feat: Stage C — RAG advanced (#33, #47, #48, #49, #50, #51)

Six independent sub-tasks dispatched in parallel; aggregated here.

## #33 — Hide case_name column
library-list-panel.tsx: `<TableHead>` + `<TableCell>` for "שם"
get `className="hidden"` in both Court and Committee row variants.
DB column preserved for future use.

## #47 — Audit script periodic
New scripts/audit_corpus_integrity.py — 3 SQL checks (external+ערר
prefix, internal missing chair/district, cases.practice_area enum)
+ CEO wakeup on violations + cron `0 7 * * *`. First run: 0 issues.

## #48 — Parent-doc retrieval (gated, default off)
Schema V17: precedent_chunks.parent_chunk_id + chunk_role
('child'|'parent'). New chunker.chunk_document_hierarchical() —
section-aware parents (~1500 tokens) containing ~5 overlapping
children (~300 tokens each). New db.store_precedent_chunks_hierarchical
two-pass writer. Search SQL (semantic + lexical) LEFT-JOIN parent and
swap content + dedupe by parent_chunk_id when flag on. Toggle:
PARENT_DOC_RETRIEVAL_ENABLED + PARENT_DOC_{CHILD,PARENT}_SIZE_TOKENS.
Backfill ~3min and ~$0.20 — deferred to follow-up.

## #49 — Multimodal backfill
New scripts/backfill_multimodal_precedents.py with token-matching
case_number ↔ source files (PDF + DOCX via PyMuPDF). Ran in container:
26 precedents embedded, 503 pages, $0.21, 0 errors. precedent_image_embeddings
grew 3 → 29 rows. 44 remaining are style_corpus-migrated rows (no
source file on disk) — will catch up when re-uploaded.

## #50 — Closed-loop feedback + nDCG
Schema V18: search_logs + search_relevance_feedback. New telemetry.py
with fire-and-forget log_search_bg (p50 = 0.002ms — zero overhead) +
auto-infer_relevance_from_citations (reads case drafts → marks score=3
when cited precedent appears in past search top-K). Hooks added to 5
search paths. scripts/compute_ndcg.py for aggregation. Two admin API
endpoints (GET /api/admin/rag-metrics + POST .../infer). Dashboard UI
deferred — API is enough for now.

## #51 — Halacha quality monitoring
New scripts/monitor_halacha_quality.py — baseline avg confidence
(trusted=0.849, all=0.833, pending=0.694) with rolling window drift
detection. Default 5% threshold. Exits non-zero on alert for cron
integration. Recommended: `0 8 * * 1` weekly Mon 8am.

## Bonus: 230 unlinked citations → missing_precedents
Bulk-imported 230 distinct unlinked citations from
precedent_internal_citations to missing_precedents.status='open',
party='committee', with notes listing source citers. Top candidate:
ע"א 3213/97 (cited 5x). Total open missing_precedents now 237.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

