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58 lines
2.3 KiB
58 lines
2.3 KiB
from app.chains.qa_chain import QAChain
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from app.chains.rag_chain import RAGChain
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from app.services.vector_store_service import VectorStoreService
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from app.core.logger import logger
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from app.core.config import settings
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from typing import List, Dict, Optional
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class QAService:
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"""问答服务类，处理问答相关的业务逻辑"""
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def __init__(self, model_name: str = None, temperature: float = None):
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"""
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初始化问答服务
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Args:
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model_name (str, optional): 使用的模型名称，默认使用配置中的值
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temperature (float, optional): 模型温度参数，默认使用配置中的值
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"""
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self.qa_chain = QAChain(
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model_name=model_name or settings.DEFAULT_MODEL,
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temperature=temperature or settings.TEMPERATURE
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)
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# 初始化 RAG 相关服务
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self.vector_store_service = VectorStoreService()
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self.rag_chain = RAGChain(self.vector_store_service)
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def get_answer(self, question: str, chat_history: Optional[List[Dict[str, str]]] = None, use_rag: bool = True) -> str:
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"""
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获取问题的回答
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Args:
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question (str): 用户问题
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chat_history (List[Dict[str, str]], optional): 聊天历史记录
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use_rag (bool): 是否使用 RAG 增强回答，默认为 True
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Returns:
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str: AI回答
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"""
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try:
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logger.info(f"处理问题: {question}")
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if use_rag:
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# 使用 RAG 获取回答
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rag_result = self.rag_chain.query(question)
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answer = rag_result["result"]
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# 如果有相关文档，添加到回答中
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if rag_result["source_documents"]:
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sources = [doc.page_content for doc in rag_result["source_documents"]]
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answer += "\n\n参考来源：\n" + "\n".join(sources)
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else:
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# 使用基础 QA 获取回答
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answer = self.qa_chain.answer(question, chat_history)
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logger.info(f"生成回答完成，长度: {len(answer)}")
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return answer
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except Exception as e:
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logger.error(f"生成回答时发生错误: {str(e)}", exc_info=True)
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raise
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