HayStackTutorial11_Pipelines.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/navneetkrc/Deep-Learning-Experiments-implemented-using-Google-Colab/blob/master/HayStackTutorial11_Pipelines.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "0WLk3TpLxbTf"
      },
      "source": [
        "# Pipelines Tutorial\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/Tutorial11_Pipelines.ipynb)\n",
        "\n",
        "In this tutorial, you will learn how the `Pipeline` class acts as a connector between all the different\n",
        "building blocks that are found in FARM. Whether you are using a Reader, Generator, Summarizer\n",
        "or Retriever (or 2), the `Pipeline` class will help you build a Directed Acyclic Graph (DAG) that\n",
        "determines how to route the output of one component into the input of another.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzHZLXp3xbTk"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "wVfOlA1HxbTl"
      },
      "source": [
        "## Setting Up the Environment\n",
        "\n",
        "Let's start by ensuring we have a GPU running to ensure decent speed in this tutorial.\n",
        "In Google colab, you can change to a GPU runtime in the menu:\n",
        "- **Runtime -> Change Runtime type -> Hardware accelerator -> GPU**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "tBy66UCHxbTm",
        "outputId": "256f4a4e-fa7c-4456-ea0c-afe4e109a705",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Apr 17 14:56:17 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 T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P8     9W /  70W |      0MiB / 15109MiB |      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": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "IdZbkLr7xbTo"
      },
      "source": [
        "These lines are to install Haystack through pip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
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        "outputId": "a4f5732a-aa8b-499d-cdad-bf77d8b77c29",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: sentence-transformers, farm-haystack, langdetect, python-docx, seqeval, tika, alembic, databricks-cli\n",
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            "Installing collected packages: tokenizers, sentencepiece, python-editor, monotonic, mmh3, azure-common, websocket-client, smmap, sacremoses, querystring-parser, pyyaml, python-docx, pyjwt, pydantic, num2words, langdetect, jarowinkler, isodate, gunicorn, grpcio, elasticsearch, elastic-apm, backoff, tika, rapidfuzz, quantulum3, posthog, Mako, huggingface-hub, gitdb, docker, databricks-cli, cryptography, azure-core, transformers, seqeval, prometheus-flask-exporter, msrest, gitpython, alembic, sentence-transformers, azure-storage-blob, azure-ai-formrecognizer, mlflow, farm-haystack\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Mako-1.2.0 alembic-1.4.1 azure-ai-formrecognizer-3.2.0b2 azure-common-1.1.28 azure-core-1.22.1 azure-storage-blob-12.11.0 backoff-1.11.1 cryptography-36.0.2 databricks-cli-0.16.6 docker-5.0.3 elastic-apm-6.9.1 elasticsearch-7.10.0 farm-haystack-1.3.1rc0 gitdb-4.0.9 gitpython-3.1.27 grpcio-1.43.0 gunicorn-20.1.0 huggingface-hub-0.5.1 isodate-0.6.1 jarowinkler-1.0.2 langdetect-1.0.9 mlflow-1.13.1 mmh3-3.0.0 monotonic-1.6 msrest-0.6.21 num2words-0.5.10 posthog-1.4.6 prometheus-flask-exporter-0.20.1 pydantic-1.9.0 pyjwt-2.3.0 python-docx-0.8.11 python-editor-1.0.4 pyyaml-6.0 quantulum3-0.7.10 querystring-parser-1.2.4 rapidfuzz-2.0.9 sacremoses-0.0.49 sentence-transformers-2.2.0 sentencepiece-0.1.96 seqeval-1.2.2 smmap-5.0.0 tika-1.24 tokenizers-0.10.3 transformers-4.13.0 websocket-client-1.3.2\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "Reading package lists... Done\n",
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            "  libgail-common libgail18 libgtk2.0-0 libgtk2.0-bin libgtk2.0-common\n",
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            "  gvfs\n",
            "The following NEW packages will be installed:\n",
            "  libgail-common libgail18 libgraphviz-dev libgtk2.0-0 libgtk2.0-bin\n",
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            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pygraphviz\n",
            "  Building wheel for pygraphviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pygraphviz: filename=pygraphviz-1.7-cp37-cp37m-linux_x86_64.whl size=165755 sha256=f7aef7c1e06952308c66e0984bd7d039c3fa0e6f072feeafbd55bad55d4b2582\n",
            "  Stored in directory: /root/.cache/pip/wheels/8c/bc/0c/ac35392b72556e75107ff610cb31b313e8471918a6d280e34c\n",
            "Successfully built pygraphviz\n",
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            "Successfully installed pygraphviz-1.7\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "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]\n",
        "\n",
        "# Install pygraphviz\n",
        "!apt install libgraphviz-dev\n",
        "!pip install pygraphviz"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "nKAMofcSxbTp"
      },
      "source": [
        "If running from Colab or a no Docker environment, you will want to start Elasticsearch from source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "bR9tlSRTxbTq"
      },
      "outputs": [],
      "source": [
        "# In Colab / No Docker environments: Start Elasticsearch from source\n",
        "! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
        "! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
        "! chown -R daemon:daemon elasticsearch-7.9.2\n",
        "\n",
        "import os\n",
        "from subprocess import Popen, PIPE, STDOUT\n",
        "\n",
        "es_server = Popen(\n",
        "    [\"elasticsearch-7.9.2/bin/elasticsearch\"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1)  # as daemon\n",
        ")\n",
        "# wait until ES has started\n",
        "! sleep 30"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "LJxRxOEYxbTs"
      },
      "source": [
        "## Initialization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "T8yT7fGHxbTs"
      },
      "source": [
        "Then let's fetch some data (in this case, pages from the Game of Thrones wiki) and prepare it so that it can\n",
        "be used indexed into our `DocumentStore`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "3Uukui71xbTs",
        "outputId": "e9b6356b-8950-4eb8-8a5a-9633f99ec67d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
            "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",
            "INFO - haystack.utils.import_utils -  Fetching from https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt11.zip to `data/tutorial11`\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/121_The_Bear_and_the_Maiden_Fair.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/57_The_Laws_of_Gods_and_Men.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/201_A_Game_of_Thrones__card_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/195_World_of_A_Song_of_Ice_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/68_The_Gift__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/53_The_Lion_and_the_Rose.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/514_Book_of_the_Stranger.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/341_Ned_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/469_Outline_of_A_Song_of_Ice_and_Fire_franchise.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/425_No_One__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/131_Mhysa.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/9_Game_of_Thrones_Tapestry.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/495_Hodor.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/332_Sansa_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/1_Dragonstone__Game_of_Thrones_episode_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/348_The_Winds_of_Winter.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/474_Bronn__character_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/198_A_Clash_of_Kings.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/304_Hardhome.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/435_White_Walker.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/334_Rickon_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/443_The_Kingsroad.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/130_Game_of_Thrones_title_sequence.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/213_Valyrian_languages.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/411_The_Iron_Throne__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/371_Cersei_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/191_Gendry.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/74_The_Prince_of_Winterfell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/86_Game_of_Thrones__season_4_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/408_The_Last_of_the_Starks.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/154_Margaery_Tyrell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/136_Game_of_Thrones__Season_8__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/379_Davos_Seaworth.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/444_Cripples__Bastards__and_Broken_Things.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/67_Unbowed__Unbent__Unbroken.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/461_The_Winds_of_Winter__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/232_Tommen_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/311_Game_of_Thrones__season_7_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/217_The_Rogue_Prince.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/369_Samwell_Tarly.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/340_Roose_Bolton.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/210_The_Mountain_and_the_Viper.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/98_Black_Friday__South_Park_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/76_Valar_Morghulis.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/224_The_Night_Lands.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/28_Jorah_Mormont.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/102_The_Princess_and_the_Queen.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/85_Game_of_Thrones__Seven_Kingdoms.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/209_Mockingbird__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/477_A_Dance_with_Dragons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/20_Light_of_the_Seven.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/193_Lord_Snow.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/347_Game_of_Thrones__season_2_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/208_Robb_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/362_Winter_Is_Coming.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/26_Game_of_Thrones__Season_3__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/55_Breaker_of_Chains.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/119_Walk_of_Punishment.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/214_Dothraki_language.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/197_A_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/22_The_Rains_of_Castamere__song_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/0_Game_of_Thrones__season_8_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/118_Dark_Wings__Dark_Words.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/51_Iron_Throne__A_Song_of_Ice_and_Fire_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/134_Game_of_Thrones__Season_6__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/485_Oathkeeper.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/97_Tyrion_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/342_Theon_Greyjoy.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/101_Titties_and_Dragons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/439_Melisandre.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/80_A_Song_of_Ice_and_Fire_fandom.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/403_Jenny_of_Oldstones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/33_David_Benioff.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/368_Jaime_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/133_Game_of_Thrones__Season_5__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/39_Renly_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/44_Daenerys_Targaryen.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/84_List_of_awards_and_nominations_received_by_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/229_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/424_Night_King.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/306_The_Dance_of_Dragons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/24_Game_of_Thrones__Season_1__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/401_Power_Is_Power.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/512_Home__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/43_Arya_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/420_Blood_of_My_Blood.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/445_The_Wolf_and_the_Lion.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/194_A_Song_of_Ice_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/317_The_Broken_Man.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/54_Two_Swords__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/410_The_Bells__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/460_Battle_of_the_Bastards.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/360_List_of_Game_of_Thrones_episodes.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/454_Music_of_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/233_Myrcella_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/373_Tywin_Lannister.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/52_Catch_the_Throne.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/127_The_Climb__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/273_High_Sparrow.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/356_Tales_of_Dunk_and_Egg.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/513_Oathbreaker__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/400_Winterfell__Game_of_Thrones_episode_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/480_Varys.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/378_A_Game_of_Thrones__board_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/310_Mother_s_Mercy.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/25_Game_of_Thrones__Season_2__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/79_Thronecast.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/10_Beyond_the_Wall__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/394_Game_of_Thrones__2014_video_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/218_Olenna_Tyrell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/21_The_Bear_and_the_Maiden_Fair__song_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/3_The_Queen_s_Justice.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/78_The_Rains_of_Castamere.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/330_Oberyn_Martell.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/207_Jon_Snow__character_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/452_Fire_and_Blood__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/442_Game_of_Thrones__season_1_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/329_Robert_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/99_A_Song_of_Ass_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/396_Game_of_Thrones__season_6_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/367_Gregor_Clegane.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/129_Second_Sons.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/146_The_Sons_of_the_Dragon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/77_Game_of_Thrones_Ascent.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/12_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/475_A_Game_of_Thrones__role-playing_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/331_Bran_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/135_Game_of_Thrones__Season_7__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/120_And_Now_His_Watch_Is_Ended.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/262_Gilly__character_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/40_Stannis_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/501_Khal_Drogo.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/212_The_Children__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/30_List_of_A_Song_of_Ice_and_Fire_characters.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/7_The_Spoils_of_War__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/453_Game_of_Thrones__2012_video_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/211_The_Watchers_on_the_Wall.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/148_Game_of_Thrones__Winter_Is_Coming.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/487_Ramsay_Bolton.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/346_Ygritte.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/402_A_Knight_of_the_Seven_Kingdoms__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/160_Viserys_Targaryen.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/75_Blackwater__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/8_Eastwatch.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/151_Ellaria_Sand.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/482_Petyr_Baelish.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/199_A_Storm_of_Swords.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/488_Brienne_of_Tarth.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/2_Stormborn.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/126_Kissed_by_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/447_You_Win_or_You_Die.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/11_The_Dragon_and_the_Wolf.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/359_Kill_the_Boy.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/87_Valar_Dohaeris.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/395_Game_of_Thrones__season_5_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/506_Game_of_Thrones_Theme.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/456_Works_based_on_A_Song_of_Ice_and_Fire.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/515_The_Door__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/299_Rani_Mahal__TV_series_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/365_A_Song_of_Ice_and_Fire_Roleplaying.