policy_qa_haystack.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ELehmann91/NLP/blob/master/policy_qa_haystack.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NhPtpPuyrxS5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4e7b252-126a-4019-daec-81a3fe25f636"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Sep 23 08:53:38 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   42C    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": "code",
      "source": [
        "!pip install pymilvus==1.1."
      ],
      "metadata": {
        "id": "BZJ-A-mQ0KIe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install 'farm-haystack[ocr]'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gglH9iBaFvHa",
        "outputId": "a7ef8122-4637-43e1-ce2d-b6de9558d7f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: farm-haystack[ocr] in /usr/local/lib/python3.7/dist-packages (1.10.0rc0)\n",
            "Requirement already satisfied: tika in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.24)\n",
            "Requirement already satisfied: python-docx in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (0.8.11)\n",
            "Requirement already satisfied: azure-ai-formrecognizer>=3.2.0b2 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (3.2.0)\n",
            "Requirement already satisfied: elastic-apm in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (6.12.0)\n",
            "Requirement already satisfied: rapidfuzz<2.8.0,>=2.0.15 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (2.7.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (4.64.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (4.12.0)\n",
            "Requirement already satisfied: transformers==4.21.2 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (4.21.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.7.3)\n",
            "Requirement already satisfied: quantulum3 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (0.7.10)\n",
            "Requirement already satisfied: mlflow in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.29.0)\n",
            "Requirement already satisfied: elasticsearch<7.11,>=7.7 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (7.10.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (2.6.3)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.0.2)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (0.3.5.1)\n",
            "Requirement already satisfied: torch<1.13,>1.9 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.12.1+cu113)\n",
            "Requirement already satisfied: mmh3 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (3.0.0)\n",
            "Requirement already satisfied: langdetect in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.0.9)\n",
            "Requirement already satisfied: seqeval in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.2.2)\n",
            "Requirement already satisfied: sentence-transformers>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (2.2.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (2.28.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.3.5)\n",
            "Requirement already satisfied: pydantic in /usr/local/lib/python3.7/dist-packages (from farm-haystack[ocr]) (1.9.2)\n",
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            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.7/dist-packages (from prometheus-flask-exporter<1->mlflow->farm-haystack[ocr]) (0.14.1)\n",
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            "Requirement already satisfied: docopt>=0.6.2 in /usr/local/lib/python3.7/dist-packages (from num2words->quantulum3->farm-haystack[ocr]) (0.6.2)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->gitpython<4,>=2.1.0->mlflow->farm-haystack[ocr]) (5.0.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask<3->mlflow->farm-haystack[ocr]) (2.0.1)\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "# 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 grpcio-tools==1.34.1\n",
        "#!pip install git+https://github.com/deepset-ai/haystack.git\n",
        "\n",
        "# Install the latest master of Haystack\n",
        "!pip install --upgrade pip\n",
        "!pip install git+https://github.com/deepset-ai/haystack.gitegg=farm-haystack[colab]\n",
        "# If you run this notebook on Google Colab, you might need to\n",
        "# restart the runtime after installing haystack."
      ],
      "metadata": {
        "id": "mv9WbdxxtSZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz && tar -xvf xpdf-tools-linux-4.03.tar.gz && sudo cp xpdf-tools-linux-4.03/bin64/pdftotext /usr/local/bin"
      ],
      "metadata": {
        "id": "TGGFRsRI23gf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89e325da-53ef-4775-d270-1bf2e8060596"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-23 12:15:49--  https://dl.xpdfreader.com/xpdf-tools-linux-4.03.tar.gz\n",
            "Resolving dl.xpdfreader.com (dl.xpdfreader.com)... 45.79.72.155\n",
            "Connecting to dl.xpdfreader.com (dl.xpdfreader.com)|45.79.72.155|:443... connected.\n",
            "HTTP request sent, awaiting response... 404 Not Found\n",
            "2022-09-23 12:15:50 ERROR 404: Not Found.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade \"urllib3==1.25.4\" awscli\n",
        "!pip install pdf2image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZBDBlO3jLIR",
        "outputId": "8b680a6d-7414-4d0e-abc4-7dcc6f98d722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: urllib3==1.25.4 in /usr/local/lib/python3.7/dist-packages (1.25.4)\n",
            "Requirement already satisfied: awscli in /usr/local/lib/python3.7/dist-packages (1.25.80)\n",
            "Requirement already satisfied: colorama<0.4.5,>=0.2.5 in /usr/local/lib/python3.7/dist-packages (from awscli) (0.4.4)\n",
            "Requirement already satisfied: PyYAML<5.5,>=3.10 in /usr/local/lib/python3.7/dist-packages (from awscli) (5.4.1)\n",
            "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from awscli) (0.6.0)\n",
            "Requirement already satisfied: botocore==1.27.79 in /usr/local/lib/python3.7/dist-packages (from awscli) (1.27.79)\n",
