XAI_Project.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Better then Bing"
      ],
      "metadata": {
        "id": "CGnZxvjZ-cJf"
      }
    },
    {
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: tensorflow-hub>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow_text) (0.12.0)\n",
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            "Requirement already satisfied: keras-preprocessing>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.10,>=2.9.0->tensorflow_text) (1.1.2)\n",
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            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.10,>=2.9.0->tensorflow_text) (0.2.0)\n",
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            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow<2.10,>=2.9.0->tensorflow_text) (0.26.0)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py>=2.9.0->tensorflow<2.10,>=2.9.0->tensorflow_text) (1.5.2)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (3.4.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (1.8.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (0.6.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (0.4.6)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (1.0.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (4.9)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (4.2.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (1.3.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow<2.10,>=2.9.0->tensorflow_text) (3.2.0)\n",
            "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.7/dist-packages (from coloredlogs->onnxruntime>=1.8.1->pyserini) (10.0)\n",
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            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from datasets->beir) (3.8.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets->beir) (0.70.13)\n",
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            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.7/dist-packages (from datasets->beir) (2022.7.1)\n",
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            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets->beir) (1.2.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets->beir) (4.0.2)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets->beir) (22.1.0)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets->beir) (2.1.1)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets->beir) (0.13.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets->beir) (1.3.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy>=3.2.1->pyserini) (2.0.1)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers->beir) (0.13.1+cu113)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers->beir) (1.12.1+cu113)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers->beir) (3.7)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy->onnxruntime>=1.8.1->pyserini) (1.2.1)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers->beir) (7.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install beir pyserini tensorflow_text"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from beir import util\n",
        "import logging\n",
        "import pathlib, os\n",
        "import json"
      ],
      "metadata": {
        "id": "cQusuH-PAegL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "712d325f-ee1d-4cac-f98c-4fcc3c8f3c53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/beir/util.py:2: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create json docs"
      ],
      "metadata": {
        "id": "6zQQFYgs-oh8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#### Download scifact.zip dataset and unzip the dataset\n",
        "dataset = \"fiqa\"\n",
        "url = \"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip\".format(dataset)\n",
        "out_dir = \".\"\n",
        "data_path = util.download_and_unzip(url, out_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7ada98eee70e4967be91400477f02634",
            "d05a9616acfc4c80a5e71969f78a0373",
            "ba2648feb80d4df08ed2cba96cadbc1f",
            "f62686c90b8249f190ebec278436ca38",
            "68709483881047f782b347da7ca6b2e4",
            "44bc7728954549b9839a60e8b67be4be",
            "28d91e6012024fab8de8e5ff818b00c6",
            "08f45ea1ce5144019f446d3b7c62d7ce",
            "5421983937de415ebbef9508f4d87c9c",
            "7afc14e42d914233a790780773b2a634",
            "294f227fc94e4e2d89d47e55a20dab1b"
          ]
        },
        "id": "8niyicYr-0Wk",
        "outputId": "5ec1395f-e24c-4d1f-e7ab-d0744a54faed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "./fiqa.zip:   0%|          | 0.00/17.1M [00:00<?, ?iB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ada98eee70e4967be91400477f02634"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = []\n",
        "with open(\"fiqa/corpus.jsonl\", \"r\") as file:\n",
        "    json_list = list(file)\n",
        "\n",
        "for json_str in json_list:\n",
        "    res = json.loads(json_str)\n",
        "    data.append({\"id\":res.get(\"_id\"), \"contents\": res.get(\"text\")})\n",
        "\n",
        "with open(\"corpus_pyserini.json\", \"w\") as out:\n",
        "    for entry in data:\n",
        "        out.write(json.dumps(entry))\n",
        "        out.write(\"\\n\")"
      ],
      "metadata": {
        "id": "6ymS_hN7Tj5q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Embed"
      ],
      "metadata": {
        "id": "qaVVTEBx-zPk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pyserini.encode input --corpus corpus_pyserini.json --fields text  --delimiter \"\\n\" --shard-id 0 --shard-num 1  output --embeddings outs encoder --device cuda:0 --encoder microsoft/xtremedistil-l6-h256-uncased --encoder-class sentence-transformers --fields text --batch 32 --max-length 256 --fp16"
      ],
      "metadata": {
        "id": "EU1MtFl5-3Ml",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "665d30f2-4450-4f54-9a4a-082858c63308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "57638it [00:00, 138929.70it/s]\n",
