weaviate-features/named-vectors/NamedVectors-ColPali-POC.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# How to use ColPali with Weaviate's Named Vectors!"
      ],
      "metadata": {
        "id": "0KEzSEYNAfSl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ColPali uses a multi-vector embedding search method similar to ColBERT, embedding an image into 1024 patch vectors.\n",
        "\n",
        "This notebook will show you how to embed images with ColPali, how to import patch vectors into Weaviate using Named Vectors, and how to query with Weaviate's Named Vectors!\n",
        "\n",
        "Note: This is a workaround solution that does not yet implement the MaxSim operator.\n",
        "\n",
        "We will test this with 3 PDFs:\n",
        "- NBA_voting.pdf\n",
        "- Boston_restaurants.pdf\n",
        "- DSPy.pdf"
      ],
      "metadata": {
        "id": "OfPSFcyWAipC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ColPali Setup"
      ],
      "metadata": {
        "id": "pk6P68QvP5ix"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pdf2image==1.17.0 > /dev/null\n",
        "!pip install peft==0.12.0 > /dev/null"
      ],
      "metadata": {
        "id": "k8-BI1jLP95C"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get update > /dev/null\n",
        "!sudo apt-get install poppler-utils > /dev/null"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CeGlNSVmQCuF",
        "outputId": "7b00acf2-a8b4-481f-9a73-6a7606d8c909"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUcvhs4NQFBr",
        "outputId": "fd6e8485-7963-4447-9799-df920f3f08b2"
      },
      "execution_count": 3,
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        {
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          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) \n",
            "Token is valid (permission: read).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from pathlib import Path\n",
        "from typing import List, cast\n",
        "from pdf2image import convert_from_path\n",
        "from PIL import Image\n",
        "import torch\n",
        "from torch import nn\n",
        "from transformers import LlamaTokenizerFast, PaliGemmaProcessor\n",
        "from transformers.models.paligemma.configuration_paligemma import PaliGemmaConfig\n",
        "from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration, PaliGemmaPreTrainedModel\n",
        "\n",
        "# Define ColPali model\n",
        "class ColPali(PaliGemmaPreTrainedModel):\n",
        "    def __init__(self, config: PaliGemmaConfig):\n",
        "        super(ColPali, self).__init__(config=config)\n",
        "        self.model = PaliGemmaForConditionalGeneration(config)\n",
        "        self.dim = 128\n",
        "        self.custom_text_proj = nn.Linear(self.model.config.text_config.hidden_size, self.dim)\n",
        "        self.main_input_name = \"doc_input_ids\"\n",
        "\n",
        "    def forward(self, *args, **kwargs) -> torch.Tensor:\n",
        "        outputs = self.model(*args, output_hidden_states=True, **kwargs)\n",
        "        last_hidden_states = outputs.hidden_states[-1]\n",
        "        proj = self.custom_text_proj(last_hidden_states)\n",
        "        proj = proj / proj.norm(dim=-1, keepdim=True)\n",
        "        '''\n",
        "        The attention_mask will zero out embeddings for any padding tokens or patches.\n",
        "        More concretely, the `proj` tensor currently has shape [batch_size, sequence_length, self.dim]\n",
        "        The `.unsqueeze` transformation will result in the attention mask having the shape [batch_size, sequence_length, 1]\n",
        "        '''\n",
        "        proj = proj * kwargs[\"attention_mask\"].unsqueeze(-1)\n",
        "        return proj\n",
        "\n",
        "# Define input classes\n",
        "class ColPaliTextInput:\n",
        "    def __init__(self, input_ids, attention_mask):\n",
        "        self.input_ids = input_ids\n",
        "        self.attention_mask = attention_mask\n",
        "\n",
        "    def to(self, device):\n",
        "        return ColPaliTextInput(\n",
        "            input_ids=self.input_ids.to(device),\n",
        "            attention_mask=self.attention_mask.to(device),\n",
        "        )\n",
        "\n",
        "class ColPaliImageInput:\n",
        "    def __init__(self, input_ids, pixel_values, attention_mask):\n",
