RAG_Database_Creation.ipynb

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            "object_fit": null,
            "object_position": null,
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            "overflow_x": null,
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        "5917c5e0066141d6aaa33a3817afa1f8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
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            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
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    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install kdbai_client --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDeEzwLUW5jk",
        "outputId": "4c6d156e-a0bd-40b6-e3a7-75a9b7642e5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.8/43.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m28.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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",
            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.1.4 which is incompatible.\n",
            "mizani 0.13.0 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.\n",
            "plotnine 0.14.1 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_ddIL32EZpo",
        "outputId": "be263bb8-b35d-4faf-c597-205f207b54ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ImageBind'...\n",
            "remote: Enumerating objects: 146, done.\u001b[K\n",
            "remote: Counting objects: 100% (88/88), done.\u001b[K\n",
            "remote: Compressing objects: 100% (49/49), done.\u001b[K\n",
            "remote: Total 146 (delta 60), reused 39 (delta 39), pack-reused 58 (from 1)\u001b[K\n",
            "Receiving objects: 100% (146/146), 2.64 MiB | 27.08 MiB/s, done.\n",
            "Resolving deltas: 100% (68/68), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/facebookresearch/ImageBind"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('./ImageBind')"
      ],
      "metadata": {
        "id": "1UZO5icnEiLI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install . --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsAUiTYjEo8t",
        "outputId": "2a9b3015-b6b6-4330-fae1-190d72ff2610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m57.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.6/13.6 MB\u001b[0m \u001b[31m91.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.0/510.0 kB\u001b[0m \u001b[31m30.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m887.5/887.5 MB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.1/317.1 MB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.0/21.0 MB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m849.3/849.3 kB\u001b[0m \u001b[31m49.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m557.1/557.1 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.7/11.7 MB\u001b[0m \u001b[31m33.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.8/44.8 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m83.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.2/24.2 MB\u001b[0m \u001b[31m77.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m56.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m94.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m41.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m230.0/230.0 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m33.1/33.1 MB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.9/268.9 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for imagebind (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for mayavi (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pytorchvideo (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import pandas as pd\n",
        "import os\n",
        "import PIL\n",
        "from PIL import Image\n",
        "import torch\n",
        "from imagebind import data\n",
        "from imagebind.models import imagebind_model\n",
        "from imagebind.models.imagebind_model import ModalityType"
      ],
      "metadata": {
        "id": "Yc9V2HE1E0x1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model = imagebind_model.imagebind_huge(pretrained=True)\n",
        "model.eval()\n",
        "model.to(device)\n",
        "print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "76120517909342a181bc12fb3b4243b3",
            "9d823f213b4e42ed9440c16a3001d2e0",
            "b1bc0f3764a34220b43ada37fe110b0d",
            "5c254a300aa1434c9d4f8563bcbd84a7",
            "ad0c6199e2ea477b97aa1d0efb725604",
            "f9c3eb8e91c0444cb7c9b2c8c02ce9f9",
            "2506d4f526654b78a79ce5109f52f7ee",
            "1bb9c34e239b45b09ec2c4f6323ec196",
            "dfe2d235c2884d37bd27b04c18db8bec",
            "5ba635ed987547c790c426d27b293af0",
            "5917c5e0066141d6aaa33a3817afa1f8"
          ]
        },
        "id": "o34dngarFoUU",
        "outputId": "6cd51008-8131-48a1-ced5-be0518179d0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading imagebind weights to .checkpoints/imagebind_huge.pth ...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/4.47G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76120517909342a181bc12fb3b4243b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "def read_context(filename):\n",
        "  context = {}\n",
        "  with open(filename, 'r') as file:\n",
        "    csv_reader = csv.reader(file)\n",
        "    header = next(csv_reader)\n",
        "    for row in csv_reader:\n",
        "      context[row[0]] = row[1]\n",
        "  return context\n",
        "\n",
        "context = read_context('/content/drive/MyDrive/Persons/context.csv')"
      ],
      "metadata": {
        "id": "0KQnwXemX36u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getEmbeddingVector(inputs):\n",
        "    with torch.no_grad():\n",
        "        embedding = model(inputs)\n",
        "    for key, value in embedding.items():\n",
        "        vec = value.reshape(-1)\n",
        "        vec = vec.cpu().numpy()\n",
        "        return(vec)\n",
        "\n",
        "def dataToEmbedding(dataIn,dtype):\n",
        "    if dtype == 'image':\n",
