awoooi/docs/proposals/IMPLEMENTATION_STEPS_REMAINING_PHASES.md

剩餘 Phase 實施步驟 (D-G)

總工時: 10h → 7h 35min (修正後) 優先級: P0-P1

---

🔍 首席架構師審查 (2026-03-29)

評分項目	分數	說明
架構合規	75/100	多處違反 leWOOOgo 積木化原則
代碼品質	80/100	結構清晰但有冗餘
測試策略	40/100	🔴 違反禁止 Mock 鐵律
API 設計	85/100	符合路徑命名規範
總分	74/100	⚠️ 條件通過

🔴 P0 嚴重問題 (必須修正)

1. Phase G 重複: 與現有 apps/api/src/services/learning_service.py 功能高度重複

   • ❌ 禁止重複實作 LearningService
   • ✅ 應擴展現有類別，新增 Redis 持久化層

2. 違反積木化: Service 直接依賴 Redis Client

   • ❌ def __init__(self, redis_client: redis.Redis):
   • ✅ 必須透過 ILearningRepository Interface

3. 硬編碼 URL: Phase F Smoke Test 硬編碼 K8s URL

   • ❌ API_BASE = "http://awoooi-api.awoooi-prod.svc.cluster.local:8000"
   • ✅ 使用 os.getenv("AWOOOI_API_BASE", "http://localhost:8000")

📊 工時調整

Phase	原工時	修正後	說明
D	1h	1h 20min	移至 SentryService
E	2h	2h 30min	建立 SignozService
F	2h	2h 15min	環境變數注入
G	3h	1h 30min	擴展現有 LearningService
總計	8h	7h 35min	-25min

詳細審查報告

→ ~/.claude/projects/-Users-ogt-awoooi/memory/project_remaining_phases_arch_review.md

---

Phase D: Sentry Comment 回寫 (1h)

現狀

# sentry_webhook.py:290-302 - 目前是 TODO
# TODO: 需要 Sentry API Token
logger.info(f"Would post comment to issue {issue_id}...")

Step D-1: 取得 Sentry API Token (10min)

# 在 Sentry Self-Hosted 管理後台
# Settings → API Tokens → Create New Token
# 權限: project:read, issue:write

# 儲存到 K8s Secret
kubectl create secret generic sentry-api-token \
  --from-literal=SENTRY_API_TOKEN=your_token_here \
  -n awoooi-prod

Step D-2: 實作 Comment 回寫 (30min)

# apps/api/src/api/v1/sentry_webhook.py
# 完成 post_sentry_comment 實作

import os

SENTRY_API_TOKEN = os.getenv("SENTRY_API_TOKEN")
SENTRY_API_URL = "http://192.168.0.110:9000"


async def post_sentry_comment(
    project_slug: str,
    issue_id: str,
    analysis: ErrorAnalysisResult,
):
    """
    回寫分析結果到 Sentry Issue Comment

    API: POST /api/0/issues/{issue_id}/comments/
    Docs: https://docs.sentry.io/api/events/create-a-comment/
    """
    if not SENTRY_API_TOKEN:
        logger.warning("SENTRY_API_TOKEN not configured, skipping comment")
        return

    comment_text = f"""## 🧠 AI 錯誤分析 (by {analysis.analyzed_by})

**根本原因 (Root Cause)**
{analysis.root_cause}

**影響範圍 (Impact)**
{analysis.impact}

**建議修復 (Fix Suggestion)**

{analysis.fix_suggestion}

**預防措施 (Prevention)**
{analysis.prevention}

---
*分析信心度: {analysis.confidence:.0%} | 分析時間: {now_taipei_iso()}*
*Powered by AWOOOI + OpenClaw*
"""

    try:
        async with httpx.AsyncClient(timeout=30.0) as client:
            response = await client.post(
                f"{SENTRY_API_URL}/api/0/issues/{issue_id}/comments/",
                headers={
                    "Authorization": f"Bearer {SENTRY_API_TOKEN}",
                    "Content-Type": "application/json",
                },
                json={"text": comment_text}
            )

            if response.status_code == 201:
                logger.info(
                    "sentry_comment_posted",
                    issue_id=issue_id,
                    comment_length=len(comment_text),
                )
            else:
                logger.warning(
                    "sentry_comment_failed",
                    issue_id=issue_id,
                    status=response.status_code,
                    response=response.text[:200],
                )

    except Exception as e:
        logger.exception("sentry_comment_error", issue_id=issue_id, error=str(e))

