genai3_rag_textsummarization.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/Vishwa0703/GenAI/blob/main/genai3_rag_textsummarization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YzOhxPxi0Fmj",
        "outputId": "aeaa1749-b6b4-4b9c-e362-6c253a6fcbd5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Workaround to avoid following error at notebook\n",
        "# NotImplementedError: A UTF-8 locale is required. Got ANSI_X3.4-1968\n",
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "ohjwoL_F41Cj"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Huggingface libraries to run LLM.\n",
        "!pip install -q -U transformers==4.40.2\n",
        "!pip install -q -U accelerate==0.30.1\n",
        "!pip install -q -U bitsandbytes==0.43.1\n",
        "\n",
        "#LangChain related libraries\n",
        "!pip install -q -U langchain==0.1.2\n",
        "\n",
        "#Open-source pure-python PDF library capable of splitting, merging, cropping,\n",
        "#and transforming the pages of PDF files\n",
        "!pip install -q -U pypdf==4.2.0"
      ],
      "metadata": {
        "id": "-P5f6DbK4_aE"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import time\n",
        "import transformers\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, BitsAndBytesConfig\n",
        "\n",
        "import torch\n",
        "\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "print(\"Device:\", device)\n",
        "if device == 'cuda':\n",
        "    print(torch.cuda.get_device_name(0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kjdx0Qc05Dzm",
        "outputId": "117b146a-aaa9-4a92-c89c-34f6d068033f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: cuda\n",
            "Tesla T4\n"
          ]
        }
      ]
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        "model_path = \"filipealmeida/Mistral-7B-Instruct-v0.1-sharded\"\n",
        "bnb_config = BitsAndBytesConfig \\\n",
        "              (\n",
        "                load_in_4bit=True,\n",
        "                bnb_4bit_use_double_quant=True,\n",
        "                bnb_4bit_quant_type=\"nf4\",\n",
        "                bnb_4bit_compute_dtype=torch.bfloat16,\n",
        "              )\n",
        "model = AutoModelForCausalLM.from_pretrained (model_path, trust_remote_code=True,\n",
        "                                              quantization_config=bnb_config,\n",
        "                                              device_map=\"auto\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(origin_model_path)"
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        },
        "id": "uWmA_Zkl5jY9",
        "outputId": "576cc904-febf-4b64-9c0f-6891704e0314"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb8aad31331648ac8e18ff1c8297f34d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13e5d94f5514419187e7cbfded3bb301"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
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        },
        {
          "output_type": "display_data",
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            ],
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        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "316013206232424087215a826e8f7a86"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb4d4deb02f24a9c8ad3919f8d911668"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40b2a7d8dbf24a74aab6e168139d6398"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "pytorch_model-00008-of-00008.bin:   0%|          | 0.00/816M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "100b0fc9a26548df86022edba739c987"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80f04c6ae3354ff6b6e82099d89c7b77"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e754c617156841009c4a006504e4bae0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.10k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "feca2a1bbd0544048de2a5715dbf268f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15de7d61b6de46c6a01d929a9e8b28ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4539bf3de5ea4b4884008b310bf76d2a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d7f095cd39b4bbabd436b78130d9fa4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_generation_pipeline = transformers.pipeline(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    task=\"text-generation\",\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    pad_token_id=tokenizer.eos_token_id,\n",
        "    batch_size=4,\n",
        "    repetition_penalty=1.1,\n",
        "    return_full_text=False,\n",
        "    max_new_tokens=600,\n",
        "    temperature = 0.3,\n",
        "    do_sample=True,\n",
        ")\n",
        "text_generation_pipeline.tokenizer.pad_token_id = text_generation_pipeline.model.config.eos_token_id# if you use batch size we have to give this line or it will give error\n",
        "mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)"
      ],
      "metadata": {
        "id": "7D2PX_eO5o2u"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Text Summarization Task"
      ],
      "metadata": {
        "id": "w0oVssrW-Q8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Librarires to count token programmatically\n",
        "!pip install -q -U tiktoken==0.6.0\n",
        "!pip install -q -U openai==1.28.1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CoHBUCOF-V8Z",
