import asyncio
import sys

import loguru
from raganything import RAGAnything, RAGAnythingConfig
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
from lightrag.utils import EmbeddingFunc
from Config.Config import *


async def main():
    # 在main函数开头添加日志配置
    loguru.logger.remove()  # 移除默认配置
    loguru.logger.add(sys.stderr, level="INFO")  # 输出INFO及以上级别到控制台

    # Create RAGAnything configuration
    config = RAGAnythingConfig(
        working_dir="./rag_storage",
        mineru_parse_method="auto",
        enable_image_processing=True,
        enable_table_processing=True,
        enable_equation_processing=True,
    )

    # Define LLM model function
    def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
        return openai_complete_if_cache(
            LLM_MODEL_NAME,
            prompt,
            system_prompt=system_prompt,
            history_messages=history_messages,
            api_key=LLM_API_KEY,
            base_url=LLM_BASE_URL,
            **kwargs,
        )

    # Define vision model function for image processing
    def vision_model_func(
            prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs
    ):
        if image_data:
            return openai_complete_if_cache(
                VISION_MODEL_NAME,  # 这里设置为了硅基流动中免费视觉模型的模型名称
                "",
                system_prompt=None,
                history_messages=[],
                messages=[
                    {"role": "system", "content": system_prompt}
                    if system_prompt
                    else None,
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": prompt},
                            {
                                "type": "image_url",
                                "image_url": {
                                    "url": f"data:image/jpeg;base64,{image_data}"
                                },
                            },
                        ],
                    }
                    if image_data
                    else {"role": "user", "content": prompt},
                ],
                api_key=VISION_API_KEY,
                base_url=VISION_BASE_URL,
                **kwargs,
            )
        else:
            return llm_model_func(prompt, system_prompt, history_messages, **kwargs)

    # Define embedding function
    embedding_func = EmbeddingFunc(
        embedding_dim=1024,  # 这里设置为了硅基流动中免费模型的嵌入维度
        max_token_size=8192,
        func=lambda texts: openai_embed(
            texts,
            model=EMBED_MODEL_NAME,
            api_key=EMBED_API_KEY,
            base_url=EMBED_BASE_URL,
        ),
    )

    # Initialize RAGAnything
    rag = RAGAnything(
        config=config,
        llm_model_func=llm_model_func,
        vision_model_func=vision_model_func,
        embedding_func=embedding_func,
    )

    # file_path = "D:\python\小乔证件\黄琬乔2023蓝桥杯省赛准考证.pdf"
    file_path = "./Txt/驿来特平台安全.docx"
    await rag.process_document_complete(
        file_path=file_path,
        output_dir="./output",
        parse_method="auto"
    )

    print("Processing complete.")

if __name__ == "__main__":
    asyncio.run(main())