Step D-3: 更新 K8s Deployment (10min)

# k8s/awoooi-prod/03-secrets.yaml
# 新增 Sentry API Token

---
apiVersion: v1
kind: Secret
metadata:
  name: sentry-api-token
  namespace: awoooi-prod
type: Opaque
stringData:
  SENTRY_API_TOKEN: "${SENTRY_API_TOKEN}"

# k8s/awoooi-prod/04-deployment-api.yaml
# 掛載環境變數

env:
  - name: SENTRY_API_TOKEN
    valueFrom:
      secretKeyRef:
        name: sentry-api-token
        key: SENTRY_API_TOKEN

Step D-4: 驗證 (10min)

# 手動觸發測試
curl -X POST http://localhost:8000/api/v1/webhooks/sentry/error \
  -H "Content-Type: application/json" \
  -d '{
    "action": "triggered",
    "data": {
      "issue": {
        "id": "12345",
        "title": "Test Error",
        "level": "error",
        "project": {"slug": "awoooi-api"}
      }
    }
  }'

# 檢查 Sentry Issue 是否有 Comment

---

Phase E: SignOz 告警規則 (2h)

現狀分析

• SignOz 只做資料收集，無告警輸出
• Error Rate / Latency 異常無法即時通知

Step E-1: SignOz 告警配置 (1h)

# signoz/alerting/rules.yaml
# SignOz 自訂告警規則

groups:
  # =========================================================================
  # API Error Rate 告警
  # =========================================================================
  - name: api_errors
    rules:
      - alert: APIHighErrorRate
        expr: |
          sum(rate(signoz_spans_total{
            service_name="awoooi-api",
            status_code=~"5.."
          }[5m])) by (service_name)
          /
          sum(rate(signoz_spans_total{
            service_name="awoooi-api"
          }[5m])) by (service_name)
          > 0.05
        for: 5m
        labels:
          severity: critical
          source: signoz
        annotations:
          summary: "API 錯誤率 > 5%"
          description: "服務 {{ $labels.service_name }} 錯誤率: {{ $value | humanizePercentage }}"
          webhook: "http://awoooi-api.awoooi-prod:8000/api/v1/webhooks/signoz"

  # =========================================================================
  # Latency 告警
  # =========================================================================
  - name: latency
    rules:
      - alert: APIHighLatencyP99
        expr: |
          histogram_quantile(0.99,
            sum(rate(signoz_spans_duration_bucket{
              service_name="awoooi-api"
            }[5m])) by (le, service_name)
          ) > 2
        for: 5m
        labels:
          severity: warning
          source: signoz
        annotations:
          summary: "API P99 延遲 > 2s"
          description: "服務 {{ $labels.service_name }} P99: {{ $value }}s"

      - alert: APIHighLatencyP95
        expr: |
          histogram_quantile(0.95,
            sum(rate(signoz_spans_duration_bucket{
              service_name="awoooi-api"
            }[5m])) by (le, service_name)
          ) > 1
        for: 10m
        labels:
          severity: warning
          source: signoz
        annotations:
          summary: "API P95 延遲 > 1s"

  # =========================================================================
  # Trace 異常告警
  # =========================================================================
  - name: traces
    rules:
      - alert: NoTracesReceived
        expr: |
          sum(rate(signoz_spans_total[15m])) == 0
        for: 15m
        labels:
          severity: warning
          source: signoz
        annotations:
          summary: "15 分鐘內無 Trace 數據"
          description: "可能是 OTEL Collector 或應用程式問題"

      - alert: HighSpanDropRate
        expr: |
          sum(rate(otelcol_exporter_send_failed_spans[5m]))
          /
          sum(rate(otelcol_exporter_sent_spans[5m]))
          > 0.01
        for: 5m
        labels:
          severity: warning
          source: signoz
        annotations:
          summary: "Span 丟棄率 > 1%"

Step E-2: 建立 SignOz Webhook Handler (30min)

