新增对话处理功能，优化意图识别逻辑，添加结果保存至Excel的功能，更新依赖项以支持新的数据库驱动和ORM，重构代码以提高可读性和维护性，删除冗余文件以简化项目结构。

rag2_0/dify/dify_tool.py

@@ -1,8 +1,17 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
 import psycopg2
-from psycopg2 import sql
 import os
 import json
-from datetime import timezone, timedelta
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from rag2_0.dify.dify_client import ChatClient
+from pydantic import BaseModel, Field
+from langchain.output_parsers import PydanticOutputParser
+
+
+class ContentSource(BaseModel):
+    score: int = Field(description="相关性分数")
+    reason: str = Field(description="评分理由")
 
 class PgSql:
     """
@@ -219,6 +228,299 @@ class DifyTool:
         finally:
             dify_pgsql.close_connection()
 
+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,
+        } 
 
 if __name__ == "__main__":
     try: