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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Implementing Colpali with Qwen2VL"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\atuli\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Verbosity is set to 1 (active). Pass verbose=0 to make quieter.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"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",
"Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 6.01it/s]\n"
]
}
],
"source": [
"from byaldi import RAGMultiModalModel\n",
"\n",
"RAG = RAGMultiModalModel.from_pretrained(\"vidore/colpali\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text and `<bos>` token after that. For this call, we will infer how many images each text has and add special tokens.\n",
"Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Added page 1 of document 0 to index.\n",
"Index exported to .byaldi\\image_index\n",
"Index exported to .byaldi\\image_index\n"
]
},
{
"data": {
"text/plain": [
"{0: 'image.png'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RAG.index(\n",
" input_path=\"image.png\",\n",
" index_name=\"image_index\",\n",
" store_collection_with_index=False,\n",
" overwrite=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special image tokens in the text, as many tokens as there are images per each text. It is recommended to add `<image>` tokens in the very beginning of your text and `<bos>` token after that. For this call, we will infer how many images each text has and add special tokens.\n"
]
},
{
"data": {
"text/plain": [
"[{'doc_id': 0, 'page_num': 1, 'score': 18.75, 'metadata': {}, 'base64': None}]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_query = \"What is the structure of the compiler?\"\n",
"results = RAG.search(text_query, k=1)\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n",
"Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'mrope_section'}\n",
"Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00, 6.88s/it]\n"
]
}
],
"source": [
"from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor\n",
"from qwen_vl_utils import process_vision_info\n",
"import torch\n",
"\n",
"model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
" \"Qwen/Qwen2-VL-2B-Instruct\",\n",
" trust_remote_code=True,\n",
" torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results[0][\"page_num\"] -1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"processor = AutoProcessor.from_pretrained(\"Qwen/Qwen2-VL-2B-Instruct\", trust_remote_code=True)\n",
"\n",
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"image\",\n",
" \"image\": Image.open(\"image.png\"),\n",
" },\n",
" {\"type\": \"text\", \"text\": text_query},\n",
" ],\n",
" }\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"text = processor.apply_chat_template(\n",
" messages, tokenize=False, add_generation_prompt=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"image_inputs, video_inputs = process_vision_info(messages)\n",
"inputs = processor(\n",
" text=[text],\n",
" images=image_inputs,\n",
" videos=video_inputs,\n",
" padding=True,\n",
" return_tensors=\"pt\",\n",
")\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"inputs = inputs.to(device)\n",
"model = model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"generated_ids = model.generate(**inputs, max_new_tokens=50)\n",
"generated_ids_trimmed = [\n",
" out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)\n",
"]\n",
"output_text = processor.batch_decode(\n",
" generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['The structure of the compiler, as described in the syllabus, includes the following components:\\n\\n1. **Lexical Analysis**: This involves the role of the lexical analyzer, input buffering, and the design of lexical analyzers, specification and recognition of tokens']\n"
]
}
],
"source": [
"print(output_text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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