mirror of
https://github.com/HKUDS/LightRAG.git
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104 lines
2.5 KiB
Python
104 lines
2.5 KiB
Python
import os
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import asyncio
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import openai_complete_if_cache
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from lightrag.llm.siliconcloud import siliconcloud_embedding
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from lightrag.utils import EmbeddingFunc
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import numpy as np
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from lightrag.kg.shared_storage import initialize_pipeline_status
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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 llm_model_func(
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prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
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) -> str:
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return await openai_complete_if_cache(
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"Qwen/Qwen2.5-7B-Instruct",
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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api_key=os.getenv("SILICONFLOW_API_KEY"),
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base_url="https://api.siliconflow.cn/v1/",
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**kwargs,
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)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await siliconcloud_embedding(
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texts,
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model="netease-youdao/bce-embedding-base_v1",
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api_key=os.getenv("SILICONFLOW_API_KEY"),
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max_token_size=512,
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)
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# function test
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async def test_funcs():
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result = await llm_model_func("How are you?")
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print("llm_model_func: ", result)
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result = await embedding_func(["How are you?"])
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print("embedding_func: ", result)
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asyncio.run(test_funcs())
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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=llm_model_func,
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embedding_func=EmbeddingFunc(
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embedding_dim=768, max_token_size=512, func=embedding_func
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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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# Initialize RAG instance
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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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# Perform naive search
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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="naive")
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)
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)
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# Perform local search
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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="local")
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)
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)
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# Perform global search
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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="global")
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)
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)
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# Perform hybrid search
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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="hybrid")
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)
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)
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if __name__ == "__main__":
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main()
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