DataChallenges.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgpkV3SuX60b"
      },
      "source": [
        "# Scraping data from Twitter feed\n",
        "If you don't want to scrape the data freshly, you can skip this step and clone the repo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lNOILMbhc8gY"
      },
      "outputs": [],
      "source": [
        "# !pip install git+https://github.com/woluxwolu/twint.git@origin/master#egg=twint"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W3BagJiMDST5"
      },
      "outputs": [],
      "source": [
        "# !twint -u \"DB_Bahn\" --year 2023 -o \"db_bahn_tweets.json\" --json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-D2sYnLstoG"
      },
      "source": [
        "A copy of `db_bahn_tweets.json` can be found in the repository below"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBlCAqWRpcEn"
      },
      "source": [
        "#Clone repository containing test data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0ot1w3VgF-5",
        "outputId": "c386d6d1-cbbd-436a-8cc2-2e5d020ef6a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'DataChallengesSoSe22'...\n",
            "remote: Enumerating objects: 30, done.\u001b[K\n",
            "remote: Counting objects: 100% (30/30), done.\u001b[K\n",
            "remote: Compressing objects: 100% (25/25), done.\u001b[K\n",
            "remote: Total 30 (delta 5), reused 13 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (30/30), done.\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "! git clone https://github.com/ToastyDom/DataChallengesSoSe22.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p-A-kjtop1Fq"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "from pprint import pprint\n",
        "\n",
        "file = open('DataChallengesSoSe22/data/db_bahn_tweets_2_years.json', 'r')\n",
        "lines = file.readlines()\n",
        "\n",
        "extracted_tweets = []\n",
        "\n",
        "for line in lines:\n",
        "  tweet = json.loads(line)\n",
        "  extracted_tweets.append([\n",
        "    tweet[\"username\"],\n",
        "    tweet[\"created_at\"],\n",
        "    tweet[\"tweet\"],\n",
        "    tweet[\"id\"],\n",
        "    tweet[\"link\"]\n",
        "  ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KfwVViBmr2fF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc79c9e3-daf5-434b-b703-75e5b63ba010"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71379\n",
            "['db_bahn', '2020-12-31 21:00:37 UTC', 'Ihr Lieben, wir verabschieden uns nun für dieses Jahr von euch! 🎉 Rutscht oder rollt (ich rolle, weil vollgefuttert 🙈 😂) ins neue Jahr 2021! 🥳 Richtet den Blick voller Zuversicht nach vorne, fühlt euch von uns gedrückt und bleibt gesund. 🍀 🥂 /at', 1344750330848931846, 'https://twitter.com/DB_Bahn/status/1344750330848931846']\n",
            "['db_bahn', '2020-12-31 20:46:37 UTC', '@atYildir Schon geschehen. /da', 1344746806383669268, 'https://twitter.com/DB_Bahn/status/1344746806383669268']\n",
            "['db_bahn', '2020-12-31 20:40:59 UTC', '@atYildir Da die Strecke von der HLB betreiben wird, haben wir von den Ticketeinnahmen nichts. Dort verkehren aber auch Züge der Linie RB 41, die von DB Regio betrieben wird. Wann möchtest du von Frankfurt Hbf nach Marburg(Lahn) fahren? /da', 1344745390076260359, 'https://twitter.com/DB_Bahn/status/1344745390076260359']\n",
            "['db_bahn', '2020-12-31 18:53:31 UTC', '@SebastianKueck Danke für die lieben Worte. Wir wünschen dir einen guten Rutsch in ein gesundes &amp; glückliches neues Jahr, Sebastian. 🥂🎇🍀 /be', 1344718345191501824, 'https://twitter.com/DB_Bahn/status/1344718345191501824']\n",
            "['db_bahn', '2020-12-31 16:30:11 UTC', '@zaini_na03 Das geben wir gerne zurück. Einen guten Start in ein gesundes und glückliches neues Jahr, Zain. 🍀🥂🎇 Liebe Grüße /be', 1344682272520339458, 'https://twitter.com/DB_Bahn/status/1344682272520339458']\n",
            "['db_bahn', '2020-12-31 16:20:12 UTC', '@User4818 🤗 Danke schön. Bleib gesund und komm gut ins neue Jahr! 🍀 🎉 🥂 /at', 1344679761298251783, 'https://twitter.com/DB_Bahn/status/1344679761298251783']\n",
            "['db_bahn', '2020-12-31 14:44:30 UTC', '@MrLemey Vielen Dank für den Hinweis. Wünsche dir auch einen guten Rutsch. /mc', 1344655677508169730, 'https://twitter.com/DB_Bahn/status/1344655677508169730']\n",
            "['db_bahn', '2020-12-31 14:34:47 UTC', '@Verti92 Dann frohes Rollen ins neue Jahr, ich werde es nachher auch so handhaben. 😂 Liebe Grüße🎉 🥂  🍀 /at', 1344653231931215874, 'https://twitter.com/DB_Bahn/status/1344653231931215874']\n",
            "['db_bahn', '2020-12-31 14:30:00 UTC', 'Wir rutschen nicht, wir rollen. 😉 Wir wünschen euch allen ein gutes Rein-Rollen ins neue Jahr 2021! Frohes Neues ✨🥂🎊   #FrohesNeues #Silvester #Happy2021  https://t.co/fqSTcLByQW', 1344652029428256771, 'https://twitter.com/DB_Bahn/status/1344652029428256771']\n",
            "['db_bahn', '2020-12-31 13:28:45 UTC', '@StoerungenBahn Vielen lieben Dank, den wünschen wir euch auch! 🤗 Bleibt gesund! /aj', 1344636612446191619, 'https://twitter.com/DB_Bahn/status/1344636612446191619']\n"
          ]
        }
      ],
      "source": [
        "\"\"\"Testing if all the tweets are shown\"\"\"\n",
        "\n",
        "print(len(extracted_tweets))\n",
        "\n",
        "for tweet in extracted_tweets[0:10]:\n",
        "  print(tweet)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6Hke_scsYsT"
      },
      "source": [
        "# Adding data to Haystack Pipeline\n",
        "Reader: Close Anaylsis of documents, perform core task of question answering. Trained from the latest transformer\n",
        "\n",
        "Retriever: Assists reader as filter for documents. Quickly identifies relevant parts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9cbhUCv1sdah"
      },
      "outputs": [],
      "source": [
        "# Make sure you have a GPU running\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yk4VUqDuGD22",
        "outputId": "2c77e7b7-bcad-49bc-fbf5-e21271b5518d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Stored in directory: /root/.cache/pip/wheels/55/c3/db/33705569425fd2bdc9ea73051a8053fa26965c2bce8a146747\n",
            "Successfully built farm-haystack sentence-transformers langdetect python-docx seqeval tika databricks-cli\n",
            "Installing collected packages: tokenizers, sentencepiece, monotonic, mmh3, azure-common, websocket-client, smmap, querystring-parser, pyyaml, python-docx, pyjwt, num2words, langdetect, jarowinkler, isodate, gunicorn, elasticsearch, elastic-apm, backoff, tika, rapidfuzz, quantulum3, posthog, Mako, huggingface-hub, gitdb, docker, databricks-cli, azure-core, transformers, seqeval, prometheus-flask-exporter, msrest, gitpython, alembic, sentence-transformers, mlflow, azure-ai-formrecognizer, farm-haystack\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed Mako-1.2.1 alembic-1.8.0 azure-ai-formrecognizer-3.2.0b2 azure-common-1.1.28 azure-core-1.22.1 backoff-1.11.1 databricks-cli-0.17.0 docker-5.0.3 elastic-apm-6.10.1 elasticsearch-7.10.1 farm-haystack-1.6.1rc0 gitdb-4.0.9 gitpython-3.1.27 gunicorn-20.1.0 huggingface-hub-0.7.0 isodate-0.6.1 jarowinkler-1.1.0 langdetect-1.0.9 mlflow-1.27.0 mmh3-3.0.0 monotonic-1.6 msrest-0.6.21 num2words-0.5.10 posthog-1.4.9 prometheus-flask-exporter-0.20.2 pyjwt-2.4.0 python-docx-0.8.11 pyyaml-6.0 quantulum3-0.7.10 querystring-parser-1.2.4 rapidfuzz-2.1.2 sentence-transformers-2.2.2 sentencepiece-0.1.96 seqeval-1.2.2 smmap-5.0.0 tika-1.24 tokenizers-0.12.1 transformers-4.20.1 websocket-client-1.3.3\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 master of Haystack\n",
        "!pip install --upgrade pip\n",
        "!pip install git+https://github.com/deepset-ai/haystack.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Bs6XCpNHx8v"
      },
      "source": [
        "Haystack looks at documents stored in a class called \"Document Store\"\n",
        "\n",
        "First we need to start an ElasticSearch (like a Bert-Model) server on local machine. Usually using docker but we can execute elastic search from source as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cSYJvovUIEEE"
      },
      "outputs": [],
      "source": [
        "# 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": "code",
      "execution_count": null,
      "metadata": {
        "id": "8AGazcdhIIcS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63c95748-7cf9-4781-dd14-4cafae191bfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.telemetry -  Haystack sends anonymous usage data to understand the actual usage and steer dev efforts towards features that are most meaningful to users. You can opt-out at anytime by calling disable_telemetry() or by manually setting the environment variable HAYSTACK_TELEMETRY_ENABLED as described for different operating systems on the documentation page. More information at https://haystack.deepset.ai/guides/telemetry\n"
          ]
        }
      ],
      "source": [
        "# Connect to Elasticsearch\n",
        "\n",
        "from haystack.document_stores import ElasticsearchDocumentStore\n",
        "from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http, print_answers\n",
        "from haystack.nodes import FARMReader, TransformersReader\n",
        "\n",
        "document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\", index=\"document\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUTCHXzRQJ7P"
      },
      "source": [
        "Now we have Elastic Search running and a yet-empty document_store. Now we need to fill our document store with our data!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bm5G0X4yLw_z"
      },
      "outputs": [],
      "source": [
        "# Write function to put json data into document store format\n",
        "\"\"\"\n",
        "dicts = [\n",
        "    {\n",
        "        'content': DOCUMENT_TEXT_HERE,\n",
        "        'meta': {'name': DOCUMENT_NAME, ...}\n",
        "    }, ...\n",
        "]\n",
        "\n",
        "[author_name, tweet_date, tweet, id, url]\n",
        "\"\"\"\n",
        "\n",
        "def to_dstore(json_data):\n",
        "  dicts = []\n",
        "  for element in json_data:\n",
        "    current_dict = {'content': element[2],\n",
        "                    'meta': {'author': element[0], 'date': element[1], 'url': element[4]}}\n",
        "    dicts.append(current_dict)\n",
        "\n",
        "  return dicts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8MsvmmE4MaPD"
      },
      "outputs": [],
      "source": [
        "docs = to_dstore(extracted_tweets)\n",
        "document_store.write_documents(docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tgFjwvD4UMLt"
      },
      "outputs": [],
      "source": [
        "# Important Imports\n",
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "from haystack.nodes import BM25Retriever\n",
        "from haystack.pipelines import GenerativeQAPipeline\n",
        "from haystack.nodes import RAGenerator\n",
        "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader\n",
        "from haystack.pipelines import DocumentSearchPipeline\n",
        "from haystack.utils import print_documents\n",
        "from haystack.nodes import BM25Retriever\n",
        "from haystack import Pipeline\n",
        "from haystack.nodes import EmbeddingRetriever"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qgu5Ip31uED2"
      },
      "source": [
        "# NEW AND IMPROVED PIPELINE\n",
        "Extractive Q&A Pipeline"
      ]
    },
    {
      "cell_type": "code",
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            "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 clips/mfaq\n"
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            "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:364: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n",
            "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",
            "INFO - haystack.document_stores.elasticsearch -  Updating embeddings for all 71351 docs ...\n"
          ]
        },
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          "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/gelectra-base-germanquad 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 -  Automatically detected language from language model name: german\n",
            "INFO - haystack.modeling.model.language_model -  Loaded deepset/gelectra-base-germanquad\n"
          ]
        },
        {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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.nodes import EmbeddingRetriever\n",
        "retriever = EmbeddingRetriever(\n",
        "    document_store=document_store,\n",
        "   embedding_model=\"clips/mfaq\",\n",
        "   model_format=\"sentence_transformers\"\n",
        ")\n",
        "document_store.update_embeddings(retriever)\n",
        "\n",
        "# Adding reader.\n",
        "reader = FARMReader(model_name_or_path=\"deepset/gelectra-base-germanquad\", use_gpu=True)\n",
        "\n",
        "p_extractive = Pipeline()\n",
        "p_extractive.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
        "p_extractive.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fxjCZgbauUdg",
        "colab": {
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          "height": 729,
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            "f4bf0d4ece7d49638b5554ae927637d4",
            "7ed70ce9bb264cf8bbfb0a1fc0d7ba20",
            "d0003575fe1e476393b5a44de2692ce1",
            "062566ea11ce4a98aa78e6999528dbf7",
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            "806bf6262ae64ea3acaf0c0ed77a6e04",
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            "8607b6bf99e149b7857aa45d8b6f3158"
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        "outputId": "e45d8dca-cd83-4601-d09a-212c1b6119c6"
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        {
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          "text": [
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  4.39 Batches/s]\n",
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            "WARNING - root -  print_answers received details='maximum', which was not understood. \n",
            "WARNING - root -  Valid values are minimum, medium and 'all'. Using 'all'.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Wiviel kostet das 9€ Ticket?\n",
            "Answers:\n",
            "[   <Answer {'answer': '46 Euro', 'type': 'extractive', 'score': 0.797111451625824, 'context': 'en Verbundshop gegangen? Ich habe es gerade auf iOS getestet und kann dort ein 10er Ticket ab Korschenbroich Geltungsbereich B für 46 Euro buchen. /ne', 'offsets_in_document': [{'start': 193, 'end': 200}], 'offsets_in_context': [{'start': 131, 'end': 138}], 'document_id': 'b0d82054126a22c2bebcd194c9404fd9', 'meta': {'date': '2021-11-12 14:20:35 UTC', 'author': 'db_bahn', 'url': 'https://twitter.com/DB_Bahn/status/1459164221464272901'}}>,\n",
            "    <Answer {'answer': '151,15 Euro', 'type': 'extractive', 'score': 0.5587798207998276, 'context': 'cht, wie du auf 194 Euro kommst, der Flexpreis mit BahnCard 25 kostet 151,15 Euro, Super Sparpreis Europa für morgen gibt es noch für 97,90 Euro. Hast', 'offsets_in_document': [{'start': 105, 'end': 116}], 'offsets_in_context': [{'start': 70, 'end': 81}], 'document_id': '62b648964e38953fcc3a9b812e20a0ea', 'meta': {'date': '2020-06-17 05:57:28 UTC', 'author': 'db_bahn', 'url': 'https://twitter.com/DB_Bahn/status/1273132635217330177'}}>,\n",
