关于【RAG&多模态】多模态RAG-ColPali：使用视觉语言模型实现高效的文档检索前面已经介绍了（供参考），这次来看看ColPali实践。
所需权重：
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多模态问答模型：Qwen2-VL-72B-Instruct，https://modelscope.cn/models/Qwen/Qwen2-VL-72B-Instruct
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基于 PaliGemma-3B 和 ColBERT 策略的视觉检索器：
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ColPali（LoRA）：https://huggingface.co/vidore/colpali
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ColPali（基座）：https://huggingface.co/vidore/colpaligemma-3b-mix-448-base
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多模态检索问答实践
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lora的adapter_config.json字段base_model_name_or_path修改地址：ColPali（基座）存储路径
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qwen_vl_utils下载地址：https://github.com/QwenLM/Qwen2-VL/tree/main/qwen-vl-utils/src/qwen_vl_utils
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byaldi安装方式：https://github.com/AnswerDotAI/byaldi
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完整代码
from byaldi import RAGMultiModalModel
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from pdf2image import convert_from_pathclass DocumentQA:def __init__(self, rag_model_name: str, vlm_model_name: str, device: str = 'cuda', system_prompt: str = None):self.rag_engine = RAGMultiModalModel.from_pretrained(rag_model_name)self.vlm = Qwen2VLForConditionalGeneration.from_pretrained(vlm_model_name,torch_dtype=torch.bfloat16,attn_implementation="flash_attention_2",device_map=device)self.processor = AutoProcessor.from_pretrained(vlm_model_name, trust_remote_code=True)self.device = deviceif system_prompt is None:self.system_prompt = ("你是一位专精于计算机科学和机器学习的AI研究助理。""你的任务是分析学术论文，尤其是关于文档检索和多模态模型的研究。""请仔细分析提供的图像和文本，提供深入的见解和解释。")else:self.system_prompt = system_promptdef index_document(self, pdf_path: str, index_name: str = 'index', overwrite: bool = True):self.pdf_path = pdf_pathself.rag_engine.index(input_path=pdf_path,index_name=index_name,store_collection_with_index=False,overwrite=overwrite)self.images = convert_from_path(pdf_path)def query(self, text_query: str, k: int = 3) -> str:results = self.rag_engine.search(text_query, k=k)print("搜索结果:", results)if not results:print("未找到相关查询结果。")return Nonetry:page_num = results[0]["page_num"]image_index = page_num - 1image = self.images[image_index]except (KeyError, IndexError) as e:print("获取页面图像时出错:", e)return Nonemessages = [{"role": "system","content": self.system_prompt},{"role": "user","content": [{"type": "image", "image": image},{"type": "text", "text": text_query},],}]text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)image_inputs, video_inputs = process_vision_info(messages)# 准备模型输入inputs = self.processor(text=[text],images=image_inputs,videos=video_inputs,padding=True,return_tensors="pt",)inputs = inputs.to(self.device)generated_ids = self.vlm.generate(**inputs, max_new_tokens=1024)generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]output_text = self.processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)return output_text[0]if __name__ == "__main__":# 初始化 DocumentQA 实例document_qa = DocumentQA(rag_model_name="./colpali",vlm_model_name="./Qwen2-VL-7B-Instruct",device='cuda')# 索引 PDF 文档document_qa.index_document("test.pdf")# 定义查询text_query = ("文中模型在哪个数据集上相比其他模型有最大的优势？""该优势的改进幅度是多少？")# 执行查询并打印答案answer = document_qa.query(text_query)print("答案:", answer)