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>
- 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>
mrleo1nidandClaude Opus 5.5