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graph/generator.py

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