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66 lines
2.2 KiB
66 lines
2.2 KiB
from app.chains.base_chain import BaseChain
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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 QAChain(BaseChain):
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"""
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问答链类，继承自基础链，专门用于处理问答任务
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该类使用预定义的提示模板来格式化问题，并通过基础链获取回答
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"""
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def __init__(self, model_name: str = None, temperature: float = None):
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"""
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初始化问答链
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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，控制输出的随机性，默认使用配置中的值
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"""
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super().__init__(
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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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logger.info("初始化问答链，创建提示模板")
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self.chain = self.create_chain(
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template="""你是一个有帮助的AI助手。请根据以下对话历史回答问题：
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历史对话:
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{chat_history}
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当前问题: {question}
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回答:""",
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input_variables=["question", "chat_history"]
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)
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def answer(self, question: str, chat_history: Optional[List[Dict[str, str]]] = None) -> 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): 用户的问题
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chat_history (List[Dict[str, str]], optional): 聊天历史记录
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Returns:
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str: 模型的回答
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"""
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logger.info(f"处理问题: {question}")
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# 格式化聊天历史
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formatted_history = ""
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if chat_history:
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formatted_history = "\n".join([
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f"{'用户' if msg['role'] == 'user' else 'AI'}: {msg['content']}"
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for msg in chat_history
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])
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# 运行链
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answer = self.run(self.chain, {
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"question": question,
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"chat_history": formatted_history
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})
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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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