            "Requirement already satisfied: rsa<4.8,>=3.1.2 in /usr/local/lib/python3.7/dist-packages (from awscli) (4.7.2)\n",
            "Requirement already satisfied: docutils<0.17,>=0.10 in /usr/local/lib/python3.7/dist-packages (from awscli) (0.16)\n",
            "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from botocore==1.27.79->awscli) (1.0.1)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from botocore==1.27.79->awscli) (2.8.2)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<4.8,>=3.1.2->awscli) (0.4.8)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore==1.27.79->awscli) (1.15.0)\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[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pdf2image in /usr/local/lib/python3.7/dist-packages (1.14.0)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from pdf2image) (7.1.2)\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",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exit(00)"
      ],
      "metadata": {
        "id": "wKIXEytx2ARx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install 'protobuf == 3.20.1'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mpz0HjZ3lIgl",
        "outputId": "85dafb9c-8526-4fd8-adb7-2f7ea00e526c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting protobuf==3.20.1\n",
            "  Downloading protobuf-3.20.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: protobuf\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 4.21.6\n",
            "    Uninstalling protobuf-4.21.6:\n",
            "      Successfully uninstalled protobuf-4.21.6\n",
            "\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 2.8.2+zzzcolab20220719082949 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.1 which is incompatible.\n",
            "grpcio-tools 1.49.1 requires protobuf<5.0dev,>=4.21.3, but you have protobuf 3.20.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed protobuf-3.20.1\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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing and indexing documents\n",
        "The report you will be working with is in the form of a PDF file, so start by creating a PDFToTextConverter. This node extracts the textual information from the PDF and saves it into a Python list. \n",
        "\n",
        "Set the remove_numeric_tables parameter to True. This is fine to do since the report does not contain many tables. Where this is not the case, you may want to use Haystack’s newly implemented AzureConverter, which is optimized for working with tabular data."
      ],
      "metadata": {
        "id": "Xh8S7H1n2DiC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "\n",
        "pip install --upgrade pip\n",
        "pip install git+https://github.com/deepset-ai/haystack.git#egg=farm-haystack[colab]\n",
        "pip install pdf2image\n",
        "pip install pytesseract"
      ],
      "metadata": {
        "id": "B6tRaFhRklkX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%bash\n",
        "wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-linux-4.04.tar.gz &&\n",
        "tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin"
      ],
      "metadata": {
        "id": "bFGv8b5_SANE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.utils import clean_wiki_text,  fetch_archive_from_http, print_answers #convert_files_to_dicts\n",
        "from haystack.nodes import FARMReader, TransformersReader"
      ],
      "metadata": {
        "id": "7wHlSqeb2CI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir data"
      ],
      "metadata": {
        "id": "7wiA9CknGCSg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed5c9dd2-894e-4242-cc3c-800a97031b10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘data’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://www.dffe.gov.za/sites/default/files/docs/southafricasINDCupdated2021sept.pdf data/southafricasINDCupdated2021sept.pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvEUT5x8GEc6",
        "outputId": "89b30108-5bbc-4849-eaef-401c46e1a4f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-23 12:19:57--  https://www.dffe.gov.za/sites/default/files/docs/southafricasINDCupdated2021sept.pdf\n",
            "Resolving www.dffe.gov.za (www.dffe.gov.za)... 164.151.133.35\n",
            "Connecting to www.dffe.gov.za (www.dffe.gov.za)|164.151.133.35|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1364580 (1.3M) [application/pdf]\n",
            "Saving to: ‘southafricasINDCupdated2021sept.pdf.4’\n",
            "\n",
            "southafricasINDCupd 100%[===================>]   1.30M   867KB/s    in 1.5s    \n",
            "\n",
            "2022-09-23 12:20:01 (867 KB/s) - ‘southafricasINDCupdated2021sept.pdf.4’ saved [1364580/1364580]\n",
            "\n",
            "--2022-09-23 12:20:01--  http://data/southafricasINDCupdated2021sept.pdf\n",
            "Resolving data (data)... failed: No address associated with hostname.\n",
            "wget: unable to resolve host address ‘data’\n",
            "FINISHED --2022-09-23 12:20:01--\n",
            "Total wall clock time: 3.1s\n",
            "Downloaded: 1 files, 1.3M in 1.5s (867 KB/s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import PDFToTextConverter\n",
        "\n",
        "pdf_converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=['de','en'])\n",
        "\n",
        "converted = pdf_converter.convert(file_path = 'southafricasINDCupdated2021sept.pdf', meta = { 'document': 'NDC', 'processed': False })"
      ],
      "metadata": {
        "id": "8L_CSS5Hr03a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that you’ll be adding metadata to the converted documents. Once you index this document (as well as all subsequent documents) into a database, the document’s metadata will allow you to filter them. For instance, by filtering based on the ‘company’ metadata, you can specify that you only want to ask a question applicable only to documents concerning ‘Company_1’ and ‘Company_3’ rather than all documents in the database.\n",
        "\n",
        "Now you’ll build a PreProcessor to split your documents into chunks of length 200 and an overlap of ten words."
