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										 |  |  | import os | 
					
						
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							|  |  |  | from lightrag import LightRAG, QueryParam | 
					
						
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										 |  |  | from lightrag.llm.openai import gpt_4o_mini_complete | 
					
						
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										 |  |  | WORKING_DIR = "./dickens" | 
					
						
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							|  |  |  | if not os.path.exists(WORKING_DIR): | 
					
						
							|  |  |  |     os.mkdir(WORKING_DIR) | 
					
						
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							|  |  |  | rag = LightRAG( | 
					
						
							|  |  |  |     working_dir=WORKING_DIR, | 
					
						
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										 |  |  |     llm_model_func=gpt_4o_mini_complete, | 
					
						
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										 |  |  |     # llm_model_func=gpt_4o_complete | 
					
						
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										 |  |  | ) | 
					
						
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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( | 
					
						
							|  |  |  |     rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")) | 
					
						
							|  |  |  | ) | 
					
						
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							|  |  |  | # Perform local search | 
					
						
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										 |  |  | print( | 
					
						
							|  |  |  |     rag.query("What are the top themes in this story?", param=QueryParam(mode="local")) | 
					
						
							|  |  |  | ) | 
					
						
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							|  |  |  | # Perform global search | 
					
						
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										 |  |  | print( | 
					
						
							|  |  |  |     rag.query("What are the top themes in this story?", param=QueryParam(mode="global")) | 
					
						
							|  |  |  | ) | 
					
						
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							|  |  |  | # Perform hybrid search | 
					
						
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										 |  |  | print( | 
					
						
							|  |  |  |     rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")) | 
					
						
							|  |  |  | ) |