# apps/api/src/api/v1/signoz_webhook.py
"""
SignOz 告警 Webhook Handler
"""

from fastapi import APIRouter, Request, BackgroundTasks
import structlog

from src.services.incident_service import get_incident_service
from src.services.telegram_gateway import get_telegram_gateway

logger = structlog.get_logger(__name__)
router = APIRouter(prefix="/webhooks/signoz", tags=["SignOz Webhook"])


@router.post("/alert")
async def handle_signoz_alert(
    request: Request,
    background_tasks: BackgroundTasks,
):
    """
    處理 SignOz 告警

    SignOz 告警格式:
    {
        "alertname": "APIHighErrorRate",
        "status": "firing",
        "labels": {...},
        "annotations": {...},
        "startsAt": "2026-03-29T10:00:00Z"
    }
    """
    payload = await request.json()
    logger.info("signoz_alert_received", payload=payload)

    alert_name = payload.get("alertname")
    status = payload.get("status")

    if status != "firing":
        return {"status": "ignored", "reason": "not firing"}

    # 轉換為標準告警格式
    normalized = {
        "labels": {
            "alertname": alert_name,
            "source": "signoz",
            **payload.get("labels", {}),
        },
        "annotations": payload.get("annotations", {}),
        "startsAt": payload.get("startsAt"),
    }

    # 建立 Incident
    incident_service = get_incident_service()
    incident, is_new = await incident_service.create_or_aggregate_incident(
        alert_data=normalized,
    )

    if is_new:
        # 發送 Telegram
        background_tasks.add_task(
            notify_signoz_alert,
            incident=incident,
            alert_data=normalized,
        )

    return {
        "status": "accepted",
        "incident_id": str(incident.id),
        "is_new": is_new,
    }


async def notify_signoz_alert(incident, alert_data: dict):
    """發送 SignOz 告警到 Telegram"""
    telegram = get_telegram_gateway()
    await telegram.initialize()

    annotations = alert_data.get("annotations", {})

    await telegram.send_alert_card(
        title=f"📊 SignOz: {alert_data['labels']['alertname']}",
        severity=alert_data['labels'].get('severity', 'warning'),
        description=annotations.get('description', annotations.get('summary', '')),
        source="signoz",
        incident_id=str(incident.id),
    )

Step E-3: 註冊路由 (10min)

# apps/api/src/main.py
from src.api.v1 import signoz_webhook

app.include_router(signoz_webhook.router, prefix="/api/v1")

Step E-4: 部署告警規則 (20min)

# 複製規則到 SignOz
scp signoz/alerting/rules.yaml 192.168.0.188:/opt/signoz/config/alerting/

# 重啟 SignOz Query Service
ssh 192.168.0.188 "docker restart signoz-query-service"

# 驗證規則載入
curl http://192.168.0.188:3301/api/v3/alerts/rules

---

Phase F: 告警鏈路 E2E 驗證 (2h)

現狀問題

• 2026-03-26: 路徑錯誤導致 2 天無告警
• 部署後無自動驗證機制

Step F-1: 建立 Smoke Test 腳本 (30min)

# ops/scripts/alert_chain_smoke_test.py
#!/usr/bin/env python3
"""
告警鏈路端到端驗證

執行:
  python ops/scripts/alert_chain_smoke_test.py

驗證項目:
1. Alertmanager Webhook 可達
2. Sentry Webhook 可達
3. SignOz Webhook 可達
4. Telegram 發送成功
5. Approval 建立成功
"""

import asyncio
import httpx
import sys
from datetime import datetime


API_BASE = "http://awoooi-api.awoooi-prod.svc.cluster.local:8000"
# 本地測試用
# API_BASE = "http://localhost:8000"

TIMEOUT = 30


async def test_alertmanager_webhook() -> bool:
    """測試 Alertmanager Webhook"""
    print("🔍 Testing Alertmanager Webhook...")

    test_payload = {
        "version": "4",
        "status": "firing",
        "alerts": [{
            "status": "firing",
            "labels": {
                "alertname": "E2E_SMOKE_TEST",
                "severity": "info",
                "service": "smoke-test",
                "namespace": "test",
            },
            "annotations": {
                "summary": "E2E Smoke Test - 請忽略",
                "description": f"自動測試 @ {datetime.now().isoformat()}",
            },
            "startsAt": datetime.now().isoformat() + "Z",
        }]
    }

    async with httpx.AsyncClient(timeout=TIMEOUT) as client:
        try:
            response = await client.post(
                f"{API_BASE}/api/v1/webhooks/alertmanager",
                json=test_payload,
            )
            if response.status_code == 200:
                print("  ✅ Alertmanager Webhook: OK")
                return True
            else:
                print(f"  ❌ Alertmanager Webhook: {response.status_code}")
                print(f"     Response: {response.text[:200]}")
                return False
        except Exception as e:
            print(f"  ❌ Alertmanager Webhook: {e}")
            return False


async def test_sentry_webhook() -> bool:
    """測試 Sentry Webhook"""
    print("🔍 Testing Sentry Webhook...")

    test_payload = {
        "action": "triggered",
        "data": {
            "issue": {
                "id": "smoke-test-" + datetime.now().strftime("%Y%m%d%H%M%S"),
                "title": "E2E Smoke Test Error",
                "level": "error",
                "culprit": "smoke_test.py:test",
                "project": {"slug": "awoooi-api"},
                "firstSeen": datetime.now().isoformat(),
                "count": 1,
            },
            "event": {
                "message": "E2E Smoke Test - 請忽略",
                "platform": "python",
            },
        },
    }

    async with httpx.AsyncClient(timeout=TIMEOUT) as client:
        try:
            response = await client.post(
                f"{API_BASE}/api/v1/webhooks/sentry/error",
                json=test_payload,
            )
            if response.status_code == 200:
                result = response.json()
                if result.get("status") in ["accepted", "deduplicated"]:
                    print("  ✅ Sentry Webhook: OK")
                    return True
            print(f"  ❌ Sentry Webhook: {response.status_code}")
            return False
        except Exception as e:
            print(f"  ❌ Sentry Webhook: {e}")
            return False


async def test_health_endpoint() -> bool:
    """測試 Health Endpoint"""
    print("🔍 Testing Health Endpoint...")

    async with httpx.AsyncClient(timeout=TIMEOUT) as client:
        try:
            response = await client.get(f"{API_BASE}/api/v1/health")
            if response.status_code == 200:
                print("  ✅ Health: OK")
                return True
            else:
                print(f"  ❌ Health: {response.status_code}")
                return False
        except Exception as e:
            print(f"  ❌ Health: {e}")
            return False


async def test_telegram_connectivity() -> bool:
    """測試 Telegram 連通性"""
    print("🔍 Testing Telegram Connectivity...")

    async with httpx.AsyncClient(timeout=TIMEOUT) as client:
        try:
            # 透過內部 API 檢查 Telegram 狀態
            response = await client.get(f"{API_BASE}/api/v1/telegram/status")
            if response.status_code == 200:
                data = response.json()
                if data.get("connected"):
                    print("  ✅ Telegram: Connected")
                    return True
                else:
                    print("  ⚠️ Telegram: Not Connected (but endpoint reachable)")
                    return True  # 端點可達即可
            else:
                print(f"  ❌ Telegram: {response.status_code}")
                return False
        except Exception as e:
            print(f"  ⚠️ Telegram: {e} (endpoint may not exist)")
            return True  # 不影響整體測試


async def main():
    print("=" * 60)
    print("🚀 AWOOOI 告警鏈路 E2E Smoke Test")
    print(f"   時間: {datetime.now().isoformat()}")
    print(f"   目標: {API_BASE}")
    print("=" * 60)

    results = await asyncio.gather(
        test_health_endpoint(),
        test_alertmanager_webhook(),
        test_sentry_webhook(),
        test_telegram_connectivity(),
    )

    print("=" * 60)
    passed = sum(results)
    total = len(results)

    if passed == total:
        print(f"✅ 全部通過 ({passed}/{total})")
        sys.exit(0)
    else:
        print(f"❌ 部分失敗 ({passed}/{total})")
        sys.exit(1)


if __name__ == "__main__":
    asyncio.run(main())

Step F-2: 整合到 CD Pipeline (30min)

# .github/workflows/cd.yaml
# 新增 smoke test 步驟

jobs:
  deploy:
    # ... 現有步驟 ...