            "    <Answer {'answer': '16,20 Euro', 'type': 'extractive', 'score': 0.42319728434085846, 'context': 'kostet. Das andere Ticket ist eine Einzelfahrkarte zum Regelpreis für regulär 16,20 Euro. Hast du mal die Kolleg*innen am Schalter gefragt gehabt? /at', 'offsets_in_document': [{'start': 180, 'end': 190}], 'offsets_in_context': [{'start': 78, 'end': 88}], 'document_id': '74179b5dea26c8424cf8dc5db2a2234b', 'meta': {'date': '2021-02-25 12:07:58 UTC', 'author': 'db_bahn', 'url': 'https://twitter.com/DB_Bahn/status/1364910005443571712'}}>]\n"
          ]
        }
      ],
      "source": [
        "# Now we can run it\n",
        "res = p_extractive.run(\n",
        "    query=\"Wiviel kostet das 9€ Ticket?\", params={\"Retriever\": {\"top_k\": 30}, \"Reader\": {\"top_k\": 3}}\n",
        ")\n",
        "print_answers(res, details=\"maximum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KAmb7DFR5bT"
      },
      "source": [
        "# Extractive Q&A Pipeline\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "d2miaxLYTJgF",
        "outputId": "cf8f893b-9170-45f9-ee84-d4a99ce59814"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
            "INFO - haystack.modeling.model.language_model -  LOADING MODEL\n",
            "INFO - haystack.modeling.model.language_model -  =============\n",
            "INFO - haystack.modeling.model.language_model -  Could not find deepset/gelectra-base-germanquad-distilled locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/778 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a00df601b3b14fe6b5da372bd0edb4f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a9d1b3736ab4a0eafc82f27c70ef8fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Automatically detected language from language model name: german\n",
            "INFO - haystack.modeling.model.language_model -  Loaded deepset/gelectra-base-germanquad-distilled\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": "665ec7f1b6404754964363c7e7e4c528"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/234k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d0cbf585130432b8d827cdfbf875466"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/468k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eaf52c9980094300b260071f49717b5e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "276ca33c08e44b83b13b5e7d03712930"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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.pipelines import ExtractiveQAPipeline\n",
        "from haystack.nodes import BM25Retriever\n",
        "\n",
        "\n",
        "# Adding reader. It will select the k-best answers. We will take a roBERTa model.\n",
        "reader = FARMReader(model_name_or_path=\"deepset/gelectra-base-germanquad-distilled\", use_gpu=True)\n",
        "\n",
        "retriever = BM25Retriever(document_store=document_store)  # Adding retriever to make scanning process faster!\n",
        "pipe = ExtractiveQAPipeline(reader, retriever)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-ahzY7bSOH3",
        "outputId": "274d85d2-8eb4-447b-920c-845da6d4961c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Ab wann gilt das 9€ Ticket?\n",
            "Answers:\n",
            "[   {   'answer': 'Wenn ein Nahverkehrszug durch einen SEV ersetzt wird',\n",
            "        'context': '@sarinamendes6 Wenn ein Nahverkehrszug durch einen SEV '\n",
            "                   'ersetzt wird, dann gilt auch dort das 9-Euro-Ticket. /si'},\n",
            "    {   'answer': 'ab Hamburg Altona',\n",
            "        'context': '@tehabe @kkklawitter Ich würde auch sagen, dass das Ticket '\n",
            "                   'ab Hamburg Altona gilt. /da'},\n",
            "    {   'answer': 'bis Enschede',\n",
            "        'context': 'mmen! Hier:  https://t.co/E2N18mjfee unter '\n",
            "                   'Beförderungsbedingungen 9-Euro-Ticket ist jetzt alles '\n",
            "                   'hinterlegt und das Ticket gilt auch bis Enschede. /an'},\n",
            "    {   'answer': 'nur in der 2. Klasse',\n",
            "        'context': '@ErkMerk Das 9- Euro-Ticket gilt nur in der 2. Klasse:  '\n",
            "                   'https://t.co/vNMJwAsWBR /si'},\n",
            "    {   'answer': 'Das 9-Euro-Ticket gilt nicht in den Fernverkehrszügen, bei '\n",
            "                  'denen andere Nahverkehrsfahrkarten gelten',\n",
            "        'context': '@mahrko @k3vk4 Ich habe nun eine Info dazu. Das '\n",
            "                   '9-Euro-Ticket gilt nicht in den Fernverkehrszügen, bei '\n",
            "                   'denen andere Nahverkehrsfahrkarten gelten. /fi'},\n",
            "    {   'answer': 'frühestens ab dem 1. Juni',\n",
            "        'context': '@Flo_Fischer_ Nein, das 9-Euro-Ticket gilt frühestens ab '\n",
            "                   'dem 1. Juni. Ist das der DB Navigator? /da'},\n",