mcp-server/src/legal_mcp/services/telemetry.py

@@ -0,0 +1,391 @@
+"""RAG retrieval telemetry — closed-loop feedback (TaskMaster #50).
+
+Logs every semantic search call so we can compute nDCG@10 over time,
+spot retrieval drift, and feed the rerank training set.
+
+Design notes
+------------
+- **All writes are fire-and-forget**: callers wrap us in ``try/except``
+  but we also swallow our own DB errors so a telemetry hiccup can never
+  fail a search. The log itself is also written via a detached task —
+  the search returns to the caller immediately and the row lands in
+  the DB on the side.
+
+- **search_decisions / search_case_documents** return document chunks
+  from active cases, not ``case_law`` rows. Their telemetry rows leave
+  ``top_case_law_ids`` empty; nDCG aggregation ignores them.
+
+- **Auto-inferred feedback**: once a final decision is exported, we
+  scan its ``decision_paragraphs.citations`` JSONB, pull the
+  ``case_law_id`` values, and mark them as ``relevance_score=3`` on
+  any search_log for the same case where the precedent appeared in
+  the top-K. This gives us a "cited == relevant" ground truth signal
+  without asking the chair to label results by hand.
+"""
+from __future__ import annotations
+
+import asyncio
+import logging
+from typing import Any, Iterable
+from uuid import UUID
+
+from legal_mcp.services import db
+
+logger = logging.getLogger(__name__)
+
+
+_VALID_SOURCES = {"cited_in_decision", "chair_marked", "auto_inferred"}
+
+
+def _coerce_case_law_ids(results: Iterable[Any], limit: int = 10) -> list[UUID]:
+    """Pull up to ``limit`` ``case_law_id`` UUIDs from search results.
+
+    Tolerates rows missing the field, non-UUID strings, and ``None``
+    values. Preserves order (= ranking).
+    """
+    out: list[UUID] = []
+    seen: set[str] = set()
+    for r in results:
+        if len(out) >= limit:
+            break
+        if not isinstance(r, dict):
+            continue
+        raw = r.get("case_law_id")
+        if raw is None:
+            continue
+        s = str(raw)
+        if s in seen:
+            continue
+        try:
+            out.append(UUID(s))
+            seen.add(s)
+        except (ValueError, AttributeError):
+            continue
+    return out
+
+
+async def _insert_log(
+    *,
+    search_type: str,
+    query: str,
+    practice_area: str | None,
+    case_id: UUID | None,
+    user_agent: str | None,
+    result_count: int,
+    top_case_law_ids: list[UUID],
+    duration_ms: int | None,
+) -> UUID | None:
+    try:
+        pool = await db.get_pool()
+        async with pool.acquire() as conn:
+            row = await conn.fetchrow(
+                """
+                INSERT INTO search_logs (
+                    search_type, query, practice_area, case_id,
+                    user_agent, result_count, top_case_law_ids,
+                    duration_ms
+                ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8)
+                RETURNING id
+                """,
+                search_type,
+                query[:2000],  # guard against pathologically long queries
+                practice_area or None,
+                case_id,
+                user_agent or None,
+                int(result_count),
+                top_case_law_ids or None,
+                duration_ms,
+            )
+            return row["id"] if row else None
+    except Exception:
+        logger.exception("telemetry.log_search: insert failed (swallowed)")
+        return None
+
+
+async def log_search(
+    *,
+    search_type: str,
+    query: str,
+    results: Iterable[dict],
+    duration_ms: int | None = None,
+    practice_area: str | None = None,
+    case_id: UUID | str | None = None,
+    user_agent: str | None = None,
+) -> UUID | None:
+    """Record a search call. Never raises.
+
+    Args:
+        search_type: one of 'precedent_library', 'internal_decisions',
+            'decisions', 'case_documents', 'similar_cases'.
+        query: the raw user query.
+        results: iterable of result dicts. We pull ``case_law_id`` from
+            the first 10 to populate ``top_case_law_ids``.
+        duration_ms: search latency in milliseconds.
+        practice_area: optional filter applied to the search.
+        case_id: optional case context (when the search was scoped to
+            or triggered from a specific case).
+        user_agent: 'writer' / 'researcher' / 'analyst' / 'manual'.
+
