feat(flywheel): W1 PR-P1 + ADR-091 T1 — 飛輪 80→90 第一波
依 onboarder 端到端閉環審計挖出的 10 條斷鏈 + critic 鐵律違反全景， W1 第一波修復飛輪鐵證 1 + 2 的核心斷鏈 C1。 ## W1 PR-P1 — matched_playbook_id 四斷點守門 (C1 修復) fullstack 探勘發現 4 斷點之前 session 已修，本 PR 補： - ENABLE_PLAYBOOK_MATCHING feature flag (default=true) rollback: kubectl set env deployment/awoooi-api ENABLE_PLAYBOOK_MATCHING=false - proposal_service._try_playbook_match_id 入口加 flag check - 7 個 e2e 測試補上保護網（之前無測試覆蓋） 斷鏈 C1 證據鏈：proposal_service.generate_proposal() → matched_playbook_id → approval_db → approval_repository → learning_service._update_playbook_stats 24h 後 playbooks.trust_score 應有真實 EWMA 更新。 ## ADR-091 T1 — auto_generate_rule 雙寫 DB (鐵證 1 第一步) 飛輪鐵證 1：alert_rule_catalog.source='ai_generated' 全 codebase 0 筆。 auto_generate_rule() 寫 alert_rules.yaml 但不寫 DB → AI 自學成果與 catalog 雙軌脫鉤。 修法（依 ADR-091 §1 D1）： - 新增 _insert_catalog_ai_generated()：YAML 寫入成功後雙寫 source='ai_generated', confidence=0.5, review_status='draft', created_by_agent - 新增 _parse_for_to_seconds() helper（"30s"/"5m"/"2h" → seconds） - ON CONFLICT (rule_name) DO NOTHING 冪等保證 - transaction 策略：YAML + DB 不在同一 transaction（YAML 已成 SoT，DB 失敗只 log） - ENABLE_AI_RULE_CATALOG_WRITE feature flag (default=true) rollback: kubectl set env deployment/awoooi-api ENABLE_AI_RULE_CATALOG_WRITE=false 13 個測試覆蓋：parse helper 8 + 業務邏輯 5（success/db_fail/idempotent/flag/SQL_lit） ## 驗證 1572 unit tests 全綠（+20 新增：PR-P1 7 + ADR-091 T1 13） ## 期望影響 飛輪自主化評分：42 → 65（+23 = C1 +3 + 鐵證 1 +20） ## 已知債（critic PR review 揭示，下一個 commit 處理） - KMWriter 統一契約 3 條 caller 路徑被旁路（C1/M1/M2） - KMWriter 冪等聲明與實作不符（M3 缺 ON CONFLICT） - Alertmanager equal:[] 爆炸抑制 + 版本未驗（M4/M5） - drift checker regex 脆弱（M7 應改 AST） - governance health score skipped 失真（M6） Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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@@ -62,6 +62,16 @@ class Settings(BaseSettings):
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description="Phase 24: True=新 AIRouter 路由, False=舊 openclaw.py fallback chain",
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)
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# ==========================================================================
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# W1 PR-P1: Playbook 匹配 Feature Flag (2026-04-28 ogt + Claude Sonnet 4.6)
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# 修復飛輪斷鏈 C1 — proposal_service 填 matched_playbook_id → EWMA 更新
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# 回滾指令: kubectl set env deployment/awoooi-api ENABLE_PLAYBOOK_MATCHING=false
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# ==========================================================================
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ENABLE_PLAYBOOK_MATCHING: bool = Field(
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default=True,
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description="W1 PR-P1: True=generate_proposal 時執行 Playbook RAG 匹配並填 matched_playbook_id, False=行為與修復前完全相同（回滾用）",
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)
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# ==========================================================================
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# P1-1: KMWriter 統一契約 (2026-04-28 ogt + Claude Sonnet 4.6)
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# KM_WRITE_AWAIT=true → 強制 await asyncio.wait_for(timeout=KM_WRITE_TIMEOUT_SECONDS)
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@@ -530,6 +540,16 @@ class Settings(BaseSettings):
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default=False,
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description="ADR-095: 啟用 12-Agent ConsensusEngine weights（預設關閉）",
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)
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# ==========================================================================
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# ADR-091 Task T1: AI 自學規則雙寫 alert_rule_catalog (2026-04-28 ogt + Claude Sonnet 4.6)
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# True=auto_generate_rule() 成功後同步寫入 DB source='ai_generated'
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# False=回滾開關，只寫 YAML，不寫 DB
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# 回滾指令: kubectl set env deployment/awoooi-api ENABLE_AI_RULE_CATALOG_WRITE=false
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# ==========================================================================
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ENABLE_AI_RULE_CATALOG_WRITE: bool = Field(
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default=True,
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description="ADR-091 T1: True=AI 自學規則雙寫 alert_rule_catalog DB, False=僅 YAML（回滾用）",
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)
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# 2026-04-27 P3.1-T2-PathA by Claude — DiagAggregator 信號分類層補 PDI
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# 路徑 A 已啟用：DA 只取 PDI 已收集的 raw 資料做業務邏輯分類（OOMKilled/CrashLoop 等），
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# 不重複呼叫 K8s/SignOz API（純邏輯分類，不打外部服務）。
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@@ -581,6 +581,118 @@ def _append_rule_to_yaml(rule_yaml: str, alertname: str) -> bool:
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return False
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def _parse_for_to_seconds(for_str: str) -> int | None:
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"""將 Prometheus 'for' 字串（如 '5m', '30s', '1h'）轉換為整數秒數。
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無法解析時回傳 None。
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"""
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if not for_str:
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return None
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for_str = for_str.strip()
