Q&A_Single_Column_OpenAI_GPT.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sarthakg2002/Basic-Pong-Game/blob/master/Q%26A_Single_Column_OpenAI_GPT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hcky9vtE1-IT"
      },
      "outputs": [],
      "source": [
        "!pip install llama_index\n",
        "!pip install openai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import openai\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"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\"\n",
        "openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n"
      ],
      "metadata": {
        "id": "DZWbzeOx2UIj"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()"
      ],
      "metadata": {
        "id": "GHG4TEbW2Ym5"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import (\n",
        "    SimpleDirectoryReader,\n",
        "    ServiceContext,\n",
        "    get_response_synthesizer,\n",
        ")\n",
        "from llama_index.indices.document_summary import DocumentSummaryIndex\n",
        "from llama_index.llms import OpenAI"
      ],
      "metadata": {
        "id": "UCTyIlie2arg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
        "service_context = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9b89sNg2fHW",
        "outputId": "44799a43-16ae-4b25-b415-ec3ed880f2a1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /tmp/llama_index...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Amazon Phones dataset"
      ],
      "metadata": {
        "id": "AIR2VY1D2lLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "file_path = '/content/Amazon Phones Oct 11.csv'\n",
        "df = pd.read_csv(file_path)"
      ],
      "metadata": {
        "id": "yLmDkm--2hzN"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "amazon_docs = []\n",
        "data_path = Path(\"data\")\n",
        "if not data_path.exists():\n",
        "  Path.mkdir(data_path)"
      ],
      "metadata": {
        "id": "VW1Qo3fi3NV7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Indexing by cluster"
      ],
      "metadata": {
        "id": "k74nhFJi3Dgp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_data = df.groupby('cluster')['text'].apply(lambda x: '. '.join(str(i) for i in x)).reset_index()"
      ],
      "metadata": {
        "id": "-KMO2ydj21sJ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "amazon_docs = []\n",
        "count=0\n",
        "for text in grouped_data['text']:\n",
        "    with open(data_path / f\"temp.txt\", \"w\") as fp:\n",
        "        fp.write(str(text))\n",
        "    docs = SimpleDirectoryReader(\n",
        "        input_files=[f\"data/temp.txt\"]\n",
        "    ).load_data()\n",
        "    docs[0].doc_id = str(count)\n",
        "    count=count+1\n",
        "    amazon_docs.extend(docs)"
      ],
      "metadata": {
        "id": "848MFd7M3VEN"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response_synthesizer = get_response_synthesizer(\n",
        "    response_mode=\"tree_summarize\", use_async=True\n",
        ")\n",
        "doc_summary_index = DocumentSummaryIndex.from_documents(\n",
        "    amazon_docs,\n",
        "    service_context=service_context,\n",
        "    response_synthesizer=response_synthesizer,\n",
        "    show_progress=True,\n",
        ")"
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      },
      "execution_count": 35,
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        {
          "output_type": "display_data",
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            "text/plain": [
              "Parsing nodes:   0%|          | 0/623 [00:00<?, ?it/s]"
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating embeddings:   0%|          | 0/623 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5f5200d9d1e40cc8c51a766bfc46c82"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = doc_summary_index.as_query_engine(\n",
        "    response_mode=\"tree_summarize\", use_async=True\n",
        ")"
      ],
      "metadata": {
        "id": "Ga_bJYhs4ldX"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "doc_summary_index.get_document_summary('0')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "jzjTVDYyCEmB",
        "outputId": "2bc3a2bc-024f-4322-c706-ca650cd7b8bc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The provided text is about the condition of various phones, specifically focusing on the presence or absence of scratches on the screens and backs of the phones. It also mentions the use of screen protectors and cases to prevent scratches. \\n\\nSome questions that this text can answer include:\\n- Are there any scratches on the screens or backs of the phones?\\n- Are screen protectors effective in preventing scratches?\\n- Are cases effective in preventing scratches?\\n- Are there any specific issues with the glass quality of certain phones?\\n- Are there any recommendations for protecting the screens and backs of phones from scratches?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"Summarize the best features in the iphones?\")\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psIOKF01CLwj",
        "outputId": "6c79d059-19d4-4121-888e-784e28f5d986"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best features of iPhones are not mentioned in the given context.\n"
          ]
        }
      ]
    }
  ]
}