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49 lines
1.2 KiB
Python
49 lines
1.2 KiB
Python
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"""
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LightRAG meets Amazon Bedrock ⛰️
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"""
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import os
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from lightrag import LightRAG, QueryParam
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from lightrag.llm import bedrock_complete, bedrock_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=bedrock_complete,
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llm_model_name="anthropic.claude-3-haiku-20240307-v1:0",
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node2vec_params = {
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'dimensions': 1024,
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'num_walks': 10,
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'walk_length': 40,
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'window_size': 2,
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'iterations': 3,
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'random_seed': 3
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},
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embedding_func=EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=8192,
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func=lambda texts: bedrock_embedding(texts)
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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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# 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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# 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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# 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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# 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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