From ac279220c4d9e2cc78ffa9844485a5d465278550 Mon Sep 17 00:00:00 2001 From: Chaim Date: Sat, 6 Jun 2026 21:52:05 +0000 Subject: [PATCH] feat(goldset): interactive gold-set tagging page (#81.7/#81.8) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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) --- mcp-server/src/legal_mcp/services/db.py | 150 +++++++++- web-ui/src/app/goldset/page.tsx | 41 +++ web-ui/src/components/app-shell.tsx | 1 + .../src/components/goldset/goldset-panel.tsx | 283 ++++++++++++++++++ web-ui/src/lib/api/goldset.ts | 105 +++++++ web/app.py | 53 ++++ 6 files changed, 632 insertions(+), 1 deletion(-) create mode 100644 web-ui/src/app/goldset/page.tsx create mode 100644 web-ui/src/components/goldset/goldset-panel.tsx create mode 100644 web-ui/src/lib/api/goldset.ts diff --git a/mcp-server/src/legal_mcp/services/db.py b/mcp-server/src/legal_mcp/services/db.py index 4ca0fd0..ca38047 100644 --- a/mcp-server/src/legal_mcp/services/db.py +++ b/mcp-server/src/legal_mcp/services/db.py @@ -1256,6 +1256,27 @@ CREATE INDEX IF NOT EXISTS idx_equiv_halacha_a ON equivalent_halachot(halacha_a) CREATE INDEX IF NOT EXISTS idx_equiv_halacha_b ON equivalent_halachot(halacha_b); """ +SCHEMA_V29_SQL = """ +-- halacha_goldset (#81.7/#81.8): a human-tagged evaluation set. A stratified +-- sample of halachot the chair/Dafna labels (is_holding / correct_type / +-- quote_complete) so we can measure the extraction validators' precision/recall +-- and recalibrate the auto-approve threshold. The tags are the ground truth — +-- they MUST be human (no AI pre-fill) to avoid circular bias. +CREATE TABLE IF NOT EXISTS halacha_goldset ( + id UUID PRIMARY KEY DEFAULT gen_random_uuid(), + halacha_id UUID NOT NULL REFERENCES halachot(id) ON DELETE CASCADE, + batch TEXT NOT NULL DEFAULT 'default', + is_holding BOOLEAN, -- NULL until tagged + correct_type TEXT DEFAULT '', -- binding | interpretive | obiter | application | '' + quote_complete BOOLEAN, + tagged_by TEXT DEFAULT '', + tagged_at TIMESTAMPTZ, + created_at TIMESTAMPTZ DEFAULT now(), + UNIQUE (halacha_id, batch) +); +CREATE INDEX IF NOT EXISTS idx_goldset_batch ON halacha_goldset(batch); +""" + async def _run_schema_migrations(pool: asyncpg.Pool) -> None: async with pool.acquire() as conn: @@ -1288,7 +1309,8 @@ async def _run_schema_migrations(pool: asyncpg.Pool) -> None: await conn.execute(SCHEMA_V26_SQL) await conn.execute(SCHEMA_V27_SQL) await conn.execute(SCHEMA_V28_SQL) - logger.info("Database schema initialized (v1-v28)") + await conn.execute(SCHEMA_V29_SQL) + logger.info("Database schema initialized (v1-v29)") async def init_schema() -> None: @@ -4270,6 +4292,132 @@ async def _annotate_equivalents(pool, out: list[dict]) -> None: d["equivalents"] = by_src.get(str(d["id"]), []) +# ── Gold-set evaluation (#81.7 / #81.8) ────────────────────────────────────── + +async def goldset_create_sample( + n: int = 150, batch: str = "default", reset: bool = False, +) -> dict: + """Stratified sample of halachot (round-robin over case×rule_type) into a + tagging batch. Idempotent (ON CONFLICT); ``reset`` clears