        "outputId": "854ffa39-0d16-4ef8-df9e-8d69589ed757"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.1/320.1 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Option-1\n",
        "LangChain StuffDocumentChain"
      ],
      "metadata": {
        "id": "OYkabuD8-h_R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tiktoken\n",
        "\n",
        "def num_tokens_from_string(string: str, encoding_name: str) -> int:\n",
        "    \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
        "    encoding = tiktoken.get_encoding(encoding_name)\n",
        "    num_tokens = len(encoding.encode(string))\n",
        "    return num_tokens"
      ],
      "metadata": {
        "id": "tdbWzGv1-tf9"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
        "from langchain.chains.llm import LLMChain\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.schema.document import Document\n",
        "\n",
        "\n",
        "def stuff_text_summarization():\n",
        "  text = \"\"\"It was a Thursday, but it felt like a Monday to John. And John loved Mondays. He thrived at work. He dismissed the old cliché of dreading Monday mornings and refused to engage in water-cooler complaints about “the grind” and empty conversations that included the familiar parry “How was your weekend?” “Too short!”. Yes, John liked his work and was unashamed.\n",
        "\n",
        "  I should probably get another latte. I’ve just been sitting here with this empty cup. But then I’ll start to get jittery. I’ll get a decaf. No, that’s stupid, it feels stupid to pay for a decaf. I can’t justify that.\n",
        "\n",
        "  John was always impatient on the weekends; he missed the formal structure of the business week. When he was younger he used to stay late after school on Fridays and come in early on Mondays, a pattern his mother referred to with equal parts admiration and disdain as “studying overtime.”\n",
        "\n",
        "  Jesus, I’ve written another loser.\n",
        "\n",
        "  Now, John spent his weekends doing yard work at the Tudor house Rebecca left him after their divorce. Rebecca, with her almond eyes—both in shape and in color—could never be his enemy.\n",
        "\n",
        "  That barista keeps looking at me. She’ll probably ask me to leave if I don’t buy something. She’s kind of attractive. Not her hair—her hair seems stringy—but her face is nice. I should really buy something.\n",
        "\n",
        "  Their divorce was remarkably amicable. In fact, John would often tell his parents, “Rebecca and I are better friends now than when we were married!” In fact, John looked forward to the days when he and Rebecca, with their new partners, would reminisce about their marriage, seeing it in a positive light, like two mature adults.\n",
        "\n",
        "  Maybe I’ll just get a pumpkin-spice loaf. That way I can still sit here without going through a whole production of buying a coffee and giving my name and feeling like an asshole while it gets made.\n",
        "\n",
        "  But if John were being honest, the house did get lonely on the weekends. Rebecca’s parents had been generous enough to leave John the house even though they had paid for it. John was still struggling to get his short-story writing—I mean, his painting—career off the ground, and Rebecca and her family had been more than supportive, even during the breakup.\"\"\"\n",
        "\n",
        "  token_count = num_tokens_from_string(text, \"cl100k_base\")\n",
        "  print(f'Token Count: {token_count}')\n",
        "\n",
        "  #Converting text to LangChain documents so that StuffDocumentsChain can understand Input\n",
        "  documents = Document(page_content=text, metadata={\"source\": \"local\"})\n",
        "\n",
        "  # Define prompt with prompt template\n",
        "  prompt_template = \"\"\"Write a concise summary of the following:\n",
        "  \"{docs}\"\n",
        "  CONCISE SUMMARY:\"\"\"\n",
        "  prompt = PromptTemplate.from_template(prompt_template)\n",
        "\n",
        "  # Define LLM chain\n",
        "  llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)\n",
        "\n",
        "  # Define StuffDocumentsChain\n",
        "  stuff_chain = StuffDocumentsChain(llm_chain=llm_chain, document_variable_name=\"docs\")\n",
        "\n",
        "  #Get the Summary of text by invoking StuffDocumentsChain\n",
        "  summary = stuff_chain.invoke([documents])\n",
        "  return summary['output_text']"
      ],
      "metadata": {
        "id": "lTvCW8Mc_A13"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stuff_summary = stuff_text_summarization()\n",
        "#Print the output text after getting summary\n",
        "print(stuff_summary)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pumjh-Eb_HZJ",
        "outputId": "52f79f8b-2dbd-4c02-ee92-b5f4dd1217cc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token Count: 487\n",
            " John enjoys his job and doesn't complain about Mondays. He misses the structure of the weekdays on weekends and does yard work at his ex-wife's house. He is attracted to a barista but doesn't want to buy anything from her. He looks forward to reminiscing about his marriage with his ex-wife and her family.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Document Loaders\n",
        "Document loaders in LangChain enable the extraction of data from various sources."