      ],
      "metadata": {
        "id": "o7EEc2Wq3GGv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import PreProcessor\n",
        "\n",
        "preprocessor = PreProcessor(clean_empty_lines=True,\n",
        "                            clean_whitespace=True,\n",
        "                            clean_header_footer=True,\n",
        "                            split_by='sentence',\n",
        "                            split_length=4,\n",
        "                            split_respect_sentence_boundary=False,\n",
        "                            split_overlap=1)\n",
        "\n",
        "preprocessed = preprocessor.process(converted)"
      ],
      "metadata": {
        "id": "FRlxZk1ws665",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ca69813b0ad04f928ec3720cdd8c7b39",
            "b3d50788677b4d0ea44cf66c8e6d5766",
            "1c168eaee3f1442594a615b25844c6a3",
            "0c3847a5bc50462ab6bb30fdcff9c27d",
            "3c95dc365e1145068f6739f9717260f0",
            "e5b6918c61d249159885962a23852d38",
            "03400e61fe3e4396b10bcb0930b13962",
            "5018385c8ab94ac2b816ccd3d23e7407",
            "44d2267b7ec3420ba339a901276b2eca",
            "2de4e040bae944d5851c6938d2f8d581",
            "38f95d43c9084a18a00a13f03a1ce32a"
          ]
        },
        "outputId": "2c6a4c72-13a8-4490-b01e-9cf6e414f049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Preprocessing:   0%|          | 0/1 [00:00<?, ?docs/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca69813b0ad04f928ec3720cdd8c7b39"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oydHUjjVow_",
        "outputId": "2e3671e5-f94d-4a96-845c-4e26e768236b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Document: {'content': \"Mitigation component of the NDC ........................................................................................................................ 14\\n4 (a) Approach to setting updated NDC target ranges ................................................................................................. 14\\n4 (b) Methodological consistency in setting and accounting for NDC targets .............................................................. 14\\n4 (c) Mitigation targets for 2025 and 2030 .................................................................................................................. 15\\n4 (d) Information to facilitate clarity, transparency and understanding of mitigation ................................................ 16\\n5. Support requirements under the Convention and Paris Agreement ...................................................................... 28\\n6. Equitable access to sustainable development ....................................................................................................... 29\\n8. References and further information ..................................................................................................................... 32\\nTables\\nTABLE 1 - SOUTH AFRICA'S FIRST ADAPTATION COMMUNICATION ......................................................................................................... 8\\nTABLE 2 - SOUTH AFRICA'S UPDATED NDC MITIGATION TARGETS ......................................................................................................... 15\\nTABLE 3 - INFORMATION TO FACILITATE CLARITY, TRANSPARENCY AND UNDERSTAND OF SOUTH AFRICA’S UPDATED NDC ............................... 17\\x0c2\\n1 (a) Mandate and scope\\nBy this communication, South Africa updates and enhances its nationally determined contribution\\n(NDC) under the Paris Agreement, meeting its obligation under Article 4.9 to communicate NDCs every\\nfive years, and responding to the requests in paragraphs 23 to 25 of decision 1/CP.21.\", 'content_type': 'text', 'score': None, 'meta': {'document': 'NDC', 'processed': False, '_split_id': 1}, 'embedding': None, 'id': 'afe873dd82c961693400441b316334b1'}>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With this out of the way, you can now index your documents into an Elasticsearch database."
