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

backend/app/api/search.py

@@ -0,0 +1,47 @@
+"""/api/v1/search/* — 搜索建议(autocomplete)。
+
+- GET /api/v1/search/suggestions?q=prefix
+  返回:{"query", "titles": [...], "keywords": [...]}
+  - titles: 真实文章标题(按 published_at DESC),B 方案
+  - keywords: 高频词(按 weight DESC),A 方案
+  - 冷启动:任一表空时自动 fallback 到实时 ILIKE / ts_stat
+- 鉴权:跟 articles 一致(需要登录)
+
+性能:两个查询都走 GIN 数组索引(prefix_keys @> ARRAY['美']),亚毫秒。
+"""
+from __future__ import annotations
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.deps import get_current_user
+from app.database import get_session
+from app.models.user import User
+from app.schemas.search import (
+    SearchKeywordItem,
+    SearchSuggestionsResponse,
+    SearchTitleSuggestionItem,
+)
+from app.services.search import SearchService
+
+router = APIRouter(prefix="/search", tags=["search"])
+
+
+@router.get("/suggestions", response_model=SearchSuggestionsResponse)
+async def get_suggestions(
+    q: str = Query(..., min_length=1, max_length=20, description="搜索前缀"),
+    limit: int = Query(10, ge=1, le=20, description="每组最多返回多少"),
+    _user: User = Depends(get_current_user),  # 需要登录,跟 articles 一致
+    session: AsyncSession = Depends(get_session),
+):
+    """搜索建议:输入 prefix,返回真实标题 + 高频词两组候选。
+
+    用法:前端搜索框 onChange 时调用,debounce 200ms。
+    """
+    svc = SearchService(session)
+    raw = await svc.suggestions(q=q, limit=limit)
+    return SearchSuggestionsResponse(
+        query=raw["query"],
+        titles=[SearchTitleSuggestionItem(**t) for t in raw["titles"]],
+        keywords=[SearchKeywordItem(**k) for k in raw["keywords"]],
+    )