e228faabf5
G-1: 메신저 Webhook Relay + _send_to_room 실제 httpx 호출 구현 G-2: POST /api/tasks/bulk SR 대량작업 엔드포인트 (최대 100건) G-3: 라이선스 만료 알림 스케줄러 (매일 09:00 KST) G-4: 체험판 upgrade_banner 필드 + license.py 배너 로직 G-5: core/auto_rca.py + incidents/problem auto-rca 엔드포인트 G-6: core/deploy_impact.py + vibe impact-analysis 엔드포인트 G-7: core/ticket_classifier.py + SR 생성 시 AI 분류 + ai-suggestion API G-8: VulnPatchRecord 모델 + vuln_scan 패치추적 4개 엔드포인트 G-9: core/jira_sync.py + gateway Jira/Confluence 연동 엔드포인트 G-10: core/push_notify.py + routers/push.py + PushSubscription 모델 G-11: approvals 다중승인 (위임/서명/기한초과/마감연장) G-12: alembic.ini + migrations/ + cicd/migrate_to_postgres.sh 하네스: guardia-orchestrator 확장기능 Phase 반영 봇명령어: /sr /status /license /bulk 슬래시 명령어 추가 설치스크립트: setup/ (Ubuntu, CentOS, RHEL, Windows) --test 옵션 포함 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
347 lines
12 KiB
Python
347 lines
12 KiB
Python
"""
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Self-Improving Learning Loop — API Router
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엔드포인트:
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GET /api/learning/stats — 학습 현황 통계
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GET /api/learning/patterns — 재발 패턴 목록
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GET /api/learning/patterns/{id} — 패턴 상세
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GET /api/learning/lessons — 검증된 교훈 목록
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GET /api/learning/thresholds — 적응형 임계값 현황
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POST /api/learning/feedback — KB 사용 피드백 기록
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POST /api/learning/feedback/{id}/check — 피드백 효과 즉시 검증
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POST /api/learning/thresholds/outcome — 이상 탐지 결과 기록
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POST /api/learning/thresholds/missed — 누락 탐지 기록
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POST /api/learning/thresholds/calibrate — 임계값 즉시 보정
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POST /api/learning/mine — 패턴 마이닝 즉시 실행
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POST /api/learning/detect-recurrence — SR 재발 감지 (수동 호출)
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"""
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from datetime import datetime
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from typing import List, Optional
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from fastapi import APIRouter, Depends, Query
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from pydantic import BaseModel
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from sqlalchemy import select, desc
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from sqlalchemy.ext.asyncio import AsyncSession
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from core.auth import get_current_user
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from database import get_db
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from models import (
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RecurrencePattern, SolutionFeedback, AdaptiveThreshold, LessonLearned, User
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)
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router = APIRouter(prefix="/api/learning", tags=["learning"])
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# ── Pydantic I/O ───────────────────────────────────────────────────────────────
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class FeedbackCreate(BaseModel):
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sr_id: str
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kb_doc_id: str
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kb_id: Optional[int] = None
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class OutcomeRecord(BaseModel):
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source: str
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metric_type: str
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was_actual_incident: bool
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base_threshold: Optional[float] = None
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class MissedRecord(BaseModel):
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source: str
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metric_type: str
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class CalibrateRequest(BaseModel):
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source: str
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metric_type: str
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class RecurrenceRequest(BaseModel):
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sr_id: str
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title: str
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description: str
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sr_type: str
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inst_id: Optional[int] = None
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class MineRequest(BaseModel):
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days_back: int = 30
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min_occurrences: int = 3
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# ── 통계 요약 ──────────────────────────────────────────────────────────────────
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@router.get("/stats")
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async def get_stats(
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import get_learning_stats
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return await get_learning_stats(db)
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# ── 재발 패턴 ──────────────────────────────────────────────────────────────────
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@router.get("/patterns")
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async def list_patterns(
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limit: int = Query(50, le=200),
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offset: int = Query(0, ge=0),
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sr_type: Optional[str] = None,
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escalated_only: bool = False,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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q = select(RecurrencePattern)
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if sr_type:
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q = q.where(RecurrencePattern.sr_type == sr_type)
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if escalated_only:
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q = q.where(RecurrencePattern.escalated == True)
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q = q.order_by(desc(RecurrencePattern.occurrence_count)).offset(offset).limit(limit)
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rows = (await db.execute(q)).scalars().all()
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return [
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{
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"id": p.id,
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"sr_type": p.sr_type,
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"tech_keywords": p.tech_keywords,
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"occurrence_count": p.occurrence_count,
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"first_seen_at": p.first_seen_at.isoformat() if p.first_seen_at else None,
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"last_seen_at": p.last_seen_at.isoformat() if p.last_seen_at else None,
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"escalated": p.escalated,
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"problem_id": p.problem_id,
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"sr_ids": (p.sr_ids or [])[-5:],
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}
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for p in rows
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]
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@router.get("/patterns/{pattern_id}")
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async def get_pattern(
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pattern_id: int,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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p = (await db.execute(
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select(RecurrencePattern).where(RecurrencePattern.id == pattern_id)
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)).scalars().first()
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if not p:
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from fastapi import HTTPException
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raise HTTPException(status_code=404, detail="패턴을 찾을 수 없습니다")
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return {
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"id": p.id,
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"pattern_key": p.pattern_key,
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"sr_type": p.sr_type,
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"inst_id": p.inst_id,
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"keyword_signature": p.keyword_signature,
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"tech_keywords": p.tech_keywords,
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"occurrence_count": p.occurrence_count,
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"first_seen_at": p.first_seen_at.isoformat() if p.first_seen_at else None,
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"last_seen_at": p.last_seen_at.isoformat() if p.last_seen_at else None,
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"escalated": p.escalated,
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"problem_id": p.problem_id,
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"sr_ids": p.sr_ids or [],
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}
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# ── 재발 감지 ──────────────────────────────────────────────────────────────────
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@router.post("/detect-recurrence")
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async def detect_recurrence(
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body: RecurrenceRequest,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import detect_recurrence as _detect
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return await _detect(
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db = db,
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sr_id = body.sr_id,
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title = body.title,
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description = body.description,
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sr_type = body.sr_type,
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inst_id = body.inst_id,
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)
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# ── 솔루션 효과 피드백 ─────────────────────────────────────────────────────────
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@router.post("/feedback")