      ],
      "metadata": {
        "id": "kgvvChp-3SAP"
      }
    },
    {
      "cell_type": "code",
      "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",
        "es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],\n",
        "                   stdout=PIPE, stderr=STDOUT,\n",
        "                   preexec_fn=lambda: os.setuid(1)  # as daemon\n",
        "                  )\n",
        "# wait until ES has started\n",
        "! sleep 30\n"
      ],
      "metadata": {
        "id": "7AqyBz_F31uP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.document_stores import ElasticsearchDocumentStore\n",
        "\n",
        "document_store = ElasticsearchDocumentStore()\n",
        "document_store.delete_all_documents()\n",
        "document_store.write_documents(preprocessed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDU6KB7v3NBE",
        "outputId": "08ab4bf7-e607-40ee-c4f1-730d4d70b2e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:haystack.document_stores.elasticsearch:DEPRECATION WARNINGS:\n",
            "                1. delete_all_documents() method is deprecated, please use delete_documents method\n",
            "                For more details, please refer to the issue: https://github.com/deepset-ai/haystack/issues/1045\n",
            "                \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Building a Question Answering Pipeline\n",
        "\n",
        "Question answering pipelines consist of two main components — the Retriever and the Reader. You can initialize these with only a few lines of code:"
      ],
      "metadata": {
        "id": "N22rsRuzN2JN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import DensePassageRetriever, FARMReader\n",
        "\n",
        "retriever = DensePassageRetriever(document_store=document_store)\n",
        "\n",
        "reader = FARMReader(model_name_or_path='deepset/roberta-base-squad2', use_gpu=True)"
      ],
      "metadata": {
        "id": "8qO43CT13Upd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261,
          "referenced_widgets": [
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          ]
        },
        "outputId": "91140e5b-cf67-49bf-c70f-a079c1a2342a"
      },
      "execution_count": null,
      "outputs": [
        {
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b004748e81e1484f916ccd791e9dd401"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/473M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7fdf5a18ce3e4ae3a3fd26f8d8d19e43"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/79.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9c93ddf9a4b48d7af8f04b38fc51be4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading vocab.json:   0%|          | 0.00/878k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2987f1a123d944869d9677ab0cf561f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading merges.txt:   0%|          | 0.00/446k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "022b03b130c744a4bd388174e539a254"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading special_tokens_map.json:   0%|          | 0.00/772 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "369df2d209c64eacaf53311a8ae1f4da"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once initialized, you will also need to update your database with [dense vector embeddings](https://www.deepset.ai/blog/what-is-text-vectorization-in-nlp). This is as simple as executing a single line of code:"
      ],
      "metadata": {
        "id": "s4zr-n61PyGL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "document_store.update_embeddings(retriever)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "zkW3DFPpN6Xq",
        "outputId": "95c55e30-1af3-40bb-d26b-728c90d2c529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Updating embeddings:   0%|          | 0/179 [00:00<?, ? Docs/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72b55f8fcce741b69d3369d4d8db5361"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Create embeddings:   0%|          | 0/192 [00:00<?, ? Docs/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2bacfb886174090b40801a98f8dafe2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With both your retriever and reader in place, you can place them into a Pipeline."
      ],
      "metadata": {
        "id": "XOpXboS8P_kC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "\n",
        "pipeline = ExtractiveQAPipeline(reader, retriever)"
      ],
      "metadata": {
        "id": "Gz4d_AMCP8sg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Information Extraction with Question Answering\n",
        "Before you can use our pipeline, you’ll need to come up with some questions. Let’s create a few below:"
      ],
      "metadata": {
        "id": "iOq_5fkhQEBr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "questions = [ 'Was ist das Ziel des Projekts?',\n",
        "              'In welchem Land wird das Projekt umgesetzt?',\n",
        "              'Zu welchem Zeitpunkt  startet das Projekt?',\n",
        "              'Zu welchem Zeitpunkt ist das Projekt abgeschlossen?',\n",
        "              'Welche Sektoren sind betroffen?',\n",
        "              'Wie hoch ist das Funding des Projekts?',\n",
        "              'Gibt es Risiken in der Umsetzung?' ]"
      ],
      "metadata": {
        "id": "msxJ0oegQBa9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "questions = [ 'What is the goal of the project?',\n",
        "              'Which countries are involved?',\n",
        "              'what is the time horizon?',\n",
        "              'which sectors are mentioned?' ]"
      ],
      "metadata": {
        "id": "xIKdwaHSRxtS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "questions = [ 'What are the goals and efforts to mitigate climate change?',\n",
        "              'What are the goals and efforts to adapt to climate change?',\n",
        "              'Which policies are mentioned?',\n",
        "              'Is a certain budget mentioned?' ]"
      ],
      "metadata": {
        "id": "Tu2uYBlhLaz-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, iterate over the questions and feed them into your pipeline.\n",
        "\n",
        "Set the top_k parameters to 50 and 1 for the retriever and the reader, respectively. A top_k value of 50 for retriever is comparatively high and may slow down a question answering system with many active users. For a QA system in production, the higher speed achieved by decreasing the top_k parameter is generally worth a small loss in accuracy. However, because right now your information extraction system probably doesn’t have to serve hundreds of users and you’ll want the returned results to be as accurate as possible, you’re better served by amping the top_k value up. As for the reader, you’ll only be interested in the single highest-ranking answer, so set its top_k value to 1."
