175 lines
6.3 KiB
Python
175 lines
6.3 KiB
Python
"""
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生成集成模块
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"""
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import logging
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import os
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import time
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from typing import List
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from openai import OpenAI
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from langchain_core.documents import Document
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logger = logging.getLogger(__name__)
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class GenerationIntegrationModule:
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"""生成集成模块 - 负责答案生成"""
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def __init__(self, model_name: str = "kimi-k2-0711-preview", temperature: float = 0.1, max_tokens: int = 2048):
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"""
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初始化生成集成模块
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"""
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self.model_name = model_name
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self.temperature = temperature
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self.max_tokens = max_tokens
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# 初始化OpenAI客户端（使用Moonshot API）
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api_key = os.getenv("MOONSHOT_API_KEY")
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if not api_key:
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raise ValueError("请设置 MOONSHOT_API_KEY 环境变量")
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self.client = OpenAI(
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api_key=api_key,
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base_url="https://api.moonshot.cn/v1"
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)
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logger.info(f"生成模块初始化完成，模型: {model_name}")
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def generate_adaptive_answer(self, question: str, documents: List[Document]) -> str:
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"""
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智能统一答案生成
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自动适应不同类型的查询，无需预先分类
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"""
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# 构建上下文
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context_parts = []
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for doc in documents:
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content = doc.page_content.strip()
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if content:
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# 添加检索层级信息（如果有的话）
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level = doc.metadata.get('retrieval_level', '')
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if level:
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context_parts.append(f"[{level.upper()}] {content}")
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else:
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context_parts.append(content)
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context = "\n\n".join(context_parts)
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# LightRAG风格的统一提示词
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prompt = f"""
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作为一位专业的烹饪助手，请基于以下信息回答用户的问题。
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检索到的相关信息：
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{context}
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用户问题：{question}
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请提供准确、实用的回答。根据问题的性质：
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- 如果是询问多个菜品，请提供清晰的列表
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- 如果是询问具体制作方法，请提供详细步骤
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- 如果是一般性咨询，请提供综合性回答
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回答：
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"""
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=self.temperature,
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max_tokens=self.max_tokens
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)
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return response.choices[0].message.content.strip()
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except Exception as e:
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logger.error(f"LightRAG答案生成失败: {e}")
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return f"抱歉，生成回答时出现错误：{str(e)}"
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def generate_adaptive_answer_stream(self, question: str, documents: List[Document], max_retries: int = 3):
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"""
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LightRAG风格的流式答案生成（带重试机制）
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"""
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# 构建上下文
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context_parts = []
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for doc in documents:
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content = doc.page_content.strip()
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if content:
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level = doc.metadata.get('retrieval_level', '')
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if level:
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context_parts.append(f"[{level.upper()}] {content}")
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else:
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context_parts.append(content)
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context = "\n\n".join(context_parts)
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# LightRAG风格的统一提示词
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prompt = f"""
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作为一位专业的烹饪助手，请基于以下信息回答用户的问题。
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检索到的相关信息：
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{context}
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用户问题：{question}
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请提供准确、实用的回答。根据问题的性质：
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- 如果是询问多个菜品，请提供清晰的列表
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- 如果是询问具体制作方法，请提供详细步骤
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- 如果是一般性咨询，请提供综合性回答
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回答：
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"""
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for attempt in range(max_retries):
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=self.temperature,
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max_tokens=self.max_tokens,
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stream=True,
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timeout=60 # 增加超时设置
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)
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if attempt == 0:
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print("开始流式生成回答...\n")
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else:
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print(f"第{attempt + 1}次尝试流式生成...\n")
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full_response = ""
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for chunk in response:
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if chunk.choices[0].delta.content:
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content = chunk.choices[0].delta.content
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full_response += content
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yield content # 使用yield返回流式内容
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# 如果成功完成，退出重试循环
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return
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except Exception as e:
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logger.warning(f"流式生成第{attempt + 1}次尝试失败: {e}")
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if attempt < max_retries - 1:
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wait_time = (attempt + 1) * 2 # 递增等待时间
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print(f"⚠️ 连接中断，{wait_time}秒后重试...")
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time.sleep(wait_time)
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continue
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else:
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# 所有重试都失败，使用非流式作为后备
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logger.error(f"流式生成完全失败，尝试非流式后备方案")
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print("⚠️ 流式生成失败，切换到标准模式...")
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try:
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fallback_response = self.generate_adaptive_answer(question, documents)
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yield fallback_response
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return
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except Exception as fallback_error:
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logger.error(f"后备生成也失败: {fallback_error}")
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error_msg = f"抱歉，生成回答时出现网络错误，请稍后重试。错误信息：{str(e)}"
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yield error_msg
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return |