    - name: Wait for Pods Ready
      run: |
        kubectl rollout status deployment/awoooi-api -n awoooi-prod --timeout=5m

    # 🆕 告警鏈路驗證
    - name: Alert Chain Smoke Test
      run: |
        # 等待服務完全啟動
        sleep 30

        # 執行 smoke test
        python ops/scripts/alert_chain_smoke_test.py

      env:
        API_BASE: "http://awoooi-api.awoooi-prod.svc.cluster.local:8000"

    - name: Notify on Smoke Test Failure
      if: failure()
      run: |
        # 直接發送 Telegram 告警 (繞過可能壞掉的 API)
        curl -X POST "https://api.telegram.org/bot${TG_BOT_TOKEN}/sendMessage" \
          -d "chat_id=${TG_CHAT_ID}" \
          -d "text=🚨 AWOOOI CD Smoke Test 失敗！告警鏈路可能中斷！"
      env:
        TG_BOT_TOKEN: ${{ secrets.OPENCLAW_TG_BOT_TOKEN }}
        TG_CHAT_ID: ${{ secrets.OPENCLAW_TG_CHAT_ID }}

Step F-3: 建立鏈路監控告警 (30min)

# k8s/monitoring/alert-chain-monitor.yaml
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: alert-chain-monitor
  namespace: monitoring
spec:
  groups:
    - name: alert_chain
      rules:
        # Alertmanager Webhook 無回應
        - alert: AlertChainBroken_Alertmanager
          expr: |
            sum(rate(http_requests_total{
              path="/api/v1/webhooks/alertmanager",
              status!="200"
            }[5m])) > 0
            or
            absent(http_requests_total{path="/api/v1/webhooks/alertmanager"})
          for: 10m
          labels:
            severity: critical
            service: alert-chain
          annotations:
            summary: "Alertmanager Webhook 鏈路異常"
            description: "告警無法送達 AWOOOI API"

        # Sentry Webhook 無回應
        - alert: AlertChainBroken_Sentry
          expr: |
            sum(rate(http_requests_total{
              path="/api/v1/webhooks/sentry/error",
              status!="200"
            }[5m])) > 0
          for: 10m
          labels:
            severity: warning
            service: alert-chain
          annotations:
            summary: "Sentry Webhook 鏈路異常"

        # 長時間無告警 (可能鏈路斷了)
        - alert: NoAlertsReceivedLong
          expr: |
            time() - max(awoooi_last_alert_received_timestamp) > 7200
          for: 5m
          labels:
            severity: warning
            service: alert-chain
          annotations:
            summary: "2 小時內未收到任何告警"
            description: "可能是告警鏈路問題或系統異常穩定"

Step F-4: 新增 Metrics (30min)

# apps/api/src/core/metrics.py
# 新增告警鏈路 metrics

from prometheus_client import Counter, Gauge, Histogram
import time

# 最後收到告警的時間戳
LAST_ALERT_RECEIVED = Gauge(
    'awoooi_last_alert_received_timestamp',
    'Timestamp of last received alert',
)

# 告警接收計數
ALERTS_RECEIVED = Counter(
    'awoooi_alerts_received_total',
    'Total alerts received',
    ['source', 'status']
)

# Webhook 處理延遲
WEBHOOK_LATENCY = Histogram(
    'awoooi_webhook_latency_seconds',
    'Webhook processing latency',
    ['webhook_type'],
    buckets=[0.1, 0.5, 1, 2, 5, 10, 30]
)


def record_alert_received(source: str, status: str = "accepted"):
    """記錄收到告警"""
    LAST_ALERT_RECEIVED.set(time.time())
    ALERTS_RECEIVED.labels(source=source, status=status).inc()

---

Phase G: Learning Service (3h)

Step G-1: 建立 learning_service.py (1.5h)

# apps/api/src/services/learning_service.py
"""
異常學習服務 - 從解決方案中學習
================================
2026-03-29 ogt: 監控戰略規劃 Section 9.4 實作

功能:
1. 記錄每次修復的效果
2. 計算各動作的成功率
3. 推薦最佳修復方案
4. 自動更新 Playbook
"""

import json
from datetime import datetime
from typing import Optional

import redis.asyncio as redis
import structlog

from src.services.anomaly_counter import get_anomaly_counter
from src.services.playbook_service import get_playbook_service

logger = structlog.get_logger(__name__)


class LearningService:
    """
    學習每次修復的效果，自動更新 Playbook
    """

