ruxia
125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
from typing import List, Dict, Any
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# from llm import llm as llm_model
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from utils.llm import search_llm as llm_model
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from utils.prompt import RESPONSE_TEMPLATE
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import time
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import logging
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import sys
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[logging.StreamHandler(sys.stdout)]
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)
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logger = logging.getLogger("ResponseGenerator")
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class ResponseGenerator:
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def __init__(self):
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"""初始化响应生成器，使用自定义的llm模型"""
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logger.info("ResponseGenerator初始化完成")
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def generate_response(self, query: str, retrieved_info: List[Dict[str, Any]], nlu_data: Dict = None) -> str:
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"""
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生成响应
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参数:
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query: 用户查询
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retrieved_info: 检索到的信息
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nlu_data: 意图识别和槽位提取的结果
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返回:
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生成的响应
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"""
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# 如果没有检索到信息，返回默认回答
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if not retrieved_info:
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logger.warning("没有检索到相关信息，返回默认回答")
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return "抱歉，我没有找到与您问题相关的信息。请尝试使用其他关键词或更具体的问题。"
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# 构建提示
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logger.info("开始构建提示")
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prompt = self._build_prompt(query, retrieved_info, nlu_data)
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logger.info(f"提示构建完成，长度: {len(prompt)}")
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# 调用自定义LLM模型生成回答
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logger.info("开始调用LLM模型生成回答")
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start_time = time.time()
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try:
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# 添加超时处理
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response = llm_model.invoke(prompt)
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logger.info(f"LLM响应完成，耗时: {time.time() - start_time:.2f}秒")
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# 检查响应是否为空
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if not response or not response.strip():
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logger.error("LLM返回了空响应")
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return "抱歉，生成回答时出现了问题。请稍后再试。"
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return response
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except Exception as e:
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logger.error(f"调用LLM模型时出错: {str(e)}")
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return f"抱歉，生成回答时出现了错误: {str(e)}"
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def _build_prompt(self, query: str, retrieved_info: List[Dict[str, Any]], nlu_data: Dict = None) -> str:
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"""
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构建提示
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参数:
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query: 用户查询
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retrieved_info: 检索到的信息
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nlu_data: 意图识别和槽位提取的结果
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返回:
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构建的提示
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"""
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context_parts = []
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for info in retrieved_info:
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node = info.get("node", {})
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text = info.get("text", "")
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node_type = node.get("labels", [""])[0] if "labels" in node else ""
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name = node.get("original_name", "") or node.get("display_name", "")
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description = node.get("描述", "")
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if node_type == "功能名称":
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context_parts.append(f"功能: {name}\n描述: {description}")
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else:
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context_parts.append(f"{node_type}: {name}")
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context = "\n\n".join(context_parts)
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# 添加意图和槽位信息
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intent_info = ""
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if nlu_data and '意图' in nlu_data:
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intent_data = nlu_data['意图']
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# 获取一级意图
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first_intent = "未知"
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if '一级意图' in intent_data and 'name' in intent_data['一级意图']:
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first_intent = intent_data['一级意图']['name']
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# 获取二级意图
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second_intent = "未知"
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if '二级意图' in intent_data and 'name' in intent_data['二级意图']:
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second_intent = intent_data['二级意图']['name']
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intent_info = f"用户一级意图: {first_intent}\n用户二级意图: {second_intent}\n"
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# 添加槽位信息
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if '二级意图' in intent_data and 'slot_lv2' in intent_data['二级意图']:
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slots = intent_data['二级意图']['slot_lv2']
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if slots:
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intent_info += "提取的槽位:\n"
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for slot_name, slot_value in slots.items():
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if slot_value and slot_value != '未知':
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intent_info += f"- {slot_name}: {slot_value}\n"
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prompt = RESPONSE_TEMPLATE.format(
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intent_info=intent_info,
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context=context,
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query=query
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)
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return prompt |