feat(vertex): 集成 Vertex AI Express API 支持

本次更新引入了对 Google Vertex AI Express API 的支持，允许用户配置和使用 Vertex AI 模型。

主要变更包括：

后端：
- 新增 `VERTEX_API_KEYS` 和 `VERTEX_EXPRESS_BASE_URL` 至系统配置 ([`.env.example`](.env.example:13), [`app/config/config.py:62`](app/config/config.py:62), [`app/database/models.py`](app/database/models.py), [`app/database/services.py`](app/database/services.py))。
- 实现 `VertexExpressChatService` ([`app/service/chat/vertex_express_chat_service.py`](app/service/chat/vertex_express_chat_service.py)) 用于处理与 Vertex AI Express API 的交互。
- 添加 `vertex_express_routes` ([`app/router/vertex_express_routes.py`](app/router/vertex_express_routes.py)) 来暴露 Vertex AI 相关的 API 端点，并集成到主应用 ([`app/core/application.py:36`](app/core/application.py:36), [`app/router/routes.py:15`](app/router/routes.py:15))。
- 更新密钥管理器 ([`app/service/key/key_manager.py`](app/service/key/key_manager.py)) 以支持 Vertex API 密钥的获取、检查和轮换。

前端 (配置编辑器):
- 在配置页面 ([`app/templates/config_editor.html:463`](app/templates/config_editor.html:463)) 添加了 Vertex API 密钥列表和 Vertex Express API 基础 URL 的表单字段。
- 实现了批量添加和删除 Vertex API 密钥的功能，包括相应的模态框和操作逻辑 ([`app/static/js/config_editor.js:550`](app/static/js/config_editor.js:550), [`app/static/js/config_editor.js:1097`](app/static/js/config_editor.js:1097), [`app/templates/config_editor.html:1657`](app/templates/config_editor.html:1657))。
- 确保新的配置项在初始化 ([`app/static/js/config_editor.js:598`](app/static/js/config_editor.js:598)) 和表单填充 ([`app/static/js/config_editor.js:671`](app/static/js/config_editor.js:671)) 时得到正确处理。
- 更新了数组项添加逻辑以识别 `VERTEX_API_KEYS` 为敏感字段 ([`app/static/js/config_editor.js:1235`](app/static/js/config_editor.js:1235))。

此功能扩展了应用支持的 AI 服务范围，为用户提供了更多模型选择。

app/database/connection.py

@@ -45,11 +45,8 @@ Base = declarative_base(metadata=metadata)
 if settings.DATABASE_TYPE == "sqlite":
     database = Database(DATABASE_URL)
 else:
-    database = Database(DATABASE_URL, min_size=5, max_size=20, pool_recycle=1800) # Reduced recycle time to 30 mins
+    database = Database(DATABASE_URL, min_size=5, max_size=20, pool_recycle=1800)
 
-# 移除了 SessionLocal 和 get_db 函数
-
-# --- Async connection functions for lifespan/async routes ---
 async def connect_to_db():
     """
     连接到数据库

app/database/models.py

@@ -2,7 +2,7 @@
 数据库模型模块
 """
 import datetime
-from sqlalchemy import Column, Integer, String, Text, DateTime, JSON, Boolean # 添加 Boolean
+from sqlalchemy import Column, Integer, String, Text, DateTime, JSON, Boolean
 
 from app.database.connection import Base
 
@@ -53,7 +53,7 @@ class RequestLog(Base):
     id = Column(Integer, primary_key=True, autoincrement=True)
     request_time = Column(DateTime, default=datetime.datetime.now, comment="请求时间")
     model_name = Column(String(100), nullable=True, comment="模型名称")
-    api_key = Column(String(100), nullable=True, comment="使用的API密钥") # 考虑安全性，后续可优化
+    api_key = Column(String(100), nullable=True, comment="使用的API密钥")
     is_success = Column(Boolean, nullable=False, comment="请求是否成功")
     status_code = Column(Integer, nullable=True, comment="API响应状态码")
     latency_ms = Column(Integer, nullable=True, comment="请求耗时(毫秒)")

app/database/services.py

@@ -189,7 +189,6 @@ async def get_error_logs(
             ErrorLog.request_time
         )
         
-        # Apply filters
         if key_search:
             query = query.where(ErrorLog.gemini_key.ilike(f"%{key_search}%"))
         if error_search:
@@ -219,14 +218,14 @@ async def get_error_logs(
         result = await database.fetch_all(query)
         return [dict(row) for row in result]
     except Exception as e:
-        logger.exception(f"Failed to get error logs with filters: {str(e)}") # Use exception for stack trace
+        logger.exception(f"Failed to get error logs with filters: {str(e)}")
         raise
 
 
 async def get_error_logs_count(
     key_search: Optional[str] = None,
     error_search: Optional[str] = None,
-    error_code_search: Optional[str] = None, # Added error code search
+    error_code_search: Optional[str] = None,
     start_date: Optional[datetime] = None,
     end_date: Optional[datetime] = None
 ) -> int:
@@ -294,7 +293,7 @@ async def get_error_log_details(log_id: int) -> Optional[Dict[str, Any]]:
                 try:
                     log_dict['request_msg'] = json.dumps(log_dict['request_msg'], ensure_ascii=False, indent=2)
                 except TypeError:
-                    log_dict['request_msg'] = str(log_dict['request_msg']) # Fallback to string
+                    log_dict['request_msg'] = str(log_dict['request_msg'])
             return log_dict
         else:
             return None
@@ -372,7 +371,6 @@ async def delete_all_error_logs() -> int:
     try:
         # 1. 获取删除前的总数
         count_query = select(func.count()).select_from(ErrorLog)
-        # fetch_val() is suitable here as we expect a single scalar value
         total_to_delete = await database.fetch_val(count_query)
  
         if total_to_delete == 0:
@@ -380,7 +378,6 @@ async def delete_all_error_logs() -> int:
             return 0
  
         # 2. 执行删除操作
-        # This creates a query like "DELETE FROM error_log"
         delete_query = delete(ErrorLog)
         await database.execute(delete_query)
         
@@ -388,7 +385,6 @@ async def delete_all_error_logs() -> int:
         return total_to_delete
     except Exception as e:
         logger.error(f"Failed to delete all error logs: {str(e)}", exc_info=True)
-        # Re-raise the exception so it can be caught by the service layer or route handler
         raise