DSAI 201 Final Project Advanced Search Engine.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwpG-0J1C33T"
      },
      "source": [
        "##**DSAI 201 Final Project**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwrWsR6xzvPq",
        "outputId": "1233bd68-1989-48d4-a294-d289bdba738b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: python-terrier, ir_measures, chest, wget, cwl-eval, warc3-wet-clueweb09, zlib-state, cbor\n",
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            "  Created wheel for cbor: filename=cbor-1.0.0-cp310-cp310-linux_x86_64.whl size=53430 sha256=e6a359eaff542e3f86867c12b3c9a896b7f658d5eff6b4a0197e44320760af80\n",
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            "Successfully built python-terrier ir_measures chest wget cwl-eval warc3-wet-clueweb09 zlib-state cbor\n",
            "Installing collected packages: wget, warc3-wet-clueweb09, warc3-wet, typish, pyjnius, multiset, ijson, heapdict, cbor, zlib-state, unlzw3, trec-car-tools, pytrec_eval_terrier, nptyping, matchpy, lz4, dill, deprecated, cwl-eval, chest, ir_measures, inscriptis, ir_datasets, python-terrier\n",
            "Successfully installed cbor-1.0.0 chest-0.2.3 cwl-eval-1.0.12 deprecated-1.2.14 dill-0.3.8 heapdict-1.0.1 ijson-3.2.3 inscriptis-2.5.0 ir_datasets-0.5.7 ir_measures-0.3.3 lz4-4.3.3 matchpy-0.5.5 multiset-2.1.1 nptyping-1.4.4 pyjnius-1.6.1 python-terrier-0.10.1 pytrec_eval_terrier-0.5.6 trec-car-tools-2.6 typish-1.9.3 unlzw3-0.2.2 warc3-wet-0.2.3 warc3-wet-clueweb09-0.2.5 wget-3.2 zlib-state-0.1.6\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
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          ]
        }
      ],
      "source": [
        "#install the Pyterrier framework\n",
        "!pip install python-terrier\n",
        "# install the nltk modules\n",
        "!pip install nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwVdio6D0-ql",
        "outputId": "02699db1-3a00-40bb-c023-7ab8c3992df1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "terrier-assemblies 5.9 jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "terrier-python-helper 0.0.8 jar not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "terrier-prf -SNAPSHOT jar not found, downloading to /root/.pyterrier...\n",
            "Done\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "PyTerrier 0.10.1 has loaded Terrier 5.9 (built by craigm on 2024-05-02 17:40) and terrier-helper 0.0.8\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import pyterrier as pt\n",
        "if not pt.started():\n",
        "\n",
        " pt.init(boot_packages=[\"com.github.terrierteam:terrier-prf:-SNAPSHOT\"])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lYz8BMlDAEa"
      },
      "source": [
        "##**Importing Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SSWxmTLpwR4l"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "import re # used to clean the data\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m9h0wMKx1IWQ"
      },
      "outputs": [],
      "source": [
        "from nltk.stem import *\n",
        "from nltk.stem.porter import *\n",
        "# Initialize Porter stemmer\n",
        "stemmer = PorterStemmer()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSdKTfJxDFCo"
      },
      "source": [
        "##**Unzipping the dataset and converting every file to csv**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-tLhTOWs7HD",
        "outputId": "03637948-591b-48e7-d311-6570c8aaad8d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CISI.ALL  CISI.QRY  CISI.REL\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "zip_file_name = 'cisi.zip'\n",
        "with zipfile.ZipFile(zip_file_name, 'r') as zip_ref:\n",
        "    zip_ref.extractall('cisi_dataset')\n",
        "!ls cisi_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eyROS2S9jU6Y"
      },
      "outputs": [],
      "source": [
        "def load_cisi_dataset(data_dir):\n",
        "    documents_path = os.path.join(data_dir, 'CISI.ALL')\n",
        "    queries_path = os.path.join(data_dir, 'CISI.QRY')\n",
        "    qrels_path = os.path.join(data_dir, 'CISI.REL')\n",
        "\n",
        "    documents_df = read_documents(documents_path)\n",
        "    queries_df = read_queries(queries_path)\n",
        "    qrels_df = read_qrels(qrels_path)\n",
        "    return documents_df, queries_df, qrels_df\n",
        "\n",
        "# Read documents from CISI.ALL file\n",
        "def read_documents(documents_path):\n",
        "    with open(documents_path, 'r') as file:\n",
        "        lines = file.readlines()\n",
        "    documents = []\n",
        "    current_document = None\n",
        "    for line in lines:\n",
        "        if line.startswith('.I'):\n",
        "            if current_document is not None:\n",
        "                current_document['Text'] = current_document['Text'].split('\\t')[0].strip()  # Remove anything after the first tab\n",
        "                documents.append(current_document)\n",
        "            current_document = {'ID': line.strip().split()[1], 'Text': ''}\n",
        "        elif line.startswith('.T'):\n",
        "            continue\n",
        "        elif line.startswith('.A') or line.startswith('.B') or line.startswith('.W') or line.startswith('.X'):\n",
        "            continue\n",
        "        else:\n",
        "            current_document['Text'] += line.strip() + ' '\n",
        "\n",
        "    # Append the last document\n",
        "    if current_document is not None:\n",
        "        current_document['Text'] = current_document['Text'].split('\\t')[0].strip()  # Remove anything after the first tab\n",
        "        documents.append(current_document)\n",
        "    documents_df = pd.DataFrame(documents)\n",
        "    return documents_df\n",
        "\n",
        "# Read queries from CISI.QRY file\n",
        "def read_queries(queries_path):\n",
        "    with open(queries_path, 'r') as file:\n",
        "        lines = file.readlines()\n",
        "    query_texts = []\n",
        "    query_ids = []\n",
        "    current_query_id = None\n",
        "    current_query_text = []\n",
        "    for line in lines:\n",
        "        if line.startswith('.I'):\n",
        "            if current_query_id is not None:\n",
        "                query_texts.append(' '.join(current_query_text))\n",
        "                current_query_text = []\n",
        "            current_query_id = line.strip().split()[1]\n",
        "            query_ids.append(current_query_id)\n",
        "        elif line.startswith('.W'):\n",
        "            continue\n",
        "        elif line.startswith('.X'):\n",
        "            break\n",
        "        else:\n",
        "            current_query_text.append(line.strip())\n",
        "    # Append the last query\n",
        "    query_texts.append(' '.join(current_query_text))\n",
        "    queries_df = pd.DataFrame({\n",
        "        'qid': query_ids,\n",
        "        'raw_query': query_texts})\n",
        "    return queries_df\n",
        "\n",
        "# Read qrels from CISI.REL file\n",
        "def read_qrels(qrels_path):\n",
        "    qrels_df = pd.read_csv(qrels_path, sep='\\s+', names=['qid','Q0','docno','label'])\n",
        "    return qrels_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9QECIC7wsinl"
      },
      "outputs": [],
      "source": [
        "documents_df, queries_df, qrels_df = load_cisi_dataset(\"cisi_dataset\")\n",
        "#documents_df = documents_df.rename(columns={\"ID\":\"docno\",\"Text\":\"raw_text\"},inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "09bU8burz3qe",
        "outputId": "4743942a-8949-4268-9078-5c875c402534"
      },
      "outputs": [
        {
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            ],
            "text/plain": [
              "  ID                                               Text\n",
              "0  1  18 Editions of the Dewey Decimal Classificatio...\n",
              "1  2  Use Made of Technical Libraries Slater, M. Thi...\n",
              "2  3  Two Kinds of Power An Essay on Bibliographic C...\n",
              "3  4  Systems Analysis of a University Library; fina...\n",
              "4  5  A Library Management Game: a report on a resea..."
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "documents_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYqJPLWFDPZg"
      },
      "source": [
        "##**Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoJpSHwBz9Hh",
        "outputId": "d3c493d4-b416-41b6-bc08-bf241592d625"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "stop_words = set(stopwords.words('english'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ak4FaCp40sEJ"
      },
      "outputs": [],
      "source": [
        "def Steem_text(text):\n",
        "\n",
        "    tokens = word_tokenize(text)\n",
        "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
        "    # print (tokens)\n",
        "    return ' '.join(stemmed_tokens)\n",
        "\n",
        "\n",
        "def clean(text):\n",
        "   text = re.sub(r\"http\\S+\", \" \", text) # remove urls\n",
        "   text = re.sub(r\"RT \", \" \", text) # remove rt\n",
        "   text = re.sub(r\"@[\\w]*\", \" \", text) # remove handles\n",
        "   text = re.sub(r\"[\\.\\,\\#_\\|\\:\\?\\?\\/\\=]\", \" \", text) # remove special characters\n",
        "   text = re.sub(r'\\t', ' ', text) # remove tabs\n",
        "   text = re.sub(r'\\n', ' ', text) # remove line jump\n",
        "   text = re.sub(r\"\\s+\", \" \", text) # remove extra white space\n",
        "   text = text.strip()\n",
        "   return text\n",
        "\n",
        "def remove_Stopwords(text):\n",
        "  tokens = word_tokenize(text)\n",
        "  tokenized = [word.lower() for word in tokens if not word.lower() in stop_words]\n",
        "  return ' '.join(tokenized)\n",
        "\n",
        "def process(sentence):\n",
        "  cleaned = clean(sentence)\n",
        "  stemed = Steem_text(cleaned)\n",
        "  removed = remove_Stopwords(stemed)\n",
        "  return removed\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "7Gv8NLZQ18pw",
        "outputId": "a225fd49-e29e-4d06-eb3f-b77577316511"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data before processing: \n"
          ]
        },
        {
          "data": {
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              "summary": "{\n  \"name\": \"documents_df\",\n  \"rows\": 1460,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1460,\n        \"samples\": [\n          \"893\",\n          \"1106\",\n          \"414\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1460,\n        \"samples\": [\n          \"The Frequency Distribution of Scientific Productivity. Lotka, A.J. It would be of interest to determine, if possible, the part which men of different calibre contribute to the progress of science. Considering first simple volume of production, a count was made of the number of names, in the decennial index of Chemical Abstracts 1907-1916, against which appeared 1, 2, 3 . . . . entries.  Names of firms (e.g. Aktiengesellschaft, etc.) were omitted from reckoning, since they represent the output, not of a single individual, but of an unknown number of persons.  The letters A and B of the alphabet only were covered.  These were treated both separately and in the aggregate, with the results shown in the table and in figures 1 and 2 below. 19\",\n          \"Efficiency of Information Service at a Research Institute (findings of a questionnaire survey) Zyabrev, V.A. Discussing the problem of evaluation of the efficiency of an information service at a research institute, the paper presents the results of a questionnaire survey conducted in 1968 at an electronics industry research establishment; the objective of the survey was to work out improvements of the special information system.. 763\",\n          \"The History of Libraries in the Western World Johnson, E.D. The present work is an attempt to trace the history of libraries in the western world:  to indicate how libraries developed and how they influenced the social and cultural history of our civilization. The book is intended for the student of library science or of cultural history and for the general reader interested in the development of western civilization.  Much has been omitted concerning libraries and librarians that could have been included; conversely, some facts could have been omitted without seriously detracting from the story as a whole but have been included to illustrate how library history developed; they point out the high spots and indicate trends. 414\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "variable_name": "documents_df"
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              "      <td>18 Editions of the Dewey Decimal Classificatio...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Use Made of Technical Libraries Slater, M. Thi...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Two Kinds of Power An Essay on Bibliographic C...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Systems Analysis of a University Library; fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>A Library Management Game: a report on a resea...</td>\n",
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              "\n",
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              "        document.querySelector('#df-c285a819-b553-4ac9-be6d-c05251471f5e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c285a819-b553-4ac9-be6d-c05251471f5e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
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              "  }\n",
              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cbfbb244-78e0-4e17-b48e-e8e55d2773fe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  ID                                               Text\n",
              "0  1  18 Editions of the Dewey Decimal Classificatio...\n",
              "1  2  Use Made of Technical Libraries Slater, M. Thi...\n",
              "2  3  Two Kinds of Power An Essay on Bibliographic C...\n",
              "3  4  Systems Analysis of a University Library; fina...\n",
              "4  5  A Library Management Game: a report on a resea..."
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Data before processing: \")\n",
        "documents_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "KguCASKf2BO2",
        "outputId": "a0d3c5d7-745d-4f58-e011-3acf048eb6f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data after processing: \n"
          ]
        },
        {
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0cf8ac8c-85e2-4ed3-abe0-20b1040302e1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0cf8ac8c-85e2-4ed3-abe0-20b1040302e1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2bff4517-405a-4baa-8d66-80a770563336\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2bff4517-405a-4baa-8d66-80a770563336')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2bff4517-405a-4baa-8d66-80a770563336 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  ID                                               Text  \\\n",
              "0  1  18 Editions of the Dewey Decimal Classificatio...   \n",
              "1  2  Use Made of Technical Libraries Slater, M. Thi...   \n",
              "2  3  Two Kinds of Power An Essay on Bibliographic C...   \n",
              "3  4  Systems Analysis of a University Library; fina...   \n",
              "4  5  A Library Management Game: a report on a resea...   \n",
              "\n",
              "                                      processed_text  \n",
              "0  18 edit dewey decim classif comaromi j p prese...  \n",
              "1  use made technic librari slater thi report ana...  \n",
              "2  two kind power essay bibliograph control wilso...  \n",
              "3  system analysi univers librari ; final report ...  \n",
              "4  librari manag game report research project bro...  "
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Data after processing: \")\n",
        "documents_df[\"processed_text\"] = documents_df[\"Text\"].apply(process)\n",
        "documents_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2apG-BLsDU87"
      },
      "source": [
        "##**Indexing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "K5uRp5-S2fXW",
        "outputId": "e6342870-8ce7-4c48-8c55-d2198108a811"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "./socuments_dfsetindex/data.properties\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'./socuments_dfsetindex/data.properties'"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "indexer = pt.DFIndexer(\"./socuments_dfsetindex\", overwrite=True)\n",
        "# index the text, record the docnos as meta documents_df\n",
        "documents_df['docno'] = documents_df['ID'].astype(str)\n",
        "index_ref = indexer.index(documents_df[\"processed_text\"], documents_df[\"docno\"])\n",
        "print(index_ref.toString())\n",
        "index_ref.toString()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p1-CL64O29FK"
      },
      "outputs": [],
      "source": [
        "index = pt.IndexFactory.of(index_ref)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R7EFDQUT3GLO"
      },
      "outputs": [],
      "source": [
        "def inverted_Index(coll):\n",
        "    punkt = [\":\", \",\", \".\", \";\", '\"']\n",
        "    fin_doc = []\n",
        "    doc_num = []\n",
        "    doc_freq = {}\n",
        "    posting_list = {}\n",
        "    for doc_index, doc in enumerate(coll, start=1):\n",
        "        splitted = doc.split()\n",
        "        for word in splitted:\n",
        "            stemed_word = ''.join(char for char in word if char not in punkt)\n",
        "\n",
        "            if stemed_word.lower():\n",
        "                fin_doc.append(stemed_word.lower())\n",
        "                doc_num.append(doc_index)\n",
        "                if doc_freq.get(stemed_word.lower(), 0) < len(coll):\n",
        "                   doc_freq[stemed_word.lower()] = doc_freq.get(stemed_word.lower(), 0) + 1\n",
        "\n",
        "\n",
        "                if stemed_word.lower() not in posting_list:\n",
