29 lines
960 B
Python
29 lines
960 B
Python
"""
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向量化
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"""
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from app.chat_qa_query.query_qa_state import QueryQAState
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from app.client.embedding_client_manager import embedding_client
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from app.repository.milvus.message_qa_repository import QARepository
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from app.core.log import logger
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async def search_embed(state:QueryQAState) :
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logger.info("search_embed")
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rewritten_query = state.get("rewritten_query", "")
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original_query = state.get("original_query", "")
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batch_embeddings = await embedding_client.aembed_query(rewritten_query)
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qa_repository = QARepository()
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result = await qa_repository.search_qa(query_vector=batch_embeddings, top_k=3)
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logger.info(f"原始问题:{original_query},改写后的问题：{rewritten_query},匹配结果：{result}")
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if result:
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question = result[0]["question"]
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answer = result[0]["answer"]
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qa_pairs={
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"question": question,
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"answer": answer,
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}
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return {"qa_pairs": qa_pairs}
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return {} |