      ],
      "metadata": {
        "id": "spKYJyGwEZFI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import PyPDFLoader\n",
        "\n",
        "def load_pdf():\n",
        "    # Load the pdf file\n",
        "    loader = PyPDFLoader('/content/drive/MyDrive/Colab Notebooks/ImpactofIndianPremierLeagueonTestCricketinIndia.pdf')\n",
        "    documents = loader.load()\n",
        "    token_count = num_tokens_from_string(str(documents), \"cl100k_base\")\n",
        "    print(f'PDF Token Count: {token_count}')\n",
        "    return documents, token_count\n"
      ],
      "metadata": {
        "id": "49n95pSfEfCB"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Option - 2\n",
        "LangChain MapReduce (For large text where LLM has context length limitation)"
      ],
      "metadata": {
        "id": "-_qiA0PAE3RI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import MapReduceDocumentsChain, LLMChain, ReduceDocumentsChain, StuffDocumentsChain\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.prompts import PromptTemplate\n",
        "\n",
        "def map_reduce_summarize_document():\n",
        "\n",
        "    #Load PDF using PyPDF\n",
        "    documents, token_count = load_pdf()\n",
        "\n",
        "    # Map Prompt template and LLM Chain\n",
        "    map_prompt_template = \"\"\"[INST] The following is a part of an pdf document:\n",
        "    {docs}\n",
        "    Based on this, please identify the main points.\n",
        "    Answer:  [/INST] \"\"\"\n",
        "    map_prompt = PromptTemplate.from_template(map_prompt_template)\n",
        "    map_chain = LLMChain(llm=mistral_llm, prompt=map_prompt)\n",
        "\n",
        "\n",
        "    # Reduce Prompt template and LLM Chain\n",
        "    reduce_prompt_template = \"\"\"[INST] The following is set of summaries from the article:\n",
        "    {doc_summaries}\n",
        "    Take these and distill it into a final, consolidated summary of the main points.\n",
        "    Construct it as a well organized summary of the main points and should be between 3 and 5 paragraphs.\n",
        "    Answer:  [/INST] \"\"\"\n",
        "    reduce_prompt = PromptTemplate.from_template(reduce_prompt_template)\n",
        "    reduce_chain = LLMChain(llm=mistral_llm, prompt=reduce_prompt)\n",
        "\n",
        "\n",
        "    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain\n",
        "    combine_documents_chain = StuffDocumentsChain(\n",
        "        llm_chain=reduce_chain, document_variable_name=\"doc_summaries\"\n",
        "    )\n",
        "\n",
        "    # Combines and iteratively reduces the mapped documents\n",
        "    reduce_documents_chain = ReduceDocumentsChain(\n",
        "        # This is final chain that is called.\n",
        "        combine_documents_chain=combine_documents_chain,\n",
        "        # If documents exceed context for `StuffDocumentsChain`\n",
        "        collapse_documents_chain=combine_documents_chain,\n",
        "        # The maximum number of tokens to group documents into.\n",
        "        token_max=4000,\n",
        "    )\n",
        "\n",
        "    # Combining documents by mapping a chain over them, then combining results\n",
        "    map_reduce_chain = MapReduceDocumentsChain(\n",
        "        # Map chain\n",
        "        llm_chain=map_chain,\n",
        "        # Reduce chain\n",
        "        reduce_documents_chain=reduce_documents_chain,\n",
        "        # The variable name in the llm_chain to put the documents in\n",
        "        document_variable_name=\"docs\",\n",
        "        # Return the results of the map steps in the output\n",
        "        return_intermediate_steps=False,\n",
        "    )\n",
        "\n",
        "    #Document Transformers in LangChain are crucial for manipulating documents,\n",
        "    #performing tasks like splitting, combining, and filtering to adapt to model\n",
        "    #context or specific application needs. The RecursiveCharacterTextSplitter\n",
        "    #is a versatile tool allowing customization of parameters such as chunk size,\n",
