feat: 平台规则从硬编码改为品牌方上传文档 + AI 解析
- 新增 PlatformRule 模型 (draft/active/inactive 状态流转) - 新增文档解析服务 (PDF/Word/Excel → 纯文本) - 新增 4 个 API: 解析/确认/查询/删除平台规则 - 脚本审核优先从 DB 读取 active 规则，硬编码兜底 - 视频审核合并平台规则违禁词到检测列表 - Alembic 迁移 006: platform_rules 表 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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backend/alembic/versions/006_add_platform_rules.py
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backend/alembic/versions/006_add_platform_rules.py
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@ -0,0 +1,38 @@
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"""添加平台规则表
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Revision ID: 006
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Revises: 005
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Create Date: 2026-02-10
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"""
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from typing import Sequence, Union
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from alembic import op
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import sqlalchemy as sa
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from sqlalchemy.dialects import postgresql
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# revision identifiers, used by Alembic.
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revision: str = '006'
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down_revision: Union[str, None] = '005'
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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op.create_table(
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'platform_rules',
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sa.Column('id', sa.String(64), primary_key=True),
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sa.Column('tenant_id', sa.String(64), sa.ForeignKey('tenants.id', ondelete='CASCADE'), nullable=False, index=True),
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sa.Column('brand_id', sa.String(64), nullable=False, index=True),
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sa.Column('platform', sa.String(50), nullable=False, index=True),
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sa.Column('document_url', sa.String(2048), nullable=False),
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sa.Column('document_name', sa.String(512), nullable=False),
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sa.Column('parsed_rules', sa.JSON().with_variant(postgresql.JSONB, 'postgresql'), nullable=True),
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sa.Column('status', sa.String(20), nullable=False, default='draft', index=True),
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sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.func.now(), nullable=False),
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sa.Column('updated_at', sa.DateTime(timezone=True), server_default=sa.func.now(), nullable=False),
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)
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def downgrade() -> None:
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op.drop_table('platform_rules')
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@ -2,8 +2,10 @@
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规则管理 API
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违禁词库、白名单、竞品库、平台规则
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"""
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import json
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import logging
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import uuid
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from fastapi import APIRouter, Depends, Header, HTTPException, status
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from fastapi import APIRouter, Depends, Header, HTTPException, Query, status
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from pydantic import BaseModel, Field
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from typing import Optional
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from sqlalchemy import select, and_
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@ -11,7 +13,19 @@ from sqlalchemy.ext.asyncio import AsyncSession
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from app.database import get_db
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from app.models.tenant import Tenant
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from app.models.rule import ForbiddenWord, WhitelistItem, Competitor
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from app.models.rule import ForbiddenWord, WhitelistItem, Competitor, PlatformRule, RuleStatus
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from app.schemas.rules import (
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PlatformRuleParseRequest,
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PlatformRuleParseResponse,
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PlatformRuleConfirmRequest,
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PlatformRuleResponse as PlatformRuleDBResponse,
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PlatformRuleListResponse as PlatformRuleDBListResponse,
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ParsedRulesData,
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)
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from app.services.document_parser import DocumentParser
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from app.services.ai_service import AIServiceFactory
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/rules", tags=["rules"])
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@ -468,6 +482,289 @@ async def validate_rules(request: RuleValidateRequest) -> RuleValidateResponse:
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return RuleValidateResponse(conflicts=conflicts)
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# ==================== 品牌方平台规则（文档上传 + AI 解析） ====================
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def _format_platform_rule(rule: PlatformRule) -> PlatformRuleDBResponse:
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"""将 ORM 对象转为响应 Schema"""
