6fdc48e5b6
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>
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
"""document 관련 telemetry — Phase E.2 (analyze_events).
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/documents/{id}/analyze 호출을 background task로 DB에 기록.
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search_telemetry.py 패턴 동일 (단독 세션 + 에러 흡수).
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"""
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from __future__ import annotations
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import logging
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from typing import Any
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from sqlalchemy.exc import SQLAlchemyError
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from core.database import async_session
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from models.analyze_event import AnalyzeEvent
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logger = logging.getLogger("document_telemetry")
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# source enum validation — 서버 강제 fallback
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VALID_SOURCES: set[str] = {
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"document_server",
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"synology_chat",
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"ui_search",
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"ui_detail",
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"eval",
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"unknown",
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}
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DEFAULT_SOURCE = "document_server"
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def sanitize_source(raw: str | None) -> str:
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"""source 값 서버 강제. enum 외 값은 unknown, None은 document_server."""
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if raw is None:
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return DEFAULT_SOURCE
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lowered = raw.strip().lower()
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if lowered in VALID_SOURCES:
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return lowered
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return "unknown"
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async def record_analyze_event(
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doc_id: int,
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user_id: int | None,
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mode: str,
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text_limit: int | None,
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truncated: bool,
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layers_returned: list[str],
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cached: bool,
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latency_ms: int,
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model_name: str | None,
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prompt_version: str | None,
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error_code: str | None,
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source: str,
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# PR-A shadow observability — 아래 6개는 routing 이 동반될 때만 세팅, 그 외는 None 유지.
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subject_domain: str | None = None,
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risk_flags: list[str] | None = None,
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high_impact_task: bool | None = None,
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escalation_reasons: list[str] | None = None,
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confidence: float | None = None,
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policy_version: str | None = None,
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shadow_would_route_to: str | None = None,
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# PR-B B-1 — 실제 호출 tier 와 R2 backlog guard
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tier: str | None = None,
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escalated_to_26b: bool | None = None,
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suppressed_reason: str | None = None,
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) -> None:
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"""analyze_events INSERT. background task에서 호출 — 에러 삼킴.
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layers_returned: 성공 시 ["evidence","summary"] 등 layer 문자열 리스트. 실패 시 [].
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error_code: None (성공) | "timeout" | "llm" | "parse" | "missing_summary" | "no_text" | "not_found"
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tier: 'triage' | 'primary' | 'fallback' — 실제 호출된 tier (PR-B B-0~B-2).
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suppressed_reason: R2 backlog guard 로 soft escalate 가 suppress 된 경우의 이유 문자열.
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"""
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try:
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async with async_session() as session:
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row = AnalyzeEvent(
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doc_id=doc_id,
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user_id=user_id,
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mode=mode,
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text_limit=text_limit,
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truncated=truncated,
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layers_returned=layers_returned,
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cached=cached,
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latency_ms=latency_ms,
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model_name=model_name,
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prompt_version=prompt_version,
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error_code=error_code,
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source=source,
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subject_domain=subject_domain,
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risk_flags=risk_flags,
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high_impact_task=high_impact_task,
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escalated_to_26b=escalated_to_26b,
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escalation_reasons=escalation_reasons,
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confidence=confidence,
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policy_version=policy_version,
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shadow_would_route_to=shadow_would_route_to,
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tier=tier,
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suppressed_reason=suppressed_reason,
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)
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session.add(row)
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await session.commit()
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except SQLAlchemyError as exc:
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logger.warning(f"analyze_event insert failed: {exc}")
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