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mapping = {"s": 1, "m": 60, "h": 3600, "d": 86400}
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m = re.fullmatch(r"(\d+)([smhd]?)", for_str)
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if not m:
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return None
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value = int(m.group(1))
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unit = m.group(2) or "s"
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return value * mapping.get(unit, 1)
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async def _insert_catalog_ai_generated(
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rule_dict: dict,
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llm_source: str,
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rule_id: str,
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alertname_safe: str,
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) -> None:
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"""寫入 alert_rule_catalog source='ai_generated'
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冪等：rule_name 唯一索引（alert_rule_catalog_rule_name_key）已存在
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使用 INSERT ON CONFLICT (rule_name) DO NOTHING
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transaction 策略：YAML + DB 不在同一 transaction
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YAML 已成功 → DB 失敗只 log warning，不回滾 YAML
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review_status='draft'：對應 DB CHECK ('draft','approved','deprecated','retired')
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confidence=0.50：新規則未驗證，保守初始值
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ADR-091 Task T1 — 2026-04-28 ogt + Claude Sonnet 4.6 (Asia/Taipei)
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"""
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from src.core.config import settings as _settings
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if not _settings.ENABLE_AI_RULE_CATALOG_WRITE:
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logger.debug(
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"ai_rule_catalog_write_skipped_flag_disabled",
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alertname=alertname_safe,
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)
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return
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from sqlalchemy import text as _sql
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import src.db.base as _db_base
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import json as _json
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# 從 rule_dict 提取欄位
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# 'expr' 在 OpenClaw YAML 規則中不存在（非 PromQL），
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# 使用 alertname 作為語意佔位（與 yaml_hardcoded 同等策略）
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expr = rule_dict.get("expr") or f'alertname="{alertname_safe}"'
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for_str = rule_dict.get("for", "0s")
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duration_seconds = _parse_for_to_seconds(str(for_str))
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labels = rule_dict.get("labels", {})
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annotations = rule_dict.get("annotations", {})
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# 若 LLM 有產出 incident_type，注入 annotations 方便後續 T3 查詢
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incident_type = rule_dict.get("incident_type")
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if incident_type and "incident_type" not in annotations:
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annotations = {**annotations, "incident_type": str(incident_type)}
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# severity 從 labels 取（Prometheus 慣例），兜底空字串
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severity = (labels.get("severity", "") or "").strip()[:50] or None
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try:
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async with _db_base.get_db_context() as db:
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await db.execute(
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_sql("""
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INSERT INTO alert_rule_catalog (
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rule_name, source, expr, duration_seconds,
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severity, labels, annotations,
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created_by_agent, confidence, review_status,
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created_at, updated_at
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) VALUES (
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:rule_name, 'ai_generated', :expr, :duration_seconds,
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:severity,
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CAST(:labels AS jsonb),
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CAST(:annotations AS jsonb),
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:created_by_agent, 0.50, 'draft',
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NOW(), NOW()
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)
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ON CONFLICT (rule_name) DO NOTHING
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"""),
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{
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"rule_name": alertname_safe[:200],
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"expr": expr[:4000],
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"duration_seconds": duration_seconds,
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"severity": severity,
