fix(api): fix dockerfile to include models.json, remove huge prompt example to fit 4K limit
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@@ -39,8 +39,9 @@ WORKDIR /app
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COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages
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COPY --from=builder /usr/local/bin /usr/local/bin
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# Copy application code
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# Copy application code and models config
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COPY apps/api/src/ ./src/
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COPY apps/api/models.json ./models.json
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# Create non-root user
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RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app
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@@ -102,46 +102,8 @@ For each optimization suggestion, provide EXECUTABLE kubectl commands:
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}
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```
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## 🔥 Example: High CPU with SignOz Data
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Given SignOz metrics: RPS=150 (↑), Error=0.5%, P99=450ms (↑)
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```json
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{
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"action_title": "擴展副本數 + 配置 HPA 自動擴展",
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"description": "api-gateway CPU 飆高,SignOz 顯示 RPS 從 80 飆升至 150 (+87%),P99 從 200ms 升至 450ms。流量突增導致資源不足。",
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"suggested_action": "SCALE_DEPLOYMENT",
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"kubectl_command": "kubectl scale deployment/api-gateway --replicas=4 -n production",
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"target_resource": "api-gateway",
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"namespace": "production",
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"risk_level": "medium",
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"blast_radius": {
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"affected_pods": 0,
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"estimated_downtime": "0",
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"related_services": [],
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"data_impact": "NONE"
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},
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"primary_responsibility": "INFRA",
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"responsibility_reasoning": "流量突增但 HPA 未配置,屬基礎設施團隊責任",
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"secondary_teams": ["BE"],
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"optimization_suggestions": [
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{
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"type": "HPA",
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"description": "配置 CPU 基準 HPA,閾值 70%,基於 SignOz RPS 趨勢",
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"kubectl_or_config": "kubectl autoscale deployment api-gateway --cpu-percent=70 --min=2 --max=10 -n production"
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},
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{
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"type": "RESOURCE_LIMIT",
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"description": "增加 CPU limit 以應對流量峰值",
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"kubectl_or_config": "kubectl set resources deployment/api-gateway --requests=cpu=500m --limits=cpu=2000m -n production"
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}
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],
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"reasoning": "SignOz 數據顯示流量突增為主因,非代碼問題。先擴容緩解,再配置 HPA 防止復發。",
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"deviation_analysis": "RPS +87%,P99 延遲 +125%,超出基準線達 +4.2σ",
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"confidence": 0.91,
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"affected_services": ["api-gateway"],
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"signoz_correlation": "RPS 與 CPU 使用率高度相關 (r=0.94),P99 上升為資源競爭導致"
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}
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```
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## 🔥 Short Example: High CPU -> SCALE_DEPLOYMENT, HPA, risk_level=medium
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Please carefully justify your confidence between 0.0 and 1.0 (e.g. 0.82) based on symptoms and metrics.
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Now analyze the following alert with SignOz data:
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"""
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