3 Commits

Include renames

Author	SHA1	Message	Date
bvandeusen	5e1996e77f	db: reconcile the models with the deployed schema (#3275) ... Milestone 328's acceptance test compared a database built by the real 0001..0087 chain against one built from the models, and found ~130 places where they disagree. This closes them. Almost all were the MODEL being wrong, so almost all of this is model edits with no DDL — the database already had these things, nothing in it changes, and no deploy is needed for this part: * 92 columns gained server_default. The models carried Python-side `default=` only, so the ORM filled the value and the column had no database default. Anything inserting outside the ORM behaved differently from production. * Eleven indexes that existed only in migrations are now declared: the three backup_run reporting indexes, the two date-ordered image_record browse indexes, import_task and presentation_review, and the three task_run history indexes. All use text() for their DESC ordering and postgresql_where for the partial one. * Two UNIQUE indexes that autogenerate silently proposed DROPPING, because neither is expressible as a UniqueConstraint: uq_tag_name_kind_fandom — an EXPRESSION index over (name, kind, COALESCE(fandom_id, 0)) uq_post_artist_external_id_null_source — PARTIAL, WHERE source_id IS NULL post.py already had a comment describing the second one. The comment was right; nothing declared it. * The two external_link enum CHECKs (host, status) — rule 36 territory, and absent from the model entirely. * Two indexes were named explicitly. A bare index=True generated ix_tag_alias_canonical_tag_id where the database has ix_tag_alias_canonical, so autogenerate proposed a drop+create of an index that was already there under another name. Same for tag_suggestion_rejection. Only ONE thing needed DDL, as 0088: tag.fandom_id is declared index=True but no migration ever created that index. Deliberately NOT here: image_record.sha256. The model says unique=True; 0001 created a plain index. Duplicates are possible today and the ORM believes otherwise. The fix depends on whether duplicates already exist — if they do, that is a dedupe decision, not a constraint — so it waits on an answer about live data. The real severity of #3275 is not the squash. It is that --autogenerate has been unsafe on this project: run against the old models it would have proposed dropping eleven indexes and two uniqueness guarantees.	2026-08-30 14:42:30 -04:00
bvandeusenandClaude Fable 5	eaea4308fc	chore: retire the tag-eval harness — it proved the heads system, job done (operator-approved) ... The head-vs-centroid eval (#1130) existed to prove the 'frozen embedding + trained head' spine; the operator accepted the tagging system and dropped the harness. Removed per rule 22: TagEvalCard + store, /api/tag_eval blueprint, tag_eval_run ml task, recover-stalled-tag-eval-runs sweep + beat entry, TagEvalRun model + table (migration 0073), and its tests. The eval's data loaders + metric helpers were NOT eval-specific — the nightly heads trainer runs on them — so they moved verbatim to services/ml/training_data.py (heads.py import updated; behavior unchanged). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM	2026-07-02 12:41:24 -04:00
bvandeusenandClaude Opus 4.8	22c3b54746	feat(heads): production per-concept heads — train + score backend (#114 A) ... The eval (#1130) proved the frozen-embedding + trained-head spine; this lands its production form (the first of three slices that make heads the suggestion source, replacing Camie + centroid). - tag_head: one logistic-regression head per general/character concept with enough labelled positives. Weights (pgvector), honest CV-derived suggest threshold + earned-auto-apply point, and per-concept quality metrics. - head_training_run: persisted batch lifecycle (mirrors tag_eval_run) so the admin card shows live + historical status across navigation. - services/ml/heads.py: TRAIN (sync, ml worker, reuses tag_eval's proven data loaders + metric math so production heads match measured eval numbers) and SCORE (async, API worker — numpy via pgvector, no scikit-learn): score one image's embedding against all heads → the rail's suggestions, cached on (count, max trained_at) so a retrain invalidates without per-request loads. - tasks.ml.train_heads (ml queue, commits per head so a kill leaves progress) + recover_stalled_head_training_runs sweep + retention(20) + 5-min beat (rule 89). - api/heads.py: POST /api/heads/train (one run at a time, 409 guard) + GET /api/heads (count, graduated, last-trained, running, per-concept table, recent runs). - ml_settings: head_min_positives + head_auto_apply_precision, tunable via /api/ml/settings. Scoring isn't wired into the rail yet (slice C) and the admin UI is slice B — this slice makes training + scoring exist and CI-verifiable. 'precision' column stored as precision_cv (SQL reserved word). Migration 0058. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-28 10:36:25 -04:00