更新API密钥管理，优化意图识别和Excel数据验证功能，增强日志记录，改进错误处理机制，支持文档检索功能，提升代码可读性和灵活性。

rag2_0/demo/validate_excel_data_batch.py

@@ -81,21 +81,21 @@ class ExcelDataValidator:
         """
         file_path = file_path or self.input_file
         if not file_path:
-            logging.error("未指定输入文件路径")
+            logging.error("未指定输入文件路径", exc_info=True)
             return None
             
         try:
             df = pd.read_excel(file_path)
-            required_columns = ["问题", "问题分类", "问题改写", "槽点信息", "检索的内容"]
+            required_columns = ["问题", "问题分类", "问题改写", "槽点信息"]
             for col in required_columns:
                 if col not in df.columns:
-                    logging.error(f"缺少必要的列: {col}")
+                    logging.error(f"缺少必要的列: {col}", exc_info=True)
                     return None
             logging.info(f"成功从{file_path}读取了{len(df)}条数据")
             self.df = df
             return df
         except Exception as e:
-            logging.error(f"读取Excel文件时出错: {e}")
+            logging.error(f"读取Excel文件时出错: {e}", exc_info=True)
             return None
     
     def validate_classification(self, llm:OpenAiLLM , query:str, vertical_class:str, sub_class:str):
@@ -413,10 +413,7 @@ class ExcelDataValidator:
             return index, True, "", "", confidence_score
         except Exception as e:
             error_msg = f"处理行 {index} 时发生错误: {str(e)}"
-            logging.error(error_msg)
-            if self.debug:
-                import traceback
-                logging.error(traceback.format_exc())
+            logging.error(error_msg, exc_info=True)
             return index, False, "处理错误", error_msg, 0.0
     
     def process_batch(self, llm, batch_data):