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/wave/service.py

@@ -0,0 +1,93 @@
+"""Сервис «Моей волны»: собирает пачку из каталога, профиля, сессии и контекста."""
+
+from __future__ import annotations
+
+from typing import Literal
+
+import numpy as np
+import psycopg
+from pydantic import BaseModel, Field
+
+from pipu_rec.wave.contexts import Contexts, Rule
+from pipu_rec.wave.engine import MODEL_VER, Pick, build_batch, taste_centroids, update_session
+from pipu_rec.wave.store import IndexHolder, SessionStore, load_profile, seed_tracks
+
+MAX_SERVED_MEMORY = 300
+
+Outcome = Literal["complete", "skip", "skip_early", "like", "dislike"]
+
+
+class Feedback(BaseModel):
+    track_id: str
+    outcome: Outcome
+
+
+class Seed(BaseModel):
+    type: Literal["track", "album", "artist"]
+    id: str
+
+
+class BatchRequest(BaseModel):
+    user_id: str
+    session_id: str
+    activity: str | None = None
+    mood: str | None = None
+    seed: Seed | None = None
+    exclude_ids: list[str] = Field(default_factory=list, max_length=1000)
+    feedback: list[Feedback] = Field(default_factory=list, max_length=200)
+    n: int = Field(default=5, ge=1, le=20)
+
+
+class BatchTrack(BaseModel):
+    id: str
+    source: str
+    score: float
+
+
+class BatchResponse(BaseModel):
+    tracks: list[BatchTrack]
+    model_ver: str
+
+
+class WaveService:
+    def __init__(self, dsn: str, redis_url: str, contexts: Contexts) -> None:
+        self.dsn = dsn
+        self.index = IndexHolder(dsn)
+        self.sessions = SessionStore(redis_url)
+        self.contexts = contexts
+
+    def rules(self, activity: str | None, mood: str | None) -> list[Rule]:
+        """Занятие и настроение комбинируются — пересечение профилей; неизвестный ключ игнорируется."""
+        rules: list[Rule] = []
+        if activity and activity in self.contexts.activities:
+            rules += self.contexts.activities[activity].rules
+        if mood and mood in self.contexts.moods:
+            rules += self.contexts.moods[mood].rules
+        return rules
+
+    def batch(self, req: BatchRequest) -> BatchResponse:
+        index = self.index.get()
+        with psycopg.connect(self.dsn) as conn:
+            profile = load_profile(conn, req.user_id)
+            seeds = seed_tracks(conn, req.seed.type, req.seed.id) if req.seed else None
+        session = self.sessions.get(req.session_id)
+        update_session(
+            index, session, [(f.track_id, f.outcome) for f in req.feedback], taste_centroids(index, profile)
+        )
+        picks: list[Pick] = build_batch(
+            index,
+            profile,
+            session,
+            self.rules(req.activity, req.mood),
+            n=req.n,
+            exclude=set(req.exclude_ids),
+            seed_ids=seeds,
+            rng=np.random.default_rng(),
+        )
+        session.served = (session.served + [p.id for p in picks])[-MAX_SERVED_MEMORY:]
+        artists = [index.artist[index.pos[p.id]] for p in picks]
+        session.recent_artists = (session.recent_artists + artists)[-10:]
+        self.sessions.put(req.session_id, session)
+        return BatchResponse(
+            tracks=[BatchTrack(id=p.id, source=p.source, score=p.score) for p in picks], model_ver=MODEL_VER
+        )