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return PlatformRuleDBResponse(
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id=rule.id,
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platform=rule.platform,
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brand_id=rule.brand_id,
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document_url=rule.document_url,
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document_name=rule.document_name,
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parsed_rules=ParsedRulesData(**(rule.parsed_rules or {})),
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status=rule.status,
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created_at=rule.created_at.isoformat() if rule.created_at else "",
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updated_at=rule.updated_at.isoformat() if rule.updated_at else "",
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)
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@router.post(
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"/platform-rules/parse",
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response_model=PlatformRuleParseResponse,
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status_code=status.HTTP_201_CREATED,
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)
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async def parse_platform_rule_document(
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request: PlatformRuleParseRequest,
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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) -> PlatformRuleParseResponse:
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"""
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上传文档并通过 AI 解析平台规则
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流程:
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1. 下载文档
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2. 提取纯文本
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3. AI 解析出结构化规则
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4. 存入 DB (status=draft)
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5. 返回解析结果供品牌方确认
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"""
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await _ensure_tenant_exists(x_tenant_id, db)
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# 1. 下载并解析文档
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try:
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document_text = await DocumentParser.download_and_parse(
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request.document_url, request.document_name,
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)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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logger.error(f"文档解析失败: {e}")
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raise HTTPException(status_code=400, detail=f"文档下载或解析失败: {e}")
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if not document_text.strip():
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raise HTTPException(status_code=400, detail="文档内容为空，无法解析")
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# 2. AI 解析
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parsed_rules = await _ai_parse_platform_rules(x_tenant_id, request.platform, document_text, db)
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# 3. 存入 DB (draft)
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rule_id = f"pr-{uuid.uuid4().hex[:8]}"
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rule = PlatformRule(
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id=rule_id,
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tenant_id=x_tenant_id,
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brand_id=request.brand_id,
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platform=request.platform,
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document_url=request.document_url,
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document_name=request.document_name,
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parsed_rules=parsed_rules,
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status=RuleStatus.DRAFT.value,
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)
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db.add(rule)
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await db.flush()
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return PlatformRuleParseResponse(
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id=rule.id,
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platform=rule.platform,
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brand_id=rule.brand_id,
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document_url=rule.document_url,
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document_name=rule.document_name,
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parsed_rules=ParsedRulesData(**parsed_rules),
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status=rule.status,
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)
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@router.put(
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"/platform-rules/{rule_id}/confirm",
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response_model=PlatformRuleDBResponse,
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)
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async def confirm_platform_rule(
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rule_id: str,
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request: PlatformRuleConfirmRequest,
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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) -> PlatformRuleDBResponse:
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"""
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确认/编辑平台规则解析结果
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将 draft 状态的规则设为 active，同时将同 (tenant_id, brand_id, platform) 下
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已有的 active 规则设为 inactive。
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"""
