So the work can be checked through an API as the agent fills in vectors (same
pattern as /api/heads/metrics):
- GET /api/ccip/overview: regions by kind, images with figure CCIP vectors, the
per-character reference counts (which characters have enough examples to match
on), and the embedding versions present.
- GET /api/ccip/images/<id>: that image's stored regions (bbox, frame_time,
has_ccip/has_siglip, versions) + the CCIP character matches it would get — for
spot-checking detector + matcher output.
Read-only, no GPU. (Queue depth is already at /api/gpu/status.)
Tests: overview coverage counts + per-character refs; per-image regions + matches.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
The thin HTTP surface over the queue so the desktop agent stays HTTP-only:
- Agent endpoints (Authorization: Bearer <token>): POST /api/gpu/jobs/lease
(returns jobs + image_url + mime + video frame cadence), /submit (stores
regions via RegionService + closes the job; 409 on a stale lease), /heartbeat,
/fail. Token validated against AppSetting (mirrors the extension-key pattern,
constant-time compare).
- Admin (browser): GET/POST /api/gpu/token[/rotate] (generate + show the agent
token), GET /api/gpu/status (queue counts), POST /api/gpu/backfill → dispatches
enqueue_gpu_backfill.
- enqueue_gpu_backfill(task): one INSERT…SELECT enqueues a job per image lacking
one for the task (scales to the full library; idempotent).
Agent flow: lease over HTTP → fetch pixels via the normal FC image URL → compute
on the GPU → submit. Redis/Postgres never exposed.
Tests: bearer required (+ wrong-token 401), lease→submit round-trip (region+CCIP
vector stored, job done via /status), stale-lease 409, backfill enqueue +
idempotency.
NEXT: the agent container + control UI, then the CCIP detector/embedder + matcher.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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
Slice 1 of milestone #114 (tagging v2). Proves the frozen-embedding + trained-
head spine on the operator's own data, reusing the SigLIP embeddings already
stored on image_record — no re-embedding, no GPU.
Per concept: train a logistic-regression HEAD (positives + negatives = explicit
rejections + sampled unlabeled) vs the old single-CENTROID baseline; report
cross-validated precision/recall/AP for both, a LEARNING CURVE (AP/F1 as tagged
positives grow 10→30→100→300), and example image ids (head-would-suggest /
head-doubts-positive) to eyeball.
Persisted so the report SURVIVES navigation (operator-flagged): the run + full
report live in a new tag_eval_run row (mirrors library_audit_run); the admin
card will rehydrate from GET on mount, not transient state.
- models.TagEvalRun + migration 0056; runs on the ml queue (only worker with
numpy/sklearn) — numpy/sklearn lazy-imported so the API can still enqueue.
- services/ml/tag_eval (compute + start helper, one-running guard), tasks.ml
.tag_eval_run, api/tag-eval (POST create, GET history light / detail w/ report).
- recover_stalled_tag_eval_runs sweep + retention (keep last 20) + 5-min beat
(rule 89). scikit-learn added to requirements-ml.
- tests: param normalization + the rehydrate read-path + create/conflict.
Frontend admin card (trigger + render persisted report) follows next.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
trigger enqueues a Celery scan_directory task and returns 202 with the
celery task id (the actual scan happens asynchronously; status surfaces
via /api/import/status and /api/system/stats). list_tasks supports
status filter and cursor pagination. retry-failed resets failed tasks
to queued and re-enqueues. clear-completed deletes finished tasks
older than the supplied age (used by the Settings UI's cleanup control).
mode='deep' is explicitly rejected with 400 until FC-2d ships it; the
frontend's UI in FC-2a only sends 'quick'.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Missed in the Task 11 commit because of a stale-cache Edit failure.
Routes /api/settings/import and /api/system/stats are now actually mounted.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Thin async blueprint delegating to TagService. Returns 400 with the
TagValidationError message on bad kind/fandom combos so the frontend can
surface the reason. List/add/remove endpoints scoped under
/api/images/<id>/tags follow REST conventions.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Thin Quart blueprint that delegates to GalleryService. Cursor-based scroll
returns images + a date_groups summary so the SPA can render year-month
headers without re-grouping client-side. Timeline returns year-month
buckets for the sidebar jump nav. Jump returns a cursor positioned at a
specific year-month so the gallery can scroll to historical periods.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Config reads from env vars (12-factor); .env.example documents defaults.
Health endpoint is liveness-only (no DB/Redis touch). Test added for CI.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen