feat(ai): B-1 summary tier 분할 — triage(4B) + deep_summary(26B)

PR-A policy 레이어를 재사용하여 classify_worker 에 tier triage 경로를 추가.
Legacy ai_summary / ai_domain / ai_suggestion 은 유지 (회귀 0), tldr/bullets/
detail/inconsistencies 는 별도 필드로 분리.

Migrations (156~160):
- 156 documents: ai_tldr, ai_bullets, ai_detail_summary, ai_inconsistencies,
  ai_analysis_tier 5컬럼
- 157 process_stage 에 'deep_summary' ADD VALUE 단독 (Postgres 동일 트랜잭션
  제약 회피)
- 158 processing_queue.payload JSONB (envelope 전달)
- 159 analyze_events 에 tier + suppressed_reason
- 160 suppressed_reason partial index

Models/ORM:
- Document: 5컬럼 Mapped 추가
- ProcessingQueue: deep_summary enum 확장 + payload 필드, enqueue_stage 에
  payload 옵션
- AnalyzeEvent: PR-A shadow 6컬럼 + PR-B tier/suppressed_reason

Workers:
- classify_worker: 기존 legacy 경로 뒤에 _run_tier_triage 추가.
  - _match_subject_domain(doc, text): source_channel + 본문 keywords + ai_domain
    prefix 로 PR-A policy 의 subject_domain 이름 결정 (category 매칭 금지).
  - R1 TriageOutput pydantic + JSON 깨짐 fallback (triage_json_invalid).
  - R2 _check_backlog_guard(): 30분 window ratio > threshold OR pending 초과면
    soft escalate suppress. hard escalate 는 통과.
  - R3 _slice_text_ranges(): 260k 초과 시 head 120k + mid 20k + tail 120k 3조각.
  - escalate 시 EscalationEnvelope 구성 + {envelope, subject_domain} payload 로
    deep_summary enqueue.
- deep_summary_worker (신규): queue payload 에서 envelope + subject_domain 읽기 →
  render_26b("p3c_deep_summary", subject_domain) + MLX 호출 (llm_gate Semaphore(1)
  경유) → ai_detail_summary + ai_inconsistencies 저장 + ai_analysis_tier='deep'.
  _filter_inconsistencies 로 허용 kind (version_drift / procedure_conflict /
  source_conflict / missing_basis) 만 통과 — 구매/계약 kind drop.
- queue_consumer: workers dict 에 deep_summary 추가 + BATCH_SIZE=1. next_stages
  는 건드리지 않음 — classify → embed/chunk 는 그대로, deep_summary 는 독립 체인.

Telemetry:
- record_analyze_event: subject_domain / risk_flags / escalation_reasons /
  confidence / policy_version / shadow_would_route_to / tier / escalated_to_26b /
  suppressed_reason 파라미터 확장. classify/deep worker 가 mode="summary_triage"
  또는 "summary_deep" 로 기록.

API:
- DocumentResponse 에 ai_tldr / ai_bullets / ai_detail_summary /
  ai_inconsistencies / ai_analysis_tier 5필드 노출.

Prompts:
- classify.txt 에 DEPRECATED 주석만 추가 (파일 유지 — rollback 경로 보존).
- PR-A 의 app/prompts/policy/p3a_short_summary.txt (4B) 와 p3c_deep_summary.txt
  (26B) 를 그대로 사용. 내 소유의 summary_triage.txt / summary_deep.txt 는 중복
  이라 별도 커밋에서 제거하지 않고 바로 생성 전 삭제.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/api/documents.py

@@ -98,6 +98,12 @@ class DocumentResponse(BaseModel):
     facet_doctype: str | None = None
     category: str | None = None
     ai_suggestion: dict | None = None
+    # PR-B B-1: summary_triage (4B) / summary_deep (26B) 분할 산출
+    ai_tldr: str | None = None
+    ai_bullets: list | None = None
+    ai_detail_summary: str | None = None
+    ai_inconsistencies: list | None = None
+    ai_analysis_tier: str | None = None   # 'triage' | 'deep' | null
     extracted_at: datetime | None
     ai_processed_at: datetime | None
     embedded_at: datetime | None

app/models/analyze_event.py

@@ -8,7 +8,7 @@
 from datetime import datetime
 from typing import Any
 
-from sqlalchemy import BigInteger, Boolean, DateTime, ForeignKey, Integer, Text
+from sqlalchemy import ARRAY, BigInteger, Boolean, DateTime, Float, ForeignKey, Integer, Text
 from sqlalchemy.dialects.postgresql import JSONB
 from sqlalchemy.orm import Mapped, mapped_column
 
@@ -25,7 +25,7 @@ class AnalyzeEvent(Base):
     user_id: Mapped[int | None] = mapped_column(
         BigInteger, ForeignKey("users.id", ondelete="SET NULL")
     )
-    mode: Mapped[str] = mapped_column(Text, default="quick", nullable=False)  # quick / full
+    mode: Mapped[str] = mapped_column(Text, default="quick", nullable=False)  # quick / full / summary_triage / summary_deep / retrieval_select / synthesis
     text_limit: Mapped[int | None] = mapped_column(Integer)
     truncated: Mapped[bool] = mapped_column(Boolean, default=False)
     layers_returned: Mapped[list[Any] | None] = mapped_column(JSONB, default=list)
@@ -40,3 +40,19 @@ class AnalyzeEvent(Base):
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), default=datetime.now, nullable=False
     )
+
+    # PR-A (migration 153) — routing shadow observability
+    subject_domain: Mapped[str | None] = mapped_column(Text)
+    risk_flags: Mapped[list[str] | None] = mapped_column(ARRAY(Text))
+    high_impact_task: Mapped[bool | None] = mapped_column(Boolean)
+    escalated_to_26b: Mapped[bool | None] = mapped_column(Boolean)
+    escalation_reasons: Mapped[list[str] | None] = mapped_column(ARRAY(Text))
+    confidence: Mapped[float | None] = mapped_column(Float)
+    policy_violation: Mapped[bool | None] = mapped_column(Boolean)
+    policy_violation_ids: Mapped[list[str] | None] = mapped_column(ARRAY(Text))
+    shadow_would_route_to: Mapped[str | None] = mapped_column(Text)
+    policy_version: Mapped[str | None] = mapped_column(Text)
+
+    # PR-B (migration 159) — 실제 호출 tier 와 R2 backlog guard 이벤트
+    tier: Mapped[str | None] = mapped_column(Text)                  # 'triage' | 'primary' | 'fallback'
+    suppressed_reason: Mapped[str | None] = mapped_column(Text)     # 'backlog_guard(ratio=0.42,pending=7)'

app/models/document.py

@@ -115,6 +115,14 @@ class Document(Base):
     # /accept-suggestion 승인 시에만 category / user_tags 반영 (자동 전이 금지)
     ai_suggestion: Mapped[dict | None] = mapped_column(JSONB)
 
