feat: import Sample SQL

backend/apps/data_training/curd/data_training.py

@@ -146,6 +146,9 @@ def get_all_data_training(session: SessionDep, name: Optional[str] = None, oid:
 
 
 def create_training(session: SessionDep, info: DataTrainingInfo, oid: int, trans: Trans):
+    """
+    创建单个数据训练记录
+    """
     # 基本验证
     if not info.question or not info.question.strip():
         raise Exception(trans("i18n_data_training.question_cannot_be_empty"))
@@ -154,45 +157,56 @@ def create_training(session: SessionDep, info: DataTrainingInfo, oid: int, trans
         raise Exception(trans("i18n_data_training.description_cannot_be_empty"))
 
     create_time = datetime.datetime.now()
+
+    # 检查数据源和高级应用不能同时为空
     if info.datasource is None and info.advanced_application is None:
         if oid == 1:
             raise Exception(trans("i18n_data_training.datasource_assistant_cannot_be_none"))
         else:
             raise Exception(trans("i18n_data_training.datasource_cannot_be_none"))
 
-    parent = DataTraining(question=info.question, create_time=create_time, description=info.description, oid=oid,
-                          datasource=info.datasource, enabled=info.enabled,
-                          advanced_application=info.advanced_application)
-
-    stmt = select(DataTraining.id).where(and_(DataTraining.question == info.question, DataTraining.oid == oid))
+    # 检查重复记录
+    stmt = select(DataTraining.id).where(
+        and_(DataTraining.question == info.question.strip(), DataTraining.oid == oid)
+    )
 
     if info.datasource is not None and info.advanced_application is not None:
         stmt = stmt.where(
-            or_(DataTraining.datasource == info.datasource,
-                DataTraining.advanced_application == info.advanced_application))
+            or_(
+                DataTraining.datasource == info.datasource,
+                DataTraining.advanced_application == info.advanced_application
+            )
+        )
     elif info.datasource is not None and info.advanced_application is None:
-        stmt = stmt.where(and_(DataTraining.datasource == info.datasource))
+        stmt = stmt.where(DataTraining.datasource == info.datasource)
     elif info.datasource is None and info.advanced_application is not None:
-        stmt = stmt.where(and_(DataTraining.advanced_application == info.advanced_application))
+        stmt = stmt.where(DataTraining.advanced_application == info.advanced_application)
 
     exists = session.query(stmt.exists()).scalar()
 
     if exists:
         raise Exception(trans("i18n_data_training.exists_in_db"))
 
-    result = DataTraining(**parent.model_dump())
+    # 创建记录
+    data_training = DataTraining(
+        question=info.question.strip(),
+        description=info.description.strip(),
+        oid=oid,
+        datasource=info.datasource,
+        advanced_application=info.advanced_application,
+        create_time=create_time,
+        enabled=info.enabled if info.enabled is not None else True
+    )
 
-    session.add(parent)
+    session.add(data_training)
     session.flush()
-    session.refresh(parent)
-
-    result.id = parent.id
+    session.refresh(data_training)
     session.commit()
 
-    # embedding
-    run_save_data_training_embeddings([result.id])
+    # 处理embedding
+    run_save_data_training_embeddings([data_training.id])
 
-    return result.id
+    return data_training.id
 
 
 def update_training(session: SessionDep, info: DataTrainingInfo, oid: int, trans: Trans):
@@ -250,14 +264,7 @@ def update_training(session: SessionDep, info: DataTrainingInfo, oid: int, trans
 
 def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo], oid: int, trans: Trans):
     """
-    批量创建数据训练记录
-    Args:
-        session: 数据库会话
-        info_list: DataTrainingInfo对象列表
-        oid: 组织ID
-        trans: 翻译对象
-    Returns:
-        dict: 包含成功数量、失败记录和统计信息的结果字典
+    批量创建数据训练记录（复用单条插入逻辑）
     """
     if not info_list:
         return {
@@ -268,17 +275,16 @@ def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo]
             'deduplicated_count': 0
         }
 
-    create_time = datetime.datetime.now()
-    failed_records = []  # 存储失败的数据和原因
+    failed_records = []
     success_count = 0
-    inserted_ids = []  # 存储成功插入的ID
+    inserted_ids = []
 
     # 第一步：数据去重
     unique_records = {}
-    duplicate_records = []  # 存储重复的数据
+    duplicate_records = []
 
     for info in info_list:
-        # 创建唯一标识：问题 + 数据源名称 + 高级应用名称
+        # 创建唯一标识
         unique_key = (
             info.question.strip().lower() if info.question else "",
             info.datasource_name.strip().lower() if info.datasource_name else "",
@@ -286,7 +292,6 @@ def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo]
         )
 
         if unique_key in unique_records:
-            # 如果是重复数据，记录到重复列表中
             duplicate_records.append(info)
         else:
             unique_records[unique_key] = info
@@ -294,14 +299,13 @@ def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo]
     # 将去重后的数据转换为列表
     deduplicated_list = list(unique_records.values())
 
-    # 预加载数据源名称到ID的映射（CoreDatasource需要判断oid）
+    # 预加载数据源和高级应用名称到ID的映射
     datasource_name_to_id = {}
     datasource_stmt = select(CoreDatasource.id, CoreDatasource.name).where(CoreDatasource.oid == oid)
     datasource_result = session.execute(datasource_stmt).all()
     for ds in datasource_result:
         datasource_name_to_id[ds.name.strip()] = ds.id
 
