import asyncio import inspect import logging import os from lightrag import LightRAG, QueryParam from lightrag.llm.ollama import ollama_embed, ollama_model_complete from lightrag.utils import EmbeddingFunc WORKING_DIR = "./dickens_age" logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO) if not os.path.exists(WORKING_DIR): os.mkdir(WORKING_DIR) # AGE os.environ["AGE_POSTGRES_DB"] = "postgresDB" os.environ["AGE_POSTGRES_USER"] = "postgresUser" os.environ["AGE_POSTGRES_PASSWORD"] = "postgresPW" os.environ["AGE_POSTGRES_HOST"] = "localhost" os.environ["AGE_POSTGRES_PORT"] = "5455" os.environ["AGE_GRAPH_NAME"] = "dickens" rag = LightRAG( working_dir=WORKING_DIR, llm_model_func=ollama_model_complete, llm_model_name="llama3.1:8b", llm_model_max_async=4, llm_model_max_token_size=32768, llm_model_kwargs={"host": "http://localhost:11434", "options": {"num_ctx": 32768}}, embedding_func=EmbeddingFunc( embedding_dim=768, max_token_size=8192, func=lambda texts: ollama_embed( texts, embed_model="nomic-embed-text", host="http://localhost:11434" ), ), graph_storage="AGEStorage", ) with open("./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)