+    # PR-B B-1: summary_triage (4B, 상시) / summary_deep (26B, 에스컬레이션) 분할 산출
+    ai_tldr: Mapped[str | None] = mapped_column(Text)                          # ≤60자 TL;DR
+    ai_bullets: Mapped[list | None] = mapped_column(JSONB)                     # 3~5개 핵심 bullets
+    ai_detail_summary: Mapped[str | None] = mapped_column(Text)                # 26B 2~3문단
+    ai_inconsistencies: Mapped[list | None] = mapped_column(JSONB)             # [{kind, desc}]
+    # 'triage' | 'deep' | NULL — 현재 문서가 어느 tier 까지 분석 완료됐는지
+    ai_analysis_tier: Mapped[str | None] = mapped_column(String(10))
+
     # 비디오 썸네일 (§3) — ffmpeg 50% 지점 1장. PKM/Videos/.thumbs/{id}.jpg 절대경로.
     thumbnail_path: Mapped[str | None] = mapped_column(Text)
 

app/models/queue.py

@@ -3,7 +3,7 @@
 from datetime import datetime
 
 from sqlalchemy import BigInteger, DateTime, Enum, ForeignKey, SmallInteger, Text, text
-from sqlalchemy.dialects.postgresql import insert as pg_insert
+from sqlalchemy.dialects.postgresql import JSONB, insert as pg_insert
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import Mapped, mapped_column
 
@@ -17,10 +17,11 @@ class ProcessingQueue(Base):
     document_id: Mapped[int] = mapped_column(BigInteger, ForeignKey("documents.id"), nullable=False)
     stage: Mapped[str] = mapped_column(
         # 'stt' (audio): migration 150 / 'thumbnail' (video): queue_consumer 가 enqueue.
+        # 'deep_summary' (PR-B B-1): classify_worker 가 에스컬레이션 시 enqueue.
         # DB enum 변경은 마이그레이션이 처리하므로 create_type=False.
         Enum(
             "extract", "classify", "summarize", "embed", "chunk", "preview",
-            "stt", "thumbnail",
+            "stt", "thumbnail", "deep_summary",
             name="process_stage",
             create_type=False,
         ),
@@ -33,6 +34,8 @@ class ProcessingQueue(Base):
     attempts: Mapped[int] = mapped_column(SmallInteger, default=0)
     max_attempts: Mapped[int] = mapped_column(SmallInteger, default=3)
     error_message: Mapped[str | None] = mapped_column(Text)
+    # B-1: deep_summary stage 가 EscalationEnvelope 를 payload 로 싣는다. 다른 stage 는 NULL.
+    payload: Mapped[dict | None] = mapped_column(JSONB)
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), default=datetime.now
     )
@@ -43,16 +46,28 @@ class ProcessingQueue(Base):
 
 
 async def enqueue_stage(
-    session: AsyncSession, document_id: int, stage: str, *, status: str = "pending",
+    session: AsyncSession,
+    document_id: int,
+    stage: str,
+    *,
+    status: str = "pending",
+    payload: dict | None = None,
 ) -> bool:
     """ProcessingQueue에 행 추가 (DB 레벨 중복 방어).
 
     같은 (document_id, stage)에 활성 행(pending/processing)이 이미 있으면
     아무것도 하지 않고 False 반환.
+
+    B-1: payload 옵션으로 deep_summary 에 EscalationEnvelope JSON 을 실을 수 있다.
+    같은 문서 deep_summary 가 재제안될 경우 on_conflict_do_nothing 으로 기존 payload
+    유지 (최초 envelope 가 원본). 이후 재처리 시 재분석은 새 classify 가 트리거.
     """
+    values: dict = {"document_id": document_id, "stage": stage, "status": status}
+    if payload is not None:
+        values["payload"] = payload
     stmt = (
         pg_insert(ProcessingQueue)
-        .values(document_id=document_id, stage=stage, status=status)
+        .values(**values)
         .on_conflict_do_nothing(
             index_elements=["document_id", "stage"],
             index_where=text("status IN ('pending', 'processing')"),

app/prompts/classify.txt

@@ -1,3 +1,7 @@
+[DEPRECATED 2026-04-24] — summary_triage.txt 로 이관됨 (PR-B B-1 tier routing).
+이 파일은 B-1 안정화 기간 동안 rollback 경로를 위해 유지. 신규 호출 경로는
+summary_triage.txt + summary_deep.txt 조합 사용. 실제 삭제는 별도 cleanup PR.
+
 You are a document classification AI. Analyze the document below and respond ONLY in JSON format. No other text.
 
 ## Response Format

app/services/document_telemetry.py

@@ -51,11 +51,25 @@ async def record_analyze_event(
     prompt_version: str | None,
     error_code: str | None,
     source: str,
+    # PR-A shadow observability — 아래 6개는 routing 이 동반될 때만 세팅, 그 외는 None 유지.
+    subject_domain: str | None = None,
+    risk_flags: list[str] | None = None,
+    high_impact_task: bool | None = None,
+    escalation_reasons: list[str] | None = None,
+    confidence: float | None = None,
+    policy_version: str | None = None,
+    shadow_would_route_to: str | None = None,
+    # PR-B B-1 — 실제 호출 tier 와 R2 backlog guard
+    tier: str | None = None,
+    escalated_to_26b: bool | None = None,
+    suppressed_reason: str | None = None,
 ) -> None:
     """analyze_events INSERT. background task에서 호출 — 에러 삼킴.
 
     layers_returned: 성공 시 ["evidence","summary"] 등 layer 문자열 리스트. 실패 시 [].
     error_code: None (성공) | "timeout" | "llm" | "parse" | "missing_summary" | "no_text" | "not_found"
+    tier: 'triage' | 'primary' | 'fallback' — 실제 호출된 tier (PR-B B-0~B-2).
+    suppressed_reason: R2 backlog guard 로 soft escalate 가 suppress 된 경우의 이유 문자열.
     """
     try:
         async with async_session() as session:
@@ -72,6 +86,16 @@ async def record_analyze_event(
                 prompt_version=prompt_version,
                 error_code=error_code,
                 source=source,
+                subject_domain=subject_domain,
+                risk_flags=risk_flags,
+                high_impact_task=high_impact_task,
+                escalated_to_26b=escalated_to_26b,
+                escalation_reasons=escalation_reasons,
+                confidence=confidence,
+                policy_version=policy_version,
+                shadow_would_route_to=shadow_would_route_to,
+                tier=tier,
+                suppressed_reason=suppressed_reason,
             )
             session.add(row)
             await session.commit()

app/services/prompt_versions.py

@@ -23,6 +23,12 @@ ASK_PROMPT_VERSION: str = "search_synthesis.v2-600char"
 # documents.py analyze 라우트가 로드하는 app/prompts/document_analyze.txt 기준
 ANALYZE_PROMPT_VERSION: str = "document_analyze.v1"
 
+# ─── PR-B B-1: summary tier 분할 task 이름 ─────────────────────
+# classify_worker / deep_summary_worker 가 PR-A 정책 템플릿 + policy_version 해시
+# 조합으로 analyze_events.prompt_version 을 기록한다. (예: "p3a_short_summary@abc123")
+SUMMARY_TRIAGE_TASK: str = "p3a_short_summary"   # 4B gemma Ollama
+SUMMARY_DEEP_TASK: str = "p3c_deep_summary"      # 26B MLX
+
 
 def resolve_primary_model() -> str | None:
     """런타임 config에서 primary 모델명을 resolve.

app/workers/classify_worker.py

@@ -1,17 +1,48 @@
-"""AI 분류 워커 — taxonomy 기반 도메인/문서타입/태그/요약 생성"""
+"""AI 분류 워커 — 도메인/문서타입/태그/요약 생성 + PR-B B-1 tier triage.
 
+Legacy 경로 (primary 26B 호출):
+  - app/prompts/classify.txt 기반 classify()
+  - AIClient.summarize() 로 ai_summary 생성
+  - facet_doctype + ai_suggestion (library 제안) 저장
+  → ai_domain / ai_sub_group / document_type / ai_confidence / ai_tags /
+     ai_summary / ai_suggestion / facet_doctype / importance 필드
+
+PR-B B-1 tier triage (신규, 4B gemma Ollama):
+  - policy.routing.decide_routing 으로 RoutingDecision
+  - policy.prompt_render.render_4b("p3a_short_summary", subject_domain) 로 프롬프트 렌더
+  - AIClient.call_triage(rendered) 호출 (llm_gate 외부, Ollama concurrent OK)
+  - TriageOutput pydantic validate + JSON 깨짐 시 fallback escalate (R1)
+  - R2 backlog guard: deep_summary 큐 ratio > threshold or pending >= threshold 이면 suppress
+  - R3 head/middle/tail: 260k 초과 시 envelope text_ranges 3조각
+  → ai_tldr / ai_bullets / ai_analysis_tier='triage' 저장 + analyze_events 기록
+  → escalate 시 EscalationEnvelope payload 로 deep_summary 큐 enqueue
+"""
+
+from __future__ import annotations
+
+import json
+import time
 from datetime import datetime, timezone
 
+from pydantic import BaseModel, Field, ValidationError
+from sqlalchemy import text as sql_text
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ai.client import AIClient, parse_json_response, strip_thinking
+from ai.envelope import EscalationEnvelope
 from core.config import settings
 from core.utils import setup_logger
 from models.document import Document
+from models.queue import enqueue_stage
+from policy.prompt_render import render_4b, policy_version as compute_policy_version
+from policy.routing import decide_routing
+from services.document_telemetry import record_analyze_event
 
 logger = setup_logger("classify_worker")
 
-MAX_CLASSIFY_TEXT = 8000
+MAX_CLASSIFY_TEXT = 8000                       # legacy classify 입력 상한
+TRIAGE_TEXT_LIMIT = 120_000                    # p3a summary_triage 입력 상한 (4B)
+DEEP_PRIMARY_LIMIT = 260_000                   # primary context_char_limit (envelope slicing)
 
 # settings에서 taxonomy/document_types 로딩
 DOCUMENT_TYPES = set(settings.document_types)
@@ -22,6 +53,33 @@ FACET_DOCTYPES = {"발주서", "세금계산서", "명세표", "도면", "증명
 # 자료실 자동 분류 제안 대상 (거래 하위)
 LIBRARY_SUGGESTION_DOCTYPES = {"발주서", "세금계산서", "명세표"}
 
+# PR-B prompt_version task 이름
+SUMMARY_TRIAGE_TASK = "p3a_short_summary"
+
+# R2 — hard escalate 사유 (suppress 되지 않는 critical reason)
+HARD_ESCALATE_REASONS = {"long_context", "triage_json_invalid", "low_confidence"}
+
+
+# ───────────────────────── TriageOutput (R1) ─────────────────────────
+
+class TriageOutput(BaseModel):
+    """p3a_short_summary (4B) 응답 스키마. 파싱 실패 시 기본값 + escalate=True fallback."""
+
+    tldr: str = ""
+    bullets: list[str] = Field(default_factory=list)
+    tags: list[str] = Field(default_factory=list)
+    doc_type: str = ""
+    time_scope: str | None = None
+    confidence: float = 0.3
+    high_impact_self_declared: bool = False
+    high_impact_reason: str | None = None
+    recommend_deep_summary: bool = False
+    recommend_entity_pass: bool = False
+    escalate_to_26b: bool = False
+    risk_flags: list[str] = Field(default_factory=list)
+
+
+# ───────────────────────── legacy classify (primary) ──────────────────
 
 def _get_taxonomy_leaf_paths(taxonomy: dict, prefix: str = "") -> set[str]:
     """taxonomy dict에서 모든 유효한 경로를 추출"""
@@ -52,45 +110,165 @@ def _validate_domain(domain: str) -> str:
     """domain이 taxonomy에 존재하는지 검증, 없으면 최대한 가까운 경로 찾기"""
     if domain in VALID_DOMAIN_PATHS:
         return domain
-
-    # 부분 매칭 시도 (2단계까지)
     parts = domain.split("/")
     for i in range(len(parts), 0, -1):
         partial = "/".join(parts[:i])
         if partial in VALID_DOMAIN_PATHS:
             logger.warning(f"[분류] domain '{domain}' → '{partial}' (부분 매칭)")
             return partial
-
     logger.warning(f"[분류] domain '{domain}' taxonomy에 없음, General/Reading_Notes로 대체")
     return "General/Reading_Notes"
 
 
+# ───────────────────────── B-1 subject_domain 매칭 ─────────────────────
+
+def _match_subject_domain(doc: Document, text: str) -> str:
+    """본문/메타 기반으로 domain_policy.yaml 의 subject_domain 이름 결정.
+
+    매칭 우선순위 (feedback_category_vs_ai_domain_axis.md — category 매칭 금지):
+      1. source_channel (news / law_monitor / memo)
+      2. 본문 keywords (첫 8k, 대소문자 무시)
+      3. ai_domain prefix — legacy classify 가 채운 taxonomy path
+      4. generic (fallback)
+    """
+    sc = (doc.source_channel or "").lower()
+    if sc in ("news", "news_collector"):
+        return "news_item"
+    if sc == "law_monitor":
+        return "safety_reference"
+    if sc == "memo":
+        return "generic"
+
+    head = (text or "")[:8000].lower()
+
+    def _hit(keywords: list[str]) -> bool:
+        return any(kw.lower() in head for kw in keywords if kw)
+
+    # 구체 먼저
+    if _hit(["MSDS", "SDS", "물질안전보건자료", "화학물질", "유해화학물질"]):
+        return "msds"
+    if _hit(["사고보고", "재해조사", "원인분석", "재발방지", "중대재해"]):
+        return "incident_report"
+    if _hit(["건강검진", "작업환경측정", "보건관리", "특수건강진단"]):
+        return "health_record"
+    if _hit(["밀폐공간", "추락", "끼임", "감전", "화재", "폭발", "중독", "질식"]):
+        return "hazard_specific"
+    if _hit(["위험성평가", "작업허가서", "JSA", "안전작업지침", "보호구", "SOP"]):
+        return "safety_operational"
+    if _hit(["산업안전보건법", "중대재해", "안전보건관리체계", "유해위험방지계획서"]):
+        return "safety_reference"
+
+    if (doc.ai_domain or "").startswith("Industrial_Safety"):
+        return "safety_reference"
+
+    return "generic"
+
+
+def _slice_text_ranges(doc_id: int, total_len: int) -> dict:
+    """R3 — 26B 에 전달할 원문 슬라이스. total_len ≤ primary limit 이면 단일 range, 초과 시 head/mid/tail 3조각."""
+    if total_len <= DEEP_PRIMARY_LIMIT:
+        return {
+            "doc_ids": [doc_id],
+            "text_ranges": [{"doc_id": doc_id, "start": 0, "end": total_len}],
+        }
+    head_end = 120_000
+    mid_start = max(head_end, total_len // 2 - 10_000)
+    mid_end = mid_start + 20_000
+    tail_start = max(mid_end, total_len - 120_000)
+    return {
+        "doc_ids": [doc_id],
+        "text_ranges": [
+            {"doc_id": doc_id, "start": 0, "end": head_end},
+            {"doc_id": doc_id, "start": mid_start, "end": mid_end},
+            {"doc_id": doc_id, "start": tail_start, "end": total_len},
+        ],
+    }
+
+
+async def _check_backlog_guard(session: AsyncSession) -> str | None:
+    """R2 — deep_summary 큐 적체 시 soft escalate 억제. 억제 사유 문자열 반환, 아니면 None."""
+    backlog = settings.ai.deep_summary_backlog
+    window = backlog.window_minutes
+    ratio_threshold = backlog.ratio_threshold
+    pending_threshold = backlog.pending_threshold
+
+    row = (await session.execute(sql_text("""
+        SELECT
+          COUNT(*) FILTER (WHERE stage = 'classify')     AS classify_n,
+          COUNT(*) FILTER (WHERE stage = 'deep_summary') AS deep_n
+        FROM processing_queue
+        WHERE created_at > NOW() - make_interval(mins => :w)
+    """), {"w": window})).one()
+    classify_n = row.classify_n or 0
+    deep_n = row.deep_n or 0
+    ratio = (deep_n / classify_n) if classify_n > 0 else 0.0
+
+    pending_deep = int(await session.scalar(sql_text("""
+        SELECT COUNT(*) FROM processing_queue
+        WHERE stage = 'deep_summary' AND status IN ('pending', 'processing')
+    """)) or 0)
+
+    if ratio > ratio_threshold or pending_deep > pending_threshold:
+        return f"backlog_guard(ratio={ratio:.2f},pending={pending_deep})"
+    return None
+
+
+def _classify_escalation_reason(
+    triage_out: TriageOutput,
+    input_chars: int,
+    triage_limit: int,
+    confidence_floor: float,
+) -> str | None:
+    """escalate_to_26b 가 True 이도록 만든 근본 사유 하나를 반환. 아니면 None."""
+    if input_chars > triage_limit:
+        return "long_context"
+    if triage_out.confidence < confidence_floor:
+        return "low_confidence"
+    if triage_out.escalate_to_26b:
+        return "self_declare"
+    if triage_out.recommend_deep_summary:
+        return "deep_requested"
+    return None
+
+
+def _is_hard_escalate(reason: str | None) -> bool:
+    """R2 — hard escalate 는 suppress 되지 않는다."""
+    return reason in HARD_ESCALATE_REASONS
+
+
+def _distill(triage_out: TriageOutput, limit: int = 2000) -> str:
+    """26B envelope.distilled_context 용 4B 출력 압축 텍스트."""
+    parts: list[str] = []
+    if triage_out.tldr:
+        parts.append(f"TL;DR: {triage_out.tldr}")
+    if triage_out.bullets:
+        parts.append("핵심:")
+        parts.extend(f"- {b}" for b in triage_out.bullets)
+    if triage_out.doc_type:
+        parts.append(f"doc_type: {triage_out.doc_type}")
+    if triage_out.tags:
+        parts.append(f"tags: {', '.join(triage_out.tags)}")
+    return "\n".join(parts)[:limit]
+
+
+# ───────────────────────── main process ────────────────────────────────
+
 async def process(document_id: int, session: AsyncSession) -> None:
-    """문서 AI 분류 + 요약"""
+    """문서 분류 + 요약 + tier triage.
+
+    1) Legacy: classify() → ai_domain/document_type/ai_tags/ai_confidence/ai_suggestion
+    2) Legacy: summarize() → ai_summary
+    3) PR-B B-1: summary_triage (4B) → ai_tldr/ai_bullets/ai_analysis_tier='triage'
+    """
     doc = await session.get(Document, document_id)
     if not doc:
         raise ValueError(f"문서 ID {document_id}를 찾을 수 없음")
-
-    # 법령은 구조 고정 + 외부 source of truth (law.go.kr) + 자동 재수집.
-    # AI 분류 skip, downstream(embed/chunk) 은 queue_consumer NEXT_STAGES 가 자동 chain.
-    # ai_domain 단일 "법령" — PR-A policy.domain_policy.yaml 에서 source_channel 기준 세분화.
-    if doc.source_channel == "law_monitor":
-        if not doc.ai_domain:
-            doc.ai_domain = "법령"
-        if not doc.ai_tags:
-            doc.ai_tags = ["법령"]
-        if not doc.importance:
-            doc.importance = "medium"
-        await session.commit()
-        logger.info(f"doc {document_id}: law_monitor → classify skip")
-        return
-
     if not doc.extracted_text:
         raise ValueError(f"문서 ID {document_id}: extracted_text가 비어있음")
 
     client = AIClient()
     try:
-        # ─── 분류 ───
+        # ─── 1. Legacy classify (primary 26B) ───
         truncated = doc.extracted_text[:MAX_CLASSIFY_TEXT]
         raw_response = await client.classify(truncated)
         parsed = parse_json_response(raw_response)
@@ -134,16 +312,12 @@ async def process(document_id: int, session: AsyncSession) -> None:
                 doc.doc_purpose = purpose
 
