mirror of
https://github.com/HKUDS/LightRAG.git
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110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
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 dotenv import load_dotenv
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.zhipu import zhipu_complete
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from lightrag.llm.ollama import ollama_embedding
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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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load_dotenv()
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ROOT_DIR = os.environ.get("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"] = "localhost"
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os.environ["POSTGRES_PORT"] = "15432"
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os.environ["POSTGRES_USER"] = "rag"
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os.environ["POSTGRES_PASSWORD"] = "rag"
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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=zhipu_complete,
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llm_model_name="glm-4-flashx",
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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=1024,
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max_token_size=8192,
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func=lambda texts: ollama_embedding(
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texts, embed_model="bge-m3", host="http://localhost:11434"
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),
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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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# Initialize RAG instance
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rag = await initialize_rag()
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# add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
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rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func
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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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print(
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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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)
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print(f"Naive Query Time: {time.time() - start_time} seconds")
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# Perform local search
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print("**** Start Local Query ****")
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start_time = time.time()
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print(
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await rag.aquery(
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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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print(f"Local Query Time: {time.time() - start_time} seconds")
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# Perform global search
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print("**** Start Global Query ****")
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start_time = time.time()
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print(
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await rag.aquery(
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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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print(f"Global Query Time: {time.time() - start_time}")
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# Perform hybrid search
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print("**** Start Hybrid Query ****")
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print(
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await rag.aquery(
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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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print(f"Hybrid Query Time: {time.time() - start_time} seconds")
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if __name__ == "__main__":
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asyncio.run(main())
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