-    # 只有在oid=1时才预加载高级应用名称到ID的映射
     assistant_name_to_id = {}
     if oid == 1:
         assistant_stmt = select(AssistantModel.id, AssistantModel.name).where(AssistantModel.type == 1)
@@ -309,7 +313,7 @@ def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo]
         for assistant in assistant_result:
             assistant_name_to_id[assistant.name.strip()] = assistant.id
 
-    # 验证和准备数据
+    # 验证和转换数据
     valid_records = []
     for info in deduplicated_list:
         error_messages = []
@@ -321,7 +325,7 @@ def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo]
         if not info.description or not info.description.strip():
             error_messages.append(trans("i18n_data_training.description_cannot_be_empty"))
 
-        # 数据源验证
+        # 数据源验证和转换
         datasource_id = None
         if info.datasource_name and info.datasource_name.strip():
             if info.datasource_name.strip() in datasource_name_to_id:
@@ -329,7 +333,7 @@ def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo]
             else:
                 error_messages.append(trans("i18n_data_training.datasource_not_found").format(info.datasource_name))
 
-        # 高级应用验证（只有在oid=1时才需要）
+        # 高级应用验证和转换
         advanced_application_id = None
         if oid == 1 and info.advanced_application_name and info.advanced_application_name.strip():
             if info.advanced_application_name.strip() in assistant_name_to_id:
@@ -346,101 +350,43 @@ def batch_create_training(session: SessionDep, info_list: List[DataTrainingInfo]
             if not datasource_id:
                 error_messages.append(trans("i18n_data_training.datasource_cannot_be_none"))
 
-        # 如果有错误，添加到失败列表
         if error_messages:
-            # 返回原始的info对象，不包含转换后的ID
             failed_records.append({
-                'data': info,  # 直接返回原始传入的数据
+                'data': info,
                 'errors': error_messages
             })
             continue
 
-        # 检查数据库中是否已存在重复记录
-        stmt = select(DataTraining.id).where(
-            and_(
-                DataTraining.question == info.question.strip(),
-                DataTraining.oid == oid
-            )
+        # 创建处理后的DataTrainingInfo对象
+        processed_info = DataTrainingInfo(
+            question=info.question.strip(),
+            description=info.description.strip(),
+            datasource=datasource_id,
+            datasource_name=info.datasource_name,
+            advanced_application=advanced_application_id,
+            advanced_application_name=info.advanced_application_name,
+            enabled=info.enabled if info.enabled is not None else True
         )
 
-        # 根据oid决定重复检查条件
-        if oid == 1:
-            if datasource_id is not None and advanced_application_id is not None:
-                stmt = stmt.where(
-                    or_(
-                        DataTraining.datasource == datasource_id,
-                        DataTraining.advanced_application == advanced_application_id
-                    )
-                )
-            elif datasource_id is not None:
-                stmt = stmt.where(DataTraining.datasource == datasource_id)
-            elif advanced_application_id is not None:
-                stmt = stmt.where(DataTraining.advanced_application == advanced_application_id)
-        else:
-            # oid != 1时，只检查数据源
-            if datasource_id is not None:
-                stmt = stmt.where(DataTraining.datasource == datasource_id)
+        valid_records.append(processed_info)
 
-        exists = session.query(stmt.exists()).scalar()
-
-        if exists:
-            # 返回原始的info对象
-            failed_records.append({
-                'data': info,  # 直接返回原始传入的数据
-                'errors': [trans("i18n_data_training.exists_in_db")]
-            })
-            continue
-
-        # 验证通过，添加到有效记录
-        valid_records.append({
-            'info': info,
-            'datasource_id': datasource_id,
-            'advanced_application_id': advanced_application_id
-        })
-
-    # 批量插入有效记录
+    # 使用事务处理有效记录
     if valid_records:
-        data_training_objects = []
-        for record in valid_records:
-            info = record['info']
-            data_training = DataTraining(
-                question=info.question.strip(),
-                description=info.description.strip(),
-                oid=oid,
-                datasource=record['datasource_id'],
-                advanced_application=record['advanced_application_id'] if oid == 1 else None,  # 只有oid=1才设置高级应用
-                create_time=create_time,
-                enabled=info.enabled if info.enabled is not None else True
-            )
-            data_training_objects.append(data_training)
+        for info in valid_records:
+            try:
+                # 直接复用create_training方法
+                training_id = create_training(session, info, oid, trans)
+                inserted_ids.append(training_id)
+                success_count += 1
 
-        try:
-            # 批量插入
-            session.bulk_save_objects(data_training_objects, return_defaults=True)
-            session.commit()
-
-            # 获取插入的ID
-            for obj in data_training_objects:
-                if obj.id is not None:  # 确保ID已经被赋值
-                    inserted_ids.append(obj.id)
-                    success_count += 1
-
-        except Exception as e:
-            session.rollback()
-            # 将所有的有效记录标记为失败
-            for record in valid_records:
-                # 返回原始的info对象
+            except Exception as e:
+                # 如果单条插入失败，回滚当前记录
+                session.rollback()
                 failed_records.append({
-                    'data': record['info'],  # 直接返回原始传入的数据
+                    'data': info,
                     'errors': [str(e)]
                 })
-            success_count = 0
 
-        # 批量处理embedding
-        if success_count > 0 and inserted_ids:
-            run_save_data_training_embeddings(inserted_ids)
-
-    # 返回结果，包含去重统计信息
     return {
         'success_count': success_count,
         'failed_records': failed_records,