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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.LightRagUtil import create_llm_model_func, create_embedding_func, create_vision_model_func, \
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format_exam_content
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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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# 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 = "Docx/《动能定理》巩固练习.docx"
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WORKING_DIR = "../Topic/WuLi"
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fileName = file_path.split('/')[-1].replace(".docx", "").replace(".doc", "")
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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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if not os.path.exists(output_dir):
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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()
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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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# 需要注意：注释掉将整理出来的文档内容插入到LightRAG的代码。
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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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# # MinerU特殊参数 - 支持的所有kwargs：
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# lang="ch", # 文档语言优化（如："ch", "en", "ja"）
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# # device="cuda:0", # 推理设备："cpu", "cuda", "cuda:0", "npu", "mps"
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# # start_page=0, # 起始页码（0为基准，适用于PDF）
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# # end_page=10, # 结束页码（0为基准，适用于PDF）
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# formula=True, # 启用公式解析
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# table=True, # 启用表格解析
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# backend="pipeline", # 解析后端："pipeline", "vlm-transformers"等
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# source="local", # 模型源："huggingface", "modelscope", "local"
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#
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# # RAGAnything标准参数
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# display_stats=True, # 显示内容统计信息
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# split_by_character=None, # 可选的文本分割字符
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# doc_id=None, # 可选的文档ID
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# )
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"""
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修正一下MinerU生成的Latex中，如果是数字加圆圈的样式 \textcircled{1}，
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无法在Typora或者PyCharm中显示的问题,改成兼容性更强的 \enclose{circle}{1}
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"""
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path = r'../output/' + fileName + '/auto'
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finalName = path + r'/' + fileName + '.md'
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with open(finalName, 'r', encoding='utf-8') as f:
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content = f.read()
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content = content.replace(r'\textcircled', r'\enclose{circle}')
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# 按【题型】分割试题
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question_types = ["不定项选择", "单选题", "多选题", "填空题", "判断题", "完型填空题", "计算题"]
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# 按 【题型】 分隔开
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content = content.replace("\n\n", "\n")
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questions = content.split('【题型】')
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idx = 0
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for q in questions:
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# 干掉 【题型】前面的文档标题，比如: # 《动能定理》巩固练习
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if idx == 0 and q != "":
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idx = idx + 1
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continue
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# 干掉空行
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if q.strip() == "" or q == '\n':
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continue
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q='【题型】'+q
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print(q)
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print("\n")
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# with open(path + r'/测试.md', 'w', encoding='utf-8') as f:
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# f.write(formatted_content)
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# 将path目录下的images目录，整体拷贝到 output下
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shutil.rmtree(output_dir + r'/images')
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shutil.copytree(path + r'/images', output_dir + r'/images')
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# 删除path目录下
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# shutil.rmtree(path)
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if __name__ == "__main__":
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asyncio.run(main())
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