c6edfb5614
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Phase 1 — affected_services 污染根治
- webhooks.py: _extract_affected_services() 從 labels 精準萃取服務名
(component > job > pod deployment name > clean target_resource > [])
- create_incident_for_approval: alert_labels 完整保留進 Signal
- alert_name 從 alertname 取，不再用 "custom"
Phase 2 — Playbook alertname 變體擴充
- alert_rules.yaml: 5 條規則新增 HostHighCpuLoad、KubePodCrashLooping 等變體
- scripts/update_playbook_alert_variants.py: Redis index 已執行更新 ✅
Phase 3 — Jaccard 通用型 Playbook 豁免
- similarity.py: affected_services=[] → 1.0 豁免（基礎設施 Playbook 不針對特定服務）
- severity_range=[] → 1.0 豁免（適用所有嚴重度）
Phase 4 — Playbook Embedding 持久化（冷啟動修復）
- migrations/flywheel_playbook_embeddings.sql: pgvector 持久化表
- services/playbook_embedding_service.py: 啟動時重建 Redis 向量快取 + 同步 DB
- main.py: lifespan 啟動時 asyncio.create_task 非阻塞執行
2026-04-10 Asia/Taipei — Claude Sonnet 4.6
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
28 lines
1.2 KiB
SQL
28 lines
1.2 KiB
SQL
-- Phase 4 飛輪修復 (ADR-067 延伸): Playbook Embeddings 持久化表
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-- 2026-04-10 Claude Sonnet 4.6 Asia/Taipei
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-- 目的: 解決冷啟動飛輪斷層 — Playbook 語義相似度查詢
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--
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-- 前置: pgvector extension 已安裝 (phase28_rag_pgvector.sql)
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-- 向量模型: nomic-embed-text (Ollama 192.168.0.188:11434) → 768 維
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--
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-- 索引策略:
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-- < 100 筆: 線性掃描 (無需索引)
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-- > 100 筆: 執行 CREATE INDEX ivfflat (phase35 已示範)
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CREATE TABLE IF NOT EXISTS playbook_embeddings (
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playbook_id TEXT PRIMARY KEY,
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embedding vector(768), -- nomic-embed-text 768 維
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alert_names TEXT[] NOT NULL DEFAULT '{}', -- 索引時的 alert_names 快照
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keywords TEXT[] NOT NULL DEFAULT '{}', -- 索引時的 keywords 快照
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indexed_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
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updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
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);
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COMMENT ON TABLE playbook_embeddings IS
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'Playbook 向量索引 — Phase 4 飛輪修復 (2026-04-10) — nomic-embed-text 768 維';
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-- 向量近鄰索引 (超過 100 筆後解開)
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-- CREATE INDEX IF NOT EXISTS ix_playbook_embeddings_vec
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-- ON playbook_embeddings USING ivfflat (embedding vector_cosine_ops)
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-- WITH (lists = 10);
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