fix: prevent bigint data from displaying in scientific notation in MCP chat

backend/apps/chat/api/chat.py

@@ -206,42 +206,14 @@ async def analysis_or_predict(session: SessionDep, current_user: CurrentUser, ch
 @router.post("/excel/export")
 async def export_excel(excel_data: ExcelData, trans: Trans):
     def inner():
-        _fields_list = []
-        data = []
+
         if not excel_data.data:
             raise HTTPException(
                 status_code=500,
                 detail=trans("i18n_excel_export.data_is_empty")
             )
 
-        # 预处理数据并记录每列的格式类型
-        col_formats = {}  # 格式类型：'text'（文本）、'number'（数字）、'default'（默认）
-        for field_idx, field in enumerate(excel_data.axis):
-            _fields_list.append(field.name)
-            col_formats[field_idx] = 'default'  # 默认不特殊处理
-
-        for _data in excel_data.data:
-            _row = []
-            for field_idx, field in enumerate(excel_data.axis):
-                value = _data.get(field.value)
-                if value is not None:
-                    # 检查是否为数字且需要特殊处理
-                    if isinstance(value, (int, float)):
-                        # 整数且超过15位 → 转字符串并标记为文本列
-                        if isinstance(value, int) and len(str(abs(value))) > 15:
-                            value = str(value)
-                            col_formats[field_idx] = 'text'
-                        # 小数且超过15位有效数字 → 转字符串并标记为文本列
-                        elif isinstance(value, float):
-                            decimal_str = format(value, '.16f').rstrip('0').rstrip('.')
-                            if len(decimal_str) > 15:
-                                value = str(value)
-                                col_formats[field_idx] = 'text'
-                        # 其他数字列标记为数字格式（避免科学记数法）
-                        elif col_formats[field_idx] != 'text':
-                            col_formats[field_idx] = 'number'
-                _row.append(value)
-            data.append(_row)
+        data, _fields_list, col_formats = LLMService.format_pd_data(excel_data.axis, excel_data.data)
 
         df = pd.DataFrame(data, columns=_fields_list)
 

backend/apps/chat/task/llm.py

@@ -31,7 +31,7 @@ from apps.chat.curd.chat import save_question, save_sql_answer, save_sql, \
     get_chat_chart_data, list_generate_sql_logs, list_generate_chart_logs, start_log, end_log, \
     get_last_execute_sql_error
 from apps.chat.models.chat_model import ChatQuestion, ChatRecord, Chat, RenameChat, ChatLog, OperationEnum, \
-    ChatFinishStep
+    ChatFinishStep, AxisObj
 from apps.data_training.curd.data_training import get_training_template
 from apps.datasource.crud.datasource import get_table_schema
 from apps.datasource.crud.permission import get_row_permission_filters, is_normal_user
@@ -414,7 +414,7 @@ class LLMService:
             if settings.TABLE_EMBEDDING_ENABLED and (
                     not self.current_assistant or (self.current_assistant and self.current_assistant.type != 1)):
                 _ds_list = get_ds_embedding(_session, self.current_user, _ds_list, self.out_ds_instance,
-                                      self.chat_question.question, self.current_assistant)
+                                            self.chat_question.question, self.current_assistant)
                 # yield {'content': '{"id":' + str(ds.get('id')) + '}'}
 
             _ds_list_dict = []
@@ -1056,23 +1056,18 @@ class LLMService:
                     if in_chat:
                         yield 'data:' + orjson.dumps({'type': 'finish'}).decode() + '\n\n'
                     else:
-                        data = []
-                        _fields_list = []
-                        _fields_skip = False
-                        for _data in result.get('data'):
-                            _row = []
-                            for field in result.get('fields'):
-                                _row.append(_data.get(field))
-                                if not _fields_skip:
-                                    _fields_list.append(field)
-                            data.append(_row)
-                            _fields_skip = True
+                        _column_list = []
+                        for field in result.get('fields'):
+                            _column_list.append(AxisObj(name=field, value=field))
+
+                        data, _fields_list, col_formats = self.format_pd_data(_column_list, result.get('data'))
 
