fix(flywheel): 四階段系統性修復 AUTO_REPAIR NO_MATCH 斷層

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>

apps/api/src/main.py

@@ -280,6 +280,18 @@ async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]:
     except Exception as e:
         logger.warning("working_memory_warmup_failed", error=str(e))
 
+    # Phase 4 飛輪修復: Playbook Embedding 冷啟動索引
+    # 2026-04-10 Claude Sonnet 4.6 Asia/Taipei
+    # 目的: 確保 playbook_embeddings 表有最新向量，供語義相似度查詢
+    # 使用 asyncio.create_task 非阻塞 — 不影響 API 啟動速度
+    try:
+        import asyncio
+        from src.services.playbook_embedding_service import ensure_playbook_embeddings_indexed
+        asyncio.create_task(ensure_playbook_embeddings_indexed())
+        logger.info("playbook_embedding_indexing_scheduled")
+    except Exception as e:
+        logger.warning("playbook_embedding_schedule_failed", error=str(e))
+
     # Phase 6.1: 啟動 Signal Worker (Redis Streams Consumer)
     # 統帥鐵律: Event Bus 解耦告警接收與處理
     await init_signal_worker()