indexing_test_without_colbert.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "id": "-140BMkMDBlX"
      },
      "outputs": [],
      "source": [
        "\n",
        "# %pip install llama-index-embeddings-huggingface\n",
        "# %pip install llama-index-embeddings-instructor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "id": "LfTIqA9DDFzx"
      },
      "outputs": [],
      "source": [
        "# !pip install llama-index\n",
        "# !pip install git+https://github.com/csebuetnlp/normalizer\n",
        "# !pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "myZzxIaaDASF"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ['MKL_THREADING_LAYER'] = 'GNU'\n",
        "import openai\n",
        "\n",
        "# needed to synthesize responses later\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
        "openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0-VR-AesDASH"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/rifat/anaconda3/envs/rag/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "from normalizer import normalize\n",
        "from llama_index.core.readers.string_iterable import StringIterableReader\n",
        "from llama_index.core.evaluation.retrieval.evaluator import RetrieverEvaluator\n",
        "from llama_index.legacy.finetuning.embeddings.common import EmbeddingQAFinetuneDataset\n",
        "from llama_index.core import SimpleDirectoryReader\n",
        "from llama_index.core.node_parser import SentenceSplitter\n",
        "from llama_index.core import VectorStoreIndex\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "from llama_index.core import Settings\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "GlXTicEWDASI"
      },
      "outputs": [],
      "source": [
        "class CustomSentenceSplitter(SentenceSplitter):\n",
        "    def _split_text(self, text: str, chunk_size: int):\n",
        "\n",
        "        text_splits = text.split(self.paragraph_separator)\n",
        "\n",
        "        return text_splits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "yydPFNw5DASJ"
      },
      "outputs": [],
      "source": [
        "EMBED_MODEL_NAME = \"FacebookAI/xlm-roberta-base\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "i0mnxF1jDASJ"
      },
      "outputs": [
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset \u001b[39m=\u001b[39m load_dataset(\u001b[39m\"\u001b[39;49m\u001b[39mcsebuetnlp/squad_bn\u001b[39;49m\u001b[39m\"\u001b[39;49m, split\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mtest\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      2\u001b[0m \u001b[39m# dataset = dataset.select(range(10))\u001b[39;00m\n\u001b[1;32m      3\u001b[0m dataset \u001b[39m=\u001b[39m dataset\u001b[39m.\u001b[39mmap(\u001b[39mlambda\u001b[39;00m x: {\u001b[39m\"\u001b[39m\u001b[39mcontext\u001b[39m\u001b[39m\"\u001b[39m: normalize(x[\u001b[39m\"\u001b[39m\u001b[39mcontext\u001b[39m\u001b[39m\"\u001b[39m]), \u001b[39m\"\u001b[39m\u001b[39mquestion\u001b[39m\u001b[39m\"\u001b[39m: normalize(x[\u001b[39m\"\u001b[39m\u001b[39mquestion\u001b[39m\u001b[39m\"\u001b[39m])})\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/datasets/load.py:2594\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[1;32m   2589\u001b[0m verification_mode \u001b[39m=\u001b[39m VerificationMode(\n\u001b[1;32m   2590\u001b[0m     (verification_mode \u001b[39mor\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mBASIC_CHECKS) \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m save_infos \u001b[39melse\u001b[39;00m VerificationMode\u001b[39m.\u001b[39mALL_CHECKS\n\u001b[1;32m   2591\u001b[0m )\n\u001b[1;32m   2593\u001b[0m \u001b[39m# Create a dataset builder\u001b[39;00m\n\u001b[0;32m-> 2594\u001b[0m builder_instance \u001b[39m=\u001b[39m load_dataset_builder(\n\u001b[1;32m   2595\u001b[0m     path\u001b[39m=\u001b[39;49mpath,\n\u001b[1;32m   2596\u001b[0m     name\u001b[39m=\u001b[39;49mname,\n\u001b[1;32m   2597\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   2598\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   2599\u001b[0m     cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m   2600\u001b[0m     features\u001b[39m=\u001b[39;49mfeatures,\n\u001b[1;32m   2601\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   