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/508_A_Game_of_Thrones__Second_Edition__card_game_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/450_Baelor.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/481_Sandor_Clegane.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/263_Tormund_Giantsbane.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/504_List_of_A_Song_of_Ice_and_Fire_video_games.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/449_The_Pointy_End.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/407_The_Long_Night__Game_of_Thrones_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/511_After_the_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/202_A_Feast_for_Crows.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/446_A_Golden_Crown.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/27_Game_of_Thrones__Season_4__soundtrack_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/145_Elio_M._García_Jr._and_Linda_Antonsson.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/73_A_Man_Without_Honor.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/37_Joffrey_Baratheon.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/56_First_of_His_Name.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/358_A_Game_of_Thrones__Genesis.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/343_Catelyn_Stark.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/349_List_of_Game_of_Thrones_characters.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/23_Game_of_Thrones_Live_Concert_Experience.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/69_The_Red_Woman.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/399_For_the_Throne__Music_Inspired_by_the_HBO_Series_Game_of_Thrones.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/345_A_Game_of_Thrones__comics_.txt\n",
            "INFO - haystack.utils.preprocessing -  Converting data/tutorial11/228_Souad_Faress.txt\n"
          ]
        }
      ],
      "source": [
        "from haystack.utils import (\n",
        "    print_answers,\n",
        "    print_documents,\n",
        "    fetch_archive_from_http,\n",
        "    convert_files_to_docs,\n",
        "    clean_wiki_text,\n",
        ")\n",
        "\n",
        "# Download and prepare data - 517 Wikipedia articles for Game of Thrones\n",
        "doc_dir = \"data/tutorial11\"\n",
        "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt11.zip\"\n",
        "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
        "\n",
        "# convert files to dicts containing documents that can be indexed to our datastore\n",
        "got_docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "Nj06Pe8AxbTu"
      },
      "source": [
        "Here we initialize the core components that we will be gluing together using the `Pipeline` class.\n",
        "We have a `DocumentStore`, an `ElasticsearchRetriever` and a `FARMReader`.\n",
        "These can be combined to create a classic Retriever-Reader pipeline that is designed\n",
        "to perform Open Domain Question Answering."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "D7GxGFpaxbTu",
        "outputId": "8d18ba7e-cc60-4605-b525-4a12a9ba0a75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING - haystack.utils.doc_store -  Tried to start Elasticsearch through Docker but this failed. It is likely that there is already an existing Elasticsearch instance running. \n",
            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
            "INFO - haystack.nodes.retriever.dense -  Init retriever using embeddings of model sentence-transformers/multi-qa-mpnet-base-dot-v1\n"
          ]
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        },
        {
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          "name": "stderr",
          "text": [
            "WARNING - haystack.nodes.retriever._embedding_encoder -  You are using a Sentence Transformer with the dot_product function. We recommend using cosine instead. This can be set when initializing the DocumentStore\n",
            "WARNING - haystack.nodes.base -  Unnamed __init__ parameters will not be saved to YAML if Pipeline.save_to_yaml() is called!\n",
            "INFO - haystack.document_stores.elasticsearch -  Updating embeddings for 2357 docs without embeddings ...\n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "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"
          ]
        },
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Loaded deepset/roberta-base-squad2\n"
          ]
        },
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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": [
        "from haystack import Pipeline\n",
        "from haystack.utils import launch_es\n",
        "from haystack.document_stores import ElasticsearchDocumentStore\n",
        "from haystack.nodes import ElasticsearchRetriever, EmbeddingRetriever, FARMReader\n",
        "\n",
        "\n",
        "# Initialize DocumentStore and index documents\n",
        "launch_es()\n",
        "document_store = ElasticsearchDocumentStore()\n",
        "document_store.delete_documents()\n",
        "document_store.write_documents(got_docs)\n",
        "\n",
        "# Initialize Sparse retriever\n",
        "es_retriever = ElasticsearchRetriever(document_store=document_store)\n",
        "\n",
        "# Initialize dense retriever\n",
        "embedding_retriever = EmbeddingRetriever(\n",
        "    document_store,\n",
        "    model_format=\"sentence_transformers\",\n",
        "    embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\",\n",
        ")\n",
        "document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)\n",
        "\n",
        "# Initialize reader\n",
        "reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "RLJbwJpuxbTv"
      },
      "source": [
        "## Prebuilt Pipelines\n",
        "\n",
        "Haystack features many prebuilt pipelines that cover common tasks.\n",
        "Here we have an `ExtractiveQAPipeline` (the successor to the now deprecated `Finder` class)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "pH6qyNZ8xbTv",
        "outputId": "933db9ae-e2da-41ec-d18d-c9c8b49ca1fd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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,  4.62 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 29.50 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  9.11 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 18.00 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 27.53 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 46.64 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 43.38 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 10.40 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.47 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 35.03 Batches/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[   {   'answer': 'Eddard',\n",
            "        'context': 's Nymeria after a legendary warrior queen. She travels '\n",
            "                   \"with her father, Eddard, to King's Landing when he is made \"\n",
            "                   'Hand of the King. Before she leaves,'},\n",
            "    {   'answer': 'Ned',\n",
            "        'context': '\\n'\n",
            "                   '====Season 1====\\n'\n",
            "                   'Arya accompanies her father Ned and her sister Sansa to '\n",
            "                   \"King's Landing. Before their departure, Arya's \"\n",
            "                   'half-brother Jon Snow gifts A'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': 'ark daughters.\\n'\n",
            "                   'During the Tourney of the Hand to honour her father Lord '\n",
            "                   'Eddard Stark, Sansa Stark is enchanted by the knights '\n",
            "                   'performing in the event.'},\n",
            "    {   'answer': 'Joffrey',\n",
            "        'context': 'laying with one of his wooden toys.\\n'\n",
            "                   \"After Eddard discovers the truth of Joffrey's paternity, \"\n",
            "                   'he tells Sansa that they will be heading back to Winterfe'},\n",
            "    {   'answer': 'Robb',\n",
            "        'context': 'allow the army to cross the river and to commit his troops '\n",
            "                   'in return for Robb and Arya Stark marrying two of his '\n",
            "                   'children.\\n'\n",
            "                   'Tyrion Lannister suspects h'}]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "\n",
        "# Prebuilt pipeline\n",
        "p_extractive_premade = ExtractiveQAPipeline(reader=reader, retriever=es_retriever)\n",
        "res = p_extractive_premade.run(\n",
        "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
        ")\n",
        "print_answers(res, details=\"minimum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "_M3PgkiexbTv"
      },
      "source": [
        "If you want to just do the retrieval step, you can use a `DocumentSearchPipeline`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "l-XprFYLxbTw",
        "outputId": "d88b7c6d-b592-4984-8d55-0b19e40d4728",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "\n",
            "{   'content': '\\n'\n",
            "               '===In the Riverlands===\\n'\n",