        "                  posting_list[stemed_word.lower()] = []\n",
        "                if doc_index not in posting_list[stemed_word.lower()]:\n",
        "                  posting_list[stemed_word.lower()].append(doc_index)\n",
        "\n",
        "\n",
        "    df = pd.DataFrame({'Term': fin_doc, 'docID': doc_num})\n",
        "    df['Posting_list'] = df['Term'].map(posting_list)\n",
        "    df['doc_Freq'] = df['Term'].map(doc_freq)\n",
        "    sorted_df = df.sort_values(by='Term').reset_index()\n",
        "\n",
        "    sorted_df = sorted_df.drop_duplicates(subset=['Term'])\n",
        "\n",
        "\n",
        "\n",
        "    return sorted_df\n",
        "#ngrock"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "hcWNb8tv3vBA",
        "outputId": "4000f7d5-a0ce-45dd-aa04-f001bbf8f83f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"indexed\",\n  \"rows\": 8247,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33618,\n        \"min\": 3,\n        \"max\": 117156,\n        \"num_unique_values\": 8247,\n        \"samples\": [\n          93840,\n          25483,\n          25939\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Term\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8247,\n        \"samples\": [\n          \"shareabl\",\n          \"remain\",\n          \"flener\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"docID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 420,\n        \"min\": 1,\n        \"max\": 1460,\n        \"num_unique_values\": 1413,\n        \"samples\": [\n          116,\n          359,\n          342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Posting_list\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"doc_Freq\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1,\n        \"max\": 1460,\n        \"num_unique_values\": 253,\n        \"samples\": [\n          161,\n          2,\n          93\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "indexed"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-c29ee71e-47ff-42df-97a2-ae535c558ded\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Term</th>\n",
              "      <th>docID</th>\n",
              "      <th>Posting_list</th>\n",
              "      <th>doc_Freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>77173</td>\n",
              "      <td>!</td>\n",
              "      <td>918</td>\n",
              "      <td>[92, 349, 918, 952, 1050]</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20000</td>\n",
              "      <td>$</td>\n",
              "      <td>220</td>\n",
              "      <td>[188, 189, 220, 391, 551, 921, 1374]</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>53181</td>\n",
              "      <td>%</td>\n",
              "      <td>614</td>\n",
              "      <td>[40, 52, 62, 76, 145, 190, 225, 253, 503, 576,...</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>36939</td>\n",
              "      <td>&amp;</td>\n",
              "      <td>426</td>\n",
              "      <td>[91, 121, 127, 153, 424, 425, 426, 427, 428, 4...</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>9301</td>\n",
              "      <td>'</td>\n",
              "      <td>109</td>\n",
              "      <td>[8, 12, 42, 88, 104, 109, 110, 113, 137, 147, ...</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117149</th>\n",
              "      <td>4718</td>\n",
              "      <td>zund</td>\n",
              "      <td>57</td>\n",
              "      <td>[57, 81]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117151</th>\n",
              "      <td>90951</td>\n",
              "      <td>zvezhinskii</td>\n",
              "      <td>1096</td>\n",
              "      <td>[1096]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117152</th>\n",
              "      <td>91489</td>\n",
              "      <td>zyabrev</td>\n",
              "      <td>1106</td>\n",
              "      <td>[1106]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117153</th>\n",
              "      <td>38181</td>\n",
              "      <td>{</td>\n",
              "      <td>443</td>\n",
              "      <td>[443]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117155</th>\n",
              "      <td>38183</td>\n",
              "      <td>}</td>\n",
              "      <td>443</td>\n",
              "      <td>[443]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8247 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c29ee71e-47ff-42df-97a2-ae535c558ded')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c29ee71e-47ff-42df-97a2-ae535c558ded button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c29ee71e-47ff-42df-97a2-ae535c558ded');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b68a1930-bc71-4780-adcd-d055d8d072c3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b68a1930-bc71-4780-adcd-d055d8d072c3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b68a1930-bc71-4780-adcd-d055d8d072c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        index         Term  docID  \\\n",
              "0       77173            !    918   \n",
              "5       20000            $    220   \n",
              "19      53181            %    614   \n",
              "132     36939            &    426   \n",
              "164      9301            '    109   \n",
              "...       ...          ...    ...   \n",
              "117149   4718         zund     57   \n",
              "117151  90951  zvezhinskii   1096   \n",
              "117152  91489      zyabrev   1106   \n",
              "117153  38181            {    443   \n",
              "117155  38183            }    443   \n",
              "\n",
              "                                             Posting_list  doc_Freq  \n",
              "0                               [92, 349, 918, 952, 1050]         5  \n",
              "5                    [188, 189, 220, 391, 551, 921, 1374]        14  \n",
              "19      [40, 52, 62, 76, 145, 190, 225, 253, 503, 576,...       113  \n",
              "132     [91, 121, 127, 153, 424, 425, 426, 427, 428, 4...        32  \n",
              "164     [8, 12, 42, 88, 104, 109, 110, 113, 137, 147, ...       188  \n",
              "...                                                   ...       ...  \n",
              "117149                                           [57, 81]         2  \n",
              "117151                                             [1096]         1  \n",
              "117152                                             [1106]         1  \n",
              "117153                                              [443]         2  \n",
              "117155                                              [443]         2  \n",
              "\n",
              "[8247 rows x 5 columns]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "indexed = inverted_Index(documents_df[\"processed_text\"])\n",
        "indexed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uv1Ld6o331j0"
      },
      "outputs": [],
      "source": [
        "tfidf_retr = pt.BatchRetrieve(index, controls = {\"wmodel\": \"TF_IDF\"},num_results=10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27ZO1duCDlyX"
      },
      "source": [
        "##**Query Expansion**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "S6cjMVxf1t1j",
        "outputId": "aeca8c29-49af-4378-e144-c175ed6403e1"
      },
      "outputs": [
        {
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              "summary": "{\n  \"name\": \"queries_df\",\n  \"rows\": 112,\n  \"fields\": [\n    {\n      \"column\": \"qid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 112,\n        \"samples\": [\n          \"41\",\n          \"66\",\n          \"5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_query\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 112,\n        \"samples\": [\n          \"Is alphabetical ordering of material considered to be a useful tool in information retrieval?  What studies have been done to compare the effectiveness of alphabetical order with other organization schemes? Is there a generally accepted form of arranging material in alphabetical order, and is there an easy way of achieving this form without going to a great amount of effort? \",\n          \".T The Technology of Library and Information Networks .A Epstein, H. Current online library network technology is described, including the physical and functional aspects of networks.  Three types of networks are distinguished:  search service (e.g., SDC, Lockheed), customized service that provide bibliographic files (e.g., OCLC, Inc., RLIN), and service center (e.g., NELINET, INCOLSA).  It is predicted that as technology evolves more services will be provided outside the library directly to the user through his home or office. .B (JASIS, Vol. 31, No. 6, November 1980, pp. 425-437) \",\n          \"What special training will ordinary researchers and businessmen need for proper information management and unobstructed use of information retrieval systems? What problems are they likely to encounter?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-fa3bf0f8-b4db-42db-9885-fbebf5428a5e');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "</svg>\n",
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              "\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "    display: none;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-44f9f0dd-6014-4f0a-886f-6774b82af2ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  qid                                          raw_query\n",
              "0   1  What problems and concerns are there in making...\n",
              "1   2  How can actually pertinent data, as opposed to...\n",
              "2   3  What is information science?  Give definitions...\n",
              "3   4  Image recognition and any other methods of aut...\n",
              "4   5  What special training will ordinary researcher..."
