###\n",
        "import os\n",
        "from langchain.tools import WikipediaQueryRun\n",
        "from langchain_community.utilities import WikipediaAPIWrapper\n",
        "from langgraph.graph import END, StateGraph, START\n",
        "from typing import Literal\n",
        "from pydantic import BaseModel, Field\n",
        "from IPython.display import Image, display\n",
        "\n",
        "### 2. Define Graph State ###\n",
        "class GraphState(BaseModel):\n",
        "    query: str\n",
        "    retrieved_docs: list = []\n",
        "    transformed_query: str = None\n",
        "    graded_docs: list = []\n",
        "    response: str = None"
      ],
      "metadata": {
        "id": "wLjJkI_I-ojG"
      },
      "id": "wLjJkI_I-ojG",
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 3. Define Nodes (Functions for Each Step) ###\n",
        "\n",
        "# 3.1 Web Search\n",
        "def web_search(state: GraphState):\n",
        "    \"\"\"Simulate web search operation\"\"\"\n",
        "    return {\"retrieved_docs\": [\"Web search result 1\", \"Web search result 2\"]}\n",
        "\n",
        "# 3.2 Wikipedia Search (New Tool)\n",
        "wikipedia_tool = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())\n",
        "\n",
        "def wikipedia_search(state: GraphState):\n",
        "    \"\"\"Search Wikipedia for relevant information.\"\"\"\n",
        "    try:\n",
        "        result = wikipedia_tool.run(state.query)\n",
        "        print(f\"Wikipedia Search Results: {result}\")\n",
        "        return {\"retrieved_docs\": [result] if result else [\"Wikipedia returned no results.\"]}\n",
        "    except Exception as e:\n",
        "        print(f\"Wikipedia search error: {e}\")\n",
        "        return {\"retrieved_docs\": [\"Wikipedia search failed.\"]}\n",
        "\n",
        "\n",
        "# 3.3 Retrieve from Vector Store\n",
        "def retrieve(state: GraphState):\n",
        "    \"\"\"Simulate vectorstore retrieval\"\"\"\n",
        "    return {\"retrieved_docs\": [\"VectorStore result 1\", \"VectorStore result 2\"]}\n",
        "\n",
        "# 3.4 Grade Documents\n",
        "def grade_documents(state: GraphState):\n",
        "    \"\"\"Ensure at least one document is passed for generation.\"\"\"\n",
        "    if not state.retrieved_docs:\n",
        "        print(\"No documents retrieved for grading.\")\n",
        "        return {\"graded_docs\": []}\n",
        "\n",
        "    # Allow more documents to pass grading\n",
        "    print(f\"Graded Documents: {state.retrieved_docs}\")\n",
        "    return {\"graded_docs\": state.retrieved_docs[:2]}  # Keep at least 2 documents\n",
        "\n",
        "\n",
        "# 3.5 Generate Response\n",
        "def generate(state: GraphState):\n",
        "    \"\"\"Generate a more meaningful response using retrieved documents.\"\"\"\n",
        "    if not state.graded_docs:\n",
        "        return {\"response\": \"No relevant documents found to generate an answer.\"}\n",
        "\n",
        "    # Create a more detailed response\n",
        "    response_text = \"Based on the retrieved information:\\n\\n\"\n",
        "    response_text += \"\\n\".join(f\"- {doc}\" for doc in state.graded_docs)\n",
        "\n",
        "    return {\"response\": response_text}\n",
        "\n",
        "\n",
        "# 3.6 Transform Query (If Needed)\n",
        "def transform_query(state: GraphState):\n",
        "    \"\"\"Transform query to improve results\"\"\"\n",
        "    return {\"transformed_query\": state.query + \" (expanded)\"}"
      ],
      "metadata": {
        "id": "KQCmFcDm-ynI"
      },
      "id": "KQCmFcDm-ynI",
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### 4. Define Routing Logic ###\n",
        "\n",
        "# 4.1 Route Query to Appropriate Tool\n",
        "def route_question(state: GraphState) -> Literal[\"web_search\", \"vectorstore\", \"wikipedia\"]:\n",
        "    \"\"\"Determine whether to use Web Search, Vector Store, or Wikipedia.\"\"\"\n",
        "    query_lower = state.query.lower()\n",
        "\n",
        "    if \"history\" in query_lower or \"who discovered\" in query_lower:\n",
        "        print(f\"Routing '{state.query}' to Wikipedia\")\n",
        "        return \"wikipedia\"\n",
        "    elif \"latest\" in query_lower or \"news\" in query_lower:\n",
        "        print(f\"Routing '{state.query}' to Web Search\")\n",
        "        return \"web_search\"\n",
        "    else:\n",
        "        print(f\"Routing '{state.query}' to Vector Store\")\n",
        "        return \"vectorstore\"\n",
        "\n",
        "\n",
        "# 4.2 Decide Whether to Transform Query\n",
        "def decide_to_generate(state: GraphState) -> Literal[\"transform_query\", \"generate\"]:\n",
        "    \"\"\"Decide whether to generate answer directly or transform query.\"\"\"\n",
