feat: add export Sample SQL

backend/apps/data_training/api/data_training.py

@@ -1,9 +1,15 @@
+import asyncio
+import io
 from typing import Optional
 
+import pandas as pd
 from fastapi import APIRouter, Query
+from fastapi.responses import StreamingResponse
 
+from apps.chat.models.chat_model import AxisObj
+from apps.chat.task.llm import LLMService
 from apps.data_training.curd.data_training import page_data_training, create_training, update_training, delete_training, \
-    enable_training
+    enable_training, get_all_data_training
 from apps.data_training.models.data_training_model import DataTrainingInfo
 from common.core.deps import SessionDep, CurrentUser, Trans
 
@@ -43,3 +49,44 @@ async def delete(session: SessionDep, id_list: list[int]):
 @router.get("/{id}/enable/{enabled}")
 async def enable(session: SessionDep, id: int, enabled: bool, trans: Trans):
     enable_training(session, id, enabled, trans)
+
+
+@router.get("/export")
+async def export_excel(session: SessionDep, trans: Trans, current_user: CurrentUser,
+                       word: Optional[str] = Query(None, description="搜索术语(可选)")):
+    def inner():
+        _list = get_all_data_training(session, word, oid=current_user.oid)
+
+        data_list = []
+        for obj in _list:
+            _data = {
+                "question": obj.question,
+                "description": obj.description,
+                "datasource_name": obj.datasource_name,
+                "advanced_application_name": obj.advanced_application_name,
+            }
+            data_list.append(_data)
+
+        fields = []
+        fields.append(AxisObj(name=trans('i18n_data_training.data_training'), value='question'))
+        fields.append(AxisObj(name=trans('i18n_data_training.problem_description'), value='description'))
+        fields.append(AxisObj(name=trans('i18n_data_training.effective_data_sources'), value='datasource_name'))
+        if current_user.oid == 1:
+            fields.append(
+                AxisObj(name=trans('i18n_data_training.advanced_application'), value='advanced_application_name'))
+
+        md_data, _fields_list = LLMService.convert_object_array_for_pandas(fields, data_list)
+
+        df = pd.DataFrame(md_data, columns=_fields_list)
+
+        buffer = io.BytesIO()
+
+        with pd.ExcelWriter(buffer, engine='xlsxwriter',
+                            engine_kwargs={'options': {'strings_to_numbers': False}}) as writer:
+            df.to_excel(writer, sheet_name='Sheet1', index=False)
+
+        buffer.seek(0)
+        return io.BytesIO(buffer.getvalue())
+
+    result = await asyncio.to_thread(inner)
+    return StreamingResponse(result, media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")

backend/apps/data_training/curd/data_training.py

@@ -18,42 +18,61 @@
 from common.utils.embedding_threads import run_save_data_training_embeddings
 
 
-def page_data_training(session: SessionDep, current_page: int = 1, page_size: int = 10, name: Optional[str] = None,
-                       oid: Optional[int] = 1):
-    _list: List[DataTrainingInfoResult] = []
-
-    current_page = max(1, current_page)
-    page_size = max(10, page_size)
-
-    total_count = 0
-    total_pages = 0
-
+def get_data_training_base_query(oid: int, name: Optional[str] = None):
+    """
+    获取数据训练查询的基础查询结构
+    """
     if name and name.strip() != "":
         keyword_pattern = f"%{name.strip()}%"
         parent_ids_subquery = (
             select(DataTraining.id)
-            .where(and_(DataTraining.question.ilike(keyword_pattern), DataTraining.oid == oid))  # LIKE查询条件
+            .where(and_(DataTraining.question.ilike(keyword_pattern), DataTraining.oid == oid))
         )
     else:
         parent_ids_subquery = (
             select(DataTraining.id).where(and_(DataTraining.oid == oid))
         )
 
+    return parent_ids_subquery
+
+
+def build_data_training_query(session: SessionDep, oid: int, name: Optional[str] = None,
+                              paginate: bool = True, current_page: int = 1, page_size: int = 10):
+    """
+    构建数据训练查询的通用方法
+    """
+    parent_ids_subquery = get_data_training_base_query(oid, name)
+
+    # 计算总数
     count_stmt = select(func.count()).select_from(parent_ids_subquery.subquery())
     total_count = session.execute(count_stmt).scalar()
-    total_pages = (total_count + page_size - 1) // page_size
 
-    if current_page > total_pages:
+    if paginate:
+        # 分页处理
+        page_size = max(10, page_size)
+        total_pages = (total_count + page_size - 1) // page_size
+        current_page = max(1, min(current_page, total_pages)) if total_pages > 0 else 1
+
+        paginated_parent_ids = (
+            parent_ids_subquery
+            .order_by(DataTraining.create_time.desc())
+            .offset((current_page - 1) * page_size)
+            .limit(page_size)
+            .subquery()
+        )
+    else:
+        # 不分页，获取所有数据
+        total_pages = 1
         current_page = 1
+        page_size = total_count if total_count > 0 else 1
 
-    paginated_parent_ids = (
-        parent_ids_subquery
-        .order_by(DataTraining.create_time.desc())
-        .offset((current_page - 1) * page_size)
-        .limit(page_size)
-        .subquery()
-    )
+        paginated_parent_ids = (
+            parent_ids_subquery
+            .order_by(DataTraining.create_time.desc())
+            .subquery()
+        )
 
+    # 构建主查询
     stmt = (
         select(
             DataTraining.id,
@@ -74,6 +93,14 @@ def page_data_training(session: SessionDep, current_page: int = 1, page_size: in
         .order_by(DataTraining.create_time.desc())
     )
 