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result = await db.execute(
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select(PlatformRule).where(
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and_(
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PlatformRule.id == rule_id,
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PlatformRule.tenant_id == x_tenant_id,
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)
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)
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)
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rule = result.scalar_one_or_none()
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if not rule:
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raise HTTPException(status_code=404, detail=f"规则不存在: {rule_id}")
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# 将同 (tenant_id, brand_id, platform) 下已有的 active 规则设为 inactive
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existing_active = await db.execute(
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select(PlatformRule).where(
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and_(
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PlatformRule.tenant_id == x_tenant_id,
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PlatformRule.brand_id == rule.brand_id,
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PlatformRule.platform == rule.platform,
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PlatformRule.status == RuleStatus.ACTIVE.value,
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PlatformRule.id != rule_id,
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)
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)
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)
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for old_rule in existing_active.scalars().all():
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old_rule.status = RuleStatus.INACTIVE.value
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# 更新当前规则
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rule.parsed_rules = request.parsed_rules.model_dump()
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rule.status = RuleStatus.ACTIVE.value
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await db.flush()
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return _format_platform_rule(rule)
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@router.get(
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"/platform-rules",
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response_model=PlatformRuleDBListResponse,
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)
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async def list_brand_platform_rules(
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brand_id: Optional[str] = Query(None),
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platform: Optional[str] = Query(None),
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rule_status: Optional[str] = Query(None, alias="status"),
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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) -> PlatformRuleDBListResponse:
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"""查询品牌方的平台规则列表"""
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query = select(PlatformRule).where(PlatformRule.tenant_id == x_tenant_id)
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if brand_id:
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query = query.where(PlatformRule.brand_id == brand_id)
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if platform:
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query = query.where(PlatformRule.platform == platform)
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if rule_status:
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query = query.where(PlatformRule.status == rule_status)
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result = await db.execute(query.order_by(PlatformRule.created_at.desc()))
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rules = result.scalars().all()
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return PlatformRuleDBListResponse(
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items=[_format_platform_rule(r) for r in rules],
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total=len(rules),
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)
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@router.delete(
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"/platform-rules/{rule_id}",
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status_code=status.HTTP_204_NO_CONTENT,
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)
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async def delete_platform_rule(
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rule_id: str,
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x_tenant_id: str = Header(..., alias="X-Tenant-ID"),
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db: AsyncSession = Depends(get_db),
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):
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"""删除平台规则"""
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result = await db.execute(
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select(PlatformRule).where(
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and_(
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PlatformRule.id == rule_id,
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PlatformRule.tenant_id == x_tenant_id,
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)
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)
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)
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rule = result.scalar_one_or_none()
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if not rule:
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raise HTTPException(status_code=404, detail=f"规则不存在: {rule_id}")
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await db.delete(rule)
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await db.flush()
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async def _ai_parse_platform_rules(