        "    if len(state.graded_docs) > 0:\n",
        "        return \"generate\"\n",
        "    return \"transform_query\"\n",
        "\n",
        "# 4.3 Grade Generated Response\n",
        "def grade_generation_v_documents_and_question(state: GraphState) -> Literal[\"useful\", \"not useful\", \"not supported\"]:\n",
        "    \"\"\"Ensure response grading allows termination.\"\"\"\n",
        "    if state.response and \"Generated answer\" in state.response:\n",
        "        return \"useful\"  # Stops the workflow\n",
        "    return \"useful\"  # Force it to end instead of looping\n",
        "\n",
        "### 5. Define the Workflow ###\n",
        "workflow = StateGraph(GraphState)\n",
        "\n",
        "# Add nodes\n",
        "workflow.add_node(\"web_search\", web_search)\n",
        "workflow.add_node(\"wikipedia_search\", wikipedia_search)  # New Wikipedia tool\n",
        "workflow.add_node(\"retrieve\", retrieve)\n",
        "workflow.add_node(\"grade_documents\", grade_documents)\n",
        "workflow.add_node(\"generate\", generate)\n",
        "workflow.add_node(\"transform_query\", transform_query)\n",
        "\n",
        "# Define query routing\n",
        "workflow.add_conditional_edges(\n",
        "    START,\n",
        "    route_question,\n",
        "    {\n",
        "        \"web_search\": \"web_search\",\n",
        "        \"vectorstore\": \"retrieve\",\n",
        "        \"wikipedia\": \"wikipedia_search\",  # Added Wikipedia as an option\n",
        "    },\n",
        ")\n",
        "\n",
        "# Define the edges for each step\n",
        "workflow.add_edge(\"web_search\", \"generate\")\n",
        "workflow.add_edge(\"wikipedia_search\", \"generate\")  # Wikipedia to generate\n",
        "workflow.add_edge(\"retrieve\", \"grade_documents\")\n",
        "\n",
        "workflow.add_conditional_edges(\n",
        "    \"grade_documents\",\n",
        "    decide_to_generate,\n",
        "    {\n",
        "        \"transform_query\": \"transform_query\",\n",
        "        \"generate\": \"generate\",\n",
        "    },\n",
        ")\n",
        "\n",
        "workflow.add_edge(\"transform_query\", \"retrieve\")\n",
        "\n",
        "workflow.add_conditional_edges(\n",
        "    \"generate\",\n",
        "    grade_generation_v_documents_and_question,\n",
        "    {\n",
        "        \"not supported\": \"generate\",\n",
        "        \"useful\": END,\n",
        "        \"not useful\": \"transform_query\",\n",
        "    },\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtCd-4QF-0zP",
        "outputId": "ad4bf3eb-f5a4-44a6-95c7-a61a074f13ef"
      },
      "id": "EtCd-4QF-0zP",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<langgraph.graph.state.StateGraph at 0x7896513a2e10>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile workflow\n",
        "graph_4_adaptiverag = workflow.compile()\n",
        "\n",
        "### 6. Run and Visualize the Graph ###\n",
        "try:\n",
        "    display(Image(graph_4_adaptiverag.get_graph(xray=True).draw_mermaid_png()))\n",
        "except Exception:\n",
        "    pass"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "dyTGcExJ-3rU",
        "outputId": "6ed53a63-8540-47c0-dc7c-e74156d9b713"
      },
      "id": "dyTGcExJ-3rU",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage of the RAG workflow\n",
        "initial_state = GraphState(query=\"What is the history of the JIO?\")\n",
        "final_output = graph_4_adaptiverag.invoke(initial_state)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPxD9liKAL4F",
        "outputId": "fd61d6cd-2078-423c-dfa0-92eb245b7333"
      },
      "id": "LPxD9liKAL4F",
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Routing 'What is the history of the JIO?' to Wikipedia\n",
            "Wikipedia Search Results: Page: Jio\n",
            "Summary: Reliance Jio Infocomm Limited (d/b/a Jio) is an Indian telecommunications company and a subsidiary of Jio Platforms, headquartered in Navi Mumbai. It operates a national LTE network with coverage across all 22 telecom circles. Jio offers 5G, 4G and 4G+ services all over India. Its 6G service is in the works.\n",
            "Jio was soft launched on 27 December 2015 with a beta for partners and employees, and became publicly available on 5 September 2016. It is the largest mobile network operator in India and the third largest mobile network operator in the world with over 46.37 crore (463.78 million) subscribers.\n",
            "\n",
            "\n",
            "\n",
            "Page: Jio Platforms\n",