        "    #overlap, and starting index for efficient text segmentation.\n",
        "\n",
        "    # Split documents into chunks using RecursiveCharacterTextSplitter\n",
        "    text_splitter = RecursiveCharacterTextSplitter(\n",
        "        chunk_size=4000, chunk_overlap=100\n",
        "    )\n",
        "    split_docs = text_splitter.split_documents(documents)\n",
        "\n",
        "    # Run the chain\n",
        "    result = map_reduce_chain.invoke(split_docs, return_only_outputs=True)\n",
        "    return result['output_text']"
      ],
      "metadata": {
        "id": "Xg4KRD17E7YN"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_response = map_reduce_summarize_document()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYeZ_s-LLMWV",
        "outputId": "cbc024a4-93f5-47cf-a98b-82e6a50aa813"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PDF Token Count: 7268\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(summary_response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE6dqvalPH4l",
        "outputId": "c8231c56-9443-4a89-be1d-c1dc4d9c7312"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. The article discusses the impact of the Indian Premier League (IPL) on Test cricket in India. The authors argue that there is a gap in the existing research on this topic and that this paper aims to fill that gap. The significance of the study includes understanding the current scenario of Test cricket in India, the popularity of different formats of the game among youth, and the influence of IPL on fundamental cricket techniques and skills.\n",
            "2. The article provides a brief overview of the history and evolution of cricket in India, including the introduction of the IPL. It highlights the impact of IPL on Indian cricket, including increased power over world cricket and the ascent of new twenty-first-century India. The article also compares IPL with the cell phone boom in the telephone industry and the changes it brought in the cricket world.\n",
            "3. The literature review section of the article discusses various researches conducted on cricket and the Indian Premier League, highlighting their opinions on the positivity or negativity of IPL. The article also mentions the controversies associated with IPL, including the Spot Fixing scandal in 2013. The article highlights the success of IPL in producing talented cricketers who have played for the Indian national team.\n",
            "4. The methodology section of the article does not provide details on the methodology used for the research. However, the study used convenience purposive sampling and a self-structured questionnaire to gather data from male participants aged 12-16, 16-25, 25-35, and 35 and above who have represented at any level of cricket mentioned.\n",
            "5. The results section of the article presents the findings of the research, including the changes brought in Indian cricket by the IPL, specifically in Test cricket. The study found that Test cricket is the most popular format for learning the skills of the game, with 63% of participants preferring it over other formats. The study also found that T20 cricket is the second most popular format for learning the skills of the game, with 25% of participants preferring it.\n",
            "6. The conclusion section of the article emphasizes the importance of understanding the trends and preferences of people regarding cricket and the IPL. The study suggests that the findings will help coaches develop measures and training plans accordingly and also provide information about the interest of youth cricketers. The uniqueness of this paper lies in the fact that it is the first paper on this particular topic.\n",
            "7. The main objective of the research was to determine how and what changes IPL has brought in Test cricket in the country. The study aimed to assess the perception of cricket in the country and evaluate the popularity of both Test cricket and IPL. The study found that Test cricket is the most popular format for learning the skills of the game, with 63% of participants\n"
          ]
        }
      ]
    }
  ]
}