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "queries_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "OJ5nX7czjrWY",
        "outputId": "c42d52f7-f689-44bc-a0cd-9fb950c3753a"
      },
      "outputs": [
        {
          "data": {
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-832bc0a0-9f6c-4c68-a7c2-97f17c1e4609 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-832bc0a0-9f6c-4c68-a7c2-97f17c1e4609');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8bb53a0b-fddf-449a-a7d6-9fa3840d9148')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8bb53a0b-fddf-449a-a7d6-9fa3840d9148 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  qid  docid docno  rank      score  \\\n",
              "0   1    428   429     0  11.848740   \n",
              "1   1    721   722     1  10.638468   \n",
              "2   1   1298  1299     2  10.292926   \n",
              "3   1     64    65     3  10.133325   \n",
              "4   1    758   759     4   9.951263   \n",
              "5   1     75    76     5   9.602290   \n",
              "6   1    927   928     6   8.948765   \n",
              "7   1   1420  1421     7   8.787256   \n",
              "8   1    665   666     8   8.569836   \n",
              "9   1   1089  1090     9   8.524600   \n",
              "\n",
              "                                               query  \n",
              "0  problem concern make descript titl difficulti ...  \n",
              "1  problem concern make descript titl difficulti ...  \n",
              "2  problem concern make descript titl difficulti ...  \n",
              "3  problem concern make descript titl difficulti ...  \n",
              "4  problem concern make descript titl difficulti ...  \n",
              "5  problem concern make descript titl difficulti ...  \n",
              "6  problem concern make descript titl difficulti ...  \n",
              "7  problem concern make descript titl difficulti ...  \n",
              "8  problem concern make descript titl difficulti ...  \n",
              "9  problem concern make descript titl difficulti ...  "
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bm25 = pt.BatchRetrieve(index, wmodel=\"BM25\",num_results=10)\n",
        "\n",
        "query = queries_df[\"raw_query\"][0]\n",
        "query = process(query)\n",
        "answer = bm25.search(query)\n",
        "answer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hakdOqSHulL6",
        "outputId": "d1fba5bf-8edb-4c96-c04d-ee17a0ac8866"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "problem^0.037500001\n",
            "relev^0.037500001\n",
            "involv^0.037500001\n",
            "physic^0.027977895\n",
            "automat^0.055471912\n",
            "pattern^0.019037995\n",
            "journal^0.063031383\n",
            "articl^0.153639466\n",
            "difficulti^0.037500001\n",
            "approxim^0.037500001\n",
            "content^0.057388879\n",
            "retriev^0.037500001\n",
            "concern^0.037500001\n",
            "catalog^0.022908255\n",
            "descript^0.037500001\n",
            "usual^0.037500001\n",
            "scatter^0.020215601\n",
            "word^0.025646402\n",
            "titl^0.217182189\n",
            "\n",
            "problem concern make descript titl difficulti involv automat retriev articl approxim titl usual relev content articl titl\n"
          ]
        }
      ],
      "source": [
        "\n",
        "bm25 = pt.BatchRetrieve(index, wmodel=\"BM25\",num_results=10)\n",
        "rm3_expander = pt.rewrite.RM3(index,fb_terms=10, fb_docs=100)\n",
        "\n",
        "rm3_qe = bm25 >> rm3_expander\n",
        "expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
        "\n",
        "expanded_query\n",
        "\n",
        "for s in expanded_query.split()[1:]:\n",
        "  print(s)\n",
        "\n",
        "print(\"\\n\" + query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEpdnxGSw7Yt",
        "outputId": "750ccd02-6454-411b-ac2b-f9c1f7a4a4af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   Before Expansion    After Expansion\n",
            "   docid_1    score_1  docid_2    score_2\n",
            "0      428  11.848740     1298  12.943719\n",
            "1      721  10.638468       75  12.512497\n",
            "2     1298  10.292926      721  12.089670\n",
            "3       64  10.133325      758  11.912545\n",
            "4      758   9.951263      428  11.700616\n"
          ]
        }
      ],
      "source": [
        "# After that you can search using the expanded query\n",
        "expanded_query_formatted = ' '.join(expanded_query.split()[1:])\n",
        "\n",
        "results_wqe = bm25.search(expanded_query_formatted)\n",
        "\n",
        "print(\"   Before Expansion    After Expansion\")\n",
        "print(pd.concat([answer[['docid','score']][0:5].add_suffix('_1'),\n",
        "            results_wqe[['docid','score']][0:5].add_suffix('_2')], axis=1).fillna(''))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4l-HUa0QCYpF"
      },
      "source": [
        "##**RM3**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "doTSZ6AmxeJE"
      },
      "outputs": [],
      "source": [
        "def process_query(query,documents_df):\n",
        "\n",
        "    query = process(query)\n",
        "    tfidf_retr = pt.BatchRetrieve(index, controls={\"wmodel\": \"TF_IDF\"}, num_results=10)\n",
        "\n",
        "    initial_results = tfidf_retr.search(query)\n",
        "    rm3_expander = pt.rewrite.RM3(index, fb_terms=10, fb_docs=100)\n",
        "\n",
        "    rm3_qe = tfidf_retr >> rm3_expander\n",
        "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
        "    expanded_query_formatted = ' '.join(expanded_query.split()[1:])\n",
        "\n",
        "\n",
        "    results_wqe = tfidf_retr.search(expanded_query_formatted)\n",
        "\n",
        "    relevant_docs = documents_df[documents_df[\"docno\"].isin(results_wqe[\"docno\"])]\n",
        "\n",
        "    return relevant_docs[\"processed_text\"]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cN0Uo7ACCQml"
      },
      "source": [
        "##**ELMO**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uVYgdmhj3VBw"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import numpy as np\n",
        "\n",
        "Elmo = hub.load(\"https://tfhub.dev/google/elmo/3\")\n",
        "\n",
        "def cosine_similarity(v1, v2):\n",
        "    dot_product = np.dot(v1,v2.T)\n",
        "    norm_v1 = np.linalg.norm(v1)\n",
        "    norm_v2 = np.linalg.norm(v2)\n",
        "    return dot_product / (norm_v1 * norm_v2 )\n",
        "def elmo(query):\n",
        "  answer = tfidf_retr.search(query)\n",
        "  documents_df[\"docno\"] = documents_df[\"docno\"].astype(str)\n",
        "  relevant_docs = documents_df[documents_df[\"docno\"].isin(answer[\"docno\"])]\n",
        "  relevant_texts = relevant_docs[\"processed_text\"].tolist()\n",
        "  embeddings = Elmo.signatures[\"default\"](tf.constant(relevant_texts))[\"elmo\"]\n",
        "  lis_query = list(query)\n",
        "  query_embeding = Elmo.signatures[\"default\"](tf.constant(lis_query))[\"elmo\"]\n",
        "  similarity_scores = {} #2d matrix\n",
        "  for i in range(len(relevant_docs[\"processed_text\"])):\n",
        "      similarity = cosine_similarity(embeddings.numpy()[i],query_embeding.numpy()[0])\n",
        "      similarity_scores[relevant_docs.iloc[i][\"docno\"]] = similarity\n",
        "  sorted_keys = sorted(similarity_scores.keys(), reverse=True)\n",
        "\n",
        "# Create a DataFrame with the sorted keys\n",
        "  df = pd.DataFrame({'keys': sorted_keys})\n",
        "\n",
        "  docs = documents_df[documents_df[\"docno\"].isin(df[\"keys\"])]\n",
        "\n",
        "\n",
        "  return pd.DataFrame(docs[\"Text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "ugBa0u-T8JUo",
        "outputId": "0bd534ad-407e-4732-b36e-a4848ec87ab6"
      },
      "outputs": [
        {
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            "text/plain": [
              "                                                   Text\n",
              "457   Information Retrieval Systems Lancaster, F.W. ...\n",
              "537   Information Retrieval Systems Lancaster, F.W. ...\n",
              "590   The Cost-Effectiveness Analysis of Information...\n",
              "614   A Cost Model for Evaluating Information Retrie...\n",
              "688   The GREMAS System, an Integral Part of the IDC...\n",
              "708   The Chemical Abstracts Service Chemical Regist...\n",
              "871   The Shared Cataloging System of the Ohio Colle...\n",
              "1037  Management Misinformation Systems Ackoff, R.L....\n",
              "1135  Data Retrieval Systems:  Specifics and Problem...\n",
              "1340  Communication Nets in Science: Status and Cita..."