            " 10% 188/1802 [00:12<01:43, 15.62it/s]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n",
            "    \"__main__\", mod_spec)\n",
            "  File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n",
            "    exec(code, run_globals)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pyserini/encode/__main__.py\", line 132, in <module>\n",
            "    embeddings = encoder.encode(**kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/pyserini/encode/_auto.py\", line 61, in encode\n",
            "    embeddings = self._mean_pooling(outputs[0], inputs['attention_mask']).detach().cpu().numpy()\n",
            "KeyboardInterrupt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Index"
      ],
      "metadata": {
        "id": "SSnojllcaqGp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pyserini.index.faiss --dim 256 --input /content/embedings --output fiqa_index --hnsw"
      ],
      "metadata": {
        "id": "kY0Qs2YAatXt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62c482ca-d026-452b-880f-8b59b5491f24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100% 1/1 [00:00<00:00,  1.74it/s]\n",
            "(6016, 256)\n",
            "hnsw_add_vertices: adding 6016 elements on top of 0 (preset_levels=0)\n",
            "  max_level = 1\n",
            "Adding 13 elements at level 1\n",
            "Adding 6003 elements at level 0\n",
            "Done in 4963.093 ms\n",
            "6016\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "CiHvjd4Kjkva"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Search"
      ],
      "metadata": {
        "id": "-36LVj1c-3hQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyserini.search.faiss import AutoQueryEncoder, FaissSearcher\n",
        "encoder = AutoQueryEncoder('microsoft/xtremedistil-l6-h256-uncased')\n",
        "\n",
        "searcher = FaissSearcher(index_dir=\"fiqa_index\", query_encoder=encoder, )"
      ],
      "metadata": {
        "id": "L7xUSJc_-vSZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hits = searcher.search('what is a lobster roll')"
      ],
      "metadata": {
        "id": "u8N-K6w4g-yo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0, 10):\n",
        "    print(f'{i+1:2} {hits[i].docid:7} {hits[i].score:.5f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdM0sl_qlpEY",
        "outputId": "c6f4e8bc-2d32-4b95-d447-e58e8c38d7e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 1 4612    14.43486\n",
            " 2 52757   14.43390\n",
            " 3 54004   14.42214\n",
            " 4 36284   14.38360\n",
            " 5 19613   14.35964\n",
            " 6 39976   14.35688\n",
            " 7 31863   14.34086\n",
            " 8 2975    14.33475\n",
            " 9 17348   14.33134\n",
            "10 14598   14.30120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hits[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1I7hWRugmD29",
        "outputId": "0fa987f5-70b4-4591-9383-5ab2d1d867b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DenseSearchResult(docid='4612', score=14.434855)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyserini.search.lucene import LuceneSearcher"
      ],
      "metadata": {
        "id": "XCT58uf7nY-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "MEkh4BEvnhPM",
        "outputId": "6c7821da-d26b-4f2a-b241-12dff566ace3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "JavaException",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mJavaException\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-d2f2830359ad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdoc_search\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLuceneSearcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"fiqa_index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyserini/search/lucene/_searcher.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, index_dir)\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_dir\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_dir\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJLuceneSearcher\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_docs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetTotalNumDocuments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mjnius/jnius_export_class.pxi\u001b[0m in \u001b[0;36mjnius.JavaClass.__init__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mjnius/jnius_export_class.pxi\u001b[0m in \u001b[0;36mjnius.JavaClass.call_constructor\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mjnius/jnius_utils.pxi\u001b[0m in \u001b[0;36mjnius.check_exception\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mJavaException\u001b[0m: JVM exception occurred: no segments* file found in MMapDirectory@/content/fiqa_index lockFactory=org.apache.lucene.store.NativeFSLockFactory@7dc19a70: files: [docid, index] org.apache.lucene.index.IndexNotFoundException"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "searcher.doc(\"3817\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "TPKiseWnlBZo",
        "outputId": "fa2f3c29-e9cd-4710-eb1b-3c778f485af9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-2f701d3de498>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msearcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3817\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyserini/search/faiss/_searcher.py\u001b[0m in \u001b[0;36mdoc\u001b[0;34m(self, docid)\u001b[0m\n\u001b[1;32m    506\u001b[0m             \u001b[0;34m:\u001b[0m\u001b[0;32mclass\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mDocument\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mcorresponding\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mdocid\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \"\"\"\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mssearcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocid\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mssearcher\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'FaissSearcher' object has no attribute 'ssearcher'"
          ]
        }
      ]
    }
  ]
}