the batch first.""" + pool = await get_pool() + if reset: + await pool.execute("DELETE FROM halacha_goldset WHERE batch = $1", batch) + rows = await pool.fetch( + "SELECT id, case_law_id, rule_type FROM halachot WHERE rule_statement <> ''" + ) + from collections import defaultdict + buckets: dict = defaultdict(list) + for r in rows: + buckets[(r["case_law_id"], r["rule_type"])].append(r["id"]) + keys = list(buckets.values()) + sample: list = [] + i = 0 + while len(sample) < n and any(keys): + b = keys[i % len(keys)] + if b: + sample.append(b.pop()) + i += 1 + if i > n * 50: + break + inserted = 0 + for hid in sample: + res = await pool.execute( + "INSERT INTO halacha_goldset (halacha_id, batch) VALUES ($1, $2) " + "ON CONFLICT (halacha_id, batch) DO NOTHING", hid, batch, + ) + if res.endswith(" 1"): + inserted += 1 + total = await pool.fetchval( + "SELECT count(*) FROM halacha_goldset WHERE batch = $1", batch) + return {"batch": batch, "inserted": inserted, "total": total} + + +async def goldset_list(batch: str = "default") -> list[dict]: + """Gold-set items joined with the halacha content + the machine's labels.""" + pool = await get_pool() + rows = await pool.fetch( + "SELECT g.id, g.halacha_id::text AS halacha_id, g.is_holding, " + " g.correct_type, g.quote_complete, g.tagged_by, g.tagged_at, " + " h.rule_statement, h.supporting_quote, h.reasoning_summary, " + " h.rule_type, h.confidence, h.quality_flags, h.review_status, " + " cl.case_number, cl.case_name " + "FROM halacha_goldset g JOIN halachot h ON h.id = g.halacha_id " + "LEFT JOIN case_law cl ON cl.id = h.case_law_id " + "WHERE g.batch = $1 ORDER BY g.created_at, g.id", batch, + ) + out = [] + for r in rows: + d = dict(r) + if d.get("tagged_at") is not None: + d["tagged_at"] = d["tagged_at"].isoformat() + if d.get("confidence") is not None: + d["confidence"] = float(d["confidence"]) + out.append(d) + return out + + +async def goldset_tag( + goldset_id: UUID, *, is_holding: bool | None = None, + correct_type: str | None = None, quote_complete: bool | None = None, + tagged_by: str = "chair", +) -> dict | None: + """Save one human tag (partial — only provided fields change).""" + pool = await get_pool() + sets = ["tagged_by = $2", "tagged_at = now()"] + params: list = [goldset_id, tagged_by] + i = 3 + if is_holding is not None: + sets.append(f"is_holding = ${i}"); params.append(is_holding); i += 1 + if correct_type is not None: + sets.append(f"correct_type = ${i}"); params.append(correct_type); i += 1 + if quote_complete is not None: + sets.append(f"quote_complete = ${i}"); params.append(quote_complete); i += 1 + row = await pool.fetchrow( + f"UPDATE halacha_goldset SET {', '.join(sets)} WHERE id = $1 RETURNING *", *params, + ) + return dict(row) if row else None + + +async def goldset_score(batch: str = "default") -> dict: + """Measure each extraction validator against the human tags (#81.8). + + A validator flag predicts "NOT a clean holding"; ground truth is + is_holding == false. truncated_quote is scored against quote_complete.""" + items = await goldset_list(batch) + labeled = [r for