HayStack_Tutorial6_Better_Retrieval_via_DensePassageRetrieval.ipynb

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        "<a href=\"https://colab.research.google.com/github/navneetkrc/Deep-Learning-Experiments-implemented-using-Google-Colab/blob/master/HayStack_Tutorial6_Better_Retrieval_via_DensePassageRetrieval.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "source": [
        "# Better Retrieval via \"Dense Passage Retrieval\"\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb)\n",
        "\n",
        "### Importance of Retrievers\n",
        "\n",
        "The Retriever has a huge impact on the performance of our overall search pipeline.\n",
        "\n",
        "\n",
        "### Different types of Retrievers\n",
        "#### Sparse\n",
        "Family of algorithms based on counting the occurrences of words (bag-of-words) resulting in very sparse vectors with length = vocab size.\n",
        "\n",
        "**Examples**: BM25, TF-IDF\n",
        "\n",
        "**Pros**: Simple, fast, well explainable\n",
        "\n",
        "**Cons**: Relies on exact keyword matches between query and text\n",
        " \n",
        "\n",
        "#### Dense\n",
        "These retrievers use neural network models to create \"dense\" embedding vectors. Within this family there are two different approaches: \n",
        "\n",
        "a) Single encoder: Use a **single model** to embed both query and passage.  \n",
        "b) Dual-encoder: Use **two models**, one to embed the query and one to embed the passage\n",
        "\n",
        "Recent work suggests that dual encoders work better, likely because they can deal better with the different nature of query and passage (length, style, syntax ...). \n",
        "\n",
        "**Examples**: REALM, DPR, Sentence-Transformers\n",
        "\n",
        "**Pros**: Captures semantinc similarity instead of \"word matches\" (e.g. synonyms, related topics ...)\n",
        "\n",
        "**Cons**: Computationally more heavy, initial training of model\n",
        "\n",
        "\n",
        "### \"Dense Passage Retrieval\"\n",
        "\n",
        "In this Tutorial, we want to highlight one \"Dense Dual-Encoder\" called Dense Passage Retriever. \n",
        "It was introdoced by Karpukhin et al. (2020, https://arxiv.org/abs/2004.04906. \n",
        "\n",
        "Original Abstract: \n",
        "\n",
        "_\"Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.\"_\n",
        "\n",
        "Paper: https://arxiv.org/abs/2004.04906  \n",
        "Original Code: https://fburl.com/qa-dpr \n",
        "\n",
        "\n",
        "*Use this* [link](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial6_Better_Retrieval_via_DPR.ipynb) *to open the notebook in Google Colab.*\n"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3K27Y5FbA6NV"
      },
      "source": [
        "### Prepare environment\n",
        "\n",
        "#### Colab: Enable the GPU runtime\n",
        "Make sure you enable the GPU runtime to experience decent speed in this tutorial.  \n",
        "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/colab_gpu_runtime.jpg\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "JlZgP8q1A6NW",
        "outputId": "74941daa-1648-4215-d4ea-5c9c5876f7df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Apr 15 11:45:15 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P8    28W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# Make sure you have a GPU running\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NM36kbRFA6Nc"
      },
      "outputs": [],
      "source": [
        "# Install the latest release of Haystack in your own environment\n",
        "#! pip install farm-haystack\n",
        "\n",
        "# Install the latest master of Haystack\n",
        "!pip install --upgrade pip\n",
        "!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmRuhTQ7A6Nh",
        "outputId": "701c7d19-ab7a-4c53-fa86-ef20ab087a3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.optimization -  apex not found, won't use it. See https://nvidia.github.io/apex/\n"
          ]
        }
      ],
      "source": [
        "from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http, print_answers\n",
        "from haystack.nodes import FARMReader, TransformersReader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3dSo7ZtA6Nl"
      },
      "source": [
        "### Document Store\n",
        "\n",
        "#### Option 1: FAISS\n",
        "\n",
        "FAISS is a library for efficient similarity search on a cluster of dense vectors.\n",
        "The `FAISSDocumentStore` uses a SQL(SQLite in-memory be default) database under-the-hood\n",
        "to store the document text and other meta data. The vector embeddings of the text are\n",
        "indexed on a FAISS Index that later is queried for searching answers.\n",
        "The default flavour of FAISSDocumentStore is \"Flat\" but can also be set to \"HNSW\" for\n",
        "faster search at the expense of some accuracy. Just set the faiss_index_factor_str argument in the constructor.\n",
        "For more info on which suits your use case: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cYgDJmrA6Nv",
        "outputId": "d16de657-1b39-45f2-d479-5c0af9991972",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.telemetry -  Haystack sends anonymous usage data to understand the actual usage and steer dev efforts towards features that are most meaningful to users. You can opt-out at anytime by calling disable_telemetry() or by manually setting the environment variable HAYSTACK_TELEMETRY_ENABLED as described for different operating systems on the documentation page. More information at https://haystack.deepset.ai/guides/telemetry\n"
          ]
        }
      ],
      "source": [
        "from haystack.document_stores import FAISSDocumentStore\n",
        "\n",
