feat(feed): broaden search to a weighted document (title + keywords + queries + description)

Makes the local search behave more like YouTube's — finding videos by the
uploader's own keywords or the query that surfaced them, not only words in the
title. A DB-generated, weighted search_vector (migration 0032) replaces the
title-only FTS index:
- keywords: the creator's snippet.tags (free — already in the snippet we fetch),
  stored on enrich.
- search_terms: distinct live-search queries that surfaced the video (across all
  users), appended by the search route — folds YouTube's relevance into local
  search (a video YT returned for a query becomes findable by it even without a
  title match), the user's own idea.
- description (truncated) for broad recall on the existing catalog.
Weighted title(A) > keywords+queries(B) > description(C) so ts_rank keeps title
hits on top. A plain GIN index on the generated column guarantees index use (no
expression/param matching). Verified on localdev: recall 146->213 for one query;
7 'eurovision' hits via the document but not the title; index scan confirmed.

backend/app/routes/search.py

@@ -16,7 +16,7 @@ from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime, timezone
 
 from fastapi import APIRouter, Depends, HTTPException
-from sqlalchemy import and_, select
+from sqlalchemy import and_, func, select, update
 from sqlalchemy.dialects.postgresql import insert as pg_insert
 from sqlalchemy.orm import Session, aliased
 
@@ -223,6 +223,20 @@ def search_youtube(
             .values([{"user_id": user.id, "video_id": vid} for vid in ordered])
             .on_conflict_do_nothing(index_elements=["user_id", "video_id"])
         )
+        # 7) Fold the query into each result's search_terms (shared across users) so local
+        #    full-text search inherits YouTube's relevance — a video YT returned for this query
+        #    becomes findable by it even when its title doesn't contain the words. Skip rows
+        #    that already include the term; the generated search_vector updates automatically.
+        db.execute(
+            update(Video)
+            .where(Video.id.in_(ordered))
+            .where(func.coalesce(Video.search_terms, "").notilike(f"%{term}%"))
+            .values(
+                search_terms=func.trim(
+                    func.coalesce(Video.search_terms, "").op("||")(" ").op("||")(term)
+                )
+            )
+        )
         db.commit()
 
     return {