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93 lines
2.8 KiB
93 lines
2.8 KiB
import asyncio
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import logging
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import os
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import time
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from lightrag import LightRAG, QueryParam
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from lightrag.utils import EmbeddingFunc
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from lightrag.kg.shared_storage import initialize_pipeline_status
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from Config.Config import EMBED_DIM, EMBED_MAX_TOKEN_SIZE, LLM_MODEL_NAME
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from Util.LightRagUtil import embedding_func, llm_model_func
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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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ROOT_DIR = '.'
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WORKING_DIR = f"{ROOT_DIR}/dickens-pg"
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logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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# AGE
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os.environ["AGE_GRAPH_NAME"] = "dickens"
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os.environ["POSTGRES_HOST"] = "10.10.14.208"
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os.environ["POSTGRES_PORT"] = "5432"
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os.environ["POSTGRES_USER"] = "postgres"
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os.environ["POSTGRES_PASSWORD"] = "postgres"
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os.environ["POSTGRES_DATABASE"] = "rag"
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async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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llm_model_name=LLM_MODEL_NAME,
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llm_model_max_async=4,
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llm_model_max_token_size=32768,
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enable_llm_cache_for_entity_extract=True,
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embedding_func=EmbeddingFunc(
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embedding_dim=EMBED_DIM,
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max_token_size=EMBED_MAX_TOKEN_SIZE,
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func=embedding_func
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),
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kv_storage="PGKVStorage",
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doc_status_storage="PGDocStatusStorage",
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graph_storage="PGGraphStorage",
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vector_storage="PGVectorStorage",
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auto_manage_storages_states=False,
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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async def main():
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try:
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rag = await initialize_rag()
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#with open(f"{ROOT_DIR}/book.txt", "r", encoding="utf-8") as f:
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# await rag.ainsert(f.read())
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print("==== Trying to test the rag queries ====")
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print("**** Start Naive Query ****")
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start_time = time.time()
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# Perform naive search
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await rag.aquery(
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"What are the top themes in this story?", param=QueryParam(mode="naive")
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)
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print(f"Naive Query Time: {time.time() - start_time} seconds")
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except Exception as e:
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#logger.error(f"Main execution error: {e}")
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pass
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finally:
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# 确保所有资源正确释放
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await asyncio.sleep(0.1) # 给pending任务完成的时间
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if __name__ == "__main__":
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asyncio.run(main()) |