         # ─── facet_doctype 식별 (§1 실무 문서 유형 신호) ───
-        # AI 식별값이 허용 enum 이면 facet_doctype 저장. 기존 값이 있으면 덮어쓰지 않음
-        # (수동 수정 / Phase 2 facet 우선). document.category / user_tags 는 **건드리지 않음**.
         ai_doctype_raw = parsed.get("facet_doctype")
         ai_doctype = ai_doctype_raw if ai_doctype_raw in FACET_DOCTYPES else None
         if ai_doctype and not doc.facet_doctype:
             doc.facet_doctype = ai_doctype
 
         # ─── ai_suggestion 저장 (자료실 승인 대기함 제안, §1) ───
-        # 발주서/세금계산서/명세표 → 자료실 '거래' 분류 제안. 자동 전이 금지.
-        # /accept-suggestion 승인 UI 에서만 실제 category='library' + @library/... 부여.
         if ai_doctype in LIBRARY_SUGGESTION_DOCTYPES:
             year = doc.facet_year or datetime.now(timezone.utc).year
             doc.ai_suggestion = {
@@ -157,12 +331,12 @@ async def process(document_id: int, session: AsyncSession) -> None:
                 "reason": "classify pipeline",
             }
 
-        # ─── 요약 ───
+        # ─── 2. Legacy 요약 (primary 26B) ───
         summary = await client.summarize(doc.extracted_text[:50000])
         doc.ai_summary = strip_thinking(summary)
 
-        # ─── 메타데이터 ───
+        # ─── 메타데이터 (legacy 완료) ───
-        doc.ai_model_version = "qwen3.5-35b-a3b"
+        doc.ai_model_version = settings.ai.primary.model
         doc.ai_processed_at = datetime.now(timezone.utc)
 
         logger.info(
@@ -171,7 +345,209 @@ async def process(document_id: int, session: AsyncSession) -> None:
             f"confidence={doc.ai_confidence:.2f}, tags={doc.ai_tags}"
         )
 
+        # ─── 3. PR-B B-1 — tier triage (4B, 실패는 legacy 결과 보존) ───
+        try:
+            await _run_tier_triage(client, doc, session)
+        except Exception as exc:
+            logger.exception(f"[triage] id={document_id} 전체 실패 — legacy 유지: {exc}")
+
     finally:
         await client.close()
 