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "elmo(\"information systems\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5eLn7-FoDr12"
      },
      "source": [
        "##**Project User Interface**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDE8TMQ58bys",
        "outputId": "1291bb48-a563-4f00-c077-813dc60fe5cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: flask in /usr/local/lib/python3.10/dist-packages (2.2.5)\n",
            "Collecting flask-ngrok\n",
            "  Downloading flask_ngrok-0.0.25-py3-none-any.whl (3.1 kB)\n",
            "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from flask) (3.0.3)\n",
            "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from flask) (3.1.4)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from flask) (2.2.0)\n",
            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from flask) (8.1.7)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from flask-ngrok) (2.31.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->flask) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->flask-ngrok) (2024.2.2)\n",
            "Installing collected packages: flask-ngrok\n",
            "Successfully installed flask-ngrok-0.0.25\n"
          ]
        }
      ],
      "source": [
        "!pip install flask flask-ngrok"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdg5tWMpBdeI",
        "outputId": "8cef9f90-9332-499c-bb75-670ad9c19a5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting pyngrok\n",
            "  Downloading pyngrok-7.1.6-py3-none-any.whl (22 kB)\n",
            "Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.10/dist-packages (from pyngrok) (6.0.1)\n",
            "Installing collected packages: pyngrok\n",
            "Successfully installed pyngrok-7.1.6\n"
          ]
        }
      ],
      "source": [
        "!pip install pyngrok\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tdl43kfX8pyX",
        "outputId": "25c18939-2047-410f-82ed-65b8bc44d836"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Authtoken saved to configuration file: /root/.config/ngrok/ngrok.yml\n"
          ]
        }
      ],
      "source": [
        "!ngrok authtoken 2gJfHmeVJCV6BWTJPfhqaUeNQEi_3gHVhY7ydnNA98kpB5Gbr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oWPafLwVNPiw"
      },
      "outputs": [],
      "source": [
        "from flask import Flask\n",
        "from pyngrok import ngrok"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2a9cG9YaNow5"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_mMLVqRINS3O"
      },
      "outputs": [],
      "source": [
        "port_no = 5000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZOlSOFfuNU4d"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "l33Zu4EGNVNX",
        "outputId": "c1c255cd-a29f-41f4-d291-8420429f314d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "To acces the Gloable link please click https://bf00-104-199-133-183.ngrok-free.app\n",
            " * Serving Flask app '__main__'\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:35:53] \"GET / HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:35:55] \"\u001b[33mGET /favicon.ico HTTP/1.1\u001b[0m\" 404 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:35:57] \"POST /search HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:38:36] \"POST /search HTTP/1.1\" 200 -\n",
            "ERROR:__main__:Exception on /search [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 2529, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1825, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1823, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1799, in dispatch_request\n",
            "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 160, in search\n",
            "    search_results = process_query(query,documents_df)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 30, in process_query\n",
            "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1103, in __getitem__\n",
            "    return self._getitem_axis(maybe_callable, axis=axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1656, in _getitem_axis\n",
            "    self._validate_integer(key, axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1589, in _validate_integer\n",
            "    raise IndexError(\"single positional indexer is out-of-bounds\")\n",
            "IndexError: single positional indexer is out-of-bounds\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:38:47] \"\u001b[35m\u001b[1mPOST /search HTTP/1.1\u001b[0m\" 500 -\n",
            "ERROR:__main__:Exception on /search [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 2529, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1825, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1823, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1799, in dispatch_request\n",
            "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 160, in search\n",
            "    search_results = process_query(query,documents_df)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 30, in process_query\n",
            "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1103, in __getitem__\n",
            "    return self._getitem_axis(maybe_callable, axis=axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1656, in _getitem_axis\n",
            "    self._validate_integer(key, axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1589, in _validate_integer\n",
            "    raise IndexError(\"single positional indexer is out-of-bounds\")\n",
            "IndexError: single positional indexer is out-of-bounds\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:38:50] \"\u001b[35m\u001b[1mPOST /search HTTP/1.1\u001b[0m\" 500 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:39:02] \"POST /search HTTP/1.1\" 200 -\n",
            "ERROR:__main__:Exception on /search [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 2529, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1825, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1823, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1799, in dispatch_request\n",
            "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 160, in search\n",
            "    search_results = process_query(query,documents_df)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 30, in process_query\n",
            "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1103, in __getitem__\n",
            "    return self._getitem_axis(maybe_callable, axis=axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1656, in _getitem_axis\n",
            "    self._validate_integer(key, axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1589, in _validate_integer\n",
            "    raise IndexError(\"single positional indexer is out-of-bounds\")\n",
            "IndexError: single positional indexer is out-of-bounds\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:39:13] \"\u001b[35m\u001b[1mPOST /search HTTP/1.1\u001b[0m\" 500 -\n",
            "ERROR:__main__:Exception on /search [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 2529, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1825, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1823, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1799, in dispatch_request\n",
            "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 160, in search\n",
            "    search_results = process_query(query,documents_df)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 30, in process_query\n",
            "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1103, in __getitem__\n",
            "    return self._getitem_axis(maybe_callable, axis=axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1656, in _getitem_axis\n",
            "    self._validate_integer(key, axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1589, in _validate_integer\n",
            "    raise IndexError(\"single positional indexer is out-of-bounds\")\n",
            "IndexError: single positional indexer is out-of-bounds\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:39:15] \"\u001b[35m\u001b[1mPOST /search HTTP/1.1\u001b[0m\" 500 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:39:21] \"POST /search HTTP/1.1\" 200 -\n",
            "ERROR:__main__:Exception on /search [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 2529, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1825, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1823, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1799, in dispatch_request\n",
            "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 160, in search\n",
            "    search_results = process_query(query,documents_df)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 30, in process_query\n",
            "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1103, in __getitem__\n",
