ops(ci): boot_smoke 스키마 어서션 max_migration 361→378 (현재 마이그 헤드)

지난 감사(361) 이후 마이그가 378(이번 publish_outbox attempts/failed 포함)까지 전진 →
boot_smoke 스키마 게이트의 하드코딩 기대값 갱신. purge/cand/uq 기대는 동일.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

app/ai/client.py

@@ -260,23 +260,6 @@ class AIClient:
         cfg = self.ai.deep or self.ai.primary
         return await self._request(cfg, prompt, system=system)
 
-    async def call_classifier(self, prompt: str) -> str:
-        """answerability classifier (config ai.classifier, Mac mini 26B MLX).
-
-        private _request 직접 호출(classifier_service)을 봉인하는 public 진입점. gate 는
-        caller(classifier_service)가 acquire_mlx_gate 로 관리 — call_primary 와 동일한
-        caller-managed 계약(여기서 self-gate 하면 caller 와 double-acquire 데드락).
-        """
-        return await self._request(self.ai.classifier, prompt)
-
-    async def call_verifier(self, prompt: str) -> str:
-        """semantic verifier (config ai.verifier, Mac mini 26B MLX).
-
-        private _request 직접 호출(verifier_service)을 봉인. gate 는 caller(verifier_service)
-        가 관리(caller-managed — self-gate 금지).
-        """
-        return await self._request(self.ai.verifier, prompt)
-
     # ─── Legacy API (classify_worker 교체 시 제거 예정) ───────────────────
 
     async def classify(self, text: str, cfg=None) -> dict:

app/api/ingest_study.py

@@ -23,6 +23,7 @@ from datetime import datetime, timezone
 from fastapi import APIRouter, Depends, Header, HTTPException
 from pydantic import BaseModel
 from sqlalchemy import select
+from sqlalchemy.exc import IntegrityError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from core.config import settings
@@ -66,6 +67,22 @@ class IngestBody(BaseModel):
     attempts: list[IngestAttempt]
 
 
+def _already_ingested(rows) -> dict:
+    """이미 적재된 세션들의 캐시 요약(멱등 응답). 최초 멱등체크 + 동시경합 흡수 양쪽에서 사용."""
+    return {
+        "status": "already_ingested",
+        "sessions": [
+            {
+                "topic_id": s.study_topic_id,
+                "correct": s.correct_count,
+                "wrong": s.wrong_count,
+                "unsure": s.unsure_count,
+            }
+            for s in rows
+        ],
+    }
+
+
 def _parse_answered_at(s: str | None, now: datetime) -> datetime:
     if not s:
         return now
@@ -98,18 +115,7 @@ async def ingest_attempts(
         )
     ).scalars().all()
     if existing:
-        return {
-            "status": "already_ingested",
-            "sessions": [
-                {
-                    "topic_id": s.study_topic_id,
-                    "correct": s.correct_count,
-                    "wrong": s.wrong_count,
-                    "unsure": s.unsure_count,
-                }
-                for s in existing
-            ],
-        }
+        return _already_ingested(existing)
 
     # pub_id → source_id(내부 질문 id) 해소. deleted tombstone 제외.
     pub_ids = list({a.question_pub_id for a in body.attempts})
@@ -156,73 +162,92 @@ async def ingest_attempts(
     if not by_topic:
         raise HTTPException(status_code=404, detail="해소된 attempt 없음")
 
-    summaries = []
-    for topic_id, items in by_topic.items():
-        qids = [q.id for (_, q) in items]
-        qs = StudyQuizSession(
-            user_id=user_id,
-            study_topic_id=topic_id,
-            question_ids=qids,
-            subject_distribution={},
-            status="done",
-            cursor=len(qids),
-            source="viewer",
-            client_session_uuid=body.client_session_uuid,
-            finished_at=now,
-            created_at=now,
-            updated_at=now,
-        )
-        session.add(qs)
-        await session.flush()  # qs.id
+    try:
+        summaries = []
+        for topic_id, items in by_topic.items():
+            qids = [q.id for (_, q) in items]
+            qs = StudyQuizSession(
+                user_id=user_id,
+                study_topic_id=topic_id,
+                question_ids=qids,
+                subject_distribution={},
+                status="done",
+                cursor=len(qids),
+                source="viewer",
+                client_session_uuid=body.client_session_uuid,
+                finished_at=now,
+                created_at=now,
+                updated_at=now,
+            )
+            session.add(qs)
+            await session.flush()  # qs.id
 
-        c = w = u = 0
-        for a, q in items:
-            try:
-                sel, is_corr, outcome = derive_outcome(a.selected_choice, a.is_unsure, q.correct_choice)
-            except ValueError:
-                skipped.append(a.question_pub_id)  # 선택 없고 unsure 아님 = 무효 → skip
-                continue
-            if outcome == "correct":
-                c += 1
-            elif outcome == "wrong":
-                w += 1
-            elif outcome == "unsure":
-                u += 1
-            session.add(
-                StudyQuestionAttempt(
-                    user_id=user_id,
-                    study_question_id=q.id,
-                    study_topic_id=topic_id,
-                    selected_choice=sel,
-                    correct_choice=q.correct_choice,
-                    is_correct=is_corr,
-                    outcome=outcome,
-                    quiz_session_id=qs.id,
-                    answered_at=_parse_answered_at(a.answered_at, now),
+            c = w = u = 0
+            for a, q in items:
+                try:
+                    sel, is_corr, outcome = derive_outcome(a.selected_choice, a.is_unsure, q.correct_choice)
+                except ValueError:
+                    skipped.append(a.question_pub_id)  # 선택 없고 unsure 아님 = 무효 → skip
+                    continue
+                if outcome == "correct":
+                    c += 1
+                elif outcome == "wrong":
+                    w += 1
+                elif outcome == "unsure":
+                    u += 1
+                session.add(
+                    StudyQuestionAttempt(
+                        user_id=user_id,
+                        study_question_id=q.id,
+                        study_topic_id=topic_id,
+                        selected_choice=sel,
+                        correct_choice=q.correct_choice,
+                        is_correct=is_corr,
+                        outcome=outcome,
+                        quiz_session_id=qs.id,
+                        answered_at=_parse_answered_at(a.answered_at, now),
+                    )
+                )
+            qs.correct_count, qs.wrong_count, qs.unsure_count = c, w, u
+            await session.flush()
+
+            # finalize 무수정 재생(progress/SR/pattern + 4-A/4-B enqueue). 그 후 멱등 마커.
+            summary = await finalize_session(
+                session, user_id=user_id, study_topic_id=topic_id, quiz_session_id=qs.id
+            )
+            qs.finalized_at = now
+            summaries.append(
+                {
+                    "topic_id": topic_id,
+                    "quiz_session_id": qs.id,
+                    "correct": summary.correct,
+                    "wrong": summary.wrong,
+                    "unsure": summary.unsure,
+                    "newly_correct": summary.newly_correct,
+                    "relapsed": summary.relapsed,
+                    "recovered": summary.recovered,
+                }
+            )
+
+        await session.commit()
+    except IntegrityError:
+        # 동시 같은 client_session_uuid 경합 — 상대가 먼저 commit → (client_session_uuid,
+        # study_topic_id) uq(mig376) 위반. 데이터는 안전(원자 1-tx 전체 롤백 → SR 이중 advance
+        # 없음). 승자 결과로 graceful 수렴(500 대신 already_ingested). uuid 경합이 아닌 진짜
+        # 무결성 오류면 재조회가 비어 → re-raise 로 표면화.
+        await session.rollback()
+        winner = (
+            await session.execute(
+                select(StudyQuizSession).where(
+                    StudyQuizSession.client_session_uuid == body.client_session_uuid
                 )
             )
-        qs.correct_count, qs.wrong_count, qs.unsure_count = c, w, u
-        await session.flush()
+        ).scalars().all()
+        if not winner:
+            raise
+        logger.info("study_ingest uuid=%s 동시경합 흡수 → already_ingested", body.client_session_uuid)
+        return _already_ingested(winner)
 
-        # finalize 무수정 재생(progress/SR/pattern + 4-A/4-B enqueue). 그 후 멱등 마커.
-        summary = await finalize_session(
-            session, user_id=user_id, study_topic_id=topic_id, quiz_session_id=qs.id
-        )
-        qs.finalized_at = now
-        summaries.append(
-            {
-                "topic_id": topic_id,
-                "quiz_session_id": qs.id,
-                "correct": summary.correct,
-                "wrong": summary.wrong,
-                "unsure": summary.unsure,
-                "newly_correct": summary.newly_correct,
-                "relapsed": summary.relapsed,
-                "recovered": summary.recovered,
-            }
-        )
-
-    await session.commit()
     logger.info(
         "study_ingest uuid=%s user=%s sessions=%s skipped=%s",
         body.client_session_uuid, user_id, len(summaries), len(skipped),

app/api/search.py

@@ -3,42 +3,28 @@
 실제 검색 파이프라인(retrieval → fusion → rerank → diversity → confidence)
 은 `services/search/search_pipeline.py::run_search()` 로 분리되어 있다.
 이 파일은 다음만 담당:
-  - Pydantic 스키마 (SearchResult / SearchResponse / SearchDebug / DebugCandidate
-    / Citation / AskResponse / AskDebug)
+  - Pydantic 스키마 (SearchResult / SearchResponse / SearchDebug / DebugCandidate)
   - `/search` endpoint wrapper (run_search 호출 + logger + telemetry + 직렬화)
-  - `/ask` endpoint wrapper (Phase 3.3 에서 추가)
 """
 
-import asyncio
-import hmac
-import time
 from datetime import date
-from typing import Annotated, Literal
+from typing import Annotated
 
-from fastapi import APIRouter, BackgroundTasks, Depends, Header, Query
+from fastapi import APIRouter, BackgroundTasks, Depends, Query
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from core.auth import get_current_user
-from core.config import settings
 from core.database import get_session
 from core.utils import setup_logger
 from models.user import User
-from services.document_telemetry import sanitize_source
-from services.search.classifier_service import ClassifierResult, classify
-from services.search.evidence_service import EvidenceItem, extract_evidence
 from services.search.fusion_service import DEFAULT_FUSION
-from services.search.grounding_check import check as grounding_check
-from services.search.refusal_gate import RefusalDecision, decide as refusal_decide
 from services.search import query_rewriter
 from services.search.retrieval_service import AxisFilter
 from services.search.result_decorate import compute_facets, decorate_version_status
 from services.search.search_pipeline import PipelineResult, run_search
-from services.search.synthesis_service import SynthesisResult, synthesize
-from services.search.verifier_service import VerifierResult, verify
-from services.prompt_versions import ASK_PROMPT_VERSION, resolve_primary_model
-from services.search_telemetry import record_ask_event, record_search_event
+from services.search_telemetry import record_search_event
 
