#!/usr/bin/env python
# -*- coding: utf-8 -*-

import os
import json
from concurrent.futures import ThreadPoolExecutor, as_completed
from rag2_0.dify.dify_client import ChatClient, DifyClient
from rag2_0.dify.dify_tool import DifyTool
from pydantic import BaseModel, Field
from langchain.output_parsers import PydanticOutputParser
from threading import Lock

class ContentSource(BaseModel):
    score: int = Field(description="相关性分数")
    reason: str = Field(description="评分理由")

class BaseWorkflowChat:
    """
    工作流对话基类，封装了与Dify API交互的基本功能
    """
    def __init__(self, api_key: str, base_url: str):
        """
        初始化工作流对话基类
        
        Args:
            api_key: Dify API的密钥
            base_url: Dify API的基础URL
        """
        self.chat_client = ChatClient(api_key=api_key, base_url=base_url)
        self.content_source_parser = PydanticOutputParser(pydantic_object=ContentSource)
        
    def create_chat_message(self, query: str):
        """
        创建聊天消息
        
        Args:
            query: 问题内容
            
        Returns:
            tuple: (聊天响应, 消息ID)
        """
        try:
            response = self.chat_client.create_chat_message(inputs={}, query=query, user="AutoTestDifyChat").json()
            return response, response["message_id"]
        except Exception as e:
            raise e
    
    def calculate_score(self, query: str, content: str) -> int:
        """
        使用LLM判断query与content之间的相关性分数
        
        Args:
            query (str): 用户问题
            content (str): 检索内容
            
        Returns:
            int: 相关性分数，1-10分，10代表完全相关，1代表完全不相关；-1表示评分失败
        """
        from rag2_0.tool.ModelTool import OpenAiLLM
        
        try:
            prompt = f"""你是一个专业的信息相关性评估助手。请根据以下标准对用户query和检索内容的相关性进行1-10评分（10=完全相关，1=完全不相关），并按指定格式输出JSON结果。

【评分标准】
10分：完全契合，主题/意图完全一致且涵盖所有关键信息
8-9分：高度相关，核心要素匹配但存在少量信息缺失
6-7分：部分相关，涉及相同主题但存在重要信息缺失
4-5分：弱相关，仅次要信息点匹配
1-3分：完全不相关或信息冲突

【评估维度】
1. 主题一致性：核心主题/意图的匹配程度
2. 内容覆盖度：是否涵盖query的关键要素
3. 信息准确性：是否存在矛盾/错误信息
4. 细节丰富度：是否提供query要求的详细信息

【输出格式】
{{
  "score": 评分,
  "reason": "简明扼要的评分理由（中文）"
}}

【示例】
query: "新冠疫苗的常见副作用"
内容: "辉瑞疫苗常见反应包括注射部位疼痛（84.1%）、疲劳（62.9%）"
输出: {{"score":8,"reason":"主题完全匹配，涵盖主要副作用但未提及发热等常见反应"}}

现在评估：
query: "{query}"
content: "{content}"
"""
            api_key = os.getenv("OPENAI_API_KEY")
            base_url = os.getenv("OPENAI_API_BASE")
            model = os.getenv("LLM_MODEL_NAME")
            llm = OpenAiLLM(api_key=api_key, base_url=base_url, model=model)
            response = llm.invoke(user_prompt=prompt, need_retry=True)

            # 解析JSON响应
            try:
                parsed_output = self.content_source_parser.parse(response.content)
                return parsed_output.score
            except Exception as e:
                return -1
        except Exception as e:
            return -1
    
    def get_retrieve_info(self, query: str, outputs: dict) -> tuple:
        """
        获取检索信息并计算分数
        
        Args:
            query (str): 用户问题
            outputs (dict): 检索输出结果
            
        Returns:
            tuple: (检索内容列表, 最高分, 最低分, 平均分)
        """
        max_score = 0
        min_score = 10
        total_score = 0
        valid_scores = 0
        retrieve_content = []
        
        # 使用线程池并发计算分数
        with ThreadPoolExecutor() as executor:
            # 创建任务列表
            future_to_content = {}
            for result in outputs["result"]:
                content = result["content"].strip()
                future = executor.submit(self.calculate_score, query=query, content=content)
                future_to_content[future] = content
            
            # 收集结果
            for future in as_completed(future_to_content):
                content = future_to_content[future]
                score = future.result()
                content_title = content.split("\n")[0]
                
                if score != -1:
                    max_score = max(max_score, score)
                    min_score = min(min_score, score)
                    total_score += score
                    valid_scores += 1
                
                if content_title:
                    retrieve_content.append(content_title + f"--相关性得分({score}分)")
                
        avg_score = total_score / valid_scores if valid_scores > 0 else 0
        return retrieve_content, max_score, min_score, avg_score


class NewWorkflowChat(BaseWorkflowChat):
    """
    新工作流对话类，用于调用新工作流发送对话并解析获取相关数据
    """
    def process_question(self, query: str) -> dict:
        """
        处理问题，获取新工作流的回答和相关信息
        
