qalangchain.ipynb

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  "cells": [
    {
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      "execution_count": null,
      "metadata": {
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        },
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        "outputId": "e5ae3a30-decd-406b-881d-209263881581"
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          "text": [
            "Collecting langchain-community\n",
            "  Downloading langchain_community-0.2.16-py3-none-any.whl.metadata (2.7 kB)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (6.0.2)\n",
            "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (2.0.32)\n",
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            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain-community)\n",
            "  Downloading dataclasses_json-0.6.7-py3-none-any.whl.metadata (25 kB)\n",
            "Collecting langchain<0.3.0,>=0.2.16 (from langchain-community)\n",
            "  Downloading langchain-0.2.16-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting langchain-core<0.3.0,>=0.2.38 (from langchain-community)\n",
            "  Downloading langchain_core-0.2.38-py3-none-any.whl.metadata (6.2 kB)\n",
            "Collecting langsmith<0.2.0,>=0.1.0 (from langchain-community)\n",
            "  Downloading langsmith-0.1.114-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (1.26.4)\n",
            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain-community) (2.32.3)\n",
            "Collecting tenacity!=8.4.0,<9.0.0,>=8.1.0 (from langchain-community)\n",
            "  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (24.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (4.0.3)\n",
            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain-community)\n",
            "  Downloading marshmallow-3.22.0-py3-none-any.whl.metadata (7.2 kB)\n",
            "Collecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain-community)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Collecting langchain-text-splitters<0.3.0,>=0.2.0 (from langchain<0.3.0,>=0.2.16->langchain-community)\n",
            "  Downloading langchain_text_splitters-0.2.4-py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain<0.3.0,>=0.2.16->langchain-community) (2.8.2)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain-core<0.3.0,>=0.2.38->langchain-community)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl.metadata (3.0 kB)\n",
            "Requirement already satisfied: packaging<25,>=23.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.38->langchain-community) (24.1)\n",
            "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.3.0,>=0.2.38->langchain-community) (4.12.2)\n",
            "Collecting httpx<1,>=0.23.0 (from langsmith<0.2.0,>=0.1.0->langchain-community)\n",
            "  Downloading httpx-0.27.2-py3-none-any.whl.metadata (7.1 kB)\n",
            "Collecting orjson<4.0.0,>=3.9.14 (from langsmith<0.2.0,>=0.1.0->langchain-community)\n",
            "  Downloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (50 kB)\n",
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            "Collecting httpcore==1.* (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.0->langchain-community)\n",
            "  Downloading httpcore-1.0.5-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.0->langchain-community) (1.3.1)\n",
            "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.0->langchain-community)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain-core<0.3.0,>=0.2.38->langchain-community)\n",
            "  Downloading jsonpointer-3.0.0-py2.py3-none-any.whl.metadata (2.3 kB)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain<0.3.0,>=0.2.16->langchain-community) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.20.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain<0.3.0,>=0.2.16->langchain-community) (2.20.1)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain-community)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl.metadata (1.1 kB)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.0->langchain-community) (1.2.2)\n",
            "Downloading langchain_community-0.2.16-py3-none-any.whl (2.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m43.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB)\n",
            "Downloading langchain-0.2.16-py3-none-any.whl (1.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading langchain_core-0.2.38-py3-none-any.whl (396 kB)\n",
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            "\u001b[?25hDownloading langsmith-0.1.114-py3-none-any.whl (290 kB)\n",
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            "\u001b[?25hDownloading tenacity-8.5.0-py3-none-any.whl (28 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n",
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            "\u001b[?25hDownloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Downloading langchain_text_splitters-0.2.4-py3-none-any.whl (25 kB)\n",
            "Downloading marshmallow-3.22.0-py3-none-any.whl (49 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.3/49.3 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading orjson-3.10.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n",
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            "\u001b[?25hDownloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading jsonpointer-3.0.0-py2.py3-none-any.whl (7.6 kB)\n",
            "Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tenacity, orjson, mypy-extensions, marshmallow, jsonpointer, h11, typing-inspect, jsonpatch, httpcore, httpx, dataclasses-json, langsmith, langchain-core, langchain-text-splitters, langchain, langchain-community\n",