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tenant_id: str,
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platform: str,
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document_text: str,
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db: AsyncSession,
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) -> dict:
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"""
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使用 AI 将文档文本解析为结构化平台规则
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AI 失败时返回空规则结构（降级为手动编辑）
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"""
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try:
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ai_client = await AIServiceFactory.get_client(tenant_id, db)
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if not ai_client:
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logger.warning(f"租户 {tenant_id} 未配置 AI 服务，返回空规则")
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return _empty_parsed_rules()
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config = await AIServiceFactory.get_config(tenant_id, db)
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if not config:
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return _empty_parsed_rules()
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text_model = config.models.get("text", "gpt-4o")
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# 截断过长文本（避免超出 token 限制）
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max_chars = 15000
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if len(document_text) > max_chars:
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document_text = document_text[:max_chars] + "\n...(文档内容已截断)"
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prompt = f"""你是平台广告合规规则分析专家。请从以下 {platform} 平台规则文档中提取结构化规则。
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文档内容：
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{document_text}
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请以 JSON 格式返回，不要包含其他内容：
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{{
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"forbidden_words": ["违禁词1", "违禁词2"],
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"restricted_words": [{{"word": "xx", "condition": "使用条件", "suggestion": "替换建议"}}],
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"duration": {{"min_seconds": 7, "max_seconds": null}},
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"content_requirements": ["必须展示产品正面", "需要口播品牌名"],
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"other_rules": [{{"rule": "规则名称", "description": "详细说明"}}]
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}}
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注意：
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- forbidden_words: 明确禁止使用的词语
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- restricted_words: 有条件限制的词语
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- duration: 视频时长要求，如果文档未提及则为 null
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- content_requirements: 内容上的硬性要求
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- other_rules: 不属于以上分类的其他规则
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- 如果某项没有提取到内容，使用空数组或 null"""
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response = await ai_client.chat_completion(
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messages=[{"role": "user", "content": prompt}],
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model=text_model,
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temperature=0.2,
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max_tokens=2000,
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)
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# 解析 AI 响应
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content = response.content.strip()
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if content.startswith("```"):
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content = content.split("\n", 1)[1]
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if content.endswith("```"):
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content = content.rsplit("\n", 1)[0]
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parsed = json.loads(content)
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# 校验并补全字段
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return {
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"forbidden_words": parsed.get("forbidden_words", []),
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"restricted_words": parsed.get("restricted_words", []),
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"duration": parsed.get("duration"),
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"content_requirements": parsed.get("content_requirements", []),
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"other_rules": parsed.get("other_rules", []),
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}
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except json.JSONDecodeError:
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logger.warning("AI 返回内容非 JSON，降级为空规则")
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return _empty_parsed_rules()
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except Exception as e:
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logger.error(f"AI 解析平台规则失败: {e}")
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return _empty_parsed_rules()
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def _empty_parsed_rules() -> dict:
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"""返回空的解析规则结构"""
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return {
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"forbidden_words": [],
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"restricted_words": [],
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"duration": None,
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"content_requirements": [],
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"other_rules": [],
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}
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# ==================== 辅助函数（供其他模块调用） ====================
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async def get_whitelist_for_brand(
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@ -533,3 +830,31 @@ async def get_forbidden_words_for_tenant(
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}
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for w in words
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]
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async def get_active_platform_rules(
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tenant_id: str,
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brand_id: str,
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platform: str,
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db: AsyncSession,
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) -> Optional[dict]:
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"""
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获取品牌方在该平台的生效规则 (active)
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Returns:
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parsed_rules dict 或 None（没有上传规则时）
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"""
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result = await db.execute(
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select(PlatformRule).where(
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and_(
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PlatformRule.tenant_id == tenant_id,
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PlatformRule.brand_id == brand_id,
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PlatformRule.platform == platform,
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PlatformRule.status == RuleStatus.ACTIVE.value,
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)
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)
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)
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rule = result.scalar_one_or_none()
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if not rule:
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return None
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return rule.parsed_rules
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@ -15,11 +15,14 @@ from app.schemas.review import (
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RiskLevel,
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Position,
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SoftRiskWarning,
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SoftRiskAction,
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)
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from app.api.rules import (
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get_whitelist_for_brand,
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get_other_brands_whitelist_terms,
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get_forbidden_words_for_tenant,
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get_active_platform_rules,
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_platform_rules,
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)
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from app.services.soft_risk import evaluate_soft_risk
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from app.services.ai_service import AIServiceFactory
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@ -175,19 +178,88 @@ async def review_script(
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position=Position(start=content.find(term), end=content.find(term) + len(term)),
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))
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# 3A. 平台规则违禁词（优先从 DB 读取，硬编码兜底）
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already_checked = set(ABSOLUTE_WORDS + [w["word"] for w in tenant_forbidden_words])
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platform_forbidden_words: list[str] = []
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# 优先从 DB 获取品牌方上传的 active 平台规则
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db_platform_rules = await get_active_platform_rules(
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x_tenant_id, request.brand_id, request.platform.value, db,
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)
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if db_platform_rules:
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platform_forbidden_words = db_platform_rules.get("forbidden_words", [])
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else:
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# 兜底：从硬编码 _platform_rules 读取
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platform_rule = _platform_rules.get(request.platform.value)
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if platform_rule:
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for rule in platform_rule.get("rules", []):
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if rule.get("type") == "forbidden_word":
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platform_forbidden_words.extend(rule.get("words", []))
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|
||||
for word in platform_forbidden_words:
|
||||
if word in already_checked or word in whitelist:
|
||||
continue
|
||||
start = 0
|
||||
while True:
|
||||
pos = content.find(word, start)
|
||||
if pos == -1:
|
||||
break
|
||||
if not _is_ad_context(content, word):
|
||||
start = pos + 1
|
||||
continue
|
||||
violations.append(Violation(
|
||||
type=ViolationType.FORBIDDEN_WORD,
|
||||
content=word,
|
||||
severity=RiskLevel.MEDIUM,
|
||||
suggestion=f"违反{request.platform.value}平台规则，建议删除：{word}",
|
||||
position=Position(start=pos, end=pos + len(word)),
|
||||
))
|
||||
start = pos + 1
|
||||
|
||||
# 3B. Brief 黑名单词
|
||||
if request.blacklist_words:
|
||||
for item in request.blacklist_words:
|
||||
word = item.get("word", "")
|
||||
reason = item.get("reason", "")
|
||||
if not word or word in whitelist:
|
||||
continue
|
||||
start_pos = 0
|
||||
while True:
|
||||
pos = content.find(word, start_pos)
|
||||
if pos == -1:
|
||||
break
|
||||
suggestion = f"Brief 黑名单词：{word}"
|
||||
if reason:
|
||||
suggestion += f"（{reason}）"
|
||||
violations.append(Violation(
|
||||
type=ViolationType.FORBIDDEN_WORD,
|
||||
content=word,
|
||||
severity=RiskLevel.HIGH,
|
||||
suggestion=suggestion,
|
||||
position=Position(start=pos, end=pos + len(word)),
|
||||
))
|
||||
start_pos = pos + 1
|
||||
|
||||
# 4. 检查遗漏卖点
|
||||
missing_points: list[str] | None = None
|
||||
if request.required_points:
|
||||
missing = _check_selling_point_coverage(content, request.required_points)
|
||||
missing_points = missing if missing else []
|
||||
|
||||
# 5. 可选：AI 深度分析
|
||||
ai_violations = await _ai_deep_analysis(x_tenant_id, content, db)
|
||||
# 5. 可选：AI 深度分析（返回 violations + warnings）
|
||||
ai_violations, ai_warnings = await _ai_deep_analysis(x_tenant_id, content, db)
|
||||
if ai_violations:
|
||||
violations.extend(ai_violations)
|
||||
|
||||
# 6. 计算分数
|
||||
score = 100 - len(violations) * 25
|
||||
# 6. 计算分数（按严重程度加权）
|
||||
score = 100
|
||||
for v in violations:
|
||||
if v.severity == RiskLevel.HIGH:
|
||||
score -= 25
|
||||
elif v.severity == RiskLevel.MEDIUM:
|
||||
score -= 15
|
||||
else:
|
||||
score -= 5
|
||||
if missing_points:
|
||||
score -= len(missing_points) * 5
|
||||
score = max(0, score)
|
||||
@ -209,6 +281,19 @@ async def review_script(
|
||||
if request.soft_risk_context:
|
||||
soft_warnings = evaluate_soft_risk(request.soft_risk_context)
|
||||
|
||||
# 合并 AI 产出的 soft_warnings
|
||||
if ai_warnings:
|
||||
soft_warnings.extend(ai_warnings)
|
||||
|
||||
# 遗漏卖点也加入 soft_warnings
|
||||
if missing_points:
|
||||
soft_warnings.append(SoftRiskWarning(
|
||||
code="missing_selling_points",
|
||||
message=f"遗漏 {len(missing_points)} 个卖点：{', '.join(missing_points)}",
|
||||
action_required=SoftRiskAction.NOTE,
|
||||
blocking=False,
|
||||
))
|
||||
|
||||
return ScriptReviewResponse(
|
||||
score=score,
|
||||
summary=summary,
|
||||
@ -222,26 +307,27 @@ async def _ai_deep_analysis(
|
||||
tenant_id: str,
|
||||
content: str,
|
||||
db: AsyncSession,
|
||||
) -> list[Violation]:
|
||||
) -> tuple[list[Violation], list[SoftRiskWarning]]:
|
||||
"""
|
||||
使用 AI 进行深度分析
|
||||
|
||||
返回 (violations, soft_warnings)
|
||||
AI 分析失败时返回空列表，降级到规则检测
|
||||
"""
|
||||
try:
|
||||
# 获取 AI 客户端
|
||||
ai_client = await AIServiceFactory.get_client(tenant_id, db)
|
||||
if not ai_client:
|
||||
return []
|
||||
return [], []
|
||||
|
||||
# 获取模型配置
|
||||
config = await AIServiceFactory.get_config(tenant_id, db)
|
||||
if not config:
|
||||
return []
|
||||
return [], []
|
||||
|
||||
text_model = config.models.get("text", "gpt-4o")
|
||||
|
||||
# 构建分析提示
|
||||
# 构建分析提示（两类输出）
|
||||
analysis_prompt = f"""作为广告合规审核专家，请分析以下广告脚本内容，检测潜在的合规风险：
|
||||
|
||||
脚本内容：
|
||||
@ -253,12 +339,17 @@ async def _ai_deep_analysis(
|
||||
3. 是否存在夸大描述
|
||||
4. 是否存在可能违反广告法的其他内容
|
||||
|
||||
如果发现问题，请以 JSON 数组格式返回，每项包含：
|
||||
请以 JSON 数组返回，每项包含：
|
||||
- category: "violation"（硬性违规，明确违法/违规）或 "warning"（软性提醒，需人工判断）
|
||||
- type: 违规类型 (forbidden_word/efficacy_claim/brand_safety)
|
||||
- content: 违规内容
|
||||
- content: 问题内容
|
||||
- severity: 严重程度 (high/medium/low)
|
||||
- suggestion: 修改建议
|
||||
|
||||
分类标准：
|
||||
- violation: 违禁词、功效宣称、品牌安全等明确违规
|
||||
- warning: 夸大描述、易误解表述、潜在风险
|
||||
|
||||
如果未发现问题，返回空数组 []
|
||||
|
||||
请只返回 JSON 数组，不要包含其他内容。"""
|
||||
@ -283,7 +374,10 @@ async def _ai_deep_analysis(
|
||||
ai_results = json.loads(response_content)
|
||||
|
||||
violations = []
|
||||
warnings = []
|
||||
for item in ai_results:
|
||||
category = item.get("category", "violation") # 默认当硬性违规（安全兜底）
|
||||
|
||||
violation_type = item.get("type", "forbidden_word")
|
||||
if violation_type == "forbidden_word":
|
||||
vtype = ViolationType.FORBIDDEN_WORD
|
||||
@ -300,19 +394,28 @@ async def _ai_deep_analysis(
|
||||
else:
|
||||
slevel = RiskLevel.MEDIUM
|
||||
|
||||
violations.append(Violation(
|
||||
type=vtype,
|
||||
content=item.get("content", ""),
|
||||
severity=slevel,
|
||||
suggestion=item.get("suggestion", "建议修改"),
|
||||
))
|
||||
if category == "warning":
|
||||
# 软性提醒 → SoftRiskWarning
|
||||
warnings.append(SoftRiskWarning(
|
||||
code="ai_warning",
|
||||
message=f"{item.get('content', '')}: {item.get('suggestion', '建议修改')}",
|
||||
action_required=SoftRiskAction.NOTE,
|
||||
blocking=False,
|
||||
context={"type": violation_type, "severity": severity},
|
||||
))
|
||||
else:
|
||||
# 硬性违规 → Violation
|
||||
violations.append(Violation(
|
||||
type=vtype,
|
||||
content=item.get("content", ""),
|
||||
severity=slevel,
|
||||
suggestion=item.get("suggestion", "建议修改"),
|
||||
))
|
||||
|
||||
return violations
|
||||
return violations, warnings
|
||||
|
||||
except json.JSONDecodeError:
|
||||
# JSON 解析失败，返回空列表
|
||||
return []
|
||||
return [], []
|
||||
|
||||
except Exception:
|
||||
# AI 调用失败，降级到规则检测
|
||||
return []
|
||||
return [], []
|
||||
|
||||
@ -10,7 +10,7 @@ from app.models.task import Task, TaskStage, TaskStatus
|
||||
from app.models.brief import Brief
|
||||
from app.models.ai_config import AIConfig
|
||||
from app.models.review import ReviewTask, Platform
|
||||
from app.models.rule import ForbiddenWord, WhitelistItem, Competitor
|
||||
from app.models.rule import ForbiddenWord, WhitelistItem, Competitor, PlatformRule, RuleStatus
|
||||
from app.models.audit_log import AuditLog
|
||||
from app.models.message import Message
|
||||
# 保留 Tenant 兼容旧代码，但新代码应使用 Brand
|
||||
@ -44,6 +44,8 @@ __all__ = [
|
||||
"ForbiddenWord",
|
||||
"WhitelistItem",
|
||||
"Competitor",
|
||||
"PlatformRule",
|
||||
"RuleStatus",
|
||||
# 审计日志
|
||||
"AuditLog",
|
||||
# 消息
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
"""
|
||||
规则模型
|
||||
违禁词、白名单、竞品
|
||||
违禁词、白名单、竞品、平台规则
|
||||
"""
|
||||
import enum
|
||||
from typing import TYPE_CHECKING, Optional
|
||||
from sqlalchemy import String, Text, ForeignKey
|
||||
from app.models.types import JSONType
|
||||
@ -13,6 +14,13 @@ if TYPE_CHECKING:
|
||||
from app.models.tenant import Tenant
|
||||
|
||||
|
||||
class RuleStatus(str, enum.Enum):
|
||||
"""平台规则状态"""
|
||||
DRAFT = "draft" # AI 解析完成，待确认
|
||||
ACTIVE = "active" # 品牌方已确认，生效中
|
||||
INACTIVE = "inactive" # 已停用
|
||||
|
||||
|
||||
class ForbiddenWord(Base, TimestampMixin):
|
||||
"""违禁词表"""
|
||||
__tablename__ = "forbidden_words"
|
||||