    # 學習門檻: 需要至少 N 次數據才能推薦
    MIN_SAMPLES = 5

    # 成功率門檻: 高於此值才會被推薦
    SUCCESS_RATE_THRESHOLD = 0.6

    # Tier 對應的動作
    TIER_ACTIONS = {
        1: ['restart_pod', 'restart_container', 'delete_pod'],
        2: ['scale_up', 'increase_memory', 'increase_cpu', 'adjust_limits'],
        3: ['apply_hotfix', 'update_config', 'patch_deployment', 'rollback'],
        4: ['create_issue', 'notify_team', 'schedule_fix', 'manual_intervention'],
    }

    def __init__(self, redis_client: redis.Redis):
        self.redis = redis_client

    async def record_repair_result(
        self,
        anomaly_key: str,
        repair_action: str,
        success: bool,
        root_cause: Optional[str] = None,
        fix_description: Optional[str] = None,
        execution_time_seconds: Optional[float] = None,
    ):
        """
        記錄修復結果，用於學習

        Args:
            anomaly_key: 異常 key
            repair_action: 修復動作
            success: 是否成功
            root_cause: 根因 (如果找到)
            fix_description: 修復說明
            execution_time_seconds: 執行時間
        """
        # 1. 記錄到 AnomalyCounter
        counter = get_anomaly_counter()
        await counter.record_repair_attempt(anomaly_key, repair_action, success)

        # 2. 記錄詳細學習數據
        learning_key = f"learning:repair:{anomaly_key}:{repair_action}"
        record = {
            'success': success,
            'root_cause': root_cause,
            'fix_description': fix_description,
            'execution_time': execution_time_seconds,
            'timestamp': datetime.now().isoformat(),
        }

        await self.redis.lpush(learning_key, json.dumps(record))
        await self.redis.ltrim(learning_key, 0, 99)  # 保留最近 100 次
        await self.redis.expire(learning_key, 90 * 24 * 3600)  # 90 天

        # 3. 如果找到根因且修復成功，考慮更新 Playbook
        if success and root_cause:
            await self._consider_playbook_update(
                anomaly_key=anomaly_key,
                repair_action=repair_action,
                root_cause=root_cause,
                fix_description=fix_description,
            )

        logger.info(
            "learning_recorded",
            anomaly_key=anomaly_key,
            action=repair_action,
            success=success,
            has_root_cause=root_cause is not None,
        )

    async def get_recommended_fix(self, anomaly_key: str) -> dict:
        """
        根據歷史學習，推薦最佳修復方案

        Returns:
            {
                'action': 'scale_up',
                'confidence': 0.85,
                'tier': 2,
                'based_on': '12 次歷史數據',
                'avg_execution_time': 45.2,
                'alternatives': [...]
            }
        """
        counter = get_anomaly_counter()
        all_stats = await counter.get_all_repair_stats(anomaly_key)

        if not all_stats:
            return self._default_recommendation()

        # 計算各動作的加權分數
        scored_actions = []
        for action, stats in all_stats.items():
            if stats['total'] >= self.MIN_SAMPLES:
                success_rate = stats['success_rate']
                if success_rate >= self.SUCCESS_RATE_THRESHOLD:
                    # 加權: 成功率 * log(樣本數)
                    import math
                    score = success_rate * math.log(stats['total'] + 1)

                    # 取得平均執行時間
                    avg_time = await self._get_avg_execution_time(anomaly_key, action)

                    scored_actions.append({
                        'action': action,
                        'score': score,
                        'success_rate': success_rate,
                        'total_samples': stats['total'],
                        'tier': self._get_action_tier(action),
                        'avg_execution_time': avg_time,
                    })

        if not scored_actions:
            return self._default_recommendation()

