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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Помощник для сотрудников ПВЗ"
      ],
      "metadata": {
        "id": "UJMp_azE0ON8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CtOziDEsz8XL"
      },
      "outputs": [],
      "source": [
        "!mkdir data\n",
        "# Сюда положить оригинальные Excel-файлы с данными, не меняя названия"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv QA_pairs.xlsx data\n",
        "!mv knowledge_base.xlsx data"
      ],
      "metadata": {
        "id": "RPzB39jk8xe3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install accelerate haystack-ai sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-jzYwfk0Mp2",
        "outputId": "481f4fc4-9538-4c26-bbb1-f4ba9f35d2ee"
      },
      "execution_count": 3,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.30.1-py3-none-any.whl (302 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting haystack-ai\n",
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            "\u001b[?25hCollecting sentence-transformers\n",
            "  Downloading sentence_transformers-2.7.0-py3-none-any.whl (171 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m171.5/171.5 kB\u001b[0m \u001b[31m26.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.0)\n",
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            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n",
            "Collecting boilerpy3 (from haystack-ai)\n",
            "  Downloading boilerpy3-1.0.7-py3-none-any.whl (22 kB)\n",
            "Collecting haystack-bm25 (from haystack-ai)\n",
            "  Downloading haystack_bm25-1.0.2-py2.py3-none-any.whl (8.8 kB)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (3.1.4)\n",
            "Collecting lazy-imports (from haystack-ai)\n",
            "  Downloading lazy_imports-0.3.1-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (10.1.0)\n",
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            "Collecting openai>=1.1.0 (from haystack-ai)\n",
            "  Downloading openai-1.30.1-py3-none-any.whl (320 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.6/320.6 kB\u001b[0m \u001b[31m38.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from haystack-ai) (2.0.3)\n",
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            "Collecting httpx<1,>=0.23.0 (from openai>=1.1.0->haystack-ai)\n",
            "  Downloading httpx-0.27.0-py3-none-any.whl (75 kB)\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\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) (2023.12.25)\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->haystack-ai) (2.1.5)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil->haystack-ai) (1.16.0)\n",
            "Collecting monotonic>=1.5 (from posthog->haystack-ai)\n",
            "  Downloading monotonic-1.6-py2.py3-none-any.whl (8.2 kB)\n",
            "Collecting backoff>=1.10.0 (from posthog->haystack-ai)\n",
            "  Downloading backoff-2.2.1-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai) (2024.2.2)\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: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai) (1.2.1)\n",
            "Collecting httpcore==1.* (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai)\n",
            "  Downloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai)\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[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai) (2.18.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: monotonic, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lazy-imports, haystack-bm25, h11, boilerpy3, backoff, posthog, nvidia-cusparse-cu12, nvidia-cudnn-cu12, httpcore, nvidia-cusolver-cu12, httpx, openai, sentence-transformers, haystack-ai, accelerate\n",
            "Successfully installed accelerate-0.30.1 backoff-2.2.1 boilerpy3-1.0.7 h11-0.14.0 haystack-ai-2.1.1 haystack-bm25-1.0.2 httpcore-1.0.5 httpx-0.27.0 lazy-imports-0.3.1 monotonic-1.6 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 openai-1.30.1 posthog-3.5.0 sentence-transformers-2.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"CMAKE_ARGS\"] = \"-DLLAMA_CUBLAS=on\"\n",
        "os.environ[\"FORCE_CMAKE\"] = \"1\""
      ],
      "metadata": {
        "id": "Nab_qsVZ1LRv"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install llama-cpp-python --verbose --force-reinstall --upgrade --no-cache-dir\n",
        "!pip3 install llama-cpp-haystack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKxAEseR1Upu",
        "outputId": "74b87313-cbbf-42a4-ec2e-e17242d6bcfd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using pip 23.1.2 from /usr/local/lib/python3.10/dist-packages/pip (python 3.10)\n",
            "Collecting llama-cpp-python\n",
            "  Downloading llama_cpp_python-0.2.74.tar.gz (49.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.2/49.2 MB\u001b[0m \u001b[31m76.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Running command pip subprocess to install build dependencies\n",
            "  Using pip 23.1.2 from /usr/local/lib/python3.10/dist-packages/pip (python 3.10)\n",
            "  Collecting scikit-build-core[pyproject]>=0.9.2\n",
            "    Downloading scikit_build_core-0.9.4-py3-none-any.whl (151 kB)\n",
            "       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 152.0/152.0 kB 2.3 MB/s eta 0:00:00\n",
            "  Collecting exceptiongroup>=1.0 (from scikit-build-core[pyproject]>=0.9.2)\n",
            "    Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB)\n",
            "  Collecting packaging>=21.3 (from scikit-build-core[pyproject]>=0.9.2)\n",
            "    Downloading packaging-24.0-py3-none-any.whl (53 kB)\n",
            "       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 53.5/53.5 kB 6.5 MB/s eta 0:00:00\n",
            "  Collecting pathspec>=0.10.1 (from scikit-build-core[pyproject]>=0.9.2)\n",
            "    Downloading pathspec-0.12.1-py3-none-any.whl (31 kB)\n",
            "  Collecting tomli>=1.2.2 (from scikit-build-core[pyproject]>=0.9.2)\n",
            "    Downloading tomli-2.0.1-py3-none-any.whl (12 kB)\n",
            "  Installing collected packages: tomli, pathspec, packaging, exceptiongroup, scikit-build-core\n",
            "  Successfully installed exceptiongroup-1.2.1 packaging-24.0 pathspec-0.12.1 scikit-build-core-0.9.4 tomli-2.0.1\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Running command Getting requirements to build wheel\n",