2602\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   2603\u001b[0m     revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   2604\u001b[0m     token\u001b[39m=\u001b[39;49mtoken,\n\u001b[1;32m   2605\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m   2606\u001b[0m     trust_remote_code\u001b[39m=\u001b[39;49mtrust_remote_code,\n\u001b[1;32m   2607\u001b[0m     _require_default_config_name\u001b[39m=\u001b[39;49mname \u001b[39mis\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m   2608\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mconfig_kwargs,\n\u001b[1;32m   2609\u001b[0m )\n\u001b[1;32m   2611\u001b[0m \u001b[39m# Return iterable dataset in case of streaming\u001b[39;00m\n\u001b[1;32m   2612\u001b[0m \u001b[39mif\u001b[39;00m streaming:\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/datasets/load.py:2266\u001b[0m, in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)\u001b[0m\n\u001b[1;32m   2264\u001b[0m     download_config \u001b[39m=\u001b[39m download_config\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m download_config \u001b[39melse\u001b[39;00m DownloadConfig()\n\u001b[1;32m   2265\u001b[0m     download_config\u001b[39m.\u001b[39mstorage_options\u001b[39m.\u001b[39mupdate(storage_options)\n\u001b[0;32m-> 2266\u001b[0m dataset_module \u001b[39m=\u001b[39m dataset_module_factory(\n\u001b[1;32m   2267\u001b[0m     path,\n\u001b[1;32m   2268\u001b[0m     revision\u001b[39m=\u001b[39;49mrevision,\n\u001b[1;32m   2269\u001b[0m     download_config\u001b[39m=\u001b[39;49mdownload_config,\n\u001b[1;32m   2270\u001b[0m     download_mode\u001b[39m=\u001b[39;49mdownload_mode,\n\u001b[1;32m   2271\u001b[0m     data_dir\u001b[39m=\u001b[39;49mdata_dir,\n\u001b[1;32m   2272\u001b[0m     data_files\u001b[39m=\u001b[39;49mdata_files,\n\u001b[1;32m   2273\u001b[0m     cache_dir\u001b[39m=\u001b[39;49mcache_dir,\n\u001b[1;32m   2274\u001b[0m     trust_remote_code\u001b[39m=\u001b[39;49mtrust_remote_code,\n\u001b[1;32m   2275\u001b[0m     _require_default_config_name\u001b[39m=\u001b[39;49m_require_default_config_name,\n\u001b[1;32m   2276\u001b[0m     _require_custom_configs\u001b[39m=\u001b[39;49m\u001b[39mbool\u001b[39;49m(config_kwargs),\n\u001b[1;32m   2277\u001b[0m )\n\u001b[1;32m   2278\u001b[0m \u001b[39m# Get dataset builder class from the processing script\u001b[39;00m\n\u001b[1;32m   2279\u001b[0m builder_kwargs \u001b[39m=\u001b[39m dataset_module\u001b[39m.\u001b[39mbuilder_kwargs\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/datasets/load.py:1876\u001b[0m, in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)\u001b[0m\n\u001b[1;32m   1869\u001b[0m \u001b[39mif\u001b[39;00m config\u001b[39m.\u001b[39mUSE_PARQUET_EXPORT \u001b[39mand\u001b[39;00m can_load_config_from_parquet_export:\n\u001b[1;32m   1870\u001b[0m     \u001b[39m# If the parquet export is ready (parquet files + info available for the current sha), we can use it instead\u001b[39;00m\n\u001b[1;32m   1871\u001b[0m     \u001b[39m# This fails when the dataset has multiple configs and a default config and\u001b[39;00m\n\u001b[1;32m   1872\u001b[0m     \u001b[39m# the user didn't specify a configuration name (_require_default_config_name=True).\u001b[39;00m\n\u001b[1;32m   1873\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1874\u001b[0m         \u001b[39mreturn\u001b[39;00m HubDatasetModuleFactoryWithParquetExport(\n\u001b[1;32m   1875\u001b[0m             path, download_config\u001b[39m=\u001b[39;49mdownload_config, revision\u001b[39m=\u001b[39;49mdataset_info\u001b[39m.\u001b[39;49msha\n\u001b[0;32m-> 1876\u001b[0m         )\u001b[39m.\u001b[39;49mget_module()\n\u001b[1;32m   1877\u001b[0m     \u001b[39mexcept\u001b[39;00m _dataset_viewer\u001b[39m.\u001b[39mDatasetViewerError:\n\u001b[1;32m   1878\u001b[0m         \u001b[39mpass\u001b[39;00m\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/datasets/load.py:1387\u001b[0m, in \u001b[0;36mHubDatasetModuleFactoryWithParquetExport.get_module\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1383\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_module\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DatasetModule:\n\u001b[1;32m   1384\u001b[0m     exported_parquet_files \u001b[39m=\u001b[39m _dataset_viewer\u001b[39m.