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60 lines
1.4 KiB
Python
60 lines
1.4 KiB
Python
"""
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LightRAG meets Amazon Bedrock ⛰️
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"""
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import os
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import logging
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.bedrock import bedrock_complete, bedrock_embed
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from lightrag.utils import EmbeddingFunc
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from lightrag.kg.shared_storage import initialize_pipeline_status
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import asyncio
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import nest_asyncio
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nest_asyncio.apply()
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logging.getLogger("aiobotocore").setLevel(logging.WARNING)
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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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async def initialize_rag():
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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 // Amazon Bedrock",
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embedding_func=EmbeddingFunc(
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embedding_dim=1024, max_token_size=8192, func=bedrock_embed
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),
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)
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await rag.initialize_storages()
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await initialize_pipeline_status()
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return rag
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def main():
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rag = asyncio.run(initialize_rag())
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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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for mode in ["naive", "local", "global", "hybrid"]:
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print("\n+-" + "-" * len(mode) + "-+")
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print(f"| {mode.capitalize()} |")
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print("+-" + "-" * len(mode) + "-+\n")
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print(
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rag.query(
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"What are the top themes in this story?", param=QueryParam(mode=mode)
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)
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)
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if __name__ == "__main__":
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main()
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