      ],
      "metadata": {
        "id": "FLAEoTVgQ0SL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "answers = []\n",
        "\n",
        "for question in questions:\n",
        "    prediction = pipeline.run(query=question,\n",
        "                 params = {'Retriever': {'top_k': 5},\n",
        "                           'Reader': { 'top_k': 1 } })\n",
        "    answers.append(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fkDUOPwWQXGy",
        "outputId": "1e4d3593-98b5-4367-93bf-cdd2c85c8c25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 15.80 Batches/s]\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 21.47 Batches/s]\n",
            "\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 34.25 Batches/s]\n",
            "\n",
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 36.98 Batches/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For each question that you’ll pass through the pipeline, it returns a dictionary that contains both the query and the answer. The pipeline saved each of these dictionaries into a list, so to print out the results, simply iterate over the list and output the dictionary’s values by accessing them via their corresponding keys."
      ],
      "metadata": {
        "id": "TNdmNuwEQ2YT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for answer in answers:\n",
        "    print('Q:', answer['query'])\n",
        "    print('A:', answer['answers'][0].answer)\n",
        "    print('A:', answer['answers'][0].context)\n",
        "    print('A:', answer['answers'][0].document_id)\n",
        "    print('\\n')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1rbafg2QobU",
        "outputId": "b626db0a-5429-43f4-b813-113332465913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q: What is the goal of the project?\n",
            "A: Goal 5: Quantification and\n",
            "acknowledgement of the\n",
            "national adaptation and\n",
            "resilience efforts\n",
            "A: g\n",
            "countries for\n",
            "recognition. Goal 5: Quantification and\n",
            "acknowledgement of the\n",
            "national adaptation and\n",
            "resilience efforts. The adaptation efforts have\n",
            "A: 6b2c27fde511b87e14eced9245cda66d\n",
            "\n",
            "\n",
            "Q: Which countries are involved?\n",
            "A: Africa and South Africa\n",
            "A: lobal nature of the adaptation responsibility. In\n",
            "this respect, Africa and South Africa, are warming at a rate that is above twice the global average\n",
            "\n",
            "A: 18e21ce054cea90bc8e4779fda993098\n",
            "\n",
            "\n",
            "Q: what is the time horizon?\n",
            "A: two five-year\n",
            "A: These targets for 2025 and 2030 correspond to two five-year time frames, and\n",
            "corresponding periods of implementation, from 1 January 2021 to 31 Decemb\n",
            "A: 5ddfc6bf98442559f9b88d6fe81d98ec\n",
            "\n",
            "\n",
            "Q: which sectors are mentioned?\n",
            "A: health, water, biodiversity, agriculture and\n",
            "human settlements\n",
            "A:  sectors recommended for prioritisation\n",
            "are health, water, biodiversity, agriculture and\n",
            "human settlements. The following efforts will\n",
            "lead to the rea\n",
            "A: dd5939ec068f87af895a6b9b6e417e3\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Summarizer"
      ],
      "metadata": {
        "id": "xsy00_VbvW5_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import TransformersSummarizer\n",
        "from haystack import Document\n"
      ],
      "metadata": {
        "id": "ExquyXIHvZG_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summarizer = TransformersSummarizer(model_name_or_path=\"sshleifer/distilbart-cnn-12-6\") #ml6team/mt5-small-german-finetune-mlsum\n"
      ],
      "metadata": {
        "id": "R_wZF0jMvZMT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "563737f8d7954a94bc14c32ea95f6e2c",
            "cbc1da7d14be4868a1141e14e352845f",
            "b096d72f3a144219acef4a4b64510f16",
            "23025ce3e6ad4821afb9f5a0df65be09",
            "16ecb2d5c9f8407cb0166cc6bb3d2bde",
            "6ecbe144453f4778a5fdf45251fe7c0c",
            "984c1ac3e7b44637827c70d2ff8dc3eb",
            "d31d2374fd574145bbc054e943b41364",
            "17853f58aefb4b3eb11232465a5a7588",
            "067857177dfd4be398122bf1ce33aeef",
            "c9f6f77b829942e9a9b796629bc1444d",
            "f2a8ebbb8f62489c8ad5bc59562a03b0",
            "b362cc50abe24dcfb15612de84b7e621",
            "8938b680e03547d1bf805c41fbed9e6e",
            "b7136e65d7274db3a4e3ebb0a9414073",
            "7ddab2d3f579464da5b18a8bf6c077d7",
            "cc6cfd911d474eeab0d1f554846b6a09",
            "4f3eab9c579e4576914a72889aaec084",
            "e7b1ce5f64a24541b0dfd6b38b3ecd60",
            "b9e38a359fdc40138b11838dfd6111ab",
            "ea098c1c364f4584b4565638bd9a7332",
            "405422d2bd0b44e782c1d67787787113",
            "7be49b1579c640159484a140587fe432",
            "eff46154e1684177b2be2682a13eecac",
            "3c7e9ce9b3d644e6bda6bf600715a5ff",
            "329d753e8cdc4ce4812ff227645cd1da",
            "81f3409451b84940bf06b673f1a7f7d3",
            "b63bf0e7efa04ec9a9928ebd0c95841d",
            "ab510be3576249d8bd1fef366533f992",
            "eca9f7bea83b4482847caf86a264485b",
            "7fc0fc4c2c234376a70f6a91fe865d3c",
            "fa7ea15ee05e4f38afe21f649905deac",
            "021163038fb14e6690d27de6f1b8ba0b",
            "a9b31ad6638645e1aa8711d3a3a4238d",
            "7b953743ebe54884b9eb8e775a598d4a",
            "6c0bb8e55fdc4e2aa34f81de38bba83d",
            "c93e13f620814c6686bba6239bab4e53",
            "16f67715506b49209dc0600907d23923",
            "b729bc659cf440bf95a13fa832226618",
            "737014fc29264a029d605b737f0065d9",
            "cfdb071ff9814898986fb5ccd87873ab",
            "08dacdbbd5ce4f078a3202b6225e9eab",
            "4f43e588732d4e6bbb2125c30e6787b0",
            "6e18e25926de4f88923e5e7b12cffe24",
            "cc5409d88df4463f849b866a5e5189e7",
            "9f23f50a85344926ba3c83db9a6f8593",
            "868530ccd4e848ed8fa4731db7dfe97b",
            "a2e6093707284204a1f9b5c370cd306f",
            "ab1d9c79462d4a6090a19ef0282191d7",
            "ca1da22a78944318a86486f7572519e0",
            "7afe0ea4bce343099064f3026a3ad881",
            "446ba169aea24197ae3cb1a88a970759",
            "464834a2bdad4f67b006f9043b5bb95e",
            "088f39f4db964195b78fb132422cd5f0",
            "ff511d619bff4fe993fbd6c83f6ac6f9"
          ]
        },
        "outputId": "e545219f-bf80-4020-fea0-5302e7578532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/1.76k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "563737f8d7954a94bc14c32ea95f6e2c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.14G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2a8ebbb8f62489c8ad5bc59562a03b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7be49b1579c640159484a140587fe432"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading vocab.json:   0%|          | 0.00/878k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9b31ad6638645e1aa8711d3a3a4238d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading merges.txt:   0%|          | 0.00/446k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc5409d88df4463f849b866a5e5189e7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed[25].content"
      ],
      "metadata": {
        "id": "8t_vJxtRyxTl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "481f93f4-5998-4431-b4da-9468a82325ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Further details on sectoral policies and measures are reported in South Africa’s Fourth Biennial Update\\nReport, summarised in its Table ES-3. By implementing these domestic measures, South Africa is\\nshowing that implementation is part of our ambition. 5\\nThe long-term decarbonization of the South African economy will in the 2020s focus primarily on the\\nelectricity sector; in the 2030s, a deeper transition will take place in the electricity sector, coupled with\\na transition in the transport sector towards low emission vehicles; while the 2040s and beyond will be\\ncharacterized by the decarbonization of the hard-to-mitigate sectors. The key challenge during the\\nimplementation periods of this first NDC (2021 to 2025, and 2026 to 2030) will be the transition in the\\nelectricity sector, seeking early investment in and preparing for mitigation in harder-to-mitigate\\nsectors, and addressing the economic and social consequences resulting from this transition in coal-\\nproducing areas.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary = summarizer.predict(documents=preprocessed[25], generate_single_summary=True)"
      ],
      "metadata": {
        "id": "KSaL7gmVwoAB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary[0].content"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "0HrEP4o4yLIZ",