            "  Could not determine CMake version via --version, got '' 'Traceback (most recent call last):\\n  File \"/usr/local/bin/cmake\", line 5, in <module>\\n    from cmake import cmake\\nModuleNotFoundError: No module named \\'cmake\\'\\n'\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Running command pip subprocess to install backend dependencies\n",
            "  Using pip 23.1.2 from /usr/local/lib/python3.10/dist-packages/pip (python 3.10)\n",
            "  Collecting ninja>=1.5\n",
            "    Downloading ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl (307 kB)\n",
            "       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 307.2/307.2 kB 4.1 MB/s eta 0:00:00\n",
            "  Collecting cmake>=3.21\n",
            "    Downloading cmake-3.29.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.7 MB)\n",
            "       ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 26.7/26.7 MB 40.9 MB/s eta 0:00:00\n",
            "  Installing collected packages: ninja, cmake\n",
            "    Creating /tmp/pip-build-env-t5ck6ear/normal/local/bin\n",
            "    changing mode of /tmp/pip-build-env-t5ck6ear/normal/local/bin/ninja to 755\n",
            "    changing mode of /tmp/pip-build-env-t5ck6ear/normal/local/bin/cmake to 755\n",
            "    changing mode of /tmp/pip-build-env-t5ck6ear/normal/local/bin/cpack to 755\n",
            "    changing mode of /tmp/pip-build-env-t5ck6ear/normal/local/bin/ctest to 755\n",
            "  Successfully installed cmake-3.29.3 ninja-1.11.1.1\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Running command Preparing metadata (pyproject.toml)\n",
            "  *** scikit-build-core 0.9.4 using CMake 3.29.3 (metadata_wheel)\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting typing-extensions>=4.5.0 (from llama-cpp-python)\n",
            "  Downloading typing_extensions-4.11.0-py3-none-any.whl (34 kB)\n",
            "Collecting numpy>=1.20.0 (from llama-cpp-python)\n",
            "  Downloading numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m286.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting diskcache>=5.6.1 (from llama-cpp-python)\n",
            "  Downloading diskcache-5.6.3-py3-none-any.whl (45 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m256.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting jinja2>=2.11.3 (from llama-cpp-python)\n",
            "  Downloading jinja2-3.1.4-py3-none-any.whl (133 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.3/133.3 kB\u001b[0m \u001b[31m312.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting MarkupSafe>=2.0 (from jinja2>=2.11.3->llama-cpp-python)\n",
            "  Downloading MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)\n",
            "Building wheels for collected packages: llama-cpp-python\n",
            "  Running command Building wheel for llama-cpp-python (pyproject.toml)\n",
            "  *** scikit-build-core 0.9.4 using CMake 3.29.3 (wheel)\n",
            "  *** Configuring CMake...\n",
            "  loading initial cache file /tmp/tmpqi2k3q16/build/CMakeInit.txt\n",
            "  -- The C compiler identification is GNU 11.4.0\n",
            "  -- The CXX compiler identification is GNU 11.4.0\n",
            "  -- Detecting C compiler ABI info\n",
            "  -- Detecting C compiler ABI info - done\n",
            "  -- Check for working C compiler: /usr/bin/cc - skipped\n",
            "  -- Detecting C compile features\n",
            "  -- Detecting C compile features - done\n",
            "  -- Detecting CXX compiler ABI info\n",
            "  -- Detecting CXX compiler ABI info - done\n",
            "  -- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "  -- Detecting CXX compile features\n",
            "  -- Detecting CXX compile features - done\n",
            "  -- Found Git: /usr/bin/git (found version \"2.34.1\")\n",
            "  -- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "  -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "  -- Found Threads: TRUE\n",
            "  CMake Warning at vendor/llama.cpp/CMakeLists.txt:389 (message):\n",
            "    LLAMA_CUBLAS is deprecated and will be removed in the future.\n",
            "\n",
            "    Use LLAMA_CUDA instead\n",
            "\n",
            "\n",
            "  -- Found CUDAToolkit: /usr/local/cuda/targets/x86_64-linux/include (found version \"12.2.140\")\n",
            "  -- CUDA found\n",
            "  -- The CUDA compiler identification is NVIDIA 12.2.140\n",
            "  -- Detecting CUDA compiler ABI info\n",
            "  -- Detecting CUDA compiler ABI info - done\n",
            "  -- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped\n",
            "  -- Detecting CUDA compile features\n",
            "  -- Detecting CUDA compile features - done\n",
            "  -- Using CUDA architectures: 52;61;70\n",
            "  -- CUDA host compiler is GNU 11.4.0\n",
            "\n",
            "  -- Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF\n",
            "  -- CMAKE_SYSTEM_PROCESSOR: x86_64\n",
            "  -- x86 detected\n",
            "  CMake Warning (dev) at CMakeLists.txt:26 (install):\n",
            "    Target llama has PUBLIC_HEADER files but no PUBLIC_HEADER DESTINATION.\n",
            "  This warning is for project developers.  Use -Wno-dev to suppress it.\n",
            "\n",
            "  CMake Warning (dev) at CMakeLists.txt:35 (install):\n",
            "    Target llama has PUBLIC_HEADER files but no PUBLIC_HEADER DESTINATION.\n",
            "  This warning is for project developers.  Use -Wno-dev to suppress it.\n",
            "\n",
            "  -- Configuring done (4.8s)\n",
            "  -- Generating done (0.0s)\n",
            "  -- Build files have been written to: /tmp/tmpqi2k3q16/build\n",
            "  *** Building project with Ninja...\n",
            "  Change Dir: '/tmp/tmpqi2k3q16/build'\n",
            "\n",
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            "  [52/54] : && /tmp/pip-build-env-t5ck6ear/normal/local/lib/python3.10/dist-packages/cmake/data/bin/cmake -E rm -f vendor/llama.cpp/examples/llava/libllava_static.a && /usr/bin/ar qc vendor/llama.cpp/examples/llava/libllava_static.a  vendor/llama.cpp/examples/llava/CMakeFiles/llava.dir/llava.cpp.o vendor/llama.cpp/examples/llava/CMakeFiles/llava.dir/clip.cpp.o && /usr/bin/ranlib vendor/llama.cpp/examples/llava/libllava_static.a && :\n",
