Generative_QA_with_Retrieval-Augmented_Generation_Tutorial7_RAG_Generator.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "AnRtpmDm6t2P"
      },
      "source": [
        "# Generative QA with \"Retrieval-Augmented Generation\"\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/Tutorial7_RAG_Generator.ipynb)\n",
        "\n",
        "While extractive QA highlights the span of text that answers a query,\n",
        "generative QA can return a novel text answer that it has composed.\n",
        "In this tutorial, you will learn how to set up a generative system using the\n",
        "[RAG model](https://arxiv.org/abs/2005.11401) which conditions the\n",
        "answer generator on a set of retrieved documents."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "_UDJ4Ed46t2T"
      },
      "source": [
        "### Prepare environment\n",
        "\n",
        "#### Colab: Enable the GPU runtime\n",
        "Make sure you enable the GPU runtime to experience decent speed in this tutorial.\n",
        "**Runtime -> Change Runtime type -> Hardware accelerator -> GPU**\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/deepset-ai/haystack/master/docs/img/colab_gpu_runtime.jpg\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
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        "outputId": "cb0c9901-9cba-45b3-f40f-86e85c7245d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Apr 18 23:59:26 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   72C    P8    34W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# Make sure you have a GPU running\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "_9BimiSg6t2V"
      },
      "source": [
        "Here are the packages and imports that we'll need:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "BuLCYg816t2W"
      },
      "outputs": [],
      "source": [
        "# Install the latest release of Haystack in your own environment\n",
        "#! pip install farm-haystack\n",
        "\n",
        "# Install the latest master of Haystack\n",
        "!pip install --upgrade pip\n",
        "!pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab,faiss]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "CYsdTiOG6t2W",
        "outputId": "bce760a0-65c5-4c66-d989-c2d33928b349",
        "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"
          ]
        }
      ],
      "source": [
        "from typing import List\n",
        "import requests\n",
        "import pandas as pd\n",
        "from haystack import Document\n",
        "from haystack.document_stores import FAISSDocumentStore\n",
        "from haystack.nodes import RAGenerator, DensePassageRetriever\n",
        "from haystack.utils import fetch_archive_from_http"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "GxR5DauT6t2X"
      },
      "source": [
        "Let's download a csv containing some sample text and preprocess the data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "hQRykYjC6t2Y",
        "outputId": "a8e21448-4a88-4dab-a4a8-e19bb20f7c2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.utils.import_utils -  Found data stored in `data/tutorial7/`. Delete this first if you really want to fetch new data.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               title  \\\n",
            "0  \"Albert Einstein\"   \n",
            "1  \"Albert Einstein\"   \n",
            "2  \"Albert Einstein\"   \n",
            "3  \"Albert Einstein\"   \n",
            "4     \"Alfred Nobel\"   \n",
            "\n",
            "                                                                              text  \n",
            "0  to Einstein in 1922. Footnotes Citations Albert Einstein Albert Einstein (; ...  \n",
            "1  Albert Einstein Albert Einstein (; ; 14 March 1879 – 18 April 1955) was a Ge...  \n",
            "2  observations were published in the international media, making Einstein worl...  \n",
            "3  model for depictions of mad scientists and absent-minded professors; his exp...  \n",
            "4  was adopted as the standard technology for mining in the \"Age of Engineering...  \n"
          ]
        }
      ],
      "source": [
        "# Download sample\n",
        "doc_dir = \"data/tutorial7/\"\n",
        "s3_url = \"https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/small_generator_dataset.csv.zip\"\n",
        "fetch_archive_from_http(url=s3_url, output_dir=doc_dir)\n",
        "\n",
        "# Create dataframe with columns \"title\" and \"text\"\n",
        "df = pd.read_csv(\"/content/data/tutorial7/small_generator_dataset.csv\", sep=\",\")\n",
        "# Minimal cleaning\n",
        "df.fillna(value=\"\", inplace=True)\n",
        "\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "BrlRCcwl6t2Z"
      },
      "source": [
        "We can cast our data into Haystack Document objects.\n",
        "Alternatively, we can also just use dictionaries with \"text\" and \"meta\" fields"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "YPPHsCki6t2a"
      },
      "outputs": [],
      "source": [
        "# Use data to initialize Document objects\n",
        "titles = list(df[\"title\"].values)\n",
        "texts = list(df[\"text\"].values)\n",
        "documents: List[Document] = []\n",
        "for title, text in zip(titles, texts):\n",
        "    documents.append(Document(content=text, meta={\"name\": title or \"\"}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "Q8X3Qd9i6t2a"
      },
      "source": [
        "Here we initialize the FAISSDocumentStore, DensePassageRetriever and RAGenerator.\n",
        "FAISS is chosen here since it is optimized vector storage."