            "  Attempting uninstall: tenacity\n",
            "    Found existing installation: tenacity 9.0.0\n",
            "    Uninstalling tenacity-9.0.0:\n",
            "      Successfully uninstalled tenacity-9.0.0\n",
            "Successfully installed dataclasses-json-0.6.7 h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jsonpatch-1.33 jsonpointer-3.0.0 langchain-0.2.16 langchain-community-0.2.16 langchain-core-0.2.38 langchain-text-splitters-0.2.4 langsmith-0.1.114 marshmallow-3.22.0 mypy-extensions-1.0.0 orjson-3.10.7 tenacity-8.5.0 typing-inspect-0.9.0\n"
          ]
        }
      ],
      "source": [
        "!pip install -U langchain-community\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain transformers faiss-cpu datasets\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "N8v9KdNTrn3S",
        "outputId": "c5723084-4be2-430f-d4aa-456dcc86eae8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.2.16)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n",
            "Requirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.2)\n",
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            "Requirement already satisfied: langsmith<0.2.0,>=0.1.17 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.1.114)\n",
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            "Requirement already satisfied: requests<3,>=2 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.32.3)\n",
            "Requirement already satisfied: tenacity!=8.4.0,<9.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (8.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.6)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Collecting pyarrow>=15.0.0 (from datasets)\n",
            "  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (2.4.0)\n",
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            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
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            "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (3.7.1)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (1.0.5)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (1.3.1)\n",
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            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.3.0,>=0.2.38->langchain) (3.0.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<1,>=0.23.0->langsmith<0.2.0,>=0.1.17->langchain) (1.2.2)\n",
            "Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m65.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading datasets-2.21.0-py3-none-any.whl (527 kB)\n",
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            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "\u001b[?25hDownloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: xxhash, pyarrow, faiss-cpu, dill, multiprocess, datasets\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.21.0 dill-0.3.8 faiss-cpu-1.8.0.post1 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pyarrow"
                ]
              },
              "id": "d88852c63f044a00861f3fbf1f71c336"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y torch torchvision torchaudio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "zai2kn1mrn4b",
        "outputId": "42b1ab8a-d89e-4b0d-b2c5-799c68b0da6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: torch 2.4.0+cu121\n",
            "Uninstalling torch-2.4.0+cu121:\n",
            "  Successfully uninstalled torch-2.4.0+cu121\n",
            "Found existing installation: torchvision 0.19.0+cu121\n",
            "Uninstalling torchvision-0.19.0+cu121:\n",
            "  Successfully uninstalled torchvision-0.19.0+cu121\n",
            "Found existing installation: torchaudio 2.4.0+cu121\n",
            "Uninstalling torchaudio-2.4.0+cu121:\n",
            "  Successfully uninstalled torchaudio-2.4.0+cu121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install torch torchvision torchaudio sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "LRgD1FvVrn7a",
        "outputId": "57d22327-9722-402b-b5b9-613c82bff93e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.4.0+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.19.0+cu121)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.10/dist-packages (2.4.0+cu121)\n",
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.26.4)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.34.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.44.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.3.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.24.6)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.15.1->sentence-transformers) (2.32.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (2024.5.15)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.4.4)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.19.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.8)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2024.8.30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U langchain-huggingface\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ntVf9wMbrn8r",
        "outputId": "715eb4b4-d639-4eaa-f52c-a701f5d5cd78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain-huggingface\n",
            "  Downloading langchain_huggingface-0.0.3-py3-none-any.whl.metadata (1.2 kB)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (0.24.6)\n",
            "Requirement already satisfied: langchain-core<0.3,>=0.1.52 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (0.2.38)\n",
            "Collecting sentence-transformers>=2.6.0 (from langchain-huggingface)\n",
            "  Downloading sentence_transformers-3.0.1-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: tokenizers>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (0.19.1)\n",