        Args:
            query: 问题内容
            
        Returns:
            dict: 包含问题、回答和相关信息的字典
        """
        response, message_id = self.create_chat_message(query)
        
        if isinstance(response, str) and response.startswith("error:"):
            raise RuntimeError(f"create_chat_message 出错：{response}")
            
        answer = response["answer"]
        workflow_info = self.get_workflow_info(query, message_id)
        
        if workflow_info is None:
            return None
            
        result = {
            "问题": query,
            "新流程答案": answer,
            "新问题改写": workflow_info["问题改写"],
            "新问题分类": workflow_info["问题分类"],
            "槽点信息": workflow_info["槽点信息"],
            "新检索词条": workflow_info["检索词条"],
            "检索内容": workflow_info["检索内容"],
            "message_id":message_id
        }
        
        return result

    def get_workflow_info(self, query: str, message_id: str) -> dict:
        """
        获取新工作流的问题分类和检索信息
        
        Args:
            query (str): 用户问题
            message_id (str): 新工作流的消息ID
            
        Returns:
            dict: 包含问题分类结果的字典
        """
        retrieve_title = []
        retrieve_content = []
        max_score = 0
        min_score = 0
        avg_score = 0
        rewrite_query = ""
        vertical_classification = ""
        sub_classification = ""
        slot_info = ""
        
        try:
            message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
            for workflow_node in message_info["workflow_node_executions_info"]:
                if workflow_node["title"] == "知识检索结果后处理":
                    outputs = json.loads(workflow_node["outputs"])  
                    retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
                    retrieve_content = outputs["result"]
                elif workflow_node["title"] == "问题优化结果解析":
                    outputs = json.loads(workflow_node["outputs"])  
                    rewrite_query = outputs["optimize_query"]
                    llm_result_json = json.loads(workflow_node['inputs'])["llm_result"]
                    json_result = json.loads(llm_result_json)
                    vertical_classification = json_result['vertical_classification']
                    sub_classification = json_result['sub_classification']
                    slot_info = json.dumps(json_result["slot_filling"], ensure_ascii=False, indent=2)
        except Exception as e:
            raise e
            
        return {
            "问题改写": rewrite_query,
            "检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
            "检索内容": retrieve_content,
            "问题分类": f"{vertical_classification} - {sub_classification}",
            "槽点信息": slot_info,

        }


class OldWorkFlowChat(BaseWorkflowChat):
    """
    旧工作流对话类，用于调用旧工作流发送对话并解析获取相关数据
    """
    def process_question(self, query: str) -> dict:
        """
        处理问题，获取旧工作流的回答和相关信息
        
        Args:
            query: 问题内容
            
        Returns:
            dict: 包含问题、回答和相关信息的字典
        """
        response, message_id = self.create_chat_message(query)
        
        if isinstance(response, str) and response.startswith("error:"):
            return None
            
        answer = response["answer"]
        workflow_info = self.get_workflow_info(query, message_id)
        
        if workflow_info is None:
            return None
            
        result = {
            "问题": query,
            "旧流程答案": answer,
            "旧问题改写": workflow_info["问题改写"],
            "旧检索词条": workflow_info["检索词条"],
            "检索内容": workflow_info["检索内容"],
            "message_id":message_id
        }
        
        return result

    def get_workflow_info(self, query: str, message_id: str) -> dict:
        """
        获取旧工作流的问题改写和检索信息
        
        Args:
            query (str): 用户问题
            message_id (str): 旧工作流的消息ID
            
        Returns:
            dict: 包含问题改写和检索信息的字典
        """
        retrieve_title = []
        retrieve_content = []
        max_score = 0
        min_score = 0
        avg_score = 0
        rewrite_query = ""
        
        try:
            message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
            for workflow_node in message_info["workflow_node_executions_info"]:
                if workflow_node["title"] == "知识检索结果后处理":
                    outputs = json.loads(workflow_node["outputs"])  
                    retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
                    retrieve_content = outputs["result"]
                elif workflow_node["title"] == "问题优化结果解析":
                    outputs = json.loads(workflow_node["outputs"])  
                    rewrite_query = outputs["optimize_query"]
        except Exception as e:
            return None
            
        return {
            "问题改写": rewrite_query,
            "检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
            "检索内容": retrieve_content,
        }