feat(enrichment): alias-aware scoring — auto-confirm non-Latin-primary artists (#772)

* feat(enrichment): loose MusicBrainz search fallback (find aliased artists)

The MB text search used a strict field-phrase query
(`recording:"<title>" AND artist:"<artist>"`). A field phrase only matches
MusicBrainz's *primary* artist/title — it never searches ALIASES — so a
recording stored under a non-Latin primary name (大橋純子) whose romanized
form ("Junko Ohashi") is only an alias returns ZERO results, even though MB
has it. Whole swaths of a community library (e.g. romanized J-pop / city-pop
charts) were unsearchable.

- `build_recording_query(..., loose=True)` drops the field scoping + phrases
  for plain AND-ed term groups (`(telephone number) AND (junko ohashi)`),
  which searches the whole document incl. aliases.
- `_mb_search_recordings` runs the strict query first (unchanged, high
  precision) and only on an EMPTY result retries once with the loose query —
  so mainstream matches are untouched and the extra throttled request is spent
  only on a miss. Results are re-scored by rank_candidates, so recall goes up
  without lowering match quality (auto-accept still needs the per-field floors).

Verified live: "Junko Ohashi / Telephone Number" and "Anri / Windy Summer"
(both 0 under the strict query) now surface the real records; "AC/DC /
Highway to Hell" still hits strict at score 1.0 with no loose retry.

Follow-up (separate): alias-aware SCORING so these can auto-confirm, not just
appear as manual candidates.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(enrichment): alias-aware scoring (auto-confirm non-Latin-primary artists)

Builds on the loose-search fallback: that surfaces a recording stored under a
Japanese primary name (大橋純子) via its romanized alias, but the SCORER still
compared the reference ("Junko Ohashi") against the primary only → artist
similarity 0 → below the auto floor, so it could only ever be a manual
candidate, never an auto-fill.

- mb_match: `cand_artist_sim` takes the best similarity over the candidate's
  primary name AND its `artist_aliases`; score_candidate + classify use it.
- server: `_mb_artist_aliases(id)` fetches an artist's aliases (one throttled
  lookup, process-cached — a one-artist discography costs ONE request) and
  `_alias_enrich` attaches them ONLY to promising near-misses (title agrees,
  primary artist doesn't) so a normal pass spends zero extra requests. Wired
  into both the auto-matcher (_enrich_one) and the manual search proxy.

Verified live: "Junko Ohashi / Telephone Number" → 大橋純子 candidate goes from
score 0.5 (loose-only) to 1.0 (auto-confirmable), ranked #1; "AC/DC / Highway
to Hell" unchanged at 1.0 with no alias lookup.

Stacks on #771 (feat/mb-loose-search-fallback).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* fix(enrichment): keep live exclusion in the loose search fallback

The loose fallback dropped the strict path's -secondarytype:Live filter, so a
studio chart whose strict query missed could fall back to — and, since
score_candidate doesn't penalize live takes, auto-confirm — a live-only
recording. Apply the same live gate to the loose query (skipped only when the
source title is itself a live take).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: byrongamatos <xasiklas@gmail.com>

lib/mb_match.py

@@ -144,12 +144,31 @@ def _duration_int(v):
         return None
 
 
+def cand_artist_sim(song: dict, cand: dict) -> float:
+    """Best artist similarity between the song's reference artist and the
+    candidate's PRIMARY name OR any of its `artist_aliases` (romanized/alternate
+    names). MusicBrainz stores many artists under a non-Latin primary name
+    (大橋純子) with the romanized form ("Junko Ohashi") only as an alias, so a
+    reference typed/derived in romaji scores 0 against the primary but 1.0
+    against the alias. The caller (server) attaches `artist_aliases` only for
+    promising near-misses, so this is a plain max when they're present and the
+    original single comparison when they're not."""
+    best = similarity(song.get("artist"), cand.get("artist"), artist=True)
+    for alias in cand.get("artist_aliases") or []:
+        if best >= 1.0:
+            break
+        s = similarity(song.get("artist"), alias, artist=True)
+        if s > best:
+            best = s
+    return best
+
+
 def score_candidate(song: dict, cand: dict) -> float:
     """Combined confidence that MusicBrainz candidate `cand` is the song the
     chart transcribes. 0.5*artist + 0.5*title, plus small year/duration
     corroboration bonuses, capped at 1.0. Missing fields score 0 on their
     half — classify() separately refuses to auto-match without both."""
-    artist_sim = similarity(song.get("artist"), cand.get("artist"), artist=True)
+    artist_sim = cand_artist_sim(song, cand)
     title_sim = similarity(song.get("title"), cand.get("title"))
     score = 0.5 * artist_sim + 0.5 * title_sim
     sy, cy = _year_int(song.get("year")), _year_int(cand.get("year"))
@@ -180,7 +199,7 @@ def classify(song: dict, cand: dict, score: float, auto_min: float | None = None
     """
     if auto_min is None:
         auto_min = AUTO_MIN
-    artist_sim = similarity(song.get("artist"), cand.get("artist"), artist=True)
+    artist_sim = cand_artist_sim(song, cand)
     title_sim = similarity(song.get("title"), cand.get("title"))
     if (score >= auto_min and artist_sim >= AUTO_ARTIST_MIN
             and title_sim >= AUTO_TITLE_MIN):