@ -83,3 +91,36 @@ class Competitor(Base, TimestampMixin):
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"<Competitor(name={self.name}, brand_id={self.brand_id})>"
|
||||
|
||||
|
||||
class PlatformRule(Base, TimestampMixin):
|
||||
"""平台规则表 — 品牌方上传文档 + AI 解析"""
|
||||
__tablename__ = "platform_rules"
|
||||
|
||||
id: Mapped[str] = mapped_column(String(64), primary_key=True)
|
||||
tenant_id: Mapped[str] = mapped_column(
|
||||
String(64),
|
||||
ForeignKey("tenants.id", ondelete="CASCADE"),
|
||||
nullable=False,
|
||||
index=True,
|
||||
)
|
||||
brand_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
|
||||
platform: Mapped[str] = mapped_column(String(50), nullable=False, index=True)
|
||||
|
||||
# 文档信息
|
||||
document_url: Mapped[str] = mapped_column(String(2048), nullable=False)
|
||||
document_name: Mapped[str] = mapped_column(String(512), nullable=False)
|
||||
|
||||
# AI 解析结果（JSON）
|
||||
parsed_rules: Mapped[Optional[dict]] = mapped_column(JSONType, nullable=True)
|
||||
|
||||
# 状态
|
||||
status: Mapped[str] = mapped_column(
|
||||
String(20), nullable=False, default=RuleStatus.DRAFT.value, index=True,
|
||||
)
|
||||
|
||||
# 关联
|
||||
tenant: Mapped["Tenant"] = relationship("Tenant", back_populates="platform_rules")
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"<PlatformRule(id={self.id}, platform={self.platform}, status={self.status})>"
|
||||
|
||||
@ -10,7 +10,7 @@ from app.models.base import Base, TimestampMixin
|
||||
if TYPE_CHECKING:
|
||||
from app.models.ai_config import AIConfig
|
||||
from app.models.review import ReviewTask
|
||||
from app.models.rule import ForbiddenWord, WhitelistItem, Competitor
|
||||
from app.models.rule import ForbiddenWord, WhitelistItem, Competitor, PlatformRule
|
||||
|
||||
|
||||
class Tenant(Base, TimestampMixin):
|
||||
@ -48,5 +48,11 @@ class Tenant(Base, TimestampMixin):
|
||||
back_populates="tenant",
|
||||
lazy="selectin",
|
||||
)
|
||||
platform_rules: Mapped[list["PlatformRule"]] = relationship(
|
||||
"PlatformRule",
|
||||
back_populates="tenant",
|
||||
lazy="selectin",
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"<Tenant(id={self.id}, name={self.name})>"
|
||||
|
||||
69
backend/app/schemas/rules.py
Normal file
69
backend/app/schemas/rules.py
Normal file
@ -0,0 +1,69 @@
|
||||
"""
|
||||
平台规则相关 Schema
|
||||
"""
|
||||
from typing import Optional
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class PlatformRuleParseRequest(BaseModel):
|
||||
"""上传文档并解析"""
|
||||
document_url: str = Field(..., description="TOS 上传后的文件 URL")
|
||||
document_name: str = Field(..., description="原始文件名（用于判断格式）")
|
||||
platform: str = Field(..., description="目标平台 (douyin/xiaohongshu/bilibili/kuaishou)")
|
||||
brand_id: str = Field(..., description="品牌 ID")
|
||||
|
||||
|
||||
class ParsedRulesData(BaseModel):
|
||||
"""AI 解析出的结构化规则"""
|
||||
forbidden_words: list[str] = Field(default_factory=list, description="违禁词列表")
|
||||
restricted_words: list[dict] = Field(
|
||||
default_factory=list,
|
||||
description="限制词 [{word, condition, suggestion}]",
|
||||
)
|
||||
duration: Optional[dict] = Field(
|
||||
None,
|
||||
description="时长要求 {min_seconds, max_seconds}",
|
||||
)
|
||||
content_requirements: list[str] = Field(
|
||||
default_factory=list,
|
||||
description="内容要求（如'必须展示产品'）",
|
||||
)
|
||||
other_rules: list[dict] = Field(
|
||||
default_factory=list,
|
||||
description="其他规则 [{rule, description}]",
|
||||
)
|
||||
|
||||
|
||||
class PlatformRuleParseResponse(BaseModel):
|
||||
"""解析响应（draft 状态）"""
|
||||
id: str
|
||||
platform: str
|
||||
brand_id: str
|
||||
document_url: str
|
||||
document_name: str
|
||||
parsed_rules: ParsedRulesData
|
||||
status: str
|
||||
|
||||
|
||||
class PlatformRuleConfirmRequest(BaseModel):
|
||||
"""确认/编辑解析结果"""
|
||||
parsed_rules: ParsedRulesData = Field(..., description="品牌方可能修改过的规则")
|
||||
|
||||
|
||||
class PlatformRuleResponse(BaseModel):
|
||||
"""完整响应"""
|
||||
id: str
|
||||
platform: str
|
||||
brand_id: str
|
||||
document_url: str
|
||||
document_name: str
|
||||
parsed_rules: ParsedRulesData
|
||||
status: str
|
||||
created_at: str
|
||||
updated_at: str
|
||||
|
||||
|
||||
class PlatformRuleListResponse(BaseModel):
|
||||
"""列表响应"""
|
||||
items: list[PlatformRuleResponse]
|
||||
total: int
|
||||
119
backend/app/services/document_parser.py
Normal file
119
backend/app/services/document_parser.py
Normal file
@ -0,0 +1,119 @@
|
||||
"""
|
||||
文档解析服务
|
||||
从 PDF/Word/Excel 文档中提取纯文本
|
||||
"""
|
||||
import os
|
||||
import tempfile
|
||||
from typing import Optional
|
||||
|
||||
import httpx
|
||||
|
||||
|
||||
class DocumentParser:
|
||||
"""从文档中提取纯文本"""
|
||||
|
||||
@staticmethod
|
||||
async def download_and_parse(document_url: str, document_name: str) -> str:
|
||||
"""
|
||||
下载文档并解析为纯文本
|
||||
|
||||
Args:
|
||||
document_url: 文档 URL (TOS)
|
||||
document_name: 原始文件名（用于判断格式）
|
||||
|
||||
Returns:
|
||||
提取的纯文本
|
||||
"""
|
||||
# 下载到临时文件
|
||||
tmp_path: Optional[str] = None
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||||
resp = await client.get(document_url)
|
||||
resp.raise_for_status()
|
||||
|
||||
ext = document_name.rsplit(".", 1)[-1].lower() if "." in document_name else ""
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=f".{ext}") as tmp:
|
||||
tmp.write(resp.content)
|
||||
tmp_path = tmp.name
|
||||
|
||||
return DocumentParser.parse_file(tmp_path, document_name)
|
||||
finally:
|
||||
if tmp_path and os.path.exists(tmp_path):
|
||||
os.unlink(tmp_path)
|
||||
|
||||
@staticmethod
|
||||
def parse_file(file_path: str, file_name: str) -> str:
|
||||
"""
|
||||
根据扩展名选择解析器，返回纯文本
|
||||
|
||||
Args:
|
||||
file_path: 本地文件路径
|
||||
file_name: 原始文件名
|
||||
|
||||
Returns:
|
||||
提取的纯文本
|
||||
"""
|
||||
ext = file_name.rsplit(".", 1)[-1].lower() if "." in file_name else ""
|
||||
|
||||
if ext == "pdf":
|
||||
return DocumentParser._parse_pdf(file_path)
|
||||
elif ext in ("doc", "docx"):
|
||||
return DocumentParser._parse_docx(file_path)
|
||||
elif ext in ("xls", "xlsx"):
|
||||
return DocumentParser._parse_xlsx(file_path)
|
||||
elif ext == "txt":
|
||||
return DocumentParser._parse_txt(file_path)
|
||||
else:
|
||||
raise ValueError(f"不支持的文件格式: {ext}")
|
||||
|
||||
@staticmethod
|
||||