\u001b[39mget_exported_parquet_files(\n\u001b[1;32m   1385\u001b[0m         dataset\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname, revision\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrevision, token\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdownload_config\u001b[39m.\u001b[39mtoken\n\u001b[1;32m   1386\u001b[0m     )\n\u001b[0;32m-> 1387\u001b[0m     exported_dataset_infos \u001b[39m=\u001b[39m _dataset_viewer\u001b[39m.\u001b[39;49mget_exported_dataset_infos(\n\u001b[1;32m   1388\u001b[0m         dataset\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mname, revision\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrevision, token\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdownload_config\u001b[39m.\u001b[39;49mtoken\n\u001b[1;32m   1389\u001b[0m     )\n\u001b[1;32m   1390\u001b[0m     dataset_infos \u001b[39m=\u001b[39m DatasetInfosDict(\n\u001b[1;32m   1391\u001b[0m         {\n\u001b[1;32m   1392\u001b[0m             config_name: DatasetInfo\u001b[39m.\u001b[39mfrom_dict(exported_dataset_infos[config_name])\n\u001b[1;32m   1393\u001b[0m             \u001b[39mfor\u001b[39;00m config_name \u001b[39min\u001b[39;00m exported_dataset_infos\n\u001b[1;32m   1394\u001b[0m         }\n\u001b[1;32m   1395\u001b[0m     )\n\u001b[1;32m   1396\u001b[0m     hfh_dataset_info \u001b[39m=\u001b[39m HfApi(config\u001b[39m.\u001b[39mHF_ENDPOINT)\u001b[39m.\u001b[39mdataset_info(\n\u001b[1;32m   1397\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname,\n\u001b[1;32m   1398\u001b[0m         revision\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mrefs/convert/parquet\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   1399\u001b[0m         token\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdownload_config\u001b[39m.\u001b[39mtoken,\n\u001b[1;32m   1400\u001b[0m         timeout\u001b[39m=\u001b[39m\u001b[39m100.0\u001b[39m,\n\u001b[1;32m   1401\u001b[0m     )\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/datasets/utils/_dataset_viewer.py:70\u001b[0m, in \u001b[0;36mget_exported_dataset_infos\u001b[0;34m(dataset, revision, token)\u001b[0m\n\u001b[1;32m     68\u001b[0m dataset_viewer_info_url \u001b[39m=\u001b[39m config\u001b[39m.\u001b[39mHF_ENDPOINT\u001b[39m.\u001b[39mreplace(\u001b[39m\"\u001b[39m\u001b[39m://\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m://datasets-server.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/info?dataset=\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     69\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 70\u001b[0m     info_response \u001b[39m=\u001b[39m http_get(\n\u001b[1;32m     71\u001b[0m         url\u001b[39m=\u001b[39;49mdataset_viewer_info_url \u001b[39m+\u001b[39;49m dataset,\n\u001b[1;32m     72\u001b[0m         temp_file\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m     73\u001b[0m         headers\u001b[39m=\u001b[39;49mget_authentication_headers_for_url(config\u001b[39m.\u001b[39;49mHF_ENDPOINT \u001b[39m+\u001b[39;49m \u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mdatasets/\u001b[39;49m\u001b[39m{\u001b[39;49;00mdataset\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m, token\u001b[39m=\u001b[39;49mtoken),\n\u001b[1;32m     74\u001b[0m         timeout\u001b[39m=\u001b[39;49m\u001b[39m100.0\u001b[39;49m,\n\u001b[1;32m     75\u001b[0m         max_retries\u001b[39m=\u001b[39;49m\u001b[39m3\u001b[39;49m,\n\u001b[1;32m     76\u001b[0m     )\n\u001b[1;32m     77\u001b[0m     info_response\u001b[39m.\u001b[39mraise_for_status()\n\u001b[1;32m     78\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mX-Revision\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m info_response\u001b[39m.