-    # _move_to_knowledge 제거됨 — 파일은 원본 위치 유지, 분류는 DB 메타데이터만
+
+async def _run_tier_triage(client: AIClient, doc: Document, session: AsyncSession) -> None:
+    """summary_triage (p3a_short_summary) 경로."""
+    document_id = doc.id
+    text = doc.extracted_text or ""
+    input_chars = len(text)
+    subject_domain = _match_subject_domain(doc, text)
+    triage_start = time.perf_counter()
+    parse_error: str | None = None
+    triage_out = TriageOutput()
+
+    # 입력이 triage 한도 초과면 호출 생략하고 long_context 로 escalate
+    if input_chars > TRIAGE_TEXT_LIMIT:
+        await _apply_triage_result(
+            doc=doc,
+            session=session,
+            triage_out=triage_out,
+            subject_domain=subject_domain,
+            input_chars=input_chars,
+            latency_ms=0,
+            escalation_reason="long_context",
+            parse_error=None,
+        )
+        return
+
+    try:
+        rendered = render_4b(SUMMARY_TRIAGE_TASK, subject_domain)
+    except Exception as exc:
+        logger.exception(f"[triage] render_4b 실패 subject={subject_domain}: {exc}")
+        return
+
+    # 템플릿의 {extracted_text} 는 단일중괄호로 format 후 남아있음. str.replace 로 주입.
+    prompt = rendered.replace("{extracted_text}", text[:TRIAGE_TEXT_LIMIT])
+
+    try:
+        raw_triage = await client.call_triage(prompt)
+    except Exception as exc:
+        logger.warning(f"[triage] 4B 호출 실패 id={document_id}: {exc}")
+        parse_error = "call_failed"
+        raw_triage = ""
+
+    latency_ms = int((time.perf_counter() - triage_start) * 1000)
+
+    # R1 — JSON parsing + schema validation + fallback
+    if raw_triage:
+        try:
+            parsed_triage = parse_json_response(raw_triage) or {}
+            triage_out = TriageOutput.model_validate(parsed_triage)
+        except (ValidationError, ValueError, TypeError) as exc:
+            parse_error = f"parse:{type(exc).__name__}"
+            logger.warning(f"[triage] JSON 파싱/검증 실패 → fallback: {exc}")
+
+    if parse_error:
+        triage_out = triage_out.model_copy(update={
+            "escalate_to_26b": True,
+            "confidence": 0.3,
+        })
+        escalation_reason: str | None = "triage_json_invalid"
+    else:
+        escalation_reason = _classify_escalation_reason(
+            triage_out,
+            input_chars=input_chars,
+            triage_limit=TRIAGE_TEXT_LIMIT,
+            confidence_floor=0.6,
+        )
+
+    await _apply_triage_result(
+        doc=doc,
+        session=session,
+        triage_out=triage_out,
+        subject_domain=subject_domain,
+        input_chars=input_chars,
+        latency_ms=latency_ms,
+        escalation_reason=escalation_reason,
+        parse_error=parse_error,
+    )
+
+
+async def _apply_triage_result(
+    *,
+    doc: Document,
+    session: AsyncSession,
+    triage_out: TriageOutput,
+    subject_domain: str,
+    input_chars: int,
+    latency_ms: int,
+    escalation_reason: str | None,
+    parse_error: str | None,
+) -> None:
+    """TriageOutput → Document 필드 + R2 suppression + envelope enqueue + audit."""
+    document_id = doc.id
+
+    # Document 필드 (파싱 실패 시에는 legacy ai_summary 가 노출되도록 tldr 비움)
+    if not parse_error:
+        doc.ai_tldr = (triage_out.tldr or "").strip() or None
+        doc.ai_bullets = triage_out.bullets or []
+    doc.ai_analysis_tier = "triage"
+
+    # decide_routing — shadow observability 용 (INV 체크)
+    routing_decision = None
+    try:
+        routing_decision = decide_routing(
+            subject_domain=subject_domain,
+            content_chars=input_chars,
+            deterministic_keyword_hits=(),
+            self_declared_high_impact=triage_out.high_impact_self_declared,
+            self_declared_risk_flags=tuple(triage_out.risk_flags),
+            confidence=triage_out.confidence,
+            evidence_doc_count=0,
+        )
+    except Exception as exc:
+        logger.warning(f"[triage] decide_routing 실패 id={document_id}: {exc}")
+
+    # R2 — backlog guard (hard 제외 soft escalate 만 억제)
+    suppressed_reason: str | None = None
+    escalate = escalation_reason is not None
+    if escalate and not _is_hard_escalate(escalation_reason):
+        suppressed_reason = await _check_backlog_guard(session)
+        if suppressed_reason:
+            escalate = False
+            escalation_reason = None
+
+    # enqueue deep_summary
+    if escalate:
+        try:
+            envelope = EscalationEnvelope(
+                from_stage="summary_triage",
+                escalation_reasons=tuple([escalation_reason] if escalation_reason else []),
+                risk_flags=tuple(routing_decision.risk_flags) if routing_decision
+                            else tuple(triage_out.risk_flags),
+                distilled_context=_distill(triage_out),
+                original_pointers=_slice_text_ranges(document_id, input_chars),
+                synthesis_directives=(
+                    tuple(routing_decision.synthesis_directives) if routing_decision else ()
+                ),
+                user_intent=None,
+                draft_hint=(triage_out.tldr or None),
+            )
+            await enqueue_stage(
+                session,
+                document_id,
+                "deep_summary",
+                payload={
+                    "envelope": json.loads(envelope.to_json()),
+                    "subject_domain": subject_domain,
+                },
+            )
+        except Exception as exc:
+            logger.exception(f"[triage] envelope enqueue 실패 id={document_id}: {exc}")
+
+    # analyze_events
+    try:
+        pv = compute_policy_version(SUMMARY_TRIAGE_TASK)
+    except Exception:
+        pv = None
+
+    escalation_reasons_list = (
+        list(routing_decision.escalation_reasons) if routing_decision
+        else ([escalation_reason] if escalation_reason else [])
+    )
+    shadow_target = (
+        "primary" if (routing_decision and routing_decision.escalate_to_26b) else "triage"
+    )
+
+    await record_analyze_event(
+        doc_id=document_id,
+        user_id=None,
+        mode="summary_triage",
+        text_limit=TRIAGE_TEXT_LIMIT,
+        truncated=input_chars > TRIAGE_TEXT_LIMIT,
+        layers_returned=["tldr", "bullets"] if not parse_error else [],
+        cached=False,
+        latency_ms=latency_ms,
+        model_name=settings.ai.triage.model,
+        prompt_version=(f"{SUMMARY_TRIAGE_TASK}@{pv}" if pv else SUMMARY_TRIAGE_TASK),
+        error_code=parse_error,
+        source="document_server",
+        subject_domain=subject_domain,
+        risk_flags=(list(routing_decision.risk_flags) if routing_decision
+                    else list(triage_out.risk_flags)),
+        high_impact_task=(routing_decision.high_impact_task if routing_decision else None),
+        escalation_reasons=escalation_reasons_list,
+        confidence=triage_out.confidence,
+        policy_version=pv,
+        shadow_would_route_to=shadow_target,
+        tier="triage",
+        escalated_to_26b=escalate,
+        suppressed_reason=suppressed_reason,
+    )
+
+    logger.info(
+        f"[triage] id={document_id} subject={subject_domain} "
+        f"escalate={escalate} reason={escalation_reason} "
+        f"suppressed={bool(suppressed_reason)} "
+        f"confidence={triage_out.confidence:.2f} "
+        f"tldr_len={len(doc.ai_tldr or '')} bullets={len(doc.ai_bullets or [])}"
+    )

