删除不再使用的脚本文件，更新数据库连接配置为环境变量，优化Dify对比测试逻辑，增强日志记录.

rag2_0/dify/dify_tool.py

@@ -39,11 +39,11 @@ class PgSql:
         try:
             # 连接数据库
             self.connection = psycopg2.connect(
-                user="postgres",
-                password="difyai123456",
-                host="172.20.0.145",
-                port=5432,
-                database="dify"
+                user=os.getenv("DIFY_PG_USER"),
+                password=os.getenv("DIFY_PG_PASSWORD"),
+                host=os.getenv("DIFY_PG_HOST"),
+                port=os.getenv("DIFY_PG_PORT"),
+                database=os.getenv("DIFY_PG_DATABASE")
             )
 
         except (Exception, psycopg2.Error) as error:
@@ -160,11 +160,11 @@ class PgSql:
                         """,
                         (workflow_run_id,)
                     )
-                result = cursor.fetchall()
-                if result:
-                    colnames = [desc[0] for desc in cursor.description]
-                    return [dict(zip(colnames, row)) for row in result]
-                return None
+                    result = cursor.fetchall()
+                    if result:
+                        colnames = [desc[0] for desc in cursor.description]
+                        return [dict(zip(colnames, row)) for row in result]
+                    return None
             except (Exception, psycopg2.Error) as error:
                 raise Exception(f"Error while getting workflow_node_executions_info: {error}")
 
@@ -263,7 +263,8 @@ class BaseWorkflowChat:
         析构函数，在对象被销毁时自动关闭数据库连接。
         确保在对象生命周期结束时释放数据库资源。
         """
-        self.dify_tool.close_connection()
+        # DifyTool类已经在其__del__方法中关闭了数据库连接，无需在此重复调用
+        pass
         
     def create_chat_message(self, query: str):
         """
@@ -464,7 +465,7 @@ class NewWorkflowChat(BaseWorkflowChat):
                 elif workflow_node["title"] == "提取处理后的知识":
                     outputs = json.loads(workflow_node["outputs"])["knowledge_list"]
                     retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs, reranker_sorce_info=reranker_sorce)
-                elif workflow_node["title"] == "问题优化结果解析":
+                elif workflow_node["title"] == "意图识别结果解析":
                     outputs = json.loads(workflow_node["outputs"])  
                     rewrite_query = outputs["optimize_query"]
                     llm_result_json = json.loads(workflow_node['inputs'])["llm_result"]