                         if not data or not _fields_list:
                             yield 'The SQL execution result is empty.\n\n'
                         else:
                             df = pd.DataFrame(data, columns=_fields_list)
-                            markdown_table = df.to_markdown(index=False)
+                            df_safe = self.safe_convert_to_string(df)
+                            markdown_table = df_safe.to_markdown(index=False)
                             yield markdown_table + '\n\n'
                 else:
                     yield json_result
@@ -1117,22 +1112,19 @@ class LLMService:
                         if chart.get('axis').get('series'):
                             _fields[chart.get('axis').get('series').get('value')] = chart.get('axis').get('series').get(
                                 'name')
-                    _fields_list = []
-                    _fields_skip = False
-                    for _data in result.get('data'):
-                        _row = []
-                        for field in result.get('fields'):
-                            _row.append(_data.get(field))
-                            if not _fields_skip:
-                                _fields_list.append(field if not _fields.get(field) else _fields.get(field))
-                        data.append(_row)
-                        _fields_skip = True
+                    _column_list = []
+                    for field in result.get('fields'):
+                        _column_list.append(
+                            AxisObj(name=field if not _fields.get(field) else _fields.get(field), value=field))
+
+                    data, _fields_list, col_formats = self.format_pd_data(_column_list, result.get('data'))
 
                     if not data or not _fields_list:
                         yield 'The SQL execution result is empty.\n\n'
                     else:
                         df = pd.DataFrame(data, columns=_fields_list)
-                        markdown_table = df.to_markdown(index=False)
+                        df_safe = self.safe_convert_to_string(df)
+                        markdown_table = df_safe.to_markdown(index=False)
                         yield markdown_table + '\n\n'
 
             if in_chat:
@@ -1179,6 +1171,64 @@ class LLMService:
             self.finish(_session)
             session_maker.remove()
 
+    @staticmethod
+    def safe_convert_to_string(df):
+        """
+        安全地将数值列转换为字符串，避免科学记数法
+        """
+        df_copy = df.copy()
+
+        for col in df_copy.columns:
+            # 只处理数值类型的列
+            if pd.api.types.is_numeric_dtype(df_copy[col]):
+                try:
+                    df_copy[col] = df_copy[col].astype(str)
+                except Exception as e:
+                    print(f"列 {col} 转换失败: {e}")
+                    # 如果转换失败，保持原样
+                    continue
+
+        return df_copy
+
+    @staticmethod
+    def format_pd_data(column_list: list, data_list: list, col_formats: dict = None):
+        # 预处理数据并记录每列的格式类型
+        # 格式类型：'text'（文本）、'number'（数字）、'default'（默认）
+        _fields_list = []
+
+        if col_formats is None:
+            col_formats = {}
+        for field_idx, field in enumerate(column_list):
+            _fields_list.append(field.name)
+            col_formats[field_idx] = 'default'  # 默认不特殊处理
+
+        data = []
+
+        for _data in data_list:
+            _row = []
+            for field_idx, field in enumerate(column_list):
+                value = _data.get(field.value)
+                if value is not None:
+                    # 检查是否为数字且需要特殊处理
+                    if isinstance(value, (int, float)):
+                        # 整数且超过15位 → 转字符串并标记为文本列
+                        if isinstance(value, int) and len(str(abs(value))) > 15:
+                            value = str(value)
+                            col_formats[field_idx] = 'text'
+                        # 小数且超过15位有效数字 → 转字符串并标记为文本列
+                        elif isinstance(value, float):
+                            decimal_str = format(value, '.16f').rstrip('0').rstrip('.')
+                            if len(decimal_str) > 15:
+                                value = str(value)
+                                col_formats[field_idx] = 'text'
+                        # 其他数字列标记为数字格式（避免科学记数法）
+                        elif col_formats[field_idx] != 'text':
+                            col_formats[field_idx] = 'number'
+                _row.append(value)
+            data.append(_row)
+
+        return data, _fields_list, col_formats
+
     def run_recommend_questions_task_async(self):
         self.future = executor.submit(self.run_recommend_questions_task_cache)