            "Requirement already satisfied: transformers>=4.39.0 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (4.44.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (3.15.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (2024.6.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (4.66.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (4.12.2)\n",
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            "Downloading langchain_huggingface-0.0.3-py3-none-any.whl (17 kB)\n",
            "Downloading sentence_transformers-3.0.1-py3-none-any.whl (227 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentence-transformers, langchain-huggingface\n",
            "Successfully installed langchain-huggingface-0.0.3 sentence-transformers-3.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.schema import Document\n",
        "from langchain.vectorstores import FAISS\n",
        "from transformers import BartTokenizer, BartForQuestionAnswering\n",
        "from langchain_huggingface import HuggingFacePipeline\n",
        "import faiss\n",
        "import numpy as np\n",
        "from transformers import pipeline\n",
        "\n",
        "# Load the dataset\n",
        "data_path = '/content/cleaned_bbc_news_articless.csv'\n",
        "df = pd.read_csv(data_path)\n",
        "\n",
        "# Determine device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Combine the columns\n",
        "def combine_columns(row, include_date=True):\n",
        "    if include_date:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']} Published on: {row['pubDate']}\"\n",
        "    else:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']}\"\n",
        "\n",
        "df['combined_text'] = df.apply(combine_columns, axis=1)\n",
        "articles = df['combined_text'].tolist()\n",
        "\n",
        "# SentenceTransformer for embedding\n",
        "model_name = 'all-MiniLM-L6-v2'\n",
        "model = SentenceTransformer(model_name, device=device)\n",
        "embeddings = model.encode(articles, convert_to_tensor=True)  # This will use GPU if available\n",
        "\n",
        "# FAISS Index creation\n",
        "embedding_dim = embeddings.shape[1]\n",
        "faiss_index = faiss.IndexFlatL2(embedding_dim)\n",
        "faiss_index.add(np.array(embeddings.cpu()))  # Ensure embeddings are on CPU for FAISS\n",
        "\n",
        "# Initialize the HuggingFace embedding model for LangChain\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
        "\n",
        "# Create Document objects for each article, adding a source identifier\n",
        "documents = [Document(page_content=article, metadata={'source': f'Article {i}'}) for i, article in enumerate(articles)]\n",
        "\n",
        "# Create the FAISS vector store from documents\n",
        "vector_store = FAISS.from_documents(documents, hf_embeddings)\n",
        "\n",
        "# Save the FAISS index and embeddings\n",
        "torch.save(embeddings.cpu(), 'bbc_embeddings.pt')  # Ensure embeddings are on CPU for saving\n",
        "faiss.write_index(faiss_index, 'bbc_faiss_index.faiss')\n",
        "\n",
        "# Load the fine-tuned BART model for answer generation\n",
        "model_name = \"phiyodr/bart-large-finetuned-squad2\"\n",
        "tokenizer = BartTokenizer.from_pretrained(model_name)\n",
        "bart_model = BartForQuestionAnswering.from_pretrained(model_name)\n",
        "bart_model.to(device)  # Move model to the appropriate device\n",
        "\n",
        "# Define the QA pipeline with BART\n",
        "hf_pipeline_object = pipeline(\n",
        "    \"question-answering\",\n",
        "    model=bart_model,\n",
        "    tokenizer=tokenizer,\n",
        "    device=0 if torch.cuda.is_available() else -1,\n",
        "    max_length=200,  # Adjust this value as needed\n",
        "    min_length=50,   # Adjust this value as needed\n",
        "    length_penalty=2.0\n",
        ")\n",
        "\n",
        "# Create the HuggingFacePipeline for use in the chain\n",
        "hf_pipeline = HuggingFacePipeline(pipeline=hf_pipeline_object)  # Pass the pipeline object\n",
        "\n",
        "# Create the stuff documents chain\n",
        "stuff_chain = load_qa_with_sources_chain(\n",
        "    llm=hf_pipeline,\n",
        "    chain_type=\"stuff\",\n",
        ")\n",
        "\n",
        "# Custom function for retrieval\n",
        "def get_answer(query, stuff_chain, vector_store):\n",
        "    # Retrieve relevant documents\n",
        "    docs = vector_store.similarity_search(query)\n",
        "\n",
        "    # Combine relevant document texts as the context\n",
        "    context = \" \".join([doc.page_content for doc in docs[:3]])  # Limit to 3 documents\n",
        "\n",
        "    # Prepare the input in a clearer format for BART\n",
        "    input_text = {\n",
        "        'question': query,\n",
        "        'context': context\n",
        "    }\n",
        "\n",
        "    # Generate the answer using the fine-tuned BART model\n",
        "    result = hf_pipeline_object(input_text)\n",
        "    return result.get('answer', 'No answer found')\n",
        "\n",
        "# Query example\n",
        "query = \"What is the impact of climate change?\"\n",
        "answer = get_answer(query, stuff_chain, vector_store)\n",
        "print(\"Answer:\", answer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910,
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        "id": "wQyIZb5ZsMIm",
        "outputId": "15851cc7-e2d3-4a71-9f4a-d2ce6854c55c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "<ipython-input-7-d4787cfa883f>:42: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
