diff --git a/apps/api/src/api/v1/github_webhook.py b/apps/api/src/api/v1/github_webhook.py index 2a2ca9bb..a6dbc4af 100644 --- a/apps/api/src/api/v1/github_webhook.py +++ b/apps/api/src/api/v1/github_webhook.py @@ -1128,7 +1128,7 @@ async def call_openclaw_ci_diagnosis( auto_fixable=False, risk_level="medium", analyzed_by="fallback", - confidence=0.3, + confidence=0.0, # 🔴 Fallback 不是 AI 分析 ) except httpx.TimeoutException: @@ -1141,7 +1141,7 @@ async def call_openclaw_ci_diagnosis( auto_fixable=False, risk_level="low", analyzed_by="fallback", - confidence=0.1, + confidence=0.0, # 🔴 Fallback 不是 AI 分析 ) except Exception as e: logger.exception("openclaw_ci_diagnosis_error", error=str(e)) diff --git a/apps/api/src/services/decision_manager.py b/apps/api/src/services/decision_manager.py index c2804f22..b3fe9c09 100644 --- a/apps/api/src/services/decision_manager.py +++ b/apps/api/src/services/decision_manager.py @@ -677,7 +677,7 @@ class DecisionManager: "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, + "confidence": 0.0, # 🔴 Playbook RAG 匹配不是 AI 分析，信心度設 0 "matched_symptoms": best_match.matched_symptoms, "from_cache": False, } @@ -839,7 +839,7 @@ class DecisionManager: "risk_level": consensus_result.risk_level, "kubectl_command": consensus_result.recommended_kubectl, "reasoning": consensus_result.final_reasoning, - "confidence": consensus_result.consensus_score, + "confidence": 0.0, # 🔴 Consensus Engine 共識分數不是 AI 信心度，設 0 "agent_count": len(consensus_result.opinions), "dissenting_opinions": consensus_result.dissenting_opinions, "from_cache": False, diff --git a/apps/api/src/services/intent_classifier.py b/apps/api/src/services/intent_classifier.py index f04ad3b8..a62ef4bf 100644 --- a/apps/api/src/services/intent_classifier.py +++ b/apps/api/src/services/intent_classifier.py @@ -518,11 +518,11 @@ class IntentClassifier: """ # TODO: 整合 Ollama qwen2.5:1b (Phase 13.4) # 預計使用 text 呼叫 Ollama API 進行分類 - # 目前先返回低信心度 UNKNOWN，規則引擎已能處理大部分情況 + # 目前先返回 UNKNOWN，規則引擎已能處理大部分情況 del text # 預留給 LLM 分類使用，避免 unused-parameter 警告 return IntentResult( intent=IntentType.UNKNOWN, - confidence=0.3, + confidence=0.0, # 🔴 LLM 未啟用，非 AI 分析 method="llm", matched_keywords=[], detected_resources=[], diff --git a/apps/api/src/services/learning_service.py b/apps/api/src/services/learning_service.py index c3e8b73a..f1a35213 100644 --- a/apps/api/src/services/learning_service.py +++ b/apps/api/src/services/learning_service.py @@ -544,12 +544,13 @@ class LearningService: return { "action": best["action"], - "confidence": best["success_rate"], + "confidence": 0.0, # 🔴 歷史學習不是 AI 分析，信心度設 0 "tier": best["tier"], "based_on": f"{best['total_samples']} 次歷史數據", "avg_execution_time": best["avg_execution_time"], + "success_rate": best["success_rate"], # 保留原始成功率作為參考 "alternatives": [ - {"action": a["action"], "confidence": a["success_rate"], "tier": a["tier"]} + {"action": a["action"], "confidence": 0.0, "success_rate": a["success_rate"], "tier": a["tier"]} for a in alternatives ], } @@ -589,12 +590,12 @@ class LearningService: """預設推薦 (無歷史數據時)""" return { "action": "restart_pod", - "confidence": 0.3, + "confidence": 0.0, # 🔴 預設推薦不是 AI 分析，信心度設 0 "tier": 1, "based_on": "無歷史數據，使用預設", "avg_execution_time": 30.0, "alternatives": [ - {"action": "delete_pod", "confidence": 0.3, "tier": 1}, + {"action": "delete_pod", "confidence": 0.0, "tier": 1}, ], } diff --git a/k8s/awoooi-prod/12-hpa.yaml b/k8s/awoooi-prod/12-hpa.yaml index a1143aa0..61d71140 100644 --- a/k8s/awoooi-prod/12-hpa.yaml +++ b/k8s/awoooi-prod/12-hpa.yaml @@ -3,6 +3,7 @@ # ============================================================================= # K3.2 2026-03-28: HPA for API/Web (based on VPA recommendations) # P3 2026-03-29: maxReplicas 4 → 6 (首席架構師建議) +# K-Worker 2026-03-29: Worker HPA (min:1 max:3, CPU 指標) # Deployed by: Claude Code (首席架構師) # VPA 建議: API target 100m, Web target 63m # ============================================================================= @@ -94,3 +95,54 @@ spec: - type: Pods value: 1 periodSeconds: 120 +--- +# ============================================================================= +# Worker HPA（K-Worker 2026-03-29） +# ============================================================================= +# 前置條件: ADR-038/039 Wave 1 完成（XCLAIM + terminationGracePeriodSeconds:90） +# 說明: Worker 消費 Redis Streams，使用 CPU 指標（未來可升級 KEDA） +# ============================================================================= +apiVersion: autoscaling/v2 +kind: HorizontalPodAutoscaler +metadata: + name: awoooi-worker-hpa + namespace: awoooi-prod + labels: + app.kubernetes.io/name: awoooi + app.kubernetes.io/component: worker + annotations: + description: "Worker 水平自動擴展 (1-3 replicas, 70% CPU)" + note: "未來可升級 KEDA Redis Stream 指標按 Queue 長度擴縮" +spec: + scaleTargetRef: + apiVersion: apps/v1 + kind: Deployment + name: awoooi-worker + minReplicas: 1 + maxReplicas: 3 + metrics: + - type: Resource + resource: + name: cpu + target: + type: Utilization + averageUtilization: 70 + - type: Resource + resource: + name: memory + target: + type: Utilization + averageUtilization: 80 + behavior: + scaleUp: + stabilizationWindowSeconds: 120 + policies: + - type: Pods + value: 1 + periodSeconds: 120 + scaleDown: + stabilizationWindowSeconds: 600 + policies: + - type: Pods + value: 1 + periodSeconds: 300