r in items if r.get("is_holding") is not None] + from collections import defaultdict + counters: dict = defaultdict(lambda: {"tp": 0, "fp": 0, "fn": 0, "tn": 0}) + + def tally(name: str, predicted_bad: bool, truly_bad: bool) -> None: + c = counters[name] + key = ("tp" if truly_bad else "fp") if predicted_bad else ("fn" if truly_bad else "tn") + c[key] += 1 + + for r in labeled: + rule = r.get("rule_statement") or "" + quote = r.get("supporting_quote") or "" + rtype = r.get("rule_type") or "binding" + qc = r["quote_complete"] if r["quote_complete"] is not None else True + truly_bad = r["is_holding"] is False + flags = halacha_quality.compute_quality_flags(rule, quote, "", qc, rtype) + tally("any_flag", bool(flags), truly_bad) + tally("application", halacha_quality.FLAG_APPLICATION in flags, truly_bad) + tally("non_decision", halacha_quality.FLAG_NON_DECISION in flags, truly_bad) + tally("thin_restatement", halacha_quality.FLAG_THIN_RESTATEMENT in flags, truly_bad) + tally("truncated_quote", halacha_quality.is_quote_truncated(quote), qc is False) + + def prf(c: dict) -> dict: + p = c["tp"] / (c["tp"] + c["fp"]) if (c["tp"] + c["fp"]) else 0.0 + rec = c["tp"] / (c["tp"] + c["fn"]) if (c["tp"] + c["fn"]) else 0.0 + f1 = 2 * p * rec / (p + rec) if (p + rec) else 0.0 + return {"precision": round(p, 3), "recall": round(rec, 3), "f1": round(f1, 3), **c} + + return { + "batch": batch, "total": len(items), "labeled": len(labeled), + "validators": {name: prf(c) for name, c in counters.items()}, + } + + async def list_corroboration_for_halacha(halacha_id: UUID) -> list[dict]: """Return all corroboration rows for one halacha, ordered by match_score DESC.""" pool = await get_pool() diff --git a/web-ui/src/app/goldset/page.tsx b/web-ui/src/app/goldset/page.tsx new file mode 100644 index 0000000..223b83e --- /dev/null +++ b/web-ui/src/app/goldset/page.tsx @@ -0,0 +1,41 @@ +"use client"; + +import Link from "next/link"; +import { AppShell } from "@/components/app-shell"; +import { GoldsetPanel } from "@/components/goldset/goldset-panel"; + +/** + * Gold-set tagging page (#81.7 / #81.8). + * + * Interactive review of a stratified halacha sample. The chair/Dafna labels each + * item (is_holding / correct_type / quote_complete); those human labels are the + * ground truth that measures the extraction validators and recalibrates the + * auto-approve threshold. Tags MUST be human — no AI pre-fill (circular bias). + */ +export default function GoldsetPage() { + return ( + +
+
+ +

מדגם-זהב לתיוג איכות

+

+ מדגם מרובד של הלכות שחולצו. לכל הלכה הכריעו שלוש שאלות — + האם זו הלכה אמיתית, מה הסוג הנכון, + והאם הציטוט שלם. ההכרעות שלכם הן אמת-המידה שמודדת את + דיוק המחלץ ומכיילת את סף-האישור האוטומטי. שיפוט משפטי אנושי בלבד — + לא תיוג-AI (כדי למנוע הטיה מעגלית). +

+
+ +
+ + +