        "        self.input_ids = input_ids\n",
        "        self.pixel_values = pixel_values\n",
        "        self.attention_mask = attention_mask\n",
        "\n",
        "    def to(self, device):\n",
        "        return ColPaliImageInput(\n",
        "            input_ids=self.input_ids.to(device),\n",
        "            pixel_values=self.pixel_values.to(device),\n",
        "            attention_mask=self.attention_mask.to(device),\n",
        "        )\n",
        "\n",
        "# Define ColPaliProcessor\n",
        "class ColPaliProcessor:\n",
        "    def __init__(self, processor: PaliGemmaProcessor):\n",
        "        self.processor = processor\n",
        "        self.tokenizer = cast(LlamaTokenizerFast, self.processor.tokenizer)\n",
        "\n",
        "    @staticmethod\n",
        "    def from_pretrained(model_name: str) -> 'ColPaliProcessor':\n",
        "        return ColPaliProcessor(processor=PaliGemmaProcessor.from_pretrained(model_name))\n",
        "\n",
        "    def process_text(self, text: str | List[str], padding: str = \"longest\", return_tensors: str = \"pt\", add_special_tokens: bool = True) -> ColPaliTextInput:\n",
        "        if add_special_tokens:\n",
        "            if isinstance(text, str):\n",
        "                text = self.tokenizer.bos_token + text + \"\\n\"\n",
        "            elif isinstance(text, list):\n",
        "                text = [self.tokenizer.bos_token + t + \"\\n\" for t in text]\n",
        "            else:\n",
        "                raise ValueError(\"text must be a string or a list of strings.\")\n",
        "\n",
        "        batch_output = self.tokenizer(text, padding=padding, return_tensors=return_tensors, add_special_tokens=add_special_tokens)\n",
        "\n",
        "        return ColPaliTextInput(\n",
        "            input_ids=batch_output[\"input_ids\"],\n",
        "            attention_mask=batch_output[\"attention_mask\"],\n",
        "        )\n",
        "\n",
        "    def process_image(self, image: Image.Image | List[Image.Image], padding: str = \"longest\", do_convert_rgb: bool = True, return_tensors: str = \"pt\", add_special_prompt: bool = True) -> ColPaliImageInput:\n",
        "        special_prompt = \"Describe the image.\" if add_special_prompt else None\n",
        "        if isinstance(image, Image.Image):\n",
        "            text_input = [special_prompt]\n",
        "        elif isinstance(image, list):\n",
        "            text_input = [special_prompt] * len(image)\n",
        "        else:\n",
        "            raise ValueError(\"image must be a PIL Image or a list of PIL Images.\")\n",
        "\n",
        "        batch_output = self.processor(\n",
        "            text=text_input,\n",
        "            images=image,\n",
        "            padding=padding,\n",
        "            do_convert_rgb=do_convert_rgb,\n",
        "            return_tensors=return_tensors,\n",
        "        )\n",
        "\n",
        "        return ColPaliImageInput(\n",
        "            input_ids=batch_output[\"input_ids\"],\n",
        "            pixel_values=batch_output[\"pixel_values\"],\n",
        "            attention_mask=batch_output[\"attention_mask\"],\n",
        "        )\n",
        "\n",
        "    def decode(self, *args, **kwargs):\n",
        "        return self.tokenizer.decode(*args, **kwargs)\n",
        "\n",
        "    def batch_decode(self, *args, **kwargs):\n",
        "        return self.tokenizer.batch_decode(*args, **kwargs)\n",
        "\n",
        "# Helper functions\n",
        "def convert_pdf_to_images(pdf_file: str, save_folder: str) -> List[Image.Image]:\n",
        "    images = convert_from_path(pdf_file)\n",
        "    os.makedirs(save_folder, exist_ok=True)\n",
        "    saved_images = []\n",
        "    for i, image in enumerate(images):\n",
        "        image_path = os.path.join(save_folder, f\"page_{i+1}.jpg\")\n",
        "        image.save(image_path, \"JPEG\")\n",
        "        saved_images.append(Image.open(image_path))\n",
        "    return saved_images\n",
        "\n",
        "def process_pdfs_with_colpali(pdf_files, output_dir, model, processor):\n",
        "    all_embeddings = []\n",
        "    all_page_info = []\n",
        "\n",
        "    for pdf_file in pdf_files:\n",
        "        pdf_images = convert_pdf_to_images(pdf_file, os.path.join(output_dir, \"pdf_images\"))\n",