        "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "ZJe51t00_gy1"
      },
      "source": [
        "#### Option 2: Milvus\n",
        "\n",
        "Milvus is an open source database library that is also optimized for vector similarity searches like FAISS.\n",
        "Like FAISS it has both a \"Flat\" and \"HNSW\" mode but it outperforms FAISS when it comes to dynamic data management.\n",
        "It does require a little more setup, however, as it is run through Docker and requires the setup of some config files.\n",
        "See [their docs](https://milvus.io/docs/v1.0.0/milvus_docker-cpu.md) for more details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "SNHzJp03_gy2"
      },
      "outputs": [],
      "source": [
        "# Milvus cannot be run on COlab, so this cell is commented out.\n",
        "# To run Milvus you need Docker (versions below 2.0.0) or a docker-compose (versions >= 2.0.0), neither of which is available on Colab.\n",
        "# See Milvus' documentation for more details: https://milvus.io/docs/install_standalone-docker.md\n",
        "\n",
        "# !pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[milvus]\n",
        "\n",
        "# from haystack.utils import launch_milvus\n",
        "# from haystack.document_stores import MilvusDocumentStore\n",
        "\n",
        "# launch_milvus()\n",
        "# document_store = MilvusDocumentStore()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06LatTJBA6N0",
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": [
        "### Cleaning & indexing documents\n",
        "\n",
        "Similarly to the previous tutorials, we download, convert and index some Game of Thrones articles to our DocumentStore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
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        "id": "iqKnu6wxA6N1",
        "outputId": "6df83604-f75b-43f2-8930-30faff1a4a38",
        "pycharm": {
          "name": "#%%\n"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.utils.import_utils -  Fetching from https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt6.zip to `data/tutorial6`\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/396_Game_of_Thrones__season_6_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/343_Catelyn_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/365_A_Song_of_Ice_and_Fire_Roleplaying.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/232_Tommen_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/474_Bronn__character_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/198_A_Clash_of_Kings.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/359_Kill_the_Boy.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/37_Joffrey_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/78_The_Rains_of_Castamere.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/233_Myrcella_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/11_The_Dragon_and_the_Wolf.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/218_Olenna_Tyrell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/44_Daenerys_Targaryen.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/87_Valar_Dohaeris.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/224_The_Night_Lands.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/469_Outline_of_A_Song_of_Ice_and_Fire_franchise.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/26_Game_of_Thrones__Season_3__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/340_Roose_Bolton.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/9_Game_of_Thrones_Tapestry.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/411_The_Iron_Throne__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/211_The_Watchers_on_the_Wall.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/424_Night_King.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/119_Walk_of_Punishment.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/373_Tywin_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/217_The_Rogue_Prince.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/131_Mhysa.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/43_Arya_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/306_The_Dance_of_Dragons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/53_The_Lion_and_the_Rose.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/399_For_the_Throne__Music_Inspired_by_the_HBO_Series_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/130_Game_of_Thrones_title_sequence.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/7_The_Spoils_of_War__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/477_A_Dance_with_Dragons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/329_Robert_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/23_Game_of_Thrones_Live_Concert_Experience.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/449_The_Pointy_End.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/127_The_Climb__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/401_Power_Is_Power.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/480_Varys.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/209_Mockingbird__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/210_The_Mountain_and_the_Viper.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/408_The_Last_of_the_Starks.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/371_Cersei_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/51_Iron_Throne__A_Song_of_Ice_and_Fire_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/446_A_Golden_Crown.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/135_Game_of_Thrones__Season_7__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/136_Game_of_Thrones__Season_8__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/212_The_Children__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/450_Baelor.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/55_Breaker_of_Chains.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/201_A_Game_of_Thrones__card_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/8_Eastwatch.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/334_Rickon_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/454_Music_of_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/460_Battle_of_the_Bastards.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/57_The_Laws_of_Gods_and_Men.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/40_Stannis_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/229_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/410_The_Bells__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/3_The_Queen_s_Justice.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/30_List_of_A_Song_of_Ice_and_Fire_characters.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/394_Game_of_Thrones__2014_video_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/120_And_Now_His_Watch_Is_Ended.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/134_Game_of_Thrones__Season_6__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/208_Robb_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/154_Margaery_Tyrell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/160_Viserys_Targaryen.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/194_A_Song_of_Ice_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/84_List_of_awards_and_nominations_received_by_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/191_Gendry.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/97_Tyrion_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/80_A_Song_of_Ice_and_Fire_fandom.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/435_White_Walker.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/99_A_Song_of_Ass_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/73_A_Man_Without_Honor.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/360_List_of_Game_of_Thrones_episodes.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/74_The_Prince_of_Winterfell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/67_Unbowed__Unbent__Unbroken.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/346_Ygritte.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/513_Oathbreaker__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/456_Works_based_on_A_Song_of_Ice_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/213_Valyrian_languages.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/85_Game_of_Thrones__Seven_Kingdoms.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/133_Game_of_Thrones__Season_5__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/145_Elio_M._García_Jr._and_Linda_Antonsson.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/447_You_Win_or_You_Die.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/228_Souad_Faress.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/488_Brienne_of_Tarth.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/356_Tales_of_Dunk_and_Egg.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/197_A_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/512_Home__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/482_Petyr_Baelish.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/511_After_the_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/75_Blackwater__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/407_The_Long_Night__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/102_The_Princess_and_the_Queen.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/379_Davos_Seaworth.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/118_Dark_Wings__Dark_Words.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/332_Sansa_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/273_High_Sparrow.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/403_Jenny_of_Oldstones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/86_Game_of_Thrones__season_4_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/146_The_Sons_of_the_Dragon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/263_Tormund_Giantsbane.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/345_A_Game_of_Thrones__comics_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/262_Gilly__character_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/101_Titties_and_Dragons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/453_Game_of_Thrones__2012_video_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/52_Catch_the_Throne.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/420_Blood_of_My_Blood.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/443_The_Kingsroad.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/98_Black_Friday__South_Park_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/378_A_Game_of_Thrones__board_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/452_Fire_and_Blood__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/515_The_Door__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/76_Valar_Morghulis.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/461_The_Winds_of_Winter__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/33_David_Benioff.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/367_Gregor_Clegane.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/311_Game_of_Thrones__season_7_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/395_Game_of_Thrones__season_5_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/304_Hardhome.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/400_Winterfell__Game_of_Thrones_episode_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/151_Ellaria_Sand.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/330_Oberyn_Martell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/487_Ramsay_Bolton.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/214_Dothraki_language.