        # 排序: 優先高成功率，其次低 Tier
        scored_actions.sort(key=lambda x: (-x['score'], x['tier']))

        best = scored_actions[0]
        alternatives = scored_actions[1:3] if len(scored_actions) > 1 else []

        return {
            'action': best['action'],
            'confidence': best['success_rate'],
            'tier': best['tier'],
            'based_on': f"{best['total_samples']} 次歷史數據",
            'avg_execution_time': best['avg_execution_time'],
            'alternatives': [
                {'action': a['action'], 'confidence': a['success_rate'], 'tier': a['tier']}
                for a in alternatives
            ],
        }

    async def _get_avg_execution_time(self, anomaly_key: str, action: str) -> float:
        """取得平均執行時間"""
        learning_key = f"learning:repair:{anomaly_key}:{action}"
        records = await self.redis.lrange(learning_key, 0, 19)  # 最近 20 次

        times = []
        for r in records:
            data = json.loads(r)
            if data.get('execution_time'):
                times.append(data['execution_time'])

        return sum(times) / len(times) if times else 0.0

    async def _consider_playbook_update(
        self,
        anomaly_key: str,
        repair_action: str,
        root_cause: str,
        fix_description: str,
    ):
        """
        考慮是否要更新 Playbook

        條件:
        1. 該動作成功率 >= 80%
        2. 至少有 5 次成功記錄
        3. Playbook 中沒有更好的方案
        """
        counter = get_anomaly_counter()
        stats = await counter.get_repair_success_rate(anomaly_key, repair_action)

        if stats['total'] >= 5 and stats['success_rate'] >= 0.8:
            # 檢查是否已有 Playbook
            playbook_service = get_playbook_service()
            existing = await playbook_service.find_by_anomaly_key(anomaly_key)

            if not existing or existing.success_rate < stats['success_rate']:
                # 建立或更新 Playbook
                await playbook_service.create_or_update(
                    anomaly_key=anomaly_key,
                    root_cause=root_cause,
                    fix_action=repair_action,
                    fix_description=fix_description,
                    success_rate=stats['success_rate'],
                    total_executions=stats['total'],
                    source='auto_learning',
                )

                logger.info(
                    "playbook_auto_updated",
                    anomaly_key=anomaly_key,
                    action=repair_action,
                    success_rate=stats['success_rate'],
                )

    def _get_action_tier(self, action: str) -> int:
        """取得動作的 Tier"""
        for tier, actions in self.TIER_ACTIONS.items():
            if action in actions:
                return tier
        return 1  # 預設 Tier 1

    def _default_recommendation(self) -> dict:
        """預設推薦 (無歷史數據時)"""
        return {
            'action': 'restart_pod',
            'confidence': 0.3,
            '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},
            ],
        }

    async def get_learning_summary(self, anomaly_key: str) -> dict:
        """
        取得學習摘要

        Returns:
            {
                'anomaly_key': 'abc123',
                'total_occurrences': 15,
                'total_repair_attempts': 8,
                'overall_success_rate': 0.625,
                'actions_tried': ['restart_pod', 'scale_up'],
                'best_action': {'action': 'scale_up', 'success_rate': 0.75},
                'learning_status': 'sufficient',  # insufficient, sufficient, excellent
            }
        """
        counter = get_anomaly_counter()

        # 取得頻率統計
        # 需要從 Redis 讀取，這裡簡化
        timeline_key = f"anomaly:timeline:{anomaly_key}"
        total_occurrences = await self.redis.zcard(timeline_key)

        # 取得所有修復統計
        all_stats = await counter.get_all_repair_stats(anomaly_key)

        total_attempts = sum(s['total'] for s in all_stats.values())
        total_success = sum(s['success'] for s in all_stats.values())
        overall_rate = total_success / total_attempts if total_attempts > 0 else 0

        # 找出最佳動作
        best_action = None
        best_rate = 0
        for action, stats in all_stats.items():
            if stats['total'] >= 3 and stats['success_rate'] > best_rate:
                best_rate = stats['success_rate']
                best_action = {'action': action, 'success_rate': best_rate}

        # 判斷學習狀態
        if total_attempts < 3:
            status = 'insufficient'
        elif total_attempts < 10:
            status = 'learning'
        elif overall_rate >= 0.8:
            status = 'excellent'
        else:
            status = 'sufficient'

        return {
            'anomaly_key': anomaly_key,
            'total_occurrences': total_occurrences,
            'total_repair_attempts': total_attempts,
            'overall_success_rate': overall_rate,
            'actions_tried': list(all_stats.keys()),
            'best_action': best_action,
            'learning_status': status,
        }