            "  hf_embeddings = HuggingFaceEmbeddings(model_name=model_name)\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-d4787cfa883f>:75: LangChainDeprecationWarning: This function is deprecated. Refer to this guide on retrieval and question answering with sources: https://python.langchain.com/v0.2/docs/how_to/qa_sources/\n",
            "See also the following migration guides for replacements based on `chain_type`:\n",
            "stuff: https://python.langchain.com/v0.2/docs/versions/migrating_chains/stuff_docs_chain\n",
            "map_reduce: https://python.langchain.com/v0.2/docs/versions/migrating_chains/map_reduce_chain\n",
            "refine: https://python.langchain.com/v0.2/docs/versions/migrating_chains/refine_chain\n",
            "map_rerank: https://python.langchain.com/v0.2/docs/versions/migrating_chains/map_rerank_docs_chain\n",
            "\n",
            "  stuff_chain = load_qa_with_sources_chain(\n",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: harming mental health\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.schema import Document\n",
        "from langchain.vectorstores import FAISS\n",
        "from transformers import BartTokenizer, BartForQuestionAnswering\n",
        "from langchain_huggingface import HuggingFacePipeline\n",
        "import faiss\n",
        "import numpy as np\n",
        "from transformers import pipeline\n",
        "from langchain.schema import Document\n",
        "# Load the dataset\n",
        "data_path = '/content/cleaned_bbc_news_articless.csv'\n",
        "df = pd.read_csv(data_path)\n",
        "\n",
        "# Determine device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Combine the columns\n",
        "def combine_columns(row, include_date=True):\n",
        "    if include_date:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']} Published on: {row['pubDate']}\"\n",
        "    else:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']}\"\n",
        "\n",
        "df['combined_text'] = df.apply(combine_columns, axis=1)\n",
        "articles = df['combined_text'].tolist()\n",
        "\n",
        "# SentenceTransformer for embedding\n",
        "model_name = 'all-MiniLM-L6-v2'\n",
        "model = SentenceTransformer(model_name, device=device)\n",
        "embeddings = model.encode(articles, convert_to_tensor=True)  # This will use GPU if available\n",
        "\n",
        "# FAISS Index creation\n",
        "embedding_dim = embeddings.shape[1]\n",
        "faiss_index = faiss.IndexFlatL2(embedding_dim)\n",
        "faiss_index.add(np.array(embeddings.cpu()))  # Ensure embeddings are on CPU for FAISS\n",
        "\n",
        "# Initialize the HuggingFace embedding model for LangChain\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
        "\n",
        "# Create Document objects for each article, adding a source identifier\n",
        "documents = [Document(page_content=article, metadata={'source': f'Article {i}'}) for i, article in enumerate(articles)]\n",
        "\n",
        "# Create the FAISS vector store from documents\n",
        "vector_store = FAISS.from_documents(documents, hf_embeddings)\n",
        "\n",
        "# Save the FAISS index and embeddings\n",
        "torch.save(embeddings.cpu(), 'bbc_embeddings.pt')  # Ensure embeddings are on CPU for saving\n",
        "faiss.write_index(faiss_index, 'bbc_faiss_index.faiss')\n",
        "\n",
        "# Load the fine-tuned BART model for answer generation\n",
        "model_name = \"phiyodr/bart-large-finetuned-squad2\"\n",
        "tokenizer = BartTokenizer.from_pretrained(model_name)\n",
        "bart_model = BartForQuestionAnswering.from_pretrained(model_name)\n",
        "bart_model.to(device)  # Move model to the appropriate device\n",
        "\n",
        "# Define the QA pipeline with BART\n",
        "hf_pipeline_object = pipeline(\n",
        "    \"question-answering\",\n",
        "    model=bart_model,\n",
        "    tokenizer=tokenizer,\n",
        "    device=0 if torch.cuda.is_available() else -1,\n",
        "    max_length=200,  # Adjust this value as needed\n",
        "    min_length=50,   # Adjust this value as needed\n",
        "    length_penalty=2.0\n",
        ")\n",
        "\n",
        "# Create the HuggingFacePipeline for use in the chain\n",
        "hf_pipeline = HuggingFacePipeline(pipeline=hf_pipeline_object)  # Pass the pipeline object\n",
        "\n",
        "# Create the stuff documents chain\n",
        "stuff_chain = load_qa_with_sources_chain(\n",
        "    llm=hf_pipeline,\n",
        "    chain_type=\"stuff\",\n",
        ")\n",
        "\n",
        "# Custom function for retrieval\n",
        "\n",
        "\n",
        "def get_answer(query, stuff_chain, vector_store):\n",
        "    # Retrieve relevant documents\n",
        "    docs = vector_store.similarity_search(query)\n",
        "\n",
        "    # Ensure documents are in Document format\n",
        "    documents = [Document(page_content=doc.page_content, metadata=doc.metadata) for doc in docs[:3]]  # Limit to 3 documents\n",
        "\n",
        "    # Prepare the input for the stuff_chain in the expected format\n",
        "    # Each item in the list is a dictionary representing a QA pair\n",
        "    inputs = [{\n",
        "        'question': query,\n",
        "        'context': '\\n\\n'.join([doc.page_content for doc in documents]) # Combine document contents\n",
        "    }]\n",
        "\n",
        "    # Use the stuff_chain to generate an answer\n",
        "    # Note: We're assuming stuff_chain is a HuggingFacePipeline object here\n",
        "    result = stuff_chain(inputs)\n",
        "\n",
        "    # Extract and return the answer\n",