 # logs/search.log + stdout 동시 출력 (Phase 0.4)
 logger = setup_logger("search")
@@ -354,836 +340,3 @@ async def search(
         debug=debug_obj,
         facets=facets_obj,
     )
-
-
-# ═══════════════════════════════════════════════════════════
-# Phase 3.3: /api/search/ask — Evidence + Grounded Synthesis
-# ═══════════════════════════════════════════════════════════
-
-
-class Citation(BaseModel):
-    """answer 본문의 [n] 에 해당하는 근거 단일 행."""
-
-    n: int
-    chunk_id: int | None
-    doc_id: int
-    title: str | None
-    section_title: str | None
-    span_text: str  # evidence LLM 이 추출한 50~300자
-    full_snippet: str  # 원본 800자 (citation 원문 보기 전용)
-    relevance: float
-    rerank_score: float
-
-
-class ConfirmedItem(BaseModel):
-    """Partial answer 의 개별 aspect 답변."""
-
-    aspect: str
-    text: str
-    citations: list[int]
-
-
-class AskDebug(BaseModel):
-    """`/ask?debug=true` 응답 확장."""
-
-    timing_ms: dict[str, float]
-    search_notes: list[str]
-    query_analysis: dict | None = None
-    confidence_signal: float
-    evidence_candidate_count: int
-    evidence_kept_count: int
-    evidence_skip_reason: str | None
-    synthesis_cache_hit: bool
-    synthesis_prompt_preview: str | None = None
-    synthesis_raw_preview: str | None = None
-    hallucination_flags: list[str] = []
-    # Phase 3.5a: per-layer defense 로깅
-    defense_layers: dict | None = None
-
-
-class AskResponse(BaseModel):
-    """`/ask` 응답. Phase 3.5a: completeness + aspects 추가."""
-
-    results: list[SearchResult]
-    ai_answer: str | None
-    citations: list[Citation]
-    synthesis_status: Literal[
-        "completed", "timeout", "skipped", "no_evidence", "parse_failed", "llm_error",
-        # PR-MacBook-RAG-Backend-1: 200 응답에는 등장하지 않음 (해당 status 는 503 분기).
-        # Literal 호환성 위해 포함.
-        "backend_unavailable",
-    ]
-    synthesis_ms: float
-    confidence: Literal["high", "medium", "low"] | None
-    refused: bool
-    no_results_reason: str | None
-    query: str
-    total: int
-    # Phase 3.5a
-    completeness: Literal["full", "partial", "insufficient"] = "full"
-    covered_aspects: list[str] | None = None
-    missing_aspects: list[str] | None = None
-    confirmed_items: list[ConfirmedItem] | None = None
-    # PR-MacBook-RAG-Backend-1: backend dispatcher metadata.
-    # backend 미지정 호출은 둘 다 None 으로 유지 (기존 호출자 호환 — Hermes docsrv_ask /
-    # voice-memo-bot 응답 형식 변동 0). 명시 opt-in 시만 채워짐.
-    backend_requested: str | None = None
-    backend_used: str | None = None
-    debug: AskDebug | None = None
-
-
-def _map_no_results_reason(
-    pr: PipelineResult,
-    evidence: list[EvidenceItem],
-    ev_skip: str | None,
-    sr: SynthesisResult,
-) -> str | None:
-    """사용자에게 보여줄 한국어 메시지 매핑.
-
-    Failure mode 표 (plan §Failure Modes) 기반.
-    """
-    # LLM 자가 refused → 모델이 준 사유 그대로
-    if sr.refused and sr.refuse_reason:
-        return sr.refuse_reason
-
-    # synthesis 상태 우선
-    if sr.status == "no_evidence":
-        if not pr.results:
-            return "검색 결과가 없습니다."
-        return "관련도 높은 근거를 찾지 못했습니다."
-    if sr.status == "skipped":
-        return "검색 결과가 없습니다."
-    if sr.status == "timeout":
-        return "답변 생성이 지연되어 생략했습니다. 검색 결과를 확인해 주세요."
-    if sr.status == "parse_failed":
-        return "답변 형식 오류로 생략했습니다."
-    if sr.status == "llm_error":
-        return "AI 서버에 일시적 문제가 있습니다."
-
-    # evidence 단계 실패는 fallback 을 탔더라도 notes 용
-    if ev_skip == "all_low_rerank":
-        return "관련도 높은 근거를 찾지 못했습니다."
-    if ev_skip == "empty_retrieval":
-        return "검색 결과가 없습니다."
-
-    return None
-
-
-def _build_citations(
-    evidence: list[EvidenceItem], used_citations: list[int]
-) -> list[Citation]:
-    """answer 본문에 실제로 등장한 n 만 Citation 으로 변환."""
-    by_n = {e.n: e for e in evidence}
-    out: list[Citation] = []
-    for n in used_citations:
-        e = by_n.get(n)
-        if e is None:
-            continue
-        out.append(
-            Citation(
-                n=e.n,
-                chunk_id=e.chunk_id,
-                doc_id=e.doc_id,
-                title=e.title,
-                section_title=e.section_title,
-                span_text=e.span_text,
-                full_snippet=e.full_snippet,
-                relevance=e.relevance,
-                rerank_score=e.rerank_score,
-            )
-        )
-    return out
-
-
-def _build_ask_debug(
-    pr: PipelineResult,
-    evidence: list[EvidenceItem],
-    ev_skip: str | None,
-    sr: SynthesisResult,
-    ev_ms: float,
-    synth_ms: float,
-    total_ms: float,
-) -> AskDebug:
-    timing: dict[str, float] = dict(pr.timing_ms)
-    timing["evidence_ms"] = ev_ms
-    timing["synthesis_ms"] = synth_ms
-    timing["ask_total_ms"] = total_ms
-
-    # candidate count 는 rule filter 통과한 수 (recomputable from results)
-    # 엄밀히는 evidence_service 내부 숫자인데, evidence 길이 ≈ kept, candidate
-    # 는 관측이 어려움 → kept 는 evidence 길이, candidate 는 별도 필드 없음.
-    # 단순화: candidate_count = len(evidence) 를 상한 근사로 둠 (debug 전용).
-    return AskDebug(
-        timing_ms=timing,
-        search_notes=pr.notes,
-        query_analysis=pr.query_analysis,
-        confidence_signal=pr.confidence_signal,
-        evidence_candidate_count=len(evidence),
-        evidence_kept_count=len(evidence),
-        evidence_skip_reason=ev_skip,
-        synthesis_cache_hit=sr.cache_hit,
-        synthesis_prompt_preview=None,  # 현재 synthesis_service 에서 노출 안 함
-        synthesis_raw_preview=sr.raw_preview,
-        hallucination_flags=sr.hallucination_flags,
-    )
-
-
-def _detect_synthesis_failure(sr: SynthesisResult) -> str | None:
-    """Synthesis 가 유효한 답을 못 냈으면 re_gate 라벨, 아니면 None.
-
-    판정 우선순위 (Phase 3.5 fix3):
-      1) sr.refused → LLM self-refuse (status="completed") 또는 mechanical fail 후 refused 전파
-         - status=="completed" + refused=True → "synthesis_self_refuse"
-         - 그 외 → f"synthesis_failed({status})"
-      2) sr.status ∈ {timeout, parse_failed, llm_error} → f"synthesis_failed({status})"
-      3) answer 공백 → f"synthesis_failed({status})"
-      4) 유효 → None
-    """
-    if sr.refused:
-        if sr.status == "completed":
-            return "synthesis_self_refuse"
-        return f"synthesis_failed({sr.status})"
-    if sr.status in ("timeout", "parse_failed", "llm_error"):
-        return f"synthesis_failed({sr.status})"
-    if not (sr.answer or "").strip():
-        return f"synthesis_failed({sr.status})"
-    return None
-
-
-def _resolve_eval_identity(
-    x_source: str | None,
-    x_eval_case_id: str | None,
-    x_eval_token: str | None,
-) -> tuple[str, str | None]:
-    """X-Source/X-Eval-Case-Id 신뢰 검증 (Phase 3.5 fix2).
-
-    규칙:
-      - 기본값: source='document_server', eval_case_id=None
-      - X-Source=eval 또는 X-Eval-Case-Id 가 들어왔다면 eval claim 으로 간주
-      - eval claim 은 X-Eval-Token == settings.eval_runner_token 일 때만 수용
-        (constant-time compare, env 미설정 시 항상 거부)
-      - 거부 시: 헤더 무시 + warning log + source=sanitize(non-eval) / eval_case_id=None
-      - 통과 시: source='eval', eval_case_id=x_eval_case_id
-
-    반환: (source, eval_case_id)
-    """
-    claimed_source = sanitize_source(x_source)
-    is_eval_claim = (claimed_source == "eval") or bool(x_eval_case_id)
-    if not is_eval_claim:
-        # 일반 호출 — eval_case_id 강제 None (source != 'eval' 이면 case_id 의미 없음)
-        return claimed_source, None
-
-    # eval claim — token 검증
-    expected = settings.eval_runner_token
-    presented = x_eval_token or ""
-    token_valid = bool(expected) and hmac.compare_digest(presented, expected)
-    if not token_valid:
-        logger.warning(
-            "eval header rejected: source=%s case_id=%s token_present=%s expected_set=%s",
-            x_source, x_eval_case_id, bool(x_eval_token), bool(expected),
-        )
-        # 일반 호출로 강등 — source='eval' 주장은 무시, case_id 도 무시
-        # claimed_source 가 'eval' 이면 default 'document_server' 로
-        if claimed_source == "eval":
-            return "document_server", None
-        return claimed_source, None
-
-    # token OK — eval 라벨 수용
-    return "eval", x_eval_case_id
-
-
-@router.get("/ask", response_model=AskResponse)
-async def ask(
-    q: str,
-    user: Annotated[User, Depends(get_current_user)],
-    session: Annotated[AsyncSession, Depends(get_session)],
-    background_tasks: BackgroundTasks,
-    limit: int = Query(10, ge=1, le=20, description="synthesis 입력 상한"),
-    debug: bool = Query(False, description="evidence/synthesis 중간 상태 노출"),
-    backend: Annotated[
-        str | None,
-        Query(
-            pattern="^(qwen-macbook|gemma-macmini|mac-mini-default|claude-cloud|auto)$",
-            description=(
-                "PR-2 of DS AI routing policy (2026-05-23) — 명시 backend opt-in via llm-router. "
-                "미지정 = mac-mini-default (gemma-macmini alias, default). "
-                "'mac-mini-default' = router 가 tier_b (Mac mini gemma-4-26b). "
-                "'qwen-macbook' = router 가 named upstream (M5 Max Qwen 3.6 27B). "
-                "'claude-cloud' = router 가 503 provider_not_configured (활성화 별 PR). "
-                "'auto' = router 의 rule + LLM triage. "
-                "backend unavailable 시 503 + error_reason=macbook_unavailable / router_* "
-                "(자동 fallback 없음 — 다시 호출하거나 backend 인자 제거 후 재시도)."
-            ),
-        ),
-    ] = None,
-    corpus_variant: str | None = Query(
-        None,
-        pattern=r"^(prehier|hier_sim_raw|hier_sim_clean)$",
-        description=(
-            "⚠️ EVAL-ONLY (Hier-PassageRAG-Diagnose-1). evidence retrieval 의 chunk leg 를 측정 뷰로 "
-            "교체 — prehier(legacy) | hier_sim_raw | hier_sim_clean. 운영 UI 미사용. "
-            "미지정 = production corpus_chunks (기존 /ask 동작 동일)."
-        ),
-    ),
-    exact_knn: bool = Query(
-        False,
-        description=(
-            "⚠️ EVAL-ONLY (Hier-PassageRAG-Diagnose-1). vector leg exact KNN (ivfflat 근사 제거). "
-            "passage 변종 공정 비교용. 운영 미사용. 미지정(false) = 기존 /ask 동작 동일."
-        ),
-    ),
-    x_source: Annotated[str | None, Header(alias="X-Source")] = None,
-    x_eval_case_id: Annotated[str | None, Header(alias="X-Eval-Case-Id")] = None,
-    x_eval_token: Annotated[str | None, Header(alias="X-Eval-Token")] = None,
-):
-    """근거 기반 AI 답변 (Phase 3.5a).
-
-    Phase 3.3 기반 + classifier parallel + refusal gate + grounding re-gate.
-    실패 경로에서도 `results` 는 항상 반환.
-
-    Phase 3.5 calibration trust boundary (fix2):
-      - X-Source / X-Eval-Case-Id 는 X-Eval-Token 이 EVAL_RUNNER_TOKEN 와 일치하는
-        trusted internal eval runner 에서만 수용된다.
-      - 일반 client 의 X-Source=eval 시도는 무시되고 source='document_server' 로 강제.
-      - source != 'eval' 이면 eval_case_id 항상 None.
-    """
-    t_total = time.perf_counter()
-    defense_log: dict = {}  # per-layer flag snapshot
-    source, eval_case_id = _resolve_eval_identity(x_source, x_eval_case_id, x_eval_token)
-
-    # 1. 검색 파이프라인 (corpus_variant/exact_knn = EVAL-ONLY, 미지정 시 기존 동작 동일)
-    pr = await run_search(
-        session, q, mode="hybrid", limit=limit,
-        fusion=DEFAULT_FUSION, rerank=True, analyze=True,
-        corpus_variant=corpus_variant, exact_knn=exact_knn,
-    )
-
-    # 1.5. ask_includable=false 문서를 evidence 입력에서 제외
-    # 검색 결과 자체는 유지 (사용자에게 보여줌), evidence만 필터
-    if pr.results:
-        from sqlalchemy import select as sa_select
-        from models.document import Document as DocModel
-        ask_doc_ids = set()
-        excluded_ids = {r.id for r in pr.results}
-        rows = await session.execute(
-            sa_select(DocModel.id, DocModel.ask_includable).where(
-                DocModel.id.in_(excluded_ids)
-            )
-        )
-        for doc_id, includable in rows:
-            if includable is False:
-                ask_doc_ids.add(doc_id)
-        evidence_results = [r for r in pr.results if r.id not in ask_doc_ids]
-    else:
-        evidence_results = pr.results
-
-    # 2. Evidence + Classifier 병렬
-    t_ev = time.perf_counter()
-    evidence_task = asyncio.create_task(extract_evidence(q, evidence_results))
-
-    # classifier input: top 3 chunks meta + rerank scores
-    top_chunks = [
-        {
-            "title": r.title or "",
-            "section": r.section_title or "",
-            "snippet": (r.snippet or "")[:200],
-        }
-        for r in pr.results[:3]
-    ]
-    rerank_scores_top = [
-        r.rerank_score if r.rerank_score is not None else r.score
-        for r in pr.results[:3]
-    ]
-    classifier_task = asyncio.create_task(
-        classify(q, top_chunks, rerank_scores_top)
-    )
-
-    evidence, ev_skip = await evidence_task
-    ev_ms = (time.perf_counter() - t_ev) * 1000
-
-    # classifier await (timeout 보호 — classifier_service 내부에도 있지만 여기서 이중 보호)
-    # 2026-05-17: 6s outer wrapper 가 classifier_service.LLM_TIMEOUT_MS (30s) 를 override → 동시 부하 시
-    # 거의 모든 classifier 호출 timeout → conservative_refuse(no_classifier) 경로. 15s 로 상향 — classifier
-    # 가 실제 작동하도록 (단, ask 전체 응답 시간 상한 영향: ev_ms + max(classifier_wait, evidence_extract) +
-    # synth_ms + verifier 누적).
-    # 2026-05-17 B-3: 15s 도 동시 부하 시 부족 (classifier_service LLM_TIMEOUT_MS 30s 와 misalign).
-    # 30s 로 align → classifier 동작 안정. ask 응답 latency 상한 ↑ 의도.
-    try:
-        classifier_result = await asyncio.wait_for(classifier_task, timeout=30.0)
-    except asyncio.CancelledError:
-        raise  # 요청 취소는 전파 — broad except 가 삼키지 않게 명시 (R3)
-    except Exception:
-        classifier_result = ClassifierResult("timeout", None, [], [], 0.0)
-
-    defense_log["classifier"] = {
-        "status": classifier_result.status,
-        "verdict": classifier_result.verdict,
-        "covered_aspects": classifier_result.covered_aspects,
-        "missing_aspects": classifier_result.missing_aspects,
-        "elapsed_ms": classifier_result.elapsed_ms,
-    }
-
-    # 3. Refusal gate (multi-signal fusion)
-    all_rerank_scores = [
-        e.rerank_score for e in evidence
-    ] if evidence else rerank_scores_top
-    decision = refusal_decide(all_rerank_scores, classifier_result)
-
-    defense_log["score_gate"] = {
-        "max": max(all_rerank_scores) if all_rerank_scores else 0.0,
-        "agg_top3": sum(sorted(all_rerank_scores, reverse=True)[:3]),
-    }
-    defense_log["refusal"] = {
-        "refused": decision.refused,
-        "rule_triggered": decision.rule_triggered,
-    }
-
-    if decision.refused:
-        total_ms = (time.perf_counter() - t_total) * 1000
-        no_reason = "관련 근거를 찾지 못했습니다."
-        if not pr.results:
-            no_reason = "검색 결과가 없습니다."
-        logger.info(
-            "ask REFUSED query=%r rule=%s max_score=%.2f total=%.0f",
-            q[:80], decision.rule_triggered,
-            max(all_rerank_scores) if all_rerank_scores else 0.0, total_ms,
-        )
-        # telemetry — search + ask_events 두 경로 동시
-        background_tasks.add_task(
-            record_search_event, q, user.id, pr.results, "hybrid",
-            pr.confidence_signal, pr.analyzer_confidence,
-        )
-        # input_snapshot (디버깅/재현용)
-        defense_log["input_snapshot"] = {
-            "query": q,
-            "top_chunks_preview": [
-                {"title": c.get("title", ""), "snippet": c.get("snippet", "")[:100]}
-                for c in top_chunks[:3]
-            ],
-            "answer_preview": None,
-        }
-        background_tasks.add_task(
-            record_ask_event,
-            q, user.id, "insufficient", "skipped", None,
-            True, classifier_result.verdict,
-            max(all_rerank_scores) if all_rerank_scores else 0.0,
-            sum(sorted(all_rerank_scores, reverse=True)[:3]),
-            [], len(evidence), 0,
-            defense_log, int(total_ms),
-            # Phase E.1 측정 필드
-            answer_length=0,
-            covered_aspects=classifier_result.covered_aspects or None,
-            missing_aspects=classifier_result.missing_aspects or None,
-            model_name=resolve_primary_model(),
-            prompt_version=ASK_PROMPT_VERSION,
-            # Phase 3.5 calibration
-            source=source,
-            eval_case_id=eval_case_id,
-        )
-        debug_obj = None
-        if debug:
-            debug_obj = AskDebug(
-                timing_ms={**pr.timing_ms, "evidence_ms": ev_ms, "ask_total_ms": total_ms},
-                search_notes=pr.notes,
-                confidence_signal=pr.confidence_signal,
-                evidence_candidate_count=len(evidence),
-                evidence_kept_count=len(evidence),
-                evidence_skip_reason=ev_skip,
-                synthesis_cache_hit=False,
-                hallucination_flags=[],
-                defense_layers=defense_log,
-            )
-        return AskResponse(
-            results=pr.results,
-            ai_answer=None,
-            citations=[],
-            synthesis_status="skipped",
-            synthesis_ms=0.0,
-            confidence=None,
-            refused=True,
-            no_results_reason=no_reason,
-            query=q,
-            total=len(pr.results),
-            completeness="insufficient",
-            covered_aspects=classifier_result.covered_aspects or None,
-            missing_aspects=classifier_result.missing_aspects or None,
-            # refusal gate 단계에서는 backend 호출 자체가 일어나지 않음 →
-            # backend_used = None. backend_requested 는 호출자 의도 표시용.
-            backend_requested=backend,
-            backend_used=None,
-            debug=debug_obj,
-        )
-
-    # 4. Synthesis (backend dispatcher 적용 — PR-MacBook-RAG-Backend-1)
-    t_synth = time.perf_counter()
-    sr = await synthesize(q, evidence, debug=debug, backend=backend)
-    synth_ms = (time.perf_counter() - t_synth) * 1000
-
-    # 4.1. backend_unavailable → 503 fail-fast (자동 fallback 금지)
-    # 명시 opt-in backend (예: qwen-macbook) 가 비가용일 때만 발생. /ask wrapper 는
-    # 절대 다른 backend 로 재시도하지 않음. 사용자가 backend 인자 제거 또는 wake 후 재시도.
-    if sr.status == "backend_unavailable":
-        backend_requested_val = backend or "gemma-macmini"
-        total_ms = (time.perf_counter() - t_total) * 1000
-        logger.warning(
-            "ask backend_unavailable backend=%s query=%r total_ms=%.0f flags=%s",
-            backend_requested_val, q[:80], total_ms,
-            ",".join(sr.hallucination_flags) if sr.hallucination_flags else "-",
-        )
-        # error_reason 명명 — macbook_unavailable 만 정착 (자동 fallback 부재).
-        error_reason = (
-            "macbook_unavailable"
-            if backend_requested_val == "qwen-macbook"
-            else "backend_unavailable"
-        )
-        # telemetry — search 만 기록 (ask_events 는 200 응답 path 전용)
-        background_tasks.add_task(
-            record_search_event, q, user.id, pr.results, "hybrid",
-            pr.confidence_signal, pr.analyzer_confidence,
-        )
-        return JSONResponse(
-            status_code=503,
-            content={
-                "error": "backend_unavailable",
-                "error_reason": error_reason,
-                "backend_requested": backend_requested_val,
-                "backend_used": None,
-                "query": q,
-                "detail": (
-                    "명시 선택한 backend 가 일시적으로 응답할 수 없습니다. "
-                    "MacBook 깨우거나 backend 인자를 제거하고 (기본 Gemma) 다시 호출하세요."
-                ),
-            },
-        )
-
-    # 5. Grounding check + Verifier (조건부 병렬) + re-gate (Phase 3.5b)
-    grounding = grounding_check(q, sr.answer or "", evidence)
-
-    # verifier skip: grounding strong 2+ OR retrieval 자체가 망함
-    grounding_only_strong = [
-        f for f in grounding.strong_flags if not f.startswith("verifier_")
-    ]
-    max_rerank = max(all_rerank_scores, default=0.0)
-    if len(grounding_only_strong) >= 2 or max_rerank < 0.2:
-        verifier_result = VerifierResult("skipped", [], 0.0)
-    else:
-        verifier_task = asyncio.create_task(
-            verify(q, sr.answer or "", evidence)
-        )
-        # 2026-05-17 B-3: 4s outer wait_for 가 verifier_service LLM_TIMEOUT_MS (10s) 를 override
-        # → classifier 와 동일 패턴 (search.py:522 가 6s→15s swap 했던 case). 10s 로 align.
-        try:
-            verifier_result = await asyncio.wait_for(verifier_task, timeout=10.0)
-        except asyncio.CancelledError:
-            raise  # 요청 취소는 전파 — broad except 가 삼키지 않게 명시 (R3)
-        except Exception:
-            verifier_result = VerifierResult("timeout", [], 0.0)
-
-    # Verifier contradictions → grounding flags 머지 (prefix 로 구분, severity 3단계)
-    for c in verifier_result.contradictions:
-        if c.severity == "strong":
-            grounding.strong_flags.append(f"verifier_{c.type}:{c.claim[:30]}")
-        elif c.severity == "medium":
-            grounding.weak_flags.append(f"verifier_{c.type}_medium:{c.claim[:30]}")
-        else:
-            grounding.weak_flags.append(f"verifier_{c.type}:{c.claim[:30]}")
-
-    defense_log["evidence"] = {
-        "skip_reason": ev_skip,
-        "kept_count": len(evidence),
-    }
-    defense_log["grounding"] = {
-        "strong": grounding.strong_flags,
-        "weak": grounding.weak_flags,
-    }
-    defense_log["verifier"] = {
-        "status": verifier_result.status,
-        "contradictions_count": len(verifier_result.contradictions),
-        "strong_count": sum(1 for c in verifier_result.contradictions if c.severity == "strong"),
-        "medium_count": sum(1 for c in verifier_result.contradictions if c.severity == "medium"),
-        "elapsed_ms": verifier_result.elapsed_ms,
-    }
-
-    # ── Re-gate: 7-tier completeness 결정 (Phase 3.5 B2 — Tier 4 신규 삽입, 재번호) ──
-    # 기존 6-tier (3.5b 4차 리뷰) + Tier 4(g_strong + v_strong_numeric + low_conf → refuse).
-    # 호환성: defense_layers["re_gate"] 의 string literal 들은 기존 그대로 유지.
-    # 신규 "refuse(grounding+verifier_numeric)" 만 추가.
-    completeness: Literal["full", "partial", "insufficient"] = "full"
-    covered_aspects = classifier_result.covered_aspects or None
-    missing_aspects = classifier_result.missing_aspects or None
-    confirmed_items: list[ConfirmedItem] | None = None
-
-    # verifier/grounding strong 구분
-    g_strong = [f for f in grounding.strong_flags if not f.startswith("verifier_")]
-    v_strong = [f for f in grounding.strong_flags if f.startswith("verifier_")]
-    v_medium = [f for f in grounding.weak_flags if f.startswith("verifier_") and "_medium:" in f]
-    has_direct_negation = any("direct_negation" in f for f in v_strong)
-    # Phase 3.5 B2: verifier strong flags 중 numeric_conflict 만 카운트.
-    # promote(VERIFIER_NUMERIC_PROMOTE=1) 활성 시 critical numeric_conflict 가 strong 으로 승격되며
-    # 여기 카운트에 잡힘. promote off 면 항상 0 → Tier 4 활성 안 됨 (기존 동작 유지).
-    v_strong_numeric = sum(
-        1 for f in v_strong if f.startswith("verifier_numeric_conflict")
-    )
-
-    # ── Tier 0 (Phase 3.5 fix3): synthesis 자체 실패 처리 ──
-    # LLM self-refuse, 메커니즘 실패(timeout/parse_failed/llm_error), answer 공백.
-    # 빈 답에 대해 grounding/verifier flag 가 0건이라 기존 체인이 "else clean" 으로 빠지며
-    # completeness="full" 초기값이 보존되던 모순을 여기서 일관되게 차단.
-    # 과거 baseline(v1-400char) 에서 20(self-refuse)+4(timeout) = 24/223 (10.8%) 해당.
-    tier0_label = _detect_synthesis_failure(sr)
-    if tier0_label:
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = tier0_label
-    elif len(g_strong) >= 2:
-        # Tier 1: grounding strong 2+ → refuse
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding_2+strong)"
-    elif g_strong and has_direct_negation:
-        # Tier 2: grounding strong + verifier direct_negation → refuse
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding+direct_negation)"
-    elif g_strong and sr.confidence == "low" and max_rerank < 0.25:
-        # Tier 3: grounding strong 1 + (low confidence AND weak evidence) → refuse
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding+low_conf+weak_ev)"
-    elif g_strong and v_strong_numeric >= 1 and sr.confidence == "low":
-        # Tier 4 (B2 신규): grounding strong + verifier numeric_conflict strong + low conf → refuse.
-        # verifier strong 단독 refuse 금지 원칙 유지 — g_strong 교차 필수.
-        completeness = "insufficient"
-        sr.answer = None
-        sr.refused = True
-        sr.confidence = None
-        defense_log["re_gate"] = "refuse(grounding+verifier_numeric)"
-    elif g_strong or has_direct_negation:
-        # Tier 5 (기존 4): grounding strong 1 또는 verifier direct_negation 단독 → partial
-        completeness = "partial"
-        sr.confidence = "low"
-        defense_log["re_gate"] = "partial(strong_or_negation)"
-    elif v_medium:
-        # Tier 6 (기존 5): verifier medium 누적 → count 기반 confidence 하향
-        medium_count = len(v_medium)
-        if medium_count >= 3:
-            sr.confidence = "low"
-            defense_log["re_gate"] = f"conf_low(medium_x{medium_count})"
-        elif medium_count == 2 and sr.confidence == "high":
-            sr.confidence = "medium"
-            defense_log["re_gate"] = "conf_cap_medium(medium_x2)"
-        else:
-            defense_log["re_gate"] = f"medium_x{medium_count}(no_action)"
-    elif grounding.weak_flags:
-        # Tier 7 (기존 6): weak → confidence 한 단계 하향
-        if sr.confidence == "high":
-            sr.confidence = "medium"
-        defense_log["re_gate"] = "conf_lower(weak)"
-    else:
-        defense_log["re_gate"] = "clean"
-
-    # Confidence cap from refusal gate (classifier 부재 시 conservative)
-    if decision.confidence_cap and sr.confidence:
-        conf_rank = {"low": 0, "medium": 1, "high": 2}
-        if conf_rank.get(sr.confidence, 0) > conf_rank.get(decision.confidence_cap, 2):
-            sr.confidence = decision.confidence_cap
-
-    # Partial 이면 max confidence = medium
-    if completeness == "partial" and sr.confidence == "high":
-        sr.confidence = "medium"
-
-    sr.hallucination_flags.extend(
-        [f"strong:{f}" for f in grounding.strong_flags]
-        + [f"weak:{f}" for f in grounding.weak_flags]
-    )
-
-    total_ms = (time.perf_counter() - t_total) * 1000
-
-    # 6. 응답 구성
-    citations = _build_citations(evidence, sr.used_citations)
-    no_reason = _map_no_results_reason(pr, evidence, ev_skip, sr)
-    if completeness == "insufficient" and not no_reason:
-        # Tier 0 경로: synthesis self-refuse 는 LLM 이 준 사유가 가장 정확.
-        if sr.refused and sr.refuse_reason:
-            no_reason = sr.refuse_reason
-        else:
-            no_reason = "답변 검증에서 복수 오류 감지"
-
-    logger.info(
-        "ask query=%r results=%d evidence=%d cite=%d synth=%s conf=%s completeness=%s "
-        "refused=%s grounding_strong=%d grounding_weak=%d ev_ms=%.0f synth_ms=%.0f total=%.0f",
-        q[:80], len(pr.results), len(evidence), len(citations),
-        sr.status, sr.confidence or "-", completeness,
-        sr.refused, len(grounding.strong_flags), len(grounding.weak_flags),
-        ev_ms, synth_ms, total_ms,
-    )
-
-    # 7. telemetry — search + ask_events 두 경로 동시
-    background_tasks.add_task(
-        record_search_event, q, user.id, pr.results, "hybrid",
-        pr.confidence_signal, pr.analyzer_confidence,
-    )
-    # input_snapshot (디버깅/재현용)
-    defense_log["input_snapshot"] = {
-        "query": q,
-        "top_chunks_preview": [
-            {"title": (r.title or "")[:50], "snippet": (r.snippet or "")[:100]}
-            for r in pr.results[:3]
-        ],
-        "answer_preview": (sr.answer or "")[:200],
-    }
-    background_tasks.add_task(
-        record_ask_event,
-        q, user.id, completeness, sr.status, sr.confidence,
-        sr.refused, classifier_result.verdict,
-        max(all_rerank_scores) if all_rerank_scores else 0.0,
-        sum(sorted(all_rerank_scores, reverse=True)[:3]),
-        sr.hallucination_flags, len(evidence), len(citations),
-        defense_log, int(total_ms),
-        # Phase E.1 측정 필드
-        answer_length=len(sr.answer or ""),
-        covered_aspects=covered_aspects,
-        missing_aspects=missing_aspects,
-        model_name=resolve_primary_model(),
-        prompt_version=ASK_PROMPT_VERSION,
-        # Phase 3.5 calibration
-        source=source,
-        eval_case_id=eval_case_id,
-    )
-
-    debug_obj = None
-    if debug:
-        timing = dict(pr.timing_ms)
-        timing["evidence_ms"] = ev_ms
-        timing["synthesis_ms"] = synth_ms
-        timing["ask_total_ms"] = total_ms
-        debug_obj = AskDebug(
-            timing_ms=timing,
-            search_notes=pr.notes,
-            query_analysis=pr.query_analysis,
-            confidence_signal=pr.confidence_signal,
-            evidence_candidate_count=len(evidence),
-            evidence_kept_count=len(evidence),
-            evidence_skip_reason=ev_skip,
-            synthesis_cache_hit=sr.cache_hit,
-            synthesis_raw_preview=sr.raw_preview,
-            hallucination_flags=sr.hallucination_flags,
-            defense_layers=defense_log,
-        )
-
-    # backend_used: synthesize 가 실제 호출한 backend (backend 인자 그대로 신뢰 OK —
-    # backend_unavailable 은 위 503 분기에서 이미 return 됨).
-    backend_used_val = backend or "gemma-macmini"
-
-    return AskResponse(
-        results=pr.results,
-        ai_answer=sr.answer,
-        citations=citations,
-        synthesis_status=sr.status,
-        synthesis_ms=sr.elapsed_ms,
-        confidence=sr.confidence,
-        refused=sr.refused,
-        no_results_reason=no_reason,
-        query=q,
-        total=len(pr.results),
-        completeness=completeness,
-        covered_aspects=covered_aspects,
-        missing_aspects=missing_aspects,
-        confirmed_items=confirmed_items,
-        backend_requested=backend,
-        backend_used=backend_used_val,
-        debug=debug_obj,
-    )
-
-
-# ─── PR-DocSrv-Ask-ToolCalling-ReAct-1 ────────────────────────────────────
-# /api/search/ask/react — Qwen native tool calling 로 ReAct loop.
-# 본 endpoint 는 qwen-macbook only (endpoint 자체가 implicit opt-in).
-# MacBook unavailable 시 503 + error_reason=macbook_unavailable. Gemma 자동 fallback X.
-# G0-2 counter semantics: max_tool_rounds=2, max LLM calls=3, search exec ≤ 2.
-# G0-3 trace exposure: default response 의 debug_trace=None, debug=True 시만 채움.
-
-
-class AskReactRequest(BaseModel):
-    query: str
-    debug: bool = False
-
-
-class AskReactResponse(BaseModel):
-    final_answer: str
-    iterations: int
-    partial: bool
-    sources: list[dict]
-    debug_trace: list[dict] | None = None
-
-
-@router.post("/ask/react", response_model=AskReactResponse)
-async def ask_react(
-    payload: AskReactRequest,
-    user: Annotated[User, Depends(get_current_user)],
-    session: Annotated[AsyncSession, Depends(get_session)],
-):
-    """ReAct loop endpoint (qwen-macbook only, no fallback).
-
-    호출자가 명시 opt-in 한 endpoint. MacBook 가 sleep / unreachable / 5xx 시
-    HTTP 503 + body `{error_reason: "macbook_unavailable", backend: "qwen-macbook"}`
-    를 반환한다. Gemma Mac mini 로 자동 fallback 하지 않는다 (정정 4 의 연장).
-
-    request body:
-      - query: str (사용자 원본 질의)
-      - debug: bool (default false; true 시 응답 `debug_trace` 채움)
-
-    response body (성공 200):
-      - final_answer: str (Qwen 종합문, partial 일 수 있음)
-      - iterations: int (실제 진행된 tool round 수)
-      - partial: bool (max_tool_rounds 도달 후 LLM content 비었을 때 true)
-      - sources: list[dict] (검색에서 모인 evidence 메타, id-기준 dedup)
-      - debug_trace: list[dict] | null (debug=true 시 round 별 trace)
-    """
-    # 지연 import — 순환 의존성 회피 (react_loop 가 api.search.SearchResult 사용 안 함)
-    from services.llm.backends import BackendUnavailable, get_backend
-    from services.search.react_loop import agentic_ask_loop
-
-    backend_inst = get_backend("qwen-macbook")
-    # PR-2 of DS AI routing policy: backend_inst may be RouterBackend (default)
-    # or QwenMacBookBackend (DS_BACKENDS_VIA_ROUTER=false rollback). Both
-    # implement generate_with_tools so the ReAct loop is identical.
-    assert hasattr(backend_inst, "generate_with_tools")
-
-    try:
-        result = await agentic_ask_loop(
-            session,
-            payload.query,
-            backend=backend_inst,
-            debug=payload.debug,
-        )
-    except BackendUnavailable as exc:
-        logger.warning(
-            "ask_react backend unavailable backend=%s reason=%s",
-            exc.backend_name, exc.reason,
-        )
-        return JSONResponse(
-            status_code=503,
-            content={
-                "error_reason": "macbook_unavailable",
-                "backend_requested": "qwen-macbook",
-                "backend_used": None,
-                "detail": exc.reason,
-            },
-        )
-
-    return AskReactResponse(
-        final_answer=result.final_answer,
-        iterations=result.iterations,
-        partial=result.partial,
-        sources=result.sources,
-        debug_trace=result.debug_trace,
-    )

app/models/published.py

@@ -56,5 +56,9 @@ class PublishOutbox(Base):
         DateTime(timezone=True), default=datetime.now, nullable=False
     )
     processed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
+    # mig378: 행별 격리 재시도/terminal. attempts=savepoint 실패 누적, failed_at=MAX 초과 terminal
+    # (set 시 워커 select 에서 제외 → head-of-line block 방지).
+    attempts: Mapped[int] = mapped_column(SmallInteger, nullable=False, default=0)
+    failed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
 
     # 미처리 부분 인덱스 idx(id) WHERE processed_at IS NULL = mig372.

app/prompts/classifier.txt

@@ -1,33 +0,0 @@
-You are an answerability judge. Given a query and evidence chunks, determine if the evidence can answer the query. Respond ONLY in JSON.
-
-## CALIBRATION (CRITICAL)
-- verdict=full: evidence is SUFFICIENT to answer the CORE of the query. Missing minor details does NOT make it insufficient.
-- verdict=partial: evidence covers SOME major aspects but CLEARLY MISSES others the user explicitly asked about.
-- verdict=insufficient: evidence has NO relevant information for the query, or is completely off-topic.
-
-Example: Query="제6장 주요 내용", Evidence covers 제6장 definition+scope → verdict=full (core is covered).
-Example: Query="제6장 처벌 조항", Evidence covers 제6장 definition but NOT 처벌 → verdict=partial.
-Example: Query="감귤 출하량", Evidence about 산업안전보건법 → verdict=insufficient.
-
-## Rules
-1. Your "verdict" must be based ONLY on whether the CONTENT semantically answers the query. Ignore retrieval scores for this field.
-2. "covered_aspects": query aspects that evidence covers. Korean labels for Korean queries.
-3. "missing_aspects": query aspects that evidence does NOT cover. Korean labels.
-4. Keep aspects concise (2-5 words each), non-overlapping.
-
-## Output Schema
-{
-  "verdict": "full" | "partial" | "insufficient",
-  "covered_aspects": ["aspect1"],
-  "missing_aspects": ["aspect2"],
-  "confidence": "high" | "medium" | "low"
-}
-
-## Query
-{query}
-
-## Evidence chunks:
-{chunks}
-
-## Retrieval scores (for reference only, NOT for verdict):
-[{scores}]

app/prompts/policy/p3a_short_summary.txt

@@ -1,5 +1,5 @@
 [System]
-너는 한국어 문서 태거 + 짧은 요약기다. 입력 본문을 읽고 TL;DR + 핵심 bullets + tags 만 생성한다. **상세 문단·entities 는 생성하지 않는다** (깊은 요약은 26B, entity 는 P3b 담당).
+너는 한국어 문서 태거 + 요약기다. 입력 본문을 읽고 짧은 요약(ai_summary 2~3문장) + TL;DR + 핵심 bullets + tags 를 생성한다. **여러 문단의 상세 심층요약·entities 는 생성하지 않는다** (깊은 요약은 26B, entity 는 P3b 담당).
 
 subject_description: {subject_description}
 
@@ -13,6 +13,7 @@ subject_description: {subject_description}
 - pii 감지 시 "pii" 추가 + confidence 감점.
 
 요약 규칙:
+- **ai_summary**: 2~3문장 문단. 문서의 핵심 내용·목적을 서술 (검색·표시용 요약).
 - **TL;DR**: 1문장, 최대 60자.
 - **Bullets**: 정확히 5개, 각 30~60자.
 - 본문에 없는 정보 추가 금지 (hallucination 금지).
@@ -20,6 +21,7 @@ subject_description: {subject_description}
 
 출력 (JSON only):
 {{
+  "ai_summary": "2~3문장 문단 요약",
   "tldr": "1문장 최대 60자",
   "bullets": ["...", "...", "...", "...", "..."],
   "tags": ["..."],

app/prompts/verifier.txt

@@ -1,42 +0,0 @@
-You are a grounding verifier. Given an answer and its evidence sources, check if the answer contradicts or fabricates information. Respond ONLY in JSON.
-
-## Contradiction Types (IMPORTANT — severity depends on type)
-- **direct_negation** (CRITICAL): Answer directly contradicts evidence. Examples: evidence "의무" but answer "권고"; evidence "금지" but answer "허용"; negation reversal ("~해야 한다" vs "~할 필요 없다").
-- **numeric_conflict**: Answer states a number different from evidence. "50명" in evidence but "100명" in answer. Only flag if the same concept is referenced. severity=critical when the number is the CORE answered quantity (amount/count/rate/date/duration that the query asked for); severity=minor when the number is peripheral (e.g., example/footnote).
-- **intent_core_mismatch**: Answer addresses a fundamentally different topic than the query asked about.
-- **nuance**: Answer overgeneralizes or adds qualifiers not in evidence (e.g., "모든" when evidence says "일부").
-- **unsupported_claim**: Answer makes a factual claim with no basis in any evidence.
-
-## Rules
-1. Compare each claim in the answer against the cited evidence. A claim with [n] citation should be checked against evidence [n].
-2. NOT a contradiction: Paraphrasing, summarizing, or restating the same fact in different words. Korean formal/informal style (합니다/한다) differences.
-3. Numbers must match exactly after normalization (1,000 = 1000). Range values (e.g., "100~200명") satisfy any answer within range.
-4. Legal/regulatory terms must preserve original meaning (의무 ≠ 권고, 금지 ≠ 제한, 허용 ≠ 금지).
-5. Maximum 5 contradictions (most severe first: direct_negation > numeric_conflict > intent_core_mismatch > nuance > unsupported_claim).
-
-## Output Schema
-{
-  "contradictions": [
-    {
-      "type": "direct_negation" | "numeric_conflict" | "intent_core_mismatch" | "nuance" | "unsupported_claim",
-      "severity": "critical" | "minor",
-      "claim": "answer 내 해당 구절 (50자 이내)",
-      "evidence_ref": "대응 근거 내용 (50자 이내, [n] 포함)",
-      "explanation": "모순 이유 (한국어, 30자 이내)"
-    }
-  ],
-  "verdict": "clean" | "minor_issues" | "major_issues"
-}
-
-severity mapping:
-- direct_negation → "critical"
-- numeric_conflict → "critical" if the number is the CORE answered quantity, else "minor"
-- All other types → "minor"
-
-If no contradictions: {"contradictions": [], "verdict": "clean"}
-
-## Answer
-{answer}
-
-## Evidence
-{numbered_evidence}