+    return stmt, total_count, total_pages, current_page, page_size
+
+
+def execute_data_training_query(session: SessionDep, stmt) -> List[DataTrainingInfoResult]:
+    """
+    执行查询并返回数据训练信息列表
+    """
+    _list = []
     result = session.execute(stmt)
 
     for row in result:
@@ -90,9 +117,34 @@ def page_data_training(session: SessionDep, current_page: int = 1, page_size: in
             advanced_application_name=row.advanced_application_name,
         ))
 
+    return _list
+
+
+def page_data_training(session: SessionDep, current_page: int = 1, page_size: int = 10,
+                       name: Optional[str] = None, oid: Optional[int] = 1):
+    """
+    分页查询数据训练（原方法保持不变）
+    """
+    stmt, total_count, total_pages, current_page, page_size = build_data_training_query(
+        session, oid, name, True, current_page, page_size
+    )
+    _list = execute_data_training_query(session, stmt)
+
     return current_page, page_size, total_count, total_pages, _list
 
 
+def get_all_data_training(session: SessionDep, name: Optional[str] = None, oid: Optional[int] = 1):
+    """
+    获取所有数据训练（不分页）
+    """
+    stmt, total_count, total_pages, current_page, page_size = build_data_training_query(
+        session, oid, name, False
+    )
+    _list = execute_data_training_query(session, stmt)
+
+    return _list
+
+
 def create_training(session: SessionDep, info: DataTrainingInfo, oid: int, trans: Trans):
     create_time = datetime.datetime.now()
     if info.datasource is None and info.advanced_application is None:

backend/locales/en.json

@@ -53,7 +53,12 @@
     "datasource_cannot_be_none": "Datasource cannot be empty",
     "datasource_assistant_cannot_be_none": "Datasource or advanced application cannot both be empty",
     "data_training_not_exists": "This example does not exist",
-    "exists_in_db": "This question already exists"
+    "exists_in_db": "This question already exists",
+    "data_training": "SQL Example Library",
+    "problem_description": "Problem Description",
+    "sample_sql": "Sample SQL",
+    "effective_data_sources": "Effective Data Sources",
+    "advanced_application": "Advanced Application"
   },
   "i18n_custom_prompt": {
     "exists_in_db": "Template name already exists",

backend/locales/ko-KR.json

@@ -53,7 +53,12 @@
     "datasource_cannot_be_none": "데이터 소스는 비울 수 없습니다",
     "datasource_assistant_cannot_be_none": "데이터 소스와 고급 애플리케이션을 모두 비울 수 없습니다",
     "data_training_not_exists": "이 예시가 존재하지 않습니다",
-    "exists_in_db": "이 질문이 이미 존재합니다"
+    "exists_in_db": "이 질문이 이미 존재합니다",
+    "data_training": "SQL 예시 라이브러리",
+    "problem_description": "문제 설명",
+    "sample_sql": "예시 SQL",
+    "effective_data_sources": "유효 데이터 소스",
+    "advanced_application": "고급 애플리케이션"
   },
   "i18n_custom_prompt": {
     "exists_in_db": "템플릿 이름이 이미 존재합니다",

backend/locales/zh-CN.json

@@ -53,7 +53,12 @@
     "datasource_cannot_be_none": "数据源不能为空",
     "datasource_assistant_cannot_be_none": "数据源或高级应用不能都为空",
     "data_training_not_exists": "该示例不存在",
-    "exists_in_db": "该问题已存在"
+    "exists_in_db": "该问题已存在",
+    "data_training": "SQL 示例库",
+    "problem_description": "问题描述",
+    "sample_sql": "示例 SQL",
+    "effective_data_sources": "生效数据源",
+    "advanced_application": "高级应用"
   },
   "i18n_custom_prompt": {
     "exists_in_db": "模版名称已存在",

frontend/src/api/training.ts

@@ -9,4 +9,10 @@ export const trainingApi = {
   deleteEmbedded: (params: any) => request.delete('/system/data-training', { data: params }),
   getOne: (id: any) => request.get(`/system/data-training/${id}`),
   enable: (id: any, enabled: any) => request.get(`/system/data-training/${id}/enable/${enabled}`),
+  export2Excel: (params: any) =>
+    request.get(`/system/data-training/export`, {
+      params,
+      responseType: 'blob',
+      requestOptions: { customError: true },
+    }),
 }

frontend/src/i18n/en.json

@@ -40,6 +40,7 @@
     "training_data_items": "Do you want to delete the {msg} selected SQL Sample items?",
     "sql_statement": "SQL Statement",
     "edit_training_data": "Edit SQL Sample",
+    "all_236_terms": "Export all {msg} sample SQL records?",
     "sales_this_year": "Do you want to delete the SQL Sample: {msg}?"
   },
   "professional": {

frontend/src/i18n/ko-KR.json

@@ -40,6 +40,7 @@
     "training_data_items": "선택된 {msg}개의 예제 SQL을 삭제하시겠습니까?",
     "sql_statement": "SQL 문",
     "edit_training_data": "예제 SQL 편집",
+    "all_236_terms": "모든 {msg}개의 예시 SQL 기록을 내보내시겠습니까?",
     "sales_this_year": "예제 SQL을 삭제하시겠습니까: {msg}?"
   },
   "professional": {

frontend/src/i18n/zh-CN.json

@@ -40,6 +40,7 @@
     "training_data_items": "是否删除选中的 {msg} 条示例 SQL？",
     "sql_statement": "SQL 语句",
     "edit_training_data": "编辑示例 SQL",
+    "all_236_terms": "是否导出全部 {msg} 条示例 SQL？",
     "sales_this_year": "是否删除示例 SQL：{msg}？"
   },
   "professional": {