# =============================================================================
# Singleton
# =============================================================================
_learning_service: LearningService | None = None


def get_learning_service() -> LearningService:
    """取得 LearningService 實例"""
    global _learning_service
    if _learning_service is None:
        from src.core.redis import get_redis_client
        _learning_service = LearningService(get_redis_client())
    return _learning_service

Step G-2: 整合到 auto_repair_service.py (1h)

# apps/api/src/services/auto_repair_service.py
# 修改執行修復的流程

from src.services.learning_service import get_learning_service
import time


class AutoRepairService:
    async def execute_repair(
        self,
        incident_id: str,
        anomaly_key: str,
        repair_action: str,
        dry_run: bool = False,
    ) -> AutoRepairResult:
        """
        執行修復並記錄學習數據
        """
        learning = get_learning_service()
        start_time = time.time()

        try:
            # 1. 執行修復
            result = await self._do_execute(repair_action, ...)

            # 2. 記錄學習數據
            execution_time = time.time() - start_time
            await learning.record_repair_result(
                anomaly_key=anomaly_key,
                repair_action=repair_action,
                success=result.success,
                root_cause=result.root_cause if hasattr(result, 'root_cause') else None,
                fix_description=result.message,
                execution_time_seconds=execution_time,
            )

            return result

        except Exception as e:
            # 記錄失敗
            await learning.record_repair_result(
                anomaly_key=anomaly_key,
                repair_action=repair_action,
                success=False,
                fix_description=str(e),
                execution_time_seconds=time.time() - start_time,
            )
            raise

    async def get_smart_recommendation(self, anomaly_key: str) -> dict:
        """
        取得智慧修復建議 (結合 AI 分析 + 歷史學習)
        """
        learning = get_learning_service()

        # 1. 取得學習推薦
        learned = await learning.get_recommended_fix(anomaly_key)

        # 2. 如果學習信心度高，直接使用
        if learned['confidence'] >= 0.8:
            return {
                'source': 'learning',
                'recommendation': learned,
            }

        # 3. 否則結合 AI 分析
        # (呼叫 OpenClaw 取得建議)
        ai_recommendation = await self._get_ai_recommendation(anomaly_key)

        # 4. 合併推薦
        return {
            'source': 'hybrid',
            'learning': learned,
            'ai': ai_recommendation,
            'final_recommendation': self._merge_recommendations(learned, ai_recommendation),
        }

Step G-3: 新增 API 端點 (30min)

# apps/api/src/api/v1/learning.py
"""
學習系統 API
"""

from fastapi import APIRouter
from src.services.learning_service import get_learning_service

router = APIRouter(prefix="/learning", tags=["Learning"])


@router.get("/summary/{anomaly_key}")
async def get_learning_summary(anomaly_key: str):
    """取得異常學習摘要"""
    learning = get_learning_service()
    return await learning.get_learning_summary(anomaly_key)


@router.get("/recommendation/{anomaly_key}")
async def get_recommendation(anomaly_key: str):
    """取得修復推薦"""
    learning = get_learning_service()
    return await learning.get_recommended_fix(anomaly_key)

---

完整實作清單總覽

Phase	項目	工時	優先級	依賴
A	AnomalyCounter	4h	P0	Redis
B	Database Exporters	3h	P0	Docker
C	Incident 頻率欄位	2h	P0	Phase A
D	Sentry Comment	1h	P1	Sentry Token
E	SignOz 告警	2h	P1	SignOz
F	Alert Chain E2E	2h	P0	Phase A
G	Learning Service	3h	P1	Phase A, C

總工時: 17h (約 2-3 天)

---

執行順序建議

Day 1 (8h):
  ├─ Phase A: AnomalyCounter (4h) ✅
  ├─ Phase B: Database Exporters (3h) ✅
  └─ Phase F: Alert Chain E2E (部分, 1h) ✅

Day 2 (6h):
  ├─ Phase C: Incident 頻率 (2h) ✅
  ├─ Phase D: Sentry Comment (1h) ✅
  └─ Phase G: Learning Service (3h) ✅

Day 3 (3h):
  ├─ Phase E: SignOz 告警 (2h) ✅
  └─ Phase F: Alert Chain E2E (完成, 1h) ✅