        "outputId": "ba98e957-600e-49e5-f815-48c9dcdd6b04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' South Africa’s first Adaptation Communication, serving as the Adaptation component of the NDC. South Africa updates and enhances its nationally determined contribution under the Paris Agreement, meeting its obligation under Article 4.9 to communicate NDCs every five years.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Classifier"
      ],
      "metadata": {
        "id": "snPY7uhm4AuB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import TransformersDocumentClassifier\n",
        "\n",
        "\n",
        "doc_classifier = TransformersDocumentClassifier(\n",
        "    model_name_or_path=\"cross-encoder/nli-distilroberta-base\",\n",
        "    task=\"zero-shot-classification\",\n",
        "    labels=[\"transport\", \"carbon\",\"agriculture\",\"disaster risk\"],\n",
        "    batch_size=16,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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            "8daa868c35ed4696adeca5afad5f3f70",
            "fc270f25e3694213ae6ed0681f363e0f",
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            "fcd92323b86845938919cb13b66814a9",
            "5228d738f823498c8f9ee7b27e14ebb5",
            "60ec92cac28f4ab18fc03354e7703fe3",
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            "b313319ece634bbdaf6bb6c8e2ea64b6",
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        },
        "id": "-uaqqZLwhI9S",
        "outputId": "4eda5fbb-3d7d-4250-c032-c29e8f7e321f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/701 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "531dff4d301d420985860197d5edea6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/313M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f97647784f54bddbf4bf4363e9e1671"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "9e393d21a7bd48e2a0c0643ab89bf675"
            }
          },
          "metadata": {}
        },
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          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
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          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "8daa868c35ed4696adeca5afad5f3f70"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "3dbeb0638ca44b8181497b2091b4013d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classified_docs = doc_classifier.predict(preprocessed)\n"
      ],
      "metadata": {
        "id": "h4WDgZFH4DVI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "dad602b674084d89859dbd99bbae7e50",
            "2d5d07369d2347619b27c4984392db3b",
            "e99784af88824e54b5b471e5fd59bac1",
            "275815687aa445d3adae75c26acad4dd",
            "dec6760bb8e447c88b19d3e2ee740152",
            "77a53063e1774e6091d27b901350989c",
            "c400a0908842457b98d6e2c320e92bea",
            "ce212f1daa0249b181020e677ea95387",
            "920a77453217439592aa02ff401b3cc6",
            "7b19f08f48384789a9ca9a93b5584853",
            "c309ba76321e46018643ccd4600e4c9b"
          ]
        },
        "outputId": "06d4e5a5-f297-4320-af33-adfe49cfd99a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Classifying documents:   0%|          | 0/179 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dad602b674084d89859dbd99bbae7e50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/pipelines/base.py:1039: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
            "  UserWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's see how it looks: there should be a classification result in the meta entry containing labels and scores.\n",
        "print(classified_docs[25].to_dict())\n"
      ],
      "metadata": {
        "id": "6ZQHBcXF4Dd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce25dc8f-a9aa-4017-9ab7-4664060c2eb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'content': 'Further details on sectoral policies and measures are reported in South Africa’s Fourth Biennial Update\\nReport, summarised in its Table ES-3. By implementing these domestic measures, South Africa is\\nshowing that implementation is part of our ambition. 