            "               'The Stark army reaches the Twins, a bridge stronghold '\n",
            "               'controlled by Walder Frey, who agrees to allow the army to '\n",
            "               'cross the river and to commit his troops in return for Robb '\n",
            "               'an...',\n",
            "    'name': '450_Baelor.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               '===On the Kingsroad===\\n'\n",
            "               'City Watchmen search the caravan for Gendry but are turned '\n",
            "               'away by Yoren. Gendry tells Arya Stark that he knows she is a '\n",
            "               'girl, and she reveals she is actually Arya Stark after ...',\n",
            "    'name': '224_The_Night_Lands.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               \"===''A Game of Thrones''===\\n\"\n",
            "               'Sansa Stark begins the novel by being betrothed to Crown '\n",
            "               'Prince Joffrey Baratheon, believing Joffrey to be a gallant '\n",
            "               'prince. While Joffrey and Sansa are walking through th...',\n",
            "    'name': '332_Sansa_Stark.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               '===Season 2===\\n'\n",
            "               \"Gendry travels North with Yoren and other Night's Watch \"\n",
            "               'recruits, including Arya Stark (disguised as an orphan boy '\n",
            "               \"named 'Arry), Lommy Greenhands, Hot Pie and Jaqen H'ghar. \"\n",
            "               'During thei...',\n",
            "    'name': '191_Gendry.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               '====Season 1====\\n'\n",
            "               \"Arya accompanies her father Ned and her sister Sansa to King's \"\n",
            "               \"Landing. Before their departure, Arya's half-brother Jon Snow \"\n",
            "               'gifts Arya a sword which she dubs \"Needle\". On the Kingsr...',\n",
            "    'name': '43_Arya_Stark.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               \"===In King's Landing===\\n\"\n",
            "               \"After Varys tells him that Sansa Stark's life is also at \"\n",
            "               'stake, Eddard \"Ned\" Stark agrees to make a false confession '\n",
            "               'and swear loyalty to King Joffrey Baratheon.\\n'\n",
            "               'Arya Stark fi...',\n",
            "    'name': '450_Baelor.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               \"==== ''A Storm of Swords'' and ''A Feast for Crows'' ====\\n\"\n",
            "               'Prior to the Red Wedding, Roose Bolton presents Robb Stark '\n",
            "               \"with a piece of Theon's skin, revealing that Ramsay has been \"\n",
            "               'flaying him; though d...',\n",
            "    'name': '487_Ramsay_Bolton.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               '===House Frey===\\n'\n",
            "               \"* '''Walder Frey''' (seasons 1, 3, 6–7) portrayed by David \"\n",
            "               'Bradley. David Bradley Lord Walder Frey, nicknamed the \"Late '\n",
            "               'Lord Frey\", is the head of House Frey, Lord of the Crossing '\n",
            "               'an...',\n",
            "    'name': '349_List_of_Game_of_Thrones_characters.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               \"==== ''A Game of Thrones'' ====\\n\"\n",
            "               'Arya adopts a direwolf cub, which she names Nymeria after a '\n",
            "               'legendary warrior queen. She travels with her father, Eddard, '\n",
            "               \"to King's Landing when he is made Hand of the...\",\n",
            "    'name': '43_Arya_Stark.txt'}\n",
            "\n",
            "{   'content': '\\n'\n",
            "               '== Character description ==\\n'\n",
            "               \"Gendry was conceived and born in King's Landing after Robert's \"\n",
            "               'Rebellion ended and is one of sixteen (twenty in the '\n",
            "               'television series) bastard children of King Robert Bara...',\n",
            "    'name': '191_Gendry.txt'}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from haystack.pipelines import DocumentSearchPipeline\n",
        "\n",
        "p_retrieval = DocumentSearchPipeline(es_retriever)\n",
        "res = p_retrieval.run(query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}})\n",
        "print_documents(res, max_text_len=200)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "vpoooJILxbTx"
      },
      "source": [
        "Or if you want to use a `Generator` instead of a `Reader`,\n",
        "you can initialize a `GenerativeQAPipeline` like this:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "QVqe3DvzxbTy",
        "outputId": "9494d947-cd6e-40f4-ae94-8fcddfa4fb30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808,
          "referenced_widgets": [
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        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.utils -  Using devices: CUDA\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/4.49k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/bart/configuration_bart.py:178: UserWarning: Please make sure the config includes `forced_bos_token_id=0` in future versions. The config can simply be saved and uploaded again to be fixed.\n",
            "  f\"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e63dac721e044a7b2b8f7f8612aabab"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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            }
          },
          "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 'RagTokenizer'. \n",
            "The class this function is called from is 'DPRQuestionEncoderTokenizer'.\n",
            "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 'RagTokenizer'. \n",
            "The class this function is called from is 'DPRQuestionEncoderTokenizerFast'.\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": "de8a508bc760448f9762ae22775d0ff7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f11984754b44bcc8e3bcdc089156cb8"
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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": "7f6f9418c0454e85ae5e26c6916d67ab"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            }
          },
          "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 'RagTokenizer'. \n",
            "The class this function is called from is 'BartTokenizer'.\n",
            "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 'RagTokenizer'. \n",
            "The class this function is called from is 'BartTokenizerFast'.\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": "dfe321f50b3b47fc997f7e86f76386aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RagTokenForGeneration were not initialized from the model checkpoint at facebook/rag-token-nq and are newly initialized: ['rag.generator.lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff53df3ea38b487e9622ea4b3748d724"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/rag/tokenization_rag.py:97: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1749: UserWarning: `max_length` is deprecated in this function, use `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.\n",
            "  UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[{'answer': ' arya stark'}, {'answer': ' eddard and catelyn stark'}]\n"
          ]
        }
      ],
      "source": [
        "from haystack.pipelines import GenerativeQAPipeline, FAQPipeline\n",
        "from haystack.nodes import RAGenerator\n",
        "\n",
        "# We set this to True so that the document store returns document embeddings with each document\n",
        "# This is needed by the Generator\n",
        "document_store.return_embedding = True\n",
        "\n",
        "# Initialize generator\n",
        "rag_generator = RAGenerator()\n",
        "\n",
        "# Generative QA\n",
        "p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever)\n",
        "res = p_generator.run(query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}})\n",
        "print_answers(res, details=\"minimum\")\n",
        "\n",
        "# We are setting this to False so that in later pipelines,\n",
        "# we get a cleaner printout\n",
        "document_store.return_embedding = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "w5rShJzqxbTy"
      },
      "source": [
        "Haystack features prebuilt pipelines to do:\n",
        "- just document search (DocumentSearchPipeline),\n",
        "- document search with summarization (SearchSummarizationPipeline)\n",
        "- generative QA (GenerativeQAPipeline)\n",
        "- FAQ style QA (FAQPipeline)\n",
        "- translated search (TranslationWrapperPipeline)\n",
        "To find out more about these pipelines, have a look at our [documentation](https://haystack.deepset.ai/docs/latest/pipelinesmd)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "SB9y9pJ7xbTz"
      },
      "source": [
        "With any Pipeline, whether prebuilt or custom constructed,\n",
        "you can save a diagram showing how all the components are connected.\n",
        "\n",