            "    return self._getitem_axis(maybe_callable, axis=axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1656, in _getitem_axis\n",
            "    self._validate_integer(key, axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1589, in _validate_integer\n",
            "    raise IndexError(\"single positional indexer is out-of-bounds\")\n",
            "IndexError: single positional indexer is out-of-bounds\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:48:52] \"\u001b[35m\u001b[1mPOST /search HTTP/1.1\u001b[0m\" 500 -\n",
            "ERROR:__main__:Exception on /search [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 2529, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1825, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1823, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1799, in dispatch_request\n",
            "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 160, in search\n",
            "    search_results = process_query(query,documents_df)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 30, in process_query\n",
            "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1103, in __getitem__\n",
            "    return self._getitem_axis(maybe_callable, axis=axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1656, in _getitem_axis\n",
            "    self._validate_integer(key, axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1589, in _validate_integer\n",
            "    raise IndexError(\"single positional indexer is out-of-bounds\")\n",
            "IndexError: single positional indexer is out-of-bounds\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:48:55] \"\u001b[35m\u001b[1mPOST /search HTTP/1.1\u001b[0m\" 500 -\n",
            "ERROR:__main__:Exception on /search [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 2529, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1825, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1823, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/flask/app.py\", line 1799, in dispatch_request\n",
            "    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 160, in search\n",
            "    search_results = process_query(query,documents_df)\n",
            "  File \"<ipython-input-56-a43e345af21e>\", line 30, in process_query\n",
            "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1103, in __getitem__\n",
            "    return self._getitem_axis(maybe_callable, axis=axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1656, in _getitem_axis\n",
            "    self._validate_integer(key, axis)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\", line 1589, in _validate_integer\n",
            "    raise IndexError(\"single positional indexer is out-of-bounds\")\n",
            "IndexError: single positional indexer is out-of-bounds\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:49:07] \"\u001b[35m\u001b[1mPOST /search HTTP/1.1\u001b[0m\" 500 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 10:49:25] \"POST /search HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 11:35:17] \"POST /search HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 11:54:10] \"POST /search HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 11:54:11] \"POST /search HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 11:54:28] \"POST /search HTTP/1.1\" 200 -\n",
            "INFO:werkzeug:127.0.0.1 - - [12/May/2024 12:08:51] \"POST /search HTTP/1.1\" 200 -\n"
          ]
        }
      ],
      "source": [
        "###the final one\n",
        "\n",
        "import time\n",
        "from flask import Flask, request, render_template_string\n",
        "import pyterrier as pt\n",
        "\n",
        "app = Flask(__name__)\n",
        "ngrok.set_auth_token(\"2gJfHmeVJCV6BWTJPfhqaUeNQEi_3gHVhY7ydnNA98kpB5Gbr\")\n",
        "public_url =  ngrok.connect(port_no).public_url\n",
        "\n",
        "#\n",
        "\n",
        "# Record the start time\n",
        "\n",
        "\n",
        "def cosine_similarity(v1, v2):\n",
        "    dot_product = np.dot(v1,v2.T)\n",
        "    norm_v1 = np.linalg.norm(v1)\n",
        "    norm_v2 = np.linalg.norm(v2)\n",
        "    return dot_product / (norm_v1 * norm_v2 )\n",
        "def process_query(query,documents_df):\n",
        "\n",
        "    query = process(query)\n",
        "    tfidf_retr = pt.BatchRetrieve(index, controls={\"wmodel\": \"TF_IDF\"}, num_results=10)\n",
        "\n",
        "    initial_results = tfidf_retr.search(query)\n",
        "    rm3_expander = pt.rewrite.RM3(index, fb_terms=10, fb_docs=100)\n",
        "\n",
        "    rm3_qe = tfidf_retr >> rm3_expander\n",
        "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
        "    expanded_query_formatted = ' '.join(expanded_query.split()[1:])\n",
        "\n",
        "\n",
        "    results_wqe = tfidf_retr.search(expanded_query_formatted)\n",
        "\n",
        "    relevant_docs = documents_df[documents_df[\"docno\"].isin(results_wqe[\"docno\"])]\n",
        "\n",
        "    return relevant_docs[\"Text\"]\n",
        "\n",
        "HTML_TEMPLATE = \"\"\"\n",
        "<!DOCTYPE html>\n",
        "<html lang=\"en\">\n",
        "<head>\n",
        "    <meta charset=\"UTF-8\">\n",
        "    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n",
        "    <title>Ahmed Ibrahim Search Engine</title>\n",
        "    <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css\">\n",
        "    <style>\n",
        "        body {\n",
        "            font-family: Arial, sans-serif;\n",
        "            margin: 0;\n",
        "            padding: 0;\n",
        "            background-color: #f5f5f5;\n",
        "            color: #333;\n",
        "        }\n",
        "        .container {\n",
        "            max-width: 800px;\n",
        "            margin: 20px auto;\n",
        "            padding: 20px;\n",
        "            background-color: #fff;\n",
        "            box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);\n",
        "        }\n",
        "        h1 {\n",
        "            text-align: center;\n",
        "            color: #333;\n",
        "        }\n",
        "        form {\n",
        "            text-align: center;\n",
        "            margin-bottom: 20px;\n",
        "        }\n",
        "        input[type=\"text\"] {\n",
        "            padding: 10px;\n",
        "            width: 70%;\n",
        "            border: 1px solid #ccc;\n",
        "            border-radius: 5px;\n",
        "            font-size: 16px;\n",
        "        }\n",
        "        input[type=\"submit\"] {\n",
        "            padding: 10px 20px;\n",
        "            background-color: #007bff;\n",
        "            color: #fff;\n",
        "            border: none;\n",
        "            border-radius: 5px;\n",
        "            cursor: pointer;\n",
        "            font-size: 16px;\n",
        "        }\n",
        "        input[type=\"submit\"]:hover {\n",
        "            background-color: #0056b3;\n",
        "        }\n",
        "        #searchResults {\n",
        "            margin-top: 20px;\n",
        "            padding: 10px;\n",
        "            background-color: #f9f9f9;\n",
        "            border-radius: 5px;\n",
        "            box-shadow: 0 0 5px rgba(0, 0, 0, 0.1);\n",
        "        }\n",
        "        ul {\n",
        "            list-style-type: none;\n",
        "            padding: 0;\n",
        "        }\n",
        "        li {\n",
        "            margin-bottom: 10px;\n",
        "            padding: 10px;\n",
        "            background-color: #fff;\n",
        "            border-radius: 5px;\n",
        "            box-shadow: 0 0 5px rgba(0, 0, 0, 0.1);\n",
        "        }\n",
        "    </style>\n",
        "</head>\n",
        "<body>\n",
        "    <div class=\"container\">\n",
        "        <h1>Ahmed Ibrahim Search Engine</h1>\n",
        "        <form id=\"searchForm\">\n",
        "            <input type=\"text\" id=\"query\" name=\"query\" placeholder=\"Search Here\">\n",
        "            <input type=\"submit\" value=\"Search\">\n",
        "        </form>\n",
        "        <div id=\"searchResults\"></div>\n",
        "    </div>\n",
        "\n",
        "    <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js\"></script>\n",
        "    <script>\n",
        "        // Function to handle form submission\n",
        "        $('#searchForm').submit(function(event) {\n",
        "            // Prevent default form submission\n",
        "            event.preventDefault();\n",
        "\n",
        "            // Get the query from the input field\n",
        "            var query = $('#query').val();\n",