            "    {   'answer': 'erst ab Enschede',\n",
            "        'context': '@Jeffrey4006 Nein, dieses Ticket gilt erst ab Enschede. '\n",
            "                   '/jn'},\n",
            "    {   'answer': 'bis nach Venlo',\n",
            "        'context': ' Hallo. Nein, das ist leider ein Fehler. Die Kolleg:innen '\n",
            "                   'sind dran, ihn schnellstmöglich zu beheben. Das '\n",
            "                   '9-Euro-Ticket gilt nicht bis nach Venlo. /jn'},\n",
            "    {   'answer': 'Das 9 Euro-Ticket gilt nicht in den Fernverkehrszügen, die '\n",
            "                  'für andere Nahverkehrstickets freigegeben sind',\n",
            "        'context': 'realdirtydan Nein, das ist kein Fehler. Das 9 Euro-Ticket '\n",
            "                   'gilt nicht in den Fernverkehrszügen, die für andere '\n",
            "                   'Nahverkehrstickets freigegeben sind. /ki'},\n",
            "    {   'answer': 'ab dem 23. Mai',\n",
            "        'context': '@DieFrauHaPunkt Hallo, das 9-Euro Ticket wird es '\n",
            "                   'voraussichtlich ab dem 23. Mai zu kaufen geben. /lu'}]\n"
          ]
        }
      ],
      "source": [
        "question = \"Ab wann gilt das 9€ Ticket?\"\n",
        "\n",
        "\n",
        "prediction = pipe.run(\n",
        "    query=question, params={\"Retriever\": {\"top_k\": 40}, \"Reader\": {\"top_k\": 10}}\n",
        ")\n",
        "\n",
        "\n",
        "# Or just have the simple output:\n",
        "print_answers(prediction, details=\"minimum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBglr7q7U1Jo"
      },
      "source": [
        "# Frontend Interface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qZM44UDYU8kb"
      },
      "outputs": [],
      "source": [
        "import logging\n",
        "# Disable info logs\n",
        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.CRITICAL)\n",
        "\n",
        "def ask_chatbot(question):\n",
        "  if question == \"\":\n",
        "    return \"\", \"\"\n",
        "  prediction = pipe.run(\n",
        "    query=question,\n",
        "    params={\"Retriever\": {\"top_k\": 10}, \"Reader\": {\"top_k\": 1}}\n",
        "  )\n",
        "  if prediction[\"answers\"]:\n",
        "    return prediction[\"answers\"][0].answer, prediction[\"answers\"][0].meta[\"url\"]\n",
        "  else:\n",
        "    return \"\", \"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 791,
          "referenced_widgets": [
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          ]
        },
        "id": "Un2eIKoDYPHZ",
        "outputId": "805f4b37-1928-4832-99c5-ba0e91222cbd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HTML(value='<style>\\n.app { \\n  background-color: #eba8a2;\\n  padding: 50px 200px;\\n  max-width: 1000px;\\n}\\n.…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "baa94436cf4648feb87d2692e5ee1a0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<div class=\"logo\"></div>'), VBox(children=(HTML(value='<div class=\"message left\">Hi…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3bec803362b48729a375a3ed4e82420"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 14.62 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 14.17 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 16.66 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 19.16 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 27.50 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 29.09 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 29.95 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 28.87 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 31.44 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 34.25 Batches/s]\n"
          ]
        }
      ],
      "source": [
        "from ipywidgets import *\n",
        "\n",
        "\n",
        "style = HTML(value=\"\"\"<style>\n",
        ".app { \n",
        "  background-color: #eba8a2;\n",
        "  padding: 50px 200px;\n",
        "  max-width: 1000px;\n",
        "}\n",
        ".logo {\n",
        "  background: url(https://raw.githubusercontent.com/ToastyDom/DataChallengesSoSe22/main/logo.png);\n",
        "  height: 80px;\n",
        "  background-position: center;\n",
        "  background-repeat: no-repeat;\n",
        "  background-size: contain;\n",
        "}\n",
        ".messages {\n",
        "  height: 300px;\n",
        "  margin: 40px 0;\n",
        "  overflow-y: scroll;\n",
        "  flex-direction: column-reverse;\n",
        "}\n",
        ".message {\n",
        "  padding: 10px;\n",
        "  margin: 5px 0px;\n",
        "  background-color: #f5e1df;\n",
        "  border-radius: 10px;\n",
        "  width: 70%;\n",
        "}\n",
        "a {\n",
        "  color: black;\n",
        "  text-decoration: none;\n",
        "}\n",
        ".message.right {\n",
        "  margin-left: 25%;\n",
        "  border-top-right-radius: 0px;\n",
        "}\n",
        ".message.left {\n",
        "  border-top-left-radius: 0px;\n",
        "}\n",
        ".input {\n",
        "  width: 75%;\n",