        "![image](https://github.com/deepset-ai/haystack/blob/master/docs/img/retriever-reader-pipeline.png)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "t7RKfeBBxbT1"
      },
      "outputs": [],
      "source": [
        "p_extractive_premade.draw(\"pipeline_extractive_premade.png\")\n",
        "p_retrieval.draw(\"pipeline_retrieval.png\")\n",
        "p_generator.draw(\"pipeline_generator.png\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "zXmfknC3xbT1"
      },
      "source": [
        "## Custom Pipelines\n",
        "\n",
        "Now we are going to rebuild the `ExtractiveQAPipelines` using the generic Pipeline class.\n",
        "We do this by adding the building blocks that we initialized as nodes in the graph."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "PlFLTmLBxbT2",
        "outputId": "8440581b-6070-4b69-8c52-10eb773283a3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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,  6.87 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 28.88 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 31.87 Batches/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[   {   'answer': 'Eddard',\n",
            "        'context': 's Nymeria after a legendary warrior queen. She travels '\n",
            "                   \"with her father, Eddard, to King's Landing when he is made \"\n",
            "                   'Hand of the King. Before she leaves,'},\n",
            "    {   'answer': 'Ned',\n",
            "        'context': '\\n'\n",
            "                   '====Season 1====\\n'\n",
            "                   'Arya accompanies her father Ned and her sister Sansa to '\n",
            "                   \"King's Landing. Before their departure, Arya's \"\n",
            "                   'half-brother Jon Snow gifts A'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': 'ark daughters.\\n'\n",
            "                   'During the Tourney of the Hand to honour her father Lord '\n",
            "                   'Eddard Stark, Sansa Stark is enchanted by the knights '\n",
            "                   'performing in the event.'},\n",
            "    {   'answer': 'Joffrey',\n",
            "        'context': 'laying with one of his wooden toys.\\n'\n",
            "                   \"After Eddard discovers the truth of Joffrey's paternity, \"\n",
            "                   'he tells Sansa that they will be heading back to Winterfe'},\n",
            "    {   'answer': 'Robb',\n",
            "        'context': 'allow the army to cross the river and to commit his troops '\n",
            "                   'in return for Robb and Arya Stark marrying two of his '\n",
            "                   'children.\\n'\n",
            "                   'Tyrion Lannister suspects h'}]\n"
          ]
        }
      ],
      "source": [
        "# Custom built extractive QA pipeline\n",
        "p_extractive = Pipeline()\n",
        "p_extractive.add_node(component=es_retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
        "p_extractive.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])\n",
        "\n",
        "# Now we can run it\n",
        "res = p_extractive.run(\n",
        "    query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 5}}\n",
        ")\n",
        "print_answers(res, details=\"minimum\")\n",
        "p_extractive.draw(\"pipeline_extractive.png\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "Q0Qp0HTXxbT3"
      },
      "source": [
        "Pipelines offer a very simple way to ensemble together different components.\n",
        "In this example, we are going to combine the power of an `EmbeddingRetriever`\n",
        "with the keyword based `ElasticsearchRetriever`.\n",
        "See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why\n",
        "we might want to combine a dense and sparse retriever.\n",
        "\n",
        "![image](https://github.com/deepset-ai/haystack/blob/master/docs/img/tutorial11_custompipelines_pipeline_ensemble.png?raw=true)\n",
        "\n",
        "Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "qUcYYcF2xbT3",
        "outputId": "08cd5609-ed7f-4c79-d5b6-5dc0fac01bfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "cfd07a5d4f0d420f83835a716835ee3f",
            "c33e0aebf1fd4ec481d7a2b3c11cf8a6",
            "254fcd10370044c4a8632add68ab9c87"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c35874c219ab4fc5bd58c8fcf3cad53b"
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          "metadata": {}
        },
        {
          "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, 13.58 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 28.23 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 31.49 Batches/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[   {   'answer': 'Ned',\n",
            "        'context': '\\n'\n",
            "                   '====Season 1====\\n'\n",
            "                   'Arya accompanies her father Ned and her sister Sansa to '\n",
            "                   \"King's Landing. Before their departure, Arya's \"\n",
            "                   'half-brother Jon Snow gifts A'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': 'ark daughters.\\n'\n",
            "                   'During the Tourney of the Hand to honour her father Lord '\n",
            "                   'Eddard Stark, Sansa Stark is enchanted by the knights '\n",
            "                   'performing in the event.'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': \"Game of Thrones'', Arya is the third child and younger \"\n",
            "                   'daughter of Lord Eddard Stark and his wife Lady Catelyn '\n",
            "                   'Stark.  She is tomboyish, headstrong, f'},\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': 'Jon Snow',\n",
            "        'context': ' of Winterfell.  She is particularly close to her bastard '\n",
            "                   'half-brother Jon Snow, who encourages her to learn how to '\n",
            "                   'fight and gives her the smallsword'},\n",
            "    {   'answer': 'Joffrey',\n",
            "        'context': 'laying with one of his wooden toys.\\n'\n",
            "                   \"After Eddard discovers the truth of Joffrey's paternity, \"\n",
            "                   'he tells Sansa that they will be heading back to Winterfe'},\n",
            "    {   'answer': 'Robb',\n",
            "        'context': 'allow the army to cross the river and to commit his troops '\n",
            "                   'in return for Robb and Arya Stark marrying two of his '\n",
            "                   'children.\\n'\n",
            "                   'Tyrion Lannister suspects h'},\n",
            "    {   'answer': 'Jon Snow',\n",
            "        'context': '.  She wields a smallsword named Needle, a gift from her '\n",
            "                   'half-brother, Jon Snow, and is trained in the Braavosi '\n",
            "                   'style of sword fighting by Syrio Forel'},\n",
            "    {   'answer': 'Ned Stark',\n",
            "        'context': ' to reveal her true identity, and is surprised to learn '\n",
            "                   \"she is in fact Ned Stark's daughter. After the Goldcloaks \"\n",
            "                   'get help from Ser Amory Lorch and hi'},\n",
            "    {   'answer': 'Ned',\n",
            "        'context': 'rya to reveal her true identity, and is surprised to learn '\n",
            "                   \"she is in fact Ned Stark's daughter. After the Goldcloaks \"\n",
            "                   'get help from Ser Amory Lorch and'}]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from haystack.nodes import JoinDocuments\n",
        "\n",
        "# Create ensembled pipeline\n",
        "p_ensemble = Pipeline()\n",
        "p_ensemble.add_node(component=es_retriever, name=\"ESRetriever\", inputs=[\"Query\"])\n",
        "p_ensemble.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"Query\"])\n",
        "p_ensemble.add_node(\n",
        "    component=JoinDocuments(join_mode=\"concatenate\"), name=\"JoinResults\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"]\n",
        ")\n",
        "p_ensemble.add_node(component=reader, name=\"Reader\", inputs=[\"JoinResults\"])\n",
        "p_ensemble.draw(\"pipeline_ensemble.png\")\n",
        "\n",
        "# Run pipeline\n",
        "res = p_ensemble.run(\n",
        "    query=\"Who is the father of Arya Stark?\", params={\"EmbeddingRetriever\": {\"top_k\": 5}, \"ESRetriever\": {\"top_k\": 5}}\n",
        ")\n",
        "print_answers(res, details=\"minimum\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run pipeline\n",
        "res = p_ensemble.run(\n",
        "    query=\"Who is the father of Arya Stark?\", params={\"EmbeddingRetriever\": {\"top_k\": 5}}\n",
        ")\n",
        "print_answers(res, details=\"minimum\")"
      ],
      "metadata": {
        "id": "B7Af-2lvBpNM",
        "outputId": "0366233b-b948-4cb2-8d31-8943fcdedeef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "e3b8879471ce4447a37988cd57961b3e"
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      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "853ddc07b6614e80bcec1e1597521132"
