big update

backend/app/schemas/delivery_schema.py

@@ -0,0 +1,72 @@
+# 推送设置相关的请求/响应模型
+from datetime import datetime
+from typing import List, Optional
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+# ==========================================
+# 推送时间表 (UserDeliverySchedule)
+# ==========================================
+class DeliveryScheduleCreate(BaseModel):
+    """新增推送时间请求体，时间格式 HH:MM"""
+    delivery_time: str = Field(..., pattern=r"^\d{2}:\d{2}$", description="每天推送的时间，格式 HH:MM")
+    is_active: bool = Field(default=True, description="是否启用此时段")
+
+
+class DeliveryScheduleUpdate(BaseModel):
+    """更新推送时间请求体"""
+    delivery_time: Optional[str] = Field(None, pattern=r"^\d{2}:\d{2}$")
+    is_active: Optional[bool] = None
+
+
+class DeliveryScheduleResponse(BaseModel):
+    """推送时间响应体"""
+    id: int
+    user_id: int
+    delivery_time: str
+    is_active: bool
+    created_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ==========================================
+# 推送渠道端点 (UserPushEndpoint)
+# ==========================================
+class PushEndpointCreate(BaseModel):
+    """新增推送渠道请求体"""
+    channel_type: str = Field(..., max_length=50, description="渠道类型，如 EMAIL / WECHAT_BOT / TELEGRAM")
+    channel_account: str = Field(..., max_length=255, description="具体接收账号（邮箱地址/Webhook等）")
+    is_active: bool = Field(default=True, description="是否启用")
+    priority_level: int = Field(default=1, ge=1, le=10, description="优先级，1最高")
+
+
+class PushEndpointUpdate(BaseModel):
+    """更新推送渠道请求体"""
+    channel_account: Optional[str] = Field(None, max_length=255)
+    is_active: Optional[bool] = None
+    priority_level: Optional[int] = Field(None, ge=1, le=10)
+
+
+class PushEndpointResponse(BaseModel):
+    """推送渠道响应体"""
+    id: int
+    user_id: int
+    channel_type: str
+    channel_account: str
+    is_active: bool
+    priority_level: int
+    created_at: datetime
+    updated_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+# ==========================================
+# 推送设置聚合响应（一次性返回全部推送配置）
+# ==========================================
+class UserDeliveryConfigResponse(BaseModel):
+    """用户的完整推送配置（时间表 + 渠道列表）"""
+    schedules: List[DeliveryScheduleResponse] = Field(default_factory=list)
+    endpoints: List[PushEndpointResponse] = Field(default_factory=list)

backend/app/schemas/event_schema.py

@@ -1,23 +1,30 @@
 # app/schemas/event_schema.py
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from typing import List, Optional
 from datetime import datetime
 
 
 class PlatformTrendResponse(BaseModel):
     source_id: int
-    platform_name: str  # 平台名称，如 "微博热搜"
-    headline: str  # 平台对应的具体热搜标题
-    url: Optional[str]  # 跳转链接
-    current_ranking: Optional[int]  # 当前排名
-    ranking_history: List[int]  # 排名历史轨迹，如 [50, 45, 20, 5, 1]，供 ApexCharts 渲染
+    platform_name: str
+    headline: str
+    url: Optional[str]
+    current_ranking: Optional[int]
+    ranking_history: List[int]
 
 
 class UnifiedEventResponse(BaseModel):
     event_id: int
-    unified_title: str  # AI 生成的统一大标题
-    summary: Optional[str]  # AI 生成的摘要
-    hot_score: int  # 总热度值
-    created_at: datetime  # 事件发现时间
-    platforms: List[PlatformTrendResponse]  # 挂载的各个平台子热搜
-    # tags: List[str] = []      # 如果后续打通了 ExtractedTopic，可以在这里返回标签
+    unified_title: str
+    summary: Optional[str]
+    hot_score: int
+    created_at: datetime
+    platforms: List[PlatformTrendResponse]
+    tags: List[str] = Field(default_factory=list)
+
+
+class PaginatedUnifiedEventResponse(BaseModel):
+    """分页包装：避免一次性返回全量数据"""
+    total: int
+    has_more: bool
+    data: List[UnifiedEventResponse]

backend/app/schemas/preference_schema.py

@@ -0,0 +1,46 @@
+from datetime import datetime
+from typing import List, Optional
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class UserTopicPreferenceCreate(BaseModel):
+    """新增用户兴趣词请求体。"""
+    interested_keyword: str = Field(..., min_length=1, max_length=100, description="用户感兴趣的关键词")
+
+
+class UserTopicPreferenceResponse(BaseModel):
+    """用户兴趣词响应体。"""
+    id: int
+    user_id: int
+    interested_keyword: str
+    created_at: datetime
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class EventMatchSemanticHit(BaseModel):
+    """语义命中的明细。"""
+    preference_keyword: str
+    topic_keyword: str
+    similarity: float
+
+
+class MatchedEventResponse(BaseModel):
+    """推荐事件响应体。"""
+    event_id: int
+    unified_title: str
+    summary: Optional[str]
+    hot_score: int
+    created_at: datetime
+    tags: List[str] = Field(default_factory=list)
+    match_score: float
+    exact_hits: List[str] = Field(default_factory=list)
+    semantic_hits: List[EventMatchSemanticHit] = Field(default_factory=list)
+
+
+class UserPreferenceRecommendationResponse(BaseModel):
+    """用户兴趣推荐结果。"""
+    user_id: int
+    total: int
+    data: List[MatchedEventResponse] = Field(default_factory=list)