      ]
    },
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            "7832d4934e4746b9b3a617bd16cd6a2c",
            "2e05512779db42008517778a0693e63e"
          ]
        }
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n"
          ]
        },
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          "text": [
            "INFO - haystack.modeling.model.language_model -  LOADING MODEL\n",
            "INFO - haystack.modeling.model.language_model -  =============\n",
            "INFO - haystack.modeling.model.language_model -  Could not find facebook/dpr-question_encoder-single-nq-base locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Loaded facebook/dpr-question_encoder-single-nq-base\n"
          ]
        },
        {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'DPRQuestionEncoderTokenizer'. \n",
            "The class this function is called from is 'DPRContextEncoderTokenizerFast'.\n",
            "INFO - haystack.modeling.model.language_model -  LOADING MODEL\n",
            "INFO - haystack.modeling.model.language_model -  =============\n",
            "INFO - haystack.modeling.model.language_model -  Could not find facebook/dpr-ctx_encoder-single-nq-base locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
          ]
        },
        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Loaded facebook/dpr-ctx_encoder-single-nq-base\n",
            "INFO - haystack.modeling.utils -  Using devices: CUDA\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n"
          ]
        },
        {
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        {
          "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"
          ]
        },
        {
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        {
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          "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"
          ]
        },
        {
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          "metadata": {}
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        {
          "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",
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          },
          "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"
          ]
        }
      ],
      "source": [
        "# Initialize FAISS document store.\n",
        "# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding\n",
        "document_store = FAISSDocumentStore(faiss_index_factory_str=\"Flat\", return_embedding=True)\n",
        "\n",
        "# Initialize DPR Retriever to encode documents, encode question and query documents\n",
        "retriever = DensePassageRetriever(\n",
        "    document_store=document_store,\n",
        "    query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n",
        "    passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n",
        "    use_gpu=True,\n",
        "    embed_title=True,\n",
        ")\n",
        "\n",
        "# Initialize RAG Generator\n",
        "generator = RAGenerator(\n",
        "    model_name_or_path=\"facebook/rag-token-nq\",\n",
        "    use_gpu=True,\n",
        "    top_k=1,\n",
        "    max_length=200,\n",
        "    min_length=2,\n",
        "    embed_title=True,\n",
        "    num_beams=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "oeHKlFHy6t2b"
      },
      "source": [
        "We write documents to the DocumentStore, first by deleting any remaining documents then calling `write_documents()`.\n",
        "The `update_embeddings()` method uses the retriever to create an embedding for each document.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "04MsJO1H6t2b",
        "outputId": "36758fc2-9f4a-44a9-f852-abd092d20811",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
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            "6126ca04327e4a63b8ddf5dedf599abb",
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            "33f78b1925754621aa09b880ac9d2f22",
            "75fb574d9e6e4bb190f1e91594bc2957"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing Documents:   0%|          | 0/75 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b035092ba0ec435e924e57d9ddabaa19"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.document_stores.faiss -  Updating embeddings for 68 docs...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Updating Embedding:   0%|          | 0/68 [00:00<?, ? docs/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "297f46b95e2b488ca2f4a1ebf597f260"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Create embeddings:   0%|          | 0/80 [00:00<?, ? Docs/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "697022b2ddfe4cbea0ca7522a438994c"
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          "metadata": {}
        }
      ],
      "source": [
        "# Delete existing documents in documents store\n",
        "document_store.delete_documents()\n",
        "\n",
        "# Write documents to document store\n",
        "document_store.write_documents(documents)\n",
