mirror of
https://github.com/HKUDS/LightRAG.git
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113 lines
2.8 KiB
Python
113 lines
2.8 KiB
Python
import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.ollama import ollama_embed, openai_complete_if_cache
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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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# WorkingDir
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ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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WORKING_DIR = os.path.join(ROOT_DIR, "myKG")
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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print(f"WorkingDir: {WORKING_DIR}")
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# redis
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os.environ["REDIS_URI"] = "redis://localhost:6379"
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# neo4j
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BATCH_SIZE_NODES = 500
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BATCH_SIZE_EDGES = 100
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os.environ["NEO4J_URI"] = "bolt://117.50.173.35:7687"
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os.environ["NEO4J_USERNAME"] = "neo4j"
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os.environ["NEO4J_PASSWORD"] = "12345678"
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# milvus
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os.environ["MILVUS_URI"] = "http://117.50.173.35:19530"
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os.environ["MILVUS_USER"] = "root"
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os.environ["MILVUS_PASSWORD"] = "Milvus"
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os.environ["MILVUS_DB_NAME"] = "lightrag"
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async def llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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"deepseek-chat",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key="",
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base_url="",
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**kwargs,
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)
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embedding_func = EmbeddingFunc(
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embedding_dim=768,
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max_token_size=512,
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func=lambda texts: ollama_embed(
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texts, embed_model="shaw/dmeta-embedding-zh", host="http://117.50.173.35:11434"
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),
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)
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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_max_token_size=32768,
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embedding_func=embedding_func,
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chunk_token_size=512,
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chunk_overlap_token_size=256,
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kv_storage="RedisKVStorage",
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graph_storage="Neo4JStorage",
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vector_storage="MilvusVectorDBStorage",
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doc_status_storage="RedisKVStorage",
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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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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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with open("./book.txt", "r", encoding="utf-8") as f:
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rag.insert(f.read())
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# Perform naive search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="naive")
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)
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)
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# Perform local search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="local")
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)
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)
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# Perform global search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="global")
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)
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)
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# Perform hybrid search
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode="hybrid")
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)
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)
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if __name__ == "__main__":
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main()
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