        "}\n",
        ".button {\n",
        "  width: 25%;\n",
        "}\n",
        "</style>\"\"\")\n",
        "\n",
        "input = Text(placeholder=\"Frage eingeben\")\n",
        "button = Button(description=\"Fragen\")\n",
        "messages = VBox([\n",
        "  HTML(value='<div class=\"message left\">Hi, wie kann ich dir helfen?</div>'),\n",
        "])\n",
        "messages.add_class('messages')\n",
        "app = VBox([\n",
        "  HTML(value='<div class=\"logo\"></div>'),\n",
        "  messages,\n",
        "  HBox([input, button]),\n",
        "])\n",
        "app.add_class('app')\n",
        "input.add_class('input')\n",
        "button.add_class('button')\n",
        "\n",
        "def on_send_message(b):\n",
        "  question = input.value\n",
        "  answer, url = ask_chatbot(question)\n",
        "  messages.children = (\n",
        "    HTML(value=f'<a href=\"{url}\" target=\"_blank\"><div class=\"message left\">{answer}</div></a>'),\n",
        "    HTML(value=f'<div class=\"message right\">{question}</div>'),\n",
        "    *messages.children,\n",
        "  )\n",
        "\n",
        "button.on_click(on_send_message)\n",
        "input.on_submit(on_send_message)\n",
        "\n",
        "\n",
        "display(style, app)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0FY-fZkGTSfU"
      },
      "source": [
        "# Generative QA-Pipeline  - This one is bad"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mLFkFDQTUASD"
      },
      "outputs": [],
      "source": [
        "from haystack.pipelines import GenerativeQAPipeline\n",
        "from haystack.nodes import RAGenerator\n",
        "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader\n",
        "\n",
        "\n",
        "# Initialize dense retriever\n",
        "embedding_retriever = EmbeddingRetriever(\n",
        "    document_store, embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\"\n",
        ")\n",
        "\n",
        "# Needed for generator\n",
        "document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)\n",
        "document_store.return_embedding = True\n",
        "\n",
        "\n",
        "\n",
        "# Initialize generator\n",
        "rag_generator = RAGenerator()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3_9PHyMTVUQ"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Generative QA\n",
        "pipe = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever)\n",
        "res = pipe.run(query=\"Wann gilt das 9€ Ticket?\", params={\"Retriever\": {\"top_k\": 10}})\n",
        "\n",
        "\n",
        "\n",
        "# Or just have the simple output:\n",
        "print_answers(prediction, details=\"minimum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVNPDOjPzSRN"
      },
      "source": [
        "# FAQ Pipeline  - This one requires a different document-store\n",
        "=> Reference: https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_FAQ_style_QA.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uiFId9_HzUWP"
      },
      "outputs": [],
      "source": [
        "from haystack.pipelines import FAQPipeline\n",
        "\n",
        "\n",
        "pipe = FAQPipeline(retriever=retriever)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D-OgrNQCzb8j"
      },
      "outputs": [],
      "source": [
        "from haystack.utils import print_answers\n",
        "\n",
        "prediction = pipe.run(query=\"Wann gilt das 9€ Ticket?\", params={\"Retriever\": {\"top_k\": 10}})\n",
        "print_answers(prediction, details=\"medium\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9B7L1rAdc6y"
      },
      "source": [
        "# Document Search Pipeline - Useless"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xmFzf47_df5b"
      },
      "outputs": [],
      "source": [
        "from haystack.pipelines import DocumentSearchPipeline\n",
        "from haystack.utils import print_documents\n",
        "\n",
        "\n",
        "bm25_retriever = BM25Retriever(document_store=document_store)  # Adding retriever to make scanning process faster!\n",
        "\n",
        "p_retrieval = DocumentSearchPipeline(bm25_retriever)\n",
        "res = p_retrieval.run(query=\"Wann gilt das 9€ Ticket?\", params={\"Retriever\": {\"top_k\": 10}})\n",
        "print_documents(res, max_text_len=200)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B29OHDzyhD1m"
      },
      "source": [
        "# Custom Build Extractive QA Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-_y7SuRr3EqK"
      },
      "outputs": [],
      "source": [
        "from haystack.nodes import BM25Retriever\n",
        "from haystack import Pipeline\n",
        "# Custom built extractive QA pipeline\n",
        "bm25_retriever = BM25Retriever(document_store=document_store)\n",
        "\n",
        "# Adding reader. It will select the k-best answers. We will take a roBERTa model.\n",
        "reader = FARMReader(model_name_or_path=\"deepset/gelectra-base-germanquad-distilled\", use_gpu=True)\n",
        "\n",
        "p_extractive = Pipeline()\n",
        "p_extractive.add_node(component=bm25_retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
