ColPali 是一种多模态检索器，直接对图像进行处理，无需OCR。
对数据建立索引后，使用 Qwen2-VL-7B 完成 RAG 的生成部分。
frompdf2imageimportconvert_from_path
images=convert_from_path("/content/climate_youth_magazine.pdf")
images[5]
byaldi 是 answer.ai 开源的工具包，可轻松使用 ColPali
frombyaldiimportRAGMultiModalModel
RAG=RAGMultiModalModel.from_pretrained("vidore/colpali")
建立索引
RAG.index(
input_path="/content/climate_youth_magazine.pdf",
index_name="image_index",#indexwillbesavedatindex_root/index_name/
store_collection_with_index=False,
overwrite=True
)
然后就可以搜索了
text_query="Howmuchdidtheworldtemperaturechangesofar?"
results=RAG.search(text_query,k=1)
results
[{'doc_id': 0, 'page_num': 6, 'score': 17.25, 'metadata': {}, 'base64': None}]
答案确实是在第6页，就是上面展示的那页pdf。现在我们可以构建一个 RAG 管道了。使用 Qwen2-VL-7B 模型。
fromtransformersimportQwen2VLForConditionalGeneration,AutoTokenizer,AutoProcessor
fromqwen_vl_utilsimportprocess_vision_info
importtorch
model=Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct",
trust_remote_code=True,torch_dtype=torch.bfloat16).cuda().eval()
processor=AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct",trust_remote_code=True)
image_index=results[0]["page_num"]-1
messages=[
{
"role":"user",
"content":[
{
"type":"image",
"image":images[image_index],
},
{"type":"text","text":text_query},
],
}
]
text=processor.apply_chat_template(
messages,tokenize=False,add_generation_prompt=True
)
image_inputs,video_inputs=process_vision_info(messages)
inputs=processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs=inputs.to("cuda")
generated_ids=model.generate(**inputs,max_new_tokens=50)
generated_ids_trimmed=[
out_ids[len(in_ids):]forin_ids,out_idsinzip(inputs.input_ids,generated_ids)
]
output_text=processor.batch_decode(
generated_ids_trimmed,skip_special_tokens=True,clean_up_tokenization_spaces=False
)
print(output_text)
["The Earth's average global temperature has increased by around 1.1°C since the late 19th century, according to the information provided in the image."] 答案正确！