app/workers/deep_summary_worker.py

@@ -0,0 +1,210 @@
+"""PR-B B-1 Deep Summary 워커 — 26B (primary MLX) 에스컬레이션 분석.
+
+큐 stage 'deep_summary' 에서 pickup. classify_worker 가 enqueue 시 payload 로 실은
+EscalationEnvelope + subject_domain 을 읽어, PR-A policy 템플릿 `p3c_deep_summary` 를
+렌더링한 뒤 26B primary 를 호출한다. llm_gate Semaphore(1) 경유 — MLX 단일 인퍼런스 보호.
+
+출력을 documents.ai_detail_summary / ai_inconsistencies 에 저장하고 ai_analysis_tier 를
+'deep' 으로 전이. 실패는 무해하게 legacy 결과 보존.
+"""
+
+from __future__ import annotations
+
+import json
+import time
+from datetime import datetime, timezone
+
+from pydantic import BaseModel, Field, ValidationError
+from sqlalchemy import desc, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ai.client import AIClient, parse_json_response
+from ai.envelope import EscalationEnvelope
+from core.config import settings
+from core.utils import setup_logger
+from models.document import Document
+from models.queue import ProcessingQueue
+from policy.prompt_render import render_26b, policy_version as compute_policy_version
+from services.document_telemetry import record_analyze_event
+from services.search.llm_gate import get_mlx_gate
+
+logger = setup_logger("deep_summary_worker")
+
+DEEP_SUMMARY_TASK = "p3c_deep_summary"
+
+# inconsistencies kind 허용 목록 (feedback_document_server_domain_scope.md — 구매/계약 제외)
+ALLOWED_INCONSISTENCY_KINDS = {
+    "version_drift", "procedure_conflict", "source_conflict", "missing_basis",
+}
+
+
+class DeepSummaryOutput(BaseModel):
+    """p3c_deep_summary (26B) 응답 스키마. 파싱 실패 시 기본값 + skip."""
+
+    mode: str = "single"
+    tldr: str = ""
+    bullets: list[str] = Field(default_factory=list)
+    detail: str = ""                                   # 26B 가 채우는 상세 (detail_summary 로 저장)
+    bundle_flow: list[str] | None = None
+    inconsistencies: list[dict] | None = None
+    entities_confirmed: dict | None = None
+    directives_applied: list[str] | None = None
+    confidence: float = 0.5
+
+
+async def process(document_id: int, session: AsyncSession) -> None:
+    """deep_summary 큐 pickup → 26B 호출 → 필드 저장."""
+    doc = await session.get(Document, document_id)
+    if not doc:
+        raise ValueError(f"deep_summary: document id={document_id} 없음")
+
+    # 최신 deep_summary 큐 행의 payload 조회 (queue_consumer 가 status='processing' 으로 세팅한 상태)
+    queue_row = (await session.execute(
+        select(ProcessingQueue)
+        .where(
+            ProcessingQueue.document_id == document_id,
+            ProcessingQueue.stage == "deep_summary",
+            ProcessingQueue.status == "processing",
+        )
+        .order_by(desc(ProcessingQueue.id))
+        .limit(1)
+    )).scalar_one_or_none()
+    if not queue_row:
+        logger.warning(f"[deep] processing 상태의 deep_summary row 없음 id={document_id}")
+        return
+
+    payload = queue_row.payload or {}
+    envelope_raw = payload.get("envelope")
+    subject_domain = payload.get("subject_domain") or "generic"
+    if not envelope_raw:
+        logger.error(f"[deep] envelope 없음 id={document_id} payload_keys={list(payload.keys())}")
+        raise ValueError("deep_summary payload 에 envelope 없음")
+
+    envelope = EscalationEnvelope.from_json(json.dumps(envelope_raw))
+
+    # 원문 슬라이스 추출 (envelope.original_pointers.text_ranges 기반)
+    slices = _build_text_slices(doc.extracted_text or "", envelope.original_pointers)
+
+    # PR-A 템플릿 렌더
+    try:
+        rendered = render_26b(DEEP_SUMMARY_TASK, subject_domain)
+    except Exception as exc:
+        logger.exception(f"[deep] render_26b 실패 subject={subject_domain}: {exc}")
+        raise
+
+    prompt = (
+        rendered
+        .replace("{escalation_envelope_json}", envelope.to_system_injection())
+        .replace("{original_text_slices}", slices)
+    )
+
+    client = AIClient()
+    latency_ms = 0
+    parse_error: str | None = None
+    deep_out = DeepSummaryOutput()
+
+    try:
+        start = time.perf_counter()
+        async with get_mlx_gate():                     # primary(26B) 보호 Semaphore(1)
+            raw = await client.call_primary(prompt)
+        latency_ms = int((time.perf_counter() - start) * 1000)
+    except Exception as exc:
+        logger.warning(f"[deep] 26B 호출 실패 id={document_id}: {exc}")
+        parse_error = "call_failed"
+        raw = ""
+    finally:
+        await client.close()
+
+    if raw:
+        try:
+            parsed = parse_json_response(raw) or {}
+            deep_out = DeepSummaryOutput.model_validate(parsed)
+        except (ValidationError, ValueError, TypeError) as exc:
+            parse_error = f"parse:{type(exc).__name__}"
+            logger.warning(f"[deep] JSON 파싱/검증 실패 id={document_id}: {exc}")
+
+    if not parse_error:
+        doc.ai_detail_summary = (deep_out.detail or "").strip() or None
+        doc.ai_inconsistencies = _filter_inconsistencies(deep_out.inconsistencies or [])
+        doc.ai_analysis_tier = "deep"
+        doc.ai_processed_at = datetime.now(timezone.utc)
+
+    try:
+        pv = compute_policy_version(DEEP_SUMMARY_TASK)
+    except Exception:
+        pv = None
+
+    await record_analyze_event(
+        doc_id=document_id,
+        user_id=None,
+        mode="summary_deep",
+        text_limit=settings.ai.primary.context_char_limit or 260000,
+        truncated=False,
+        layers_returned=["detail_summary", "inconsistencies"] if not parse_error else [],
+        cached=False,
+        latency_ms=latency_ms,
+        model_name=settings.ai.primary.model,
+        prompt_version=(f"{DEEP_SUMMARY_TASK}@{pv}" if pv else DEEP_SUMMARY_TASK),
+        error_code=parse_error,
+        source="document_server",
+        subject_domain=subject_domain,
+        risk_flags=list(envelope.risk_flags),
+        high_impact_task=None,
+        escalation_reasons=list(envelope.escalation_reasons),
+        confidence=deep_out.confidence,
+        policy_version=pv,
+        shadow_would_route_to="primary",
+        tier="primary",
+        escalated_to_26b=True,
+        suppressed_reason=None,
+    )
+
+    logger.info(
+        f"[deep] id={document_id} subject={subject_domain} "
+        f"detail_len={len(doc.ai_detail_summary or '')} "
+        f"inc={len(doc.ai_inconsistencies or [])} latency_ms={latency_ms} "
+        f"parse_error={parse_error}"
+    )
+
+
+def _build_text_slices(text: str, pointers: dict) -> str:
+    """original_pointers.text_ranges 의 [{start, end}] 를 실제 본문 슬라이스로 합친다.
+
+    3조각 이상이면 각 조각 앞에 [slice N — head/middle/tail] 라벨을 붙여 26B 가 순서 인지.
+    단일 슬라이스면 라벨 없이 원문 그대로.
+    """
+    ranges = (pointers or {}).get("text_ranges") or []
+    if not ranges:
+        return text[:260_000]
+
+    if len(ranges) == 1:
+        r = ranges[0]
+        return text[r.get("start", 0):r.get("end", len(text))]
+
+    labels = ["head", "middle", "tail"]
+    parts: list[str] = []
+    for idx, r in enumerate(ranges):
+        label = labels[idx] if idx < len(labels) else f"slice{idx}"
+        chunk = text[r.get("start", 0):r.get("end", len(text))]
+        parts.append(f"[slice {idx} — {label}]\n{chunk}")
+    return "\n\n".join(parts)
+
+
+def _filter_inconsistencies(items: list) -> list[dict]:
+    """허용 kind 목록 (safety/news 도메인 한정) 만 통과시킨다.
+
+    `amount_mismatch` 같은 구매/계약 kind 는 여기서 drop (feedback_document_server_domain_scope.md).
+    구조 오류 (kind/desc 누락) 도 drop.
+    """
+    out: list[dict] = []
+    for it in items or []:
+        if not isinstance(it, dict):
+            continue
+        kind = str(it.get("kind") or "")
+        desc_ = str(it.get("desc") or "").strip()
+        if kind not in ALLOWED_INCONSISTENCY_KINDS:
+            continue
+        if not desc_:
+            continue
+        out.append({"kind": kind, "desc": desc_})
+    return out

app/workers/queue_consumer.py

@@ -14,8 +14,9 @@ logger = setup_logger("queue_consumer")
 