            "Summary: Jio Platforms is an Indian multinational technology company, headquartered in Mumbai. It is a subsidiary of Reliance Industries. Established in 2019, it acts as a holding company for India's largest mobile network operator, Jio, and other digital businesses of Reliance. Since April 2020, Reliance Industries has raised ₹152,056 crore (US$18 billion) by selling a 32.97% equity stake in the company. In August 2021, it was ranked 155th on the 2021 Fortune Global 500 list of the world's biggest corporations.\n",
            "\n",
            "Page: JioCinema\n",
            "Summary: JioCinema is an Indian subscription video-on-demand over-the-top streaming service owned by JioStar, a joint venture between Reliance Industries, Viacom18 and Disney India. the service was launched on 4 May 2016.\n",
            "In April 2022, the service was brought under Reliance's Viacom18 joint venture, backed by new funding from Uday Shankar and James Murdoch's Bodhi Tree Systems. JioCinema began to subsume Viacom18's existing streaming platform Voot later that year, beginning with sports programming in October, and Voot being officially discontinued in favour of JioCinema in August 2023.\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "initial_state = GraphState(query=\"What is jio platforms\")\n",
        "final_output = graph_4_adaptiverag.invoke(initial_state)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbrZyMuMCkCw",
        "outputId": "63c00536-09e5-4005-df2e-e3f14fa67766"
      },
      "id": "ZbrZyMuMCkCw",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Routing 'What is jio platforms' to Vector Store\n",
            "Graded Documents: ['VectorStore result 1', 'VectorStore result 2']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "initial_state = GraphState(query=\"Who discovered gravity?\")\n",
        "final_output = graph_4_adaptiverag.invoke(initial_state)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAmDYgHTE3jH",
        "outputId": "35ddd086-d7cb-483a-84fb-83d61190fac8"
      },
      "id": "RAmDYgHTE3jH",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Routing 'Who discovered gravity?' to Wikipedia\n",
            "Wikipedia Search Results: Page: Gravity\n",
            "Summary: In physics, gravity (from Latin  gravitas 'weight') is a fundamental interaction primarily observed as mutual attraction between all things that have mass. Gravity is, by far, the weakest of the four fundamental interactions, approximately 1038 times weaker than the strong interaction, 1036 times weaker than the electromagnetic force and 1029 times weaker than the weak interaction. As a result, it has no significant influence at the level of subatomic particles. However, gravity is the most significant interaction between objects at the macroscopic scale, and it determines the motion of planets, stars, galaxies, and even light.\n",
            "On Earth, gravity gives weight to physical objects, and the Moon's gravity is responsible for sublunar tides in the oceans. The corresponding antipodal tide is caused by the inertia of the Earth and Moon orbiting one another. Gravity also has many important biological functions, helping to guide the growth of plants through the process of gravitropism and influencing the circulation of fluids in multicellular organisms.\n",
            "The gravitational attraction between the original gaseous matter in the universe caused it to coalesce and form stars which eventually condensed into galaxies, so gravity is responsible for many of the large-scale structures in the universe. Gravity has an infinite range, although its effects become weaker as objects get farther away.\n",
            "Gravity is most accurately described by the general theory of relativity, proposed by Albert Einstein in 1915, which describes gravity not as a force, but as the curvature of spacetime, caused by the uneven distribution of mass, and causing masses to move along geodesic lines. The most extreme example of this curvature of spacetime is a black hole, from which nothing—not even light—can escape once past the black hole's event horizon. However, for most applications, gravity is well approximated by Newton's law of universal gravitation, which describes gravity as a force causing any two bodies to be attracted toward each other, with magnitude proportional to the product of their masses and inversely proportional to the square of the distance between them.\n",
            "Current models of particle physics imply that the earliest instance of gravity in the universe, possibly in the form of quantum gravity, supergravity or a gravitational singularity, along with ordinary space and time, developed during the Planck epoch (up to 10−43 seconds after the birth of the universe), possibly from a primeval state, such as a false vacuum, quantum vacuum or virtual particle, in a currently unknown manner. Scientists are currently working to develop a theory of gravity consistent with quantum mechanics, a quantum gravity theory, which would allow gravity to be united in a common mathematical framework (a theory of everything) with the other three fundamental interactions of physics.\n",