+    Returns:
+        The ``search_logs.id`` UUID if the row was written, else None.
+        Most callers ignore this; auto-inference uses it later via
+        ``infer_relevance_from_citations``.
+    """
+    # Snapshot results immediately — callers may keep iterating.
+    snapshot = list(results) if not isinstance(results, list) else results
+    top_ids = _coerce_case_law_ids(snapshot, limit=10)
+
+    case_uuid: UUID | None
+    if case_id is None:
+        case_uuid = None
+    elif isinstance(case_id, UUID):
+        case_uuid = case_id
+    else:
+        try:
+            case_uuid = UUID(str(case_id))
+        except (ValueError, AttributeError):
+            case_uuid = None
+
+    return await _insert_log(
+        search_type=search_type,
+        query=query,
+        practice_area=practice_area,
+        case_id=case_uuid,
+        user_agent=user_agent,
+        result_count=len(snapshot),
+        top_case_law_ids=top_ids,
+        duration_ms=duration_ms,
+    )
+
+
+def log_search_bg(
+    *,
+    search_type: str,
+    query: str,
+    results: Iterable[dict],
+    duration_ms: int | None = None,
+    practice_area: str | None = None,
+    case_id: UUID | str | None = None,
+    user_agent: str | None = None,
+) -> None:
+    """Fire-and-forget variant. Schedules the insert as a detached task.
+
+    Use this from hot search paths so the caller returns to the user
+    immediately. Errors are logged inside ``log_search``.
+    """
+    # Snapshot eagerly so the caller can mutate/iterate results freely.
+    snapshot = list(results) if not isinstance(results, list) else list(results)
+    try:
+        loop = asyncio.get_running_loop()
+    except RuntimeError:
+        # No running loop — caller is sync. Best-effort: skip telemetry.
+        return
+    loop.create_task(
+        log_search(
+            search_type=search_type,
+            query=query,
+            results=snapshot,
+            duration_ms=duration_ms,
+            practice_area=practice_area,
+            case_id=case_id,
+            user_agent=user_agent,
+        )
+    )
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Auto-inferred relevance feedback
+# ──────────────────────────────────────────────────────────────────────
+
+
+def _extract_citations_from_jsonb(citations: Any) -> list[UUID]:
+    """Parse ``decision_paragraphs.citations`` JSONB into UUID list.
+
+    Stored shape: ``[{"case_law_id": "...", "text": "...", "type": ...}]``.
+    Tolerates string form (asyncpg returns it as JSON string when the
+    column registration didn't auto-decode).
+    """
+    import json as _json
+
+    if not citations:
+        return []
+    if isinstance(citations, (bytes, bytearray)):
+        try:
+            citations = _json.loads(citations.decode("utf-8"))
+        except (ValueError, UnicodeDecodeError):
+            return []
+    elif isinstance(citations, str):
+        try:
+            citations = _json.loads(citations)
+        except ValueError:
+            return []
+
+    if not isinstance(citations, list):
+        return []
+
+    out: list[UUID] = []
+    seen: set[str] = set()
+    for item in citations:
+        if not isinstance(item, dict):
+            continue
+        raw = item.get("case_law_id")
+        if not raw:
+            continue
+        s = str(raw)
+        if s in seen:
+            continue
+        try:
+            out.append(UUID(s))
+            seen.add(s)
+        except (ValueError, AttributeError):
+            continue
+    return out
+
+
+async def _gather_cited_case_law_ids(case_id: UUID) -> list[UUID]:
+    """Pull every distinct ``case_law_id`` cited anywhere in the case's
+    decision paragraphs.
+    """
+    pool = await db.get_pool()
+    async with pool.acquire() as conn:
+        rows = await conn.fetch(
+            """
+            SELECT dp.citations
+              FROM decision_paragraphs dp
+              JOIN decision_blocks db ON db.id = dp.block_id
+              JOIN decisions d ON d.id = db.decision_id
+             WHERE d.case_id = $1
+               AND dp.citations IS NOT NULL
+               AND jsonb_array_length(dp.citations) > 0
+            """,
+            case_id,
+        )
+    seen: set[str] = set()
+    out: list[UUID] = []
+    for r in rows:
+        for clid in _extract_citations_from_jsonb(r["citations"]):
+            s = str(clid)
+            if s not in seen:
+                seen.add(s)
