PDF 解析（MiniCPM-V 4.7）

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_path = "openbmb/MiniCPM-V-4.7-1B"  # 或 "openbmb/MiniCPM-V-4.7-35B-A3B"

processor = AutoProcessor.from_pretrained(model_path)
model = AutoModelForImageTextToText.from_pretrained(
    model_path,
    dtype="auto",
    device_map="auto",
    attn_implementation="sdpa",
).eval()

# 依赖 poppler-utils：
#   sudo apt-get update && sudo apt-get install poppler-utils
from pdf2image import convert_from_path


def pdf_to_images(pdf_path: str, dpi: int = 200):
    images = convert_from_path(pdf_path, dpi=dpi)
    return [image.convert("RGB") for image in images]

prompt = """
You are an OCR assistant. Your task is to identify and extract all visible text from the image provided. Preserve the original formatting as closely as possible, including:

- Line breaks and paragraphs
- Headings and subheadings
- Any tables, lists, bullet points, or numbered items
- Special characters, spacing, and alignment

Output strictly the extracted text in Markdown format, reflecting the layout and structure of the original image. Do not add commentary, interpretation, or summarization—only return the raw text content with its formatting.
"""

images = pdf_to_images("assets/parse.pdf")

content = [{"type": "image", "image": img} for img in images]
content.insert(0, {"type": "text", "text": prompt})

messages = [{"role": "user", "content": content}]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    chat_template_kwargs={"enable_thinking": False},
).to(model.device)

out_ids = model.generate(**inputs, max_new_tokens=4096)
answer = processor.decode(
    out_ids[0][inputs["input_ids"].shape[-1]:],
    skip_special_tokens=True,
)
print(answer)