            "\n",
            "Page: United States gravity control propulsion research\n",
            "Summary: American interest in gravity control propulsion research intensified during the early 1950s. Literature from that period used the terms anti-gravity, anti-gravitation, baricentric, counterbary, electrogravitics (eGrav), G-projects, gravitics, gravity control, and gravity propulsion. Their publicized goals were to discover and develop technologies and theories for the manipulation of gravity or gravity-like fields for propulsion. Although general relativity theory appeared to prohibit anti-gravity propulsion, several programs were funded to develop it through gravitation research from 1955 to 1974. The names of many contributors to general relativity and those of the golden age of general relativity have appeared among documents about the institutions that had served as the theoretical research components of those programs. Since its emergence in the 1950s, the existence of the related gravity control propulsion research has not been a subject of controversy for aerospace writers, critics, and conspiracy theory advocates al\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Enable LangSmith tracing\n",
        "\n",
        "# Enable LangSmith tracing with a custom project\n",
        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
        "os.environ[\"LANGCHAIN_PROJECT\"] = \"Adaptive_RAG_Wikipedia\"\n",
        "os.environ[\"LANGCHAIN_API_KEY\"] = \"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\"  # Replace with your LangSmith API key\n"
      ],
      "metadata": {
        "id": "RLYKwsvRIYEe"
      },
      "id": "RLYKwsvRIYEe",
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "print(\"\\n🔹 Checking LangSmith API Setup:\")\n",
        "print(\"LANGCHAIN_TRACING_V2:\", os.getenv(\"LANGCHAIN_TRACING_V2\"))\n",
        "print(\"LANGCHAIN_PROJECT:\", os.getenv(\"LANGCHAIN_PROJECT\"))\n",
        "print(\"LANGCHAIN_API_KEY:\", \"SET\" if os.getenv(\"LANGCHAIN_API_KEY\") else \"NOT SET\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkMZk2ThJ4Y7",
        "outputId": "9e8feee5-c9b1-457a-a6ab-1392450edcb7"
      },
      "id": "LkMZk2ThJ4Y7",
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 Checking LangSmith API Setup:\n",
            "LANGCHAIN_TRACING_V2: true\n",
            "LANGCHAIN_PROJECT: Adaptive_RAG_Wikipedia\n",
            "LANGCHAIN_API_KEY: SET\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.callbacks import tracing_v2_enabled\n",
        "\n",
        "for i in range(1):\n",
        "    print(f\"Running iteration {i+1}...\")\n",
        "    with tracing_v2_enabled(project_name=\"Adaptive_RAG_Wikipedia\"):  # Make sure project name is correct\n",
        "      # Execute your script here\n",
        "        initial_state = GraphState(query=\"Who discovered gravity?\")\n",
        "        final_output = graph_4_adaptiverag.invoke(initial_state)\n",
        "    print(f\"Iteration {i+1} completed.\")\n",
        "\n",
        "print(\"\\nTraces generated in LangSmith.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXb7ZHQJNLlx",
        "outputId": "71b5a8f7-9a68-4303-a582-160cf3afbb26"
      },
      "id": "cXb7ZHQJNLlx",
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running iteration 1...\n",
            "Routing 'Who discovered gravity?' to Wikipedia\n",
            "Wikipedia Search Results: Page: Gravity\n",
            "Summary: In physics, gravity (from Latin  gravitas 'weight') is a fundamental interaction primarily observed as mutual attraction between all things that have mass. Gravity is, by far, the weakest of the four fundamental interactions, approximately 1038 times weaker than the strong interaction, 1036 times weaker than the electromagnetic force and 1029 times weaker than the weak interaction. As a result, it has no significant influence at the level of subatomic particles. However, gravity is the most significant interaction between objects at the macroscopic scale, and it determines the motion of planets, stars, galaxies, and even light.\n",
            "On Earth, gravity gives weight to physical objects, and the Moon's gravity is responsible for sublunar tides in the oceans. The corresponding antipodal tide is caused by the inertia of the Earth and Moon orbiting one another. Gravity also has many important biological functions, helping to guide the growth of plants through the process of gravitropism and influencing the circulation of fluids in multicellular organisms.\n",
            "The gravitational attraction between the original gaseous matter in the universe caused it to coalesce and form stars which eventually condensed into galaxies, so gravity is responsible for many of the large-scale structures in the universe. Gravity has an infinite range, although its effects become weaker as objects get farther away.\n",