\u001b[39mheaders:\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/datasets/utils/file_utils.py:424\u001b[0m, in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries, desc, disable_tqdm)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[39mif\u001b[39;00m resume_size \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m    423\u001b[0m     headers[\u001b[39m\"\u001b[39m\u001b[39mRange\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbytes=\u001b[39m\u001b[39m{\u001b[39;00mresume_size\u001b[39m:\u001b[39;00m\u001b[39md\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m-\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 424\u001b[0m response \u001b[39m=\u001b[39m _request_with_retry(\n\u001b[1;32m    425\u001b[0m     method\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mGET\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    426\u001b[0m     url\u001b[39m=\u001b[39;49murl,\n\u001b[1;32m    427\u001b[0m     stream\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m    428\u001b[0m     proxies\u001b[39m=\u001b[39;49mproxies,\n\u001b[1;32m    429\u001b[0m     headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m    430\u001b[0m     cookies\u001b[39m=\u001b[39;49mcookies,\n\u001b[1;32m    431\u001b[0m     max_retries\u001b[39m=\u001b[39;49mmax_retries,\n\u001b[1;32m    432\u001b[0m     timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m    433\u001b[0m )\n\u001b[1;32m    434\u001b[0m \u001b[39mif\u001b[39;00m temp_file \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    435\u001b[0m     \u001b[39mreturn\u001b[39;00m response\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/datasets/utils/file_utils.py:336\u001b[0m, in \u001b[0;36m_request_with_retry\u001b[0;34m(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)\u001b[0m\n\u001b[1;32m    334\u001b[0m tries \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    335\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 336\u001b[0m     response \u001b[39m=\u001b[39m requests\u001b[39m.\u001b[39;49mrequest(method\u001b[39m=\u001b[39;49mmethod\u001b[39m.\u001b[39;49mupper(), url\u001b[39m=\u001b[39;49murl, timeout\u001b[39m=\u001b[39;49mtimeout, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mparams)\n\u001b[1;32m    337\u001b[0m     success \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    338\u001b[0m \u001b[39mexcept\u001b[39;00m (requests\u001b[39m.\u001b[39mexceptions\u001b[39m.\u001b[39mConnectTimeout, requests\u001b[39m.\u001b[39mexceptions\u001b[39m.\u001b[39mConnectionError) \u001b[39mas\u001b[39;00m err:\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[39m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[39m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[39m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[39mwith\u001b[39;00m sessions\u001b[39m.\u001b[39mSession() \u001b[39mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m     \u001b[39mreturn\u001b[39;00m session\u001b[39m.\u001b[39;49mrequest(method\u001b[39m=\u001b[39;49mmethod, url\u001b[39m=\u001b[39;49murl, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[39m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtimeout\u001b[39m\u001b[39m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mallow_redirects\u001b[39m\u001b[39m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[39m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(prep, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msend_kwargs)\n\u001b[1;32m    591\u001b[0m \u001b[39mreturn\u001b[39;00m resp\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[39m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[39m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[39m=\u001b[39m adapter\u001b[39m.\u001b[39;49msend(request, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    705\u001b[0m \u001b[39m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[39m=\u001b[39m preferred_clock() \u001b[39m-\u001b[39m start\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    664\u001b[0m     timeout \u001b[39m=\u001b[39m TimeoutSauce(connect\u001b[39m=\u001b[39mtimeout, read\u001b[39m=\u001b[39mtimeout)\n\u001b[1;32m    666\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49murlopen(\n\u001b[1;32m    668\u001b[0m         method\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mmethod,\n\u001b[1;32m    669\u001b[0m         url\u001b[39m=\u001b[39;49murl,\n\u001b[1;32m    670\u001b[0m         body\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mbody,\n\u001b[1;32m    671\u001b[0m         headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[1;32m    672\u001b[0m         redirect\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    673\u001b[0m         assert_same_host\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    674\u001b[0m         preload_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    675\u001b[0m         decode_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    676\u001b[0m         retries\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_retries,\n\u001b[1;32m    677\u001b[0m         timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m    678\u001b[0m         chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m    679\u001b[0m     )\n\u001b[1;32m    681\u001b[0m \u001b[39mexcept\u001b[39;00m (ProtocolError, \u001b[39mOSError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m    682\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m(err, request\u001b[39m=\u001b[39mrequest)\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/urllib3/connectionpool.py:789\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    786\u001b[0m response_conn \u001b[39m=\u001b[39m conn \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m release_conn \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    788\u001b[0m \u001b[39m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 789\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[1;32m    790\u001b[0m     conn,\n\u001b[1;32m    791\u001b[0m     method,\n\u001b[1;32m    792\u001b[0m     url,\n\u001b[1;32m    793\u001b[0m     timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[1;32m    794\u001b[0m     body\u001b[39m=\u001b[39;49mbody,\n\u001b[1;32m    795\u001b[0m     headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m    796\u001b[0m     chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m    797\u001b[0m     retries\u001b[39m=\u001b[39;49mretries,\n\u001b[1;32m    798\u001b[0m     response_conn\u001b[39m=\u001b[39;49mresponse_conn,\n\u001b[1;32m    799\u001b[0m     preload_content\u001b[39m=\u001b[39;49mpreload_content,\n\u001b[1;32m    800\u001b[0m     decode_content\u001b[39m=\u001b[39;49mdecode_content,\n\u001b[1;32m    801\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mresponse_kw,\n\u001b[1;32m    802\u001b[0m )\n\u001b[1;32m    804\u001b[0m \u001b[39m# Everything went great!\u001b[39;00m\n\u001b[1;32m    805\u001b[0m clean_exit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/urllib3/connectionpool.py:536\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    534\u001b[0m \u001b[39m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m    535\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 536\u001b[0m     response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49mgetresponse()\n\u001b[1;32m    537\u001b[0m \u001b[39mexcept\u001b[39;00m (BaseSSLError, \u001b[39mOSError\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    538\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_raise_timeout(err\u001b[39m=\u001b[39me, url\u001b[39m=\u001b[39murl, timeout_value\u001b[39m=\u001b[39mread_timeout)\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/site-packages/urllib3/connection.py:464\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    461\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mresponse\u001b[39;00m \u001b[39mimport\u001b[39;00m HTTPResponse\n\u001b[1;32m    463\u001b[0m \u001b[39m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 464\u001b[0m httplib_response \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mgetresponse()\n\u001b[1;32m    466\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    467\u001b[0m     assert_header_parsing(httplib_response\u001b[39m.\u001b[39mmsg)\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/http/client.py:1375\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1373\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1374\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1375\u001b[0m         response\u001b[39m.\u001b[39;49mbegin()\n\u001b[1;32m   1376\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m:\n\u001b[1;32m   1377\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/http/client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[39m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    317\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m--> 318\u001b[0m     version, status, reason \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_status()\n\u001b[1;32m    319\u001b[0m     \u001b[39mif\u001b[39;00m status \u001b[39m!