Files
xiaoxia-saas/apps/api/app/schemas/asset.py
T
CI Bot 7cb36a5c3d
CI/CD Pipeline / Check if frontend-only change (pull_request) Successful in 3m9s
PR Automation / Auto Merge on CI Green + Approved (pull_request) Successful in 3m39s
CI/CD Pipeline / Validate - Code Quality (pull_request) Failing after 3m43s
CI/CD Pipeline / Validate - Migration (alembic) (pull_request) Successful in 4m0s
Preview Deploy / Deploy Preview Environment (pull_request) Successful in 4m3s
CI/CD Pipeline / Validate - Type Check (mypy) (pull_request) Successful in 4m7s
CI/CD Pipeline / PR Build API Image (pull_request) Successful in 1m14s
CI/CD Pipeline / PR Build Worker Image (pull_request) Successful in 1m24s
AI Code Review / AI Code Review (pull_request) Failing after 4m42s
PR Automation / Auto Approve on CI Green (pull_request) Successful in 5m49s
CI/CD Pipeline / Integration Tests (pull_request) Successful in 2m58s
CI/CD Pipeline / Unit Tests (pull_request) Successful in 9m19s
CI/CD Pipeline / CI Gate (pull_request) Failing after 24s
CI/CD Pipeline / Production Browser E2E (pull_request) Failing after 420h56m34s
CI/CD Pipeline / Build Production Worker Image (pull_request) Failing after 420h56m38s
CI/CD Pipeline / Build Production Web Image (pull_request) Failing after 420h56m38s
CI/CD Pipeline / PR Build Web Image (pull_request) Failing after 421h5m58s
CI/CD Pipeline / Canary Release to Production (pull_request) Failing after 420h56m36s
CI/CD Pipeline / Frontend Unit Tests (pull_request) Failing after 421h5m58s
CI/CD Pipeline / ACR Image Cleanup (pull_request) Failing after 421h8m58s
CI/CD Pipeline / Frontend Lint (pull_request) Failing after 421h5m59s
CI/CD Pipeline / Staging E2E Tests (pull_request) Failing after 421h8m59s
CI/CD Pipeline / Retag skipped Staging Worker Image (pull_request) Failing after 421h9m2s
CI/CD Pipeline / Retag skipped Staging Web Image (pull_request) Failing after 421h9m2s
CI/CD Pipeline / Build Staging Worker Image (pull_request) Failing after 421h9m6s
CI/CD Pipeline / Build Staging Web Image (pull_request) Failing after 421h9m7s
CI/CD Pipeline / Build Staging API Image (pull_request) Failing after 421h9m8s
CI/CD Pipeline / Check push changed paths (pull_request) Failing after 421h9m10s
CI/CD Pipeline / Deploy Production (pull_request) Failing after 421h30m41s
CI/CD Pipeline / Build Production API Image (pull_request) Failing after 421h30m43s
CI/CD Pipeline / Staging API Integration Tests (pull_request) Failing after 421h43m3s
CI/CD Pipeline / Deploy Staging (Watchtower auto-deploy) (pull_request) Failing after 421h43m6s
CI/CD Pipeline / Retag skipped Staging API Image (pull_request) Failing after 421h43m8s
style: auto-format with black + isort + prettier [skip ci-format-check]
2026-08-29 18:16:26 +00:00

145 lines
5.5 KiB
Python
Executable File
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
from pydantic import BaseModel, Field
class CreateAssetRequest(BaseModel):
project_id: str | None = Field(default=None, description="可选,不传时从 library.project_id 自动推导")
library_id: str = Field(..., min_length=1)
name: str = Field(..., min_length=1, max_length=100)
storage_key: str = Field(..., min_length=1, max_length=255)
mime_type: str = Field(..., min_length=1, max_length=100)
metadata: dict[str, object] = Field(default_factory=dict)
file_size: int = Field(default=0, ge=0)
thumbnail_url: str | None = None
duration: float | None = Field(default=None, ge=0)
width: int | None = Field(default=None, ge=0)
height: int | None = Field(default=None, ge=0)
fps: float | None = Field(default=None, ge=0)
codec: str | None = None
status: str = Field(default="uploading")
classification_status: str = Field(default="pending")