            "Gravity is most accurately described by the general theory of relativity, proposed by Albert Einstein in 1915, which describes gravity not as a force, but as the curvature of spacetime, caused by the uneven distribution of mass, and causing masses to move along geodesic lines. The most extreme example of this curvature of spacetime is a black hole, from which nothing—not even light—can escape once past the black hole's event horizon. However, for most applications, gravity is well approximated by Newton's law of universal gravitation, which describes gravity as a force causing any two bodies to be attracted toward each other, with magnitude proportional to the product of their masses and inversely proportional to the square of the distance between them.\n",
            "Current models of particle physics imply that the earliest instance of gravity in the universe, possibly in the form of quantum gravity, supergravity or a gravitational singularity, along with ordinary space and time, developed during the Planck epoch (up to 10−43 seconds after the birth of the universe), possibly from a primeval state, such as a false vacuum, quantum vacuum or virtual particle, in a currently unknown manner. Scientists are currently working to develop a theory of gravity consistent with quantum mechanics, a quantum gravity theory, which would allow gravity to be united in a common mathematical framework (a theory of everything) with the other three fundamental interactions of physics.\n",
            "\n",
            "Page: United States gravity control propulsion research\n",
            "Summary: American interest in gravity control propulsion research intensified during the early 1950s. Literature from that period used the terms anti-gravity, anti-gravitation, baricentric, counterbary, electrogravitics (eGrav), G-projects, gravitics, gravity control, and gravity propulsion. Their publicized goals were to discover and develop technologies and theories for the manipulation of gravity or gravity-like fields for propulsion. Although general relativity theory appeared to prohibit anti-gravity propulsion, several programs were funded to develop it through gravitation research from 1955 to 1974. The names of many contributors to general relativity and those of the golden age of general relativity have appeared among documents about the institutions that had served as the theoretical research components of those programs. Since its emergence in the 1950s, the existence of the related gravity control propulsion research has not been a subject of controversy for aerospace writers, critics, and conspiracy theory advocates al\n",
            "Iteration 1 completed.\n",
            "\n",
            "Traces generated in LangSmith.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1):\n",
        "    print(f\"Running iteration {i+1}...\")\n",
        "    with tracing_v2_enabled(project_name=\"Adaptive_RAG_Wikipedia\"):  # Make sure project name is correct\n",
        "      # Execute your script here\n",
        "        initial_state = GraphState(query=\"Latest advancements in AI\")\n",
        "        final_output = graph_4_adaptiverag.invoke(initial_state)\n",
        "    print(f\"Iteration {i+1} completed.\")\n",
        "\n",
        "print(\"\\nTraces generated in LangSmith.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EzwYa_eyPe0m",
        "outputId": "8eb5bb7a-b563-4f5a-c9f6-2ab6ed9afbc9"
      },
      "id": "EzwYa_eyPe0m",
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running iteration 1...\n",
            "Routing 'Latest advancements in AI' to Web Search\n",
            "Iteration 1 completed.\n",
            "\n",
            "Traces generated in LangSmith.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1):\n",
        "    print(f\"Running iteration {i+1}...\")\n",
        "    with tracing_v2_enabled(project_name=\"Adaptive_RAG_Wikipedia\"):  # Make sure project name is correct\n",
        "      # Execute your script here\n",
        "        initial_state = GraphState(query=\"What is machine learning?\")\n",
        "        final_output = graph_4_adaptiverag.invoke(initial_state)\n",
        "    print(f\"Iteration {i+1} completed.\")\n",
        "\n",
        "print(\"\\nTraces generated in LangSmith.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Fe-USAnPn6V",
        "outputId": "39af5311-34cf-40d0-d635-8db326af4f56"
      },
      "id": "0Fe-USAnPn6V",
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running iteration 1...\n",
            "Routing 'What is machine learning?' to Vector Store\n",
            "Graded Documents: ['VectorStore result 1', 'VectorStore result 2']\n",
            "Iteration 1 completed.\n",
            "\n",
            "Traces generated in LangSmith.\n"
          ]
        }
      ]
    }
  ],
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