            "  [53/54] : && /usr/bin/c++ -O3 -DNDEBUG  vendor/llama.cpp/examples/llava/CMakeFiles/llava.dir/llava.cpp.o vendor/llama.cpp/examples/llava/CMakeFiles/llava.dir/clip.cpp.o vendor/llama.cpp/examples/llava/CMakeFiles/llava-cli.dir/llava-cli.cpp.o -o vendor/llama.cpp/examples/llava/llava-cli  -Wl,-rpath,/tmp/tmpqi2k3q16/build/vendor/llama.cpp:/usr/local/cuda-12.2/targets/x86_64-linux/lib:  vendor/llama.cpp/common/libcommon.a  vendor/llama.cpp/libllama.so  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudart.so  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublas.so  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libculibos.a  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublasLt.so  /usr/local/cuda/targets/x86_64-linux/lib/stubs/libcuda.so  -ldl  /usr/lib/x86_64-linux-gnu/librt.a && :\n",
            "  [54/54] : && /usr/bin/c++ -fPIC -O3 -DNDEBUG   -shared -Wl,-soname,libllava.so -o vendor/llama.cpp/examples/llava/libllava.so vendor/llama.cpp/examples/llava/CMakeFiles/llava.dir/llava.cpp.o vendor/llama.cpp/examples/llava/CMakeFiles/llava.dir/clip.cpp.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml.c.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-alloc.c.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-backend.c.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-quants.c.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/acc.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/arange.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/argsort.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/binbcast.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/clamp.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/concat.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/convert.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/cpy.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/diagmask.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/dmmv.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/fattn.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/getrows.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/im2col.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/mmq.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/mmvq.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/norm.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/pad.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/pool2d.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/quantize.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/rope.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/scale.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/softmax.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/sumrows.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/tsembd.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/unary.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda/upscale.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/ggml-cuda.cu.o vendor/llama.cpp/CMakeFiles/ggml.dir/sgemm.cpp.o  -Wl,-rpath,/tmp/tmpqi2k3q16/build/vendor/llama.cpp:/usr/local/cuda-12.2/targets/x86_64-linux/lib:  vendor/llama.cpp/libllama.so  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudart.so  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublas.so  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libculibos.a  /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcublasLt.so  /usr/local/cuda/targets/x86_64-linux/lib/stubs/libcuda.so  -ldl  /usr/lib/x86_64-linux-gnu/librt.a && :\n",
            "\n",
            "  *** Installing project into wheel...\n",
            "  -- Install configuration: \"Release\"\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/lib/libggml_shared.so\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/tmpqi2k3q16/wheel/platlib/lib/libggml_shared.so\" to \"\"\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/lib/cmake/Llama/LlamaConfig.cmake\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/lib/cmake/Llama/LlamaConfigVersion.cmake\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/include/ggml.h\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/include/ggml-alloc.h\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/include/ggml-backend.h\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/include/ggml-cuda.h\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/lib/libllama.so\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/tmpqi2k3q16/wheel/platlib/lib/libllama.so\" to \"\"\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/include/llama.h\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/bin/convert.py\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/llama_cpp/libllama.so\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/tmpqi2k3q16/wheel/platlib/llama_cpp/libllama.so\" to \"\"\n",
            "  -- Installing: /tmp/pip-install-9v53xanj/llama-cpp-python_22142dc83a5e4ff896e7985658bbbec8/llama_cpp/libllama.so\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/pip-install-9v53xanj/llama-cpp-python_22142dc83a5e4ff896e7985658bbbec8/llama_cpp/libllama.so\" to \"\"\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/lib/libllava.so\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/tmpqi2k3q16/wheel/platlib/lib/libllava.so\" to \"\"\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/bin/llava-cli\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/tmpqi2k3q16/wheel/platlib/bin/llava-cli\" to \"\"\n",
            "  -- Installing: /tmp/tmpqi2k3q16/wheel/platlib/llama_cpp/libllava.so\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/tmpqi2k3q16/wheel/platlib/llama_cpp/libllava.so\" to \"\"\n",
            "  -- Installing: /tmp/pip-install-9v53xanj/llama-cpp-python_22142dc83a5e4ff896e7985658bbbec8/llama_cpp/libllava.so\n",
            "  -- Set non-toolchain portion of runtime path of \"/tmp/pip-install-9v53xanj/llama-cpp-python_22142dc83a5e4ff896e7985658bbbec8/llama_cpp/libllava.so\" to \"\"\n",
            "  *** Making wheel...\n",
            "  *** Created llama_cpp_python-0.2.74-cp310-cp310-linux_x86_64.whl...\n",
            "  Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for llama-cpp-python: filename=llama_cpp_python-0.2.74-cp310-cp310-linux_x86_64.whl size=61856854 sha256=943dc62950a4fe3a1f6ff9d835fb79a3454f616f9b80bb016e97df705bb463d0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ftqsz2q1/wheels/21/1f/6d/ee16805ea65ed00d89776a8ce82f3631340a43fc41edabed97\n",
            "Successfully built llama-cpp-python\n",
            "Installing collected packages: typing-extensions, numpy, MarkupSafe, diskcache, jinja2, llama-cpp-python\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.11.0\n",
            "    Uninstalling typing_extensions-4.11.0:\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/__pycache__/typing_extensions.cpython-310.pyc\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/typing_extensions-4.11.0.dist-info/\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/typing_extensions.py\n",