+ + ); +} diff --git a/web-ui/src/components/app-shell.tsx b/web-ui/src/components/app-shell.tsx index a243879..872ff7d 100644 --- a/web-ui/src/components/app-shell.tsx +++ b/web-ui/src/components/app-shell.tsx @@ -51,6 +51,7 @@ const NAV_GROUPS: NavGroup[] = [ items: [ { href: "/precedents", label: "ספריית פסיקה" }, { href: "/missing-precedents", label: "פסיקה חסרה" }, + { href: "/goldset", label: "מדגם-זהב" }, { href: "/training", label: "אימון סגנון" }, { href: "/methodology", label: "מתודולוגיה" }, ], diff --git a/web-ui/src/components/goldset/goldset-panel.tsx b/web-ui/src/components/goldset/goldset-panel.tsx new file mode 100644 index 0000000..aaeb3e5 --- /dev/null +++ b/web-ui/src/components/goldset/goldset-panel.tsx @@ -0,0 +1,283 @@ +"use client"; + +import { useEffect, useMemo, useState } from "react"; +import { Check, X, ChevronDown, ChevronLeft } from "lucide-react"; +import { toast } from "sonner"; +import { Button } from "@/components/ui/button"; +import { Badge } from "@/components/ui/badge"; +import { Skeleton } from "@/components/ui/skeleton"; +import { + useGoldset, useGoldsetScore, useTagGoldset, useCreateGoldsetSample, + type GoldsetItem, +} from "@/lib/api/goldset"; + +const TYPES: { value: string; label: string }[] = [ + { value: "binding", label: "מחייבת" }, + { value: "interpretive", label: "פרשני" }, + { value: "application", label: "יישום" }, + { value: "obiter", label: "אמרת-אגב" }, + { value: "procedural", label: "פרוצדורלי" }, + { value: "persuasive", label: "משכנע" }, +]; + +const FLAG_LABELS: Record = { + non_decision: "אי-הכרעה", truncated_quote: "ציטוט קטוע", thin_restatement: "ניסוח דק", + quote_unverified: "ציטוט לא מאומת", nli_unsupported: "כלל לא נגזר", application: "יישום", + near_duplicate: "כפילות-קרובה", nevo_preamble_leak: "דליפת רציו", +}; + +function cleanCitation(s: string | null | undefined): string { + if (!s) return "—"; + return s.replace(/[‎‏‪-‮⁦-⁩]/g, "").trim(); +} + +function isTagged(it: GoldsetItem): boolean { + return it.is_holding !== null; +} + +// ─── Score panel ────────────────────────────────────────────────────────────── + +function ScorePanel({ batch }: { batch: string }) { + const { data } = useGoldsetScore(batch); + const [open, setOpen] = useState(false); + if (!data || data.labeled === 0) return null; + const rows = Object.entries(data.validators); + return ( +
+ + {open && ( +
+ + + + + + + + + + + + {rows.map(([name, v]) => ( + + + + + + + + ))} + +
ולידטורPrecisionRecallF1tp/fp/fn/tn
{name}{v.precision.toFixed(2)}{v.recall.toFixed(2)}{v.f1.toFixed(2)} + {v.tp}/{v.fp}/{v.fn}/{v.tn} +
+
+ )} +
+ ); +} + +// ─── Tag card ───────────────────────────────────────────────────────────────── + +function TagCard({ + it, focused, onTag, +}: { + it: GoldsetItem; + focused: boolean; + onTag: (tag: { is_holding?: boolean; correct_type?: string; quote_complete?: boolean }) => void; +}) { + const tagged = isTagged(it); + return ( +
+
+ {cleanCitation(it.case_number)} + מכונה: {it.rule_type} + {it.confidence != null && ( + ביטחון {it.confidence.toFixed(2)} + )} + {(it.quality_flags ?? []).map((f) => ( + + {FLAG_LABELS[f] ?? f} + + ))} + {tagged && ( + + תויג + + )} +
+ +

{it.rule_statement}

+
+ “{it.supporting_quote}” +
+ +
+ {/* is_holding */} +
+
האם זו הלכה אמיתית?
+
+ + +
+
+ {/* correct_type */} +
+
הסוג הנכון
+
+ {TYPES.map((t) => ( + + ))} +
+
+ {/* quote_complete */} +
+
הציטוט שלם?
