3 Commits

Include renames

Author	SHA1	Message	Date
bvandeusenandClaude Opus 5	573228b9da	db: finish reconciling the models with the deployed schema (#3275) ... Closes the residue the first reconciliation pass left, and corrects a factual error I put into the record. sha256 was NOT missing a uniqueness guarantee. I read `op.create_index("ix_image_record_sha256", ...)` at 0001 line 151 and concluded duplicates were possible, without reading line 149 two lines above it: sa.UniqueConstraint("sha256", name="uq_image_record_sha256"), Uniqueness has held since the initial schema. The database expresses it as a CONSTRAINT plus a separate non-unique lookup index; the model said `unique=True, index=True`, which is one UNIQUE index under a different name. Same guarantee, different objects — which is exactly why the two schemas did not line up. The model now declares both objects. No DDL. 0088's docstring, which repeated the claim, is corrected in place. Two real divergences, both the MODEL over-claiming: * source: uq_source_artist_platform_url (alembic 0010) was declared nowhere in the models — source.py had no __table_args__ at all — so autogenerate would have proposed DROPPING it. * head_metrics_snapshot.tag_id: model said NOT NULL, 0060 created it nullable. Left nullable; the FK already cascades. Seven constraints renamed to what the chain actually created, rather than what base.py's naming convention renders: uq_series_page_image, uq_series_chapter_anchor_page, fk_series_chapter_anchor_page, fk_image_record_artist_id, fk_image_provenance_from_attachment, and the two hand-shortened fk_tsr_* names from 0003. Float server_defaults now mirror their own migration, per column. The chain is MIXED: a plain string renders DEFAULT '0.90'::double precision, sa.text() renders DEFAULT 0.90, and the migrations used both. Seven columns take text(); the rest stay strings. Two literals also disagreed outright — process_{auto_apply,conflict}_threshold said 0.9/0.5 against the migration's 0.90/0.50. baseline.yml gains two things. A repair for a SECOND generator defect in the same class as the missing pgvector import: base.py's ck convention contains %(constraint_name)s, so it applies even to a NAMED CheckConstraint — autogenerate writes the already-rendered name into the migration and running it applies the convention again, yielding ck_ml_settings_ck_ml_settings_singleton. That is round-tripping damage, not a claim the models make, so it is undone rather than counted. And the diff now runs twice. Column ORDER differs permanently between a schema built by 87 ADD COLUMNs and one built in a single shot — the operator's database keeps chain order forever, a fresh install gets model order — so a check that failed on it could never pass. The second pass SORTS column lines within each CREATE TABLE instead of deleting them, which cannot hide a column present on one side only, or one whose type, nullability or default differs. Ordered diff is reported as information; the order-insensitive one is the verdict. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017QHszn9H8VBvx5Ke8x1hvw	2026-08-31 00:24:00 -04:00
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 Opus 4.8	48c8811d69	feat(heads): auto-apply observability + on by default (#114 auto-apply B) ... Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration 0059 + model default flipped. The support (>=30) + measured-precision gates keep it safe and every auto-tag is reversible. Observability so the operator can tune from real data: - MISFIRE = an auto-applied (source='head_auto') tag the operator later removes. UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it). Both captured at correction time in TagService.add_to_image/remove_from_image (source is lost on delete) into durable per-tag counters (head_metric), keyed by tag so they survive head retrain/prune. - Daily snapshot_head_metrics writes a per-concept time-series point (head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires + head quality; 180-day retention; daily beat. - GET /api/heads/metrics: per-concept current counts + realized misfire rate + head quality, plus the snapshot time-series — the report to tune the precision target + support floor. Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual removal isn't a misfire, headless manual add isn't an under-fire), snapshot time-series, metrics API. What's the autofire threshold? There's no single number — each graduated head derives its OWN probability cutoff from its PR curve: the operating point that holds precision >= head_auto_apply_precision (0.97) at max recall. The global knobs are that target + the >=30 support floor. NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-29 00:36:58 -04:00