        "    return result[0]['answer']  # Extract the answer from the first result\n",
        "\n",
        "# Query example\n",
        "query = \"What is the impact of climate change?\"\n",
        "answer = get_answer(query, stuff_chain, vector_store)\n",
        "print(\"Answer:\", answer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "12-9c_mpSJW9",
        "outputId": "2acc496b-19a7-45fe-dd8d-3cde5716a27a",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'langchain'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-c7b7a96d8a2b>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchains\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqa_with_sources\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_qa_with_sources_chain\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msentence_transformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSentenceTransformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlangchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membeddings\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHuggingFaceEmbeddings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'langchain'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, BartTokenizer, BartForQuestionAnswering\n",
        "\n",
        "# Load model and tokenizer\n",
        "model_name = \"phiyodr/bart-large-finetuned-squad2\"\n",
        "tokenizer = BartTokenizer.from_pretrained(model_name)\n",
        "bart_model = BartForQuestionAnswering.from_pretrained(model_name)\n",
        "\n",
        "# Define the QA pipeline with adjusted parameters\n",
        "hf_pipeline_object = pipeline(\n",
        "    \"question-answering\",\n",
        "    model=bart_model,\n",
        "    tokenizer=tokenizer,\n",
        "    device=0 if torch.cuda.is_available() else -1,\n",
        "    max_length=400,  # Set higher to allow longer answers\n",
        "    min_length=150,  # Minimum length for more detailed answers\n",
        "    length_penalty=2.0,  # Encourage longer answers\n",
        "    num_beams=4,  # Beam search for more diverse answers\n",
        "    do_sample=True  # Sampling for a varied response\n",
        ")\n",
        "\n",
        "def chunk_context(context, chunk_size=512):\n",
        "    \"\"\"Split context into chunks if it's too long for the model to handle at once.\"\"\"\n",
        "    return [context[i:i + chunk_size] for i in range(0, len(context), chunk_size)]\n",
        "\n",
        "def test_pipeline(query, context):\n",
        "    inputs = {\n",
        "        'question': query,\n",
        "        'context': context\n",
        "    }\n",
        "    result = hf_pipeline_object(inputs)\n",
        "    return result.get('answer', 'No answer found')\n",
        "\n",
        "def get_detailed_answer(query, context):\n",
        "    # Split the context into chunks\n",
        "    context_chunks = chunk_context(context)\n",
        "\n",
        "    detailed_answer = \"\"\n",
        "\n",
        "    # Run the model on each chunk and append answers\n",
        "    for chunk in context_chunks:\n",
        "        answer = test_pipeline(query, chunk)\n",
        "        detailed_answer += f\" {answer}\"\n",
        "\n",
        "    # Optionally extend or elaborate the answer if needed\n",
        "    return detailed_answer.strip()\n",
        "\n",
        "# Test example\n",
        "context = \"climate change harming mental health former climate scientist among growing number people overwhelmed ecoanxiety climate change let doom win project tell worrier bbc get exclusive look new project help student deal rising climate anxiety climate change deal ecoanxiety rachel hurst student university east anglia helping others address climate anxiety\"\n",
        "query = \"What is the impact of climate change?\"\n",
        "print(get_detailed_answer(query, context))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAZ4JsP8ZMJS",
        "outputId": "0633d103-df87-4b71-e752-b761e987afa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "climate change harming mental health\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel\n",
        "\n",
        "# Load a generative model for more elaborate responses\n",
        "model_name = \"gpt2-large\"  # Use GPT-2 large, but you can use GPT-3 (via OpenAI API) or GPT-4\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "gpt_model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "\n",
        "# Define the pipeline for generation\n",
        "generative_pipeline = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=gpt_model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=300,  # Larger max_length for more detailed answers\n",
        "    min_length=100,  # Ensure the answer isn't too short\n",
        "    length_penalty=1.2,\n",
        "    num_beams=4,\n",
        "    no_repeat_ngram_size=2,  # Prevent repetitive answers\n",
        "    do_sample=True  # Sampling for creative answers\n",
        ")\n",
        "\n",
        "def get_generative_answer(query, context):\n",
        "    prompt = f\"Question: {query}\\nContext: {context}\\nAnswer:\"\n",
        "    generated_text = generative_pipeline(prompt, max_length=400, min_length=100, num_return_sequences=1)[0]['generated_text']\n",
        "    return generated_text\n",
        "\n",
        "# Test example\n",
        "context = \"climate change harming mental health former climate scientist among growing number people overwhelmed ecoanxiety\"\n",
        "query = \"What is the impact of climate change?\"\n",
        "print(get_generative_answer(query, context))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382,
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          "output_type": "display_data",
