0ef7650c09
后端: - 审核结果拆分为 4 个独立维度 (法规合规/平台规则/品牌安全/Brief匹配度) - 卖点优先级从 required:bool 改为三级 (core/recommended/reference) - AI 语义匹配卖点覆盖 + AI 整体 Brief 匹配度分析 - BriefMatchDetail 评分详情 (覆盖率+亮点+问题点) - min_selling_points 代理商可配置最少卖点数 + Alembic 迁移 - AI 语境复核过滤误报 - Brief AI 解析 + 规则 AI 解析 - AI 未配置/异常时通知品牌方 - 种子数据更新 (新格式审核结果+brief_match_detail) 前端: - 三端审核页面展示四维度评分卡片 - 卖点编辑改为三级优先级选择器 - BriefMatchDetail 展示 (覆盖率进度条+亮点+问题) - min_selling_points 配置 UI - AI 配置页未配置时静默处理 - 文件预览/下载/签名 URL 优化 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
224 lines
9.3 KiB
Python
224 lines
9.3 KiB
Python
"""
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审核相关的 Pydantic 模型（API 契约定义）
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所有测试和实现必须遵循此契约
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"""
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from typing import Optional
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from datetime import datetime
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from pydantic import BaseModel, Field, HttpUrl
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from enum import Enum
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# ==================== 枚举定义 ====================
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class Platform(str, Enum):
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"""支持的投放平台"""
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DOUYIN = "douyin"
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XIAOHONGSHU = "xiaohongshu"
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BILIBILI = "bilibili"
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KUAISHOU = "kuaishou"
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class TaskStatus(str, Enum):
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"""任务状态"""
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PENDING = "pending"
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PROCESSING = "processing"
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COMPLETED = "completed"
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FAILED = "failed"
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APPROVED = "approved"
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REJECTED = "rejected"
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class RiskLevel(str, Enum):
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"""风险等级"""
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HIGH = "high" # 法律违规（广告法极限词）
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MEDIUM = "medium" # 平台规则违规
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LOW = "low" # 品牌规范违规
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class ViolationType(str, Enum):
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"""违规类型"""
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FORBIDDEN_WORD = "forbidden_word" # 违禁词
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EFFICACY_CLAIM = "efficacy_claim" # 功效宣称
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COMPETITOR_LOGO = "competitor_logo" # 竞品露出
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DURATION_SHORT = "duration_short" # 时长不足
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MENTION_MISSING = "mention_missing" # 品牌提及不足
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BRAND_SAFETY = "brand_safety" # 品牌安全风险
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class ViolationSource(str, Enum):
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"""违规来源"""
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TEXT = "text" # 文本/脚本
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SPEECH = "speech" # 语音（ASR）
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SUBTITLE = "subtitle" # 字幕（OCR）
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VISUAL = "visual" # 画面（CV）
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class SoftRiskAction(str, Enum):
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"""软性风控动作"""
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CONFIRM = "confirm" # 需要二次确认
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NOTE = "note" # 需要填写备注
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class SoftRiskWarning(BaseModel):
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"""软性风控提示（Warn-only）"""
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code: str = Field(..., description="提示类型代码")
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message: str = Field(..., description="提示内容")
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action_required: SoftRiskAction = Field(..., description="要求动作")
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blocking: bool = Field(default=False, description="是否阻断（默认不阻断）")
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context: Optional[dict] = Field(None, description="附加上下文")
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class SoftRiskContext(BaseModel):
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"""软性风控输入上下文"""
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violation_rate: Optional[float] = Field(None, ge=0, le=1, description="违规率")
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violation_threshold: Optional[float] = Field(None, ge=0, le=1, description="违规率阈值")
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asr_confidence: Optional[float] = Field(None, ge=0, le=1, description="ASR 置信度")
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ocr_confidence: Optional[float] = Field(None, ge=0, le=1, description="OCR 置信度")
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has_history_violation: Optional[bool] = Field(None, description="是否有历史类似违规")
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# ==================== 通用模型 ====================
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class Position(BaseModel):
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"""文本位置"""
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start: int = Field(..., description="起始位置")
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end: int = Field(..., description="结束位置")
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class Violation(BaseModel):
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"""违规项（统一结构）"""
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type: ViolationType = Field(..., description="违规类型")
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content: str = Field(..., description="违规内容")
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severity: RiskLevel = Field(..., description="严重程度")
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suggestion: str = Field(..., description="修改建议")
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dimension: Optional[str] = Field(None, description="所属维度: legal/platform/brand_safety/brief_match")
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# 文本审核字段
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position: Optional[Position] = Field(None, description="文本位置（脚本审核）")
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# 视频审核字段
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timestamp: Optional[float] = Field(None, description="开始时间戳（秒）")
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timestamp_end: Optional[float] = Field(None, description="结束时间戳（秒）")
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source: Optional[ViolationSource] = Field(None, description="违规来源（视频审核）")
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# ==================== 多维度审核 ====================
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class ReviewDimension(BaseModel):
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"""审核维度评分"""
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score: int = Field(..., ge=0, le=100)
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passed: bool
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issue_count: int = 0
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class ReviewDimensions(BaseModel):
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"""四维度审核结果"""
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legal: ReviewDimension # 法规合规（违禁词、功效词、Brief黑名单词）
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platform: ReviewDimension # 平台规则
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brand_safety: ReviewDimension # 品牌安全（竞品、其他品牌词）
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brief_match: ReviewDimension # Brief 匹配度（卖点覆盖）