        "\n",
        "        for page_num, image in enumerate(pdf_images):\n",
        "            image_input = processor.process_image(image).to(model.device)\n",
        "            with torch.no_grad():\n",
        "                page_embedding = model(**vars(image_input))\n",
        "\n",
        "            all_embeddings.append(page_embedding.cpu().numpy().squeeze()) # the last .squeeze() call removes the batch dimension\n",
        "            all_page_info.append({\"pdf\": pdf_file, \"page\": page_num})\n",
        "\n",
        "    embeddings_array = np.array(all_embeddings)\n",
        "\n",
        "    np.save(Path(output_dir) / \"embeddings.npy\", embeddings_array)\n",
        "    np.save(Path(output_dir) / \"page_info.npy\", all_page_info)\n",
        "\n",
        "    return embeddings_array, all_page_info\n",
        "\n",
        "def answer_query_with_colpali(query, embeddings_array, page_info, model, processor):\n",
        "    query_input = processor.process_text(query).to(model.device)\n",
        "    with torch.no_grad():\n",
        "        query_embedding = model(**vars(query_input))\n",
        "\n",
        "    # Reshape embeddings if necessary\n",
        "    if len(embeddings_array.shape) == 3:\n",
        "        embeddings_array = embeddings_array.mean(axis=1)  # Average over sequence dimension\n",
        "    if len(query_embedding.shape) == 3:\n",
        "        query_embedding = query_embedding.mean(axis=1)  # Average over sequence dimension\n",
        "\n",
        "    # Ensure both embeddings are 2D\n",
        "    embeddings_array = embeddings_array.squeeze()\n",
        "    query_embedding = query_embedding.cpu().numpy().squeeze() # remove batch_dimension with the last squeeze call\n",
        "\n",
        "    # Compute similarity scores\n",
        "    similarity_scores = np.dot(embeddings_array, query_embedding.T)\n",
        "\n",
        "    K = 5\n",
        "    top_k_indices = np.argsort(similarity_scores.flatten())[-K:][::-1]\n",
        "\n",
        "    top_results = [\n",
        "        {\"score\": similarity_scores.flatten()[i], \"info\": page_info[i]}\n",
        "        for i in top_k_indices\n",
        "    ]\n",
        "\n",
        "    return top_results"
      ],
      "metadata": {
        "id": "iRmHzqaOQSjS"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model_path = \"google/paligemma-3b-mix-448\" # can also try smaller models\n",
        "lora_path = \"vidore/colpali\"\n",
        "\n",
        "model = ColPali.from_pretrained(model_path) # torch_dtype = torch.bfloat16 # there is also a `bitsandbytes` setup for 8-bit / 4-bit\n",
        "model.load_adapter(lora_path, adapter_name=\"colpali\")\n",
        "model.to(device);"
      ],
      "metadata": {
        "colab": {
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          "height": 617,
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        },
        "id": "vLoj4lrwQj3F",
        "outputId": "18113ee9-72c5-4c0d-c3cd-07464dacbd17"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d39837b65574ef4b76bbb79eb0d778b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors.index.json:   0%|          | 0.00/62.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ccf4f08c8ce44eeca6cbd796f492a703"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba565541b01c4aa683a0488a2747ffc3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00001-of-00003.safetensors:   0%|          | 0.00/4.96G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "384e1d62abe2465f9d7fea03d258caba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00002-of-00003.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b368e287dfd42b8ae88e13ab23b9499"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00003-of-00003.safetensors:   0%|          | 0.00/1.74G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "677c640d3fe54219afd7fb27d17e4fb9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n",
            "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n",
            "`config.hidden_activation` if you want to override this behaviour.\n",