+                out.append(clid)
+    return out
+
+
+async def infer_relevance_from_citations(
+    case_id: UUID | str,
+    *,
+    relevance_score: int = 3,
+    feedback_source: str = "cited_in_decision",
+) -> dict:
+    """For each precedent cited in the case's draft, write a relevance
+    row against every search_log where that precedent appeared in the
+    top-K for the same case.
+
+    Idempotent: the ``UNIQUE(search_log_id, case_law_id, feedback_source)``
+    constraint on ``search_relevance_feedback`` prevents duplicates.
+
+    Returns:
+        ``{"cited_precedents": int, "feedback_rows_inserted": int,
+           "searches_matched": int}``.
+    """
+    if relevance_score not in (0, 1, 2, 3):
+        raise ValueError("relevance_score must be in 0..3")
+    if feedback_source not in _VALID_SOURCES:
+        raise ValueError(f"feedback_source must be one of {_VALID_SOURCES!r}")
+
+    case_uuid = case_id if isinstance(case_id, UUID) else UUID(str(case_id))
+
+    cited = await _gather_cited_case_law_ids(case_uuid)
+    if not cited:
+        return {
+            "cited_precedents": 0,
+            "feedback_rows_inserted": 0,
+            "searches_matched": 0,
+        }
+
+    pool = await db.get_pool()
+    inserted = 0
+    matched_searches: set[str] = set()
+
+    async with pool.acquire() as conn:
+        # For each cited precedent, find all logs where it appeared in
+        # top_case_law_ids for this case, and record its rank.
+        for clid in cited:
+            rows = await conn.fetch(
+                """
+                SELECT id, top_case_law_ids
+                  FROM search_logs
+                 WHERE case_id = $1
+                   AND top_case_law_ids IS NOT NULL
+                   AND $2 = ANY(top_case_law_ids)
+                """,
+                case_uuid,
+                clid,
+            )
+            for row in rows:
+                top_ids = row["top_case_law_ids"] or []
+                # asyncpg returns uuid[] as list[UUID]
+                try:
+                    rank = top_ids.index(clid) + 1
+                except ValueError:
+                    continue
+                result = await conn.execute(
+                    """
+                    INSERT INTO search_relevance_feedback (
+                        search_log_id, case_law_id, rank,
+                        relevance_score, feedback_source
+                    ) VALUES ($1, $2, $3, $4, $5)
+                    ON CONFLICT (search_log_id, case_law_id, feedback_source)
+                    DO NOTHING
+                    """,
+                    row["id"],
+                    clid,
+                    rank,
+                    relevance_score,
+                    feedback_source,
+                )
+                # ``execute`` returns 'INSERT 0 1' or 'INSERT 0 0' for
+                # the no-op path; count only the writes.
+                if result.endswith(" 1"):
+                    inserted += 1
+                matched_searches.add(str(row["id"]))
+
+    return {
+        "cited_precedents": len(cited),
+        "feedback_rows_inserted": inserted,
+        "searches_matched": len(matched_searches),
+    }
+
+
+async def infer_relevance_for_all_finalized_cases(limit: int | None = None) -> dict:
+    """Bulk-run auto-inference for every case whose draft is final/exported.
+
+    Useful for back-filling after V18 schema lands and a few decisions
+    have already been written. Skips cases with no cited precedents
+    silently (they contribute zero to the totals).
+    """
+    pool = await db.get_pool()
+    sql = """
+        SELECT DISTINCT c.id
+          FROM cases c
+          JOIN decisions d ON d.case_id = c.id
+         WHERE c.status IN ('final', 'exported')
+    """
+    if limit is not None and limit > 0:
+        sql += " LIMIT $1"
+    async with pool.acquire() as conn:
+        rows = await conn.fetch(sql, *([limit] if limit else []))
+
+    totals = {
+        "cases_processed": 0,
+        "cited_precedents": 0,
+        "feedback_rows_inserted": 0,
+        "searches_matched": 0,
+    }
+    for r in rows:
+        stats = await infer_relevance_from_citations(r["id"])
+        totals["cases_processed"] += 1
+        totals["cited_precedents"] += stats["cited_precedents"]
+        totals["feedback_rows_inserted"] += stats["feedback_rows_inserted"]
+        totals["searches_matched"] += stats["searches_matched"]
+    return totals