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/368_Jaime_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/28_Jorah_Mormont.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/39_Renly_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/362_Winter_Is_Coming.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/442_Game_of_Thrones__season_1_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/129_Second_Sons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/425_No_One__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/347_Game_of_Thrones__season_2_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/506_Game_of_Thrones_Theme.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/495_Hodor.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/369_Samwell_Tarly.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/317_The_Broken_Man.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/202_A_Feast_for_Crows.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/349_List_of_Game_of_Thrones_characters.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/299_Rani_Mahal__TV_series_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/341_Ned_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/12_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/481_Sandor_Clegane.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/195_World_of_A_Song_of_Ice_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/207_Jon_Snow__character_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/485_Oathkeeper.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/0_Game_of_Thrones__season_8_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/199_A_Storm_of_Swords.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/2_Stormborn.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/68_The_Gift__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/10_Beyond_the_Wall__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/348_The_Winds_of_Winter.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/310_Mother_s_Mercy.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/193_Lord_Snow.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/504_List_of_A_Song_of_Ice_and_Fire_video_games.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/25_Game_of_Thrones__Season_2__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/358_A_Game_of_Thrones__Genesis.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/148_Game_of_Thrones__Winter_Is_Coming.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/121_The_Bear_and_the_Maiden_Fair.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/508_A_Game_of_Thrones__Second_Edition__card_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/402_A_Knight_of_the_Seven_Kingdoms__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/514_Book_of_the_Stranger.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/21_The_Bear_and_the_Maiden_Fair__song_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/1_Dragonstone__Game_of_Thrones_episode_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/20_Light_of_the_Seven.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/475_A_Game_of_Thrones__role-playing_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/79_Thronecast.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/445_The_Wolf_and_the_Lion.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/54_Two_Swords__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/56_First_of_His_Name.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/501_Khal_Drogo.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/24_Game_of_Thrones__Season_1__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/126_Kissed_by_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/342_Theon_Greyjoy.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/27_Game_of_Thrones__Season_4__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/444_Cripples__Bastards__and_Broken_Things.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/69_The_Red_Woman.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/331_Bran_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/22_The_Rains_of_Castamere__song_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/439_Melisandre.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial6/77_Game_of_Thrones_Ascent.txt\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing Documents:   0%|          | 0/2497 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c3217489252401b8bff3335863cfebd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Let's first get some files that we want to use\n",
        "doc_dir = \"data/tutorial6\"\n",
        "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt6.zip\"\n",
        "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
        "\n",
        "# Convert files to dicts\n",
        "docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)\n",
        "\n",
        "# Now, let's write the dicts containing documents to our DB.\n",
        "document_store.write_documents(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgjedxx_A6N6"
      },
      "source": [
        "### Initalize Retriever, Reader & Pipeline\n",
        "\n",
        "#### Retriever\n",
        "\n",
        "**Here:** We use a `DensePassageRetriever`\n",
        "\n",
        "**Alternatives:**\n",
        "\n",
        "- The `ElasticsearchRetriever`with custom queries (e.g. boosting) and filters\n",
        "- Use `EmbeddingRetriever` to find candidate documents based on the similarity of embeddings (e.g. created via Sentence-BERT)\n",
        "- Use `TfidfRetriever` in combination with a SQL or InMemory Document store for simple prototyping and debugging"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
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          "height": 646,
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        },