            "See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c772c4c91d547aa862cbdef6157064f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ColPali were not initialized from the model checkpoint at google/paligemma-3b-mix-448 and are newly initialized: ['custom_text_proj.bias', 'custom_text_proj.weight', 'language_model.lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\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": "e62f6acc49d64ed6a2f0ff9eb2e0d293"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/78.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a13b0d978fc456cbc2e8f20257bd830"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processor = ColPaliProcessor.from_pretrained(model_path)\n",
        "\n",
        "pdf_files = [\"Boston-Restaurants.pdf\", \"NBA-mvp-voting.pdf\", \"DSPy.pdf\"]\n",
        "output_dir = \"colpali_output\"\n",
        "\n",
        "# Process PDFs and save embeddings\n",
        "embeddings, page_info = process_pdfs_with_colpali(pdf_files, output_dir, model, processor)\n",
        "\n",
        "# Answer a query\n",
        "query = \"Where is Uni located in Boston?\" # The answer should be contained in Boston-Restaurants.pdf\n",
        "results = answer_query_with_colpali(query, embeddings, page_info, model, processor)\n",
        "\n",
        "# Print results\n",
        "for result in results:\n",
        "  print(f\"Score: {result['score']}, PDF: {result['info']['pdf']}, Page: {result['info']['page']}\")"
      ],
      "metadata": {
        "colab": {
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            "c45bd30428994b20b3ff09a62d4697fb",
            "4a72c889ad8b4d238dfd58c22098fe9b",
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            "539c4e8ee6aa4f9a826985aa9d13623e",
            "6c668bb6c9cb40b687ce2fdf38a2009b",
            "fccfc948e8c54804a8e89b4fe2ec844c",
            "ec82c8a2fe7f4e978450ed384adb7a1e",
            "e932d00a75ce44ae80944f6857afac16",
            "4e2a8563db70458099dad99ec7733189",
            "75488f9014874236af4f8ddd9f3831c1",
            "527eb535dc224de0a113e7fb39c6657b",
            "98355fd4bf384e5c82b5d76bf740cf8b",
            "81b3dfa2d0fb48fbaab6196da9e76ce7",
            "1bd06afb57754a129434ccd849957dc1",
            "09492ab7c28345c3a74852f1946769f5",
            "1aa4ae9e9f174fb8b34b31e6a05cab72"
          ]
        },
        "id": "otSYlrZYQ6ZQ",
        "outputId": "a694787e-8016-4df6-d810-c469965e10e8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/700 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c626e107ebfd4ba0b9b8917606337623"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/40.0k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4fe9c61a6804a50b3adb971db68030c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/4.26M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5669a182602d49dfbac8e59e12cf8f9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e5f8928c2df422d9cce9a3721309126"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/24.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba709ccbf3684737a07ef2dafc375b6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/607 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fccfc948e8c54804a8e89b4fe2ec844c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 0.32659846544265747, PDF: Boston-Restaurants.pdf, Page: 8\n",
            "Score: 0.321824848651886, PDF: Boston-Restaurants.pdf, Page: 9\n",
            "Score: 0.3206213414669037, PDF: Boston-Restaurants.pdf, Page: 7\n",
            "Score: 0.3198925852775574, PDF: Boston-Restaurants.pdf, Page: 6\n",
            "Score: 0.3106686770915985, PDF: Boston-Restaurants.pdf, Page: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"How many votes did Jayson Tatum get for MVP?\" # The answer should be contained in NBA-mvp-voting.pdf\n",
        "results = answer_query_with_colpali(query, embeddings, page_info, model, processor)\n",
        "\n",
        "# Print results\n",
        "for result in results:\n",
        "  print(f\"Score: {result['score']}, PDF: {result['info']['pdf']}, Page: {result['info']['page']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXp3nf5BSBEN",
        "outputId": "83e0ed5c-6ab5-4243-cd57-a5cfdae7c4e9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 0.3444867730140686, PDF: NBA-mvp-voting.pdf, Page: 0\n",
            "Score: 0.2413223832845688, PDF: DSPy.pdf, Page: 19\n",
            "Score: 0.21714499592781067, PDF: DSPy.pdf, Page: 10\n",
            "Score: 0.21266475319862366, PDF: Boston-Restaurants.pdf, Page: 7\n",
            "Score: 0.20665661990642548, PDF: Boston-Restaurants.pdf, Page: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"What is an LLM programming language?\" # The answer should be contained in DSPy.pdf\n",