        "p_extractive.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbvkF47tInhV"
      },
      "source": [
        "Trying out new retrievers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673,
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        },
        "id": "NVcwBXkx7KOx",
        "outputId": "9b32ab75-9e62-4498-e6a4-f50a310a3e09"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "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",
            "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",
            "INFO - haystack.document_stores.elasticsearch -  Updating embeddings for all 71351 docs ...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3602546e44cb4ae295209b925c5f0b65",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Updating embeddings:   0%|          | 0/71351 [00:00<?, ? Docs/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "adf7c07300424a358da0bbb9cb4f8e0c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Batches:   0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e717ff909e64c2ba0d3c5829451c370",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Batches:   0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f95168747b5945439978b672a6d73c6c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Batches:   0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88e410acdacc448ab9d917ac966b7346",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Batches:   0%|          | 0/313 [00:00<?, ?it/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
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        {
          "name": "stderr",
          "output_type": "stream",
          "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/gelectra-base-germanquad locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n",
            "INFO - haystack.modeling.model.language_model -  Automatically detected language from language model name: german\n",
            "INFO - haystack.modeling.model.language_model -  Loaded deepset/gelectra-base-germanquad\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"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nWas ist die DB-Navigator App? - Reise-App\\nWann gilt das 9€ Ticket? - nur in der 2. Klasse\\nBis wann gilt das 9€ Ticket? - nur in der 2. Klasse\\n\\n'"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# New retriever?\n",
        "from haystack.nodes import EmbeddingRetriever\n",
        "retriever = EmbeddingRetriever(\n",
        "    document_store=document_store,\n",
        "   embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\",\n",
        "   model_format=\"sentence_transformers\"\n",
        ")\n",
        "document_store.update_embeddings(retriever)\n",
        "\n",
        "# Adding reader. It will select the k-best answers. We will take a roBERTa model.\n",
        "reader = FARMReader(model_name_or_path=\"deepset/gelectra-base-germanquad\", use_gpu=True)\n",
        "\n",
        "p_extractive = Pipeline()\n",
        "p_extractive.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
        "p_extractive.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "Was ist die DB-Navigator App? - Reise-App\n",
        "Wann gilt das 9€ Ticket? - nur in der 2. Klasse\n",
        "Bis wann gilt das 9€ Ticket? - nur in der 2. Klasse\n",
        "\n",
        "\"\"\""
      ]
    },
    {
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "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 clips/mfaq\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:364: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n",
            "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",
            "INFO - haystack.document_stores.elasticsearch -  Updating embeddings for all 71351 docs ...\n"
          ]
        },
        {
          "data": {
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              "model_id": "684a40f613fd47199a21460e2c19d997",
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            "text/plain": [
              "Updating embeddings:   0%|          | 0/71351 [00:00<?, ? Docs/s]"
            ]
          },
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        },
        {
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        },
        {
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          "output_type": "stream",
          "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/gelectra-base-germanquad locally.\n",
            "INFO - haystack.modeling.model.language_model -  Looking on Transformers Model Hub (in local cache and online)...\n"
          ]
        },
        {
          "data": {
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              "model_id": "dfa4154818ea4759878bacee3617bf42",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO - haystack.modeling.model.language_model -  Automatically detected language from language model name: german\n",
            "INFO - haystack.modeling.model.language_model -  Loaded deepset/gelectra-base-germanquad\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a35ff1dde3264b2eb710573ede0b8535",
