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/feed.py

@@ -221,22 +221,19 @@ def _filtered_query(
             published_before, datetime.min.time(), tzinfo=timezone.utc
         ) + timedelta(days=1)
         query = query.where(Video.published_at < end)
-    # Full-text relevance search on the title: YouTube-like "fuzzy" matching (word-order-
-    # independent, multi-word AND, prefix on the word being typed) + accent-insensitive via
-    # the unaccent_simple config. The channel name still matches as a substring so channel
-    # searches work. `rank_expr` (ts_rank) drives the optional "relevance" sort. The
-    # to_tsvector expression must match the GIN index (migration 0031) verbatim to be used.
+    # Full-text relevance search over the weighted search document (title > creator keywords +
+    # the queries that surfaced the video > description; see Video.search_vector). YouTube-like
+    # "fuzzy" matching: word-order-independent, multi-word AND, prefix on the word being typed,
+    # accent-insensitive (unaccent_simple). The channel name still matches as a substring so
+    # channel searches work. `rank_expr` (weight-aware ts_rank) drives the "relevance" sort.
     rank_expr = None
     if q:
         ts_str = _to_tsquery_str(q)
         if ts_str:
             tsq = func.to_tsquery("public.unaccent_simple", ts_str)
-            title_vec = func.to_tsvector(
-                "public.unaccent_simple", func.coalesce(Video.title, "")
-            )
             query = query.where(
                 or_(
-                    title_vec.op("@@")(tsq),
+                    Video.search_vector.op("@@")(tsq),
                     func.unaccent(Channel.title).ilike(func.unaccent(f"%{q}%")),
                 )
             )
@@ -244,7 +241,7 @@ def _filtered_query(
             # EXACTLY — a raw float4 vs the float8 cursor value mismatches on round-trip and
             # breaks paging (the same page repeats). 1e6 granularity is ample; ties break on
             # published_at then id.
-            rank_expr = cast(func.ts_rank(title_vec, tsq) * 1000000, BigInteger)
+            rank_expr = cast(func.ts_rank(Video.search_vector, tsq) * 1000000, BigInteger)
         else:
             # No usable tokens (e.g. only punctuation): fall back to a plain substring match.
             like = func.unaccent(f"%{q}%")