feat(rec): audio analyzer and My Wave v1 engine (M4)

- migration 00006: track features and embeddings (pgvector, HNSW),
  analysis queue filled by a trigger on new tracks, wave sessions and
  impressions, recommender profiles, model versions
- analyzer basic-1 (ADR-014): ffmpeg decode, three 30 s excerpts,
  256-d timbre/rhythm embedding, tempo by onset autocorrelation, key,
  loudness, energy, danceability, acousticness, intro length; worker
  with SKIP LOCKED claims, retries and re-analysis on version change
- wave engine v1: candidates (session/taste similarity, fresh,
  liked artists, rediscover, popular, exploration, seed), filters
  (dislikes, played in 3 h, already served), heuristic score, activity
  and mood fit from contexts.yaml with relaxing thresholds, MMR with
  one track per artist, exploration share, session vector updated by
  reactions and a change of course after three skips
- POST /internal/wave/batch with the catalog index cached in memory
  and the session kept in Redis

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

services/recommender/pipu_rec/api/app.py

@@ -10,6 +10,8 @@ from fastapi import Depends, FastAPI, Header, HTTPException, status
 from fastapi.responses import JSONResponse
 
 from pipu_rec.settings import Settings
+from pipu_rec.wave.engine import MODEL_VER
+from pipu_rec.wave.service import BatchRequest, BatchResponse, WaveService
 
 Check = Callable[[], Awaitable[None]]
 
@@ -18,7 +20,9 @@ log = logging.getLogger(__name__)
 CHECK_TIMEOUT_S = 2.0
 
 
-def create_app(settings: Settings, checks: dict[str, Check] | None = None) -> FastAPI:
+def create_app(
+    settings: Settings, checks: dict[str, Check] | None = None, wave: WaveService | None = None
+) -> FastAPI:
     app = FastAPI(title="pipu-recommender", version=settings.version, docs_url=None, redoc_url=None)
     checks = checks or {}
 
@@ -54,6 +58,13 @@ def create_app(settings: Settings, checks: dict[str, Check] | None = None) -> Fa
 
     @app.get("/internal/status", dependencies=[Depends(require_token)])
     async def internal_status() -> dict[str, object]:
-        return {"version": settings.version, "models": [], "ranker": "heuristic-v1"}
+        return {"version": settings.version, "models": [], "ranker": MODEL_VER}
+
+    @app.post("/internal/wave/batch", dependencies=[Depends(require_token)])
+    async def wave_batch(req: BatchRequest) -> BatchResponse:
+        if wave is None:
+            raise HTTPException(status.HTTP_503_SERVICE_UNAVAILABLE, detail="wave.unavailable")
+        # Сборка — numpy и синхронные запросы к БД: в пуле потоков, чтобы не блокировать цикл событий.
+        return await asyncio.to_thread(wave.batch, req)
 
     return app