quality_score: float | None = Field(default=None, ge=0, le=100)
uploaded_by_user_id: str = Field(default="", max_length=100)
class UpdateAssetReviewRequest(BaseModel):
review_status: str = Field(..., pattern="^(pending_review|approved|rejected)$")
class UpdateAssetRequest(BaseModel):
name: str | None = Field(default=None, min_length=1, max_length=100)
metadata: dict[str, object] | None = None
tags: list[str] | None = None
class AssetResponse(BaseModel):
id: str
project_id: str
library_id: str
name: str
storage_key: str
mime_type: str
metadata: dict[str, object]
file_size: int
file_url: str | None = None
thumbnail_url: str | None = None
duration: float | None = None
width: int | None = None
height: int | None = None
fps: float | None = None
codec: str | None = None
status: str
classification_status: str
quality_score: float | None = None
created_at: str
uploaded_by_user_id: str
tag_ids: list[str] = Field(default_factory=list)
# 片段级余量信息(仅视频素材返回,非视频/无时长记录为 None,前端按可用处理)
used_duration: float | None = Field(default=None, description="已使用片段时长(秒,历史区间合并去重后)")
available_duration: float | None = Field(default=None, description="剩余可用时长(秒)= 素材总时长 - 已用时长")
used_ratio: float | None = Field(default=None, description="已用时长占比(0~1)")
usable: bool = Field(
default=True,
description="是否仍可用于新片段:零重复可切区间耗尽且所有历史区间复用次数" "(use_count)均达上限时为 false",
)
MAX_BATCH_SIZE = 200
class BatchGetRequest(BaseModel):
"""批量获取素材详情请求。"""
ids: list[str] = Field(..., min_length=1, max_length=MAX_BATCH_SIZE, description="素材 ID 列表")
class BatchDeleteRequest(BaseModel):
"""批量删除请求(软删除)。"""
asset_ids: list[str] = Field(..., min_length=1, max_length=MAX_BATCH_SIZE, description="要删除的素材 ID 列表")
class BatchOperationResponse(BaseModel):
"""批量操作通用响应。"""
success_count: int = Field(..., ge=0, description="成功数量")
failed_ids: list[str] = Field(default_factory=list, description="失败的 ID 列表")
failed_details: dict[str, str] = Field(default_factory=dict, description="失败详情 {asset_id: reason}")
class BatchTagRequest(BaseModel):
"""批量打标签请求。"""
asset_ids: list[str] = Field(..., min_length=1, max_length=MAX_BATCH_SIZE, description="素材 ID 列表")
tag_ids: list[str] = Field(..., min_length=1, max_length=50, description="标签 ID 列表")
mode: str = Field(default="add", pattern="^(add|replace)$", description="add=添加合并,replace=全量替换")
class BatchClassifyRequest(BaseModel):
"""批量修改分类请求。"""
asset_ids: list[str] = Field(..., min_length=1, max_length=MAX_BATCH_SIZE, description="素材 ID 列表")
category: str = Field(..., min_length=1, max_length=50, description="内容分类,如 person/scenic/product")
class BatchMarkRequest(BaseModel):
"""批量设置智能视图标记请求。"""
asset_ids: list[str] = Field(..., min_length=1, max_length=MAX_BATCH_SIZE, description="素材 ID 列表")
smart_view: str = Field(
..., pattern="^(recommended|caution|high_risk)$", description="智能视图标记:recommended/caution/high_risk"
)
class ListAssetsResponse(BaseModel):
items: list[AssetResponse]
total: int = Field(default=0, ge=0)
skip: int = Field(default=0, ge=0)
limit: int = Field(default=100, ge=1)
class SmartMatchRequest(BaseModel):
"""智能选素材请求。"""
library_id: str = Field(..., min_length=1, description="素材库 ID")
limit: int | None = Field(default=None, ge=1, le=200, description="最大返回数量,不传则返回全部匹配素材")
kind: str | None = Field(
default=None,
pattern="^(video|image|audio)$",
description="按文件类型过滤,不传则返回所有类型",
)
class SmartMatchItem(BaseModel):
"""智能选素材结果条目。"""
asset: AssetResponse
score: float = Field(..., ge=0, le=100, description="综合得分 0-100")
breakdown: dict[str, float] = Field(default_factory=dict, description="各维度得分明细")
class SmartMatchResponse(BaseModel):
"""智能选素材响应。"""
items: list[SmartMatchItem]
total_candidates: int = Field(default=0, ge=0, description="参与评分的候选素材总数")