        "\n",
        "            // Send the query to the server using AJAX\n",
        "            $.ajax({\n",
        "                type: 'POST',\n",
        "                url: '/search',\n",
        "                data: { query: query },\n",
        "                success: function(response) {\n",
        "                    // Update the search results div with the response\n",
        "                    $('#searchResults').html(response);\n",
        "                },\n",
        "                error: function(xhr, status, error) {\n",
        "                    // Handle errors\n",
        "                    console.error(error);\n",
        "                }\n",
        "            });\n",
        "        });\n",
        "    </script>\n",
        "</body>\n",
        "</html>\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "@app.route(\"/\", methods=[\"GET\"])\n",
        "def home():\n",
        "    return render_template_string(HTML_TEMPLATE)\n",
        "\n",
        "@app.route(\"/search\", methods=[\"POST\"])\n",
        "def search():\n",
        "    start_time = time.time()\n",
        "    query = request.form.get(\"query\")\n",
        "    # Perform search operation and retrieve search results\n",
        "    search_results = process_query(query,documents_df)\n",
        "\n",
        "    # Record the end time\n",
        "    end_time = time.time()\n",
        "\n",
        "    # Calculate the elapsed time\n",
        "    elapsed_time = end_time - start_time\n",
        "\n",
        "    # Format search results and include elapsed time\n",
        "    results_html = f\"<p>Effectiveness: {len(search_results)}</p>\"\n",
        "    results_html += f\"<p>Efficiency: {elapsed_time:.2f} seconds</p>\"\n",
        "    results_html += \"<h2>Search Results</h2>\"\n",
        "    results_html += \"<ul>\"\n",
        "    results_html += \"\".join([f\"<li>{result}</li>\" for result in search_results])\n",
        "    results_html += \"</ul>\"\n",
        "\n",
        "\n",
        "    return results_html\n",
        "\n",
        "print(f\"To acces the Gloable link please click {public_url}\")\n",
        "\n",
        "app.run(port=port_no)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7APtaqRRO3A0",
        "outputId": "5d030c73-d7c3-402f-aeb4-417bb292923c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "253     chemic inform system hyde e ash j e purpos che...\n",
              "457     inform retriev system lancast f w thi book con...\n",
              "458     inform retriev on-lin lancast f w thi book dea...\n",
              "537     inform retriev system lancast f w 1972 thi boo...\n",
              "669     chemic structur storag search system develop d...\n",
              "689     experi mechan chemic biolog inform retriev sys...\n",
              "706     french nation polici chemic inform darc system...\n",
              "1037    manag misinform system ackoff r l five assumpt...\n",
              "1091    method relat structur properti chemic compound...\n",
              "1124    contribut theori system inform flow kozachkov ...\n",
              "Name: processed_text, dtype: object"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def process_query(query,documents_df):\n",
        "\n",
        "    query = process(query)\n",
        "    tfidf_retr = pt.BatchRetrieve(index, controls={\"wmodel\": \"TF_IDF\"}, num_results=10)\n",
        "\n",
        "    initial_results = tfidf_retr.search(query)\n",
        "    rm3_expander = pt.rewrite.RM3(index, fb_terms=10, fb_docs=100)\n",
        "\n",
        "    rm3_qe = tfidf_retr >> rm3_expander\n",
        "    expanded_query = rm3_qe.search(query).iloc[0][\"query\"]\n",
        "    expanded_query_formatted = ' '.join(expanded_query.split()[1:])\n",
        "\n",
        "\n",
        "    results_wqe = tfidf_retr.search(expanded_query_formatted)\n",
        "\n",
        "    relevant_docs = documents_df[documents_df[\"docno\"].isin(results_wqe[\"docno\"])]\n",
        "\n",
        "    return relevant_docs[\"processed_text\"]\n",
        "\n",
        "# Example usage\n",
        "process_query(\"information systems\",documents_df)\n",
        "\n"
      ]
    },
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        },
        "id": "-RTvMvACO2DE",
        "outputId": "c5cb3690-abb4-4af3-8da0-e8dda22893ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading vaswani index to /root/.pyterrier/corpora/vaswani/index\n"
          ]
        },
        {
          "data": {
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading vaswani topics to /root/.pyterrier/corpora/vaswani/query-text.trec\n"
          ]
        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading vaswani qrels to /root/.pyterrier/corpora/vaswani/qrels\n"
          ]
        },
        {
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            "text/plain": [
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              "        const element = document.querySelector('#df-146c6f5b-0c86-4e73-9cf9-0b512d9dac41');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-5b072eaa-6da3-4f02-852c-4336ded52d5c button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "         name       map  recip_rank\n",
              "0  BR(TF_IDF)  0.290905    0.699168\n",
              "1    BR(BM25)  0.296517    0.725665"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = pt.get_dataset(\"vaswani\")\n",
        "# vaswani dataset provides an index, topics and qrels\n",
        "tfidf = pt.BatchRetrieve(dataset.get_index(), wmodel=\"TF_IDF\")\n",
        "bm25 = pt.BatchRetrieve(dataset.get_index(), wmodel=\"BM25\")\n",
        "\n",
        "# Perform experiment\n",
        "exp = experiment = pt.Experiment(\n",
        "    [tfidf, bm25],\n",
        "    dataset.get_topics(),\n",
        "    dataset.get_qrels(),\n",
        "    eval_metrics=[\"map\", \"recip_rank\"],\n",
        "\n",
        ")\n",
        "exp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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          "text": [
            "['Information Retrieval Systems Lancaster, F.W. This book is concerned primarily with those \"intellectual\" factors that significantly affect the performance of all information retrieval systems; namely,  - indexing policy and practice - vocabulary control - searching strategies - interaction between the system and its users  My viewpoint is that of the evaluator of information systems.  I have therefore paid considerable attention to a discussion of the requirements of users of information systems and the measurement of system performance in terms of the efficient and economical satisfaction of these requirements. The book does not concern itself, except indirectly, with equipment for the implementation of retrieval systems, a topic that is adequately covered by other volumes in this seris.  Moreover, it is my contention that the importance of \"hardware\" and \"data processing\" aspects of information systems has been exaggerated in the United States, with some detriment to the performance of many systems. 29', 'Information Retrieval Systems Lancaster, F.W. 1972 This book is concerned primarily with those \"intellectual\" factors that significantly affect the performance of all information retrieval systems; namely, - indexing policy and practice - vocabulary control - searching control - interaction between the system and its users My viewpoint is that of the evaluator of information systems.  I have therefore paid considerable attention to a discussion of the requirements of users of information systems and the measurement of system performance in terms of the efficient and economical satisfaction of these requirements. The book does not concern itself, except indirectly, with equipment for the implementation of retrieval systems, a topic that is adequately covered by other volumes in this series.  Moreover, it is my contention that the importance of \"hardware\" and \"data processing\" aspects of information systems has been exaggerated in the United States, with some detriment to the performance of many systems. 538', 'The Cost-Effectiveness Analysis of Information Retrieval and Dissemination Systems Lancaster, F.W. A distinction is made between cost-effectiveness analysis and cost-benefits analysis as applied to information systems; and the relationship between costs, performance, and benefits is discussed.. Some factors influencing the cost-effectiveness of retrieval and dissemination systems are identified.. Various aspects of system operation that are susceptible to cost-effectiveness analysis are discussed, including system coverage, indexing policies and procedures, system vocabulary, searching procedures, and mode of interaction between system and user.. Possible tradeoffs between input and output costs, and the effects of these tradeoffs on cost-effectiveness are presented. 18', 'A Cost Model for Evaluating Information Retrieval Systems Cooper, Michael D. This paper develops a mathematical model for use in studying how to minimize the cost of operating a mechanized retrieval system.. Through the use of cost analysis, the model provides a method for comparative evaluation between information retrieval systems.. The cost model divides the costs of a retrieval system into two components:  system costs and user costs.. In addition, it suggests that a trade off exists between the performance level of the system and the combination of user and systems time that is expended in working with the system.. This minimizes the total cost of operating the system.. The allocation is done for a given performance level and for a given cost per unit of user and system time.. 5', 'The GREMAS System, an Integral Part of the IDC System for Chemical Documentation Rossler, Sigrid Kolb, Arthur The Genealogical Retrieval by Magnetic Tapes Storage (GREMAS) system and the potential it offers for searches are described.. The input and retrieval procedures of the system are explained as well as the integration of the GREMAS system into the IDC system - i.e., machine generation of the GREMAS coding from topological input and of the superimposed bit code from the GREMAS coding.. 29', 'The Chemical Abstracts Service Chemical Registry System.  I. General Design Dittmar, P.G. Stobaugh, R.E. Watson, C.E. The Chemical Abstracts Service (CAS) Chemical Registry System is a computer- based system that uniquely identifies chemical substances on the basis of composition and structure.  Since initial operation in 1964 as a stand-alone input, storage, and retrieval system for structural representations of organic chemical compounds, the scope of the CAS Registry System has steadily increased to include all types of chemical substances and the entire system has been integrated into CAS indexing operations.  The third refinement of this system, Registry III, which has been in operation for over a year, involves major changes in Registry recods but no change in the basic algorithmic techniques for registering chemical substances.  The previous format for listing atoms and bonds has been modified so that each ring system is now separately identified, and this ring-system identifier is used in the record for each substance that contains that ring.  These modifications support CAS nomenclature derivation and also a computer-based structure output system.  The general design of Registry III, which involves a structure record of cyclic and acyclic segments, is presented. 422', 'The Shared Cataloging System of the Ohio College Library Center Kilgour, Frederick G. Long, Philip L. Landgraf, Alan L. Wyckoff, John A. Development and implementation of an off-line catalog card production system and on-line shared cataloging system are described.. In off-line production, average cost per card for 529,893 catalog cards in finished form and alphabetized for filing was 6.57c.. An account is given of system design and equipment selection for the on-line system.. File organization and programs are described, and the on-line cataloging system is discussed.. The system is easy to use, efficient, reliable, and cost beneficial.. 90', 'Management Misinformation Systems Ackoff, R.L. Five assumptions commonly made by designers of management information systems are identified.  It is argued that these are not justified in many (if not most) cases and hence lead to major deficiencies in the resulting systems. These assumptions are: (1) the critical deficiency under which most managers operate is the lack of relevant information, (2) the manager needs the information he wants, (3) if a manager has the information he needs his decision making will improve, (4) better communication between managers improves organizational performance, and (5) a manager does not have to understand how his information system works, only how to use it.  To overcome these assumptions and the deficiencies which result from them, a management information system should be imbedded in a management control system. A procedure for designing such a system is proposed and an example is given of the type of control system which it produces. 29', 'Data Retrieval Systems:  Specifics and Problems Shtein, V. S. The essential differences between data retrieval system and document retrieval systems are considered.. The notion of \"fact\" is discussed, analyzing the influence of the definition adopted on the structure of a data retrieval system.. A proposition is advanced that a factographic JRS is a rudimentary but indispensable form on the way to a logical information system.. The latter type of system by a capability for automatic analysis of input data and synthesis of new information.. The problem of the information retrieval language for data retrieval system is discussed, as is its machine organization, intricately tied up with the specifics and functions of a system of that kind.. 179', 'Communication Nets in Science: Status and Citation Patterns in Animal Physiology Whitley, R.D. By virtue of its peculiar links with the reward system in science, the communication system plays a central part in the maintenance and growth of science.  It is the means by which the individual scientist relates to the social system:  he publishes his work to gain recognition, and reads the publications of others to maintain his knowledge.  The formal communication system also forms the basis for the allocation of rewards: instrumental and consumatory.  Thus it is a means of exercising social control. The informal communication system, although important, is the distaff side.  Its recognition is personal with more immediate and consumatory rewards.  Legitimation of objective or methods of work is rarely given by the social system through informal systems of communication, though it is growing in importance as an information dissemination system. 33']\n"
          ]
        }
      ],
      "source": [
        "def elmo(query):\n",
        "    answer = tfidf_retr.search(query)\n",
        "    relevant_docs = documents_df[documents_df[\"docno\"].isin(answer[\"docno\"])]\n",
        "    relevant_texts = relevant_docs[\"processed_text\"].tolist()\n",
        "    embeddings = Elmo.signatures[\"default\"](tf.constant(relevant_texts, dtype=tf.string))[\"elmo\"]\n",
        "    lis_query = [query]  # Wrap query in a list to make it a list of strings\n",
        "    query_embedding = Elmo.signatures[\"default\"](tf.constant(lis_query, dtype=tf.string))[\"elmo\"]\n",
        "    similarity_scores = {}\n",
        "    for i in range(len(relevant_docs[\"processed_text\"])):\n",
        "        similarity = cosine_similarity(embeddings.numpy()[i], query_embedding.numpy()[0])\n",
        "        similarity_scores[relevant_docs.iloc[i][\"docno\"]] = similarity\n",
        "    sorted_keys = sorted(similarity_scores.keys(), reverse=True)\n",
        "\n",
        "    # Create a DataFrame with the sorted keys\n",
        "    df = pd.DataFrame({'keys': sorted_keys})\n",
        "    docs = documents_df[documents_df[\"docno\"].isin(df[\"keys\"])]\n",
        "\n",
        "    return docs[\"Text\"]\n",
        "\n",
        "type(elmo(\"information systems\"))\n",
        "print([result for result in elmo(\"information systems\")])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2-AS67sLBDN3"
      },
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      "source": [
        "#\n",
        "def elmo(query):\n",
        "  answer = tfidf_retr.search(query)\n",
        "  documents_df[\"docno\"] = documents_df[\"docno\"].astype(str)\n",
        "  relevant_docs = documents_df[documents_df[\"docno\"].isin(answer[\"docno\"])]\n",
        "  relevant_texts = relevant_docs[\"processed_text\"].tolist()\n",
        "  embeddings = Elmo.signatures[\"default\"](tf.constant(relevant_texts))[\"elmo\"]\n",
        "  lis_query = list(query)\n",
        "  query_embeding = Elmo.signatures[\"default\"](tf.constant(lis_query))[\"elmo\"]\n",
        "  similarity_scores = {} #2d matrix\n",
        "  for i in range(len(relevant_docs[\"processed_text\"])):\n",
        "      similarity = cosine_similarity(embeddings.numpy()[i],query_embeding.numpy()[0])\n",
        "      similarity_scores[relevant_docs.iloc[i][\"docno\"]] = similarity\n",
        "  sorted_keys = sorted(similarity_scores.keys(), reverse=True)\n",
        "\n",
        "# Create a DataFrame with the sorted keys\n",
        "  df = pd.DataFrame({'keys': sorted_keys})\n",
        "\n",
        "  docs = documents_df[documents_df[\"docno\"].isin(df[\"keys\"])]\n",
        "\n",
        "\n",
        "  return docs[\"Text\"]"
      ]
    }
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