        "        data_path = [dataIn]\n",
        "        inputs = {\n",
        "          ModalityType.VISION: data.load_and_transform_vision_data(data_path, device)\n",
        "        }\n",
        "    elif dtype == 'text':\n",
        "        txt = [dataIn]\n",
        "        inputs = {\n",
        "          ModalityType.TEXT: data.load_and_transform_text(txt, device)\n",
        "        }\n",
        "    vec = getEmbeddingVector(inputs)\n",
        "    return(vec)\n",
        "\n",
        "def queryToEmbedding(dataIn):\n",
        "    data_path = [dataIn]\n",
        "    inputs = {\n",
        "        ModalityType.VISION: data.load_and_transform_vision_data(data_path, device)\n",
        "    }\n",
        "    vec = getEmbeddingVector(inputs)\n",
        "    return(vec)"
      ],
      "metadata": {
        "id": "8jGX_d6sGIhX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns = ['path', 'embeddings', 'context']\n",
        "df = pd.DataFrame(columns=columns)"
      ],
      "metadata": {
        "id": "cUDPM6cnRUsn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyheif --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-GbRT_X3sJB",
        "outputId": "bea0703d-1395-4797-aefa-65b997386ec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m40.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pyheif\n",
        "from PIL import Image\n",
        "from tqdm.auto import tqdm\n",
        "def convert_heic_to_jpg(input_path, output_path):\n",
        "    heif_file = pyheif.read(input_path)\n",
        "    image = Image.frombytes(\n",
        "        heif_file.mode,\n",
        "        heif_file.size,\n",
        "        heif_file.data,\n",
        "        \"raw\",\n",
        "        heif_file.mode,\n",
        "        heif_file.stride,\n",
        "    )\n",
        "    image.save(output_path, format=\"JPEG\")\n",
        "\n",
        "input_dir = '/content/drive/MyDrive/LLM_Mid1/images/'\n",
        "output_dir = '/content/drive/MyDrive/RAG/'\n",
        "\n",
        "if not os.path.exists(output_dir):\n",
        "    os.makedirs(output_dir)\n",
        "\n",
        "for image_name in tqdm(os.listdir(input_dir)):\n",
        "    image_path = os.path.join(input_dir, image_name)\n",
        "    try:\n",
        "        with open(image_path, 'rb') as f:\n",
        "            header = f.read(12)\n",
        "            if b'ftypheic' in header:\n",
        "                output_path = os.path.join(output_dir, image_name.replace('.jpeg', '_converted.jpg'))\n",
        "                convert_heic_to_jpg(image_path, output_path)\n",
        "    except Exception as e:\n",
        "        print(f\"Error handling {image_name}: {e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6f24f07992ed4809a1454d2708341c57",
            "6a94a3b8a6ed4075aef2d5192f695e3e",
            "034a9fada85b4c3c884a06e1f1709be2",
            "1ac6752566634a19b94f32040c5c6932",
            "402f60ff24f543bc92464c46ea7f5412",
            "11ff158a5f514d2f8b003b9b1aa55eb4",
            "1be815d3d5cf45b691f097c32d61d290",
            "b7a76c9c0cb54dd1893740305b275cf7",
            "a629ae1c1f4e463294cb7507405c8b04",
            "f91c7c553a6c46f1a6362d75b2f179ba",
            "8a01eee775724f30b38ff5c635d0ba0b"
          ]
        },
        "id": "0tGX6E1t4pWk",
        "outputId": "08eb0cab-3179-4167-c323-0e61e5e133c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/282 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f24f07992ed4809a1454d2708341c57"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "for image in tqdm(os.listdir('/content/drive/MyDrive/Persons/Images')):\n",
        "    path = f'/content/drive/MyDrive/Persons/Images/{image}'\n",
        "    name = image.split('.')[0]\n",
        "    print(name)\n",
        "    con = context[name]\n",
        "    if con == '':\n",
        "        continue\n",
        "    embedding = dataToEmbedding(path, 'image')\n",
        "    new_row = {'path': path,\n",
        "               'embeddings':embedding,\n",
        "               'context':con}\n",
        "    df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)"
      ],
      "metadata": {
        "id": "5TayffuKU8QV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "import kdbai_client as kdbai\n",
        "import time"
      ],
      "metadata": {
        "id": "RwEKfY9aWouY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "joHzBaDEZqos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "KDBAI_ENDPOINT = '<KDBAI ENDPOINT>'\n",
        "KDBAI_API_KEY = 'KDBAI API KEY'"
      ],
      "metadata": {
        "id": "M_LPAcSpXNXz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "session = kdbai.Session(api_key=KDBAI_API_KEY, endpoint=KDBAI_ENDPOINT)"
      ],
      "metadata": {
        "id": "f9cGh6TUXq8k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "db = session.database('default')"
      ],
      "metadata": {
        "id": "tCG3E5KBAxHz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# try:\n",
        "#     db.table(\"LLM\").drop()\n",
        "#     time.sleep(5)\n",
        "# except kdbai.KDBAIException:\n",
        "#     pass"
      ],
      "metadata": {
        "id": "id0A4gtBZRUp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# schema = [\n",
        "# {'name': 'path', 'type': 'str'},\n",
        "# {'name': 'context', 'type': 'str'},\n",
        "# {'name': 'embeddings', 'type': 'float64s'}]\n",
        "\n",
        "# indexes = [ {'name': 'vectorIndex', 'type': 'flat',\n",
        "# 'params': {'dims': 1024, 'metric': 'CS'},\n",
        "# 'column': 'embeddings'}]\n",
        "table = db.table('LLM')"
      ],
      "metadata": {
        "id": "HHiCFCQnDS3K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "n = 2000\n",
        "for i in tqdm(range(0, df.shape[0], n)):\n",
        "    table.insert(df[i:i+n].reset_index(drop=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSrDAf6zXuns",
        "outputId": "dea38b91-f2b8-4e92-b1cf-cd68a4c8d7d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1/1 [00:00<00:00,  2.04it/s]\n"
          ]
        }
      ]
    }
  ]
}