        "id": "kFwiPP60A6N7",
        "outputId": "fb6e168c-b29f-4840-ebe3-ced8fc23d6cf",
        "pycharm": {
          "is_executing": true
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03463f49533a48bbb7f3bb3b6b885a1c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "288f684bd758453eb1b63c1815aca1f0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "923740ff7a2b4c68be03e517f0614a17"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/493 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30395f6218414094b5413d6648b3259b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  LOADING MODEL\n",
            "INFO - haystack.modeling.model.language_model -  =============\n",
            "INFO - haystack.modeling.model.language_model -  Could not find facebook/dpr-question_encoder-single-nq-base locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3601056f641d40928372b11ff40c620d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Loaded facebook/dpr-question_encoder-single-nq-base\n"
          ]
        },
        {
          "output_type": "display_data",
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            }
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            }
          },
          "metadata": {}
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              "version_major": 2,
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              "model_id": "914c0f351d284ad98884adcf736527d5"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "9473ea1565dd4283b33208c91a5612e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'DPRQuestionEncoderTokenizer'. \n",
            "The class this function is called from is 'DPRContextEncoderTokenizerFast'.\n",
            "INFO - haystack.modeling.model.language_model -  LOADING MODEL\n",
            "INFO - haystack.modeling.model.language_model -  =============\n",
            "INFO - haystack.modeling.model.language_model -  Could not find facebook/dpr-ctx_encoder-single-nq-base locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86fb09fc21df42509694123fdbb7230d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Loaded facebook/dpr-ctx_encoder-single-nq-base\n",
            "INFO - haystack.document_stores.faiss -  Updating embeddings for 2357 docs...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Updating Embedding:   0%|          | 0/2357 [00:00<?, ? docs/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d816350d5f5415087d762867768f478"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Create embeddings:   0%|          | 0/2368 [00:00<?, ? Docs/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6cbef84d44ba41499a23c36badfea83e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from haystack.nodes import DensePassageRetriever\n",
        "\n",
        "retriever = DensePassageRetriever(\n",
        "    document_store=document_store,\n",
        "    query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
        "    passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
        "    max_seq_len_query=64,\n",
        "    max_seq_len_passage=256,\n",
        "    batch_size=16,\n",
        "    use_gpu=True,\n",
        "    embed_title=True,\n",
        "    use_fast_tokenizers=True,\n",
        ")\n",
        "# Important:\n",
        "# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all\n",
        "# previously indexed documents and update their embedding representation.\n",
        "# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.\n",
        "# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.\n",
        "document_store.update_embeddings(retriever)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnVR28OXA6OA"
      },
      "source": [
        "#### Reader\n",
        "\n",
        "Similar to previous Tutorials we now initalize our reader.\n",
        "\n",
        "Here we use a FARMReader with the *deepset/roberta-base-squad2* model (see: https://huggingface.co/deepset/roberta-base-squad2)\n",
        "\n",
        "\n",
        "\n",
        "##### FARMReader"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "fyIuWVwhA6OB",
        "outputId": "0fa74925-704a-47c7-9740-56d646c0f0ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
            "INFO - haystack.modeling.model.language_model -  LOADING MODEL\n",
            "INFO - haystack.modeling.model.language_model -  =============\n",
            "INFO - haystack.modeling.model.language_model -  Could not find deepset/roberta-base-squad2 locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "7c92602adb274cb4911a3eb65afea1f8"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Loaded deepset/roberta-base-squad2\n",
            "WARNING - haystack.modeling.logger -  Failed to log params: Changing param values is not allowed. Param with key='prediction_heads' was already logged with value='TextSimilarityHead' for run ID='c49f2e4604d04005a98ff0b290ef9ec4'. Attempted logging new value 'QuestionAnsweringHead'.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "206e676c9acc46519efd04b3fc065a0d"
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c98d1eb886be42c19c8b6e7b70956e97"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0edca24dcc764f4eb29cba75712eb0e5"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4848711568b2495697689921d3039338"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING - haystack.modeling.logger -  Failed to log params: Changing param values is not allowed. Param with key='processor' was already logged with value='TextSimilarityProcessor' for run ID='c49f2e4604d04005a98ff0b290ef9ec4'. Attempted logging new value 'SquadProcessor'.\n",
            "INFO - haystack.modeling.logger -  ML Logging is turned off. No parameters, metrics or artifacts will be logged to MLFlow.\n",
            "INFO - haystack.modeling.utils -  Using devices: CUDA\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