            "      Successfully uninstalled typing_extensions-4.11.0\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.25.2\n",
            "    Uninstalling numpy-1.25.2:\n",
            "      Removing file or directory /usr/local/bin/f2py\n",
            "      Removing file or directory /usr/local/bin/f2py3\n",
            "      Removing file or directory /usr/local/bin/f2py3.10\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/numpy-1.25.2.dist-info/\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/numpy.libs/\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/numpy/\n",
            "      Successfully uninstalled numpy-1.25.2\n",
            "  changing mode of /usr/local/bin/f2py to 755\n",
            "  Attempting uninstall: MarkupSafe\n",
            "    Found existing installation: MarkupSafe 2.1.5\n",
            "    Uninstalling MarkupSafe-2.1.5:\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/MarkupSafe-2.1.5.dist-info/\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/markupsafe/\n",
            "      Successfully uninstalled MarkupSafe-2.1.5\n",
            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 3.1.4\n",
            "    Uninstalling Jinja2-3.1.4:\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/jinja2-3.1.4.dist-info/\n",
            "      Removing file or directory /usr/local/lib/python3.10/dist-packages/jinja2/\n",
            "      Successfully uninstalled Jinja2-3.1.4\n",
            "Successfully installed MarkupSafe-2.1.5 diskcache-5.6.3 jinja2-3.1.4 llama-cpp-python-0.2.74 numpy-1.26.4 typing-extensions-4.11.0\n",
            "Collecting llama-cpp-haystack\n",
            "  Downloading llama_cpp_haystack-0.4.0-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: haystack-ai in /usr/local/lib/python3.10/dist-packages (from llama-cpp-haystack) (2.1.1)\n",
            "Requirement already satisfied: llama-cpp-python in /usr/local/lib/python3.10/dist-packages (from llama-cpp-haystack) (0.2.74)\n",
            "Requirement already satisfied: boilerpy3 in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (1.0.7)\n",
            "Requirement already satisfied: haystack-bm25 in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (1.0.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (3.1.4)\n",
            "Requirement already satisfied: lazy-imports in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (0.3.1)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (10.1.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (3.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (1.26.4)\n",
            "Requirement already satisfied: openai>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (1.30.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (2.0.3)\n",
            "Requirement already satisfied: posthog in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (3.5.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (2.8.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (6.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (2.31.0)\n",
            "Requirement already satisfied: tenacity in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (8.3.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (4.66.4)\n",
            "Requirement already satisfied: typing-extensions>=4.7 in /usr/local/lib/python3.10/dist-packages (from haystack-ai->llama-cpp-haystack) (4.11.0)\n",
            "Requirement already satisfied: diskcache>=5.6.1 in /usr/local/lib/python3.10/dist-packages (from llama-cpp-python->llama-cpp-haystack) (5.6.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->haystack-ai->llama-cpp-haystack) (2.1.5)\n",
            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai->llama-cpp-haystack) (3.7.1)\n",
            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai>=1.1.0->haystack-ai->llama-cpp-haystack) (1.7.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai->llama-cpp-haystack) (0.27.0)\n",
            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai->llama-cpp-haystack) (2.7.1)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai>=1.1.0->haystack-ai->llama-cpp-haystack) (1.3.1)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai->llama-cpp-haystack) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->haystack-ai->llama-cpp-haystack) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil->haystack-ai->llama-cpp-haystack) (1.16.0)\n",
            "Requirement already satisfied: monotonic>=1.5 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai->llama-cpp-haystack) (1.6)\n",
            "Requirement already satisfied: backoff>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from posthog->haystack-ai->llama-cpp-haystack) (2.2.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai->llama-cpp-haystack) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai->llama-cpp-haystack) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai->llama-cpp-haystack) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->haystack-ai->llama-cpp-haystack) (2024.2.2)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai>=1.1.0->haystack-ai->llama-cpp-haystack) (1.2.1)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai->llama-cpp-haystack) (1.0.5)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai>=1.1.0->haystack-ai->llama-cpp-haystack) (0.14.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai->llama-cpp-haystack) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai>=1.1.0->haystack-ai->llama-cpp-haystack) (2.18.2)\n",
            "Installing collected packages: llama-cpp-haystack\n",
            "Successfully installed llama-cpp-haystack-0.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://huggingface.co/Futyn-Maker/saiga_llama3_8b_wildberries_4bit_gguf/resolve/main/saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwxOj6S81jov",
        "outputId": "b46b1b40-d832-4715-aff0-6a15871be7a4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-14 21:22:17--  https://huggingface.co/Futyn-Maker/saiga_llama3_8b_wildberries_4bit_gguf/resolve/main/saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf\n",
            "Resolving huggingface.co (huggingface.co)... 13.33.30.23, 13.33.30.49, 13.33.30.76, ...\n",