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
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              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a4fde2d2ea24710b73bf03ef90fe93b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n",
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: What is the impact of climate change?\n",
            "Context: climate change harming mental health former climate scientist among growing number people overwhelmed ecoanxiety\n",
            "Answer: Climate change is having an impact on our health and wellbeing. It is causing more extreme weather events such as floods, droughts, heatwaves and storms, increasing the risk of heat-related illness and death, and reducing the availability of water and food. The impacts are already being felt in the UK and around the world.\n",
            "For example, the number of people suffering from anxiety disorders has increased by more than 50% over the past 30 years, while the proportion of children and young people who suffer from depression has also increased. There are also concerns that the impacts will continue to grow as the planet warms and sea levels rise. This is particularly true for those living in low-lying coastal regions, where the effects of sea level rise are likely to be more severe than for other parts of the globe due to their higher population densities and the fact that they are more vulnerable to flooding and storm surges. As a result, there is a growing body of research that suggests that a range of health problems, including depression and anxiety, will become more common as a consequence of global warming. For instance, a recent study by the University of Oxford found that people with high levels of anxiety and depression are three times more likely than those without these conditions to die in a hurricane or flood event. Another study published in Nature Climate Change found a correlation between the severity of a person's anxiety disorder and their likelihood of suffering a major depressive episode, which is associated with increased mortality risk. These findings are backed up by other studies that show a link between stress and increased rates of heart disease, diabetes, cancer and certain types of cancer, all of which can be linked to a lack of physical activity and a sedentary lifestyle. In addition, recent research has shown that increased exposure to air pollution can lead to changes in mood and behaviour, as well\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.schema import Document\n",
        "from langchain.vectorstores import FAISS\n",
        "from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel\n",
        "import faiss\n",
        "import numpy as np\n",
        "\n",
        "# Load the dataset\n",
        "data_path = '/content/cleaned_bbc_news_articless.csv'\n",
        "df = pd.read_csv(data_path)\n",
        "\n",
        "# Determine device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Combine the columns\n",
        "def combine_columns(row, include_date=True):\n",
        "    if include_date:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']} Published on: {row['pubDate']}\"\n",
        "    else:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']}\"\n",
        "\n",
        "df['combined_text'] = df.apply(combine_columns, axis=1)\n",
        "articles = df['combined_text'].tolist()\n",
        "\n",
        "# SentenceTransformer for embedding\n",
        "model_name = 'all-MiniLM-L6-v2'\n",
        "model = SentenceTransformer(model_name, device=device)\n",
        "embeddings = model.encode(articles, convert_to_tensor=True)  # This will use GPU if available\n",
        "\n",
        "# FAISS Index creation\n",
        "embedding_dim = embeddings.shape[1]\n",
        "faiss_index = faiss.IndexFlatL2(embedding_dim)\n",
        "faiss_index.add(np.array(embeddings.cpu()))  # Ensure embeddings are on CPU for FAISS\n",
        "\n",
        "# Initialize the HuggingFace embedding model for LangChain\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
        "\n",
        "# Create Document objects for each article, adding a source identifier\n",
        "documents = [Document(page_content=article, metadata={'source': f'Article {i}'}) for i, article in enumerate(articles)]\n",
        "\n",
        "# Create the FAISS vector store from documents\n",
        "vector_store = FAISS.from_documents(documents, hf_embeddings)\n",
        "\n",
        "# Save the FAISS index and embeddings\n",
        "torch.save(embeddings.cpu(), 'bbc_embeddings.pt')  # Ensure embeddings are on CPU for saving\n",
        "faiss.write_index(faiss_index, 'bbc_faiss_index.faiss')\n",
        "\n",
        "# Load GPT-2 model and tokenizer\n",
        "model_name = \"gpt2-large\"  # Use GPT-2 large\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "gpt_model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "gpt_model.to(device)  # Move the model to the appropriate device\n",
        "\n",
        "# Define the pipeline for GPT-2 text generation\n",
        "generative_pipeline = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=gpt_model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=300,  # Larger max_length for more detailed answers\n",
        "    min_length=100,  # Ensure the answer isn't too short\n",
        "    length_penalty=1.2,\n",
        "    num_beams=4,\n",
        "    truncation=True,\n",
        "    no_repeat_ngram_size=2,  # Prevent repetitive answers\n",
        "    do_sample=True,  # Sampling for creative answers\n",
        "    device=0 if torch.cuda.is_available() else -1  # Adjust for GPU or CPU\n",
        ")\n",
        "\n",
        "# Function to retrieve relevant documents and generate an answer\n",
        "def get_generative_answer(query, vector_store, num_docs=3):\n",
        "    # Retrieve the top `num_docs` relevant documents using FAISS\n",
        "    docs = vector_store.similarity_search(query, k=num_docs)\n",
        "\n",
        "    # Combine the content of the retrieved documents into a context\n",
        "    context = \" \".join([doc.page_content for doc in docs])\n",
        "\n",
        "    # Prepare the input prompt for GPT-2\n",
        "    prompt = f\"Question: {query}\\nContext: {context}\\nAnswer:\"\n",
        "\n",
        "    # Generate the answer using GPT-2\n",
        "    generated_text = generative_pipeline(prompt, max_length=300, min_length=100, num_return_sequences=1)[0]['generated_text']\n",
        "\n",
        "    return generated_text\n",
        "\n",
        "# Example query\n",
        "# Allow the user to input a query\n",
        "user_query = input(\"Please enter your query: \")\n",
        "\n",
        "# Get the answer based on the user's query\n",
        "answer = get_generative_answer(user_query, vector_store)\n",
        "\n",
        "# Print the generated answer\n",
        "print(\"Answer:\", answer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtEwhz4G3w51",
        "outputId": "97abf247-b8ee-4ddc-950d-c0360ff66c72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please enter your query: what about ukraine russia war\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: Question: what about ukraine russia war\n",
            "Context: ukraine war happening russia total fear lithuania russian opposing vladimir putin war say home country like huge prison Published on: 22/09/2022 23:06 ukraine happened day russia invasion vladimir putin pave way foreign fighter join war russia widens attack Published on: 11/03/2022 21:12 watch look back ukraine war two year bbcs joe inwood reflects two year since russia launched full scale invasion ukraine Published on: 24/02/2024 1:41\n",
            "Answer:\n",
            "The war in Ukraine started in April 2014, when Russia annexed the Ukrainian peninsula of Crimea. Since then, the conflict has claimed the lives of more than 6,000 people, according to the United Nations.\n",
            "Russia has been accused of violating international law by sending troops and weapons into eastern Ukraine, where separatists have been fighting Ukrainian forces for nearly two years. The fighting has resulted in the deaths of hundreds of Ukrainian soldiers and civilians, and the displacement of millions of people. Russia has denied the allegations, insisting that it is only defending its own territory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.schema import Document\n",
        "from langchain.vectorstores import FAISS\n",
        "from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel\n",
        "import faiss\n",
        "import numpy as np\n",
        "\n",
        "# Load the dataset\n",
        "data_path = '/content/cleaned_bbc_news_articless.csv'\n",
        "df = pd.read_csv(data_path)\n",
        "\n",
        "# Determine device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Combine the columns\n",
        "def combine_columns(row, include_date=True):\n",
        "    if include_date:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']} Published on: {row['pubDate']}\"\n",
        "    else:\n",
        "        return f\"{row['cleaned_title']} {row['cleaned_text']}\"\n",
        "\n",
        "df['combined_text'] = df.apply(combine_columns, axis=1)\n",
        "articles = df['combined_text'].tolist()\n",
        "\n",
        "# SentenceTransformer for embedding\n",
        "model_name = 'all-MiniLM-L6-v2'\n",
        "model = SentenceTransformer(model_name, device=device)\n",
        "embeddings = model.encode(articles, convert_to_tensor=True)  # This will use GPU if available\n",
        "\n",
        "# FAISS Index creation\n",
        "embedding_dim = embeddings.shape[1]\n",
        "faiss_index = faiss.IndexFlatL2(embedding_dim)\n",
        "faiss_index.add(np.array(embeddings.cpu()))  # Ensure embeddings are on CPU for FAISS\n",
        "\n",
        "# Initialize the HuggingFace embedding model for LangChain\n",
        "hf_embeddings = HuggingFaceEmbeddings(model_name=model_name)\n",
        "\n",
        "# Create Document objects for each article, adding a source identifier\n",
        "documents = [Document(page_content=article, metadata={'source': f'Article {i}'}) for i, article in enumerate(articles)]\n",
        "\n",
        "# Create the FAISS vector store from documents\n",
        "vector_store = FAISS.from_documents(documents, hf_embeddings)\n",
        "\n",
        "# Save the FAISS index and embeddings\n",
        "torch.save(embeddings.cpu(), 'bbc_embeddings.pt')  # Ensure embeddings are on CPU for saving\n",
        "faiss.write_index(faiss_index, 'bbc_faiss_index.faiss')\n",
        "\n",
        "# Load GPT-2 model and tokenizer\n",
        "model_name = \"gpt2-large\"  # Use GPT-2 large\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "gpt_model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "gpt_model.to(device)  # Move the model to the appropriate device\n",
        "\n",
        "# Define the pipeline for GPT-2 text generation\n",
        "generative_pipeline = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=gpt_model,\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=300,  # Larger max_length for more detailed answers\n",
        "    min_length=100,  # Ensure the answer isn't too short\n",
        "    length_penalty=1.2,\n",
        "    num_beams=4,\n",
        "    truncation=True,\n",
        "    no_repeat_ngram_size=2,  # Prevent repetitive answers\n",
        "    do_sample=True,  # Sampling for creative answers\n",
