Chaim
ac279220c4
Replaces the CSV-edit workflow with an in-app tagging page so the chair/Dafna
can label the extraction-quality gold-set by clicking, and see validator
precision/recall live.
Schema (V29): halacha_goldset — a stratified, human-tagged evaluation batch
(is_holding / correct_type / quote_complete, NULL until tagged).
db.py:
- goldset_create_sample (stratified round-robin over case×rule_type, idempotent),
- goldset_list (items + halacha content + the machine's own labels),
- goldset_tag (partial — one field at a time for keyboard tagging),
- goldset_score (ports the script's P/R/F1: each validator scored as a
not-a-holding detector against the human tags — the #81.8 input).
API: GET /api/goldset, POST /api/goldset/sample, GET /api/goldset/score,
PATCH /api/goldset/{id}.
web-ui:
- lib/api/goldset.ts (hooks),
- components/goldset/goldset-panel.tsx — card-per-item, keyboard-first
(J/K nav, H/N holding, C/X quote), progress bar, hide-tagged toggle, and a
collapsible live score table,
- app/goldset/page.tsx + nav link "מדגם-זהב" under ידע ולמידה.
Methodology guard kept explicit in UI + docstrings: tags are HUMAN ground truth,
no AI pre-fill (circular bias). Populated a 150-item stratified batch.
Verified: backend create/list/tag/score against the live DB; tsc --noEmit 0;
py_compile ok. (Local Turbopack build blocked by worktree symlink — CI builds clean.)
Invariants: G1 (eval set modeled at source in its own table); G2 (reuses the same
halacha_quality validators the extractor runs — no parallel scoring logic).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>