            "Connecting to huggingface.co (huggingface.co)|13.33.30.23|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://cdn-lfs-us-1.huggingface.co/repos/64/0d/640d8853e91b22175679bac7eb005b9614a21295547cf1ac39f9414abe6fe4a0/3a279ee55e31e216a051ebafa058b9d2544b10ee843998d7f1877b4d37bf9781?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf%3B+filename%3D%22saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf%22%3B&Expires=1715980937&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNTk4MDkzN319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzY0LzBkLzY0MGQ4ODUzZTkxYjIyMTc1Njc5YmFjN2ViMDA1Yjk2MTRhMjEyOTU1NDdjZjFhYzM5Zjk0MTRhYmU2ZmU0YTAvM2EyNzllZTU1ZTMxZTIxNmEwNTFlYmFmYTA1OGI5ZDI1NDRiMTBlZTg0Mzk5OGQ3ZjE4NzdiNGQzN2JmOTc4MT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=OXkmzPJOvMYZjmGcYw0Mm71ak5EA4DeZO8mnr1i0fft0QkS8qDcNhx9mYfxgNv9yfsa72PhkFGMTFOc9GDsI0rzdhhUlvcxFyfmMu5JP7Bj18n0ihJyeK%7EsEN7yttJY5tN6QlRK6mFl5ox%7EmIb%7EI8hXT-CCCS8ZLHTsni69dXDOx3qbX8oIUoiAqf0m3Qd8%7E3Bw30K9kh3O7NF1puWvhBoiszL3G-4GLKLnQhn%7EgxfsXJxJ-Sr7QxAEcrbNd99jIULBT6PfYu2Yf3tqkqKxYAbTLekd6g6VMBo7bXEhWJNMV-btpVvREGIyvlqwGjzPErSx1YQ22EMovdQW7nyNg0w__&Key-Pair-Id=KCD77M1F0VK2B [following]\n",
            "--2024-05-14 21:22:17--  https://cdn-lfs-us-1.huggingface.co/repos/64/0d/640d8853e91b22175679bac7eb005b9614a21295547cf1ac39f9414abe6fe4a0/3a279ee55e31e216a051ebafa058b9d2544b10ee843998d7f1877b4d37bf9781?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf%3B+filename%3D%22saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf%22%3B&Expires=1715980937&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNTk4MDkzN319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzY0LzBkLzY0MGQ4ODUzZTkxYjIyMTc1Njc5YmFjN2ViMDA1Yjk2MTRhMjEyOTU1NDdjZjFhYzM5Zjk0MTRhYmU2ZmU0YTAvM2EyNzllZTU1ZTMxZTIxNmEwNTFlYmFmYTA1OGI5ZDI1NDRiMTBlZTg0Mzk5OGQ3ZjE4NzdiNGQzN2JmOTc4MT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=OXkmzPJOvMYZjmGcYw0Mm71ak5EA4DeZO8mnr1i0fft0QkS8qDcNhx9mYfxgNv9yfsa72PhkFGMTFOc9GDsI0rzdhhUlvcxFyfmMu5JP7Bj18n0ihJyeK%7EsEN7yttJY5tN6QlRK6mFl5ox%7EmIb%7EI8hXT-CCCS8ZLHTsni69dXDOx3qbX8oIUoiAqf0m3Qd8%7E3Bw30K9kh3O7NF1puWvhBoiszL3G-4GLKLnQhn%7EgxfsXJxJ-Sr7QxAEcrbNd99jIULBT6PfYu2Yf3tqkqKxYAbTLekd6g6VMBo7bXEhWJNMV-btpVvREGIyvlqwGjzPErSx1YQ22EMovdQW7nyNg0w__&Key-Pair-Id=KCD77M1F0VK2B\n",
            "Resolving cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)... 108.157.254.82, 108.157.254.63, 108.157.254.89, ...\n",
            "Connecting to cdn-lfs-us-1.huggingface.co (cdn-lfs-us-1.huggingface.co)|108.157.254.82|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4920734048 (4.6G) [binary/octet-stream]\n",
            "Saving to: ‘saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf’\n",
            "\n",
            "saiga_llama3_8b_wil 100%[===================>]   4.58G  20.5MB/s    in 3m 48s  \n",
            "\n",
            "2024-05-14 21:26:06 (20.6 MB/s) - ‘saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf’ saved [4920734048/4920734048]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Перезагружаем ядро, чтобы использовать установленные библиотеки\n",
        "\n",
        "import os\n",
        "os.kill(os.getpid(), 9)"
      ],
      "metadata": {
        "id": "2pDb2RHU1pWW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Подготовка данных"
      ],
      "metadata": {
        "id": "Gtp5WkSD0M87"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Очистка данных"
      ],
      "metadata": {
        "id": "NbPPO1Ec1wzO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "64Dz_G-X1_kI"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "knowledge_base = pd.read_excel(\"data/knowledge_base.xlsx\", sheet_name=None)\n",
        "kb_chunks = knowledge_base[\"Knowledge_base\"]\n",
        "kb_sources = knowledge_base[\"Sources\"]\n",
        "qa_pairs = pd.read_excel(\"data/QA_pairs.xlsx\")"
      ],
      "metadata": {
        "id": "-m8q4oqR2LVe"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Duplicate search\n",
        "duplicate_questions = qa_pairs[qa_pairs.duplicated(\"question\", keep=False)]\n",
        "grouped = duplicate_questions.groupby(\"question\").filter(\n",
        "    lambda x: len(x[\"answer\"].unique()) > 1)\n",
        "\n",
        "# Keep only later occurrences and remove complete duplicates\n",
        "grouped_sorted = grouped.sort_values(\"id\", ascending=True)\n",
        "cleaned_qa_pairs = grouped_sorted.drop_duplicates(\n",
        "    subset=[\"question\"], keep=\"last\")\n",
        "\n",
        "qa_pairs = qa_pairs.drop_duplicates(subset=[\"question\"], keep=False)\n",
        "\n",
        "qa_pairs = pd.concat([qa_pairs, cleaned_qa_pairs])\n",
        "\n",
        "qa_pairs = qa_pairs.sort_values(\"id\", ascending=True)"
      ],
      "metadata": {
        "id": "ltarJwZD2PA3"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicate chunks\n",
        "kb_chunks = kb_chunks.drop_duplicates(\n",
        "    subset=[\"chunk\"], keep=\"first\").sort_values(\"id\")"
      ],
      "metadata": {
        "id": "YoJED_Gg2SHQ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill in the missing part_id\n",
        "kb_chunks[\"part_id\"] = kb_chunks[\"part_id\"].fillna(0)"
      ],
      "metadata": {
        "id": "ePKabc-Z2T13"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clear chunks\n",
        "pattern = re.compile(\"<[^>]+>\", re.UNICODE)\n",
        "\n",
        "kb_chunks[\"chunk\"] = kb_chunks[\"chunk\"].replace(\n",
        "    to_replace=\"/n\", value=\"\\n\", regex=False)\n",
        "kb_chunks[\"chunk\"] = kb_chunks[\"chunk\"].replace(\n",
        "    to_replace=pattern, value=\"\\n\", regex=True)"
      ],
      "metadata": {
        "id": "zkW_fS0q2VkN"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save data\n",
        "qa_pairs.to_csv(\"data/qa_pairs_cleaned.csv\", index=False)\n",
        "kb_chunks.to_csv(\"data/kb_chunks_cleaned.csv\", index=False)"
      ],
      "metadata": {
        "id": "tPmMlAfi2ZFI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Подготовка данных"
      ],
      "metadata": {
        "id": "kSFZo8HK2bAc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "kb_chunks = pd.read_csv(\"data/kb_chunks_cleaned.csv\")\n",
        "qa_pairs = pd.read_csv(\"data/qa_pairs_cleaned.csv\")"
      ],
      "metadata": {
        "id": "o5GNEIF42bSO"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine questions with answers\n",
        "qa_pairs[\"combined\"] = \"Query: \" + qa_pairs[\"question\"] + \\\n",
        "    \"\\n\\nAnswer: \" + qa_pairs[\"answer\"]"
      ],
      "metadata": {
        "id": "AcdlatF02mPW"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add question-answers to the knowledge base\n",
        "qa_combined = qa_pairs[\"combined\"].tolist()\n",
        "\n",
        "new_rows = [{\"id\": 2000 + i, \"chunk\": text, \"document_id\": 14, \"part_id\": 0}\n",
        "            for i, text in enumerate(qa_combined)]\n",
        "\n",
        "kb_chunks = pd.concat([kb_chunks, pd.DataFrame(new_rows)], ignore_index=True)"
      ],
      "metadata": {
        "id": "TkRvnfJ62osH"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save data\n",
        "kb_chunks.to_csv(\"data/kb_chunks_combined.csv\", index=False)"
      ],
      "metadata": {
        "id": "OTEMuO1k2rR2"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Создание эмбеддингов документов"
      ],
      "metadata": {
        "id": "C5Hr4HwU2sI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack import Document\n",
        "from haystack import Pipeline\n",
        "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
        "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
        "from haystack.components.writers import DocumentWriter"
      ],
      "metadata": {
        "id": "-iiMIYFP2sca"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_model = \"Futyn-Maker/wb_questions\""
      ],
      "metadata": {
        "id": "CZJrucVA3lAo"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "kb_chunks = pd.read_csv(\"data/kb_chunks_combined.csv\")\n",
        "\n",
        "documents = [Document(content=row[\"chunk\"])\n",
        "             for index, row in kb_chunks.iterrows()]"
      ],
      "metadata": {
        "id": "CAx4ukWS3afm"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the DocumentStore\n",
        "document_store = InMemoryDocumentStore()"
      ],
      "metadata": {
        "id": "YZoTvZzq3dhR"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create indexing pipeline\n",
        "indexing_pipeline = Pipeline()\n",
        "indexing_pipeline.add_component(\n",
        "    instance=SentenceTransformersDocumentEmbedder(\n",
        "        model=embedding_model,\n",
        "        normalize_embeddings=True),\n",
        "    name=\"embedder\")\n",
        "indexing_pipeline.add_component(\n",
        "    instance=DocumentWriter(\n",
        "        document_store=document_store),\n",
        "    name=\"writer\")\n",
        "indexing_pipeline.connect(\"embedder.documents\", \"writer.documents\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNXGlyFj3o-u",
        "outputId": "fc787cd5-3718-40b2-98b7-2c2adad87a38"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7f854c24beb0>\n",
              "🚅 Components\n",
              "  - embedder: SentenceTransformersDocumentEmbedder\n",
              "  - writer: DocumentWriter\n",
              "🛤️ Connections\n",
              "  - embedder.documents -> writer.documents (List[Document])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indexing_pipeline.run({\"documents\": documents})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511,
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            "c191f62f11f54d7a8d16d8c665e327fc",
            "31bd0e2aecba477aa7946f95d6b6856e",
            "b791cffcb3534e838d263edeb334753a",
            "5175e90e1b9348208b46693315953868",
            "81e1711fdf434d31a4642ad94157a487",
            "dee349b4c7a54267a4e33e194756df00",
            "fa2e6561d5d54f1a97826b580a044c8c",
            "fee19225e0d54dfaa60ba763f867d733",
            "36f42dfc754746b885012f007bf0394c",
            "7ff25bda737c407dad1d65a763e456a3"
          ]
        },
        "id": "B-ZVFLh93wLG",
        "outputId": "4f910a2d-8d8a-427a-b70a-d302e43d0b55"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "916f3655c94f42a69b68c04d7a7b5a7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "config_sentence_transformers.json:   0%|          | 0.00/176 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b1fa572184244c195aa9f6c8e4806d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d78bab5fbb4f449f899dd45817253ddb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "efa0c26d6a5a4af79c2d9a9e680c8515"
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d7dbfc4798a74c69941786df70c6dc0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/2.24G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c23af9a8d664b2893b6a61ebc5c794f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1cca5b2875bc473096c16f9506ca0f48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a45a02f7893e4b738e79e4788e998eda"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1b80bfa4a164419bec23329cbf813e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/214 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f186e2f226542fa93eed9eeab69b629"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'writer': {'documents_written': 6830}}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to retrieve answer on already existed question\n",
        "def get_answer_from_qa_pairs(question):\n",
        "    result = qa_pairs[qa_pairs[\"question\"].str.lower() == question.lower()]\n",
        "    if not result.empty:\n",
        "        return result[\"answer\"].iloc[0]\n",
        "    return None"
      ],
      "metadata": {
        "id": "k40kMW4d4Xtl"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Помощник"
      ],
      "metadata": {
        "id": "PbOlWBFa36Xy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
        "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
        "from haystack.components.builders import PromptBuilder\n",
        "from haystack_integrations.components.generators.llama_cpp import LlamaCppGenerator"
      ],
      "metadata": {
        "id": "t3PVAc2436ry"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "template = \"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "Ты — Помощник, русскоязычный автоматический ассистент. Ты работаешь в технической поддержке компании Wildberries, крупного российского маркетплейса. Ты помогаешь сотрудникам пунктов выдачи заказов (ПВЗ), отвечая на их вопросы. Ответь на вопрос сотрудника ПВЗ, используя вспомогательную информацию из базы знаний. Отвечай последовательно и по существу. Не предоставляй нерелевантную или слишком общую информацию, давай такой ответ, который прямо отвечает на поставленный вопрос, при этом следи, чтобы релевантная информация была передана полностью. Отвечай только на основе вспомогательной информации из базы знаний. Вежливо отказывайся отвечать на вопросы, которые никак не связаны с Wildberries.<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "Вспомогательная информация из базы знаний:\n",
        "{% for document in documents %}\n",
        "    {{ document.content }}\n",
        "{% endfor %}\n",
        "\n",
        "Вопрос: {{question}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
        "\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "EB1ORXHU5RlO"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_embedder = SentenceTransformersTextEmbedder(\n",
        "    model=embedding_model,\n",
        "    prefix=\"Instruct: Given a question, retrieve relevant documents that best answer the question\\nQuery: \",\n",
        "    progress_bar=False,\n",
        "    normalize_embeddings=True)\n",