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          "text": [
            "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"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
              "\"\\nWas ist die DB-Navigator App? - Reise-App\\nWann gilt das 9€ Ticket? - frühestens ab dem 1. Juni\\nBis wann gilt das 9€ Ticket? - frühestens ab dem 1. Juni'\\n\\n\""
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# New retriever?\n",
        "from haystack.nodes import EmbeddingRetriever\n",
        "retriever = EmbeddingRetriever(\n",
        "    document_store=document_store,\n",
        "   embedding_model=\"clips/mfaq\",\n",
        "   model_format=\"sentence_transformers\"\n",
        ")\n",
        "document_store.update_embeddings(retriever)\n",
        "\n",
        "# Adding reader. It will select the k-best answers. We will take a roBERTa model.\n",
        "#reader = FARMReader(model_name_or_path=\"deepset/gelectra-base-germanquad-distilled\", use_gpu=True)\n",
        "reader = FARMReader(model_name_or_path=\"deepset/gelectra-base-germanquad\", use_gpu=True)\n",
        "\n",
        "p_extractive = Pipeline()\n",
        "p_extractive.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
        "p_extractive.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "Was ist die DB-Navigator App? - Reise-App\n",
        "Wann gilt das 9€ Ticket? - frühestens ab dem 1. Juni\n",
        "Bis wann gilt das 9€ Ticket? - frühestens ab dem 1. Juni'\n",
        "\n",
        "\n",
        "\n",
        "Neuer reader:\n",
        "\n",
        "Was ist die DB-Navigator App? - 'eine App mit der du z.B. Zugverbindungen suchen und Tickets '\n",
        "                  'kaufen kannst'\n",
        "Wann gilt das 9€ Ticket? - frühestens ab dem 1. Juni\n",
        "Bis wann gilt das 9€ Ticket? - frühestens ab dem 1. Juni'\n",
        "\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJd8Vs0PIqdz"
      },
      "source": [
        "Trying out new reader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzFHRDetIs_Y"
      },
      "source": [
        "Ask the question"
      ]
    },
    {
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              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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          "text": [
            "Inferencing Samples:   0%|          | 0/1 [00:00<?, ? Batches/s]/usr/local/lib/python3.7/dist-packages/haystack/modeling/model/prediction_head.py:483: 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,  8.51 Batches/s]\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00,  9.00 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 29.02 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 23.43 Batches/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Query: Was ist die MyBahnCard?\n",
            "Answers:\n",
            "[   {   'answer': 'Jugend BahnCard',\n",
            "        'context': '@Gitarre3 @L1zha_ Nach der Jugend BahnCard gibt es die My '\n",
            "                   'BahnCard.  https://t.co/WomrqmjZJP Liebe Grüße /ti'},\n",
            "    {   'answer': 'ein Aboprodukt',\n",
            "        'context': 'a_TheKPanda Moin, online geht das leider nicht. Die My '\n",
            "                   'BahnCard ist ein Aboprodukt und darf nicht von '\n",
            "                   'Jugendlichen unter 18 Jahren erworben werden, so'},\n",
            "    {   'answer': 'eine ermäßigte BahnCard 50 2. Klasse',\n",
            "        'context': '@karinpatzer Hallo. Hast du vielleicht die My BahnCard '\n",
            "                   'oder eine ermäßigte BahnCard 50 2. Klasse? /jn'},\n",
            "    {   'answer': 'die normale BahnCard',\n",
            "        'context': '@ZainCh02 Hi, da die Aktion gestern ausgelaufen ist, wird '\n",
            "                   'es nur noch die normale BahnCard geben. /lu'},\n",
            "    {   'answer': 'BahnCard 100 1. Klasse',\n",
            "        'context': 'ebiet 6000 fährst, kannst du die Stadtbahn Bielefeld mit '\n",
            "                   'deiner BahnCard 100 1. Klasse nutzen. In dieser Seite '\n",
            "                   'siehst du den Geltungsbereich City-Tick'}]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "# Now we can run it\n",
        "res = p_extractive.run(\n",
        "    query=\"Was ist die MyBahnCard?\", params={\"Retriever\": {\"top_k\": 30}, \"Reader\": {\"top_k\": 5}}\n",
        ")\n",
        "print_answers(res, details=\"minimum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TkYodbeki3h"
      },
      "source": [
        "# Huggingface Summarizer  - Der ist nicht gut\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u0zrATpekkWb"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "summarizer = pipeline(\"summarization\", model = \"ml6team/mt5-small-german-finetune-mlsum\")\n",
        "summarizer(\"Das 9 Euro-Ticket gibt es erst ab Juni. Hallo, das 9-Euro Ticket wird es voraussichtlich ab dem 23. Mai zu kaufen geben\", min_length=5, max_length=50)"
      ]
    }
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