Your Name
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09256be62c
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fix(rag): use bge embeddings on GCP Ollama lane
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2026-05-06 05:49:37 +08:00 |
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Your Name
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c2c0b1ec82
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fix(alerts): let GCP Ollama finish before cloud fallback
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CD Pipeline / build-and-deploy (push) Successful in 4m21s
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2026-05-06 05:27:55 +08:00 |
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Your Name
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c4854bb355
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fix(ai): isolate heavy Ollama workloads from GCP alert lane
CD Pipeline / tests (push) Successful in 54s
Code Review / ai-code-review (push) Successful in 10s
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2026-05-05 23:06:07 +08:00 |
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OG T
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df3ef9006c
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fix(auto-repair): 首席架構師 Review — 4 Critical/Important 修復
CD Pipeline / build-and-deploy (push) Successful in 7m2s
Critical #1: KM write task 移出 try/except
- _trigger_learning 的 KM 寫入原在 try 內，learning 失敗時不寫 KM
- 移至 except 後確保成功/失敗都寫入
- 移除冗餘 import asyncio（已在頂層 import）
- Minor: approval.incident_id or None 防空字串
Important #2: migration 加 PRIMARY KEY
- playbook_id 從 UNIQUE 升為 PRIMARY KEY
- prod DB 已執行 ALTER TABLE ADD PRIMARY KEY
Important #3: s.sequence→s.step_number, s.description→s.command
- embed_playbook() 使用不存在的欄位名，RAG 向量索引靜默失敗
- RepairStep 正確欄位: step_number, command
Important #1: PlaybookService._get_rag_service 不再 Service 層快取
- 改為每次呼叫工廠 get_playbook_rag_service()
- 避免舊實例繞過工廠的 is_closed 重建邏輯
冷啟動修復 (首席架構師建議B+C):
- _trigger_playbook_extraction 執行成功後自動設定
execution_success=True, effectiveness_score=4, status=RESOLVED
- skip 路徑 logger.debug → logger.info 提升可觀測性
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-04 12:02:03 +08:00 |
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OG T
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d0f09705e5
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fix(auto-repair): 修復三個阻礙自動修復的根本原因
CD Pipeline / build-and-deploy (push) Has been cancelled
1. playbook_rag: Ollama embedding http_client 滾動重啟後 is_closed
- 新增 _get_http_client() 偵測 is_closed 自動重建
- singleton get_playbook_rag_service() 加 is_closed 重建判斷
2. telegram: 加入 ai_model 欄位顯示底層判斷模型
- TelegramMessage.ai_model 欄位
- format() / format_with_nemotron() 顯示 "Nemotron (nemotron-70b)"
- openclaw proposal_dict 加入 model 欄位
- decision_manager / send_approval_card 串接
3. DB: 清除 9 筆 3/26 殭屍 PENDING (mock_fallback CRITICAL 測試記錄)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2026-04-04 11:46:25 +08:00 |
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OG T
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138ef0c2db
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fix(api): 修復 7 個 Lint 錯誤 (unused imports + zip strict + dict comprehension)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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2026-03-27 14:42:47 +08:00 |
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OG T
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f1b037bb0c
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refactor(api): playbook_rag.py 模組化改造 (P1 違規修復)
修復 P1 違規:
- Line 29: Service 直接 import Redis → Repository Pattern
- Line 156: 自建 httpx.AsyncClient → DI 注入
變更:
- 新增 IEmbeddingCacheRepository Protocol (interfaces.py)
- 新增 EmbeddingCacheRepository 實作 (embedding_repository.py)
- PlaybookRAGService 改用 DI 注入 http_client + embedding_cache
- get_playbook_rag_service() 改為 async factory
- PlaybookService 改用 lazy initialization
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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2026-03-27 10:07:30 +08:00 |
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OG T
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3c034526a5
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feat(api): ADR-030 Phase 3 Playbook RAG 向量搜尋
實作 Playbook 語意搜尋能力：
1. playbook_rag.py - RAG 向量服務
- Ollama nomic-embed-text 生成 embedding
- Redis 儲存向量 (JSON 格式)
- 餘弦相似度搜尋
- 混合搜尋 (Vector 60% + Jaccard 40%)
2. playbook_service.py - 整合 RAG
- extract_from_incident 後自動建立向量索引
- get_recommendations 支援混合搜尋
- RAG 失敗時 fallback 到純 Jaccard
功能:
- embed_text(): 文字向量化
- embed_playbook(): Playbook 向量化
- search_similar(): 向量相似度搜尋
- hybrid_search(): 混合搜尋
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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2026-03-26 22:08:15 +08:00 |
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