        "results = answer_query_with_colpali(query, embeddings, page_info, model, processor)\n",
        "\n",
        "# Print results\n",
        "for result in results:\n",
        "  print(f\"Score: {result['score']}, PDF: {result['info']['pdf']}, Page: {result['info']['page']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uZfYogdvSIiY",
        "outputId": "9477b85e-7521-4ccd-8048-3043cc12ca37"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 0.32342493534088135, PDF: DSPy.pdf, Page: 1\n",
            "Score: 0.32004794478416443, PDF: DSPy.pdf, Page: 5\n",
            "Score: 0.31236791610717773, PDF: DSPy.pdf, Page: 10\n",
            "Score: 0.3096577525138855, PDF: DSPy.pdf, Page: 7\n",
            "Score: 0.30225279927253723, PDF: DSPy.pdf, Page: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_image = Image.open(\"./colpali_output/pdf_images/page_1.jpg\")  # Get the first page of the first PDF\n",
        "image_input = processor.process_image(sample_image).to(model.device)\n",
        "with torch.no_grad():\n",
        "    sample_output = model(**vars(image_input))\n",
        "\n",
        "# The shape of sample_output should be [batch_size, num_patches, embedding_dim]\n",
        "num_patches = sample_output.shape[1]\n",
        "\n",
        "print(f\"Number of patches per image: {num_patches}\")\n",
        "# 1030 ==> 1024 image patches + 5 special tokens (described in original ColBERT paper + 1[CLS] token)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzwiMKQKSQyu",
        "outputId": "6e0b8bdf-e725-406d-aefa-d25b4ef14189"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of patches per image: 1030\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Weaviate Setup"
      ],
      "metadata": {
        "id": "cjC3A0Y5P8AX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install weaviate-client==4.7.1 > /dev/null"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuNadxxqIhCk",
        "outputId": "889422ce-c459-4a0d-ea6e-832f6f92ce41"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires protobuf<5,>=3.20, but you have protobuf 5.27.2 which is incompatible.\n",
            "google-ai-generativelanguage 0.6.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
            "google-cloud-aiplatform 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.25.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
            "google-cloud-datastore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
            "google-cloud-firestore 2.16.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n",
            "tensorflow 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.2 which is incompatible.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.27.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZawdLCN8jtP",
        "outputId": "ac97364e-34d8-47da-cea8-063eb151034d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:weaviate-client:Binary /root/.cache/weaviate-embedded did not exist. Downloading binary from https://github.com/weaviate/weaviate/releases/download/v1.26.1/weaviate-v1.26.1-Linux-amd64.tar.gz\n",
            "INFO:weaviate-client:Started /root/.cache/weaviate-embedded: process ID 2880\n"
          ]
        }
      ],
      "source": [
        "import weaviate\n",
        "import os\n",
        "\n",
        "client = weaviate.connect_to_embedded(\n",
        "    version=\"1.26.1\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import weaviate.classes as wvc\n",
        "\n",
        "patch_vector_properties = [\n",
        "    wvc.config.Property(name=f\"patch_{idx}\", data_type=wvc.config.DataType.TEXT) for idx in range(num_patches)\n",
        "]\n",
        "\n",
        "patch_vector_names = [f\"patch_{idx}\" for idx in range(num_patches)] # used for querying\n",
        "\n",
        "patch_vectorizer_config = [\n",
        "    wvc.config.Configure.NamedVectors.none(\n",
        "        name=f\"patch_{idx}\"\n",
        "    ) for idx in range(num_patches)\n",
        "]\n",
        "\n",
        "paper_pdfs = client.collections.create(\n",
        "    name=\"PaperPDFs\",\n",
        "    description=\"Scientific paper PDFs\",\n",
        "    vectorizer_config=patch_vectorizer_config,\n",
        "    properties=patch_vector_properties\n",
        ")"
      ],
      "metadata": {