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        {
          "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,  7.48 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 31.13 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.47 Batches/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[   {   'answer': 'Eddard',\n",
            "        'context': 's Nymeria after a legendary warrior queen. She travels '\n",
            "                   \"with her father, Eddard, to King's Landing when he is made \"\n",
            "                   'Hand of the King. Before she leaves,'},\n",
            "    {   'answer': 'Ned',\n",
            "        'context': '\\n'\n",
            "                   '====Season 1====\\n'\n",
            "                   'Arya accompanies her father Ned and her sister Sansa to '\n",
            "                   \"King's Landing. Before their departure, Arya's \"\n",
            "                   'half-brother Jon Snow gifts A'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': 'ark daughters.\\n'\n",
            "                   'During the Tourney of the Hand to honour her father Lord '\n",
            "                   'Eddard Stark, Sansa Stark is enchanted by the knights '\n",
            "                   'performing in the event.'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': \"Game of Thrones'', Arya is the third child and younger \"\n",
            "                   'daughter of Lord Eddard Stark and his wife Lady Catelyn '\n",
            "                   'Stark.  She is tomboyish, headstrong, f'},\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': 'Jon Snow',\n",
            "        'context': ' of Winterfell.  She is particularly close to her bastard '\n",
            "                   'half-brother Jon Snow, who encourages her to learn how to '\n",
            "                   'fight and gives her the smallsword'},\n",
            "    {   'answer': 'Joffrey',\n",
            "        'context': 'laying with one of his wooden toys.\\n'\n",
            "                   \"After Eddard discovers the truth of Joffrey's paternity, \"\n",
            "                   'he tells Sansa that they will be heading back to Winterfe'},\n",
            "    {   'answer': 'Robb',\n",
            "        'context': 'allow the army to cross the river and to commit his troops '\n",
            "                   'in return for Robb and Arya Stark marrying two of his '\n",
            "                   'children.\\n'\n",
            "                   'Tyrion Lannister suspects h'},\n",
            "    {   'answer': 'Eddard \"Ned\" Stark',\n",
            "        'context': '=\\n'\n",
            "                   \"After Varys tells him that Sansa Stark's life is also at \"\n",
            "                   'stake, Eddard \"Ned\" Stark agrees to make a false '\n",
            "                   'confession and swear loyalty to King Joffr'},\n",
            "    {   'answer': 'Jon Snow',\n",
            "        'context': '.  She wields a smallsword named Needle, a gift from her '\n",
            "                   'half-brother, Jon Snow, and is trained in the Braavosi '\n",
            "                   'style of sword fighting by Syrio Forel'}]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run pipeline\n",
        "res = p_ensemble.run(\n",
        "    query=\"Who is the father of Arya Stark?\", params={\"ESRetriever\": {\"top_k\": 5}}\n",
        ")\n",
        "print_answers(res, details=\"minimum\")"
      ],
      "metadata": {
        "id": "9ymKCOoFBqk5",
        "outputId": "aa2a9b8f-0f61-49a4-e1f3-cfed97cac612",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "88fbb724962a4fe099869b37739c5644"
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      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
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        {
          "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,  5.98 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.80 Batches/s]\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[   {   'answer': 'Eddard',\n",
            "        'context': 's Nymeria after a legendary warrior queen. She travels '\n",
            "                   \"with her father, Eddard, to King's Landing when he is made \"\n",
            "                   'Hand of the King. Before she leaves,'},\n",
            "    {   'answer': 'Ned',\n",
            "        'context': '\\n'\n",
            "                   '====Season 1====\\n'\n",
            "                   'Arya accompanies her father Ned and her sister Sansa to '\n",
            "                   \"King's Landing. Before their departure, Arya's \"\n",
            "                   'half-brother Jon Snow gifts A'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': 'ark daughters.\\n'\n",
            "                   'During the Tourney of the Hand to honour her father Lord '\n",
            "                   'Eddard Stark, Sansa Stark is enchanted by the knights '\n",
            "                   'performing in the event.'},\n",
            "    {   'answer': 'Lord Eddard Stark',\n",
            "        'context': \"Game of Thrones'', Arya is the third child and younger \"\n",
            "                   'daughter of Lord Eddard Stark and his wife Lady Catelyn '\n",
            "                   'Stark.  She is tomboyish, headstrong, f'},\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': 'Jon Snow',\n",
            "        'context': ' of Winterfell.  She is particularly close to her bastard '\n",
            "                   'half-brother Jon Snow, who encourages her to learn how to '\n",
            "                   'fight and gives her the smallsword'},\n",
            "    {   'answer': 'Brynden Tully',\n",
            "        'context': 'o the weather. Sandor decides to instead take her to her '\n",
            "                   'great-uncle Brynden Tully. On their way to Riverrun, they '\n",
            "                   \"encounter two men on Arya's death l\"},\n",
            "    {   'answer': 'Lord Eddard and Catelyn Stark',\n",
            "        'context': 'rk of House Stark is the younger daughter and third child '\n",
            "                   'of Lord Eddard and Catelyn Stark of Winterfell. Ever the '\n",
            "                   'tomboy, Arya would rather be traini'},\n",
            "    {   'answer': 'Joffrey',\n",
            "        'context': 'laying with one of his wooden toys.\\n'\n",
            "                   \"After Eddard discovers the truth of Joffrey's paternity, \"\n",
            "                   'he tells Sansa that they will be heading back to Winterfe'},\n",
            "    {   'answer': 'Robb',\n",
            "        'context': 'allow the army to cross the river and to commit his troops '\n",
            "                   'in return for Robb and Arya Stark marrying two of his '\n",
            "                   'children.\\n'\n",
            "                   'Tyrion Lannister suspects h'}]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "CetmgsvhxbT4"
      },
      "source": [
        "## Custom Nodes\n",
        "\n",
        "Nodes are relatively simple objects\n",
        "and we encourage our users to design their own if they don't see on that fits their use case\n",
        "\n",
        "The only requirements are:\n",
        "- Create a class that inherits `BaseComponent`.\n",
        "- Add a method run() to your class. Add the mandatory and optional arguments it needs to process. These arguments must be passed as input to the pipeline, inside `params`, or output by preceding nodes.\n",
        "- Add processing logic inside the run() (e.g. reformatting the query).\n",
        "- Return a tuple that contains your output data (for the next node)\n",
        "and the name of the outgoing edge (by default \"output_1\" for nodes that have one output)\n",
        "- Add a class attribute outgoing_edges = 1 that defines the number of output options from your node. You only need a higher number here if you have a decision node (see below).\n",
        "\n",
        "Here we have a template for a Node:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "V65nHLKtxbT5"
      },
      "outputs": [],
      "source": [
        "from haystack import BaseComponent\n",
        "from typing import Optional\n",
        "\n",
        "\n",
        "class CustomNode(BaseComponent):\n",
        "    outgoing_edges = 1\n",
        "\n",
        "    def run(self, query: str, my_optional_param: Optional[int]):\n",
        "        # process the inputs\n",
        "        output = {\"my_output\": ...}\n",
        "        return output, \"output_1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "PeMX6spHxbT5"
      },
      "source": [
        "## Decision Nodes\n",
        "\n",
        "Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.\n",
        "One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to EmbeddingRetriever + Reader.\n",
        "With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.\n",
        "\n",
        "![image](https://github.com/deepset-ai/haystack/blob/master/docs/img/tutorial11_decision_nodes_pipeline_classifier.png?raw=true)\n",
        "\n",
        "Though this looks very similar to the ensembled pipeline shown above,\n",