            "INFO - haystack.modeling.infer -  Got ya 2 parallel workers to do inference ...\n",
            "INFO - haystack.modeling.infer -   0     0  \n",
            "INFO - haystack.modeling.infer -  /w\\   /w\\ \n",
            "INFO - haystack.modeling.infer -  /'\\   / \\ \n"
          ]
        }
      ],
      "source": [
        "# Load a  local model or any of the QA models on\n",
        "# Hugging Face's model hub (https://huggingface.co/models)\n",
        "\n",
        "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "unhLD18yA6OF"
      },
      "source": [
        "### Pipeline\n",
        "\n",
        "With a Haystack `Pipeline` you can stick together your building blocks to a search pipeline.\n",
        "Under the hood, `Pipelines` are Directed Acyclic Graphs (DAGs) that you can easily customize for your own use cases.\n",
        "To speed things up, Haystack also comes with a few predefined Pipelines. One of them is the `ExtractiveQAPipeline` that combines a retriever and a reader to answer our questions.\n",
        "You can learn more about `Pipelines` in the [docs](https://haystack.deepset.ai/docs/latest/pipelinesmd)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "TssPQyzWA6OG"
      },
      "outputs": [],
      "source": [
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "\n",
        "pipe = ExtractiveQAPipeline(reader, retriever)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXlBBxKXA6OL"
      },
      "source": [
        "## Voilà! Ask a question!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zi97Hif2A6OM",
        "outputId": "ba34f0fd-0efa-459c-9405-9ba4beff4910"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inferencing Samples:   0%|          | 0/1 [00:00<?, ? Batches/s]/usr/local/lib/python3.7/dist-packages/haystack/modeling/model/prediction_head.py:478: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  start_indices = flat_sorted_indices // max_seq_len\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 10.46 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  4.17 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 17.10 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 17.34 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 16.68 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 17.76 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 17.15 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  4.07 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 19.69 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  8.93 Batches/s]\n"
          ]
        }
      ],
      "source": [
        "# You can configure how many candidates the reader and retriever shall return\n",
        "# The higher top_k for retriever, the better (but also the slower) your answers.\n",
        "prediction = pipe.run(\n",
        "    query=\"Who created the Dothraki vocabulary?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ANN7dPvw_gzj",
        "outputId": "e235edc3-f509-4d97-9ee7-e0b85c1cf410",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[   {   'answer': 'Eddard and Catelyn Stark',\n",
            "        'context': 'Background ===\\n'\n",
            "                   'Arya is the third child and younger daughter of Eddard and '\n",
            "                   'Catelyn Stark and is nine years old at the beginning of '\n",
            "                   'the book series.  Sh'},\n",
            "    {   'answer': 'Rhaegar',\n",
            "        'context': ', Aemon Targaryen, Jorah Mormont, Meera Reed, Jon '\n",
            "                   'Connington and Gilly.\\n'\n",
            "                   'Rhaegar married the Dornish princess Elia Martell of '\n",
            "                   'Sunspear, and fathered wi'},\n",
            "    {   'answer': 'Eddard Stark',\n",
            "        'context': 'e from House Tully in the Riverlands region prior to her '\n",
            "                   'marriage to Eddard Stark. She has her hair dyed dark brown '\n",
            "                   'later on while in the Vale, disgui'},\n",
            "    {   'answer': 'Eddard Stark and Catelyn Stark',\n",
            "        'context': 'ces==\\n'\n",
            "                   'Sansa Stark is the second child and elder daughter of '\n",
            "                   'Eddard Stark and Catelyn Stark. She was born and raised in '\n",
            "                   'Winterfell, until leaving with '},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': 'Catelyn Stark ===\\n'\n",
            "                   'Catelyn Stark is the Lady of Winterfell, wife of Lord '\n",
            "                   'Eddard Stark, and mother to his children Robb, Sansa, '\n",
            "                   'Arya, Bran, and Rickon. '}]\n"
          ]
        }
      ],
      "source": [
        "print_answers(prediction, details=\"minimum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "GcF4kEb__gzj"
      },
      "source": [
        "## About us\n",
        "\n",
        "This [Haystack](https://github.com/deepset-ai/haystack/) notebook was made with love by [deepset](https://deepset.ai/) in Berlin, Germany\n",
        "\n",
        "We bring NLP to the industry via open source!  \n",
        "Our focus: Industry specific language models & large scale QA systems.  \n",
        "  \n",
        "Some of our other work: \n",
        "- [German BERT](https://deepset.ai/german-bert)\n",
        "- [GermanQuAD and GermanDPR](https://deepset.ai/germanquad)\n",
        "- [FARM](https://github.com/deepset-ai/FARM)\n",
        "\n",
        "Get in touch:\n",
        "[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Slack](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)\n",
        "\n",
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