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async def create_feedback(
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body: FeedbackCreate,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import record_kb_usage
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return await record_kb_usage(db, body.sr_id, body.kb_doc_id, body.kb_id)
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@router.post("/feedback/{feedback_id}/check")
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async def check_feedback(
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feedback_id: int,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import check_solution_effectiveness
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return await check_solution_effectiveness(db, feedback_id)
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@router.get("/feedback")
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async def list_feedback(
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limit: int = Query(50, le=200),
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offset: int = Query(0, ge=0),
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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rows = (await db.execute(
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select(SolutionFeedback)
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.order_by(desc(SolutionFeedback.applied_at))
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.offset(offset).limit(limit)
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)).scalars().all()
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return [
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{
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"id": f.id,
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"sr_id": f.sr_id,
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"kb_doc_id": f.kb_doc_id,
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"applied_at": f.applied_at.isoformat() if f.applied_at else None,
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"effectiveness_score": f.effectiveness_score,
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"recurred_within_days": f.recurred_within_days,
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"checked_at": f.checked_at.isoformat() if f.checked_at else None,
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}
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for f in rows
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]
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# ── 교훈 목록 ──────────────────────────────────────────────────────────────────
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@router.get("/lessons")
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async def list_lessons(
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limit: int = Query(50, le=200),
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offset: int = Query(0, ge=0),
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verified_only: bool = False,
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category: Optional[str] = None,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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q = select(LessonLearned)
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if verified_only:
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q = q.where(LessonLearned.is_verified == True)
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if category:
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q = q.where(LessonLearned.category == category)
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q = q.order_by(desc(LessonLearned.confidence_score)).offset(offset).limit(limit)
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rows = (await db.execute(q)).scalars().all()
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return [
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{
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"id": l.id,
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"lesson_id": l.lesson_id,
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"title": l.title,
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"category": l.category,
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"problem_pattern": l.problem_pattern,
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"root_cause": l.root_cause,
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"effective_solution": l.effective_solution,
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"prevention": l.prevention,
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"confidence_score": l.confidence_score,
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"is_verified": l.is_verified,
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"created_at": l.created_at.isoformat() if l.created_at else None,
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}
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for l in rows
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]
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# ── 적응형 임계값 ──────────────────────────────────────────────────────────────
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@router.get("/thresholds")
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async def list_thresholds(
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source: Optional[str] = None,
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metric_type: Optional[str] = None,
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adapted_only: bool = False,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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q = select(AdaptiveThreshold)
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if source:
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q = q.where(AdaptiveThreshold.source == source)
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if metric_type:
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q = q.where(AdaptiveThreshold.metric_type == metric_type)
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if adapted_only:
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q = q.where(AdaptiveThreshold.adaptation_count > 0)
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q = q.order_by(desc(AdaptiveThreshold.adaptation_count)).limit(200)
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rows = (await db.execute(q)).scalars().all()
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return [
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{
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"id": t.id,
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"source": t.source,
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"metric_type": t.metric_type,
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"base_threshold": t.base_threshold,
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"adapted_threshold": t.adapted_threshold,
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"change_pct": round(
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(t.adapted_threshold - t.base_threshold) / t.base_threshold * 100, 1
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) if t.base_threshold else 0,
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"true_positive": t.true_positive,
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"false_positive": t.false_positive,
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"missed_count": t.missed_count,
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"adaptation_count": t.adaptation_count,
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"last_adapted_at": t.last_adapted_at.isoformat() if t.last_adapted_at else None,
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}
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for t in rows
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]
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@router.post("/thresholds/outcome")
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async def record_outcome(
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body: OutcomeRecord,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import record_anomaly_outcome
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return await record_anomaly_outcome(
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db, body.source, body.metric_type,
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body.was_actual_incident, body.base_threshold,
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)
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@router.post("/thresholds/missed")
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async def record_missed(
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body: MissedRecord,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import record_missed_detection
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return await record_missed_detection(db, body.source, body.metric_type)
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@router.post("/thresholds/calibrate")
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async def calibrate(
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body: CalibrateRequest,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import calibrate_threshold
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return await calibrate_threshold(db, body.source, body.metric_type)
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@router.post("/thresholds/calibrate-all")
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async def calibrate_all(
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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"""모든 적응형 임계값 일괄 보정."""
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rows = (await db.execute(select(AdaptiveThreshold))).scalars().all()
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results = []
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from core.learning import calibrate_threshold
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for rec in rows:
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r = await calibrate_threshold(db, rec.source, rec.metric_type)
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if r.get("adjusted"):
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results.append(r)
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return {"calibrated": len(results), "results": results}
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# ── 패턴 마이닝 ────────────────────────────────────────────────────────────────
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@router.post("/mine")
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async def mine_lessons(
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body: MineRequest,
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db: AsyncSession = Depends(get_db),
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_: User = Depends(get_current_user),
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):
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from core.learning import run_lesson_mining
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return await run_lesson_mining(db, body.days_back, body.min_occurrences)
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