        "retriever = InMemoryEmbeddingRetriever(document_store)\n",
        "prompt_builder = PromptBuilder(template=template)"
      ],
      "metadata": {
        "id": "kdkqE_aE5dMO"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = LlamaCppGenerator(\n",
        "    model=\"saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf\",\n",
        "    n_ctx=8192,\n",
        "    n_batch=128,\n",
        "    model_kwargs={\"n_parts\": 1, \"verbose\": True, \"n_gpu_layers\": 32, \"seed\": 42},\n",
        "\t\tgeneration_kwargs={\"max_tokens\": 1024, \"temperature\": 0.6, \"top_k\": 30, \"top_p\": 0.8, \"repeat_penalty\": 1.1,},\n",
        ")\n",
        "generator.warm_up()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQndYc5I53D6",
        "outputId": "0023abaa-3e4a-451a-9f91-fbf3e3f0c544"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "llama_model_loader: loaded meta data with 23 key-value pairs and 291 tensors from saiga_llama3_8b_wildberries_4bit_gguf-unsloth.Q4_K_M.gguf (version GGUF V3 (latest))\n",
            "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
            "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
            "llama_model_loader: - kv   1:                               general.name str              = saiga_llama3_8b_wildberries_4bit_gguf\n",
            "llama_model_loader: - kv   2:                          llama.block_count u32              = 32\n",
            "llama_model_loader: - kv   3:                       llama.context_length u32              = 8192\n",
            "llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096\n",
            "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336\n",
            "llama_model_loader: - kv   6:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv   7:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv   8:                       llama.rope.freq_base f32              = 500000.000000\n",
            "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  10:                          general.file_type u32              = 15\n",
            "llama_model_loader: - kv  11:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv  12:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = llama-bpe\n",
            "llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = [\"Ġ Ġ\", \"Ġ ĠĠĠ\", \"ĠĠ ĠĠ\", \"...\n",
            "llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 128000\n",
            "llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...\n",
            "llama_model_loader: - kv  22:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - type  f32:   65 tensors\n",
            "llama_model_loader: - type q4_K:  193 tensors\n",
            "llama_model_loader: - type q6_K:   33 tensors\n",
            "llm_load_vocab: special tokens definition check successful ( 256/128256 ).\n",
            "llm_load_print_meta: format           = GGUF V3 (latest)\n",
            "llm_load_print_meta: arch             = llama\n",
            "llm_load_print_meta: vocab type       = BPE\n",
            "llm_load_print_meta: n_vocab          = 128256\n",
            "llm_load_print_meta: n_merges         = 280147\n",
            "llm_load_print_meta: n_ctx_train      = 8192\n",
            "llm_load_print_meta: n_embd           = 4096\n",
            "llm_load_print_meta: n_head           = 32\n",
            "llm_load_print_meta: n_head_kv        = 8\n",
            "llm_load_print_meta: n_layer          = 32\n",
            "llm_load_print_meta: n_rot            = 128\n",
            "llm_load_print_meta: n_embd_head_k    = 128\n",
            "llm_load_print_meta: n_embd_head_v    = 128\n",
            "llm_load_print_meta: n_gqa            = 4\n",
            "llm_load_print_meta: n_embd_k_gqa     = 1024\n",
            "llm_load_print_meta: n_embd_v_gqa     = 1024\n",
            "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
            "llm_load_print_meta: f_norm_rms_eps   = 1.0e-05\n",
            "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
            "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
            "llm_load_print_meta: f_logit_scale    = 0.0e+00\n",
            "llm_load_print_meta: n_ff             = 14336\n",
            "llm_load_print_meta: n_expert         = 0\n",
            "llm_load_print_meta: n_expert_used    = 0\n",
            "llm_load_print_meta: causal attn      = 1\n",
            "llm_load_print_meta: pooling type     = 0\n",
            "llm_load_print_meta: rope type        = 0\n",
            "llm_load_print_meta: rope scaling     = linear\n",
            "llm_load_print_meta: freq_base_train  = 500000.0\n",
            "llm_load_print_meta: freq_scale_train = 1\n",
            "llm_load_print_meta: n_yarn_orig_ctx  = 8192\n",
            "llm_load_print_meta: rope_finetuned   = unknown\n",
            "llm_load_print_meta: ssm_d_conv       = 0\n",
            "llm_load_print_meta: ssm_d_inner      = 0\n",
            "llm_load_print_meta: ssm_d_state      = 0\n",
            "llm_load_print_meta: ssm_dt_rank      = 0\n",
            "llm_load_print_meta: model type       = 8B\n",
            "llm_load_print_meta: model ftype      = Q4_K - Medium\n",
            "llm_load_print_meta: model params     = 8.03 B\n",
            "llm_load_print_meta: model size       = 4.58 GiB (4.89 BPW) \n",
            "llm_load_print_meta: general.name     = saiga_llama3_8b_wildberries_4bit_gguf\n",
            "llm_load_print_meta: BOS token        = 128000 '<|begin_of_text|>'\n",
            "llm_load_print_meta: EOS token        = 128009 '<|eot_id|>'\n",
            "llm_load_print_meta: PAD token        = 128000 '<|begin_of_text|>'\n",
            "llm_load_print_meta: LF token         = 128 'Ä'\n",
            "llm_load_print_meta: EOT token        = 128009 '<|eot_id|>'\n",
            "llm_load_tensors: ggml ctx size =    0.30 MiB\n",
            "llm_load_tensors: offloading 32 repeating layers to GPU\n",
            "llm_load_tensors: offloaded 32/33 layers to GPU\n",
            "llm_load_tensors:        CPU buffer size =  4685.30 MiB\n",
            "llm_load_tensors:      CUDA0 buffer size =  3992.50 MiB\n",
            "........................................................................................\n",
            "llama_new_context_with_model: n_ctx      = 8192\n",
            "llama_new_context_with_model: n_batch    = 128\n",
            "llama_new_context_with_model: n_ubatch   = 128\n",
            "llama_new_context_with_model: flash_attn = 0\n",
            "llama_new_context_with_model: freq_base  = 500000.0\n",
            "llama_new_context_with_model: freq_scale = 1\n",
