ci: integration tests on PostgreSQL + pgvector for migrations and store SQL

New `ci / db` job with a pgvector service: Go applies all migrations,
rolls them back to zero and applies them again, then runs the scanner,
fingerprint, write-template, top-tracks and upload-queue store queries;
Python runs the analysis worker, catalog / profile loading and a full
ALS training run against the same schema. Until now this SQL ran
nowhere before reaching a real server.

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

services/recommender/tests/test_db_integration.py

@@ -0,0 +1,141 @@
+"""SQL рекомендателя на настоящей схеме (CI-job `ci / db`; схему заранее накатывают миграции api).
+
+Локально: TEST_DATABASE_URL=postgresql://… uv run pytest tests/test_db_integration.py
+"""
+
+import os
+import time
+import uuid
+from typing import Any
+
+import numpy as np
+import psycopg
+import pytest
+
+from pipu_rec.analysis.features import Features
+from pipu_rec.analysis.worker import Worker
+from pipu_rec.train.pipeline import load_interactions, train_all
+from pipu_rec.wave.engine import Profile, Session, build_batch
+from pipu_rec.wave.store import load_index, load_profile
+
+DSN = os.environ.get("TEST_DATABASE_URL", "")
+pytestmark = pytest.mark.skipif(not DSN, reason="TEST_DATABASE_URL is not set")
+
+
+def uid() -> str:
+    return str(uuid.uuid4())
+
+
+@pytest.fixture()
+def world() -> dict[str, Any]:
+    """Хранилище, 2 исполнителя × 10 треков, 6 слушателей с событиями и реакциями."""
+    with psycopg.connect(DSN, autocommit=True) as conn:
+        src = uid()
+        conn.execute(
+            "INSERT INTO library_sources (id, name, type, local_path, status) VALUES (%s, 'it', 'local', '', 'mounted')",
+            (src,),
+        )
+        tracks: list[str] = []
+        for a in range(2):
+            artist = uid()
+            conn.execute(
+                "INSERT INTO artists (id, name, sort_name, name_norm) VALUES (%s, %s, %s, %s)",
+                (artist, f"A{a}", f"A{a}", f"a{a}-{artist[:6]}"),
+            )
+            album = uid()
+            conn.execute(
+                "INSERT INTO albums (id, title, title_norm, album_artist_id) VALUES (%s, %s, %s, %s)",
+                (album, f"Al{a}", f"al{a}", artist),
+            )
+            for i in range(10):
+                t, f = uid(), uid()
+                conn.execute(
+                    "INSERT INTO tracks (id, title, title_norm, album_id, duration_ms) VALUES (%s, %s, %s, %s, 200000)",
+                    (t, f"T{a}{i}", f"t{a}{i}", album),
+                )
+                conn.execute(
+                    "INSERT INTO media_files (id, source_id, rel_path, size_bytes, mtime, format, duration_ms, track_id) "
+                    "VALUES (%s, %s, %s, 1, now(), 'flac', 200000, %s)",
+                    (f, src, f"{a}/{i}.flac", t),
+                )
+                conn.execute("UPDATE tracks SET primary_file_id = %s WHERE id = %s", (f, t))
+                conn.execute(
+                    "INSERT INTO track_artists (track_id, artist_id, role, position) VALUES (%s, %s, 'main', 0)",
+                    (t, artist),
+                )
+                tracks.append(t)
+        users = []
+        for u in range(6):
+            user = uid()
+            conn.execute(
+                "INSERT INTO users (id, username, password_hash) VALUES (%s, %s, 'x')",
+                (user, f"u{u}-{user[:6]}"),
+            )
+            users.append(user)
+            group = tracks[:10] if u % 2 == 0 else tracks[10:]
+            for k, t in enumerate(group):
+                kind = "play_end" if k < 8 else "skip"
+                conn.execute(
+                    "INSERT INTO play_events (id, user_id, track_id, type, occurred_at, played_ms, client_type) "
+                    "VALUES (%s, %s, %s, %s, now() - interval '20 days', %s, 'web')",
+                    (uid(), user, t, kind, 200000 if kind == "play_end" else 3000),
+                )
+                conn.execute(
+                    "INSERT INTO user_track_stats (user_id, track_id, play_count, complete_count, last_played_at) "
+                    "VALUES (%s, %s, 1, %s, now() - interval '20 days')",
+                    (user, t, 1 if kind == "play_end" else 0),
+                )
+            conn.execute(
+                "INSERT INTO track_reactions (user_id, track_id, reaction) VALUES (%s, %s, 'like')",
+                (user, group[0]),
+            )
+        return {"tracks": tracks, "users": users}
+
+
+def test_analysis_worker_saves_features(world: dict[str, Any]) -> None:
+    w = Worker(DSN, "/library", "/library-host", 1)
+    emb = np.zeros(256, dtype=np.float32)
+    emb[0] = 1.0
+    feats = Features(
+        bpm=120.0,
+        bpm_confidence=0.9,
+        key="C",
+        scale="major",
+        loudness_lufs=-12.0,
+        energy=0.6,
+        danceability=0.5,
+        valence=0.5,
+        acousticness=0.2,
+        intro_ms=1000,
+        embedding=emb,
+    )
+    with psycopg.connect(DSN, autocommit=True) as conn:
+        jobs = w.claim(conn, 5)
+        assert jobs, "every new track is queued for analysis by the trigger"
+        w.save(conn, jobs[0].track_id, feats)
+        w.fail(conn, jobs[1].track_id, "boom")
+        w.requeue_stale(conn)
+        row = conn.execute(
+            "SELECT bpm FROM track_features WHERE track_id = %s", (jobs[0].track_id,)
+        ).fetchone()
+        assert row is not None and row[0] == pytest.approx(120.0)
+
+
+def test_training_and_wave_queries(world: dict[str, Any]) -> None:
+    with psycopg.connect(DSN) as conn:
+        assert len(load_interactions(conn)) >= 60
+    result = train_all(DSN, now=time.time())
+    assert result.get("activated") is True, result
+    with psycopg.connect(DSN) as conn:
+        index = load_index(conn)
+        assert set(world["tracks"]) <= set(index.ids)
+        assert index.has_cf.sum() >= 20
+        profile: Profile = load_profile(conn, world["users"][0])
+        assert profile.taste_cf is not None
+        assert profile.recent_likes
+        active = conn.execute(
+            "SELECT count(*) FROM model_versions WHERE kind = 'als' AND is_active"
+        ).fetchone()
+        assert active is not None and active[0] == 1
+    picks = build_batch(index, profile, Session(), [], n=5, rng=np.random.default_rng(0))
+    assert len(picks) == 5