Hyungi Ahn
8074be6b6d
Phase 4-B v1 첫 검증 결과 자료 부족 토픽인데도 모델이 confidence='high'
박는 케이스 발견. 정의 (high = 자료 + 다른 ai_explanation 으로 패턴 명확)
보다 과신 — UX 신뢰도 위험. 자동 cap 보정 + 운영 관찰 SQL 추가.
confidence calibration (services/study/session_summary_guard):
- calibrate_confidence(c, ctx_docs_count, ready_explanation_count) 신규
· ctx_docs_count == 0 AND ready_explanation_count == 0 → 'low' cap
· ctx_docs_count == 0 (ready 만 있음) → 'medium' cap
· ctx_docs_count >= 1 → 모델 값 그대로
- 모델이 정의보다 더 보수적인 값 박은 경우 (모델 'low' + cap 'medium') 는
보존 — 더 보수적인 값을 절대 올리지 않음
worker 적용 (study_session_analysis_worker):
- ctx_docs_count = len(ctx_docs)
- ready_explanation_count = sum(1 for a in prompt_attempts if a.get('ai_explanation'))
- calibrate_confidence 호출 → study_quiz_session_analysis.confidence 박힘
- job.payload 에 운영 분석 metadata 보존:
· ctx_docs_count / ready_explanation_count
· model_confidence_raw (모델 응답) vs calibrated_confidence (cap 후)
· prompt_attempts / valid_attempts_total / summary_len
→ SQL 4 번 쿼리가 cap 작동 빈도 측정
scripts/phase4_health.sql (신규 운영 점검 SQL 7 섹션):
1. 4-A study_question_jobs status × error_code 분포
2. 4-B study_quiz_session_jobs status × error_code 분포
3. 4-B confidence 분포 (calibrated)
4. 4-B model_confidence_raw vs calibrated 차이 (cap 작동 빈도)
5. 4-A/4-B 최근 7일 처리 지연 p50/p95/max/avg
6. 4-A/4-B skipped 사유 분포
7. 4-B guard_fail / parse_fail / llm_timeout 비율
ship gate (단위 테스트):
- test_calibrate_confidence_no_evidence_caps_to_low (3 케이스)
- test_calibrate_confidence_only_explanations_caps_to_medium (3 케이스)
- test_calibrate_confidence_with_documents_passthrough (3 케이스)
- test_calibrate_confidence_normalizes_invalid_first (2 케이스)
Plan: ~/.claude/plans/nifty-sparking-spindle.md (Phase 4-B v1 후속)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
120 lines
4.0 KiB
SQL
120 lines
4.0 KiB
SQL
-- Phase 4 운영 점검 SQL — 4-A (study_question_jobs) + 4-B (study_quiz_session_jobs)
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-- 사용:
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-- ssh gpu 'docker exec -i hyungi_document_server-postgres-1 psql -U pkm pkm' < scripts/phase4_health.sql
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-- 또는 개별 SECTION 만 골라 실행. 모든 섹션은 read-only.
