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41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
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import os
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import ollama_model_complete, ollama_embedding
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from lightrag.utils import EmbeddingFunc
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WORKING_DIR = "./dickens"
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=ollama_model_complete,
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llm_model_name='your_model_name',
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embedding_func=EmbeddingFunc(
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embedding_dim=768,
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max_token_size=8192,
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func=lambda texts: ollama_embedding(
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texts,
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embed_model="nomic-embed-text"
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)
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),
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)
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with open("./book.txt") 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")))
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