5\\nThe long-term decarbonization of the South African economy will in the 2020s focus primarily on the\\nelectricity sector; in the 2030s, a deeper transition will take place in the electricity sector, coupled with\\na transition in the transport sector towards low emission vehicles; while the 2040s and beyond will be\\ncharacterized by the decarbonization of the hard-to-mitigate sectors. The key challenge during the\\nimplementation periods of this first NDC (2021 to 2025, and 2026 to 2030) will be the transition in the\\nelectricity sector, seeking early investment in and preparing for mitigation in harder-to-mitigate\\nsectors, and addressing the economic and social consequences resulting from this transition in coal-\\nproducing areas.', 'content_type': 'text', 'score': None, 'meta': {'document': 'NDC', 'processed': False, '_split_id': 25, 'classification': {'label': 'transport', 'score': 0.7231297492980957, 'details': {'transport': 0.7231297492980957, 'disaster risk': 0.11923302710056305, 'carbon': 0.09363766014575958, 'agriculture': 0.06399954110383987}}}, 'embedding': None, 'id': 'c60e425132f78d6b28a19745b4bff694'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classified_docs[25]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTRjcLdDhtc7",
        "outputId": "5ed30aba-e189-4aea-c502-e061f73b1512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Document: {'content': 'Further details on sectoral policies and measures are reported in South Africa’s Fourth Biennial Update\\nReport, summarised in its Table ES-3. By implementing these domestic measures, South Africa is\\nshowing that implementation is part of our ambition. 5\\nThe long-term decarbonization of the South African economy will in the 2020s focus primarily on the\\nelectricity sector; in the 2030s, a deeper transition will take place in the electricity sector, coupled with\\na transition in the transport sector towards low emission vehicles; while the 2040s and beyond will be\\ncharacterized by the decarbonization of the hard-to-mitigate sectors. The key challenge during the\\nimplementation periods of this first NDC (2021 to 2025, and 2026 to 2030) will be the transition in the\\nelectricity sector, seeking early investment in and preparing for mitigation in harder-to-mitigate\\nsectors, and addressing the economic and social consequences resulting from this transition in coal-\\nproducing areas.', 'content_type': 'text', 'score': None, 'meta': {'document': 'NDC', 'processed': False, '_split_id': 25, 'classification': {'label': 'transport', 'score': 0.7231297492980957, 'details': {'transport': 0.7231297492980957, 'disaster risk': 0.11923302710056305, 'carbon': 0.09363766014575958, 'agriculture': 0.06399954110383987}}}, 'embedding': None, 'id': 'c60e425132f78d6b28a19745b4bff694'}>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Entity"
      ],
      "metadata": {
        "id": "dsnOHS6t43K1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import EntityExtractor\n",
        "\n",
        "entity_extractor = EntityExtractor(model_name_or_path=\"fhswf/bert_de_ner\")\n"
      ],
      "metadata": {
        "id": "g3x_kOMA45Np"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "entities = entity_extractor.extract(\n",
        "    text= preprocessed[1]['content']\n",
        " )\n",
        "entities"
      ],
      "metadata": {
        "id": "cDPjPXNG452f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pipeline = Pipeline()\n",
        "#pipeline.add_node(text_converter, \"TextConverter\", [\"File\"])\n",
        "#pipeline.add_node(preprocessor, \"PreProcessor\", [\"TextConverter\"])\n",
        "#pipeline.add_node(entity_extractor, \"EntityExtractor\", [\"PreProcessor\"])\n",
        "#pipeline.add_node(document_store, \"DocumentStore\", [\"EntityExtractor\"])\n",
        "\n",
        "#pipeline.run(file_paths=file_paths)\n"
      ],
      "metadata": {
        "id": "KUcRA_LI8MQD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Crawl"
      ],
      "metadata": {
        "id": "aa_8mYs8V-S-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir 'crawled_files_giz'"
      ],
      "metadata": {
        "id": "WRHROibEV_92"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install webdriver_manager\n",
        "!pip install selenium\n",
        "!apt-get update\n",
        "!apt install chromium-driver\n",
        "!cp /usr/lib/chromium-browser/chromedriver /usr/bin"
      ],
      "metadata": {
        "id": "uCf6UtuaWL-o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.nodes import Crawler\n",
        "\n",
        "crawler = Crawler(output_dir=\"crawled_files_giz\")\n",
        "docs = crawler.crawl(\n",
        "    urls=[\"https://www.giz.de/de/weltweit/324.html\"],\n",
        "    filter_urls=[\"haystack\"],\n",
        "    crawler_depth=1\n",
        ")"
      ],
      "metadata": {
        "id": "ar6qkcehREzp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qqJZxyakWIaY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}