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\echo '── 1. 4-A study_question_jobs status × error_code 분포 ──'
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SELECT
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status,
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COALESCE(error_code, '(none)') AS error_code,
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COUNT(*) AS cnt
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FROM study_question_jobs
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GROUP BY status, error_code
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ORDER BY status, error_code;
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\echo ''
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\echo '── 2. 4-B study_quiz_session_jobs status × error_code 분포 ──'
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SELECT
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status,
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COALESCE(error_code, '(none)') AS error_code,
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COUNT(*) AS cnt
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FROM study_quiz_session_jobs
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GROUP BY status, error_code
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ORDER BY status, error_code;
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\echo ''
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\echo '── 3. 4-B study_quiz_session_analysis confidence 분포 (calibrated) ──'
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SELECT
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COALESCE(confidence, '(null)') AS confidence,
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COUNT(*) AS cnt,
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COUNT(*) FILTER (WHERE is_stale) AS stale_count
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FROM study_quiz_session_analysis
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GROUP BY confidence
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ORDER BY
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CASE COALESCE(confidence, '(null)')
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WHEN 'high' THEN 0
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WHEN 'medium' THEN 1
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WHEN 'low' THEN 2
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ELSE 3
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END;
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\echo ''
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\echo '── 4. 4-B confidence calibration 차이 (job.payload 기반) ──'
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\echo ' model_confidence_raw vs calibrated_confidence — 자료 부족 cap 작동 빈도 측정'
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SELECT
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payload->>'model_confidence_raw' AS model_raw,
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payload->>'calibrated_confidence' AS calibrated,
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(payload->>'ctx_docs_count')::int AS docs_n,
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(payload->>'ready_explanation_count')::int AS ready_n,
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COUNT(*) AS cnt
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FROM study_quiz_session_jobs
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WHERE status = 'completed'
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AND payload IS NOT NULL
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AND payload ? 'model_confidence_raw'
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GROUP BY model_raw, calibrated, docs_n, ready_n
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ORDER BY cnt DESC
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LIMIT 20;
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\echo ''
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\echo '── 5. 4-A/4-B 최근 7일 처리 지연 (created_at → completed_at) ──'
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\echo ' p50/p95/max 단순 ROUND(EXTRACT). 4-A 와 4-B 분리.'
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SELECT
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'study_question_jobs' AS source,
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COUNT(*) AS terminal_n,
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ROUND(AVG(EXTRACT(EPOCH FROM (completed_at - created_at)))::numeric, 1) AS avg_sec,
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ROUND(MAX(EXTRACT(EPOCH FROM (completed_at - created_at)))::numeric, 1) AS max_sec,
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ROUND((PERCENTILE_CONT(0.5) WITHIN GROUP (
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ORDER BY EXTRACT(EPOCH FROM (completed_at - created_at))
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))::numeric, 1) AS p50_sec,
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ROUND((PERCENTILE_CONT(0.95) WITHIN GROUP (
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ORDER BY EXTRACT(EPOCH FROM (completed_at - created_at))
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))::numeric, 1) AS p95_sec
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FROM study_question_jobs
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WHERE created_at >= NOW() - INTERVAL '7 days'
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AND completed_at IS NOT NULL
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UNION ALL
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SELECT
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'study_quiz_session_jobs',
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COUNT(*),
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ROUND(AVG(EXTRACT(EPOCH FROM (completed_at - created_at)))::numeric, 1),
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ROUND(MAX(EXTRACT(EPOCH FROM (completed_at - created_at)))::numeric, 1),
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ROUND((PERCENTILE_CONT(0.5) WITHIN GROUP (
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ORDER BY EXTRACT(EPOCH FROM (completed_at - created_at))
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))::numeric, 1),
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ROUND((PERCENTILE_CONT(0.95) WITHIN GROUP (
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ORDER BY EXTRACT(EPOCH FROM (completed_at - created_at))
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))::numeric, 1)
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FROM study_quiz_session_jobs
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WHERE created_at >= NOW() - INTERVAL '7 days'
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AND completed_at IS NOT NULL;
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\echo ''
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\echo '── 6. 4-A/4-B skipped 사유 분포 (어떤 데이터 부족이 가장 많이 막는가) ──'
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SELECT
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'study_question_jobs' AS source,
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error_code,
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COUNT(*) AS cnt
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FROM study_question_jobs
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WHERE status = 'skipped'
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GROUP BY error_code
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UNION ALL
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SELECT
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'study_quiz_session_jobs',
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error_code,
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COUNT(*)
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FROM study_quiz_session_jobs
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WHERE status = 'skipped'
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GROUP BY error_code
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ORDER BY source, cnt DESC;
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\echo ''
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\echo '── 7. 4-B guard_fail / parse_fail / llm_timeout 비율 (전체 job 대비) ──'
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SELECT
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error_code,
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COUNT(*) AS cnt,
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ROUND(100.0 * COUNT(*) / NULLIF((SELECT COUNT(*) FROM study_quiz_session_jobs), 0), 1) AS pct
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FROM study_quiz_session_jobs
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WHERE error_code IN ('guard_fail', 'parse_fail', 'llm_timeout', 'unknown')
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GROUP BY error_code
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ORDER BY cnt DESC;
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