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ff30c61c4c
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fix(rls): 收斂 API DB access context
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2026-05-12 19:55:13 +08:00 |
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Your Name
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22453161e9
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fix(ai): restore dynamic baseline holt winters fit
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2026-05-06 15:30:31 +08:00 |
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OG T
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4a6aa16a94
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fix(Phase 4): 修正呼叫點遺漏傳入參數 — promql 和 sample_log
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關聯節點檢查發現：
- dynamic_baseline_service.py: _save_baseline() 在 train_baseline() 中
未傳入 promql/lookback_hours → PG 記錄無法追蹤訓練來源
- log_anomaly_detector.py: _save_new_cluster() 未傳入 sample_log →
PG 記錄 LogCluster 時 sample_log 欄位為空
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-15 15:34:33 +08:00 |
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OG T
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bf45b80bd2
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feat(Phase 3.5 + Phase 4): AI 學習成果持久化到 PostgreSQL — 修正「AI 失憶」架構缺陷
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ADR-085: AI 學習成果不可存在 Cache
架構鐵律確立：
- PostgreSQL = System of Record（AI 的永久記憶）
- Redis = Warm Cache（加速讀取，TTL 到期從 PG 復原）
核心變更：
1. models.py: 新增 PlaybookRecord / DynamicBaselineRecord / LogClusterRecord ORM
2. base.py: ALTER TABLE playbooks 補加 trust_score / requires_approval_level 等欄位
3. playbook_repository.py: 完整雙寫實作（PG upsert + Redis cache）
4. dynamic_baseline_service.py: Holt-Winters 訓練結果寫入 PG，Redis 只作 24h warm cache
5. log_anomaly_detector.py: Drain3 cluster template 寫入 PG（UPSERT on cluster_id）
6. main.py: 啟動時執行 backfill_redis_to_pg()（Redis → PG 冪等補救）
修正的問題：
- Playbook 7天 Redis TTL 到期 → AI 失去所有修復知識
- trust_score EWMA 隨 Redis TTL 歸零 → AI 重新回到初始信任度 0.3
- Holt-Winters 基線 24h TTL → AI 每天重新學習「正常」的定義
- Drain3 cluster 沒有持久化 → AI 把已知 log pattern 反覆當新 pattern
Phase 4 新服務（requirements.txt 已加入 statsmodels + drain3 + numpy）
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-15 15:34:04 +08:00 |
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