=\u001b[39m CONTINUE:\n\u001b[1;32m    320\u001b[0m         \u001b[39mbreak\u001b[39;00m\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/http/client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_read_status\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 279\u001b[0m     line \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfp\u001b[39m.\u001b[39;49mreadline(_MAXLINE \u001b[39m+\u001b[39;49m \u001b[39m1\u001b[39;49m), \u001b[39m\"\u001b[39m\u001b[39miso-8859-1\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    280\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(line) \u001b[39m>\u001b[39m _MAXLINE:\n\u001b[1;32m    281\u001b[0m         \u001b[39mraise\u001b[39;00m LineTooLong(\u001b[39m\"\u001b[39m\u001b[39mstatus line\u001b[39m\u001b[39m\"\u001b[39m)\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/socket.py:705\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    703\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m    704\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 705\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sock\u001b[39m.\u001b[39;49mrecv_into(b)\n\u001b[1;32m    706\u001b[0m     \u001b[39mexcept\u001b[39;00m timeout:\n\u001b[1;32m    707\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_timeout_occurred \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/ssl.py:1307\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1303\u001b[0m     \u001b[39mif\u001b[39;00m flags \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m   1304\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   1305\u001b[0m           \u001b[39m\"\u001b[39m\u001b[39mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m   1306\u001b[0m           \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m)\n\u001b[0;32m-> 1307\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mread(nbytes, buffer)\n\u001b[1;32m   1308\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1309\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
            "File \u001b[0;32m~/anaconda3/envs/rag/lib/python3.10/ssl.py:1163\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1162\u001b[0m     \u001b[39mif\u001b[39;00m buffer \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1163\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mread(\u001b[39mlen\u001b[39;49m, buffer)\n\u001b[1;32m   1164\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1165\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sslobj\u001b[39m.\u001b[39mread(\u001b[39mlen\u001b[39m)\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "dataset = load_dataset(\"csebuetnlp/squad_bn\", split=\"test\")\n",
        "# dataset = dataset.select(range(10))\n",
        "dataset = dataset.map(lambda x: {\"context\": normalize(x[\"context\"]), \"question\": normalize(x[\"question\"])})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V63-fsZeDASJ"
      },
      "outputs": [],
      "source": [
        "collection = list(set(dataset[\"context\"]))\n",
        "documents = StringIterableReader().load_data(texts=collection)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lxUnV-lODASJ"
      },
      "outputs": [],
      "source": [
        "delimiter= \"andolon\"  # Define your delimiter here\n",
        "joined_collection = delimiter.join(collection)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vUWktGtTDASK"
      },
      "outputs": [],
      "source": [
        "!mkdir -p 'data/squad_bn/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b-IP0aNEDASK"
      },
      "outputs": [],
      "source": [
        "file_path = \"data/squad_bn/joined_collections.txt\"  # Specify the file path here\n",
        "\n",
        "# Write the joined string to the file\n",
        "with open(file_path, \"w\") as file:\n",
        "    file.write(joined_collection)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IfvjnC8WDASK"
      },
      "outputs": [],
      "source": [
        "documents = SimpleDirectoryReader(\"./data/squad_bn/\").load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HiSWSPvvDASL"
      },
      "outputs": [],
      "source": [
        "node_parser = CustomSentenceSplitter(paragraph_separator= delimiter)\n",
        "nodes = node_parser.get_nodes_from_documents(documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lD4Ari5DDASL"
      },
      "outputs": [],
      "source": [
        "for idx, node in enumerate(nodes):\n",
