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import asyncio
from lightrag import LightRAG
from lightrag . kg . shared_storage import initialize_pipeline_status
from raganything import RAGAnything
from Util . RagUtil import create_llm_model_func , create_vision_model_func , create_embedding_func
async def load_existing_lightrag ( ) :
# 索引位置
WORKING_DIR = " ./Topic/DongHua "
# 创建 LLM 模型自定义函数
llm_model_func = create_llm_model_func ( )
# 创建可视模型自定义函数
vision_model_func = create_vision_model_func ( llm_model_func )
# 创建嵌入模型自定义函数
embedding_func = create_embedding_func ( )
# 声明LightRAG实例
lightrag_instance = LightRAG (
working_dir = WORKING_DIR ,
llm_model_func = llm_model_func ,
embedding_func = embedding_func
)
# 初始化
await lightrag_instance . initialize_storages ( )
await initialize_pipeline_status ( )
# 创建RAGAnything实例，
rag = RAGAnything (
lightrag = lightrag_instance ,
vision_model_func = vision_model_func ,
)
# 查询
#user_prompt = "如果资料中提供了图片，需要把图片也展示出来。有图片路径的，需要转为![]() 这样的markdown格式展示。"
#user_prompt = "如需画图使用mermaid格式， ，
result = await rag . aquery (
#user_prompt=user_prompt,
#query="本文档讲述了什么内容？",
query = " 吉林动画学院招聘岗位有哪些？ " ,
mode = " hybrid "
)
print ( " 查询结果: " , result )
if __name__ == " __main__ " :
asyncio . run ( load_existing_lightrag ( ) )
