Backend for the halacha approval-queue triage (#84). The keyboard UI, batch
actions and defer/reject (#84.4–6) already shipped; this adds the gating,
prioritization and metrics the queue was missing.
db.list_halachot — two opt-in triage controls:
* exclude_low_quality (#84.1): drop items carrying ANY quality_flag
(application / quote_unverified / truncated / non_decision / thin /
nli_unsupported / near_duplicate) — they belong in a 'needs extraction fix'
bucket, not the chair's approve queue.
* order_by_priority (#84.3): active-learning order — negatively-treated
first, then most-uncertain (lowest confidence), then oldest — instead of
FIFO, so the highest-value decisions surface first.
halachot_pending (MCP) — now gated + prioritized BY DEFAULT; include_low_quality=
true reveals the needs-fix bucket. The agent review path benefits immediately.
GET /api/halachot — same two params, default OFF (non-breaking; the UI opts in).
metrics.halacha_backlog (#84.7) — splits pending into clean vs flagged, adds
deferred, reviewed_total, approve_ratio, and a pending_by_flag breakdown, so the
backlog distinguishes real review work from extraction noise.
Deferred (documented): #84.2 near-duplicate cluster cards and wiring the UI
fetch to the new params require frontend work + an api:types regen AFTER this
deploys (the new query params aren't in prod's OpenAPI until then) — a clean
follow-up. The backend fully supports both now.
Verified against the live DB (read-only):
- pending 177 → gated-clean 110, 0 flagged items leak into the clean queue.
- priority order surfaces the lowest-confidence items first (0.55, 0.55, ...).
- backlog: pending_clean=110 / pending_flagged=67 / approve_ratio=0.916,
pending_by_flag={nli_unsupported:59, quote_unverified:3, thin:3, truncated:2}.
- pytest tests/test_halacha_quality.py — 52 passed (no regression).
Invariants: G1 (gate at source — SQL filter, not post-hoc); G2 (no parallel
path — same list_halachot); §6 (flagged items routed to a bucket, never dropped).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Covers GAP-11 (INV-RET4/G8) and GAP-14 (INV-QA1/G10). Retrieval quality was
never measured (only telemetry observation) and the halacha review backlog was
invisible (the 10/19 gap was found by accident).
Unit B — backlog visibility (pure code, container):
- metrics.halacha_backlog(conn) → {pending_review, approved, rejected, published,
total, oldest_pending_at}; surfaced in metrics.get_dashboard() (get_metrics MCP
tool) and /api/system/diagnostics. Live count revealed 178 pending / 1552 total,
oldest from 2026-05-03 — previously invisible.
Unit A — retrieval eval harness (host-side scripts):
- scripts/eval_gold_bootstrap.py — seeds data/eval/gold-set.jsonl. Two sources:
citations (cited==relevant via search_relevance_feedback — empty until decisions
cite precedents) and known_item (query=case_name → relevant=self; a real
citation-free signal, the methodology #52 checked by hand). Idempotent; preserves
source='chair' rows.
- scripts/eval_retrieval.py — runs the production retrieval path (search_library /
search_internal) over the gold-set; computes precision@k, recall@k, MRR, nDCG@k
(k=5,10); aggregates overall + per-corpus + per-practice_area; writes a report and
a delta vs committed baseline.json (which records the retrieval_config it reflects).
--self-test unit-checks the metric math offline.
Gold-set strategy = hybrid (chair decision): bootstrap + chair review. The citation
source is empty today (0 cited precedents in decisions), so the seed is known-item
(77 queries: 54 internal_decisions + 23 precedent_library). The gold-set is
PROVISIONAL until Dafna reviews it (the domain chair-gate).
Baseline (production config: multimodal+rerank on): R@10=0.987, MRR=0.837,
nDCG@10=0.872. Finding: MULTIMODAL_ENABLED=true slightly lowers known-item recall
(image-page results displace exact name matches) — relevant to #15. precedent_library
weaker than internal (R@10 0.957 vs 1.0) — one external precedent unfindable by name.
"CI gate" realized as discipline (re-runnable harness + committed baseline + run
before/after any retrieval-layer change) — retrieval needs prod DB + Voyage, no CI
runner has that access.
Spec: docs/superpowers/specs/2026-05-31-fu5-eval-harness-design.md
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
