d838a9bea2
- T-013 错误处理: 增强 API 错误处理，添加日志记录 - T-015 视频链接: 已在 ResultTable 中实现点击跳转 - T-016 部署配置: 添加前后端 Dockerfile 和 docker-compose.yml - 新增 11 个错误处理测试用例，共 55 个测试全部通过 - 测试覆盖率达到 93% Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
from fastapi import APIRouter, Depends
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.exc import SQLAlchemyError
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from app.database import get_db
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from app.schemas.query import QueryRequest, QueryResponse, VideoData
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from app.services.query_service import query_videos
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from app.services.calculator import calculate_metrics
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from app.services.brand_api import get_brand_names
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from app.api.v1.export import set_export_data
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from app.core.logging import get_logger
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router = APIRouter()
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logger = get_logger(__name__)
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@router.post("/query", response_model=QueryResponse)
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async def query(
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request: QueryRequest,
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db: AsyncSession = Depends(get_db),
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) -> QueryResponse:
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"""
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批量查询 KOL 视频数据.
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支持三种查询方式:
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- star_id: 按星图ID精准匹配
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- unique_id: 按达人unique_id精准匹配
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- nickname: 按达人昵称模糊匹配
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"""
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try:
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# 1. 查询数据库
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logger.info(f"Querying videos: type={request.type}, count={len(request.values)}")
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videos = await query_videos(db, request.type, request.values)
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if not videos:
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logger.info("No videos found for query")
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return QueryResponse(success=True, data=[], total=0)
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# 2. 提取品牌ID并批量获取品牌名称
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brand_ids = [v.brand_id for v in videos if v.brand_id]
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brand_map = {}
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if brand_ids:
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try:
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brand_map = await get_brand_names(brand_ids)
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except Exception as brand_err:
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logger.warning(f"Failed to fetch brand names, using fallback: {brand_err}")
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# 降级处理：使用 brand_id 作为名称
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# 3. 转换为响应模型并计算指标
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data = []
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for video in videos:
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video_data = VideoData.model_validate(video)
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# 填充品牌名称
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if video.brand_id:
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video_data.brand_name = brand_map.get(video.brand_id, video.brand_id)
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# 计算预估指标
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metrics = calculate_metrics(
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estimated_video_cost=video.estimated_video_cost,
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natural_play_cnt=video.natural_play_cnt,
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total_play_cnt=video.total_play_cnt,
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after_view_search_uv=video.after_view_search_uv,
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)
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video_data.estimated_natural_cpm = metrics["estimated_natural_cpm"]
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video_data.estimated_natural_search_uv = metrics["estimated_natural_search_uv"]
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video_data.estimated_natural_search_cost = metrics["estimated_natural_search_cost"]
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data.append(video_data)
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# 缓存数据供导出使用
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set_export_data([d.model_dump() for d in data])
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logger.info(f"Query successful: {len(data)} videos found")
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return QueryResponse(success=True, data=data, total=len(data))
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except SQLAlchemyError as db_err:
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logger.error(f"Database error: {db_err}")
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return QueryResponse(
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success=False,
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data=[],
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total=0,
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error="数据库连接失败，请稍后重试"
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)
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except Exception as e:
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logger.error(f"Query error: {e}")
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return QueryResponse(success=False, data=[], total=0, error=str(e))
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