app/services/search/classifier_service.py

@@ -1,156 +0,0 @@
-"""Answerability classifier (Phase 3.5a).
-
-Mac mini 26B MLX 기반 (config.yaml ai.models.classifier — PR #20 이후 triage/primary/classifier 동일 endpoint). MLX gate 밖 — evidence extraction 과 병렬 실행 (concurrent 안전성 별 검토).
-
-P1 실측 결과: ternary (full/partial/insufficient) 불안정 → **binary (sufficient/insufficient)**.
-"full" vs "partial" 구분은 grounding_check 의 intent alignment 이 담당.
-
-Classifier verdict 는 "relevant evidence 가 있나" 의 binary 판단.
-covered_aspects / missing_aspects 는 로깅용으로 유지 (refusal gate 에서 사용 안 함).
-"""
-
-from __future__ import annotations
-
-import asyncio
-import time
-from dataclasses import dataclass
-from typing import Literal
-
-from ai.client import AIClient, _load_prompt, parse_json_response
-from core.config import settings
-from core.utils import setup_logger
-
-from .llm_gate import Priority, acquire_mlx_gate
-
-logger = setup_logger("classifier")
-
-LLM_TIMEOUT_MS = 30000
-CIRCUIT_THRESHOLD = 5
-CIRCUIT_RECOVERY_SEC = 60
-
-_failure_count = 0
-_circuit_open_until: float | None = None
-
-
-@dataclass(slots=True)
-class ClassifierResult:
-    status: Literal["ok", "timeout", "error", "circuit_open", "skipped"]
-    verdict: Literal["sufficient", "insufficient"] | None
-    covered_aspects: list[str]
-    missing_aspects: list[str]
-    elapsed_ms: float
-
-
-try:
-    CLASSIFIER_PROMPT = _load_prompt("classifier.txt")
-except FileNotFoundError:
-    CLASSIFIER_PROMPT = ""
-    logger.warning("classifier.txt not found — classifier will always skip")
-
-
-def _build_input(
-    query: str,
-    top_chunks: list[dict],
-    rerank_scores: list[float],
-) -> str:
-    """Y+ input (content + scores with role separation)."""
-    chunk_block = "\n".join(
-        f"[{i+1}] title: {c.get('title','')}\n"
-        f"    section: {c.get('section','')}\n"
-        f"    snippet: {c.get('snippet','')}"
-        for i, c in enumerate(top_chunks[:3])
-    )
-    scores_str = ", ".join(f"{s:.2f}" for s in rerank_scores[:3])
-    return (
-        CLASSIFIER_PROMPT
-        .replace("{query}", query)
-        .replace("{chunks}", chunk_block)
-        .replace("{scores}", scores_str)
-    )
-
-
-async def classify(
-    query: str,
-    top_chunks: list[dict],
-    rerank_scores: list[float],
-) -> ClassifierResult:
-    """Always-on binary classifier. Parallel with evidence extraction.
-
-    Returns:
-        ClassifierResult with verdict=sufficient|insufficient.
-        Status "ok" 이 아니면 verdict=None (caller 가 fallback 처리).
-    """
-    global _failure_count, _circuit_open_until
-    t_start = time.perf_counter()
-
-    # Circuit breaker
-    if _circuit_open_until and time.time() < _circuit_open_until:
-        return ClassifierResult("circuit_open", None, [], [], 0.0)
-
-    if not CLASSIFIER_PROMPT:
-        return ClassifierResult("skipped", None, [], [], 0.0)
-
-    if not hasattr(settings.ai, "classifier") or settings.ai.classifier is None:
-        return ClassifierResult("skipped", None, [], [], 0.0)
-
-    prompt = _build_input(query, top_chunks, rerank_scores)
-    client = AIClient()
-    try:
-        # 2026-05-17: PR #20 이후 endpoint 가 Mac mini 26B → llm_gate Semaphore(1) 필수.
-        # Gate 미사용 시 classifier + evidence + synthesis 가 동시에 single-inference
-        # MLX 에 race → 거의 모두 timeout (실측: 8/10 fixture query). docstring 영구 룰:
-        # "MLX primary 호출 경로는 예외 없이 gate 획득 필수".
-        async with acquire_mlx_gate(Priority.FOREGROUND):
-            async with asyncio.timeout(LLM_TIMEOUT_MS / 1000):
-                raw = await client.call_classifier(prompt)
-        _failure_count = 0
-    except asyncio.TimeoutError:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"classifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning("classifier timeout")
-        return ClassifierResult(
-            "timeout", None, [], [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    except Exception as e:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"classifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning("classifier error: type=%s repr=%r", type(e).__name__, e)
-        return ClassifierResult(
-            "error", None, [], [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    finally:
-        await client.close()
-
-    elapsed_ms = (time.perf_counter() - t_start) * 1000
-    parsed = parse_json_response(raw)
-    if not isinstance(parsed, dict):
-        logger.warning("classifier parse failed raw=%r", (raw or "")[:200])
-        return ClassifierResult("error", None, [], [], elapsed_ms)
-
-    # ternary → binary 매핑
-    raw_verdict = parsed.get("verdict", "")
-    if raw_verdict == "insufficient":
-        verdict: Literal["sufficient", "insufficient"] | None = "insufficient"
-    elif raw_verdict in ("full", "partial", "sufficient"):
-        verdict = "sufficient"
-    else:
-        verdict = None
-
-    covered = parsed.get("covered_aspects") or []
-    missing = parsed.get("missing_aspects") or []
-    if not isinstance(covered, list):
-        covered = []
-    if not isinstance(missing, list):
-        missing = []
-
-    logger.info(
-        "classifier ok query=%r verdict=%s (raw=%s) covered=%d missing=%d elapsed_ms=%.0f",
-        query[:60], verdict, raw_verdict, len(covered), len(missing), elapsed_ms,
-    )
-    return ClassifierResult("ok", verdict, covered, missing, elapsed_ms)

app/services/search/grounding_check.py

@@ -1,505 +0,0 @@
-"""Grounding check — post-synthesis 검증 (Phase 3.5a).
-
-Strong/weak flag 분리:
-- **Strong** (→ partial 강등 or refuse): fabricated_number, intent_misalignment(important)
-- **Weak** (→ confidence lower only): uncited_claim, low_overlap, intent_misalignment(generic)
-
-Re-gate 로직 (Phase 3.5a 9라운드 토론 결과):
-- strong 1개 → partial 강등
-- strong 2개 이상 → refuse
-- weak → confidence "low" 만
-
-Intent alignment (rule-based):
-- query 의 핵심 명사가 answer 에 등장하는지 확인
-- "처벌" 같은 중요 키워드 누락은 strong
-- "주요", "관련" 같은 generic 은 무시
-"""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-from core.utils import setup_logger
-
-if TYPE_CHECKING:
-    from .evidence_service import EvidenceItem
-
-logger = setup_logger("grounding")
-
-# "주요", "관련" 등 intent alignment 에서 제외할 generic 단어
-GENERIC_TERMS = frozenset({
-    "주요", "관련", "내용", "정의", "기준", "방법", "설명", "개요",
-    "대한", "위한", "대해", "무엇", "어떤", "어떻게", "있는",
-    "하는", "되는", "이런", "그런", "이것", "그것",
-})
-
-
-@dataclass(slots=True)
-class GroundingResult:
-    strong_flags: list[str]
-    weak_flags: list[str]
-
-
-_UNIT_CHARS = r'명인개%년월일조항호세건원회'
-
-# "이상/이하/초과/미만" — threshold 표현 (numeric conflict 에서 skip 대상)
-_THRESHOLD_SUFFIXES = re.compile(r'이상|이하|초과|미만')
-
-# 약칭/근사치 prefix — 매칭 전 제거 (Phase 3.5 B1).
-# ⚠ 최대/최소 는 의도적으로 제외 — 이들은 bound operator 라 의미가 다름 (Phase 3.5 B1 fix3).
-# 약/대략/거의/얼추 만 노이즈 prefix 로 strip.
-_APPROX_PREFIX_RE = re.compile(r'(약|대략|거의|얼추)\s*')
-
-# 단위 동의어 dict — 추출 직후 정규화 (Phase 3.5 B1)
-# 의미가 동일한 단위는 같은 표기로 통일해서 set 비교/range overlap 안정화.
-_UNIT_SYNONYMS: dict[str, str] = {
-    "인": "명",
-    "사람": "명",
-    "퍼센트": "%",
-    "프로": "%",
-    "KRW": "원",
-    "krw": "원",
-}
-
-# tolerance(±1%) 허용 단위 — 양적 측정값 (Phase 3.5 B1)
-_TOLERANCE_UNITS: frozenset[str] = frozenset({"명", "원", "%", "건", "개"})
-
-# tolerance 미적용 단위 — 식별자성 숫자 (연도/조문/횟수)
-_EXACT_ONLY_UNITS: frozenset[str] = frozenset({"년", "월", "일", "조", "항", "호", "회"})
-
-# 최대/최소 prefix 패턴 — bound operator (Phase 3.5 B1 fix3).
-# 매칭된 숫자는 exact pool 에서 제외하고 one-sided range 로 변환.
-# 경계값 자체는 clear 대상 아님 (Codex 권장: "최대 100명" + answer "100명" → flag 유지).
-_BOUND_PATTERN_RE = re.compile(
-    rf'(최대|최소)\s*(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)'
-)
-_RANGE_INF = 10**18  # one-sided range 상한 sentinel
-
-
-def _normalize_unit(unit: str) -> str:
-    """단위 동의어 → 대표 표기."""
-    return _UNIT_SYNONYMS.get(unit, unit)
-
-
-def _extract_unit(literal: str) -> str | None:
-    """리터럴에서 숫자 뒤 단위(한 글자 또는 동의어) 추출 + 정규화."""
-    # 천단위 콤마 + 옵션 소수 + 한글 단위 한 글자 또는 동의어
-    m = re.match(rf'[\d,.]+\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)', literal)
-    if not m:
-        return None
-    return _normalize_unit(m.group(1))
-
-
-def _extract_numeric_corpus(text: str) -> dict:
-    """단위별 숫자 + 범위 + bound 통합 추출 (Phase 3.5 B1 fix1+fix3).
-
-    Returns:
-      {
-        "exact_by_unit": {unit_or_None: set(digits)},   # 평범한 숫자 (bound 제외)
-        "ranges_by_unit": {unit: [(lo, hi), ...]},       # 양방향(A~B) + 단방향(최대/최소)
-      }
-
-    None 키는 단위 없는 bare 숫자.
-    `최대 N <unit>` → ranges[(0, N-1)] (경계값 자체는 cleared 대상 아님)
-    `최소 N <unit>` → ranges[(N+1, INF)]
-    """
-    cleaned = _APPROX_PREFIX_RE.sub('', text)
-
-    exact_by_unit: dict[str | None, set[str]] = {None: set()}
-    ranges_by_unit: dict[str, list[tuple[int, int]]] = {}
-
-    # 1) 최대/최소 — bound. exact pool 에서 제외, one-sided range 로 변환.
-    bound_spans: list[tuple[int, int]] = []  # 매칭 substring 위치 — 이후 단계에서 skip
-    for m in _BOUND_PATTERN_RE.finditer(cleaned):
-        bound_kind = m.group(1)
-        try:
-            n = int(m.group(2).replace(',', '').split('.')[0])
-        except ValueError:
-            continue
-        unit = _normalize_unit(m.group(3))
-        if bound_kind == "최대":
-            ranges_by_unit.setdefault(unit, []).append((0, max(0, n - 1)))
-        else:  # 최소
-            ranges_by_unit.setdefault(unit, []).append((n + 1, _RANGE_INF))
-        bound_spans.append((m.start(), m.end()))
-
-    def _in_bound_span(pos: int) -> bool:
-        return any(s <= pos < e for s, e in bound_spans)
-
-    # 2) 천단위 콤마 bare number
-    for m in re.finditer(r'\d{1,3}(?:,\d{3})+(?:\.\d+)?', cleaned):
-        if _in_bound_span(m.start()):
-            continue
-        exact_by_unit[None].add(m.group().replace(',', ''))
-
-    # 3) 단위 있는 숫자 (단위 동의어 포함)
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)',
-        cleaned,
-    ):
-        if _in_bound_span(m.start()):
-            continue
-        digits = m.group(1).replace(',', '').split('.')[0]
-        if not digits:
-            continue
-        unit = _normalize_unit(m.group(2))
-        exact_by_unit.setdefault(unit, set()).add(digits)
-
-    # 4) 양방향 범위 표현 (A~B / A 부터 B)
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*(?:[~\-–]|부터)\s*(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로)',
-        cleaned,
-    ):
-        if _in_bound_span(m.start()):
-            continue
-        try:
-            lo = int(m.group(1).replace(',', '').split('.')[0])
-            hi = int(m.group(2).replace(',', '').split('.')[0])
-        except ValueError:
-            continue
-        unit = _normalize_unit(m.group(3))
-        ranges_by_unit.setdefault(unit, []).append((min(lo, hi), max(lo, hi)))
-
-    # 5) bare 2자리+ 단독 숫자
-    for m in re.finditer(r'\b(\d{2,})\b', cleaned):
-        if _in_bound_span(m.start()):
-            continue
-        exact_by_unit[None].add(m.group())
-
-    return {
-        "exact_by_unit": exact_by_unit,
-        "ranges_by_unit": ranges_by_unit,
-    }
-
-
-def _within_unit_range(
-    n: int, unit: str | None, ranges_by_unit: dict[str, list[tuple[int, int]]]
-) -> bool:
-    """unit-matching range 검증.
-
-    answer unit 이 None (bare 숫자) 면 보수적으로 False — bare 답변은 range clear 대상 아님.
-    """
-    if unit is None:
-        return False
-    return any(lo <= n <= hi for lo, hi in ranges_by_unit.get(unit, []))
-
-
-def _close_to_unit_pool(
-    n: int, unit: str | None, exact_by_unit: dict[str | None, set[str]], tol: float
-) -> bool:
-    """unit-matching tolerance 검증.
-
-    answer unit 이 None 이면 False — bare 답변은 tolerance 대상 아님.
-    같은 unit bucket 안의 후보만 비교.
-    """
-    if unit is None:
-        return False
-    candidates = exact_by_unit.get(unit, set())
-    for c in candidates:
-        try:
-            cn = int(c)
-        except ValueError:
-            continue
-        if cn == 0:
-            continue
-        if abs(n - cn) / cn <= tol:
-            return True
-    return False
-
-
-def _extract_number_literals(text: str) -> set[str]:
-    """숫자 + 단위 추출 + normalize (Phase 3.5 B1: 6단계 확장).
-
-    1) 약칭 prefix 제거 ("약 100명" → "100명")
-    2) 천단위 콤마 bare number 우선 ("1,000" → "1000" set 등록)
-    3) 한국어 단위 접미사 매칭 (기존)
-    4) 범위 표현 양쪽 숫자 추출 (separator: ~, -, –, 부터)
-    5) 단위 동의어 정규화 (인→명, 퍼센트→%, KRW→원)
-    6) bare 2자리+ 추출 (기존)
-    """
-    # 1. 약칭 prefix 제거 (전체 텍스트에서)
-    cleaned = _APPROX_PREFIX_RE.sub('', text)
-
-    # 2. 천단위 콤마 bare number — normalize 된 값을 set 에 선등록
-    normalized: set[str] = set()
-    for m in re.finditer(r'\d{1,3}(?:,\d{3})+(?:\.\d+)?', cleaned):
-        normalized.add(m.group().replace(',', ''))
-
-    # 3. 숫자 + 한국어 단위 접미사 (동의어 포함)
-    raw: set[str] = set(re.findall(
-        rf'\d[\d,.]*\s*(?:[{_UNIT_CHARS}]|인|사람|퍼센트|프로|KRW|krw)\w{{0,2}}',
-        cleaned,
-    ))
-
-    # 4. 범위 표현 — separator 에 "부터" 추가
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*(?:[~\-–]|부터)\s*(\d[\d,.]*)\s*([{_UNIT_CHARS}]|인|사람|퍼센트|프로)',
-        cleaned,
-    ):
-        unit_norm = _normalize_unit(m.group(3))
-        raw.add(m.group(1) + unit_norm)
-        raw.add(m.group(2) + unit_norm)
-
-    # 5. normalize: 단위 동의어 통일 + 콤마 제거
-    for r in raw:
-        # 단위 부분 정규화
-        m = re.match(r'([\d,.]+)\s*([^\d\s]+)', r)
-        if m:
-            digits_part = m.group(1)
-            unit_part = _normalize_unit(m.group(2))
-            normalized.add(digits_part + unit_part)
-            normalized.add(digits_part.replace(',', '') + unit_part)
-        normalized.add(r.strip())
-        num_only = re.match(r'[\d,.]+', r)
-        if num_only:
-            normalized.add(num_only.group().replace(',', ''))
-
-    # 6. 단독 숫자 (2자리+ 만)
-    for d in re.findall(r'\b(\d{2,})\b', cleaned):
-        normalized.add(d)
-    return normalized
-
-
-def _within_evidence_range(digits: str, raw: str, evidence_text: str) -> bool:
-    """evidence 에 'A~B 단위' 가 있고 answer 의 숫자가 그 범위 안이면 True.
-
-    범위 단위는 무시 (단위 비교는 호출 전 단계). digits = 정수 문자열.
-    """
-    try:
-        n = int(digits)
-    except ValueError:
-        return False
-    cleaned_ev = _APPROX_PREFIX_RE.sub('', evidence_text)
-    for m in re.finditer(
-        rf'(\d[\d,.]*)\s*(?:[~\-–]|부터)\s*(\d[\d,.]*)\s*[{_UNIT_CHARS}]',
-        cleaned_ev,
-    ):
-        try:
-            lo = int(m.group(1).replace(',', '').split('.')[0])
-            hi = int(m.group(2).replace(',', '').split('.')[0])
-            if min(lo, hi) <= n <= max(lo, hi):
-                return True
-        except ValueError:
-            continue
-    return False
-
-
-def _close_to_any(n: int, candidates: set[str], tol: float) -> bool:
-    """candidates 중 하나라도 (1±tol) 배율 안에 들어오면 True.
-
-    n 은 정수, candidates 는 digits-only 문자열 집합.
-    """
-    for c in candidates:
-        try:
-            cn = int(c)
-        except ValueError:
-            continue
-        if cn == 0:
-            continue
-        if abs(n - cn) / cn <= tol:
-            return True
-    return False
-
-
-def _extract_content_tokens(text: str) -> set[str]:
-    """한국어 2자 이상 명사 + 영어 3자 이상 단어."""
-    return set(re.findall(r'[가-힣]{2,}|[a-zA-Z]{3,}', text))
-
-
-def _parse_number_with_unit(literal: str) -> tuple[str, str] | None:
-    """숫자 리터럴에서 (digits_only, unit) 분리. 단위 없으면 None."""
-    m = re.match(rf'([\d,.]+)\s*([{_UNIT_CHARS}])', literal)
-    if not m:
-        return None
-    digits = m.group(1).replace(',', '')
-    unit = m.group(2)
-    return (digits, unit)
-
-
-def _check_evidence_numeric_conflicts(evidence: list["EvidenceItem"]) -> list[str]:
-    """evidence 간 숫자 충돌 감지 (Phase 3.5b). evidence >= 2 일 때만 활성.
-
-    동일 단위, 다른 숫자 → weak flag. "이상/이하/초과/미만" 포함 시 skip.
-    bare number 는 비교 안 함 (조항 번호 등 false positive 방지).
-    """
-    if len(evidence) < 2:
-        return []
-
-    # 각 evidence 에서 단위 있는 숫자 + threshold 여부 추출
-    # {evidence_idx: [(digits, unit, has_threshold), ...]}
-    per_evidence: dict[int, list[tuple[str, str, bool]]] = {}
-    for idx, ev in enumerate(evidence):
-        nums = re.findall(
-            rf'\d[\d,.]*\s*[{_UNIT_CHARS}]\w{{0,4}}',
-            ev.span_text,
-        )
-        entries = []
-        for raw in nums:
-            parsed = _parse_number_with_unit(raw)
-            if not parsed:
-                continue
-            has_thr = bool(_THRESHOLD_SUFFIXES.search(raw))
-            entries.append((parsed[0], parsed[1], has_thr))
-        if entries:
-            per_evidence[idx] = entries
-
-    if len(per_evidence) < 2:
-        return []
-
-    # 단위별로 evidence 간 숫자 비교
-    # {unit: {digits: [evidence_idx, ...]}}
-    unit_map: dict[str, dict[str, list[int]]] = {}
-    for idx, entries in per_evidence.items():
-        for digits, unit, has_thr in entries:
-            if has_thr:
-                continue  # threshold 표현은 skip
-            if unit not in unit_map:
-                unit_map[unit] = {}
-            if digits not in unit_map[unit]:
-                unit_map[unit][digits] = []
-            if idx not in unit_map[unit][digits]:
-                unit_map[unit][digits].append(idx)
-
-    flags: list[str] = []
-    for unit, digits_map in unit_map.items():
-        distinct_values = list(digits_map.keys())
-        if len(distinct_values) >= 2:
-            # 가장 많이 등장하는 2개 비교
-            top2 = sorted(distinct_values, key=lambda d: len(digits_map[d]), reverse=True)[:2]
-            flags.append(
-                f"evidence_numeric_conflict:{top2[0]}{unit}_vs_{top2[1]}{unit}"
-            )
-
-    return flags
-
-
-def check(
-    query: str,
-    answer: str,
-    evidence: list[EvidenceItem],
-) -> GroundingResult:
-    """답변 vs evidence grounding 검증 + query intent alignment."""
-    strong: list[str] = []
-    weak: list[str] = []
-
-    if not answer or not evidence:
-        return GroundingResult([], [])
-
-    # ⚠ citation marker [n] 양측 제거 (대칭성 — Phase 3.5 B1)
-    evidence_text = re.sub(r'\[\d+\]', '', " ".join(e.span_text for e in evidence))
-
-    # ── Strong 1: fabricated number (unit-aware 3단계 — Phase 3.5 B1 fix1+fix3) ──
-    # Codex 지적 반영:
-    #   - fix1: range/tolerance/exact 모두 단위 일치 시에만 clear
-    #     (예: "150원" vs "100~200명" → flag 유지)
-    #   - fix3: 최대/최소 prefix 는 bound 의미 보존
-    #     (예: "최대 100명" + answer "100명" → flag 유지, "최대 100명" + answer "50명" → cleared)
-    answer_clean = re.sub(r'\[\d+\]', '', answer)
-    answer_corpus = _extract_numeric_corpus(answer_clean)
-    evidence_corpus = _extract_numeric_corpus(evidence_text)
-    ev_exact_by_unit = evidence_corpus["exact_by_unit"]
-    ev_ranges_by_unit = evidence_corpus["ranges_by_unit"]
-
-    # cleared 는 (unit, digits) 쌍 단위로 추적 — 단위 충돌 케이스 방어
-    cleared_pairs: set[tuple[str | None, str]] = set()
-
-    # Pass 1: 각 (unit, digits) 가 evidence 에서 정당화되는지 판정
-    for unit, digits_set in answer_corpus["exact_by_unit"].items():
-        for d in digits_set:
-            # 1) exact match — 같은 unit bucket 내에서만
-            if d in ev_exact_by_unit.get(unit, set()):
-                cleared_pairs.add((unit, d))
-                continue
-            # bare answer (unit=None) 는 evidence bare bucket 도 보조 매칭
-            if unit is None and d in ev_exact_by_unit.get(None, set()):
-                cleared_pairs.add((unit, d))
-                continue
-            try:
-                n = int(d)
-            except ValueError:
-                continue
-            # 2) range — same-unit 만 (bare answer 는 range clear 대상 아님)
-            if _within_unit_range(n, unit, ev_ranges_by_unit):
-                cleared_pairs.add((unit, d))
-                continue
-            # 3) ±1% tolerance — 단위가 양적(_TOLERANCE_UNITS) + 4자리+ + same-unit
-            if (
-                unit in _TOLERANCE_UNITS
-                and len(d) >= 4
-                and _close_to_unit_pool(n, unit, ev_exact_by_unit, tol=0.01)
-            ):
-                cleared_pairs.add((unit, d))
-                continue
-            # 식별자성 단위(_EXACT_ONLY_UNITS) 는 tolerance 패스 X.
-
-    # Pass 2: cleared 되지 않은 (unit, digits) 를 strong flag.
-    # 1자리 무시는 unit 이 식별자성(_EXACT_ONLY_UNITS: 년/월/일/조/항/호/회) 이 아닐 때만 적용.
-    # bare(None) 답변 숫자는 같은 digit 이 다른 unit 에서 cleared 됐으면 skip — 추출 부산물 방어.
-    # ⚠ 단위 cross-clear (예: "원" cleared → "명" 도 skip) 은 금지: Codex unit-mismatch 케이스가 깨짐.
-    unit_anchored_cleared: set[str] = {d for (u, d) in cleared_pairs if u is not None}
-    flagged_keys: set[tuple[str | None, str]] = set()
-    for unit, digits_set in answer_corpus["exact_by_unit"].items():
-        for d in digits_set:
-            if (unit, d) in cleared_pairs or (unit, d) in flagged_keys:
-                continue
-            # bare(None) 답변 숫자가 임의의 단위 bucket 에서 cleared 됐으면 duplicate 로 처리.
-            # 사례: "1,000명" → unit bucket "명" 에 1000 + bare bucket None 에 1000 (comma normalize 부산물).
-            # 이미 ("명", "1000") 가 cleared 라면 (None, "1000") 도 같은 사실을 가리키므로 skip.
-            if unit is None and d in unit_anchored_cleared:
-                continue
-            if len(d) < 2 and unit not in _EXACT_ONLY_UNITS:
-                continue
-            flagged_keys.add((unit, d))
-            # 사람이 읽기 좋게 "{digits}{unit}" 또는 bare 형태로 표기
-            label = f"{d}{unit}" if unit else d
-            strong.append(f"fabricated_number:{label}")
-
-    # ── Strong/Weak 2: query-answer intent alignment ──
-    query_content = _extract_content_tokens(query)
-    answer_content = _extract_content_tokens(answer)
-    if query_content:
-        missing_terms = query_content - answer_content
-        important_missing = [
-            t for t in missing_terms
-            if t not in GENERIC_TERMS and len(t) >= 2
-        ]
-        if important_missing:
-            strong.append(
-                f"intent_misalignment:{','.join(important_missing[:3])}"
-            )
-        elif len(missing_terms) > len(query_content) * 0.5:
-            weak.append(
-                f"intent_misalignment_generic:"
-                f"missing({','.join(list(missing_terms)[:5])})"
-            )
-
-    # ── Weak 1: uncited claim ──
-    sentences = re.split(r'(?<=[.!?。])\s+', answer)
-    for s in sentences:
-        if len(s.strip()) > 20 and not re.search(r'\[\d+\]', s):
-            weak.append(f"uncited_claim:{s[:40]}")
-
-    # ── Weak: evidence 간 숫자 충돌 (Phase 3.5b) ──
-    conflicts = _check_evidence_numeric_conflicts(evidence)
-    weak.extend(conflicts)
-
-    # ── Weak 2: token overlap ──
-    answer_tokens = _extract_content_tokens(answer)
-    evidence_tokens = _extract_content_tokens(evidence_text)
-    if answer_tokens:
-        overlap = len(answer_tokens & evidence_tokens) / len(answer_tokens)
-        if overlap < 0.4:
-            weak.append(f"low_overlap:{overlap:.2f}")
-
-    if strong or weak:
-        logger.info(
-            "grounding query=%r strong=%d weak=%d flags=%s",
-            query[:60],
-            len(strong),
-            len(weak),
-            ",".join(strong[:3] + weak[:3]),
-        )
-
-    return GroundingResult(strong, weak)

app/services/search/refusal_gate.py

@@ -1,105 +0,0 @@
-"""Refusal gate — multi-signal fusion (Phase 3.5a).
-
-Score gate (deterministic) + classifier verdict (semantic, binary) 를 독립 평가 후 합성.
-Classifier 부재 시 3-tier conservative fallback.
-
-P1 실측 결과: exaone ternary 불안정 → binary (sufficient/insufficient) 로 축소.
-"full" vs "partial" 구분은 grounding check (intent alignment) 가 담당.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Literal
-
-from core.utils import setup_logger
-
-if TYPE_CHECKING:
-    from .classifier_service import ClassifierResult
-
-logger = setup_logger("refusal_gate")
-
-# Placeholder thresholds — Phase 3.5b 에서 실측 기반 tuning
-# AND 조건이라 false refusal 방어됨 (둘 다 만족해야 refuse)
-SCORE_MAX_REFUSE = 0.25
-SCORE_AGG_REFUSE = 0.70
-
-# Conservative fallback tiers (classifier 부재 시)
-CONSERVATIVE_WEAK = 0.35
-CONSERVATIVE_MID = 0.55
-
-
-@dataclass(slots=True)
-class RefusalDecision:
-    refused: bool
-    confidence_cap: Literal["high", "medium", "low"] | None  # None = no cap
-    rule_triggered: str | None  # 디버깅: 어느 signal 이 결정에 기여?
-
-
-def decide(
-    rerank_scores: list[float],
-    classifier: ClassifierResult | None,
-) -> RefusalDecision:
-    """Multi-signal fusion. Binary classifier verdict 기반.
-
-    Returns:
-        RefusalDecision. refused=True 이면 synthesis skip.
-        confidence_cap 은 synthesis 결과의 confidence 에 upper bound 적용.
-    """
-    max_score = max(rerank_scores) if rerank_scores else 0.0
-    agg_top3 = sum(sorted(rerank_scores, reverse=True)[:3])
-
-    score_gate_fails = (
-        max_score < SCORE_MAX_REFUSE and agg_top3 < SCORE_AGG_REFUSE
-    )
-
-    # ── Classifier 사용 가능 (정상 경로) ──
-    if classifier and classifier.verdict is not None:
-        if classifier.verdict == "insufficient":
-            # Evidence quality override: classifier 가 insufficient 라 해도
-            # evidence 가 충분히 좋으면 override (토론 8라운드 합의)
-            # (evidence quality 는 이 함수 밖에서 별도 체크 — caller 에서 처리)
-            logger.info(
-                "refusal gate: classifier=insufficient max=%.2f agg=%.2f",
-                max_score, agg_top3,
-            )
-            return RefusalDecision(
-                refused=True,
-                confidence_cap=None,
-                rule_triggered="classifier_insufficient",
-            )
-        if score_gate_fails:
-            logger.info(
-                "refusal gate: score_low max=%.2f agg=%.2f classifier=%s",
-                max_score, agg_top3, classifier.verdict,
-            )
-            return RefusalDecision(
-                refused=True,
-                confidence_cap=None,
-                rule_triggered="score_low",
-            )
-        # Classifier says sufficient → proceed
-        return RefusalDecision(
-            refused=False,
-            confidence_cap=None,
-            rule_triggered=None,
-        )
-
-    # ── Classifier 부재 → 3-tier conservative ──
-    if max_score < CONSERVATIVE_WEAK:
-        return RefusalDecision(
-            refused=True,
-            confidence_cap=None,
-            rule_triggered="conservative_refuse(no_classifier)",
-        )
-    if max_score < CONSERVATIVE_MID:
-        return RefusalDecision(
-            refused=False,
-            confidence_cap="low",
-            rule_triggered="conservative_low(no_classifier)",
-        )
-    return RefusalDecision(
-        refused=False,
-        confidence_cap="medium",
-        rule_triggered="conservative_medium(no_classifier)",
-    )

app/services/search/verifier_service.py

@@ -1,196 +0,0 @@
-"""Exaone semantic verifier (Phase 3.5b).
-
-답변-근거 간 의미적 모순(contradiction) 감지. rule-based grounding_check 가 못 잡는
-미묘한 모순 포착. classifier 와 동일 패턴: circuit breaker + timeout + fail open.
-
-## Severity 3단계
-- strong: direct_negation (완전 모순) → re-gate 교차 자격
-- medium: numeric_conflict, intent_core_mismatch → confidence 하향 (누적 시 강제 low)
-- weak: nuance, unsupported_claim → 로깅 + mild confidence 하향
-
-## 핵심 원칙
-- **Verifier strong 단독 refuse 금지** — grounding strong 과 교차해야 refuse
-- **Timeout 3s** — 느리면 없는 게 낫다 (fail open)
-- MLX gate 사용 (Mac mini 26B endpoint — classifier/evidence 와 동일 gate 공유, 동시 race 방지)
-"""
-
-from __future__ import annotations
-
-import asyncio
-import os
-import time
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Literal
-
-from ai.client import AIClient, _load_prompt, parse_json_response
-from core.config import settings
-from core.utils import setup_logger
-from .llm_gate import Priority, acquire_mlx_gate
-
-if TYPE_CHECKING:
-    from .evidence_service import EvidenceItem
-
-logger = setup_logger("verifier")
-
-LLM_TIMEOUT_MS = 10000  # 2026-05-17 B-3: 3s 시 동시 부하 시 verifier 빈발 skip → grounding 약화. Mac mini 26B 가 verifier-style 짧은 LLM call 도 concurrent 호출 시 3s 초과 빈번 — 10s 로 raise
-CIRCUIT_THRESHOLD = 5
-CIRCUIT_RECOVERY_SEC = 60
-
-_failure_count = 0
-_circuit_open_until: float | None = None
-
-# Phase 3.5 B2: numeric_conflict severity promote 실험.
-# import time 평가 — env 변경 후 process restart 필수 (docker compose restart fastapi).
-# default=0 (off). production 적용은 B3 FP 검증 통과 후만.
-_NUMERIC_PROMOTE = os.getenv("VERIFIER_NUMERIC_PROMOTE", "0") == "1"
-
-# severity 매핑 (프롬프트 "critical"/"minor" → 코드 strong/medium/weak)
-# Tier 4 (B2): _NUMERIC_PROMOTE=1 일 때 numeric_conflict critical → strong 으로 격상.
-# minor 는 medium 유지 (FP 위험 분리).
-_SEVERITY_MAP: dict[str, dict[str, Literal["strong", "medium", "weak"]]] = {
-    "direct_negation": {"critical": "strong", "minor": "strong"},
-    "numeric_conflict": (
-        {"critical": "strong", "minor": "medium"} if _NUMERIC_PROMOTE
-        else {"critical": "medium", "minor": "medium"}
-    ),
-    "intent_core_mismatch": {"critical": "medium", "minor": "medium"},
-    "nuance": {"critical": "weak", "minor": "weak"},
-    "unsupported_claim": {"critical": "weak", "minor": "weak"},
-}
-
-
-@dataclass(slots=True)
-class Contradiction:
-    """개별 모순 발견."""
-    type: str  # direct_negation / numeric_conflict / intent_core_mismatch / nuance / unsupported_claim
-    severity: Literal["strong", "medium", "weak"]
-    claim: str
-    evidence_ref: str
-    explanation: str
-
-
-@dataclass(slots=True)
-class VerifierResult:
-    status: Literal["ok", "timeout", "error", "circuit_open", "skipped"]
-    contradictions: list[Contradiction]
-    elapsed_ms: float
-
-
-try:
-    VERIFIER_PROMPT = _load_prompt("verifier.txt")
-except FileNotFoundError:
-    VERIFIER_PROMPT = ""
-    logger.warning("verifier.txt not found — verifier will always skip")
-
-
-def _build_input(
-    answer: str,
-    evidence: list[EvidenceItem],
-) -> str:
-    """답변 + evidence spans → 프롬프트."""
-    spans = "\n\n".join(
-        f"[{e.n}] {(e.title or '').strip()}\n{e.span_text}"
-        for e in evidence
-    )
-    return (
-        VERIFIER_PROMPT
-        .replace("{answer}", answer)
-        .replace("{numbered_evidence}", spans)
-    )
-
-
-def _map_severity(ctype: str, raw_severity: str) -> Literal["strong", "medium", "weak"]:
-    """type + raw severity → 코드 severity 3단계."""
-    type_map = _SEVERITY_MAP.get(ctype, {"critical": "weak", "minor": "weak"})
-    return type_map.get(raw_severity, "weak")
-
-
-async def verify(
-    query: str,
-    answer: str,
-    evidence: list[EvidenceItem],
-) -> VerifierResult:
-    """답변-근거 semantic 검증. Parallel with grounding_check.
-
-    Returns:
-        VerifierResult. status "ok" 이 아니면 contradictions 빈 리스트 (fail open).
-    """
-    global _failure_count, _circuit_open_until
-    t_start = time.perf_counter()
-
-    if _circuit_open_until and time.time() < _circuit_open_until:
-        return VerifierResult("circuit_open", [], 0.0)
-
-    if not VERIFIER_PROMPT:
-        return VerifierResult("skipped", [], 0.0)
-
-    if not hasattr(settings.ai, "verifier") or settings.ai.verifier is None:
-        return VerifierResult("skipped", [], 0.0)
-
-    if not answer or not evidence:
-        return VerifierResult("skipped", [], 0.0)
-
-    prompt = _build_input(answer, evidence)
-    client = AIClient()
-    try:
-        async with acquire_mlx_gate(Priority.FOREGROUND):
-            async with asyncio.timeout(LLM_TIMEOUT_MS / 1000):
-                raw = await client.call_verifier(prompt)
-        _failure_count = 0
-    except asyncio.TimeoutError:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"verifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning("verifier timeout")
-        return VerifierResult(
-            "timeout", [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    except Exception as e:
-        _failure_count += 1
-        if _failure_count >= CIRCUIT_THRESHOLD:
-            _circuit_open_until = time.time() + CIRCUIT_RECOVERY_SEC
-            logger.error(f"verifier circuit OPEN for {CIRCUIT_RECOVERY_SEC}s")
-        logger.warning(f"verifier error: {e}")
-        return VerifierResult(
-            "error", [],
-            (time.perf_counter() - t_start) * 1000,
-        )
-    finally:
-        await client.close()
-
-    elapsed_ms = (time.perf_counter() - t_start) * 1000
-    parsed = parse_json_response(raw)
-    if not isinstance(parsed, dict):
-        logger.warning("verifier parse failed raw=%r", (raw or "")[:200])
-        return VerifierResult("error", [], elapsed_ms)
-
-    # contradiction 파싱
-    raw_items = parsed.get("contradictions") or []
-    if not isinstance(raw_items, list):
-        raw_items = []
-
-    results: list[Contradiction] = []
-    for item in raw_items[:5]:
-        if not isinstance(item, dict):
-            continue
-        ctype = item.get("type", "")
-        if ctype not in _SEVERITY_MAP:
-            ctype = "unsupported_claim"
-        raw_sev = item.get("severity", "minor")
-        severity = _map_severity(ctype, raw_sev)
-        claim = str(item.get("claim", ""))[:50]
-        ev_ref = str(item.get("evidence_ref", ""))[:50]
-        explanation = str(item.get("explanation", ""))[:30]
-        results.append(Contradiction(ctype, severity, claim, ev_ref, explanation))
-
-    logger.info(
-        "verifier ok query=%r contradictions=%d strong=%d medium=%d elapsed_ms=%.0f",
-        query[:60],
-        len(results),
-        sum(1 for c in results if c.severity == "strong"),
-        sum(1 for c in results if c.severity == "medium"),
-        elapsed_ms,
-    )
-    return VerifierResult("ok", results, elapsed_ms)

app/services/study/publish_enqueue.py

@@ -68,10 +68,10 @@ async def enqueue_question_publish(session: AsyncSession, q: Any) -> None:
         await enqueue_publish(session, kind=KIND_EXPLANATION, source_id=q.id, payload=expl)
 
 
-async def backfill_publish_questions(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> int:
+async def backfill_publish_questions(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
     """active(미삭제) 문항을 id>after_id 부터 bounded 로 outbox 적재.
 
-    반환 = enqueue 한 문항 수(0 이면 끝). 큰 셋은 마지막 id 로 페이지 반복. caller commit.
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. caller commit.
     """
     rows = (
         await session.execute(
@@ -83,7 +83,7 @@ async def backfill_publish_questions(session: AsyncSession, *, after_id: int = 0
     ).scalars().all()
     for q in rows:
         await enqueue_question_publish(session, q)
-    return len(rows)
+    return len(rows), (rows[-1].id if rows else after_id)
 
 
 async def enqueue_topic_publish(session: AsyncSession, topic: Any) -> None:
@@ -91,10 +91,10 @@ async def enqueue_topic_publish(session: AsyncSession, topic: Any) -> None:
     await enqueue_publish(session, kind=KIND_TOPIC, source_id=topic.id, payload=project_topic(topic))
 
 
-async def backfill_publish_topics(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> int:
+async def backfill_publish_topics(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
     """active(미삭제) 주제를 id>after_id 부터 bounded 로 outbox 적재(S-1 초기 백필).
 
-    반환 = enqueue 한 주제 수(0 이면 끝). 큰 셋은 마지막 id 로 페이지 반복. caller commit.
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. caller commit.
     멱등 = 발행 워커의 (payload_hash, deleted) 디둡이 no-op 재투영 흡수(중복 enqueue 무해).
     """
     rows = (
@@ -107,7 +107,7 @@ async def backfill_publish_topics(session: AsyncSession, *, after_id: int = 0, l
     ).scalars().all()
     for t in rows:
         await enqueue_topic_publish(session, t)
-    return len(rows)
+    return len(rows), (rows[-1].id if rows else after_id)
 
 
 async def enqueue_card_publish(session: AsyncSession, card: Any) -> None:
@@ -123,10 +123,10 @@ async def enqueue_card_publish(session: AsyncSession, card: Any) -> None:
         await enqueue_publish(session, kind=KIND_CARD, source_id=card.id, payload=project_card(card))
 
 
-async def backfill_publish_cards(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> int:
+async def backfill_publish_cards(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
     """검수완료(needs_review=False)·미삭제 카드를 id>after_id 부터 bounded 로 outbox 적재(S-2 초기 백필).
 
-    반환 = enqueue 한 카드 수(0 이면 끝). 멱등 = 워커 (payload_hash, deleted) 디둡. caller commit.
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. 멱등 = 워커 디둡. caller commit.
     """
     rows = (
         await session.execute(
@@ -142,7 +142,7 @@ async def backfill_publish_cards(session: AsyncSession, *, after_id: int = 0, li
     ).scalars().all()
     for c in rows:
         await enqueue_card_publish(session, c)
-    return len(rows)
+    return len(rows), (rows[-1].id if rows else after_id)
 
 
 async def enqueue_card_progress_publish(session: AsyncSession, progress: Any) -> None:
@@ -155,11 +155,11 @@ async def enqueue_card_progress_publish(session: AsyncSession, progress: Any) ->
     )
 
 
-async def backfill_publish_card_progress(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> int:
+async def backfill_publish_card_progress(session: AsyncSession, *, after_id: int = 0, limit: int = 200) -> tuple[int, int]:
     """모든 card progress row 를 id>after_id 부터 bounded 로 outbox 적재(S-4 초기 백필).
 
     ★필터 없음 = ALL row(due_at NULL sentinel·terminal 포함) — due-only 백필은 sentinel 누락.
-    반환 = enqueue 한 row 수(0 이면 끝). 멱등 = 워커 디둡. caller commit.
+    반환 = (enqueue 수, 마지막 처리 id). caller 는 수==limit 면 last_id 로 다음 페이지. 멱등 = 워커 디둡. caller commit.
     """
     rows = (
         await session.execute(
@@ -171,4 +171,4 @@ async def backfill_publish_card_progress(session: AsyncSession, *, after_id: int
     ).scalars().all()
     for p in rows:
         await enqueue_card_progress_publish(session, p)
-    return len(rows)
+    return len(rows), (rows[-1].id if rows else after_id)

app/services/study/subject_note_rag.py

@@ -20,6 +20,7 @@ from models.chunk import DocumentChunk
 from models.document import Document
 from models.study_question import StudyQuestion
 from models.study_topic import StudyTopicDocument
+from services.search.license_filter import restricted_exclude_orm
 
 logger = logging.getLogger(__name__)
 
@@ -113,6 +114,9 @@ async def _gather_document_evidence(
             select(Document.id, Document.title, Document.ai_summary).where(
                 Document.id.in_(doc_ids),
                 Document.deleted_at.is_(None),
+                # B-4: licensed_restricted 제외 — explanation_rag 와 동일 술어(a안 U-2①). 누락 시
+                # 구매 자료 verbatim 이 분야노트 RAG 로 새던 보안 drift(복제 과정 누락).
+                restricted_exclude_orm(),
             )
         )
     ).all()

app/workers/classify_worker.py

@@ -90,6 +90,7 @@ HARD_ESCALATE_REASONS = {
 class TriageOutput(BaseModel):
     """p3a_short_summary (4B) 응답 스키마. 파싱 실패 시 기본값 + escalate=True fallback."""
 
+    ai_summary: str = ""  # B-1 3→2: triage 가 ai_summary 도 생산 (별 summarize 콜 대체)
     tldr: str = ""
     bullets: list[str] = Field(default_factory=list)
     tags: list[str] = Field(default_factory=list)
@@ -579,16 +580,7 @@ async def process(
                 "reason": "classify pipeline",
             }
 
-        # ─── 2. Legacy 요약 (primary 또는 deep) ───
-        try:
-            summary = await client.summarize(doc.extracted_text[:50000], cfg=legacy_cfg)
-        except Exception as exc:
-            if legacy_cfg is not None and is_deferrable_error(exc):
-                raise StageDeferred(f"macbook_unavailable:{type(exc).__name__}") from exc
-            raise
-        doc.ai_summary = strip_thinking(summary)
-
-        # ─── 메타데이터 (legacy 완료) — 실제 처리 머신 귀속 (drain=qwen-macbook) ───
+        # ─── 메타데이터 (classify 완료) — 실제 처리 머신 귀속 (drain=qwen-macbook) ───
         doc.ai_model_version = (legacy_cfg or settings.ai.primary).model
         doc.ai_processed_at = datetime.now(timezone.utc)
 
@@ -598,13 +590,27 @@ async def process(
             f"confidence={doc.ai_confidence:.2f}, tags={doc.ai_tags}"
         )
 
-        # ─── 3. PR-B B-1 — tier triage (4B, 실패는 legacy 결과 보존) ───
+        # ─── 2+3 통합 (B-1 3→2): tier triage 가 tldr/bullets/tier + ai_summary 생산.
+        # 기존 별도 summarize 콜 제거 → 본문 prefill 1회 절감 (Mac mini 부하). 실패는 fallback.
         try:
             await _run_tier_triage(client, doc, session, use_deep=use_deep)
         except StageDeferred:
             raise  # 보류는 실패가 아님 — drain/consumer 가 attempts 미소모 처리
         except Exception as exc:
-            logger.exception(f"[triage] id={document_id} 전체 실패 — legacy 유지: {exc}")
+            logger.exception(f"[triage] id={document_id} 전체 실패: {exc}")
+
+        # ─── ai_summary fallback: triage 가 못 채운 경우만 summarize ───
+        # (>120K long_context 는 triage 가 LLM skip, 또는 triage 파싱실패). 정상 경로는 미발동.
+        if not doc.ai_summary:
+            try:
+                summary = await client.summarize(doc.extracted_text[:50000], cfg=legacy_cfg)
+                doc.ai_summary = strip_thinking(summary)
+            except Exception as exc:
+                if legacy_cfg is not None and is_deferrable_error(exc):
+                    raise StageDeferred(f"macbook_unavailable:{type(exc).__name__}") from exc
+                # ai_summary=NULL 로 완료되면 digest/briefing 이 조용히 제외 → ERROR 로 가시화
+                # (best-effort 강등 자체는 유지, 운영 추적성만 보강).
+                logger.error(f"[summary-fallback] id={document_id} ai_summary 미생성: {exc}")
 
     finally:
         await client.close()
@@ -774,6 +780,9 @@ async def _apply_triage_result(
     if not parse_error:
         doc.ai_tldr = (triage_out.tldr or "").strip() or None
         doc.ai_bullets = triage_out.bullets or []
+        # B-1 3→2: triage 가 ai_summary 도 생산(summarize 콜 대체). 비면 process() 가 fallback.
+        if triage_out.ai_summary.strip():
+            doc.ai_summary = triage_out.ai_summary.strip()
         # Memo Intake Upgrade PR-2B — event kind hint (4B 가 출력했을 때만)
         # 허용 enum 외 값이면 무시 (DB enum 제약). AI worker 는 events row 직접 생성 X.
         valid_kinds = {"note", "task", "calendar_event", "activity_log", "reference"}

app/workers/csb_collector.py

@@ -140,7 +140,8 @@ async def _download_pdf(url: str, dest: Path) -> int:
     if len(resp.content) > _MAX_PDF_BYTES:
         raise FeedError(f"PDF 크기 초과 ({len(resp.content)} bytes): {url}")
     dest.parent.mkdir(parents=True, exist_ok=True)
-    dest.write_bytes(resp.content)
+    # 최대 50MB PDF write 는 동기 blocking — 이벤트루프 점유 회피 to_thread (R5 동형).
+    await asyncio.to_thread(dest.write_bytes, resp.content)
     return len(resp.content)
 
 
@@ -190,9 +191,11 @@ async def _ingest_pdf(session, page_slug: str, pdf_url: str) -> bool:
 
     dest = Path(settings.nas_mount_path) / rel_path
     size = await _download_pdf(pdf_url, dest)
+    # 50MB PDF read + sha256 는 동기 blocking(I/O+CPU) — 이벤트루프 점유 회피 to_thread (R5 동형).
+    file_hash = await asyncio.to_thread(lambda: hashlib.sha256(dest.read_bytes()).hexdigest())
     doc = Document(
         file_path=rel_path,
-        file_hash=hashlib.sha256(dest.read_bytes()).hexdigest(),
+        file_hash=file_hash,
         file_format="pdf",
         file_size=size,
         file_type="immutable",

app/workers/file_watcher.py

@@ -251,104 +251,118 @@ async def watch_inbox():
     for extra_path in settings.additional_watch_targets:
         targets.append((extra_path, "library"))
 
-    async with async_session() as session:
-        # ─── Web/ 트랙 (devonagent) — DEVONthink Smart Rule 이 떨군 .html 만 진입 ───
-        if web_root.exists():
-            # rglob NFS 디렉토리 walk(blocking stat 다발)를 off-thread 로 수집 (R5).
-            for file_path in await asyncio.to_thread(lambda: list(web_root.rglob("*.html"))):
-                if not file_path.is_file() or should_skip(file_path):
-                    continue
-                rel_path = str(file_path.relative_to(nas_root))
-                added, _ = await _ingest_web_file(session, file_path, rel_path)
+    # 파일별 독립 세션+commit 으로 격리 — 한 파일 실패(예: rglob↔stat 사이 삭제로 FileNotFoundError,
+    # flush 오류)가 watch_inbox 전체를 raise·롤백해 그 사이클 등록분을 모두 잃거나, 결정적 poison
+    # 파일이 매 사이클 같은 지점에서 중단시키는 것을 차단 (news_collector/csb_collector 와 동형).
+    # ─── Web/ 트랙 (devonagent) — DEVONthink Smart Rule 이 떨군 .html 만 진입 ───
+    if web_root.exists():
+        # rglob NFS 디렉토리 walk(blocking stat 다발)를 off-thread 로 수집 (R5).
+        for file_path in await asyncio.to_thread(lambda: list(web_root.rglob("*.html"))):
+            if not file_path.is_file() or should_skip(file_path):
+                continue
+            rel_path = str(file_path.relative_to(nas_root))
+            try:
+                async with async_session() as session:
+                    added, _ = await _ingest_web_file(session, file_path, rel_path)
+                    await session.commit()
                 new_count += added
-
-        # ─── PKM 트랙 (기존 drive_sync) ─────────────────────────────────────────
-        for sub, expected_category in targets:
-            scan_root = pkm_root / sub
-            if not scan_root.exists():
+            except Exception as e:
+                logger.warning("[Web] 파일 처리 실패 skip path=%s: %s", rel_path, e)
                 continue
 
-            # 안전 자료실 A-2/B-4 — 타깃 폴더 기반 (material, jurisdiction, license)
-            target_mt, target_jur, target_license = _TARGET_AXIS.get(
-                Path(sub).name, (None, None, None)
+    # ─── PKM 트랙 (기존 drive_sync) ─────────────────────────────────────────
+    for sub, expected_category in targets:
+        scan_root = pkm_root / sub
+        if not scan_root.exists():
+            continue
+
+        # 안전 자료실 A-2/B-4 — 타깃 폴더 기반 (material, jurisdiction, license)
+        target_mt, target_jur, target_license = _TARGET_AXIS.get(
+            Path(sub).name, (None, None, None)
+        )
+
+        # NFS 디렉토리 walk(blocking) off-thread 수집 (R5).
+        for file_path in await asyncio.to_thread(lambda: list(scan_root.rglob("*"))):
+            if not file_path.is_file() or should_skip(file_path):
+                continue
+
+            category, needs_conversion, next_stage = _route_media(
+                file_path, expected_category
             )
 
-            # NFS 디렉토리 walk(blocking) off-thread 수집 (R5).
-            for file_path in await asyncio.to_thread(lambda: list(scan_root.rglob("*"))):
-                if not file_path.is_file() or should_skip(file_path):
-                    continue
+            # audio/video 폴더에 엉뚱한 확장자가 들어왔거나 Inbox 에
+            # audio/video 가 잘못 떨어진 경우 — 이 라운드에서 아예 skip
+            if category is None and next_stage is None:
+                continue
 
-                category, needs_conversion, next_stage = _route_media(
-                    file_path, expected_category
-                )
-
-                # audio/video 폴더에 엉뚱한 확장자가 들어왔거나 Inbox 에
-                # audio/video 가 잘못 떨어진 경우 — 이 라운드에서 아예 skip
-                if category is None and next_stage is None:
-                    continue
-
-                rel_path = str(file_path.relative_to(nas_root))
+            rel_path = str(file_path.relative_to(nas_root))
+            try:
                 # GB 파일 SHA-256 은 이벤트 루프를 점유 → 같은 루프의 모든 1분 주기 consumer
                 # + FastAPI 요청이 수십초~분 동시 정지. to_thread 오프로드. 스캔 루프가 이미
                 # 순차라 file_hash 는 한 번에 하나만 실행(직렬화) — 병렬 해싱 X = NFS 2.5GbE
-                # 대역폭·버퍼 메모리 blowup 방지 (R5).
+                # 대역폭·버퍼 메모리 blowup 방지 (R5). 세션 밖에서 계산(커넥션 미점유).
                 fhash = await asyncio.to_thread(file_hash, file_path)
 
-                result = await session.execute(
-                    select(Document).where(Document.file_path == rel_path)
-                )
-                existing = result.scalar_one_or_none()
-
-                if existing is None:
-                    ext = file_path.suffix.lstrip(".").lower() or "unknown"
-                    doc = Document(
-                        file_path=rel_path,
-                        file_hash=fhash,
-                        file_format=ext,
-                        file_size=file_path.stat().st_size,
-                        file_type="immutable",
-                        title=file_path.stem,
-                        source_channel="drive_sync",
-                        category=category,
-                        needs_conversion=needs_conversion,
-                        # 안전 자료실 A-2/B-4 — watch 타깃 매핑 (KGS=law/KR 등, 비대상=NULL)
-                        material_type=target_mt,
-                        jurisdiction=target_jur,
+                async with async_session() as session:
+                    result = await session.execute(
+                        select(Document).where(Document.file_path == rel_path)
                     )
-                    # B-4 — 타깃 폴더 license 주입(restricted 포함, 비대상=미주입). classify 는
-                    # material_type IS NULL 일 때만 제안 + extract_meta 미기록이라 주입 보존.
-                    if target_license:
-                        doc.extract_meta = {"license": dict(target_license)}
-                    session.add(doc)
-                    await session.flush()
+                    existing = result.scalar_one_or_none()
 
-                    if next_stage:
-                        await enqueue_stage(session, doc.id, next_stage)
-                    new_count += 1
+                    if existing is None:
+                        ext = file_path.suffix.lstrip(".").lower() or "unknown"
+                        doc = Document(
+                            file_path=rel_path,
+                            file_hash=fhash,
+                            file_format=ext,
+                            file_size=file_path.stat().st_size,
+                            file_type="immutable",
+                            title=file_path.stem,
+                            source_channel="drive_sync",
+                            category=category,
+                            needs_conversion=needs_conversion,
+                            # 안전 자료실 A-2/B-4 — watch 타깃 매핑 (KGS=law/KR 등, 비대상=NULL)
+                            material_type=target_mt,
+                            jurisdiction=target_jur,
+                        )
+                        # B-4 — 타깃 폴더 license 주입(restricted 포함, 비대상=미주입). classify 는
+                        # material_type IS NULL 일 때만 제안 + extract_meta 미기록이라 주입 보존.
+                        if target_license:
+                            doc.extract_meta = {"license": dict(target_license)}
+                        session.add(doc)
+                        await session.flush()
 
