diff --git a/app/api/search.py b/app/api/search.py
index 023ffc9..5d6f559 100644
--- a/app/api/search.py
+++ b/app/api/search.py
@@ -592,631 +592,3 @@ def _resolve_eval_identity(
     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.
-    """
-    # 오케스트레이션은 _run_ask 로 분리(라우터=입력+deps 해소만). 동작 무변경 (refactor: ask-orchestrator).
-    return await _run_ask(
-        q=q,
-        user=user,
-        session=session,
-        background_tasks=background_tasks,
-        limit=limit,
-        debug=debug,
-        backend=backend,
-        corpus_variant=corpus_variant,
-        exact_knn=exact_knn,
-        x_source=x_source,
-        x_eval_case_id=x_eval_case_id,
-        x_eval_token=x_eval_token,
-    )
-
-
-async def _run_ask(
-    q: str,
-    user: User,
-    session: AsyncSession,
-    background_tasks: BackgroundTasks,
-    limit: int,
-    debug: bool,
-    backend: str | None,
-    corpus_variant: str | None,
-    exact_knn: bool,
-    x_source: str | None,
-    x_eval_case_id: str | None,
-    x_eval_token: str | None,
-) -> "AskResponse | JSONResponse":
-    """/ask 오케스트레이션 — 검색→evidence/classifier→refusal→synthesis→grounding/verifier→
-    7-tier 재게이트→telemetry. ask() 가 FastAPI deps 해소 후 호출. 동작은 기존 핸들러와 동일."""
-    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,
-    )