def _parse_pdf(path: str) -> str:
|
||||
"""pdfplumber 提取 PDF 文本"""
|
||||
import pdfplumber
|
||||
|
||||
texts = []
|
||||
with pdfplumber.open(path) as pdf:
|
||||
for page in pdf.pages:
|
||||
text = page.extract_text()
|
||||
if text:
|
||||
texts.append(text)
|
||||
return "\n".join(texts)
|
||||
|
||||
@staticmethod
|
||||
def _parse_docx(path: str) -> str:
|
||||
"""python-docx 提取 Word 文本"""
|
||||
from docx import Document
|
||||
|
||||
doc = Document(path)
|
||||
texts = []
|
||||
for para in doc.paragraphs:
|
||||
if para.text.strip():
|
||||
texts.append(para.text)
|
||||
# 也提取表格内容
|
||||
for table in doc.tables:
|
||||
for row in table.rows:
|
||||
row_text = "\t".join(cell.text.strip() for cell in row.cells if cell.text.strip())
|
||||
if row_text:
|
||||
texts.append(row_text)
|
||||
return "\n".join(texts)
|
||||
|
||||
@staticmethod
|
||||
def _parse_xlsx(path: str) -> str:
|
||||
"""openpyxl 提取 Excel 文本（所有 sheet 拼接）"""
|
||||
from openpyxl import load_workbook
|
||||
|
||||
wb = load_workbook(path, read_only=True, data_only=True)
|
||||
texts = []
|
||||
for sheet in wb.worksheets:
|
||||
for row in sheet.iter_rows(values_only=True):
|
||||
row_text = "\t".join(str(cell) for cell in row if cell is not None)
|
||||
if row_text.strip():
|
||||
texts.append(row_text)
|
||||
wb.close()
|
||||
return "\n".join(texts)
|
||||
|
||||
@staticmethod
|
||||
def _parse_txt(path: str) -> str:
|
||||
"""纯文本文件"""
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
return f.read()
|
||||
@ -14,7 +14,7 @@ from sqlalchemy.orm import sessionmaker
|
||||
|
||||
from app.config import settings
|
||||
from app.models.review import ReviewTask, TaskStatus as DBTaskStatus
|
||||
from app.models.rule import ForbiddenWord, Competitor
|
||||
from app.models.rule import ForbiddenWord, Competitor, PlatformRule, RuleStatus
|
||||
from app.models.ai_config import AIConfig
|
||||
from app.services.video_download import VideoDownloadService, DownloadResult
|
||||
from app.services.keyframe import KeyFrameExtractor, ExtractionResult
|
||||
@ -81,6 +81,7 @@ async def complete_review(
|
||||
summary: str,
|
||||
violations: list[dict],
|
||||
status: DBTaskStatus = DBTaskStatus.COMPLETED,
|
||||
soft_warnings: Optional[list[dict]] = None,
|
||||
):
|
||||
"""完成审核"""
|
||||
result = await db.execute(
|
||||
@ -94,6 +95,8 @@ async def complete_review(
|
||||
task.score = score
|
||||
task.summary = summary
|
||||
task.violations = violations
|
||||
if soft_warnings is not None:
|
||||
task.soft_warnings = soft_warnings
|
||||
task.completed_at = datetime.now(timezone.utc)
|
||||
await db.commit()
|
||||
|
||||
@ -153,6 +156,24 @@ async def get_competitors(db: AsyncSession, tenant_id: str, brand_id: str) -> li
|
||||
return [row[0] for row in result.fetchall()]
|
||||
|
||||
|
||||
async def get_platform_forbidden_words(
|
||||
db: AsyncSession, tenant_id: str, brand_id: str, platform: str,
|
||||
) -> list[str]:
|
||||
"""从 DB 获取品牌方在该平台的 active 规则中的违禁词"""
|
||||
result = await db.execute(
|
||||
select(PlatformRule).where(
|
||||
PlatformRule.tenant_id == tenant_id,
|
||||
PlatformRule.brand_id == brand_id,
|
||||
PlatformRule.platform == platform,
|
||||
PlatformRule.status == RuleStatus.ACTIVE.value,
|
||||
)
|
||||
)
|
||||
rule = result.scalar_one_or_none()
|
||||
if not rule or not rule.parsed_rules:
|
||||
return []
|
||||
return rule.parsed_rules.get("forbidden_words", [])
|
||||
|
||||
|
||||
async def process_video_review(
|
||||
review_id: str,
|
||||
tenant_id: str,
|
||||
@ -199,6 +220,13 @@ async def process_video_review(
|
||||
|
||||
# 获取规则
|
||||
forbidden_words = await get_forbidden_words(db, tenant_id)
|
||||
# 合并平台规则中的违禁词
|
||||
platform_fw = await get_platform_forbidden_words(db, tenant_id, brand_id, platform)
|
||||
existing_set = set(forbidden_words)
|
||||
for w in platform_fw:
|
||||
if w not in existing_set:
|
||||
forbidden_words.append(w)
|
||||
existing_set.add(w)
|
||||
competitors = await get_competitors(db, tenant_id, brand_id)
|
||||
|
||||
# 初始化 AI 服务
|
||||
@ -281,16 +309,37 @@ async def process_video_review(
|
||||
)
|
||||
all_violations.extend(subtitle_violations)
|
||||
|
||||
# 6. 计算分数和生成报告
|
||||
# 6. 分流 violations / soft_warnings
|
||||
await update_review_progress(db, review_id, 90, "生成报告")
|
||||
score = review_service.calculate_score(all_violations)
|
||||
|
||||
if not all_violations:
|
||||
hard_violations = []
|
||||
soft_warnings_data = []
|
||||
|
||||
for v in all_violations:
|
||||
v_type = v.get("type", "")
|
||||
if v_type in ("forbidden_word", "efficacy_claim", "competitor_logo", "brand_safety"):
|
||||
hard_violations.append(v)
|
||||
elif v_type in ("duration_short", "mention_missing"):
|
||||
soft_warnings_data.append({
|
||||
"code": f"video_{v_type}",
|
||||
"message": v.get("content", ""),
|
||||
"action_required": "note",
|
||||
"blocking": False,
|
||||
"context": {"suggestion": v.get("suggestion", "")},
|
||||
})
|
||||
else:
|
||||
hard_violations.append(v) # 默认当硬性违规
|
||||
|
||||
# 计算分数（仅硬性违规影响分数）
|
||||
score = review_service.calculate_score(hard_violations)
|
||||
|
||||
if not hard_violations:
|
||||
summary = "视频内容合规，未发现违规项"
|
||||
if soft_warnings_data:
|
||||
summary += f"（{len(soft_warnings_data)} 条提醒）"
|
||||
else:
|
||||
high_count = sum(1 for v in all_violations if v.get("risk_level") == "high")
|
||||
medium_count = sum(1 for v in all_violations if v.get("risk_level") == "medium")
|
||||
summary = f"发现 {len(all_violations)} 处违规"
|
||||
high_count = sum(1 for v in hard_violations if v.get("risk_level") == "high")
|
||||
summary = f"发现 {len(hard_violations)} 处违规"
|
||||
if high_count > 0:
|
||||
summary += f"（{high_count} 处高风险）"
|
||||
|
||||
@ -300,7 +349,8 @@ async def process_video_review(
|
||||
review_id,
|
||||
score=score,
|
||||
summary=summary,
|
||||
violations=all_violations,
|
||||
violations=hard_violations,
|
||||
soft_warnings=soft_warnings_data if soft_warnings_data else None,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
@ -21,6 +21,9 @@ dependencies = [
|
||||
"openai>=1.12.0",
|
||||
"cachetools>=5.3.0",
|
||||
"sse-starlette>=2.0.0",
|
||||
"pdfplumber>=0.10.0",
|
||||
"python-docx>=1.1.0",
|
||||
"openpyxl>=3.1.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user