RAG__Langchain.ipynb

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  "cells": [
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      "source": [
        "# Will be using:\n",
        " 1. Langchain framework for RAG Architecture.\n",
        " 2. Faiss for Vector DB\n",
        " 3. PyPDF for pdf text extraction.\n",
        " 4. sentence transformer for vector embedding.\n",
        " 5. Free Chatgpt API from RAPID API."
      ],
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            "Collecting langchain\n",
            "  Downloading langchain-0.1.16-py3-none-any.whl (817 kB)\n",
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            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.1)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.0.29)\n",
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            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain)\n",
            "  Downloading dataclasses_json-0.6.4-py3-none-any.whl (28 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langchain-community<0.1,>=0.0.32 (from langchain)\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langchain-core<0.2.0,>=0.1.42 (from langchain)\n",
            "  Downloading langchain_core-0.1.45-py3-none-any.whl (291 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m291.3/291.3 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langchain-text-splitters<0.1,>=0.0.1 (from langchain)\n",
            "  Downloading langchain_text_splitters-0.0.1-py3-none-any.whl (21 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.17 (from langchain)\n",
            "  Downloading langsmith-0.1.49-py3-none-any.whl (115 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.2/115.2 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain)\n",
            "  Downloading jsonpointer-2.4-py2.py3-none-any.whl (7.8 kB)\n",
            "Collecting packaging<24.0,>=23.2 (from langchain-core<0.2.0,>=0.1.42->langchain)\n",
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            "\u001b[?25hCollecting orjson<4.0.0,>=3.9.14 (from langsmith<0.2.0,>=0.1.17->langchain)\n",
            "  Downloading orjson-3.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n",
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            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: packaging, orjson, mypy-extensions, jsonpointer, typing-inspect, marshmallow, jsonpatch, langsmith, dataclasses-json, langchain-core, langchain-text-splitters, langchain-community, langchain\n",
            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 24.0\n",
            "    Uninstalling packaging-24.0:\n",
            "      Successfully uninstalled packaging-24.0\n",
            "Successfully installed dataclasses-json-0.6.4 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.16 langchain-community-0.0.34 langchain-core-0.1.45 langchain-text-splitters-0.0.1 langsmith-0.1.49 marshmallow-3.21.1 mypy-extensions-1.0.0 orjson-3.10.1 packaging-23.2 typing-inspect-0.9.0\n",
            "Collecting faiss-gpu\n",
            "  Downloading faiss_gpu-1.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (85.5 MB)\n",
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            "\u001b[?25hInstalling collected packages: faiss-gpu\n",
            "Successfully installed faiss-gpu-1.7.2\n",
            "Collecting pypdf\n",
            "  Downloading pypdf-4.2.0-py3-none-any.whl (290 kB)\n",
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            "Installing collected packages: pypdf\n",
            "Successfully installed pypdf-4.2.0\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-2.7.0-py3-none-any.whl (171 kB)\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
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            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.11.0->sentence-transformers)\n",
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            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.11.0->sentence-transformers)\n",
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            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.11.0->sentence-transformers)\n",
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            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.11.0->sentence-transformers)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (2.2.0)\n",
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            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-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, sentence-transformers\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 sentence-transformers-2.7.0\n"
          ]
        }
      ],
      "source": [
        "!pip install langchain\n",
        "!pip install faiss-gpu\n",
        "!pip install pypdf\n",
        "!pip install sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.schema.output_parser import StrOutputParser\n",
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.vectorstores.utils import filter_complex_metadata\n",
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.vectorstores import DocArrayInMemorySearch\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.chains import ConversationalRetrievalChain\n",
        "from langchain.indexes import VectorstoreIndexCreator\n",
        "from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings"
      ],
      "metadata": {
        "id": "Vq3f_t_6uRqv"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "# Uploading the file named Doc1.pdf\n",
        "txt_file_path = 'Doc1.pdf'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Cvld9SwYutkC",
        "outputId": "4fe0e05f-4339-431e-9d67-612b7f101f8f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2fdc92e4-0bf1-4a4e-9413-f7b471d399e9\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2fdc92e4-0bf1-4a4e-9413-f7b471d399e9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Doc1.pdf to Doc1.pdf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Extracting the text and splitting into chunks to be vectorized.\n",
        "docs = PyPDFLoader(file_path=txt_file_path).load()\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=80)\n",
        "chunks = text_splitter.split_documents(docs)\n",
        "chunks = filter_complex_metadata(chunks)\n",
