import asyncio import os import inspect import logging from dotenv import load_dotenv from lightrag import LightRAG, QueryParam from lightrag.llm import openai_complete_if_cache, ollama_embedding from lightrag.utils import EmbeddingFunc load_dotenv() WORKING_DIR = "./examples/output" logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO) async def llm_model_func( prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs ) -> str: return await openai_complete_if_cache( "deepseek-chat", prompt, system_prompt=system_prompt, history_messages=history_messages, api_key=os.getenv("DEEPSEEK_API_KEY"), base_url=os.getenv("DEEPSEEK_ENDPOINT"), **kwargs, ) if not os.path.exists(WORKING_DIR): os.mkdir(WORKING_DIR) rag = LightRAG( working_dir=WORKING_DIR, llm_model_func=llm_model_func, embedding_func=EmbeddingFunc( embedding_dim=1024, max_token_size=8192, func=lambda texts: ollama_embedding( texts, embed_model="bge-m3:latest", host="http://m4.lan.znipower.com:11434" ), ), ) with open("./examples/input/book.txt", "r", encoding="utf-8") as f: rag.insert(f.read()) # Perform naive search print( rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")) ) # Perform local search print( rag.query("What are the top themes in this story?", param=QueryParam(mode="local")) ) # Perform global search print( rag.query("What are the top themes in this story?", param=QueryParam(mode="global")) ) # Perform hybrid search print( rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")) ) # stream response resp = rag.query( "What are the top themes in this story?", param=QueryParam(mode="hybrid", stream=True), ) async def print_stream(stream): async for chunk in stream: print(chunk, end="", flush=True) if inspect.isasyncgen(resp): asyncio.run(print_stream(resp)) else: print(resp)