        "    device=0 if torch.cuda.is_available() else -1  # Adjust for GPU or CPU\n",
        ")\n",
        "\n",
        "#  function to retrieve relevant documents and generate an answer with article publish dates\n",
        "def get_generative_answer(query, vector_store, num_docs=3):\n",
        "    # Retrieve the top `num_docs` relevant documents using FAISS\n",
        "    docs = vector_store.similarity_search(query, k=num_docs)\n",
        "\n",
        "    # Summarize and combine the content of the retrieved documents into a structured context\n",
        "    context = \"\"\n",
        "    for doc in docs:\n",
        "        article_text = doc.page_content[:300]  # Reduce the length of each document snippet\n",
        "        publish_date = doc.metadata.get('source')  # Retrieve the 'source' metadata for the publish date\n",
        "        context += f\"Article published on {publish_date}:\\n{article_text}\\n\\n\"\n",
        "\n",
        "    # Prepare the input prompt for GPT-2\n",
        "    prompt = (\n",
        "        f\"Question: {query}\\n\"\n",
        "        f\"Context: Please provide a detailed answer based on the following articles. Include references to the publishing dates of the articles mentioned.\\n\"\n",
        "        f\"{context}\\nAnswer:\"\n",
        "    )\n",
        "\n",
        "    # Generate the answer using GPT-2 with improved generation control\n",
        "    generated_text = generative_pipeline(\n",
        "        prompt,\n",
        "        max_length=600,  # Increase max_length for more detailed responses\n",
        "        min_length=200,  # Ensure the answer has enough substance\n",
        "        num_return_sequences=1,\n",
        "        temperature=0.6,  # Lower temperature for more focused generation\n",
        "        num_beams=6,  # Increase beams for more thoughtful generation\n",
        "        no_repeat_ngram_size=2  # Prevent repetition\n",
        "    )[0]['generated_text']\n",
        "\n",
        "    return generated_text\n",
        "\n",
        "\n",
        "# Example query\n",
        "# Allow the user to input a query\n",
        "user_query = input(\"Please enter your query: \")\n",
        "\n",
        "# Get the answer based on the user's query\n",
        "answer = get_generative_answer(user_query, vector_store)\n",
        "\n",
        "# Print the generated answer\n",
        "print(\"Answer:\", answer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scF6GfCWB_n5",
        "outputId": "59bd1609-c9f2-400c-beda-08bdd51d21a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please enter your query: tell me about russia ukraine war\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: Question: tell me about russia ukraine war\n",
            "Context: Please provide a detailed answer based on the following articles. Include references to the publishing dates of the articles mentioned.\n",
            "Article published on Article 26:\n",
            "ukraine war kherson mariupol key russian success strategic historical reason offensive southern ukraine vital russia Published on: 04/03/2022 12:54\n",
            "\n",
            "Article published on Article 302:\n",
            "ukraine happened day russia invasion vladimir putin pave way foreign fighter join war russia widens attack Published on: 11/03/2022 21:12\n",
            "\n",
            "Article published on Article 8256:\n",
            "ukraine war happening russia total fear lithuania russian opposing vladimir putin war say home country like huge prison Published on: 22/09/2022 23:06\n",
            "\n",
            "\n",
            "Answer: The war in Ukraine began on April 12, 2014, when Russia annexed the Crimean peninsula from Ukraine. The conflict has since claimed the lives of more than 6,000 people and displaced millions of others. Russia's annexation of Crimea has been condemned by the United States, the European Union, and the Organization for Security and Co-operation in Europe (OSCE) as a violation of international law and a threat to international peace and security. Russian President Vladimir Putin has said that Russia will not recognize the results of a referendum in Crimea on whether to join Russia or remain part of Ukraine, which is scheduled to take place on March 16. In response, Ukraine's President Petro Poroshenko has called on Russia to withdraw its forces from the peninsula and to allow international monitors to monitor the situation in the region. On April 16, Russia and Ukraine agreed to a cease-fire that was to last for 72 hours, but the truce was violated by both sides, resulting in a new round of fighting between Ukrainian forces and Russian-backed separatists in eastern Ukraine that continues to this day. As a result of this conflict, over 4,500 people have been killed, including over 1,200 civilians, according to United Nations estimates. Ukraine and Russia have accused each other of committing war crimes and crimes against humanity during the conflict. According to Human Rights Watch (HRW), Russian forces have used cluster munitions in populated areas, while the Ukrainian military has used indiscriminate weapons, such as artillery and mortars, against civilian areas. HRW has also documented the use of torture and other cruel, inhuman, or degrading treatment or punishment by Ukrainian security forces, as well as by pro-Russian separatists, on a large number of civilians in Donetsk and Luhansk regions. For more information, please visit: http://www.hrw.org/sites/default/files/documents/human-rights-report-2014-03-16-en.pdf#page=1#mediaviewer/File:HumanRights_Report_2014_03_16_en_Ukrainian_Security_forces_and_\n"
          ]
        }
      ]
    }
  ]
}