        "the key difference is that only one of the retrievers is run for each request.\n",
        "By contrast both retrievers are always run in the ensembled approach.\n",
        "\n",
        "Below, we define a very naive `QueryClassifier` and show how to use it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "yphspV9kxbT5",
        "outputId": "460a7e83-f23c-43fb-9ba7-a2fed108afb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971,
          "referenced_widgets": [
            "b40b20bc1c2f41f6adb54cc030b1cedf",
            "c012aa67aa0a452590e822e497b3db6a",
            "803206055a5f4528bd6b262697862706",
            "b94958ab53af416ca364e86a978ae008",
            "f6b4e22b330948dda4d2213c2b2f56e0",
            "ee63f98327064adc9b79ae20926c6507",
            "367f716c4dc24ea899cc39fbee2718fb",
            "d17916979a484320bf46741de9c1e379",
            "1ad3c3e58d29423c94c55c3bdeaa91dc",
            "4f2fc5f925de42bf9002bcff8fe10cd3",
            "8f9e9c5fbcf94af69e5dd6ea9d41d112"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b40b20bc1c2f41f6adb54cc030b1cedf"
            }
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          "metadata": {}
        },
        {
          "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",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 20.85 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 28.61 Batches/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Embedding Retriever Results\n",
            "===============\n",
            "\n",
            "Query: Who is the father of Arya Stark?\n",
            "Answers:\n",
            "[   <Answer {'answer': 'Eddard', 'type': 'extractive', 'score': 0.9919579923152924, 'context': \"s Nymeria after a legendary warrior queen. She travels with her father, Eddard, to King's Landing when he is made Hand of the King. Before she leaves,\", 'offsets_in_document': [{'start': 147, 'end': 153}], 'offsets_in_context': [{'start': 72, 'end': 78}], 'document_id': 'ba2a8e87ddd95e380bec55983ee7d55f', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Ned', 'type': 'extractive', 'score': 0.9767242670059204, 'context': \"\\n====Season 1====\\nArya accompanies her father Ned and her sister Sansa to King's Landing. Before their departure, Arya's half-brother Jon Snow gifts A\", 'offsets_in_document': [{'start': 46, 'end': 49}], 'offsets_in_context': [{'start': 46, 'end': 49}], 'document_id': '180c2a6b36369712b361a80842e79356', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Lord Eddard Stark', 'type': 'extractive', 'score': 0.8793495893478394, 'context': \"Game of Thrones'', Arya is the third child and younger daughter of Lord Eddard Stark and his wife Lady Catelyn Stark.  She is tomboyish, headstrong, f\", 'offsets_in_document': [{'start': 419, 'end': 436}], 'offsets_in_context': [{'start': 67, 'end': 84}], 'document_id': '2ee56bdd46dfd30b23f91bcc046456a4', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Eddard and Catelyn Stark', 'type': 'extractive', 'score': 0.8221823871135712, 'context': 'Background ===\\nArya is the third child and younger daughter of Eddard and Catelyn Stark and is nine years old at the beginning of the book series.  Sh', 'offsets_in_document': [{'start': 68, 'end': 92}], 'offsets_in_context': [{'start': 63, 'end': 87}], 'document_id': 'd7a98cb66f592540fa7de20bf46a5e64', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Jon Snow', 'type': 'extractive', 'score': 0.821662425994873, 'context': ' of Winterfell.  She is particularly close to her bastard half-brother Jon Snow, who encourages her to learn how to fight and gives her the smallsword', 'offsets_in_document': [{'start': 656, 'end': 664}], 'offsets_in_context': [{'start': 71, 'end': 79}], 'document_id': 'a64bb94eab347d5cc10686c16b52a4dd', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Brynden Tully', 'type': 'extractive', 'score': 0.7207880318164825, 'context': \"o the weather. Sandor decides to instead take her to her great-uncle Brynden Tully. On their way to Riverrun, they encounter two men on Arya's death l\", 'offsets_in_document': [{'start': 1021, 'end': 1034}], 'offsets_in_context': [{'start': 69, 'end': 82}], 'document_id': 'e06a1c067e1556cf86e391f91f422572', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Lord Eddard and Catelyn Stark', 'type': 'extractive', 'score': 0.686543732881546, 'context': 'rk of House Stark is the younger daughter and third child of Lord Eddard and Catelyn Stark of Winterfell. Ever the tomboy, Arya would rather be traini', 'offsets_in_document': [{'start': 134, 'end': 163}], 'offsets_in_context': [{'start': 61, 'end': 90}], 'document_id': '623e446a1b048d81130a29e262152cd5', 'meta': {'name': '349_List_of_Game_of_Thrones_characters.txt'}}>,\n",
            "    <Answer {'answer': 'Dondarrion', 'type': 'extractive', 'score': 0.6121854186058044, 'context': \"o trial by combat. The Hound wins, to Arya's fury, and is released by Dondarrion. Arya is further enraged when the Brotherhood sells Gendry to Melisan\", 'offsets_in_document': [{'start': 521, 'end': 531}], 'offsets_in_context': [{'start': 70, 'end': 80}], 'document_id': '536e2d431a9ea2ad7eb3e15a4e5fb440', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Jon Snow', 'type': 'extractive', 'score': 0.5814972221851349, 'context': '.  She wields a smallsword named Needle, a gift from her half-brother, Jon Snow, and is trained in the Braavosi style of sword fighting by Syrio Forel', 'offsets_in_document': [{'start': 662, 'end': 670}], 'offsets_in_context': [{'start': 71, 'end': 79}], 'document_id': '2ee56bdd46dfd30b23f91bcc046456a4', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Joffrey', 'type': 'extractive', 'score': 0.4483480304479599, 'context': \"Mycah, sparring in the woods with broomsticks.  Arya defends Mycah from Joffrey's torments and her direwolf Nymeria helps Arya fight off Joffrey, woun\", 'offsets_in_document': [{'start': 573, 'end': 580}], 'offsets_in_context': [{'start': 72, 'end': 79}], 'document_id': 'ba2a8e87ddd95e380bec55983ee7d55f', 'meta': {'name': '43_Arya_Stark.txt'}}>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 12.01 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  6.94 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 50.91 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 30.92 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.30 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.22 Batches/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ES Results\n",
            "===============\n",
            "\n",
            "Query: Arya Stark father\n",
            "Answers:\n",
            "[   <Answer {'answer': 'Eddard', 'type': 'extractive', 'score': 0.9085888862609863, 'context': \"s Nymeria after a legendary warrior queen. She travels with her father, Eddard, to King's Landing when he is made Hand of the King. Before she leaves,\", 'offsets_in_document': [{'start': 147, 'end': 153}], 'offsets_in_context': [{'start': 72, 'end': 78}], 'document_id': 'ba2a8e87ddd95e380bec55983ee7d55f', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Ned', 'type': 'extractive', 'score': 0.7877896726131439, 'context': \"\\n====Season 1====\\nArya accompanies her father Ned and her sister Sansa to King's Landing. Before their departure, Arya's half-brother Jon Snow gifts A\", 'offsets_in_document': [{'start': 46, 'end': 49}], 'offsets_in_context': [{'start': 46, 'end': 49}], 'document_id': '180c2a6b36369712b361a80842e79356', 'meta': {'name': '43_Arya_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Lord Eddard Stark', 'type': 'extractive', 'score': 0.7179791331291199, 'context': 'ark daughters.\\nDuring the Tourney of the Hand to honour her father Lord Eddard Stark, Sansa Stark is enchanted by the knights performing in the event.', 'offsets_in_document': [{'start': 659, 'end': 676}], 'offsets_in_context': [{'start': 67, 'end': 84}], 'document_id': 'd1f36ec7170e4c46cde65787fe125dfe', 'meta': {'name': '332_Sansa_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Balon', 'type': 'extractive', 'score': 0.40754663944244385, 'context': \"sgusted, Robb acquiesces to Theon's further captivity, as Theon's father Balon has recently died and Theon's absence presents a succession crisis for \", 'offsets_in_document': [{'start': 274, 'end': 279}], 'offsets_in_context': [{'start': 73, 'end': 78}], 'document_id': 'fc56eb160221cbdc74d223383680dbeb', 'meta': {'name': '487_Ramsay_Bolton.txt'}}>,\n",
            "    <Answer {'answer': 'Robert', 'type': 'extractive', 'score': 0.21348517388105392, 'context': \"d Gendry over to them - King Joffrey has ordered that all of his father Robert's bastards be killed, but Yoren turns the Goldcloaks away. Later, Gendr\", 'offsets_in_document': [{'start': 362, 'end': 368}], 'offsets_in_context': [{'start': 72, 'end': 78}], 'document_id': 'dd4e070a22896afa81748d6510006d2', 'meta': {'name': '191_Gendry.txt'}}>,\n",
            "    <Answer {'answer': 'Season 7', 'type': 'extractive', 'score': 0.13554804772138596, 'context': '\\n====Season 7====\\nJon travels to Dragonstone to negotiate with Daenerys Targaryen for her support against the White Walkers, leaving Sansa as regent i', 'offsets_in_document': [{'start': 5, 'end': 13}], 'offsets_in_context': [{'start': 5, 'end': 13}], 'document_id': '8ed3e3264c4db55f1545eafdad30a38b', 'meta': {'name': '332_Sansa_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Robb', 'type': 'extractive', 'score': 0.12908092141151428, 'context': 'ost, Walder was able to negotiate marriage contracts for his children to Robb and Arya Stark. But during Season 2 Robb broke his word and married Lady', 'offsets_in_document': [{'start': 487, 'end': 491}], 'offsets_in_context': [{'start': 73, 'end': 77}], 'document_id': 'e60cb63e43a5a01694aea3a5cab14281', 'meta': {'name': '349_List_of_Game_of_Thrones_characters.txt'}}>,\n",