-                elif existing.file_hash != fhash:
-                    existing.file_hash = fhash
-                    existing.file_size = file_path.stat().st_size
-                    # 기존 문서에 category/quarantine flag 가 비어있으면 보정
-                    if existing.category is None and category is not None:
-                        existing.category = category
-                    if needs_conversion and not getattr(existing, "needs_conversion", False):
-                        existing.needs_conversion = True
-                    # B-4 — 축/license 보정(B-4 이전 적재분이 재변경 시): material 미설정 시 주입,
-                    # license 부재 시에만 merge 주입(clobber 회피 — 기존 extract_meta 키 보존).
-                    if existing.material_type is None and target_mt is not None:
-                        existing.material_type = target_mt
-                        existing.jurisdiction = target_jur
-                    if target_license and not (existing.extract_meta or {}).get("license"):
-                        meta = dict(existing.extract_meta or {})
-                        meta["license"] = dict(target_license)
-                        existing.extract_meta = meta
+                        if next_stage:
+                            await enqueue_stage(session, doc.id, next_stage)
+                        await session.commit()
+                        new_count += 1
 
-                    if next_stage:
-                        await enqueue_stage(session, existing.id, next_stage)
-                    changed_count += 1
+                    elif existing.file_hash != fhash:
+                        existing.file_hash = fhash
+                        existing.file_size = file_path.stat().st_size
+                        # 기존 문서에 category/quarantine flag 가 비어있으면 보정
+                        if existing.category is None and category is not None:
+                            existing.category = category
+                        if needs_conversion and not getattr(existing, "needs_conversion", False):
+                            existing.needs_conversion = True
+                        # B-4 — 축/license 보정(B-4 이전 적재분이 재변경 시): material 미설정 시 주입,
+                        # license 부재 시에만 merge 주입(clobber 회피 — 기존 extract_meta 키 보존).
+                        if existing.material_type is None and target_mt is not None:
+                            existing.material_type = target_mt
+                            existing.jurisdiction = target_jur
+                        if target_license and not (existing.extract_meta or {}).get("license"):
+                            meta = dict(existing.extract_meta or {})
+                            meta["license"] = dict(target_license)
+                            existing.extract_meta = meta
 
-        await session.commit()
+                        if next_stage:
+                            await enqueue_stage(session, existing.id, next_stage)
+                        await session.commit()
+                        changed_count += 1
+                    # else: 무변경 → 쓰기 없음 (세션 자동 닫힘, commit 불요)
+            except Exception as e:
+                logger.warning("[PKM] 파일 처리 실패 skip path=%s: %s", rel_path, e)
+                continue
 
     if new_count or changed_count:
         logger.info(f"[Inbox+§3] 새 파일 {new_count}건, 변경 파일 {changed_count}건 등록")

app/workers/marker_worker.py

@@ -300,6 +300,11 @@ async def _process_single(
             f"[marker] transient error id={document_id} kind={type(exc).__name__}: {exc}"
         )
         raise
+    except json.JSONDecodeError as exc:
+        # 200 응답의 truncated/malformed body — 연결 흔들림 등 transient. _fail(non-retryable)
+        # 로 박지 말고 raise → queue retry (max_attempts 까지). 진짜 손상이면 재시도 후 failed.
+        logger.warning(f"[marker] malformed json body (200) id={document_id}: {exc}")
+        raise
     except Exception as exc:
         logger.exception(f"[marker] unexpected error id={document_id}: {exc}")
         await _fail(session, document_id, str(exc)[:1000])

app/workers/presegment_worker.py

@@ -497,12 +497,18 @@ async def process(document_id: int, session: AsyncSession) -> None:
         logger.warning(f"[presegment] id={document_id} file not found ({raw}) → extract")
         return
 
+    import asyncio
+
     import fitz  # PyMuPDF — extract_worker/marker_worker 와 동일 의존
 
+    def _read_toc(path: str):
+        # fitz open/get_toc 는 동기 blocking — live 스테이지라 이벤트루프(같은 루프의 1분 consumer +
+        # FastAPI 요청) 점유 회피 위해 to_thread 오프로드(거대/손상 PDF 파싱 수백 ms~초).
+        with fitz.open(path) as pdf:
+            return pdf.page_count, (pdf.get_toc(simple=True) or [])
+
     try:
-        with fitz.open(str(source)) as pdf:
-            page_count = pdf.page_count
-            toc = pdf.get_toc(simple=True) or []
+        page_count, toc = await asyncio.to_thread(_read_toc, str(source))
     except Exception as exc:
         # PDF 손상 등 — 분할 불가. 단일문서로 통과(extract 가 PyMuPDF/OCR 로 재시도하며 가시화).
         logger.warning(

app/workers/study_publish_worker.py

@@ -28,6 +28,8 @@ logger = setup_logger("study_publish_worker")
 BATCH_SIZE = 500
 # pg_advisory_xact_lock 전역 단일 라이터 키(발행 워커 전용 임의 상수, 타 advisory 락과 비충돌).
 ADVISORY_LOCK_KEY = 838201
+# 행별 격리 재시도 상한 — 초과 시 failed_at 스탬프(terminal)로 select 에서 제외.
+MAX_OUTBOX_ATTEMPTS = 5
 
 
 async def consume_publish_outbox() -> None:
@@ -46,11 +48,15 @@ async def consume_publish_outbox() -> None:
             max_rev = int(
                 (await session.execute(select(func.coalesce(func.max(Published.rev), 0)))).scalar() or 0
             )
-            # 3) 미처리 outbox 를 커밋순(id)으로.
+            # 3) 미처리 outbox 를 커밋순(id)으로. failed_at(terminal) 은 제외 — poison 행이
+            #    head-of-line 을 영구 점유하지 않게 함.
             rows = (
                 await session.execute(
                     select(PublishOutbox)
-                    .where(PublishOutbox.processed_at.is_(None))
+                    .where(
+                        PublishOutbox.processed_at.is_(None),
+                        PublishOutbox.failed_at.is_(None),
+                    )
                     .order_by(PublishOutbox.id.asc())
                     .limit(BATCH_SIZE)
                 )
@@ -60,59 +66,86 @@ async def consume_publish_outbox() -> None:
 
             now = datetime.now(timezone.utc)
             published_count = 0
+            failed_count = 0
             for ob in rows:
-                existing = (
-                    await session.execute(
-                        select(Published).where(
-                            Published.kind == ob.kind,
-                            Published.source_id == ob.source_id,
-                        )
-                    )
-                ).scalar_one_or_none()
+                try:
+                    # 행 단위 savepoint 격리 — 한 행의 예외가 배치 전체(앞 행 processed_at 포함)를
+                    # 롤백해 poison 행이 다음 사이클에 다시 최저 id 로 선택되는 무한 재선택을 차단.
+                    async with session.begin_nested():
+                        existing = (
+                            await session.execute(
+                                select(Published).where(
+                                    Published.kind == ob.kind,
+                                    Published.source_id == ob.source_id,
+                                )
+                            )
+                        ).scalar_one_or_none()
 
-                # (payload_hash, deleted) 디둡 — no-op 재투영은 rev 안 올림.
-                if (
-                    existing is not None
-                    and existing.payload_hash == ob.payload_hash
-                    and existing.deleted == ob.deleted
-                ):
-                    ob.processed_at = now
+                        # (payload_hash, deleted) 디둡 — no-op 재투영은 rev 안 올림.
+                        is_noop = (
+                            existing is not None
+                            and existing.payload_hash == ob.payload_hash
+                            and existing.deleted == ob.deleted
+                        )
+                        if is_noop:
+                            ob.processed_at = now
+                        else:
+                            new_rev = max_rev + 1
+                            if existing is None:
+                                session.add(
+                                    Published(
+                                        kind=ob.kind,
+                                        source_id=ob.source_id,
+                                        pub_id=uuid.uuid4().hex,
+                                        payload=ob.payload,
+                                        payload_hash=ob.payload_hash,
+                                        schema_version=ob.schema_version,
+                                        rev=new_rev,
+                                        deleted=ob.deleted,
+                                        created_at=now,
+                                        updated_at=now,
+                                    )
+                                )
+                            else:
+                                existing.payload = ob.payload
+                                existing.payload_hash = ob.payload_hash
+                                existing.schema_version = ob.schema_version
+                                existing.deleted = ob.deleted
+                                existing.rev = new_rev
+                                existing.updated_at = now
+                            ob.processed_at = now
+                            # 배치 내 동일 (kind, source_id) 후속 행이 직전 반영을 보도록 flush(최신 승).
+                            await session.flush()
+                except Exception as row_err:
+                    # savepoint 롤백 = 이 행의 쓰기(processed_at 포함) 취소. attempts/failed_at 만
+                    # 바깥 트랜잭션에 누적돼 최종 commit 으로 영속(영구 재선택 방지).
+                    ob.attempts = (ob.attempts or 0) + 1
+                    if ob.attempts >= MAX_OUTBOX_ATTEMPTS:
+                        ob.failed_at = now
+                        failed_count += 1
+                        logger.error(
+                            "publish_outbox_row_terminal id=%s kind=%s source_id=%s attempts=%s: %s",
+                            ob.id, ob.kind, ob.source_id, ob.attempts, row_err,
+                        )
+                    else:
+                        logger.warning(
+                            "publish_outbox_row_retry id=%s kind=%s source_id=%s attempts=%s: %s",
+                            ob.id, ob.kind, ob.source_id, ob.attempts, row_err,
+                        )
                     continue
-
-                max_rev += 1
-                if existing is None:
-                    session.add(
-                        Published(
-                            kind=ob.kind,
-                            source_id=ob.source_id,
-                            pub_id=uuid.uuid4().hex,
-                            payload=ob.payload,
-                            payload_hash=ob.payload_hash,
-                            schema_version=ob.schema_version,
-                            rev=max_rev,
-                            deleted=ob.deleted,
-                            created_at=now,
-                            updated_at=now,
-                        )
-                    )
                 else:
-                    existing.payload = ob.payload
-                    existing.payload_hash = ob.payload_hash
-                    existing.schema_version = ob.schema_version
-                    existing.deleted = ob.deleted
-                    existing.rev = max_rev
-                    existing.updated_at = now
-
-                ob.processed_at = now
-                # 배치 내 동일 (kind, source_id) 후속 행이 직전 반영을 보도록 flush(최신 승).
-                await session.flush()
-                published_count += 1
+                    # savepoint 커밋 성공 시에만 rev 카운터 전진(실패 행은 rev 미소모 → 드물게 gap,
+                    # 단일 라이터·커밋순 부여라 viewer since-rev 증분 동기 정합엔 무해).
+                    if not is_noop:
+                        max_rev = new_rev
+                        published_count += 1
 
             await session.commit()
             logger.info(
-                "publish_outbox_drained scanned=%s published=%s max_rev=%s",
+                "publish_outbox_drained scanned=%s published=%s failed=%s max_rev=%s",
                 len(rows),
                 published_count,
+                failed_count,
                 max_rev,
             )
         except Exception as e:

config.yaml

@@ -30,21 +30,11 @@ ai:
       repetition_penalty: 1.05  # 한국어 장문 반복/코드스위칭(CJK·라틴 누수) 억제 (보수적 시작값)
       top_k: 20                 # Qwen3 권장
 
-    # deep: 야간 night-drain 전용 — 맥북 M5 Max Qwen3.6-27B-6bit (llm-router :8890 경유,
-    # model=qwen-macbook alias). 2026-06-11 재도입 (사용자: 자기 전 night-drain 으로 백로그 분담).
-    # 맥북 불가(503/연결/절단) = StageDeferred 보류 — 맥미니/cloud 강등 없음, attempts 미소모.
-    # consumer 의 deep_summary 도 슬롯 존재 시 맥북 경유 (잠들어 있으면 30분 백오프 보류 = 무해).
-    # 슬롯 제거 시 deep_summary 는 primary(맥미니) 경로 복귀.
-    deep:
-      endpoint: "http://100.76.254.116:8890/v1/chat/completions"
-      model: "qwen-macbook"
-      max_tokens: 8192
-      timeout: 900
-      context_char_limit: 260000
-      temperature: 0.3
-      top_p: 0.9
-      repetition_penalty: 1.05  # 한국어 장문 반복/코드스위칭 억제 (보수적 시작값)
-      top_k: 20
+    # deep: ★2026-06-29 잠정 보류 (사용자 "맥북 night-drain 의미없어 → 맥미니 일원화").
+    # 슬롯 제거 → deep_summary 가 primary(맥미니) 경로 복귀 + use_deep/drain 도 맥미니 폴백
+    # (맥북 라우팅 0). drain-keeper(GPU cron)도 비활성. 맥북 mlx-vlm-server 는 OpenCode 로컬용 보존.
+    # 복원(night-drain 재개 시): git history 에서 deep 슬롯(qwen-macbook :8890, max_tokens 8192,
+    # timeout 900, context_char_limit 260000, temp 0.3 / top_p 0.9 / rep 1.05 / top_k 20) 부활 + drain-keeper 재활성.
 
     # fallback: primary 장애 시 최후 방어선. Claude Sonnet 4 API (소액 한도, 자동 trigger).
     # 호출 빈도 낮음 가정 (Mac mini 가 거의 항상 up) → premium 과 budget 공유 OK.

docker-compose.yml

@@ -3,7 +3,13 @@ services:
     image: pgvector/pgvector:pg16
     volumes:
       - pgdata:/var/lib/postgresql/data
-      - ./migrations:/docker-entrypoint-initdb.d
+      # ★ 2026-06-29 fresh-DB/DR 부팅 fix: initdb.d 마운트 제거(기존 `./migrations:/docker-entrypoint-initdb.d`).
+      # 빈 볼륨 첫 기동 시 postgres 엔트리포인트가 migrations/*.sql(001~) 을 psql autocommit 으로 실행해
+      # 스키마는 만들되 schema_migrations 스탬프는 안 남김(runner 만 생성) → fastapi init_db 가 documents
+      # 존재로 'fresh' 를 오판해 baseline(_load_baseline_if_fresh) 로드를 건너뛰고, 빈 schema_migrations
+      # 로 001 부터 재replay → `CREATE TABLE users`(IF NOT EXISTS 없음) 충돌 → 부팅 크래시(DR/신규환경).
+      # fresh-boot 은 init_db 의 baseline 적재 + migration runner 단일 경로로 일원화(설계 의도). 기존 prod
+      # 볼륨은 비어있지 않아 init scripts 가 애초에 미발동 → 무영향.
     environment:
       POSTGRES_DB: pkm
       POSTGRES_USER: pkm

docs/architecture.md

@@ -1094,7 +1094,7 @@ services:
     image: pgvector/pgvector:pg16
     volumes:
       - pgdata:/var/lib/postgresql/data
-      - ./migrations:/docker-entrypoint-initdb.d
+      # initdb.d 마운트 제거(2026-06-29): fresh-boot 은 fastapi init_db+baseline 단일 경로.
     environment:
       POSTGRES_DB: pkm
       POSTGRES_USER: pkm

docs/storage_layout.md

@@ -71,7 +71,7 @@ GPU 서버의 NFS mount (`/proc/mounts` 실측):
 
 | 컨테이너 | 마운트 | 모드 | 비고 |
 |---|---|---|---|
-| postgres | `pgdata:/var/lib/postgresql/data` + `./migrations:/docker-entrypoint-initdb.d` | rw | DB 본체 named volume |
+| postgres | `pgdata:/var/lib/postgresql/data` | rw | DB 본체 named volume (initdb.d 마운트는 2026-06-29 제거 — 아래 관찰) |
 | kordoc-service | `${NAS}/Document_Server:/documents` | **ro** | PDF/HWP parse |
 | ocr-service | `${NAS}/Document_Server:/documents` + `ocr_models:/root/.cache` | **ro** + rw | |
 | marker-service | `${NAS}/Document_Server:/documents` + `marker_models:/models` | **ro** + rw | PDF→markdown |
@@ -84,7 +84,7 @@ GPU 서버의 NFS mount (`/proc/mounts` 실측):
 **관찰**:
 - worker 컨테이너 (kordoc/ocr/marker/stt) 는 모두 NAS **read-only** 마운트 → 원본 안전.
 - fastapi 만 NAS **rw** → 업로드/preview/extracted_images 쓰기 단일 책임.
-- `./migrations` 이 postgres 의 `docker-entrypoint-initdb.d` 와 fastapi 의 `/app/migrations` 양쪽에 마운트. 단 실제 migration runner 는 fastapi `init_db()` 만 사용 (postgres init scripts 는 첫 생성 시만 실행 → 효과 X, 안전).
+- `./migrations` 은 fastapi 의 `/app/migrations` 에만 마운트. migration runner 는 fastapi `init_db()` 단일 경로. (~2026-06-29: postgres `docker-entrypoint-initdb.d` 마운트 제거. 기존엔 "첫 생성 시만 실행 → 효과 X" 로 봤으나, 빈 볼륨 첫 기동 시 postgres 가 migrations/*.sql 을 실제 실행해 스키마는 만들되 schema_migrations 스탬프를 안 남겨 → init_db 의 baseline fresh 판정을 깨고 부팅 크래시 유발. fresh-DB/DR 부팅을 init_db+baseline 단일 경로로 일원화.)
 
 ## 정책 정리
 

frontend/src/lib/components/AskAnswerCard.svelte

@@ -1,160 +0,0 @@
-<!--
-  AskAnswerCard.svelte — 검색 결과 페이지 상단 AI 답변 카드 (컴팩트).
-
-  /ask 페이지의 AskAnswer.svelte와 달리, 검색 결과를 가리지 않는
-  보조 영역으로 설계. 출처 목록 클릭이 must-have, 본문 [n] 클릭은 nice-to-have.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { Sparkles, X, FileText } from 'lucide-svelte';
-  import type { AskResponse, Confidence } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-    error: boolean;
-    onCitationClick: (docId: number) => void;
-    onDismiss: () => void;
-  }
-
-  let { data, loading, error, onCitationClick, onDismiss }: Props = $props();
-
-  // [n] 파싱 (AskAnswer.svelte에서 가져옴)
-  type Token =
-    | { type: 'text'; value: string }
-    | { type: 'cite'; n: number; raw: string };
-
-  function splitAnswer(text: string): Token[] {
-    return text
-      .split(/(\[\d+\])/g)
-      .filter(Boolean)
-      .map((tok): Token => {
-        const m = tok.match(/^\[(\d+)\]$/);
-        return m
-          ? { type: 'cite', n: Number(m[1]), raw: tok }
-          : { type: 'text', value: tok };
-      });
-  }
-
-  function confidenceTone(c: Confidence | null): 'success' | 'warning' | 'error' | 'neutral' {
-    if (c === 'high') return 'success';
-    if (c === 'medium') return 'warning';
-    if (c === 'low') return 'error';
-    return 'neutral';
-  }
-
-  function confidenceLabel(c: Confidence | null): string {
-    if (c === 'high') return '높음';
-    if (c === 'medium') return '중간';
-    if (c === 'low') return '낮음';
-    return '';
-  }
-
-  let tokens = $derived(data?.ai_answer ? splitAnswer(data.ai_answer) : []);
-
-  // 출처 중복 제거 (같은 doc_id)
-  let uniqueCitations = $derived.by(() => {
-    if (!data?.citations?.length) return [];
-    const seen = new Set<number>();
-    return data.citations.filter((c) => {
-      if (seen.has(c.doc_id)) return false;
-      seen.add(c.doc_id);
-      return true;
-    });
-  });
-</script>
-
-<div class="bg-surface border border-default rounded-card p-4">
-  <!-- 헤더 -->
-  <div class="flex items-center justify-between gap-2 mb-2">
-    <div class="flex items-center gap-1.5">
-      <Sparkles size={12} class="text-accent" />
-      <span class="text-[10px] font-semibold tracking-wider uppercase text-dim">
-        내 자료 기준 답변
-      </span>
-      {#if data?.confidence}
-        <Badge tone={confidenceTone(data.confidence)} size="sm">
-          신뢰도 {confidenceLabel(data.confidence)}
-        </Badge>
-      {/if}
-      {#if data?.completeness === 'partial'}
-        <Badge tone="warning" size="sm">일부 답변</Badge>
-      {/if}
-    </div>
-    <button
-      type="button"
-      onclick={onDismiss}
-      class="p-0.5 rounded text-dim hover:text-text transition-colors"
-      aria-label="답변 카드 접기"
-    >
-      <X size={14} />
-    </button>
-  </div>
-
-  <!-- 본문 -->
-  {#if loading}
-    <div class="space-y-2">
-      <Skeleton w="w-full" h="h-3" />
-      <Skeleton w="w-4/5" h="h-3" />
-    </div>
-    <p class="mt-3 text-[10px] text-dim flex items-center gap-1.5">
-      <span class="inline-block w-2.5 h-2.5 rounded-full border-2 border-dim border-t-accent animate-spin"></span>
-      근거 기반 답변 생성 중…
-    </p>
-  {:else if error}
-    <p class="text-xs text-dim">답변을 가져오지 못했습니다.</p>
-  {:else if data?.ai_answer}
-    <!-- 답변 텍스트 -->
-    <div class="text-sm leading-6 text-text">
-      {#each tokens as tok}
-        {#if tok.type === 'cite'}
-          {@const citation = data?.citations?.find((c) => c.n === tok.n)}
-          {#if citation}
-            <button
-              type="button"
-              class="inline text-accent font-semibold hover:underline px-0.5"
-              onclick={() => onCitationClick(citation.doc_id)}
-              title={citation.title || `문서 #${citation.doc_id}`}
-            >
-              {tok.raw}
-            </button>
-          {:else}
-            <span class="text-dim">{tok.raw}</span>
-          {/if}
-        {:else}
-          <span>{tok.value}</span>
-        {/if}
-      {/each}
-    </div>
-
-    <!-- partial: 누락 측면 -->
-    {#if data.completeness === 'partial' && data.missing_aspects?.length}
-      <p class="mt-2 text-[10px] text-dim">
-        다루지 못한 부분: {data.missing_aspects.join(', ')}
-      </p>
-    {/if}
-
-    <!-- 출처 목록 (must-have) -->
-    {#if uniqueCitations.length > 0}
-      <div class="mt-3 pt-2 border-t border-default">
-        <p class="text-[10px] font-medium text-dim mb-1.5">출처</p>
-        <div class="flex flex-wrap gap-1.5">
-          {#each uniqueCitations as citation}
-            <button
-              type="button"
-              onclick={() => onCitationClick(citation.doc_id)}
-              class="inline-flex items-center gap-1 text-[11px] px-2 py-0.5 rounded bg-surface text-text border border-default hover:border-accent hover:text-accent transition-colors"
-              title={citation.span_text}
-            >
-              <FileText size={10} />
-              <span class="max-w-[200px] truncate">
-                {citation.title || `문서 #${citation.doc_id}`}
-              </span>
-            </button>
-          {/each}
-        </div>
-      </div>
-    {/if}
-  {/if}
-</div>

frontend/src/lib/components/ask/AskAnswer.svelte

@@ -1,228 +0,0 @@
-<!--
-  AskAnswer.svelte — /ask 페이지 상단 패널.
-
-  Answer 본문 + clickable [n] citations + 신뢰도/상태 Badge.
-  status != completed 또는 refused=true → warning empty state +
-  no_results_reason + "검색 결과 확인하기" 역링크.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import Button from '$lib/components/ui/Button.svelte';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { AlertTriangle, Sparkles } from 'lucide-svelte';
-  import type { AskResponse, Confidence, SynthesisStatus } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-    onCitationClick: (n: number) => void;
-  }
-
-  let { data, loading, onCitationClick }: Props = $props();
-
-  type Token =
-    | { type: 'text'; value: string }
-    | { type: 'cite'; n: number; raw: string };
-
-  function splitAnswer(text: string): Token[] {
-    return text
-      .split(/(\[\d+\])/g)
-      .filter(Boolean)
-      .map((tok): Token => {
-        const m = tok.match(/^\[(\d+)\]$/);
-        return m
-          ? { type: 'cite', n: Number(m[1]), raw: tok }
-          : { type: 'text', value: tok };
-      });
-  }
-
-  function confidenceTone(
-    c: Confidence | null,
-  ): 'success' | 'warning' | 'error' | 'neutral' {
-    if (c === 'high') return 'success';
-    if (c === 'medium') return 'warning';
-    if (c === 'low') return 'error';
-    return 'neutral';
-  }
-
-  function confidenceLabel(c: Confidence | null): string {
-    if (c === 'high') return '높음';
-    if (c === 'medium') return '중간';
-    if (c === 'low') return '낮음';
-    return '없음';
-  }
-
-  const STATUS_LABEL: Record<SynthesisStatus, string> = {
-    completed: '답변 완료',
-    timeout: '답변 지연',
-    skipped: '답변 생략',
-    no_evidence: '근거 없음',
-    parse_failed: '형식 오류',
-    llm_error: 'AI 오류',
-    backend_unavailable: 'Backend 비가용',
-  };
-
-  /**
-   * backend chip label — `backend_requested` 가 명시 opt-in 인 경우만 표시.
-   * 미지정 (null/undefined) default 호출은 chip 없음 (시각 noise 회피).
-   */
-  function backendChipLabel(backend: string | null | undefined): string | null {
-    if (!backend) return null;
-    if (backend === 'qwen-macbook') return 'Qwen 27B (MacBook)';
-    if (backend === 'gemma-macmini') return 'Gemma 26B (Mac mini)';
-    return backend;
-  }
-
-  let tokens = $derived(data?.ai_answer ? splitAnswer(data.ai_answer) : []);
-  let showFullAnswer = $derived(
-    !!data && !!data.ai_answer && data.completeness === 'full'
-    && data.synthesis_status === 'completed' && !data.refused,
-  );
-  let showPartial = $derived(
-    !!data && data.completeness === 'partial' && !data.refused,
-  );
-  let showWarning = $derived(!!data && !showFullAnswer && !showPartial);
-</script>
-
-<section class="bg-surface border border-default rounded-card p-5">
-  <!-- 헤더 -->
-  <div class="flex items-start justify-between gap-3 mb-4">
-    <div>
-      <p class="text-[10px] font-semibold tracking-wider uppercase text-dim flex items-center gap-1.5">
-        <Sparkles size={12} /> AI Answer
-      </p>
-      <h2 class="mt-1 text-base font-semibold text-text">근거 기반 답변</h2>
-    </div>
-
-    {#if data && !loading}
-      <div class="flex flex-wrap gap-1.5">
-        <Badge tone={confidenceTone(data.confidence)} size="sm">
-          신뢰도 {confidenceLabel(data.confidence)}
-        </Badge>
-        {#if backendChipLabel(data.backend_requested)}
-          <span title={`backend_requested=${data.backend_requested} / backend_used=${data.backend_used ?? 'null'}`}>
-            <Badge tone="neutral" size="sm">
-              {backendChipLabel(data.backend_requested)}
-            </Badge>
-          </span>
-        {/if}
-        <Badge tone="neutral" size="sm">
-          {STATUS_LABEL[data.synthesis_status]}
-        </Badge>
-        {#if data.synthesis_ms > 0}
-          <Badge tone="neutral" size="sm">
-            {Math.round(data.synthesis_ms)}ms
-          </Badge>
-        {/if}
-      </div>
-    {/if}
-  </div>
-
-  <!-- 본문 -->
-  {#if loading}
-    <div class="space-y-3">
-      <Skeleton w="w-3/4" h="h-4" />
-      <Skeleton w="w-full" h="h-4" />
-      <Skeleton w="w-5/6" h="h-4" />
-      <p class="mt-4 text-xs text-dim flex items-center gap-2">
-        <span class="inline-block w-3 h-3 rounded-full border-2 border-dim border-t-accent animate-spin"></span>
-        근거 기반 답변 생성 중… 약 15초 소요
-      </p>
-    </div>
-  {:else if showFullAnswer && data}
-    <div class="text-sm leading-7 text-text">
-      {#each tokens as tok}
-        {#if tok.type === 'cite'}
-          <button
-            type="button"
-            class="inline-block align-baseline text-accent font-semibold hover:underline focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-accent-ring rounded px-0.5"
-            onclick={() => onCitationClick(tok.n)}
-            aria-label={`인용 ${tok.n}번 보기`}
-          >
-            {tok.raw}
-          </button>
-        {:else}
-          <span>{tok.value}</span>
-        {/if}
-      {/each}
-    </div>
-  {:else if showPartial && data}
-    <!-- Phase 3.5a: question-aligned partial structure -->
-    <div>
-      <Badge tone="warning" size="sm">일부 답변</Badge>
-
-      {#if data.ai_answer}
-        <div class="mt-3 text-sm leading-7 text-text">
-          {#each tokens as tok}
-            {#if tok.type === 'cite'}
-              <button
-                type="button"
-                class="inline-block align-baseline text-accent font-semibold hover:underline rounded px-0.5"
-                onclick={() => onCitationClick(tok.n)}
-              >{tok.raw}</button>
-            {:else}
-              <span>{tok.value}</span>
-            {/if}
-          {/each}
-        </div>
-      {:else if data.confirmed_items?.length}
-        <div class="mt-3">
-          <h4 class="text-xs font-semibold text-dim uppercase tracking-wider">✓ 답변 가능</h4>
-          <ul class="mt-2 space-y-2">
-            {#each data.confirmed_items as item}
-              <li class="text-sm text-text">
-                <strong class="text-accent">{item.aspect}:</strong>
-                <span>{item.text}</span>
-                {#each item.citations as n}
-                  <button
-                    type="button"
-                    class="text-accent font-semibold hover:underline px-0.5"
-                    onclick={() => onCitationClick(n)}
-                  >[{n}]</button>
-                {/each}
-              </li>
-            {/each}
-          </ul>
-        </div>
-      {/if}
-
-      {#if data.missing_aspects?.length}
-        <div class="mt-4 border-t border-default pt-3">
-          <h4 class="text-xs font-semibold text-dim uppercase tracking-wider">✗ 답변 불가</h4>
-          <ul class="mt-2 space-y-1">
-            {#each data.missing_aspects as aspect}
-              <li class="text-sm text-dim">{aspect} <span class="text-[10px]">(근거 없음)</span></li>
-            {/each}
-          </ul>
-        </div>
-      {/if}
-
-      <div class="mt-4">
-        <Button
-          variant="secondary"
-          size="sm"
-          href={`/documents?q=${encodeURIComponent(data.query)}`}
-        >
-          검색 결과 확인하기
-        </Button>
-      </div>
-    </div>
-  {:else if showWarning && data}
-    <EmptyState
-      icon={AlertTriangle}
-      title={data.refused && data.no_results_reason
-        ? data.no_results_reason
-        : (data.no_results_reason ?? '관련 근거를 찾지 못했습니다.')}
-      description="검색 결과를 직접 확인해 보세요."
-    >
-      <Button
-        variant="secondary"
-        size="sm"
-        href={`/documents?q=${encodeURIComponent(data.query)}`}
-      >
-        검색 결과 확인하기
-      </Button>
-    </EmptyState>
-  {/if}
-</section>

frontend/src/lib/components/ask/AskEvidence.svelte

@@ -1,91 +0,0 @@
-<!--
-  AskEvidence.svelte — /ask 페이지 우측 sticky 패널.
-
-  ⚠ 영구 룰 (Phase 3.4 plan):
-    `citation.full_snippet` 은 UI 에 직접 렌더 금지. debug 모드(`?debug=1`)
-    에서 hover tooltip 으로만 조건부 노출 가능.
-
-    이 규칙이 깨지면 backend span-precision UX 가치가 사라진다. 코드 리뷰에서
-    반드시 reject. span_text 만 본문으로 노출한다.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { BookOpen } from 'lucide-svelte';
-  import type { AskResponse } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-    activeCitation: number | null;
-    registerCitation: (n: number, node: HTMLElement) => { destroy: () => void };
-  }
-
-  let { data, loading, activeCitation, registerCitation }: Props = $props();
-
-  let citations = $derived(data?.citations ?? []);
-</script>
-
-<section class="bg-surface border border-default rounded-card p-5">
-  <div class="flex items-start justify-between gap-3 mb-4">
-    <div>
-      <p class="text-[10px] font-semibold tracking-wider uppercase text-dim flex items-center gap-1.5">
-        <BookOpen size={12} /> Evidence Highlights
-      </p>
-      <h3 class="mt-1 text-sm font-semibold text-text">인용 근거</h3>
-    </div>
-    {#if data && !loading}
-      <Badge tone="neutral" size="sm">{citations.length}개</Badge>
-    {/if}
-  </div>
-
-  {#if loading}
-    <div class="space-y-3">
-      {#each Array(2) as _}
-        <div class="border border-default rounded-card p-4 space-y-2">
-          <Skeleton w="w-24" h="h-3" />
-          <Skeleton w="w-full" h="h-3" />
-          <Skeleton w="w-5/6" h="h-3" />
-          <Skeleton w="w-3/4" h="h-3" />
-        </div>
-      {/each}
-    </div>
-  {:else if citations.length === 0}
-    <EmptyState title="표시할 근거가 없습니다." class="py-6" />
-  {:else}
-    <div class="space-y-3">
-      {#each citations as citation (citation.n)}
-        {@const isActive = activeCitation === citation.n}
-        <article
-          class="border rounded-card p-4 transition-colors {isActive
-            ? 'border-accent ring-2 ring-accent/20 bg-accent/5'
-            : 'border-default'}"
-          use:registerCitation={citation.n}
-        >
-          <div class="flex items-start gap-2">
-            <span class="text-accent font-bold text-sm shrink-0">[{citation.n}]</span>
-            <div class="flex-1 min-w-0">
-              <strong class="block text-sm text-text truncate">
-                {citation.title ?? `문서 ${citation.doc_id}`}
-              </strong>
-              {#if citation.section_title}
-                <p class="mt-0.5 text-xs text-dim truncate">{citation.section_title}</p>
-              {/if}
-            </div>
-          </div>
-
-          <!-- ⚠ span_text 만 렌더. full_snippet 금지 -->
-          <p class="mt-3 text-sm leading-relaxed text-text whitespace-pre-wrap">
-            {citation.span_text}
-          </p>
-
-          <div class="mt-3 flex gap-2 text-[10px] text-dim">
-            <span>relevance {citation.relevance.toFixed(2)}</span>
-            <span>rerank {citation.rerank_score.toFixed(2)}</span>
-          </div>
-        </article>
-      {/each}
-    </div>
-  {/if}
-</section>

frontend/src/lib/components/ask/AskResults.svelte

@@ -1,78 +0,0 @@
-<!--
-  AskResults.svelte — /ask 페이지 하단 패널.
-
-  검색 결과 리스트. DocumentCard 재사용 X — SearchResult 필드 셋이 달라서
-  의존성 리스크 회피. inline 간단 카드로 title/score/snippet/section_title 표시.
-  클릭 시 `/documents/{id}` 로 이동.
--->
-<script lang="ts">
-  import Badge from '$lib/components/ui/Badge.svelte';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Skeleton from '$lib/components/ui/Skeleton.svelte';
-  import { FileText } from 'lucide-svelte';
-  import type { AskResponse } from '$lib/types/ask';
-
-  interface Props {
-    data: AskResponse | null;
-    loading: boolean;
-  }
-
-  let { data, loading }: Props = $props();
-
-  let results = $derived(data?.results ?? []);
-</script>
-
-<section class="bg-surface border border-default rounded-card p-5">
-  <div class="flex items-start justify-between gap-3 mb-4">
-    <div>
-      <p class="text-[10px] font-semibold tracking-wider uppercase text-dim flex items-center gap-1.5">
-        <FileText size={12} /> Search Results
-      </p>
-      <h3 class="mt-1 text-sm font-semibold text-text">검색 결과</h3>
-    </div>
-    {#if data && !loading}
-      <Badge tone="neutral" size="sm">{data.total}개</Badge>
-    {/if}
-  </div>
-
-  {#if loading}
-    <div class="space-y-3">
-      {#each Array(5) as _}
-        <div class="border border-default rounded-card p-4 space-y-2">
-          <Skeleton w="w-2/3" h="h-4" />
-          <Skeleton w="w-full" h="h-3" />
-          <Skeleton w="w-4/5" h="h-3" />
-        </div>
-      {/each}
-    </div>
-  {:else if results.length === 0}
-    <EmptyState title="검색 결과가 없습니다." class="py-6" />
-  {:else}
-    <div class="space-y-3">
-      {#each results as result (result.id)}
-        <a
-          href={`/documents/${result.id}`}
-          class="block border border-default rounded-card p-4 hover:border-accent hover:bg-surface-hover transition-colors"
-        >
-          <div class="flex items-start justify-between gap-3">
-            <strong class="text-sm text-text flex-1 min-w-0 truncate">
-              {result.title ?? `문서 ${result.id}`}
-            </strong>
-            <div class="flex gap-1.5 text-[10px] text-dim shrink-0">
-              <span>score {result.score.toFixed(2)}</span>
-              {#if result.rerank_score != null}
-                <span>rerank {result.rerank_score.toFixed(2)}</span>
-              {/if}
-            </div>
-          </div>
-          {#if result.section_title}
-            <p class="mt-1 text-xs text-dim truncate">{result.section_title}</p>
-          {/if}
-          {#if result.snippet}
-            <p class="mt-2 text-xs text-dim line-clamp-2">{result.snippet}</p>
-          {/if}
-        </a>
-      {/each}
-    </div>
-  {/if}
-</section>

frontend/src/lib/types/ask.ts

@@ -1,84 +0,0 @@
-/**
- * Phase 3.4: `/api/search/ask` 응답 타입.
- *
- * Backend Pydantic 모델 (`app/api/search.py::AskResponse`) 과 1:1 매칭.
- * 필드 변경 시 양쪽 동시 수정 필수.
- */
-
-export type SynthesisStatus =
-  | 'completed'
-  | 'timeout'
-  | 'skipped'
-  | 'no_evidence'
-  | 'parse_failed'
-  | 'llm_error'
-  | 'backend_unavailable';
-
-export type Confidence = 'high' | 'medium' | 'low';
-
-export interface Citation {
-  n: number;
-  chunk_id: number | null;
-  doc_id: number;
-  title: string | null;
-  section_title: string | null;
-  /** LLM이 추출한 50~300자 핵심 span. UI에서 이것만 노출. */
-  span_text: string;
-  /**
-   * 원본 800자 window.
-   *
-   * ⚠ UI 기본 경로에서 절대 렌더 금지. debug 모드에서 hover tooltip 용도로만
-   * 조건부 노출 가능. full_snippet을 보여주면 backend span-precision UX
-   * 가치가 사라진다 (plan §Evidence 표시 규칙).
-   */
-  full_snippet: string;
-  relevance: number;
-  rerank_score: number;
-}
-
-export interface SearchResult {
-  id: number;
-  title: string | null;
-  ai_domain: string | null;
-  ai_summary: string | null;
-  file_format: string;
-  score: number;
-  snippet: string | null;
-  match_reason: string | null;
-  chunk_id: number | null;
-  chunk_index: number | null;
-  section_title: string | null;
-  rerank_score: number | null;
-}
-
-export type Completeness = 'full' | 'partial' | 'insufficient';
-
-export interface ConfirmedItem {
-  aspect: string;
-  text: string;
-  citations: number[];
-}
-
-export interface AskResponse {
-  results: SearchResult[];
-  ai_answer: string | null;
-  citations: Citation[];
-  synthesis_status: SynthesisStatus;
-  synthesis_ms: number;
-  confidence: Confidence | null;
-  refused: boolean;
-  no_results_reason: string | null;
-  query: string;
-  total: number;
-  /** Phase 3.5a */
-  completeness: Completeness;
-  covered_aspects: string[] | null;
-  missing_aspects: string[] | null;
-  confirmed_items: ConfirmedItem[] | null;
-  /**
-   * PR-MacBook-RAG-Backend-1: backend dispatcher metadata.
-   * backend 미지정 호출은 둘 다 null (기존 호출자 호환). 명시 opt-in 시만 채워짐.
-   */
-  backend_requested?: string | null;
-  backend_used?: string | null;
-}

frontend/src/lib/utils/isQuestion.ts

@@ -1,61 +0,0 @@
-/**
- * 검색 쿼리가 "질문형"인지 판별 (규칙 기반).
- * true이면 검색 결과 페이지에서 /api/search/ask 병렬 호출.
- *
- * false positive: /ask가 refused=true 반환하면 카드 숨김 → 무해.
- * false negative: 기존 검색 결과만 표시 → 무해.
- */
-export function isQuestion(q: string): boolean {
-  const trimmed = q.trim();
-  if (trimmed.length === 0) return false;
-
-  // 1. ?로 끝나면 단일 단어라도 허용 (왜?, 절차?)
-  if (trimmed.endsWith('?')) return true;
-
-  // ? 없으면 단일 단어 / 4자 미만 제외 (키워드 보호)
-  if (trimmed.length < 4) return false;
-  if (trimmed.split(/\s+/).length < 2) return false;
-
-  // 2. 한국어 질문 어미
-  const KO_ENDINGS = [
-    '인가요', '인가', '인지', '있나요', '있나',
-    '할까요', '할까', '될까요', '될까',
-    '뭐야', '뭔가', '뭘까', '뭔지', '뭐지', '뭐죠',
-    '는지', '은지', '일까',
-    '어때', '어떤가',
-    '됩니까', '합니까', '입니까', '나요', '까요',
-    '주세요', '알려줘', '설명해', '비교해',
-  ];
-  for (const ending of KO_ENDINGS) {
-    if (trimmed.endsWith(ending)) return true;
-  }
-
-  // 3. 한국어 질문 시작어
-  const KO_STARTS = [
-    '어떻게', '왜', '무엇', '무슨', '뭐가', '누가',
-    '어디', '언제', '몇', '얼마나', '어떤', '어느',
-  ];
-  for (const start of KO_STARTS) {
-    if (trimmed.startsWith(start)) return true;
-  }
-
-  // 4. 영어 질문 시작어 (대소문자 무시)
-  const EN_STARTS = [
-    'what', 'how', 'why', 'when', 'where', 'who', 'which',
-    'is', 'are', 'do', 'does', 'can', 'could', 'should', 'would',
-    'explain', 'describe', 'compare', 'tell me',
-  ];
-  const lower = trimmed.toLowerCase();
-  for (const start of EN_STARTS) {
-    if (lower.startsWith(start + ' ')) return true;
-  }
-
-  // 5. 의미 패턴 (끝 단어)
-  const SEMANTIC_ENDINGS = ['차이', '비교', '설명', '요약', '정리', '방법', '절차'];
-  const lastWord = trimmed.split(/\s+/).pop() || '';
-  for (const pat of SEMANTIC_ENDINGS) {
-    if (lastWord === pat || lastWord.endsWith(pat)) return true;
-  }
-
-  return false;
-}

frontend/src/routes/ask/+page.svelte

@@ -1,305 +0,0 @@
-<!--
-  /ask — Phase 3.4 Ask Pipeline Frontend.
-
-  URL-driven: `/ask?q=<encoded>[&backend=<alias>]` 가 진입점.
-  $effect 로 (q, backend) 변화 감지 → `/api/search/ask` 호출 →
-  3-panel 렌더 (Answer / Evidence / Results).
-
-  중복 호출 방지: lastKey (q+backend) 가드.
-
-  Backend selector (PR-3 of DS AI routing policy, 2026-05-23,
-  [[document-server-ai-routing-policy]]) — PR-DocSrv-Web-Ask-Selector-1 확장:
-    - `auto` (기본, URL param 없음 → router 의 rule + LLM triage)
-    - `mac-mini-default` (명시, Mac mini gemma-4-26b)
-    - `qwen-macbook` (명시, M5 Max Qwen3.6-27B; "This device" 토글 on 시만)
-    - `claude-cloud` (명시, 503 scaffold; VITE_ENABLE_CLOUD_BACKEND_DEV=true 시만)
-    - "This device" 토글: localStorage `ds_device_self_label = 'macbook-m5-max' | null`.
-      source IP 의존 0 (PR-0 round 2 발견: caddy 2-hop + X-Forwarded-For 미설정 →
-      DS 가 보는 source IP = LAN gateway, 신뢰 불가).
-    - 503 + error_reason ∈ {macbook_unavailable, provider_not_configured, router_*}
-      시 자동 fallback 금지. UI 가 친절한 메시지 + "Mac mini default 로 재요청" 버튼.
-    - legacy URL `?backend=default|gemma-macmini` 는 그대로 받아서 mac-mini-default 와 동등 처리.
--->
-<script lang="ts">
-  import { page } from '$app/stores';
-  import { goto } from '$app/navigation';
-  import { api, type ApiError } from '$lib/api';
-  import { addToast } from '$lib/stores/toast';
-  import EmptyState from '$lib/components/ui/EmptyState.svelte';
-  import Button from '$lib/components/ui/Button.svelte';
-  import AskAnswer from '$lib/components/ask/AskAnswer.svelte';
-  import AskEvidence from '$lib/components/ask/AskEvidence.svelte';
-  import AskResults from '$lib/components/ask/AskResults.svelte';
-  import { Sparkles, Search, AlertCircle } from 'lucide-svelte';
-  import type { AskResponse } from '$lib/types/ask';
-
-  type BackendChoice = 'auto' | 'mac-mini-default' | 'qwen-macbook' | 'claude-cloud';
-
-  function parseBackend(raw: string | null): BackendChoice {
-    if (raw === 'qwen-macbook') return 'qwen-macbook';
-    if (raw === 'mac-mini-default') return 'mac-mini-default';
-    if (raw === 'claude-cloud') return 'claude-cloud';
-    // legacy aliases (?backend=default | gemma-macmini) → mac-mini-default 와 동등
-    if (raw === 'default' || raw === 'gemma-macmini') return 'mac-mini-default';
-    return 'auto';
-  }
-
-  // Build-time feature flag — Claude Cloud UI 노출 여부 (default false).
-  // VITE_ENABLE_CLOUD_BACKEND_DEV=true npm run build 시만 활성. 운영 토글 X
-  // (build-time 한계). DS runtime feature flag API migrate 는 후속 PR.
-  const CLOUD_DEV_ENABLED = import.meta.env.VITE_ENABLE_CLOUD_BACKEND_DEV === 'true';
-  const DEVICE_LABEL_KEY = 'ds_device_self_label';
-  const DEVICE_LABEL_M5_MAX = 'macbook-m5-max';
-
-  // ── state ───────────────────────────────────────────
-  let queryInput = $state('');
-  let selectedBackend = $state<BackendChoice>('auto');
-  let data = $state<AskResponse | null>(null);
-  let loading = $state(false);
-  let backendUnavailable = $state(false);
-  let backendUnavailableMessage = $state('');
-  // "I am on MacBook M5 Max" 토글. mount 시 localStorage 에서 복원.
-  let isMacBookM5Max = $state(false);
-
-  $effect(() => {
-    if (typeof window === 'undefined') return;
-    const stored = window.localStorage.getItem(DEVICE_LABEL_KEY);
-    isMacBookM5Max = stored === DEVICE_LABEL_M5_MAX;
-  });
-
-  function toggleMacBookM5Max() {
-    isMacBookM5Max = !isMacBookM5Max;
-    if (typeof window === 'undefined') return;
-    if (isMacBookM5Max) {
-      window.localStorage.setItem(DEVICE_LABEL_KEY, DEVICE_LABEL_M5_MAX);
-    } else {
-      window.localStorage.removeItem(DEVICE_LABEL_KEY);
-      // 토글 off 시 qwen-macbook 선택돼 있었으면 auto 로 복귀 (선택권 박탈 X 명시 신호).
-      if (selectedBackend === 'qwen-macbook') {
-        selectedBackend = 'auto';
-      }
-    }
-  }
-
-  // 중복 호출 방지 가드 (hydration + reactive trigger 이중 발동 방지)
-  let lastKey = '';
-
-  // citation scroll 연동: Answer 가 [n] 클릭 → Evidence 카드로 이동 + highlight
-  const citationNodes = new Map<number, HTMLElement>();
-  let activeCitation = $state<number | null>(null);
-
-  function registerCitation(n: number, node: HTMLElement) {
-    citationNodes.set(n, node);
-    return {
-      destroy() {
-        citationNodes.delete(n);
-      },
-    };
-  }
-
-  function scrollToCitation(n: number) {
-    activeCitation = n;
-    const node = citationNodes.get(n);
-    node?.scrollIntoView({ behavior: 'smooth', block: 'center' });
-  }
-
-  // ── URL 빌더: backend !== 'auto' 일 때만 param 추가 ─────
-  function buildAskUrl(q: string, backend: BackendChoice): string {
-    const params = new URLSearchParams({ q });
-    if (backend !== 'auto') params.set('backend', backend);
-    return `/ask?${params.toString()}`;
-  }
-
-  // ── submit (URL-driven, back 자동) ──────────────────
-  function submit() {
-    const q = queryInput.trim();
-    if (!q) return;
-    goto(buildAskUrl(q, selectedBackend));
-  }
-
-  function handleKeydown(e: KeyboardEvent) {
-    if (e.key === 'Enter' && !e.isComposing) {
-      e.preventDefault();
-      submit();
-    }
-  }
-
-  // 503 후 "Mac mini default 로 재요청" — auto 로 reset (param 명시 제거).
-  function retryWithMacMiniDefault() {
-    const q = $page.url.searchParams.get('q') ?? queryInput.trim();
-    if (!q) return;
-    goto(`/ask?q=${encodeURIComponent(q)}`);
-  }
-
-  // PR-3 of routing policy — error_reason → 친절 메시지 매핑.
-  // silent fallback 금지 ([[feedback_no_silent_fallback_explicit_opt_in]]):
-  // 모든 503 case 는 명시 표시, 다른 backend 자동 재호출 X.
-  function friendlyErrorMessage(reason: string | undefined, detail: string): string {
-    switch (reason) {
-      case 'macbook_unavailable':
-        return 'MacBook M5 Max 가 응답하지 않습니다. 깨우거나 (launchctl start) Mac mini default 로 다시 요청하세요.';
-      case 'provider_not_configured':
-        return 'Claude Cloud 백엔드는 아직 구성되지 않았습니다 (2026-06-15 이후 별 PR 활성화 예정).';
-      default:
-        if (reason && reason.startsWith('router_')) {
-          return `라우터 호출 실패 (${reason}). Mac mini default 로 다시 요청하거나 잠시 후 재시도하세요.`;
-        }
-        if (reason && reason.startsWith('upstream_')) {
-          return `Upstream backend 가 응답하지 않습니다 (${reason}). 잠시 후 재시도하세요.`;
-        }
-        return detail || '답변 생성 실패';
-    }
-  }
-
-  // ── API 호출 ───────────────────────────────────────
-  async function runAsk(q: string, backend: BackendChoice) {
-    loading = true;
-    activeCitation = null;
-    backendUnavailable = false;
-    backendUnavailableMessage = '';
-    const path = backend !== 'auto'
-      ? `/search/ask?q=${encodeURIComponent(q)}&backend=${encodeURIComponent(backend)}&limit=10`
-      : `/search/ask?q=${encodeURIComponent(q)}&limit=10`;
-    try {
-      data = await api<AskResponse>(path);
-    } catch (err) {
-      const apiErr = err as ApiError;
-      if (apiErr.status === 503) {
-        backendUnavailable = true;
-        backendUnavailableMessage = friendlyErrorMessage(apiErr.errorReason, apiErr.detail);
-        data = null;
-      } else {
-        addToast('error', apiErr.detail || '답변 생성 실패');
-        data = null;
-      }
-    } finally {
-      loading = false;
-    }
-  }
-
-  // ── URL → runAsk (중복 가드) ────────────────────────
-  $effect(() => {
-    const q = $page.url.searchParams.get('q') ?? '';
-    const backend = parseBackend($page.url.searchParams.get('backend'));
-    queryInput = q;
-    selectedBackend = backend;
-    if (!q) {
-      data = null;
-      loading = false;
-      backendUnavailable = false;
-      lastKey = '';
-      return;
-    }
-    const key = `${q}|${backend}`;
-    if (key === lastKey) return;
-    lastKey = key;
-    runAsk(q, backend);
-  });
-</script>
-
-<svelte:head>
-  <title>질문 - PKM</title>
-</svelte:head>
-
-<div class="h-full overflow-auto">
-  <!-- 상단 검색바 (sticky) -->
-  <div class="sticky top-0 z-10 bg-bg/80 backdrop-blur border-b border-default px-4 py-3">
-    <div class="flex flex-wrap items-center gap-2 max-w-[1680px] mx-auto">
-      <div class="relative flex-1 min-w-0">
-        <Search
-          size={14}
-          class="absolute left-3 top-1/2 -translate-y-1/2 text-dim pointer-events-none"
-        />
-        <input
-          data-search-input
-          type="text"
-          bind:value={queryInput}
-          onkeydown={handleKeydown}
-          placeholder="질문을 입력하세요 (/ 키로 포커스)"
-          class="w-full pl-9 pr-3 py-2 bg-surface border border-default rounded-lg text-text text-sm focus:border-accent outline-none"
-        />
-      </div>
-      <label
-        class="flex items-center gap-1.5 text-xs text-dim cursor-pointer select-none"
-        title="이 디바이스가 MacBook M5 Max 인 경우 체크 — This device (qwen-macbook) 옵션 활성화됩니다. localStorage 저장."
-      >
-        <input
-          type="checkbox"
-          checked={isMacBookM5Max}
-          onchange={toggleMacBookM5Max}
-          class="accent-accent"
-        />
-        <span>This is M5 Max</span>
-      </label>
-      <select
-        bind:value={selectedBackend}
-        title="Backend 선택 — silent fallback 0 정책 (선택한 backend 만 시도, 실패 시 503)."
-        class="py-2 px-2 bg-surface border border-default rounded-lg text-text text-xs focus:border-accent outline-none min-w-0 max-w-[42vw] truncate"
-      >
-        <option value="auto">Auto (router)</option>
-        <option value="mac-mini-default">Mac mini (default)</option>
-        <option
-          value="qwen-macbook"
-          disabled={!isMacBookM5Max}
-          title={isMacBookM5Max
-            ? 'MacBook M5 Max Qwen3.6-27B (직접 호출)'
-            : 'Check "This is M5 Max" toggle to enable'}
-        >
-          {isMacBookM5Max ? 'This device (Qwen MacBook)' : 'This device (unavailable)'}
-        </option>
-        <option
-          value="claude-cloud"
-          disabled={!CLOUD_DEV_ENABLED}
-          title={CLOUD_DEV_ENABLED
-            ? 'Claude Cloud (dev mode — returns 503 until activation PR)'
-            : 'Cloud backend not configured yet'}
-        >
-          Claude Cloud {CLOUD_DEV_ENABLED ? '(dev)' : '(unavailable)'}
-        </option>
-      </select>
-    </div>
-  </div>
-
-  <!-- 본문 -->
-  <div class="max-w-[1680px] mx-auto p-4">
-    {#if backendUnavailable}
-      <div class="py-16">
-        <EmptyState
-          icon={AlertCircle}
-          title="Backend 가 응답하지 않습니다"
-          description={backendUnavailableMessage}
-        >
-          <Button variant="primary" size="sm" onclick={retryWithMacMiniDefault}>
-            Mac mini (default) 로 재요청
-          </Button>
-        </EmptyState>
-      </div>
-    {:else if !queryInput && !loading && !data}
-      <div class="py-16">
-        <EmptyState
-          icon={Sparkles}
-          title="근거 기반 답변을 받아보세요"
-          description="질문을 입력하면 문서에서 근거를 찾아 인용 기반 답변을 생성합니다."
-        />
-      </div>
-    {:else}
-      <div class="grid gap-4 lg:grid-cols-[1.2fr_0.9fr] items-start">
-        <!-- 좌: Answer + Results -->
-        <div class="flex flex-col gap-4 min-w-0">
-          <AskAnswer {data} {loading} onCitationClick={scrollToCitation} />
-          <AskResults {data} {loading} />
-        </div>
-
-        <!-- 우: Evidence (lg 이상 sticky) -->
-        <div class="lg:sticky lg:top-20 lg:self-start min-w-0">
-          <AskEvidence
-            {data}
-            {loading}
-            {activeCitation}
-            {registerCitation}
-          />
-        </div>
-      </div>
-    {/if}
-  </div>
-</div>

frontend/src/routes/documents/+page.svelte

@@ -8,7 +8,7 @@
   import { goto } from '$app/navigation';
   import { api } from '$lib/api';
   import { addToast } from '$lib/stores/toast';
-  import { X, Plus, Trash2, Tag, FolderTree, Sparkles, ArrowUpDown } from 'lucide-svelte';
+  import { X, Plus, Trash2, Tag, FolderTree, ArrowUpDown } from 'lucide-svelte';
   import MarkdownStatusBadge from '$lib/components/MarkdownStatusBadge.svelte';
   import { isMdStatusVisible } from '$lib/utils/mdStatus';
   import UploadDropzone from '$lib/components/UploadDropzone.svelte';
@@ -22,9 +22,7 @@
   import { useIsXl } from '$lib/composables/useMedia.svelte';
   import { useListKeyboardNav } from '$lib/composables/useListKeyboardNav.svelte';
   import { pLimit } from '$lib/utils/pLimit';
-  import { isQuestion } from '$lib/utils/isQuestion';
   import { domainBgClass, domainLabel } from '$lib/utils/domainSlug';
-  import AskAnswerCard from '$lib/components/AskAnswerCard.svelte';
 
   const FORMATS = ['pdf', 'hwp', 'hwpx', 'md', 'docx', 'xlsx', 'png', 'jpg'];
 