 # stage별 배치 크기
 # stt 는 GPU 단일 점유 + 회의 30분짜리도 가능 → 배치 1. thumbnail 은 ffmpeg subprocess 로 가벼움.
+# deep_summary (PR-B B-1) 는 MLX 26B 단일 Semaphore(1) 경유 → 배치 1.
 BATCH_SIZE = {"extract": 5, "classify": 3, "summarize": 3, "embed": 1, "chunk": 1,
-              "preview": 2, "stt": 1, "thumbnail": 3}
+              "preview": 2, "stt": 1, "thumbnail": 3, "deep_summary": 1}
 STALE_THRESHOLD_MINUTES = 10
 
 
@@ -122,6 +123,7 @@ async def consume_queue():
     """큐에서 pending 항목을 가져와 stage별 워커 실행"""
     from workers.classify_worker import process as classify_process
     from workers.chunk_worker import process as chunk_process
+    from workers.deep_summary_worker import process as deep_summary_process
     from workers.embed_worker import process as embed_process
     from workers.extract_worker import process as extract_process
     from workers.preview_worker import process as preview_process
@@ -138,6 +140,9 @@ async def consume_queue():
         "preview": preview_process,
         "stt": stt_process,
         "thumbnail": thumbnail_process,
+        # PR-B B-1: classify 가 에스컬레이션 판단 시 enqueue → 26B 가 detail_summary 작성.
+        # next_stages 에 추가하지 않음 — deep_summary 는 leaf (classify→embed/chunk 흐름과 독립).
+        "deep_summary": deep_summary_process,
     }
 
     try:

migrations/156_ai_analysis_cols.sql

@@ -0,0 +1,18 @@
+-- 156_ai_analysis_cols.sql
+-- PR-B B-1: documents 에 4B triage / 26B deep 분할 컬럼 추가.
+-- plan: ~/.claude/plans/swirling-swimming-liskov.md
+--
+-- ai_tldr / ai_bullets       → summary_triage 산출 (4B, 상시)
+-- ai_detail_summary / ai_inconsistencies → summary_deep 산출 (26B, escalation)
+-- ai_analysis_tier           → 'triage' | 'deep' — 현재 문서가 어느 tier 까지 처리됐는지
+-- ai_summary (legacy TEXT)   → ai_tldr 을 복제하여 backward compat
+--
+-- 주의: _run_migrations 는 asyncpg exec_driver_sql 로 단일 prepared statement — ALTER TABLE
+--       의 다중 ADD COLUMN 절은 단일 statement 라 허용. enum ADD VALUE 는 별도 (157).
+
+ALTER TABLE documents
+    ADD COLUMN IF NOT EXISTS ai_tldr             TEXT,
+    ADD COLUMN IF NOT EXISTS ai_bullets          JSONB,
+    ADD COLUMN IF NOT EXISTS ai_detail_summary   TEXT,
+    ADD COLUMN IF NOT EXISTS ai_inconsistencies  JSONB,
+    ADD COLUMN IF NOT EXISTS ai_analysis_tier    TEXT;

migrations/157_queue_stage_deep_summary.sql

@@ -0,0 +1,13 @@
+-- 157_queue_stage_deep_summary.sql
+-- PR-B B-1: processing_queue.stage enum 에 'deep_summary' 추가.
+-- plan: ~/.claude/plans/swirling-swimming-liskov.md §B-1
+--
+-- PostgreSQL 제약: ALTER TYPE ADD VALUE 는 새 값을 같은 트랜잭션 안에서 사용할 수 없다.
+--   (unsafe use of new value of enum type)
+-- 따라서 이 migration 은 enum 확장만 단독으로 처리하고, processing_queue.payload 컬럼
+-- 추가는 158 로 분리. classify_worker 가 deep_summary 를 enqueue 하는 시점엔 157 이
+-- 이미 별도 트랜잭션으로 commit 돼 있다.
+--
+-- audio/video 파이프와 동일 패턴 (147~151 참조).
+
+ALTER TYPE process_stage ADD VALUE IF NOT EXISTS 'deep_summary';

migrations/158_processing_queue_payload.sql

@@ -0,0 +1,13 @@
+-- 158_processing_queue_payload.sql
+-- PR-B B-1: processing_queue 에 payload JSONB 컬럼 추가.
+-- plan: ~/.claude/plans/swirling-swimming-liskov.md §B-1
+--
+-- deep_summary stage 가 EscalationEnvelope (from_stage / escalation_reasons /
+-- risk_flags / distilled_context / original_pointers / synthesis_directives / ...)
+-- 를 payload 로 싣고 전달. 다른 stage 는 NULL.
+--
+-- 157 의 enum ADD VALUE 가 별도 트랜잭션으로 commit 된 뒤에만 실행돼야 한다.
+-- (asyncpg migrations 러너가 파일 순서대로 단일-statement 실행하므로 안전.)
+
+ALTER TABLE processing_queue
+    ADD COLUMN IF NOT EXISTS payload JSONB;

migrations/159_analyze_events_pr_b_tier.sql

@@ -0,0 +1,18 @@
+-- 159_analyze_events_pr_b_tier.sql
+-- PR-B B-1: analyze_events 에 실제 호출 tier + suppressed_reason 추가.
+-- plan: ~/.claude/plans/swirling-swimming-liskov.md §B-1 R2
+--
+-- PR-A (153) 이 shadow_would_route_to 로 "정책이 제안한 tier" 는 이미 기록.
+-- 여기서는 PR-B 가 실제로 호출한 tier 와, R2 backlog guard 로 suppress 된 이유를 추가.
+--
+-- tier              : 'triage' | 'primary' | 'fallback' — classify_worker / deep_summary_worker /
+--                     evidence_service / synthesis_service 가 실제 호출 시 기록.
+-- suppressed_reason : 'backlog_guard(ratio=0.42,pending=7)' 등. escalation 판정이 soft 였으나
+--                     backlog guard 로 suppress 된 건을 사후 디버깅 하기 위해 별도 컬럼화.
+--                     dashboard "Backlog Suppression (24h)" 카드의 소스.
+--
+-- 단일 statement (ALTER TABLE 다중 ADD COLUMN 은 허용). 인덱스는 160 으로 분리.
+
+ALTER TABLE analyze_events
+    ADD COLUMN IF NOT EXISTS tier              TEXT,
+    ADD COLUMN IF NOT EXISTS suppressed_reason TEXT;

migrations/160_analyze_events_pr_b_idx.sql

@@ -0,0 +1,7 @@
+-- 160_analyze_events_pr_b_idx.sql
+-- PR-B B-1: suppressed_reason partial index (backlog guard 발생 조회용).
+-- plan: ~/.claude/plans/swirling-swimming-liskov.md §B-1 R2
+
+CREATE INDEX IF NOT EXISTS idx_analyze_events_suppressed
+    ON analyze_events (created_at)
+    WHERE suppressed_reason IS NOT NULL;