            "    <Answer {'answer': 'Joffrey', 'type': 'extractive', 'score': 0.07759711891412735, 'context': \"laying with one of his wooden toys.\\nAfter Eddard discovers the truth of Joffrey's paternity, he tells Sansa that they will be heading back to Winterfe\", 'offsets_in_document': [{'start': 1161, 'end': 1168}], 'offsets_in_context': [{'start': 72, 'end': 79}], 'document_id': 'd1f36ec7170e4c46cde65787fe125dfe', 'meta': {'name': '332_Sansa_Stark.txt'}}>,\n",
            "    <Answer {'answer': 'Eddard \"Ned\" Stark agrees to make a false confession and swear loyalty to King Joffrey Baratheon.\\nArya Stark finds a crowd gathering to watch her father be judged', 'type': 'extractive', 'score': 0.06236855685710907, 'context': 'Eddard \"Ned\" Stark agrees to make a false confession and swear loyalty to King Joffrey Baratheon.\\nArya Stark finds a crowd gathering to watch her father be judged', 'offsets_in_document': [{'start': 89, 'end': 251}], 'offsets_in_context': [{'start': 0, 'end': 162}], 'document_id': '956aa2b653c6debcb6cb217531a6be58', 'meta': {'name': '450_Baelor.txt'}}>,\n",
            "    <Answer {'answer': 'Robb', 'type': 'extractive', 'score': 0.058649828657507896, 'context': 'allow the army to cross the river and to commit his troops in return for Robb and Arya Stark marrying two of his children.\\nTyrion Lannister suspects h', 'offsets_in_document': [{'start': 193, 'end': 197}], 'offsets_in_context': [{'start': 73, 'end': 77}], 'document_id': '6b181174d1237878b706e6a12d69e92', 'meta': {'name': '450_Baelor.txt'}}>]\n"
          ]
        }
      ],
      "source": [
        "class CustomQueryClassifier(BaseComponent):\n",
        "    outgoing_edges = 2\n",
        "\n",
        "    def run(self, query: str):\n",
        "        if \"?\" in query:\n",
        "            return {}, \"output_2\"\n",
        "        else:\n",
        "            return {}, \"output_1\"\n",
        "\n",
        "\n",
        "# Here we build the pipeline\n",
        "p_classifier = Pipeline()\n",
        "p_classifier.add_node(component=CustomQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"])\n",
        "p_classifier.add_node(component=es_retriever, name=\"ESRetriever\", inputs=[\"QueryClassifier.output_1\"])\n",
        "p_classifier.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"QueryClassifier.output_2\"])\n",
        "p_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"])\n",
        "p_classifier.draw(\"pipeline_classifier.png\")\n",
        "\n",
        "# Run only the dense retriever on the full sentence query\n",
        "res_1 = p_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
        "print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
        "print_answers(res_1)\n",
        "\n",
        "# Run only the sparse retriever on a keyword based query\n",
        "res_2 = p_classifier.run(query=\"Arya Stark father\")\n",
        "print(\"ES Results\" + \"\\n\" + \"=\" * 15)\n",
        "print_answers(res_2)"
      ]
    },
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          "name": "#%% md\n"
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      "source": [
        "## Evaluation Nodes\n",
        "\n",
        "We have also designed a set of nodes that can be used to evaluate the performance of a system.\n",
        "Have a look at our [tutorial](https://haystack.deepset.ai/docs/latest/tutorial5md) to get hands on with the code and learn more about Evaluation Nodes!"
      ]
    },
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      "source": [
        "## Debugging Pipelines\n",
        "\n",
        "You can print out debug information from nodes in your pipelines in a few different ways."
      ]
    },
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              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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            "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, 11.64 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  8.54 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 25.95 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.55 Batches/s]\n"
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 14.59 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  9.24 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  9.67 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 33.04 Batches/s]\n"
          ]
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          "ename": "KeyError",
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-104fac63384e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp_classifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Who is the father of Arya Stark?\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"debug\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\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      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"_debug\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m: '_debug'"
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      "source": [
        "# 1) You can set the `debug` attribute of a given node.\n",
        "es_retriever.debug = True\n",
        "\n",
        "# 2) You can provide `debug` as a parameter when running your pipeline\n",
        "result = p_classifier.run(query=\"Who is the father of Arya Stark?\", params={\"ESRetriever\": {\"debug\": True}})\n",
        "\n",
        "# 3) You can provide the `debug` paramter to all nodes in your pipeline\n",
        "result = p_classifier.run(query=\"Who is the father of Arya Stark?\", params={\"debug\": True})\n",
        "\n",
        "result[\"_debug\"]"
      ]
    },
    {
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      "source": [
        "## YAML Configs\n",
        "\n",
        "A full `Pipeline` can be defined in a YAML file and simply loaded.\n",
        "Having your pipeline available in a YAML is particularly useful\n",
        "when you move between experimentation and production environments.\n",
        "Just export the YAML from your notebook / IDE and import it into your production environment.\n",
        "It also helps with version control of pipelines,\n",
        "allows you to share your pipeline easily with colleagues,\n",
        "and simplifies the configuration of pipeline parameters in production.\n",
        "\n",
        "It consists of two main sections: you define all objects (e.g. a reader) in components\n",
        "and then stick them together to a pipeline in pipelines.\n",
        "You can also set one component to be multiple nodes of a pipeline or to be a node across multiple pipelines.\n",
        "It will be loaded just once in memory and therefore doesn't hurt your resources more than actually needed.\n",
        "\n",
        "The contents of a YAML file should look something like this:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
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      "source": [
        "```yaml\n",
        "version: '0.7'\n",
        "components:    # define all the building-blocks for Pipeline\n",
        "- name: MyReader       # custom-name for the component; helpful for visualization & debugging\n",
        "  type: FARMReader    # Haystack Class name for the component\n",
        "  params:\n",
        "    no_ans_boost: -10\n",
        "    model_name_or_path: deepset/roberta-base-squad2\n",
        "- name: MyESRetriever\n",
        "  type: ElasticsearchRetriever\n",
        "  params:\n",
        "    document_store: MyDocumentStore    # params can reference other components defined in the YAML\n",
        "    custom_query: null\n",
        "- name: MyDocumentStore\n",
        "  type: ElasticsearchDocumentStore\n",
        "  params:\n",
        "    index: haystack_test\n",
        "pipelines:    # multiple Pipelines can be defined using the components from above\n",
        "- name: my_query_pipeline    # a simple extractive-qa Pipeline\n",
        "  nodes:\n",
        "  - name: MyESRetriever\n",
        "    inputs: [Query]\n",
        "  - name: MyReader\n",
        "    inputs: [MyESRetriever]\n",
        "```"
      ]
    },
    {
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          "name": "#%% md\n"
        },
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      "source": [
        "To load, simply call:\n",
        "``` python\n",
        "pipeline.load_from_yaml(Path(\"sample.yaml\"))\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "wV_3VIT_xbT-"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "The possibilities are endless with the `Pipeline` class and we hope that this tutorial will inspire you\n",
        "to build custom pipeplines that really work for your use case!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "3-Xu0nToxbT-"
      },
      "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",
        "By the way: [we're hiring!](https://www.deepset.ai/jobs)"
      ]
    }
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