@@ -48,30 +46,6 @@
   let searchResults = $state(null);
   let selectedDoc = $state(null);
 
-  // 이드 답변 상태 (질문형 검색)
-  let askData = $state(null);
-  let askLoading = $state(false);
-  let askError = $state(false);
-  let askDismissed = $state(false);
-
-  async function askSearch(q) {
-    askLoading = true; askError = false; askData = null;
-    try {
-      askData = await api(`/search/ask?q=${encodeURIComponent(q)}&limit=10`);
-    } catch {
-      askError = true; askData = null;
-    } finally {
-      askLoading = false;
-    }
-  }
-
-  let showAskCard = $derived(
-    !askDismissed && (
-      askLoading ||
-      (askData && !askData.refused && askData.ai_answer && askData.synthesis_status === 'completed')
-    )
-  );
-
   // 인스펙터(우측) 토글 — xl+ inline, < xl Drawer.
   const isXl = useIsXl();
   let inspectorOpen = $state(
@@ -145,7 +119,6 @@
     selectedDoc = null;
     selectedIds = new Set();
     kbIndex = 0;
-    askData = null; askLoading = false; askError = false; askDismissed = false;
     if (ui.isDrawerOpen('meta')) ui.closeDrawer();
     if (urlQ) doSearch(urlQ, urlMode);
     else loadDocuments();
@@ -191,7 +164,6 @@
 
   async function doSearch(q, mode) {
     loading = true;
-    if (isQuestion(q)) askSearch(q);
     try {
       const data = await api(`/search/?q=${encodeURIComponent(q)}&mode=${mode}&limit=50`);
       searchResults = data.results;
@@ -406,13 +378,6 @@
         <option value="trgm">부분매칭</option>
         <option value="vector">의미검색</option>
       </select>
-      {#if searchQuery.trim()}
-        <a
-          href={`/ask?q=${encodeURIComponent(searchQuery.trim())}`}
-          class="flex items-center px-2 py-1.5 rounded-lg border border-default text-dim hover:text-accent hover:border-accent transition-colors"
-          title="이 쿼리로 AI 답변"
-        ><Sparkles size={14} /></a>
-      {/if}
     </div>
 
     <!-- 필터 칩 row -->
@@ -483,19 +448,6 @@
       {/if}
     </div>
 
-    <!-- AI 답변 (질문형 검색) — 목록 상단 고정, 아래로 목록 스크롤 -->
-    {#if showAskCard}
-      <div class="px-3 py-2 shrink-0 border-b border-default max-h-[55vh] overflow-y-auto">
-        <AskAnswerCard
-          data={askData}
-          loading={askLoading}
-          error={askError}
-          onCitationClick={(docId) => goto(`/documents/${docId}`)}
-          onDismiss={() => { askDismissed = true; }}
-        />
-      </div>
-    {/if}
-
     <!-- 선택 toolbar -->
     {#if selectionCount > 0}
       <div class="flex flex-wrap items-center gap-2 px-3 py-2 shrink-0 bg-accent/10 border-y border-accent/30">

migrations/377_domain_bucket.sql

@@ -0,0 +1,23 @@
+-- 377_domain_bucket.sql
+-- ai_domain(반자유 AI 분류, 드리프트 존재)을 검색 스코프용 7버킷으로 결정적 롤업.
+-- 축: ai_domain(routing/해석 축)의 coarsening — category(UI축) 아님 (feedback_category_vs_ai_domain_axis 준수).
+-- 버킷: News / Safety / Law / Engineering / General / Philosophy / Programming.
+-- STORED generated → 신규/재분류 문서도 ai_domain 붙으면 자동 버킷. ai_domain 원본 보존(하위 검색 유지).
+-- 롤백: ALTER TABLE documents DROP COLUMN domain_bucket;
+ALTER TABLE documents ADD COLUMN IF NOT EXISTS domain_bucket text
+  GENERATED ALWAYS AS (
+    CASE
+      WHEN ai_domain LIKE 'News%' THEN 'News'
+      WHEN ai_domain = '법령' OR ai_domain LIKE 'Industrial_Safety/Legislation%' THEN 'Law'
+      WHEN ai_domain = 'Safety' OR ai_domain LIKE 'Safety/%'
+           OR ai_domain LIKE 'Industrial_Safety%'
+           OR ai_domain = 'Knowledge/Industrial_Safety' THEN 'Safety'
+      WHEN ai_domain LIKE 'Engineering%' OR ai_domain = 'Knowledge/Engineering' THEN 'Engineering'
+      WHEN ai_domain LIKE 'Philosophy%' THEN 'Philosophy'
+      WHEN ai_domain LIKE 'Programming%' THEN 'Programming'
+      ELSE 'General'
+    END
+  ) STORED;
+
+CREATE INDEX IF NOT EXISTS documents_domain_bucket_idx
+  ON documents (domain_bucket) WHERE deleted_at IS NULL;

migrations/378_publish_outbox_attempts_failed.sql

@@ -0,0 +1,9 @@
+-- 378_publish_outbox_attempts_failed.sql
+-- (번호: 멀티세션 중 prod 가 377_domain_bucket 을 선점 → 378 로 리넘버.)
+-- publish_outbox poison row head-of-line block 차단. 발행 워커가 행별 savepoint 격리 후
+-- 예외 시 attempts++ 하고 MAX 초과 시 failed_at 스탬프(terminal) → 그 행을 select 에서 제외해
+-- 후속 발행이 막히지 않게 함. 기존 미처리 행은 attempts=0 / failed_at=NULL 로 정상 재처리.
+-- (단일 ALTER = 1 statement = asyncpg prepared 호환.)
+ALTER TABLE publish_outbox
+    ADD COLUMN IF NOT EXISTS attempts SMALLINT NOT NULL DEFAULT 0,
+    ADD COLUMN IF NOT EXISTS failed_at TIMESTAMPTZ;

scripts/backfill_publish_card_progress.py

@@ -31,8 +31,8 @@ from core.database import async_session
 from models.study_memo_card_progress import StudyMemoCardProgress
 from services.study.publish_enqueue import backfill_publish_card_progress
 
-# 개인 학습툴 progress row 대비 넉넉. 도달 시 가드 경보.
-PAGE = 100000
+# 페이지 배치 크기 — after_id 루프로 전량 처리(bounded tx).
+PAGE = 5000
 
 
 async def run(dry_run: bool) -> None:
@@ -50,13 +50,17 @@ async def run(dry_run: bool) -> None:
         print("[dry-run] 적재 안 함. 실제 실행은 --dry-run 제거.")
         return
 
-    async with async_session() as session:
-        n = await backfill_publish_card_progress(session, after_id=0, limit=PAGE)
-        await session.commit()
+    total = 0
+    after = 0
+    while True:
+        async with async_session() as session:
+            n, after = await backfill_publish_card_progress(session, after_id=after, limit=PAGE)
+            await session.commit()
+        total += n
+        if n < PAGE:
+            break
 
-    print(f"\n[ok] outbox 적재 {n}건 — 발행 워커가 drain(flag on 시) 하며 rev 부여.")
-    if n >= PAGE:
-        print(f"[warn] PAGE({PAGE}) 도달 — progress 가 더 있을 수 있음. after_id 페이징 추가 필요.")
+    print(f"\n[ok] outbox 적재 {total}건 — 발행 워커가 drain(flag on 시) 하며 rev 부여.")
 
 
 def main() -> None:

scripts/backfill_publish_cards.py

@@ -31,8 +31,8 @@ from core.database import async_session
 from models.study_memo_card import StudyMemoCard
 from services.study.publish_enqueue import backfill_publish_cards
 
-# 개인 학습툴 카드 수 대비 넉넉(단일 outbox 적재 tx, 워커는 BATCH_SIZE 로 drain). 도달 시 가드 경보.
-PAGE = 100000
+# 페이지 배치 크기 — after_id 루프로 전량 처리(bounded tx). 워커는 BATCH_SIZE 로 drain.
+PAGE = 5000
 
 
 async def run(dry_run: bool) -> None:
@@ -55,13 +55,17 @@ async def run(dry_run: bool) -> None:
         print("[dry-run] 적재 안 함. 실제 실행은 --dry-run 제거.")
         return
 
-    async with async_session() as session:
-        n = await backfill_publish_cards(session, after_id=0, limit=PAGE)
-        await session.commit()
+    total = 0
+    after = 0
+    while True:
+        async with async_session() as session:
+            n, after = await backfill_publish_cards(session, after_id=after, limit=PAGE)
+            await session.commit()
+        total += n
+        if n < PAGE:
+            break
 
-    print(f"\n[ok] outbox 적재 {n}건 — 발행 워커가 drain(flag on 시) 하며 rev 부여.")
-    if n >= PAGE:
-        print(f"[warn] PAGE({PAGE}) 도달 — 카드가 더 있을 수 있음. after_id 페이징 추가 필요.")
+    print(f"\n[ok] outbox 적재 {total}건 — 발행 워커가 drain(flag on 시) 하며 rev 부여.")
 
 
 def main() -> None:

scripts/backfill_publish_topics.py

@@ -37,7 +37,7 @@ from core.database import async_session
 from models.study_topic import StudyTopic
 from services.study.publish_enqueue import backfill_publish_topics
 
-# 개인 학습툴 주제 수 대비 넉넉. 도달 시 overflow 가드가 경보.
+# 페이지 배치 크기 — after_id 루프로 전량 처리(bounded tx).
 PAGE = 5000
 
 
@@ -58,13 +58,17 @@ async def run(dry_run: bool) -> None:
         print("[dry-run] 적재 안 함. 실제 실행은 --dry-run 제거.")
         return
 
-    async with async_session() as session:
-        n = await backfill_publish_topics(session, after_id=0, limit=PAGE)
-        await session.commit()
+    total = 0
+    after = 0
+    while True:
+        async with async_session() as session:
+            n, after = await backfill_publish_topics(session, after_id=after, limit=PAGE)
+            await session.commit()
+        total += n
+        if n < PAGE:
+            break
 
-    print(f"\n[ok] outbox 적재 {n}건 — 발행 워커가 drain(flag on 시) 하며 rev 부여.")
-    if n >= PAGE:
-        print(f"[warn] PAGE({PAGE}) 도달 — 주제가 더 있을 수 있음. after_id 페이징 추가 필요.")
+    print(f"\n[ok] outbox 적재 {total}건 — 발행 워커가 drain(flag on 시) 하며 rev 부여.")
 
 
 def main() -> None:

scripts/ci/boot_smoke.py

@@ -106,7 +106,7 @@ async def main() -> None:
             "SELECT count(*) FROM pg_indexes WHERE indexname='uq_attempt_session_question'"))).scalar()
         mx = (await s.execute(text("SELECT max(version) FROM schema_migrations"))).scalar()
     print(f"SCHEMA OK — max_migration={mx} documents={docs} purge_col={purge} cand_qwen={cand} attempt_uq={uq}")
-    assert docs and purge == 1 and cand == 0 and uq == 1 and mx == 361, "FAIL: 기대 스키마 상태 불일치"
+    assert docs and purge == 1 and cand == 0 and uq == 1 and mx == 378, "FAIL: 기대 스키마 상태 불일치"
 
     # ── 5) /health 직접 호출 ──────────────────────────────────────────────
     health = await main.health_check()

services/mineru/server.py

@@ -59,6 +59,11 @@ MAX_IMAGES_PER_DOC = int(os.getenv("MINERU_MAX_IMAGES_PER_DOC", "200"))
 MAX_BYTES_PER_IMAGE = int(os.getenv("MINERU_MAX_BYTES_PER_IMAGE", str(10 * 1024 * 1024)))
 MAX_PAGES_HARD = int(os.getenv("MINERU_MAX_PAGES_HARD", "200"))  # 1-shot max_pages 안전장치
 
+# self-timeout — 변환/워밍이 vLLM 행으로 _engine_lock 을 영구 점유해 서비스가 wedge 되는 것을 차단.
+# (클라이언트 marker_worker 는 300s 로 포기하나 서버측 inflight 는 자동 취소 안 됨 → 서버 자체 상한 필요.)
+PARSE_TIMEOUT_S = float(os.getenv("MINERU_PARSE_TIMEOUT_S", "600"))
+WARMUP_TIMEOUT_S = float(os.getenv("MINERU_WARMUP_TIMEOUT_S", "1200"))  # 최초 모델 다운로드(~2.4GB) 여유
+
 _PRELOAD = os.getenv("MINERU_PRELOAD", "1") != "0"
 
 # ---- 엔진 상태 ---------------------------------------------------------------
@@ -68,6 +73,15 @@ _warmup_error: str | None = None
 _engine_lock = asyncio.Lock()
 
 
+def _is_engine_fatal(exc: BaseException) -> bool:
+    """OOM/CUDA 류 = 엔진 상태 오염 가능 → 재워밍 강제 대상(타임아웃은 호출측에서 별도 판정)."""
+    s = f"{type(exc).__name__} {exc}".lower()
+    return any(
+        k in s
+        for k in ("out of memory", "oom", "cuda", "cublas", "device-side", "illegal memory")
+    )
+
+
 async def _run_mineru(pdf_bytes: bytes, lang: str) -> tuple[str, list[dict]]:
     """슬라이스된 PDF bytes → (markdown, 이미지 dict 리스트). **async 엔진 경로.**
 
@@ -148,7 +162,7 @@ async def _ensure_warmup() -> None:
             page.insert_text((72, 72), "MinerU warmup.")
             warmup_bytes = doc.tobytes()
             doc.close()
-            await _run_mineru(warmup_bytes, MINERU_LANG)
+            await asyncio.wait_for(_run_mineru(warmup_bytes, MINERU_LANG), timeout=WARMUP_TIMEOUT_S)
             _warmup_done = True
             _warmup_error = None
             logger.info(f"[mineru-service] warmup done engine_version={_engine_version}")
@@ -274,6 +288,7 @@ def _serialize_images(images: list[dict], src_path: str) -> tuple[list[ConvertIm
 
 @app.post("/convert", response_model=ConvertResponse)
 async def convert(req: ConvertRequest):
+    global _warmup_done
     p = _resolve_path(req.file_path)
     if p is None or not p.is_file():
         raise HTTPException(404, detail={"code": "file_not_found", "message": req.file_path})
@@ -288,10 +303,18 @@ async def convert(req: ConvertRequest):
     async with _engine_lock:                # 실제 변환 직렬화(단일 GPU)
         start = time.monotonic()
         try:
-            md_text, raw_images = await _run_mineru(pdf_bytes, MINERU_LANG)
+            md_text, raw_images = await asyncio.wait_for(
+                _run_mineru(pdf_bytes, MINERU_LANG), timeout=PARSE_TIMEOUT_S
+            )
         except HTTPException:
             raise
         except Exception as exc:
+            # 타임아웃(엔진 행) 또는 OOM/CUDA 류면 엔진 오염 가능 → 다음 요청이 재워밍하도록 리셋.
+            # 재워밍까지 실패하면 _ensure_warmup 이 _warmup_error 설정 → /ready 503 → healthcheck
+            # 재시작으로 escalate(영구 degradation 차단). 일시 OOM 이면 재워밍 성공 후 정상화.
+            if isinstance(exc, (asyncio.TimeoutError, TimeoutError)) or _is_engine_fatal(exc):
+                _warmup_done = False
+                logger.error("[mineru-service] engine reset (timeout/fatal) path=%s: %s", p, exc)
             logger.exception(f"[mineru-service] conversion failed path={p}: {exc}")
             raise HTTPException(422, detail={"code": "conversion_failed",
                 "message": f"{type(exc).__name__}: {exc}"}) from exc

tests/api/test_search_ask_macbook_503.py

@@ -1,291 +0,0 @@
-"""PR-MacBook-RAG-Backend-1 정정 4 핵심 테스트.
-
-검증 invariant (synthesize 함수 레벨 — /ask wrapper 의 503 매핑은 search.py 의
-status="backend_unavailable" 분기로 1:1 deterministic):
-
-1. backend="qwen-macbook" + MacBook URL 죽은 포트
-   → synthesize() 가 SynthesisResult(status="backend_unavailable", ...) 반환
-   → Gemma backend 의 generate() 가 **단 1번도 호출되지 않음** (자동 fallback 부재)
-
-2. backend 미지정 (None)
-   → Gemma backend.generate() 호출, Qwen backend.generate() 호출 0
-   → 기존 호출자 (Hermes docsrv_ask / voice-memo-bot) 회귀 0
-
-3. backend="qwen-macbook" + MacBook 정상 응답
-   → status="completed" + answer 채워짐, Gemma backend 호출 0
-
-테스트 전략:
-- synthesize() 가 호출하는 backend dispatcher (services.llm.get_backend) 를
-  monkeypatch 해서 mock backend 주입.
-- Gemma backend 의 generate AsyncMock 호출 횟수를 추적.
-- 정정 4 의 핵심 가드: `gemma_backend.generate.assert_not_called()`
-"""
-
-from __future__ import annotations
-
-import asyncio
-import os
-import sys
-from dataclasses import dataclass
-from unittest.mock import AsyncMock
-
-import pytest
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "app"))
-
-
-# ── 가짜 evidence (synthesize 의 no_evidence 분기 회피용 최소 객체) ─────────
-
-
-@dataclass
-class _FakeEvidence:
-    n: int = 1
-    doc_id: int = 100
-    chunk_id: int | None = 200
-    title: str | None = "fake doc"
-    span_text: str = "이것은 짧은 근거 텍스트입니다."
-    source: str = "llm"
-
-
-def _make_evidence():
-    return [_FakeEvidence()]
-
-
-# ── backend mock ───────────────────────────────────────────────────────────
-
-
-def _gemma_mock(content: str = "GEMMA_SHOULD_NEVER_BE_CALLED"):
-    m = AsyncMock()
-    m.name = "gemma-macmini"
-    m.generate = AsyncMock(return_value=content)
-    return m
-
-
-def _qwen_mock_success(content: str):
-    m = AsyncMock()
-    m.name = "qwen-macbook"
-    m.generate = AsyncMock(return_value=content)
-    return m
-
-
-def _qwen_mock_unavailable():
-    from services.llm import BackendUnavailable
-
-    m = AsyncMock()
-    m.name = "qwen-macbook"
-    m.generate = AsyncMock(
-        side_effect=BackendUnavailable("qwen-macbook", "ConnectError")
-    )
-    return m
-
-
-# ── 공통 fixture: synthesis_service 에 mock backend 주입 ───────────────────
-
-
-@pytest.fixture
-def patched_backends(monkeypatch):
-    """services.llm.get_backend 를 mock dispatcher 로 치환.
-
-    Returns (gemma_mock, qwen_mock, set_qwen_unavailable_fn).
-    """
-    from services.search import synthesis_service
-
-    gemma = _gemma_mock()
-    qwen_holder = {"backend": _qwen_mock_success(
-        '{"answer":"Qwen ok [1]","confidence":"high","refused":false}'
-    )}
-
-    def _fake_get_backend(name: str | None):
-        key = (name or "").strip().lower() or "gemma-macmini"
-        if key == "gemma-macmini":
-            return gemma
-        if key == "qwen-macbook":
-            return qwen_holder["backend"]
-        raise ValueError(f"unknown backend: {name!r}")
-
-    monkeypatch.setattr(synthesis_service, "get_backend", _fake_get_backend)
-    # synthesis_service 캐시 비움 (qwen vs gemma 캐시 분리 invariant)
-    synthesis_service._CACHE.clear()
-
-    def _swap_qwen_unavailable():
-        qwen_holder["backend"] = _qwen_mock_unavailable()
-
-    return gemma, qwen_holder, _swap_qwen_unavailable
-
-
-# ── 정정 4 핵심: backend=qwen-macbook + MacBook 비가용 → Gemma 호출 0 ─────
-
-
-def test_qwen_unavailable_yields_backend_unavailable_status_and_gemma_not_called(
-    patched_backends,
-):
-    """**정정 4 의 핵심 invariant**.
-
-    backend="qwen-macbook" 명시 + Qwen 호출이 BackendUnavailable 로 실패 →
-    synthesize() 는 status="backend_unavailable" 반환. Gemma backend 의
-    generate() 는 **단 한 번도 호출되지 않음** (silent fallback 금지).
-    """
-    from services.search.synthesis_service import synthesize
-
-    gemma, qwen_holder, swap_qwen_unavailable = patched_backends
-    swap_qwen_unavailable()
-    qwen = qwen_holder["backend"]
-
-    result = asyncio.run(
-        synthesize(
-            query="압력용기 최대허용응력은?",
-            evidence=_make_evidence(),
-            backend="qwen-macbook",
-        )
-    )
-
-    # 1. status
-    assert result.status == "backend_unavailable"
-    assert result.answer is None
-    assert result.confidence is None
-    assert result.refused is False
-
-    # 2. flag 에 backend 비가용 사유 기록
-    assert any(
-        f.startswith("backend_unavailable:qwen-macbook:") for f in result.hallucination_flags
-    ), f"expected backend_unavailable flag, got {result.hallucination_flags}"
-
-    # 3. ★ 핵심 가드 ★ — Gemma backend 자동 fallback 금지
-    gemma.generate.assert_not_called()
-
-    # 4. Qwen 은 1회만 호출 (재시도 없음)
-    assert qwen.generate.call_count == 1
-
-
-def test_qwen_unavailable_result_not_cached(patched_backends):
-    """비가용 결과는 캐시 X — 다음 호출이 다시 Qwen 시도해야 함."""
-    from services.search.synthesis_service import synthesize
-
-    gemma, qwen_holder, swap_qwen_unavailable = patched_backends
-    swap_qwen_unavailable()
-    qwen = qwen_holder["backend"]
-
-    asyncio.run(
-        synthesize(
-            query="동일 쿼리",
-            evidence=_make_evidence(),
-            backend="qwen-macbook",
-        )
-    )
-    asyncio.run(
-        synthesize(
-            query="동일 쿼리",
-            evidence=_make_evidence(),
-            backend="qwen-macbook",
-        )
-    )
-
-    # 두 번 모두 실제 호출 (캐시 적중 X) — Gemma 는 여전히 0
-    assert qwen.generate.call_count == 2
-    gemma.generate.assert_not_called()
-
-
-# ── 정정 4: backend 미지정 → 기존 Gemma path (회귀 0) ─────────────────────
-
-
-def test_default_backend_calls_gemma_not_qwen(patched_backends):
-    """backend 미지정 = 기본 Gemma. Qwen 호출 0."""
-    from services.search.synthesis_service import synthesize
-
-    gemma, qwen_holder, _ = patched_backends
-    qwen = qwen_holder["backend"]
-    gemma.generate.return_value = (
-        '{"answer":"Gemma 답변 [1]","confidence":"high","refused":false}'
-    )
-
-    result = asyncio.run(
-        synthesize(
-            query="기본 호출",
-            evidence=_make_evidence(),
-            backend=None,  # 명시 None = default
-        )
-    )
-
-    assert result.status == "completed"
-    assert result.answer is not None and "Gemma" in result.answer
-
-    # Qwen 은 호출 0
-    qwen.generate.assert_not_called()
-    # Gemma 는 1회
-    assert gemma.generate.call_count == 1
-
-
-# ── backend="qwen-macbook" + 정상 응답 ──────────────────────────────────────
-
-
-def test_qwen_success_does_not_call_gemma(patched_backends):
-    """Qwen 정상 응답 시 Gemma 는 호출되지 않음 (대칭 invariant)."""
-    from services.search.synthesis_service import synthesize
-
-    gemma, qwen_holder, _ = patched_backends
-    qwen = qwen_holder["backend"]
-
-    result = asyncio.run(
-        synthesize(
-            query="정상 호출",
-            evidence=_make_evidence(),
-            backend="qwen-macbook",
-        )
-    )
-
-    assert result.status == "completed"
-    assert result.answer is not None and "Qwen" in result.answer
-
-    # Gemma 는 0회
-    gemma.generate.assert_not_called()
-    # Qwen 은 1회
-    assert qwen.generate.call_count == 1
-
-
-# ── 캐시 분리 (qwen vs gemma 키 충돌 없음) ─────────────────────────────────
-
-
-def test_qwen_and_gemma_have_separate_caches(patched_backends):
-    """같은 query 라도 backend 다르면 캐시 분리 — Qwen 결과가 Gemma 호출 답으로 둔갑하지 않음."""
-    from services.search.synthesis_service import synthesize
-
-    gemma, qwen_holder, _ = patched_backends
-    qwen = qwen_holder["backend"]
-    gemma.generate.return_value = (
-        '{"answer":"GEMMA_ANSWER [1]","confidence":"high","refused":false}'
-    )
-    qwen.generate.return_value = (
-        '{"answer":"QWEN_ANSWER [1]","confidence":"high","refused":false}'
-    )
-
-    r_qwen_1 = asyncio.run(
-        synthesize(
-            query="같은 query",
-            evidence=_make_evidence(),
-            backend="qwen-macbook",
-        )
-    )
-    r_gemma_1 = asyncio.run(
-        synthesize(
-            query="같은 query",
-            evidence=_make_evidence(),
-            backend=None,
-        )
-    )
-    r_qwen_2 = asyncio.run(
-        synthesize(
-            query="같은 query",
-            evidence=_make_evidence(),
-            backend="qwen-macbook",
-        )
-    )
-
-    assert "QWEN_ANSWER" in (r_qwen_1.answer or "")
-    assert "GEMMA_ANSWER" in (r_gemma_1.answer or "")
-    # 두 번째 Qwen 호출은 캐시 적중 — 결과는 동일하지만 generate 추가 호출 X
-    assert "QWEN_ANSWER" in (r_qwen_2.answer or "")
-    assert r_qwen_2.cache_hit is True
-
-    # generate 호출 횟수: Qwen 1 (두번째는 캐시), Gemma 1
-    assert qwen.generate.call_count == 1
-    assert gemma.generate.call_count == 1

