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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/Chemistry"
#WORKING_DIR = "./Topic/DongHua"
#WORKING_DIR = "./Topic/Chinese"
WORKING_DIR = "./Topic/Math"
# 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实例依托于LightRAG实例
rag = RAGAnything(
lightrag=lightrag_instance,
vision_model_func=vision_model_func,
)
# 查询
result = await rag.aquery(
#query="氧化铁和硝酸的反应方程式?",
query="文档介绍了哪些内容?",
mode="hybrid"
)
print("查询结果:", result)
if __name__ == "__main__":
asyncio.run(load_existing_lightrag())