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