        "id": "NfuiUdHRCXgw"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def import_pdfs_with_colpali_to_weaviate(weaviate_collection, pdf_files, model, processor):\n",
        "    device = next(model.parameters()).device  # Get the device of the model\n",
        "\n",
        "    for pdf_file in pdf_files:\n",
        "        pdf_images = convert_pdf_to_images(pdf_file, \"pdf_images_temp\")\n",
        "\n",
        "        for page_num, image in enumerate(pdf_images):\n",
        "            # Process the image\n",
        "            image_input = processor.process_image(image)\n",
        "            # Move the processed input to the correct device\n",
        "            image_input = ColPaliImageInput(\n",
        "                input_ids=image_input.input_ids.to(device),\n",
        "                pixel_values=image_input.pixel_values.to(device),\n",
        "                attention_mask=image_input.attention_mask.to(device)\n",
        "            )\n",
        "\n",
        "            with torch.no_grad():\n",
        "                patch_embeddings = model(**vars(image_input))\n",
        "\n",
        "            # Create a dictionary for the Weaviate object\n",
        "            properties = {\n",
        "                \"fileName\": pdf_file,\n",
        "                \"pageNumber\": page_num,\n",
        "            }\n",
        "            vector = {}\n",
        "\n",
        "            # Add each patch embedding to the data object\n",
        "            for idx, patch_embedding in enumerate(patch_embeddings[0]):\n",
        "                properties[f\"patch_{idx}\"] = \"\"\n",
        "                vector[f\"patch_{idx}\"] = patch_embedding.cpu().numpy().tolist()\n",
        "\n",
        "            # Add to Weaviate\n",
        "            paper_pdfs.data.insert(properties=properties, vector=vector)\n",
        "\n",
        "    print(f\"Processed {len(pdf_files)} PDF files.\")"
      ],
      "metadata": {
        "id": "jBoT_XiRHjXX"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import_pdfs_with_colpali_to_weaviate(paper_pdfs, pdf_files, model, processor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRmWq5grS2VJ",
        "outputId": "69054d55-6b99-4a59-ac0b-79609dc699db"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed 3 PDF files.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def embed_query_with_colpali(query, model, processor):\n",
        "    query_input = processor.process_text(query).to(model.device)\n",
        "    with torch.no_grad():\n",
        "        query_embedding = model(**vars(query_input))\n",
        "\n",
        "    # Print shape for debugging\n",
        "    print(f\"Query embedding shape: {query_embedding.shape}\")\n",
        "\n",
        "    query_vectors = {}\n",
        "    num_tokens = query_embedding.shape[1]\n",
        "    for idx in range(num_patches):\n",
        "        # Use modulo to cycle through token embeddings if num_tokens < num_patches\n",
        "        token_idx = idx % num_tokens\n",
        "        query_vectors[f\"patch_{idx}\"] = query_embedding[0, token_idx].cpu().numpy().tolist()\n",
        "\n",
        "    return query_vectors"
      ],
      "metadata": {
        "id": "vd_6sYlAUvSy"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_vectors = embed_query_with_colpali(\"Where is Uni located?\", model, processor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dr0G5SWKVzB0",
        "outputId": "1347bfe4-33f8-4ee1-a311-e08be70629ae"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Query embedding shape: torch.Size([1, 7, 128])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = paper_pdfs.query.near_vector(\n",
        "    # Specify the query vectors for each target vector\n",
        "    near_vector=query_vectors,\n",
        "    limit=2,\n",
        "    target_vector=patch_vector_names\n",
        ")\n",
        "\n",
        "for o in response.objects:\n",
        "  fileName, pageNumber = o.properties[\"fileName\"], o.properties[\"pageNumber\"]\n",
        "  print(f\"Filename: {fileName} on page {pageNumber}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9kLIzx3S-zv",
        "outputId": "c3f43ac5-75eb-4aad-a11a-bd2e72acf5de"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filename: Boston-Restaurants.pdf on page 9.0.\n",
            "Filename: Boston-Restaurants.pdf on page 6.0.\n"
          ]
        }
      ]
    }
  ]
}