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"labels": _json.dumps(labels, ensure_ascii=False),
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"annotations": _json.dumps(annotations, ensure_ascii=False),
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"created_by_agent": llm_source,
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},
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)
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logger.info(
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"ai_rule_catalog_insert_success",
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alertname=alertname_safe,
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rule_id=rule_id,
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llm_source=llm_source,
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outcome="success",
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)
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except Exception as db_err:
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# DB 失敗只 warning，不影響已成功的 YAML 寫入
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logger.warning(
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"ai_rule_catalog_insert_failed",
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alertname=alertname_safe,
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rule_id=rule_id,
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error=str(db_err),
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outcome="failed",
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)
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async def _call_ollama(prompt: str, ollama_url: str, model: str) -> str | None:
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"""呼叫 Ollama 生成規則 YAML"""
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try:
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@@ -749,6 +861,25 @@ async def auto_generate_rule(
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# 立即為新規則建立 APPROVED Playbook（不等下次重啟）
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from src.services.playbook_seed_service import seed_playbooks_from_rules
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_asyncio.create_task(seed_playbooks_from_rules())
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# ADR-091 T1: 雙寫 alert_rule_catalog source='ai_generated'
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# 獨立 try/except — DB 失敗不回滾已成功的 YAML 寫入
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try:
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parsed_rules = yaml.safe_load(yaml_block)
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rule_dict = parsed_rules[0] if isinstance(parsed_rules, list) and parsed_rules else {}
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_asyncio.create_task(
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_insert_catalog_ai_generated(
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rule_dict=rule_dict,
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llm_source=llm_source or "unknown",
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rule_id=rule_id,
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alertname_safe=alertname_safe,
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)
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)
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except Exception as _catalog_err:
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logger.warning(
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"ai_rule_catalog_task_create_failed",
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alertname=alertname_safe,
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error=str(_catalog_err),
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)
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else:
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logger.warning(
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"auto_rule_auto_generate_failed",
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@@ -22,6 +22,7 @@ from datetime import UTC, datetime
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import structlog
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from src.core.config import get_settings
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from src.db.base import get_db_context
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from src.db.models import IncidentRecord
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from src.models.approval import (
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@@ -373,7 +374,17 @@ class ProposalService:
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讓學習服務 EWMA 能在人工審核後更新 Playbook trust score。
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邏輯與 decision_manager._try_playbook_match 相同，但只回傳 ID 不改 action。
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失敗時靜默返回 None（不阻塞主流程）。
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W1 PR-P1 Feature Flag (2026-04-28 ogt + Claude Sonnet 4.6):
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ENABLE_PLAYBOOK_MATCHING=false → 回傳 None，行為與修復前完全相同（回滾用）。
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"""
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if not get_settings().ENABLE_PLAYBOOK_MATCHING:
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logger.debug(
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"playbook_matching_disabled",
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incident_id=getattr(incident, "incident_id", "?"),
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)
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return None
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PLAYBOOK_SIMILARITY_THRESHOLD = 0.85
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try:
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from src.models.playbook import SymptomPattern
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