        "\n",
        "# Add documents embeddings to index\n",
        "document_store.update_embeddings(retriever=retriever)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "9VcYwTAU6t2c"
      },
      "source": [
        "Here are our questions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "nwwMoqYT6t2c"
      },
      "outputs": [],
      "source": [
        "QUESTIONS = [\n",
        "    \"who got the first nobel prize in physics\",\n",
        "    \"when is the next deadpool movie being released\",\n",
        "    \"which mode is used for short wave broadcast service\",\n",
        "    \"who is the owner of reading football club\",\n",
        "    \"when is the next scandal episode coming out\",\n",
        "    \"when is the last time the philadelphia won the superbowl\",\n",
        "    \"what is the most current adobe flash player version\",\n",
        "    \"how many episodes are there in dragon ball z\",\n",
        "    \"what is the first step in the evolution of the eye\",\n",
        "    \"where is gall bladder situated in human body\",\n",
        "    \"what is the main mineral in lithium batteries\",\n",
        "    \"who is the president of usa right now\",\n",
        "    \"where do the greasers live in the outsiders\",\n",
        "    \"panda is a national animal of which country\",\n",
        "    \"what is the name of manchester united stadium\",\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "UEG4Gigl6t2d"
      },
      "source": [
        "Now let's run our system!\n",
        "The retriever will pick out a small subset of documents that it finds relevant.\n",
        "These are used to condition the generator as it generates the answer.\n",
        "What it should return then are novel text spans that form and answer to your question!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "8FobmQHQ6t2d",
        "outputId": "80da0cae-51c4-4079-daa3-e16b7a600264",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "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 got the first nobel prize in physics\n",
            "Answers:\n",
            "[{'answer': ' albert einstein'}]\n",
            "\n",
            "Query: when is the next deadpool movie being released\n",
            "Answers:\n",
            "[{'answer': ' september 22, 2017'}]\n",
            "\n",
            "Query: which mode is used for short wave broadcast service\n",
            "Answers:\n",
            "[{'answer': ' amplitude modulation'}]\n",
            "\n",
            "Query: who is the owner of reading football club\n",
            "Answers:\n",
            "[{'answer': ' stefan persson'}]\n",
            "\n",
            "Query: when is the next scandal episode coming out\n",
            "Answers:\n",
            "[{'answer': ' april 20, 2018'}]\n",
            "\n",
            "Query: when is the last time the philadelphia won the superbowl\n",
            "Answers:\n",
            "[{'answer': ' the 1970s'}]\n",
            "\n",
            "Query: what is the most current adobe flash player version\n",
            "Answers:\n",
            "[{'answer': ' 7.1. 2'}]\n",
            "\n",
            "Query: how many episodes are there in dragon ball z\n",
            "Answers:\n",
            "[{'answer': ' 13'}]\n",
            "\n",
            "Query: what is the first step in the evolution of the eye\n",
            "Answers:\n",
            "[{'answer': ' step by step'}]\n",
            "\n",
            "Query: where is gall bladder situated in human body\n",
            "Answers:\n",
            "[{'answer': ' stomach'}]\n",
            "\n",
            "Query: what is the main mineral in lithium batteries\n",
            "Answers:\n",
            "[{'answer': ' spodumene'}]\n",
            "\n",
            "Query: who is the president of usa right now\n",
            "Answers:\n",
            "[{'answer': ' obama'}]\n",
            "\n",
            "Query: where do the greasers live in the outsiders\n",
            "Answers:\n",
            "[{'answer': ' northern new jersey'}]\n",
            "\n",
            "Query: panda is a national animal of which country\n",
            "Answers:\n",
            "[{'answer': ' india'}]\n",
            "\n",
            "Query: what is the name of manchester united stadium\n",
            "Answers:\n",
            "[{'answer': ', united kingdom'}]\n"
          ]
        }
      ],
      "source": [
        "# Or alternatively use the Pipeline class\n",
        "from haystack.pipelines import GenerativeQAPipeline\n",
        "from haystack.utils import print_answers\n",
        "\n",
        "pipe = GenerativeQAPipeline(generator=generator, retriever=retriever)\n",
        "for question in QUESTIONS:\n",
        "    res = pipe.run(query=question, params={\"Generator\": {\"top_k\": 1}, \"Retriever\": {\"top_k\": 5}})\n",
        "    print_answers(res, details=\"minimum\")"
      ]
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      "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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