+
+ + +
+
+
+
+ ); +} + +// ─── Main panel ─────────────────────────────────────────────────────────────── + +export function GoldsetPanel() { + const batch = "default"; + const { data, isPending, error } = useGoldset(batch); + const tag = useTagGoldset(batch); + const createSample = useCreateGoldsetSample(batch); + const [focusedId, setFocusedId] = useState(null); + const [hideTagged, setHideTagged] = useState(false); + + const items = useMemo(() => data?.items ?? [], [data]); + const taggedCount = items.filter(isTagged).length; + const visible = useMemo( + () => (hideTagged ? items.filter((i) => !isTagged(i)) : items), + [items, hideTagged], + ); + + const focused = focusedId ? visible.find((i) => i.id === focusedId) ?? null : null; + + useEffect(() => { + if (focusedId && visible.some((i) => i.id === focusedId)) return; + setFocusedId(visible[0]?.id ?? null); + }, [focusedId, visible]); + + useEffect(() => { + if (!focusedId) return; + document.querySelector(`[data-goldset-id="${focusedId}"]`) + ?.scrollIntoView({ block: "nearest", behavior: "smooth" }); + }, [focusedId]); + + const move = (delta: 1 | -1) => { + if (!visible.length) return; + const idx = focusedId ? visible.findIndex((i) => i.id === focusedId) : -1; + const next = idx < 0 ? (delta > 0 ? 0 : visible.length - 1) + : Math.max(0, Math.min(visible.length - 1, idx + delta)); + setFocusedId(visible[next].id); + }; + + const doTag = async ( + it: GoldsetItem, + t: { is_holding?: boolean; correct_type?: string; quote_complete?: boolean }, + ) => { + try { + await tag.mutateAsync({ id: it.id, tag: t }); + } catch (e) { + toast.error(e instanceof Error ? e.message : "שגיאה"); + } + }; + + useEffect(() => { + const onKey = (e: KeyboardEvent) => { + const tagName = (e.target as HTMLElement)?.tagName?.toLowerCase(); + if (tagName === "input" || tagName === "textarea" || tagName === "select") return; + if (e.key === "j") { e.preventDefault(); move(1); } + else if (e.key === "k") { e.preventDefault(); move(-1); } + else if (focused && (e.key === "h" || e.key === "H")) { e.preventDefault(); doTag(focused, { is_holding: true }); } + else if (focused && (e.key === "n" || e.key === "N")) { e.preventDefault(); doTag(focused, { is_holding: false }); } + else if (focused && (e.key === "c" || e.key === "C")) { e.preventDefault(); doTag(focused, { quote_complete: true }); } + else if (focused && (e.key === "x" || e.key === "X")) { e.preventDefault(); doTag(focused, { quote_complete: false }); } + }; + window.addEventListener("keydown", onKey); + return () => window.removeEventListener("keydown", onKey); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [focused, visible]); + + if (error) { + return
{error.message}
; + } + if (isPending) { + return
{[...Array(3)].map((_, i) => )}
; + } + if (!items.length) { + return ( +
+

אין מדגם-זהב עדיין.

+ +
+ ); + } + + const pct = items.length ? Math.round((taggedCount / items.length) * 100) : 0; + + return ( +
+ + +
+ {taggedCount}/{items.length} תויגו +
+
+
+ + מקלדת: J/K + {" "}· הלכה H / לא N + {" "}· ציטוט שלם C / קטוע X + + +
+ +
+ {visible.map((it) => ( + doTag(it, t)} /> + ))} +
+
+ ); +} diff --git a/web-ui/src/lib/api/goldset.ts b/web-ui/src/lib/api/goldset.ts new file mode 100644 index 0000000..7e389ad --- /dev/null +++ b/web-ui/src/lib/api/goldset.ts @@ -0,0 +1,105 @@ +/** + * Gold-set tagging API (#81.7 / #81.8). + * + * The chair/Dafna manually labels a stratified sample of halachot + * (is_holding / correct_type / quote_complete). Those human labels are the + * ground truth used to measure the extraction validators and recalibrate the + * auto-approve threshold. Endpoints under /api/goldset. + */ +import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query"; +import { apiRequest } from "./client"; + +export type GoldsetItem = { + id: string; + halacha_id: string; + // human tags (null until tagged) + is_holding: boolean | null; + correct_type: string; + quote_complete: boolean | null; + tagged_by: string; + tagged_at: string | null; + // halacha content + the machine's own labels + rule_statement: string; + supporting_quote: string; + reasoning_summary: string; + rule_type: string; + confidence: number | null; + quality_flags?: string[]; + review_status: string; + case_number: string | null; + case_name: string | null; +}; + +export type GoldsetScore = { + batch: string; + total: number; + labeled: number; + validators: Record< + string, + { precision: number; recall: number; f1: number; tp: number; fp: number; fn: number; tn: number } + >; +}; + +export type GoldsetTag = { + is_holding?: boolean | null; + correct_type?: string; + quote_complete?: boolean | null; +}; + +const keys = { + all: ["goldset"] as const, + list: (batch: string) => ["goldset", "list", batch] as const, + score: (batch: string) => ["goldset", "score", batch] as const, +}; + +export function useGoldset(batch = "default") { + return useQuery({ + queryKey: keys.list(batch), + queryFn: ({ signal }) => + apiRequest<{ items: GoldsetItem[]; batch: string }>( + `/api/goldset?batch=${encodeURIComponent(batch)}`, + { signal }, + ), + staleTime: 5_000, + refetchOnMount: "always", + }); +} + +export function useGoldsetScore(batch = "default") { + return useQuery({ + queryKey: keys.score(batch), + queryFn: ({ signal }) => + apiRequest( + `/api/goldset/score?batch=${encodeURIComponent(batch)}`, + { signal }, + ), + staleTime: 5_000, + }); +} + +export function useTagGoldset(batch = "default") { + const qc = useQueryClient(); + return useMutation({ + mutationFn: ({ id, tag }: { id: string; tag: GoldsetTag }) => + apiRequest<{ ok: boolean }>(`/api/goldset/${encodeURIComponent(id)}`, { + method: "PATCH", + body: { ...tag, tagged_by: "chair" }, + }), + onSuccess: () => { + qc.invalidateQueries({ queryKey: keys.list(batch) }); + qc.invalidateQueries({ queryKey: keys.score(batch) }); + }, + }); +} + +export function useCreateGoldsetSample(batch = "default") { + const qc = useQueryClient(); + return useMutation({ + mutationFn: (n: number) => + apiRequest<{ batch: string; inserted: number; total: number }>( + "/api/goldset/sample", + { method: "POST", body: { n, batch } }, + ), + onSuccess: () => qc.invalidateQueries({ queryKey: keys.list(batch) }), + }); +} diff --git a/web/app.py b/web/app.py index 4959917..50b1d3b 100644 --- a/web/app.py +++ b/web/app.py @@ -6099,6 +6099,59 @@ async def halacha_equivalents_unlink(halacha_id: str, other_id: str): return {"ok": await db.unlink_equivalent_halachot(hid, oid)} +# ── Gold-set tagging (#81.7 / #81.8) ───────────────────────────────────────── + +class GoldsetSampleRequest(BaseModel): + n: int = 150 + batch: str = "default" + reset: bool = False + + +class GoldsetTagRequest(BaseModel): + is_holding: bool | None = None + correct_type: str | None = None + quote_complete: bool | None = None + tagged_by: str = "chair" + + +@app.get("/api/goldset") +async def goldset_list_ep(batch: str = "default"): + """The gold-set tagging queue (halacha content + machine labels + human tags).""" + return {"items": await db.goldset_list(batch), "batch": batch} + + +@app.post("/api/goldset/sample") +async def goldset_sample_ep(req: GoldsetSampleRequest): + """Create/extend a stratified gold-set batch for tagging (#81.7).""" + return await db.goldset_create_sample(n=req.n, batch=req.batch, reset=req.reset) + + +@app.get("/api/goldset/score") +async def goldset_score_ep(batch: str = "default"): + """Measure the extraction validators against the human tags (#81.8).""" + return await db.goldset_score(batch) + + +@app.patch("/api/goldset/{goldset_id}") +async def goldset_tag_ep(goldset_id: str, req: GoldsetTagRequest): + """Save one human tag on a gold-set item.""" + try: + gid = UUID(goldset_id) + except ValueError: + raise HTTPException(400, "מזהה לא תקין") + if req.correct_type and req.correct_type not in ( + "binding", "interpretive", "obiter", "application", "procedural", "persuasive", + ): + raise HTTPException(400, "correct_type לא תקין") + row = await db.goldset_tag( + gid, is_holding=req.is_holding, correct_type=req.correct_type, + quote_complete=req.quote_complete, tagged_by=req.tagged_by, + ) + if not row: + raise HTTPException(404, "פריט לא נמצא") + return {"ok": True} + + @app.patch("/api/halachot/{halacha_id}") async def halacha_update(halacha_id: str, req: HalachaUpdateRequest): """Approve / reject / edit a halacha. Used by the chair review queue.""" -- 2.49.1