feat(api): Phase 7.5-7.6 Playbook 整合決策與自動萃取

Phase 7.5: DecisionManager 三軌決策
- 新增 Playbook 優先匹配 (similarity >= 85%)
- 三軌決策順序: Playbook > LLM > Expert System
- 整合 PlaybookService 推薦引擎

Phase 7.6: 自動萃取機制
- approval_execution.py 成功執行後觸發萃取
- 條件: RESOLVED/CLOSED + effectiveness >= 4
- 滿分 (5) 自動核准 Playbook

測試:
- 13 個 Playbook 單元測試全部通過
- 修復 Incident 模型欄位對應 (reasoning_steps)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

apps/api/src/services/approval_execution.py

@@ -8,9 +8,11 @@ Approval Execution Service - Phase 16 R4.2 瘦身 Router 抽取
 - ApprovalDBService: 更新狀態
 - TimelineService: 記錄事件
 - NotificationManager: 發送通知
+- Phase 7.6: Playbook 自動萃取
 
-版本: v1.0
+版本: v1.1
 建立: 2026-03-25 (台北時區)
+更新: 2026-03-26 (Phase 7.6 自動萃取)
 建立者: Claude Code (Phase 16 R4.2)
 """
 
@@ -144,6 +146,11 @@ class ApprovalExecutionService:
                     duration_ms=result.duration_ms,
                 )
             )
+
+            # Phase 7.6: 觸發 Playbook 自動萃取 (fire-and-forget)
+            asyncio.create_task(
+                self._trigger_playbook_extraction(approval)
+            )
         else:
             logger.error(
                 "background_execution_failed",
@@ -250,6 +257,134 @@ class ApprovalExecutionService:
                 error=str(e),
             )
 
+    async def _trigger_playbook_extraction(
+        self,
+        approval: ApprovalRequest,
+    ) -> None:
+        """
+        Phase 7.6: 觸發 Playbook 自動萃取
+
+        條件:
+        - 執行成功
+        - 關聯的 Incident 狀態為 RESOLVED 或 CLOSED
+        - effectiveness_score >= 4
+
+        此函數為 fire-and-forget，失敗不影響主流程
+        """
+        try:
+            # 1. 從 approval 取得關聯的 incident_id
+            # approval.requested_by 可能包含 incident 資訊，或從 metadata 取得
+            # 暫時從 description 或 action 解析
+            incident_id = self._extract_incident_id_from_approval(approval)
+            if not incident_id:
+                logger.debug(
+                    "playbook_extraction_skip",
+                    approval_id=str(approval.id),
+                    reason="No incident_id found",
+                )
+                return
+
+            # 2. 取得 Incident
+            from src.services.incident_service import get_incident_service
+
+            incident_service = get_incident_service()
+            incident = await incident_service.get_incident(incident_id)
+
+            if not incident:
+                logger.debug(
+                    "playbook_extraction_skip",
+                    approval_id=str(approval.id),
+                    incident_id=incident_id,
+                    reason="Incident not found",
+                )
+                return
+
+            # 3. 檢查 Incident 狀態
+            from src.models.incident import IncidentStatus
+
+            if incident.status not in [IncidentStatus.RESOLVED, IncidentStatus.CLOSED]:
+                logger.debug(
+                    "playbook_extraction_skip",
+                    approval_id=str(approval.id),
+                    incident_id=incident_id,
+                    incident_status=incident.status.value,
+                    reason="Incident not resolved",
+                )
+                return
+
+            # 4. 檢查 effectiveness_score
+            effectiveness = incident.outcome.effectiveness_score if incident.outcome else 0
+            if effectiveness < 4:
+                logger.debug(
+                    "playbook_extraction_skip",
+                    approval_id=str(approval.id),
+                    incident_id=incident_id,
+                    effectiveness=effectiveness,
+                    reason="Low effectiveness score",
+                )
+                return
+
+            # 5. 觸發萃取
+            from src.services.playbook_service import get_playbook_service
+
+            playbook_service = get_playbook_service()
+            playbook = await playbook_service.extract_from_incident(
+                incident=incident,
+                auto_approve=effectiveness >= 5,  # 滿分自動核准
+            )
+
+            if playbook:
+                logger.info(
+                    "playbook_auto_extracted",
+                    approval_id=str(approval.id),
+                    incident_id=incident_id,
+                    playbook_id=playbook.playbook_id,
+                    playbook_name=playbook.name,
+                    auto_approved=playbook.status.value == "approved",
+                )
+            else:
+                logger.debug(
+                    "playbook_extraction_no_result",
+                    approval_id=str(approval.id),
+                    incident_id=incident_id,
+                )
+
+        except Exception as e:
+            # 萃取失敗不影響主流程
+            logger.warning(
+                "playbook_extraction_error",
+                approval_id=str(approval.id),
+                error=str(e),
+            )
+
+    def _extract_incident_id_from_approval(
+        self,
+        approval: ApprovalRequest,
+    ) -> str | None:
+        """
+        從 approval 提取關聯的 incident_id
+
+        嘗試以下來源:
+        1. approval.metadata (如果有)
+        2. approval.description 中的 INC- 模式
+        3. approval.requested_by 中的 incident 資訊
+        """
+        import re
+
+        # 從 description 或 action 中尋找 INC-XXXXXX 模式
+        text = f"{approval.description or ''} {approval.action or ''}"
+        match = re.search(r"INC-([A-Z0-9-]+)", text)
+        if match:
+            return match.group(0)  # 返回完整的 INC-XXXXX
+
+        # 從 requested_by 尋找
+        if approval.requested_by and "INC-" in approval.requested_by:
+            match = re.search(r"INC-([A-Z0-9-]+)", approval.requested_by)
+            if match:
+                return match.group(0)
+
+        return None
+
 
 # =============================================================================
 # Singleton Instance

apps/api/src/services/decision_manager.py

@@ -30,10 +30,15 @@ import structlog
 from src.core.config import settings
 from src.core.redis_client import get_redis
 from src.models.incident import Incident
+from src.models.playbook import SymptomPattern
 from src.services.openclaw import get_openclaw
+from src.services.playbook_service import get_playbook_service
 
 logger = structlog.get_logger(__name__)
 
+# Phase 7.5: Playbook 優先閾值
+PLAYBOOK_SIMILARITY_THRESHOLD = 0.85  # 相似度 >= 85% 直接使用 Playbook
+
 
 # =============================================================================
 # Telegram 推送 (Phase 6.5: 決策就緒通知)
@@ -394,13 +399,20 @@ class DecisionManager:
         incident: Incident,
     ) -> dict[str, Any]:
         """
-        雙軌決策分析
+        三軌決策分析 (Phase 7.5 升級)
 
         策略:
-        - 同時啟動 LLM 和 Expert System
-        - LLM 成功則用 LLM (更智能)
-        - LLM 失敗則用 Expert System (保底)
+        1. 先檢查 Playbook 是否有高度匹配 (similarity >= 85%)
+        2. Playbook 命中則直接使用 (最快、經驗驗證)
+        3. 否則 LLM + Expert System 雙軌
+
+        優先順序: Playbook > LLM > Expert System
         """
+        # Phase 7.5: 先嘗試 Playbook 匹配
+        playbook_result = await self._try_playbook_match(incident)
+        if playbook_result:
+            return playbook_result
+
         # Expert System 同步執行 (立即可用)
         expert_result = expert_analyze(incident)
 
@@ -440,6 +452,108 @@ class DecisionManager:
         )
         return expert_result
 
+    async def _try_playbook_match(
+        self,
+        incident: Incident,
+    ) -> dict[str, Any] | None:
+        """
+        Phase 7.5: 嘗試 Playbook 匹配
+
+        條件:
+        - 相似度 >= PLAYBOOK_SIMILARITY_THRESHOLD (85%)
+        - Playbook 狀態為 APPROVED
+        - 成功率 >= 80% (如果有執行紀錄)
+
+        Returns:
+            匹配成功返回 proposal_data，否則 None
+        """
+        try:
+            playbook_service = get_playbook_service()
+
+            # 建構症狀模式
+            alert_names = [s.alert_name for s in incident.signals] if incident.signals else []
+            symptoms = SymptomPattern(
+                alert_names=alert_names,
+                affected_services=incident.affected_services or [],
+                severity_range=[incident.severity.value] if incident.severity else ["P2"],
+            )
+
+            # 取得推薦 (只取 Top 1)
+            recommendations = await playbook_service.get_recommendations(
+                symptoms=symptoms,
+                top_k=1,
+            )
+
+            if not recommendations:
+                logger.debug(
+                    "playbook_no_match",
+                    incident_id=incident.incident_id,
+                )
+                return None
+
+            best_match = recommendations[0]
+            playbook = best_match.playbook
+
+            # 檢查相似度閾值
+            if best_match.similarity_score < PLAYBOOK_SIMILARITY_THRESHOLD:
+                logger.debug(
+                    "playbook_similarity_below_threshold",
+                    incident_id=incident.incident_id,
+                    playbook_id=playbook.playbook_id,
+                    similarity=best_match.similarity_score,
+                    threshold=PLAYBOOK_SIMILARITY_THRESHOLD,
+                )
+                return None
+
+            # 檢查成功率 (如果有執行紀錄)
+            if playbook.total_executions > 0 and playbook.success_rate < 0.8:
+                logger.debug(
+                    "playbook_low_success_rate",
+                    incident_id=incident.incident_id,
+                    playbook_id=playbook.playbook_id,
+                    success_rate=playbook.success_rate,
+                )
+                return None
+
+            # Playbook 命中！
+            # 取得第一個修復步驟的指令
+            kubectl_command = ""
+            if playbook.repair_steps:
+                # 將 target 替換為實際服務名稱
+                target = incident.affected_services[0] if incident.affected_services else "unknown"
+                kubectl_command = playbook.repair_steps[0].command.format(target=target)
+
+            logger.info(
+                "playbook_match_success",
+                incident_id=incident.incident_id,
+                playbook_id=playbook.playbook_id,
+                playbook_name=playbook.name,
+                similarity=best_match.similarity_score,
+                success_rate=playbook.success_rate,
+            )
+
+            return {
+                "source": "playbook",
+                "playbook_id": playbook.playbook_id,
+                "playbook_name": playbook.name,
+                "action": kubectl_command,
+                "kubectl_command": kubectl_command,
+                "description": playbook.description,
+                "risk_level": playbook.repair_steps[0].risk_level.value.lower() if playbook.repair_steps else "medium",
+                "reasoning": f"Playbook 匹配 ({best_match.similarity_score:.0%} 相似度, {playbook.success_rate:.0%} 成功率): {best_match.reason}",
+                "confidence": min(best_match.similarity_score, playbook.success_rate) if playbook.total_executions > 0 else best_match.similarity_score,
+                "matched_symptoms": best_match.matched_symptoms,
+                "from_cache": False,
+            }
+
+        except Exception as e:
+            logger.warning(
+                "playbook_match_error",
+                incident_id=incident.incident_id,
+                error=str(e),
+            )
+            return None
+
     async def _find_existing_token(
         self,
         incident_id: str,

apps/api/src/services/playbook_service.py

@@ -322,31 +322,39 @@ class PlaybookService:
     def _extract_repair_steps(self, incident: Incident) -> list[RepairStep]:
         """從 Incident 萃取修復步驟"""
         steps: list[RepairStep] = []
+        step_number = 1
 
-        # 從 decision_chain 提取
-        if incident.decision_chain:
-            for i, step in enumerate(incident.decision_chain.steps, 1):
-                if step.executed_action:
-                    steps.append(
-                        RepairStep(
-                            step_number=i,
-                            action_type=ActionType.KUBECTL,
-                            command=step.executed_action,
-                            expected_result=step.result or None,
-                            risk_level=RiskLevel.MEDIUM,
+        # 從 decision_chain.reasoning_steps 提取 kubectl 命令
+        if incident.decision_chain and incident.decision_chain.reasoning_steps:
+            for reasoning in incident.decision_chain.reasoning_steps:
+                # 尋找包含 kubectl 的步驟
+                if "kubectl" in reasoning.lower():
+                    # 嘗試提取 kubectl 命令
+                    import re
+                    kubectl_match = re.search(r"kubectl\s+\S+.*", reasoning)
+                    if kubectl_match:
+                        steps.append(
+                            RepairStep(
+                                step_number=step_number,
+                                action_type=ActionType.KUBECTL,
+                                command=kubectl_match.group(0).strip(),
+                                risk_level=RiskLevel.MEDIUM,
+                            )
                         )
-                    )
+                        step_number += 1
 
-        # 如果沒有從 decision_chain 取得，嘗試從 outcome 取得
-        if not steps and incident.outcome and incident.outcome.repair_action:
-            steps.append(
-                RepairStep(
-                    step_number=1,
-                    action_type=ActionType.KUBECTL,
-                    command=incident.outcome.repair_action,
-                    risk_level=RiskLevel.MEDIUM,
+        # 如果沒有從 reasoning_steps 取得，嘗試從 learning_notes 取得
+        if not steps and incident.outcome and incident.outcome.learning_notes:
+            notes = incident.outcome.learning_notes
+            if "kubectl" in notes.lower():
+                steps.append(
+                    RepairStep(
+                        step_number=1,
+                        action_type=ActionType.KUBECTL,
+                        command=notes,
+                        risk_level=RiskLevel.MEDIUM,
+                    )
                 )
-            )
 
         return steps
 
@@ -358,7 +366,7 @@ class PlaybookService:
         effectiveness_bonus = (effectiveness - 3) * 0.2
 
         # 有 decision_chain 加分
-        if incident.decision_chain and incident.decision_chain.steps:
+        if incident.decision_chain and incident.decision_chain.reasoning_steps:
             base_score += 0.1
 
         # 有多個 signals 加分 (更多資料)
@@ -385,8 +393,9 @@ class PlaybookService:
         if incident.affected_services:
             parts.append(f"影響服務: {', '.join(incident.affected_services)}")
 
-        if incident.outcome and incident.outcome.repair_action:
-            parts.append(f"修復動作: {incident.outcome.repair_action[:100]}")
+        # 從 decision_chain.hypothesis 取得 AI 分析結果
+        if incident.decision_chain and incident.decision_chain.hypothesis:
+            parts.append(f"AI 分析: {incident.decision_chain.hypothesis[:100]}")
 
         return ". ".join(parts) if parts else "從成功案例自動萃取的修復劇本"