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class SellingPointMatch(BaseModel):
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"""卖点匹配结果"""
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content: str
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priority: str # "core" | "recommended" | "reference"
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matched: bool
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evidence: Optional[str] = None # AI 给出的匹配依据
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class BriefMatchDetail(BaseModel):
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"""Brief 匹配度评分详情"""
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# 卖点覆盖
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total_points: int = Field(0, description="需要检查的卖点总数（core + recommended）")
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matched_points: int = Field(0, description="实际匹配的卖点数")
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required_points: int = Field(0, description="代理商要求至少体现的卖点条数（min_selling_points）")
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coverage_score: int = Field(0, ge=0, le=100, description="卖点覆盖率得分")
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# AI 整体匹配分析
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overall_score: int = Field(0, ge=0, le=100, description="整体 Brief 匹配度得分")
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highlights: list[str] = Field(default_factory=list, description="内容亮点（AI 分析）")
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issues: list[str] = Field(default_factory=list, description="问题点（AI 分析）")
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explanation: str = Field("", description="评分说明（一句话总结）")
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# ==================== 脚本预审 ====================
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class ScriptReviewRequest(BaseModel):
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"""脚本预审请求"""
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content: str = Field(..., min_length=1, description="脚本内容")
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platform: Platform = Field(..., description="投放平台")
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brand_id: str = Field(..., description="品牌 ID")
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selling_points: Optional[list[dict]] = Field(None, description="卖点列表 [{content, priority}]")
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min_selling_points: Optional[int] = Field(None, ge=0, description="代理商要求至少体现的卖点条数")
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blacklist_words: Optional[list[dict]] = Field(None, description="Brief 黑名单词 [{word, reason}]")
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soft_risk_context: Optional[SoftRiskContext] = Field(None, description="软性风控上下文")
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file_url: Optional[str] = Field(None, description="脚本文件 URL（用于自动解析文本和提取图片）")
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file_name: Optional[str] = Field(None, description="原始文件名（用于判断格式）")
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class ScriptReviewResponse(BaseModel):
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"""
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脚本预审响应
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结构：
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- score: 加权总分（向后兼容）
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- summary: 整体摘要
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- dimensions: 四维度评分（法规/平台/品牌安全/Brief匹配）
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- selling_point_matches: 卖点匹配详情
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- violations: 违规项列表，每项带 dimension 标签
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- missing_points: 遗漏的核心卖点（向后兼容）
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"""
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score: int = Field(..., ge=0, le=100, description="加权总分")
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summary: str = Field(..., description="审核摘要")
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dimensions: ReviewDimensions = Field(..., description="四维度评分")
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selling_point_matches: list[SellingPointMatch] = Field(default_factory=list, description="卖点匹配详情")
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brief_match_detail: Optional[BriefMatchDetail] = Field(None, description="Brief 匹配度评分详情")
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violations: list[Violation] = Field(default_factory=list, description="违规项列表")
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missing_points: Optional[list[str]] = Field(None, description="遗漏的核心卖点")
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soft_warnings: list[SoftRiskWarning] = Field(default_factory=list, description="软性风控提示")
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ai_available: bool = Field(True, description="AI 服务是否可用（False 表示降级为纯关键词检测）")
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# ==================== 视频审核 ====================
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class VideoReviewRequest(BaseModel):
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"""视频审核请求"""
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video_url: HttpUrl = Field(..., description="视频 URL")
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platform: Platform = Field(..., description="投放平台")
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brand_id: str = Field(..., description="品牌 ID")
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creator_id: str = Field(..., description="达人 ID")
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competitors: Optional[list[str]] = Field(None, description="竞品列表")
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requirements: Optional[dict] = Field(None, description="审核要求（时长、频次等）")
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class VideoReviewSubmitResponse(BaseModel):
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"""视频审核提交响应（202 Accepted）"""
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review_id: str = Field(..., description="审核任务 ID")
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status: TaskStatus = Field(default=TaskStatus.PENDING, description="任务状态")
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class VideoReviewProgressResponse(BaseModel):
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"""视频审核进度响应"""
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review_id: str = Field(..., description="审核任务 ID")
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status: TaskStatus = Field(..., description="任务状态")
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progress: int = Field(..., ge=0, le=100, description="进度百分比")
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current_step: str = Field(..., description="当前处理步骤")
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class VideoReviewResultResponse(BaseModel):
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"""
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视频审核结果响应（200 OK）
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结构与脚本审核一致：
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- score: 合规分数
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- summary: 整体摘要
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- violations: 违规项列表，每项包含 timestamp 和 suggestion
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"""
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review_id: str = Field(..., description="审核任务 ID")
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status: TaskStatus = Field(default=TaskStatus.COMPLETED, description="任务状态")
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score: int = Field(..., ge=0, le=100, description="合规分数")
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summary: str = Field(..., description="审核摘要")
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violations: list[Violation] = Field(default_factory=list, description="违规项列表")
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soft_warnings: list[SoftRiskWarning] = Field(default_factory=list, description="软性风控提示")
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