        "#Using the embedding model named all-MiniLM-L6-v2, more can be read in HuggingFace\n",
        "embedding_function = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
        "#Storing the chunks in vector DB\n",
        "db = FAISS.from_documents(documents=chunks, embedding=embedding_function)"
      ],
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        "id": "6crBlzE4wJP1",
        "outputId": "a77c8212-ef47-4c74-907b-2e19184e6c20"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
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            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
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              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
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          "metadata": {}
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          "metadata": {}
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        {
          "output_type": "display_data",
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              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "a5549ff2ec794e4eab8bb535237a2949"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17d35cb97a9c424fa9605527e32547db"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Moding the retrival algorithm to pick particular number of chunks after it crosses the threshold.\n",
        "retriever = db.as_retriever(\n",
        "    search_type=\"similarity_score_threshold\",\n",
        "    search_kwargs={\n",
        "        \"score_threshold\": 0.4,\n",
        "        \"k\":3},\n",
        ")"
      ],
      "metadata": {
        "id": "Ix0wtPcSwbI2"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## This question can be found on the 6th page in the first line of the Doc1.pdf"
      ],
      "metadata": {
        "id": "aWgiT5dhIBEQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "what_to_ask=\"What was a program for animating choropleth maps was created?\""
      ],
      "metadata": {
        "id": "I-Q13AH4wu_p"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retrieves=retriever.invoke(what_to_ask)"
      ],
      "metadata": {
        "id": "WSuFLOwpw6vC"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the view of top 3 chunks gotten from the DB.\n",
        "retrieves"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S00dFcJ7w_Px",
        "outputId": "cd454c02-4539-4244-86db-cd751c5716fe"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(page_content='6 cartographic perspectives Number 13, Fall 1992 \\nREQUIRED RESEARCH addition, a program for animating choropleth maps was created (MacChoro \\nII 1989). \\nAll these works employed the application of cartographic animation.', metadata={'source': 'Doc1.pdf', 'page': 6}),\n",
              " Document(page_content='II 1989). \\nAll these works employed the application of cartographic animation. \\nMost of them exclusively deal with the reali7ation of an animated map \\nsequence for one special issue. Only the later works pick up a more', metadata={'source': 'Doc1.pdf', 'page': 6}),\n",
              " Document(page_content='well as on animation techniques and their uses. \\nCampbell, C. S. and Egbert, S. L. 1990. Animated cartography: thirty \\nyears of scratching the surface. Cartogmphicn. 27 (2):24-46. \\nCornwell, B. and Robinson, A. 1966. Possibilities for computer ani\\xad', metadata={'source': 'Doc1.pdf', 'page': 8}),\n",
              " Document(page_content=\"perception and production of animated map sequences is not available. \\nThere are no principles that tell cartographers hO\\\\'\\\\' to make and design \\nanimated maps. Without extensive knowledge of the creation of animated\", metadata={'source': 'Doc1.pdf', 'page': 6}),\n",
              " Document(page_content='creation, and use of animated map sequences. Because of the potential of \\nanimation for cartography, more attention to research is required in this \\nfield. \\nThe potential of computer animation has already been realized by differ\\xad', metadata={'source': 'Doc1.pdf', 'page': 7})]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "str_retrieves = \" \".join([str(element) for element in retrieves])"
      ],
      "metadata": {
        "id": "kyxRJKemFrCa"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Instuction to be passed to the chatgpt.\n",
        "instruction=\"Answer the user quesestion as accuractely as possible, based on the following context :\"+str_retrieves"
      ],
      "metadata": {
        "id": "WDOfzt3QF3cc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Using RAPID API's free Chatgpt API to get answer based on user query.\n",
        "\n",
        "# You can get your key from here : https://rapidapi.com/haxednet/api/chatgpt-api8\n",
        "import requests\n",
        "\n",
        "url = \"https://chatgpt-api8.p.rapidapi.com/\"\n",
        "\n",
        "payload = [\n",
        "\t{\n",
        "\t\t\"content\": instruction,\n",
        "\t\t\"role\": \"system\"\n",
        "\t},\n",
        "\t{\n",
        "\t\t\"content\": what_to_ask,\n",
        "\t\t\"role\": \"user\"\n",
        "\t}\n",
        "]\n",
        "headers = {\n",
        "\t\"content-type\": \"...\",\n",
        "\t\"X-RapidAPI-Key\": \"...\",\n",
        "\t\"X-RapidAPI-Host\": \"...\"\n",
        "}\n",
        "\n",
        "\n",
        "response = requests.post(url, json=payload, headers=headers)\n",
        "\n",
        "print(response.json())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8JVblcGxFps",
        "outputId": "185c045b-bcfb-4507-fc02-114706e0638c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'text': 'A program called MacChoro II was created for animating choropleth maps in 1989.', 'finish_reason': 'stop', 'model': 'gpt-3.5-turbo-030', 'server': 'backup-K'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(response.json()['text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7JFV25WiHSAA",
        "outputId": "258ce9c8-b17d-450e-8d43-c3135472a63c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A program called MacChoro II was created for animating choropleth maps in 1989.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-Jr9mK3cHnC-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}