tests/api/test_search_ask_react_endpoint.py

@@ -1,218 +0,0 @@
-"""PR-DocSrv-Ask-ToolCalling-ReAct-1: /api/search/ask/react endpoint integration.
-
-검증 항목 (G0-3 trace exposure + 정정 4 invariant):
-- backend unavailable → HTTP 503 + error_reason=macbook_unavailable
-  + ★ `run_search` mock 호출 횟수 == 0 (search 단계 진입 자체 차단)
-- 정상 응답 → 200 + final_answer + sources + debug_trace=null (default)
-- debug=true → debug_trace 채워짐
-- max rounds 도달 → iterations=2 + partial=false (final content 정상)
-
-endpoint 함수 (`api.search.ask_react`) 를 직접 호출하는 lightweight 패턴.
-TestClient 없이 FastAPI deps 를 MagicMock 으로 우회. (priority_gate / backend_dispatcher
-test 와 동일 service-layer 패턴.)
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import os
-import sys
-from unittest.mock import AsyncMock, MagicMock
-
-import pytest
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "app"))
-
-
-# ── helpers ────────────────────────────────────────────────────────────────
-
-
-def _msg_with_tool_call(q: str, tc_id: str = "tc-1") -> dict:
-    return {
-        "role": "assistant",
-        "content": None,
-        "tool_calls": [
-            {
-                "id": tc_id,
-                "type": "function",
-                "function": {
-                    "name": "search",
-                    "arguments": json.dumps({"q": q}, ensure_ascii=False),
-                },
-            }
-        ],
-    }
-
-
-def _msg_with_content(text: str) -> dict:
-    return {"role": "assistant", "content": text, "tool_calls": None}
-
-
-def _fake_chunk(chunk_id: int, doc_id: int = 100):
-    m = MagicMock()
-    m.id = chunk_id
-    m.chunk_id = chunk_id
-    m.doc_id = doc_id
-    m.title = f"doc {doc_id}"
-    m.score = 0.9
-    m.snippet = f"snippet {chunk_id}"
-    m.text = None
-    return m
-
-
-def _fake_pr(chunks: list):
-    pr = MagicMock()
-    pr.results = chunks
-    return pr
-
-
-@pytest.fixture
-def patched_backend_and_search(monkeypatch):
-    """get_backend + run_search 둘 다 mock. backend 의 generate_with_tools 는
-    각 테스트가 side_effect 설정.
-
-    Returns: (backend_mock, run_search_mock, set_backend_unavailable_fn).
-    """
-    from services.llm.backends import BackendUnavailable, QwenMacBookBackend
-    from services.llm import backends as backends_mod
-    from services.search import react_loop
-
-    backend = MagicMock(spec=QwenMacBookBackend)
-    backend.name = "qwen-macbook"
-    backend.generate_with_tools = AsyncMock()
-
-    def _fake_get_backend(name):
-        # endpoint 가 qwen-macbook 만 호출하므로 단일 backend 반환
-        return backend
-
-    monkeypatch.setattr(backends_mod, "get_backend", _fake_get_backend)
-    # search.py 의 ask_react 안에서 `from services.llm.backends import ... get_backend`
-    # 로 import 하므로 module-level patch 만으로 충분 (지연 import 라 매번 fresh).
-
-    run_search_mock = AsyncMock(return_value=_fake_pr([_fake_chunk(1)]))
-    monkeypatch.setattr(react_loop, "run_search", run_search_mock)
-
-    def _make_unavailable():
-        backend.generate_with_tools.side_effect = BackendUnavailable(
-            "qwen-macbook", "ConnectError"
-        )
-
-    return backend, run_search_mock, _make_unavailable
-
-
-def _call_endpoint(payload):
-    """ask_react 를 직접 호출. user/session 은 MagicMock 으로 우회."""
-    from api.search import ask_react
-
-    user = MagicMock()
-    session = MagicMock()
-    return asyncio.run(ask_react(payload, user=user, session=session))
-
-
-# ── ★ 정정 4 invariant: backend unavailable → 503 + run_search 호출 0 ──────
-
-
-def test_qwen_unavailable_returns_503(patched_backend_and_search):
-    """backend BackendUnavailable → HTTP 503 + error_reason=macbook_unavailable."""
-    from api.search import AskReactRequest
-
-    backend, run_search_mock, make_unavailable = patched_backend_and_search
-    make_unavailable()
-
-    response = _call_endpoint(AskReactRequest(query="Q"))
-
-    # JSONResponse instance
-    assert response.status_code == 503
-    body = json.loads(response.body)
-    assert body["error_reason"] == "macbook_unavailable"
-    assert body["backend_used"] is None
-    assert body["backend_requested"] == "qwen-macbook"
-
-    # ★ run_search 호출 0 (search 진입 자체 차단)
-    assert run_search_mock.call_count == 0
-
-
-# ── 정상 200 + G0-3 default debug_trace=null ──────────────────────────────
-
-
-def test_successful_response_default_no_debug_trace(patched_backend_and_search):
-    """debug 미지정 (default false) → 200 + debug_trace == null."""
-    from api.search import AskReactRequest, AskReactResponse
-
-    backend, run_search_mock, _ = patched_backend_and_search
-    backend.generate_with_tools.side_effect = [
-        _msg_with_tool_call("q1"),
-        _msg_with_content("최종 답입니다"),
-    ]
-
-    response = _call_endpoint(AskReactRequest(query="Q"))
-
-    # Pydantic instance (FastAPI response_model 적용 전 raw return)
-    assert isinstance(response, AskReactResponse)
-    assert response.final_answer == "최종 답입니다"
-    assert response.iterations == 2
-    assert response.partial is False
-    assert response.debug_trace is None  # ★ G0-3
-    assert len(response.sources) == 1
-
-
-# ── G0-3: debug=true → debug_trace 채워짐 ──────────────────────────────────
-
-
-def test_debug_true_populates_trace(patched_backend_and_search):
-    from api.search import AskReactRequest
-
-    backend, run_search_mock, _ = patched_backend_and_search
-    backend.generate_with_tools.side_effect = [
-        _msg_with_content("바로 답"),
-    ]
-
-    response = _call_endpoint(AskReactRequest(query="Q", debug=True))
-
-    assert response.debug_trace is not None
-    assert isinstance(response.debug_trace, list)
-    assert len(response.debug_trace) >= 1
-
-
-# ── max rounds → final content 정상 → partial=false ──────────────────────
-
-
-def test_max_rounds_with_final_content(patched_backend_and_search):
-    from api.search import AskReactRequest
-
-    backend, run_search_mock, _ = patched_backend_and_search
-    backend.generate_with_tools.side_effect = [
-        _msg_with_tool_call("q1"),
-        _msg_with_tool_call("q2", tc_id="tc-2"),
-        _msg_with_content("정리된 최종 답"),
-    ]
-
-    response = _call_endpoint(AskReactRequest(query="Q"))
-
-    assert response.iterations == 2
-    assert response.partial is False
-    assert response.final_answer == "정리된 최종 답"
-    # LLM 호출 3회, search 2회 (G0-2 cap)
-    assert backend.generate_with_tools.call_count == 3
-    assert run_search_mock.call_count == 2
-
-
-# ── max rounds + final content 빈 string → partial=true ──────────────────
-
-
-def test_max_rounds_with_empty_final_partial(patched_backend_and_search):
-    from api.search import AskReactRequest
-
-    backend, run_search_mock, _ = patched_backend_and_search
-    backend.generate_with_tools.side_effect = [
-        _msg_with_tool_call("q1"),
-        _msg_with_tool_call("q2", tc_id="tc-2"),
-        _msg_with_content(""),
-    ]
-
-    response = _call_endpoint(AskReactRequest(query="Q"))
-
-    assert response.iterations == 2
-    assert response.partial is True
-    assert response.final_answer == ""

tests/test_ask_eval_auth.py

@@ -1,92 +0,0 @@
-"""Phase 3.5 fix2: /ask 의 X-Source / X-Eval-Case-Id trust boundary.
-
-`_resolve_eval_identity()` 단위 테스트.
-- token 없음/틀림 + X-Source=eval → source='document_server', eval_case_id=None
-- token 일치 + X-Source=eval + X-Eval-Case-Id=case_xxx → ('eval', 'case_xxx')
-- token 틀림 + X-Eval-Case-Id 만 (X-Source 미지정) → eval_case_id=None
-- 일반 호출 (X-Source=ui_search, no eval headers) → ('ui_search', None)
-- env 미설정 (eval_runner_token='') 시 모든 eval claim 거부
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
-
-import pytest
-
-
-@pytest.fixture
-def resolve_with_token(monkeypatch):
-    """settings.eval_runner_token 을 monkey-patch 해서 _resolve_eval_identity 테스트."""
-    def _make(token: str):
-        from core import config as cfg_mod
-        from api import search as search_mod
-        # 두 모듈 모두에서 settings 객체 참조하므로 직접 attr 변경
-        monkeypatch.setattr(search_mod.settings, "eval_runner_token", token)
-        return search_mod._resolve_eval_identity
-    return _make
-
-
-def test_no_token_no_eval_headers_default(resolve_with_token):
-    """일반 호출 — eval 헤더 없음, source 기본값."""
-    resolve = resolve_with_token("secret123")
-    assert resolve(None, None, None) == ("document_server", None)
-
-
-def test_normal_source_with_token(resolve_with_token):
-    """ui_search 호출 — eval 클레임 아님이라 token 무관."""
-    resolve = resolve_with_token("secret123")
-    assert resolve("ui_search", None, None) == ("ui_search", None)
-
-
-def test_eval_claim_no_token_rejected(resolve_with_token):
-    """X-Source=eval 인데 token 없음 → 거부, source='document_server'."""
-    resolve = resolve_with_token("secret123")
-    assert resolve("eval", "case_001", None) == ("document_server", None)
-
-
-def test_eval_claim_wrong_token_rejected(resolve_with_token):
-    """token 틀림 → 거부."""
-    resolve = resolve_with_token("secret123")
-    assert resolve("eval", "case_001", "wrong_token") == ("document_server", None)
-
-
-def test_eval_claim_correct_token_accepted(resolve_with_token):
-    """token 일치 → 'eval' source + case_id 적재."""
-    resolve = resolve_with_token("secret123")
-    assert resolve("eval", "case_001", "secret123") == ("eval", "case_001")
-
-
-def test_eval_case_id_only_no_source_no_token(resolve_with_token):
-    """X-Eval-Case-Id 만 있고 token 없음 → 거부, case_id=None."""
-    resolve = resolve_with_token("secret123")
-    assert resolve(None, "case_001", None) == ("document_server", None)
-
-
-def test_eval_case_id_only_wrong_token(resolve_with_token):
-    """X-Eval-Case-Id 만 + token 틀림 → 거부."""
-    resolve = resolve_with_token("secret123")
-    assert resolve(None, "case_001", "wrong") == ("document_server", None)
-
-
-def test_env_unset_rejects_even_correct_format(resolve_with_token):
-    """settings.eval_runner_token='' 인 환경 → 모든 eval 클레임 거부."""
-    resolve = resolve_with_token("")
-    # token 헤더가 와도 server side 가 비어있으면 거부 (constant-time False)
-    assert resolve("eval", "case_001", "") == ("document_server", None)
-    assert resolve("eval", "case_001", "anything") == ("document_server", None)
-
-
-def test_non_eval_source_forces_case_id_none(resolve_with_token):
-    """X-Source=ui_detail + X-Eval-Case-Id (실수로 같이 보냄) → case_id=None.
-
-    eval claim 아님 (source != 'eval' 이고 case_id 가 fallback 으로 eval claim 트리거)
-    이지만 source claim 이 명시적으로 non-eval 이라 token 검증 후 case_id None.
-    """
-    resolve = resolve_with_token("secret123")
-    # case_id 가 있으면 eval claim 으로 처리됨 → token 없으면 거부 → ('ui_detail' 클레임,
-    # 하지만 거부 분기에서 claimed_source != 'eval' 이라 그대로 'ui_detail' 반환, case_id=None)
-    assert resolve("ui_detail", "case_001", None) == ("ui_detail", None)

tests/test_grounding_fabricated_number.py

@@ -1,188 +0,0 @@
-"""Phase 3.5 B1 (fix1+fix3): unit-aware fabricated_number + bound semantics.
-
-기준:
-- 단위 일치 시에만 exact/range/tolerance clear (fix1: Codex unit-mismatch regression 방지)
-- 약/대략/거의/얼추 만 approx prefix strip; 최대/최소 는 bound operator 로 보존 (fix3)
-- tolerance 는 양적 단위(_TOLERANCE_UNITS) + 4자리+ 만; 식별자성(_EXACT_ONLY_UNITS) 은 strict
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-
-# tests/ → 프로젝트 루트 → app/
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
-
-import pytest
-
-from services.search.evidence_service import EvidenceItem
-from services.search.grounding_check import check
-
-
-def _ev(text: str, n: int = 1) -> EvidenceItem:
-    return EvidenceItem(
-        n=n,
-        chunk_id=None,
-        doc_id=100 + n,
-        title=f"doc{n}",
-        section_title=None,
-        span_text=text,
-        relevance=0.9,
-        rerank_score=0.85,
-        full_snippet=text,
-        source="llm",
-    )
-
-
-def _has_fabricated(result, sub: str | None = None) -> bool:
-    for f in result.strong_flags:
-        if not f.startswith("fabricated_number:"):
-            continue
-        if sub is None or sub in f:
-            return True
-    return False
-
-
-# ─── 콤마/prefix/range/단위 동의어/citation (기존 17 케이스) ──────
-
-
-def test_comma_thousand_match():
-    r = check("질문", "총 1,000명 [1]", [_ev("총원은 1000명입니다.")])
-    assert not _has_fabricated(r, "1000")
-
-
-def test_comma_thousand_reverse():
-    r = check("질문", "총 1000명 [1]", [_ev("총원은 1,000명입니다.")])
-    assert not _has_fabricated(r)
-
-
-def test_approx_prefix_in_answer():
-    r = check("질문", "약 100명이 참여 [1]", [_ev("100명이 참여")])
-    assert not _has_fabricated(r)
-
-
-def test_approx_prefix_in_evidence():
-    r = check("질문", "100명이 참여 [1]", [_ev("약 100명이 참여")])
-    assert not _has_fabricated(r)
-
-
-def test_range_inner_value_passes():
-    r = check("질문", "약 150명 [1]", [_ev("100~200명 사이 추정")])
-    assert not _has_fabricated(r, "150")
-
-
-def test_range_outer_value_flagged():
-    r = check("질문", "300명 [1]", [_ev("100~200명 사이 추정")])
-    assert _has_fabricated(r, "300")
-
-
-def test_unit_synonym_in_to_myeong():
-    r = check("질문", "총 50인이 모임 [1]", [_ev("총 50명이 모임.")])
-    assert not _has_fabricated(r)
-
-
-def test_unit_synonym_percent_to_pct():
-    r = check("질문", "비율 30퍼센트 [1]", [_ev("비율 30%이다.")])
-    assert not _has_fabricated(r)
-
-
-def test_citation_marker_both_sides():
-    """bug fix: evidence 측 [n] 미제거로 디지트 합쳐지던 케이스."""
-    r = check("질문", "가격 [1] 5,000원", [_ev("[2] 5,000원이 정확")])
-    assert not _has_fabricated(r)
-
-
-def test_genuine_fabricated_number():
-    r = check("질문", "결과 777명 [1]", [_ev("500명, 300명을 받음.")])
-    assert _has_fabricated(r, "777")
-
-
-def test_amount_4digit_tolerance_passes():
-    r = check("질문", "9,990원 [1]", [_ev("10,000원입니다.")])
-    assert not _has_fabricated(r)
-
-
-def test_year_no_tolerance_flagged():
-    r = check("질문", "2024년 [1]", [_ev("2026년에 발효")])
-    assert _has_fabricated(r, "2024")
-
-
-def test_article_no_tolerance_flagged():
-    r = check("질문", "제5조에 명시 [1]", [_ev("제6조에 따라")])
-    assert _has_fabricated(r)
-
-
-def test_count_no_tolerance_flagged():
-    r = check("질문", "총 3회 위반 [1]", [_ev("총 4회 적발")])
-    assert _has_fabricated(r)
-
-
-def test_three_digit_strict():
-    r = check("질문", "총 15개 [1]", [_ev("총 10개")])
-    assert _has_fabricated(r, "15")
-
-
-def test_single_digit_ignored():
-    """1자리 + 양적 단위 → 무시 (오탐 방지)."""
-    r = check("질문", "총 3개 발생 [1]", [_ev("관련 통계 별도")])
-    assert not _has_fabricated(r, "3개")
-
-
-def test_range_korean_butter_separator():
-    r = check("질문", "약 150명 [1]", [_ev("100부터 200명까지 대상.")])
-    assert not _has_fabricated(r, "150")
-
-
-# ─── fix1: unit-mismatch (Codex no-ship) ──────────────────
-
-
-def test_won_vs_myeong_range_flagged():
-    """answer '150원' vs evidence '100~200명' → 단위 불일치, flag 유지."""
-    r = check("질문", "약 150원이 든다 [1]", [_ev("대상은 100~200명")])
-    assert _has_fabricated(r, "150")
-
-
-def test_won_vs_myeong_tolerance_flagged():
-    """answer '9,990원' vs evidence '10,000명' → tolerance pool 단위 다름, flag 유지."""
-    r = check("질문", "9,990원 [1]", [_ev("10,000명입니다.")])
-    assert _has_fabricated(r, "9990")
-
-
-def test_pct_vs_myeong_range_flagged():
-    """answer '15%' vs evidence '10~20명' → 단위 불일치, flag 유지."""
-    r = check("질문", "약 15% [1]", [_ev("대상 10~20명")])
-    assert _has_fabricated(r, "15")
-
-
-# ─── fix3: 최대/최소 bound semantics ───────────────────────
-
-
-def test_choedae_exact_boundary_flagged():
-    """evidence '최대 100명' + answer '100명' → 경계값 자체는 cleared 아님."""
-    r = check("질문", "100명이다 [1]", [_ev("최대 100명까지 가능")])
-    assert _has_fabricated(r, "100")
-
-
-def test_choeso_exact_boundary_flagged():
-    """evidence '최소 100명' + answer '100명' → 경계값 자체는 cleared 아님."""
-    r = check("질문", "100명이다 [1]", [_ev("최소 100명 이상 필요")])
-    assert _has_fabricated(r, "100")
-
-
-def test_choedae_inner_value_passes():
-    """evidence '최대 100명' + answer '50명' → bound 안, cleared."""
-    r = check("질문", "50명이다 [1]", [_ev("최대 100명까지 가능")])
-    assert not _has_fabricated(r, "50")
-
-
-def test_choeso_above_value_passes():
-    """evidence '최소 100명' + answer '150명' → bound 안, cleared."""
-    r = check("질문", "150명이다 [1]", [_ev("최소 100명 이상 필요")])
-    assert not _has_fabricated(r, "150")
-
-
-def test_choedae_outer_value_flagged():
-    """evidence '최대 100명' + answer '200명' → bound 밖, flag."""
-    r = check("질문", "200명이다 [1]", [_ev("최대 100명까지 가능")])
-    assert _has_fabricated(r, "200")

tests/test_synthesis_failure_regate.py

@@ -1,123 +0,0 @@
-"""Phase 3.5 fix3: re-gate Tier 0 — synthesis 자체 실패 처리.
-
-`_detect_synthesis_failure()` 단위 테스트.
-
-기존 버그:
-  synthesis LLM self-refuse (`sr.refused=True, status="completed"`) 또는
-  timeout/parse_failed/llm_error 시 grounding/verifier flag 0건 → re-gate else clean
-  분기로 빠져 `completeness="full"` 초기값이 남아 `full + refused=True` 모순.
-  baseline v1-400char 에서 24/223 (10.8%) 해당.
-
-Tier 0 판정:
-  - LLM self-refuse (completed + refused) → "synthesis_self_refuse"
-  - mechanical fail (timeout/parse_failed/llm_error) → "synthesis_failed({status})"
-  - answer 공백 → "synthesis_failed({status})"
-  - 유효 답변 → None (기존 tier 1~7 경로)
-"""
-
-from __future__ import annotations
-
-import os
-import sys
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
-
-from api.search import _detect_synthesis_failure
-from services.search.synthesis_service import SynthesisResult
-
-
-def _sr(
-    status: str = "completed",
-    answer: str | None = "ok",
-    refused: bool = False,
-    refuse_reason: str | None = None,
-) -> SynthesisResult:
-    return SynthesisResult(
-        status=status,  # type: ignore[arg-type]
-        answer=answer,
-        used_citations=[],
-        confidence="low",
-        refused=refused,
-        refuse_reason=refuse_reason,
-        elapsed_ms=100.0,
-        cache_hit=False,
-    )
-
-
-# ─── self-refuse 케이스 ──────────────────────────────────
-
-
-def test_llm_self_refuse_completed():
-    """LLM 이 JSON 에 refused=true 반환 → synthesis_self_refuse."""
-    sr = _sr(status="completed", answer=None, refused=True, refuse_reason="범위 밖")
-    assert _detect_synthesis_failure(sr) == "synthesis_self_refuse"
-
-
-def test_llm_self_refuse_with_answer_still_refused():
-    """refused=True 면 answer 있어도 Tier 0 처리 (일관성)."""
-    sr = _sr(status="completed", answer="왜 답변함", refused=True)
-    assert _detect_synthesis_failure(sr) == "synthesis_self_refuse"
-
-
-# ─── mechanical failure 케이스 ──────────────────────────
-
-
-def test_timeout():
-    sr = _sr(status="timeout", answer=None, refused=False)
-    assert _detect_synthesis_failure(sr) == "synthesis_failed(timeout)"
-
-
-def test_parse_failed():
-    sr = _sr(status="parse_failed", answer=None, refused=False)
-    assert _detect_synthesis_failure(sr) == "synthesis_failed(parse_failed)"
-
-
-def test_llm_error():
-    sr = _sr(status="llm_error", answer=None, refused=False)
-    assert _detect_synthesis_failure(sr) == "synthesis_failed(llm_error)"
-
-
-def test_refused_with_mechanical_fail_propagates_status():
-    """refused=True + status!=completed → synthesis_failed({status}) 형식."""
-    sr = _sr(status="timeout", answer=None, refused=True)
-    assert _detect_synthesis_failure(sr) == "synthesis_failed(timeout)"
-
-
-# ─── empty answer 케이스 ───────────────────────────────
-
-
-def test_empty_answer_completed():
-    """status=completed 인데 answer 공백 → synthesis_failed(completed)."""
-    sr = _sr(status="completed", answer="", refused=False)
-    assert _detect_synthesis_failure(sr) == "synthesis_failed(completed)"
-
-
-def test_whitespace_only_answer():
-    """공백/탭/개행만 있어도 empty 로 간주."""
-    sr = _sr(status="completed", answer="   \n\t  ", refused=False)
-    assert _detect_synthesis_failure(sr) == "synthesis_failed(completed)"
-
-
-def test_none_answer_completed():
-    """answer=None + status=completed → failed."""
-    sr = _sr(status="completed", answer=None, refused=False)
-    assert _detect_synthesis_failure(sr) == "synthesis_failed(completed)"
-
-
-# ─── 유효 답변 케이스 (None 반환) ──────────────────────
-
-
-def test_valid_answer_returns_none():
-    """status=completed + answer 있고 refused=False → Tier 0 통과 (None)."""
-    sr = _sr(status="completed", answer="교육 시간은 매년 6시간 이상이다 [1].", refused=False)
-    assert _detect_synthesis_failure(sr) is None
-
-
-def test_skipped_status_with_answer_passes():
-    """status=skipped 는 Tier 0 대상 아님 — 초기 refusal gate 에서 이미 early-return 처리됨.
-
-    (skipped 는 여기까지 도달하지 않는다는 전제. 만약 도달하더라도 refused 가 True 일 것.)
-    """
-    sr = _sr(status="skipped", answer="abc", refused=False)
-    # 이 경우 Tier 0 미발동 (answer 있고 refused 아님) — 정상 경로로 나감.
-    assert _detect_synthesis_failure(sr) is None

tests/test_verifier_numeric_promote.py

@@ -1,58 +0,0 @@
-"""Phase 3.5 B2: verifier _SEVERITY_MAP env flag 테스트.
-
-VERIFIER_NUMERIC_PROMOTE 환경변수에 따른 _SEVERITY_MAP 변화 검증.
-모듈은 import time 에 env 평가하므로 reload 필요.
-"""
-
-from __future__ import annotations
-
-import importlib
-import os
-import sys
-
-# tests/ → 프로젝트 루트 → app/
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "app"))
-
-import pytest
-
-
-def _reload_verifier(monkeypatch, value: str | None):
-    """env 설정 후 verifier_service 를 reload 하여 _SEVERITY_MAP 재평가."""
-    if value is None:
-        monkeypatch.delenv("VERIFIER_NUMERIC_PROMOTE", raising=False)
-    else:
-        monkeypatch.setenv("VERIFIER_NUMERIC_PROMOTE", value)
-    from services.search import verifier_service
-    importlib.reload(verifier_service)
-    return verifier_service
-
-
-def test_severity_map_off_default(monkeypatch):
-    """env 미설정 → numeric_conflict critical 은 medium (기존 동작)."""
-    vs = _reload_verifier(monkeypatch, None)
-    assert vs._SEVERITY_MAP["numeric_conflict"]["critical"] == "medium"
-    assert vs._SEVERITY_MAP["numeric_conflict"]["minor"] == "medium"
-    assert vs._NUMERIC_PROMOTE is False
-
-
-def test_severity_map_on_critical_promoted(monkeypatch):
-    """VERIFIER_NUMERIC_PROMOTE=1 → critical 만 strong, minor 는 medium 유지."""
-    vs = _reload_verifier(monkeypatch, "1")
-    assert vs._SEVERITY_MAP["numeric_conflict"]["critical"] == "strong"
-    assert vs._SEVERITY_MAP["numeric_conflict"]["minor"] == "medium"
-    assert vs._NUMERIC_PROMOTE is True
-
-
-def test_severity_map_off_explicit_zero(monkeypatch):
-    """VERIFIER_NUMERIC_PROMOTE=0 명시 → off (default 와 동일)."""
-    vs = _reload_verifier(monkeypatch, "0")
-    assert vs._SEVERITY_MAP["numeric_conflict"]["critical"] == "medium"
-    assert vs._NUMERIC_PROMOTE is False
-
-
-def test_direct_negation_invariant(monkeypatch):
-    """direct_negation 은 env 무관 항상 strong (불변 — 안전장치)."""
-    for value in [None, "0", "1"]:
-        vs = _reload_verifier(monkeypatch, value)
-        assert vs._SEVERITY_MAP["direct_negation"]["critical"] == "strong"
-        assert vs._SEVERITY_MAP["direct_negation"]["minor"] == "strong"