        "    node.id_ = f\"node_{idx}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "bMoJVOlEDASL",
        "outputId": "991fabdb-4df8-4943-89ff-2202085a5fc1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No sentence-transformers model found with name FacebookAI/xlm-roberta-base. Creating a new one with mean pooling.\n",
            "/home/rifat/anaconda3/envs/rag/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "Generating embeddings:  28%|██▊       | 579/2048 [02:00<05:04,  4.82it/s]\n"
          ]
        }
      ],
      "source": [
        "embed_model = HuggingFaceEmbedding(model_name= EMBED_MODEL_NAME)\n",
        "Settings.embed_model = embed_model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "id": "3m48-e6MDASL",
        "outputId": "3495c8af-2cd5-41b6-f532-28d84c0e180c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Generating embeddings: 100%|██████████| 2048/2048 [05:31<00:00,  6.19it/s]\n",
            "Generating embeddings: 100%|██████████| 201/201 [00:32<00:00,  6.13it/s]\n"
          ]
        }
      ],
      "source": [
        "vector_index = VectorStoreIndex(\n",
        "    nodes=nodes,\n",
        "    show_progress=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bAkycPAEDASL"
      },
      "outputs": [],
      "source": [
        "retriever = vector_index.as_retriever(similarity_top_k=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NVMzOPqfDASM"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "metrics = [\"mrr\", \"hit_rate\", \"precision\"]\n",
        "\n",
        "\n",
        "retriever_evaluator = RetrieverEvaluator.from_metric_names(\n",
        "    metrics, retriever=retriever\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JPNNRzt8DASM"
      },
      "outputs": [],
      "source": [
        "import nest_asyncio\n",
        "nest_asyncio.apply()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tku8_1HgDASM"
      },
      "outputs": [],
      "source": [
        "\n",
        "query_texts = dataset[\"question\"]  # List of query texts\n",
        "query_ids=[]\n",
        "for i in range(len(query_texts)):\n",
        "    query_ids.append(str(i))\n",
        "query_dict = dict(zip(query_ids, query_texts))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jEkgFUGLDASM"
      },
      "outputs": [],
      "source": [
        "reverse_doc_dict = {}\n",
        "doc_dict = {}\n",
        "\n",
        "for key, value in vector_index.docstore.docs.items():\n",
        "    reverse_doc_dict[value.text] = key\n",
        "    doc_dict[key] = value.text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1kkS4iTdDASM"
      },
      "outputs": [],
      "source": [
        "relevent_doc_dict = {query_id: [reverse_doc_dict[dataset[\"context\"][i]]] for i, query_id in enumerate(query_ids)}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4YZguknGDASM"
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      "source": [
        "qa_dataset = EmbeddingQAFinetuneDataset(\n",
        "    queries= query_dict,\n",
        "    corpus= doc_dict,\n",
        "    relevant_docs= relevent_doc_dict\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "VHcB9emrDASN"
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      "source": [
        "eval_results = await retriever_evaluator.aevaluate_dataset(qa_dataset)"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "6Mumz5ADDASN"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "def display_results(name, eval_results):\n",
        "    \"\"\"Display results from evaluate.\"\"\"\n",
        "\n",
        "    metric_dicts = []\n",
        "    for eval_result in eval_results:\n",
        "        metric_dict = eval_result.metric_vals_dict\n",
        "        metric_dicts.append(metric_dict)\n",
        "\n",
        "    full_df = pd.DataFrame(metric_dicts)\n",
        "\n",
        "    hit_rate = full_df[\"hit_rate\"].mean()\n",
        "    mrr = full_df[\"mrr\"].mean()\n",
        "    columns = {\"retrievers\": [name], \"hit_rate\": [hit_rate], \"mrr\": [mrr]}\n",
        "\n",
        "    metric_df = pd.DataFrame(columns)\n",
        "\n",
        "    return metric_df"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "GBQdDGinDASN",
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