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68 lines
2.1 KiB
68 lines
2.1 KiB
import asyncio
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import os
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import shutil
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from raganything import RAGAnything, RAGAnythingConfig
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from Util.RagUtil import create_llm_model_func, create_vision_model_func, create_embedding_func
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import logging
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# 在程序开始时添加以下配置
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logging.basicConfig(
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level=logging.INFO, # 设置日志级别为INFO
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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# 或者如果你想更详细地控制日志输出
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logger = logging.getLogger('lightrag')
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logger.setLevel(logging.INFO)
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handler = logging.StreamHandler()
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handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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logger.addHandler(handler)
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async def main():
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# 要处理的文件路径
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file_path = "static/Txt/吉林动画学院一览表.pdf"
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# 索引生成目录
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WORKING_DIR = "./Topic/DongHua"
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# 删除output目录下的所有文件
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output_dir = "./output"
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shutil.rmtree(output_dir, ignore_errors=True)
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os.makedirs(output_dir, exist_ok=True)
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# 删除WORKING_DIR下的所有文件
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shutil.rmtree(WORKING_DIR, ignore_errors=True)
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os.makedirs(WORKING_DIR, exist_ok=True)
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# 指定最终的索引生成目录，启动索引生成
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config = RAGAnythingConfig(
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working_dir=WORKING_DIR,
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mineru_parse_method="auto",
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enable_image_processing=True, # 处理图片
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enable_table_processing=True, # 处理表格
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enable_equation_processing=True, # 处理公式
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)
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# 自定义的大模型函数
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llm_model_func = create_llm_model_func(history_messages=[])
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# 自定义的可视模型函数
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vision_model_func = create_vision_model_func(llm_model_func)
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# 自定义的嵌入函数
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embedding_func = create_embedding_func()
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rag = RAGAnything(
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config=config,
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llm_model_func=llm_model_func,
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vision_model_func=vision_model_func,
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embedding_func=embedding_func,
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)
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await rag.process_document_complete(
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file_path=file_path,
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output_dir=output_dir,
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parse_method="auto"
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)
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if __name__ == "__main__":
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asyncio.run(main())
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