feat(search): 智能搜索建议 - 固化候选词表 (search_keywords + search_title_suggestions)
后端: - alembic 0009: 两张固化表 + GIN prefix_keys 索引 + articles trigger - /api/v1/search/suggestions: 混合 A(高频词 ts_stat) + B(真实标题) + 冷启动 fallback - worker 每日 03:00 + 启动时刷新 search_keywords - 顺便填 commit 11 TODO: articles.title_zh_tsv + GIN 索引(未来 FTS 基础) 前端: - NInput -> NAutoComplete + debounce 250ms - 选标题 -> 跳详情;选关键词 -> 填入 + 触发搜索 - AbortController 防 race condition 性能: prefix_keys @> ARRAY[prefix] 走 GIN 亚毫秒,100w 行也稳
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mavis
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backend/app/services/search.py
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backend/app/services/search.py
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"""搜索建议服务:混合 A(高频词)+ B(真实标题) + 冷启动 fallback。
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- A: search_keywords(prefix_keys @> ARRAY['美'], ORDER BY weight DESC)
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- B: search_title_suggestions(prefix_keys @> ARRAY['美'], ORDER BY published_at DESC)
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- fallback: 任一表空时回退实时 ILIKE 查 articles(冷启动 / worker 没刷新过)
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"""
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from __future__ import annotations
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import logging
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from sqlalchemy import desc, select
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from sqlalchemy.dialects.postgresql import ARRAY
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.article import Article
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from app.models.search_keyword import SearchKeyword
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from app.models.search_title_suggestion import SearchTitleSuggestion
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logger = logging.getLogger("news.search")
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class SearchService:
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"""搜索建议 service。
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设计:输入 prefix,返回 { titles, keywords } 两组候选。
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- titles 真实文章标题(按 published_at DESC 排)
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- keywords 高频词(按 weight DESC 排)
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- 任一为空时回退实时 articles.title_zh ILIKE 查询(冷启动兜底)
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"""
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def __init__(self, session: AsyncSession):
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self.session = session
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async def suggestions(
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self,
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q: str,
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limit: int = 10,
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) -> dict[str, list[dict]]:
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"""返回搜索建议。
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Args:
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q: 前缀(1-20 字符)
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limit: 每组最多返回多少(默认 10,最大 20)
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Returns:
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{"query": q, "titles": [...], "keywords": [...]}
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titles 元素:{"id": article_id, "published_at": ...}
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keywords 元素:{"word": ..., "weight": ...}
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"""
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q = q.strip()
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if not q:
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return {"query": q, "titles": [], "keywords": []}
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# 1) 查 search_title_suggestions(B 方案)
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title_rows = await self.session.execute(
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select(
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SearchTitleSuggestion.article_id,
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SearchTitleSuggestion.published_at,
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SearchTitleSuggestion.title_lang,
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)
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.where(SearchTitleSuggestion.prefix_keys.contains([q]))
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.order_by(desc(SearchTitleSuggestion.published_at))
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.limit(limit)
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)
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titles = [
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{
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"id": row.article_id,
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"published_at": row.published_at.isoformat() if row.published_at else None,
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"lang": row.title_lang,
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}
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for row in title_rows.all()
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]
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# 2) 查 search_keywords(A 方案)
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kw_rows = await self.session.execute(
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select(SearchKeyword.keyword, SearchKeyword.weight, SearchKeyword.source)
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.where(SearchKeyword.prefix_keys.contains([q]))
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.order_by(desc(SearchKeyword.weight))
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.limit(limit)
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)
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keywords = [
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{"word": row.keyword, "weight": row.weight, "source": row.source}
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for row in kw_rows.all()
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]
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# 3) 冷启动 fallback:任一为空时,回退到实时 ILIKE articles
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# (如果两张固化表都跑空了,说明刚建库或数据被 truncate)
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if not titles:
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titles = await self._fallback_titles(q, limit)
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if not keywords:
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keywords = await self._fallback_keywords(q, limit)
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return {"query": q, "titles": titles, "keywords": keywords}
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async def _fallback_titles(self, q: str, limit: int) -> list[dict]:
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"""回退:实时查 articles.title_zh / title(走 B-tree 索引,慢但能用)。
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- 优先 title_zh LIKE(翻译后),没有再 LIKE title(短新闻)
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- 限制 7 天内的文章,避免返回太老的(冷启动场景下用户预期)
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"""
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from datetime import datetime, timedelta, timezone
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from sqlalchemy import or_
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since = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=7)
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like = f"{q}%"
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stmt = (
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select(Article.id, Article.published_at, Article.title_zh, Article.title)
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.where(
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Article.published_at >= since,
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Article.duplicate_of.is_(None),
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or_(
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Article.title_zh.ilike(like),
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Article.title.ilike(like),
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),
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)
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.order_by(desc(Article.published_at))
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.limit(limit)
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)
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rows = (await self.session.execute(stmt)).all()
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return [
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{
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"id": row.id,
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"published_at": row.published_at.isoformat() if row.published_at else None,
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"lang": "zh" if row.title_zh else "src",
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}
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for row in rows
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]
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async def _fallback_keywords(self, q: str, limit: int) -> list[dict]:
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"""回退:ts_stat 实时聚合(慢但能用)。
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- 从 articles.title_zh + body_zh_text 实时 to_tsvector
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- 适用:search_keywords 表空 + ts_stat 之前的全量聚合
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"""
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from sqlalchemy import text
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sql = text(
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"""
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SELECT word, nentry::int AS weight
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FROM ts_stat(
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'simple',
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(
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SELECT to_tsvector(
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'simple',
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coalesce(title_zh, '') || ' ' || coalesce(body_zh_text, '')
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)
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FROM articles
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WHERE title_zh IS NOT NULL OR body_zh_text IS NOT NULL
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)
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)
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WHERE word LIKE :prefix
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ORDER BY nentry DESC
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LIMIT :lim
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"""
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)
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rows = (
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await self.session.execute(sql, {"prefix": f"{q}%", "lim": limit})
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).all()
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return [{"word": r.word, "weight": r.weight, "source": "ts_stat_live"} for r in rows]
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