3 Commits

Include renames

Author	SHA1	Message	Date
bvandeusen	6959e1220c	Revert "db: collapse alembic 0001..0087 into one baseline" ... This reverts 2529b51. Not a retreat — a reordering, on the operator's call, and the better sequence. The squash's acceptance test (run 4971) found ~130 places where the ORM models do not describe the deployed schema (#3275), including a unique=True the database never had and two UNIQUE indexes that exist only in migrations. Collapsing now would have baked all of that into the one file a public installer starts from. So: fix the drift first as ordinary migrations on the intact chain, let the operator deploy so their database moves to the corrected head, and only then collapse. The baseline is then generated from reconciled models and reproduces a schema worth reproducing. Nothing is lost by reverting. The baseline was never deployed, and regenerating it after the fixes is strictly better than patching this copy — it will come out of autogenerate correct rather than needing the same hand-finishing twice.	2026-08-30 14:34:28 -04:00
bvandeusen	2529b516e6	db: collapse alembic 0001..0087 into one baseline (milestone 328 step 1) ... 87 revisions narrating this project's build-out become one file that creates the schema in a single step. They cost nothing at runtime — all 86 upgrade steps ran in 0.2s (note #3260) — so this is a presentation change, not a performance one: a new installer should not inherit our development history to stand up a database. Deleted: 87 revisions (6,052 lines), the 10 tests/test_migration_*.py files (483 lines) that asserted intermediate states and backfills which no longer exist, and backend/app/utils/artist_backfill.py — the only live module a migration imported, with no other consumer anywhere. That last one satisfies the operator's separate request to inline it into 0008 and delete the module; the squash removes both outright. THE REVISION ID IS "0087", NOT "0001", ON PURPOSE. It is the id of the last revision collapsed, so an existing database is already at head and `alembic upgrade head` does nothing. The alternative is `alembic stamp` against live data, and stamp validates NOTHING — it writes a version string whether or not the schema matches, so a wrong baseline surfaces later, via the next real migration, with no clean way back. This removes that operation rather than making it safe. Future revisions run from 0088. Four things are hand-written because SQLAlchemy metadata does not carry them, and none fail at generation time: 1. CREATE EXTENSION vector — the VECTOR columns cannot be created without it, so it is ordered first in upgrade(). 2. CREATE EXTENSION tsm_system_rows — surfaces only when the random sample query runs. 3. the HNSW index on image_record.siglip_embedding, raw SQL because create_index cannot express USING hnsw (... vector_cosine_ops). The quietest of the four: everything works, similarity search just stops using an index. 4. import pgvector.sqlalchemy.vector — autogenerate EMITS pgvector.sqlalchemy.vector.VECTOR references without importing it, so the generated file dies with NameError on first run. The candidate came out of CI (run 4967) as checksummed base64 rather than a plain cat, because run 4964's cat was truncated mid-line inside a column definition with the step still green — 29 tables instead of 42, and it looked entirely plausible. Verified here: 56,582 bytes, sha256 471acfca69c0…, 42 tables, 66 indexes, 42 drops. NOT YET PROVEN against the old chain. baseline.yml does that, and it is step 2's gate; this commit does not claim the schemas match.	2026-08-30 13:45:35 -04:00
bvandeusenandClaude Fable 5	19b962f1a7	feat(b3): ml-worker becomes optional — embed-only role, decoupled GPU coordination, cpu-embed switch ... The ml-worker's ONLY processing role is now the CPU whole-image embed fallback (tag_and_embed renamed embed_image — Camie tagging was retired #1189 and the name kept implying otherwise; videos were already handled agent-style: frame sampling + mean-pool). Detection/cropping/CCIP stay GPU-agent-only, and their completion is judged per-pipeline: ccip by gpu_job rows, siglip by concept regions at the current model version — never by image_record.siglip_embedding. A CPU embed therefore can NEVER close crop work for the agent (regression test pins this; only the whole-image 'embed' job, the same artifact, is satisfied). Making removal actually safe (operator will drop the container): - GPU-queue coordination (enqueue_gpu_backfill, recover_orphaned_gpu_jobs, reprocess_gpu_jobs) moved verbatim to tasks/gpu_queue.py on the maintenance quick lane — it lived on the 'ml' queue only by module colocation, which made the ml-worker a hard dependency of the whole agent pipeline. - New ml_settings.cpu_embed_enabled (migration 0074, default ON so agent-less installs keep working): OFF stops the four import hooks queueing embed work nothing will consume and no-ops the manual backfill; switch lives on the renamed 'CPU embedding backfill' card. - NB heads training / auto-apply still run on the ml image (sklearn) — a stack that removes the container gives those up too. Deploy note: in-flight messages under the old task names are dropped by the new workers; the 60s orphan sweep + hourly backfill re-fire under the new names immediately. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM	2026-07-02 16:53:08 -04:00