Gemini_ChatPDF.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN5Se2cWEHsjMjNgFwBd1HT",
      "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/Anil-matcha/ChatPDF/blob/main/Gemini_ChatPDF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ChatPDF flow\n",
        "\n",
        "![ChatPDF flow](https://miro.medium.com/v2/resize:fit:1400/1*leoW-Pn0ohWalrUBbzdidA.png)"
      ],
      "metadata": {
        "id": "0V6pj2_r8Pbx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setup Google api key"
      ],
      "metadata": {
        "id": "_H6RsKxj4Rxt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"GOOGLE_API_KEY\"] = \"gemini-api-key\""
      ],
      "metadata": {
        "id": "5PpWJiu32_rI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install requried dependancies"
      ],
      "metadata": {
        "id": "cT2EMASV4aCm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6O5CTvJzPZ6T",
        "outputId": "38b6eac2-828c-47ba-b9d9-606cefd5ec8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m811.8/811.8 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m50.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.8/132.8 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.4/239.4 kB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m45.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.7/55.7 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.4/55.4 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install -U --quiet langchain-google-genai langchain faiss-cpu pypdf sentence-transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add necessary imports"
      ],
      "metadata": {
        "id": "ba_-ophd4nwH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "from langchain import FAISS"
      ],
      "metadata": {
        "id": "NsKZYeSf4im_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the documents"
      ],
      "metadata": {
        "id": "e156mdeh4xZq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loader = PyPDFLoader(\"./Apps.pdf\")\n",
        "pages = loader.load_and_split()"
      ],
      "metadata": {
        "id": "Bpef2RpP3N_o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a vector db index"
      ],
      "metadata": {
        "id": "lhNGhtHe4y5a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "db = FAISS.from_documents(pages, embeddings)"
      ],
      "metadata": {
        "id": "KZa9CsMY3Peo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Search relevant docs"
      ],
      "metadata": {
        "id": "w4bkl40C45nl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What is Gista?\"\n",
        "docs = db.similarity_search(query)"
      ],
      "metadata": {
        "id": "TQ8-IEnG3Rhd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Invoke Gemini api with the qa prompt"
      ],
      "metadata": {
        "id": "P8i6Xnes5Kg2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "content = \"\\n\".join([x.page_content for x in docs])\n",
        "qa_prompt = \"Use the following pieces of context to answer the user's question. If you don't know the answer, just say that you don't know, don't try to make up an answer.----------------\"\n",
        "input_text = qa_prompt+\"\\nContext:\"+content+\"\\nUser question:\\n\"+query\n",
        "llm = ChatGoogleGenerativeAI(model=\"gemini-pro\")\n",
        "result = llm.invoke(input_text)\n",
        "result.content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "qw6FRgUz3ZBc",
        "outputId": "af31e57c-c3ee-4fbb-81d6-0728ab05f764"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Gista is a professional AI tool that empowers businesses to build their own ChatGPT Plugin, an AI assistant, and deploy custom chatbots on their websites.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    }
  ]
}