{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM7DVBQbBv7iSjrA/U71HaV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "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/towardsai/ai-tutor-rag-system/blob/main/notebooks/Web_Search_API.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JboB5VaCJUrb",
        "outputId": "2433bc46-9d7f-476e-bfe9-0e4be5f4e51a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.5/12.5 MB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q llama-index==0.10.5 openai==1.12.0 tiktoken==0.6.0 llama-index-tools-google==0.1.3"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Set the \"OPENAI_API_KEY\" in the Python environment. Will be used by OpenAI client later.\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_OPENAI_KEY>\""
      ],
      "metadata": {
        "id": "1NKAn5scN_g9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define Google Search Tool"
      ],
      "metadata": {
        "id": "0LMypoqUyuXq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.tools.google import GoogleSearchToolSpec\n",
        "\n",
        "tool_spec = GoogleSearchToolSpec(key=\"[GOOGLE_API_KEY]\", engine=\"[GOOGLE_ENGINE_ID]\")"
      ],
      "metadata": {
        "id": "4Q7sc69nJvWI"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import and initialize our tool spec\n",
        "from llama_index.core.tools.tool_spec.load_and_search import LoadAndSearchToolSpec\n",
        "\n",
        "# Wrap the google search tool to create an index on top of the returned Google search\n",
        "wrapped_tool = LoadAndSearchToolSpec.from_defaults(\n",
        "    tool_spec.to_tool_list()[0],\n",
        ").to_tool_list()"
      ],
      "metadata": {
        "id": "VrbuIOaMeOIf"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create the Agent"
      ],
      "metadata": {
        "id": "T3ENpLyBy7UL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.agent.openai import OpenAIAgent\n",
        "\n",
        "agent = OpenAIAgent.from_tools(wrapped_tool, verbose=False)"
      ],
      "metadata": {
        "id": "-_Ab47ppK8b2"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res = agent.chat(\"How many parameters LLaMA2 model has?\")"
      ],
      "metadata": {
        "id": "YcUyz1-FlCQ8"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res.response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "w4wK5sY-lOOv",
        "outputId": "8090a106-6fac-4514-fdbd-c72a01b28169"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The LLaMA2 model has parameters available in three different sizes: 7 billion, 13 billion, and 70 billion.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res.sources"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TM_cvBA1nTJM",
        "outputId": "0bf3533a-c62d-4d0d-bd76-76c043477042"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[ToolOutput(content='Content loaded! You can now search the information using read_google_search', tool_name='google_search', raw_input={'args': (), 'kwargs': {'query': 'parameters of LLaMA2 model'}}, raw_output='Content loaded! You can now search the information using read_google_search', is_error=False),\n",
              " ToolOutput(content='Answer: The parameters of the LLaMA2 model are available in three different sizes: 7 billion, 13 billion, and 70 billion.', tool_name='read_google_search', raw_input={'args': (), 'kwargs': {'query': 'parameters of LLaMA2 model'}}, raw_output='Answer: The parameters of the LLaMA2 model are available in three different sizes: 7 billion, 13 billion, and 70 billion.', is_error=False)]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SPUgKiKpygLn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}