            "llama_kv_cache_init:      CUDA0 KV buffer size =  1024.00 MiB\n",
            "llama_new_context_with_model: KV self size  = 1024.00 MiB, K (f16):  512.00 MiB, V (f16):  512.00 MiB\n",
            "llama_new_context_with_model:  CUDA_Host  output buffer size =     0.49 MiB\n",
            "llama_new_context_with_model:      CUDA0 compute buffer size =   475.60 MiB\n",
            "llama_new_context_with_model:  CUDA_Host compute buffer size =     6.00 MiB\n",
            "llama_new_context_with_model: graph nodes  = 1030\n",
            "llama_new_context_with_model: graph splits = 4\n",
            "AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | \n",
            "Model metadata: {'tokenizer.chat_template': \"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}{% endif %}\", 'tokenizer.ggml.padding_token_id': '128000', 'tokenizer.ggml.eos_token_id': '128009', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'gpt2', 'general.architecture': 'llama', 'llama.rope.freq_base': '500000.000000', 'tokenizer.ggml.pre': 'llama-bpe', 'llama.context_length': '8192', 'general.name': 'saiga_llama3_8b_wildberries_4bit_gguf', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '14336', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'tokenizer.ggml.bos_token_id': '128000', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8', 'general.file_type': '15', 'llama.vocab_size': '128256', 'llama.rope.dimension_count': '128'}\n",
            "Available chat formats from metadata: chat_template.default\n",
            "Guessed chat format: llama-3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rag_pipeline = Pipeline()\n",
        "\n",
        "rag_pipeline.add_component(\"text_embedder\", text_embedder)\n",
        "rag_pipeline.add_component(\"retriever\", retriever)\n",
        "rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n",
        "rag_pipeline.add_component(\"llm\", generator)\n",
        "\n",
        "rag_pipeline.connect(\n",
        "    \"text_embedder.embedding\",\n",
        "    \"retriever.query_embedding\")\n",
        "rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n",
        "rag_pipeline.connect(\"prompt_builder\", \"llm\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0gElg4g6Q5I",
        "outputId": "0f074821-5c21-4c77-a7a0-fd91f1d6008b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<haystack.core.pipeline.pipeline.Pipeline object at 0x7f842289e7d0>\n",
              "🚅 Components\n",
              "  - text_embedder: SentenceTransformersTextEmbedder\n",
              "  - retriever: InMemoryEmbeddingRetriever\n",
              "  - prompt_builder: PromptBuilder\n",
              "  - llm: LlamaCppGenerator\n",
              "🛤️ Connections\n",
              "  - text_embedder.embedding -> retriever.query_embedding (List[float])\n",
              "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
              "  - prompt_builder.prompt -> llm.prompt (str)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_answer(question):\n",
        "    result = rag_pipeline.run(\n",
        "        {\"text_embedder\": {\"text\": question}, \"prompt_builder\": {\"question\": question}})\n",
        "    return result[\"llm\"][\"replies\"][0].strip()"
      ],
      "metadata": {
        "id": "NA8NTuXA6Ryz"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Интерфейс"
      ],
      "metadata": {
        "id": "ZoAvWe906jW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJUovqlm7d3J",
        "outputId": "a6c6ede9-fca4-4ab2-dc68-31e2d157779e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting gradio\n",
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            "  Downloading websockets-11.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (129 kB)\n",
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            "Collecting ujson!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,>=4.0.1 (from fastapi->gradio)\n",
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            "\u001b[?25hCollecting email_validator>=2.0.0 (from fastapi->gradio)\n",
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            "  Downloading httptools-0.6.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (341 kB)\n",
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            "\u001b[?25hCollecting python-dotenv>=0.13 (from uvicorn>=0.14.0->gradio)\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Collecting uvloop!=0.15.0,!=0.15.1,>=0.14.0 (from uvicorn>=0.14.0->gradio)\n",
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            "\u001b[?25hCollecting watchfiles>=0.13 (from uvicorn>=0.14.0->gradio)\n",
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            "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.2)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=bcf53a51521a917c5fd9e95907a92af6498932155d95e2fdc31ef65f89cc3fe8\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, uvloop, uvicorn, ujson, tomlkit, shellingham, semantic-version, ruff, python-multipart, python-dotenv, orjson, httptools, dnspython, aiofiles, watchfiles, starlette, email_validator, typer, gradio-client, fastapi-cli, fastapi, gradio\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.9.4\n",
            "    Uninstalling typer-0.9.4:\n",
            "      Successfully uninstalled typer-0.9.4\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",
            "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
            "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 dnspython-2.6.1 email_validator-2.1.1 fastapi-0.111.0 fastapi-cli-0.0.3 ffmpy-0.3.2 gradio-4.31.2 gradio-client-0.16.3 httptools-0.6.1 orjson-3.10.3 pydub-0.25.1 python-dotenv-1.0.1 python-multipart-0.0.9 ruff-0.4.4 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.37.2 tomlkit-0.12.0 typer-0.12.3 ujson-5.10.0 uvicorn-0.29.0 uvloop-0.19.0 watchfiles-0.21.0 websockets-11.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr"
      ],
      "metadata": {
        "id": "-mrXWihD7fWR"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def return_answer(question):\n",
        "    answer = get_answer_from_qa_pairs(question)\n",
        "    if answer is None:\n",
        "        answer = get_answer(question)\n",
        "    return answer"
      ],
      "metadata": {
        "id": "d9m6bgt76sc4"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "helper = gr.Interface(fn=return_answer, inputs=\"text\", outputs=\"text\")"
      ],
      "metadata": {
        "id": "RDA7iYRC7jU2"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "helper.launch(debug=True)"
      ],
      "metadata": {
        "id": "7EZwcVMPFn5J"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}