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											2024-12-13 20:41:38 +01:00
										 |  |  | import asyncio | 
					
						
							|  |  |  | import inspect | 
					
						
							|  |  |  | import logging | 
					
						
							|  |  |  | import os | 
					
						
							|  |  |  | 
 | 
					
						
							|  |  |  | from lightrag import LightRAG, QueryParam | 
					
						
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											2025-01-25 00:11:00 +01:00
										 |  |  | from lightrag.llm.ollama import ollama_embed, ollama_model_complete | 
					
						
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										 |  |  | from lightrag.utils import EmbeddingFunc | 
					
						
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							|  |  |  | WORKING_DIR = "./dickens_age" | 
					
						
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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): | 
					
						
							|  |  |  |     os.mkdir(WORKING_DIR) | 
					
						
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 | 
					
						
							|  |  |  | # 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" | 
					
						
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							|  |  |  | 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, | 
					
						
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										 |  |  |         func=lambda texts: ollama_embed( | 
					
						
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										 |  |  |             texts, embed_model="nomic-embed-text", host="http://localhost:11434" | 
					
						
							|  |  |  |         ), | 
					
						
							|  |  |  |     ), | 
					
						
							|  |  |  |     graph_storage="AGEStorage", | 
					
						
							|  |  |  | ) | 
					
						
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							|  |  |  | with open("./book.txt", "r", encoding="utf-8") as f: | 
					
						
							|  |  |  |     rag.insert(f.read()) | 
					
						
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 | 
					
						
							|  |  |  | # Perform naive search | 
					
						
							|  |  |  | print( | 
					
						
							|  |  |  |     rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")) | 
					
						
							|  |  |  | ) | 
					
						
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 | 
					
						
							|  |  |  | # Perform local search | 
					
						
							|  |  |  | print( | 
					
						
							|  |  |  |     rag.query("What are the top themes in this story?", param=QueryParam(mode="local")) | 
					
						
							|  |  |  | ) | 
					
						
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 | 
					
						
							|  |  |  | # Perform global search | 
					
						
							|  |  |  | print( | 
					
						
							|  |  |  |     rag.query("What are the top themes in this story?", param=QueryParam(mode="global")) | 
					
						
							|  |  |  | ) | 
					
						
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 | 
					
						
							|  |  |  | # 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), | 
					
						
							|  |  |  | ) | 
					
						
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							|  |  |  | 
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							|  |  |  | async def print_stream(stream): | 
					
						
							|  |  |  |     async for chunk in stream: | 
					
						
							|  |  |  |         print(chunk, end="", flush=True) | 
					
						
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							|  |  |  | if inspect.isasyncgen(resp): | 
					
						
							|  |  |  |     asyncio.run(print_stream(resp)) | 
					
						
							|  |  |  | else: | 
					
						
							|  |  |  |     print(resp) |