feat(api): Phase 6.4 LLM-based proposal generation with cache
- Add _call_with_cache wrapper in OpenClaw (Redis-based LLM cache) - Add generate_incident_proposal method for incident analysis - Integrate ProposalService with OpenClaw LLM - Fallback to template-based proposals if LLM fails - Include LLM metadata (provider, confidence, cache status) in proposals 憲法條款: 必須使用快取保護算力資源，嚴禁無快取裸奔調用 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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OG T
@@ -18,6 +18,7 @@ Features:
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- SignOz 失敗時優雅降級 (不阻塞主流程)
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"""
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import hashlib
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import json
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import re
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import time
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@@ -27,6 +28,7 @@ import httpx
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import structlog
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from src.core.config import settings
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from src.core.redis_client import get_redis
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from src.models.ai import (
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AIRiskLevel,
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AIBlastRadius,
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@@ -714,6 +716,100 @@ class OpenClawService:
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return json.dumps(mock_response)
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# =========================================================================
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# LLM Cache Layer (憲法要求: 嚴禁無快取裸奔)
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# =========================================================================
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def _generate_cache_key(self, prompt: str, context_hash: str = "") -> str:
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"""
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生成 LLM 快取鍵
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使用 prompt 內容的 SHA256 作為快取鍵，確保相同問題不重複呼叫 LLM
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"""
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content = f"{prompt}:{context_hash}"
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hash_digest = hashlib.sha256(content.encode()).hexdigest()[:16]
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return f"llm_cache:{hash_digest}"
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async def _call_with_cache(
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self,
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prompt: str,
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alert_context: dict | None = None,
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signoz_metrics: GoldMetrics | None = None,
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cache_ttl: int = 3600, # 1 hour default
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) -> tuple[str, str, bool, bool]:
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"""
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帶快取的 LLM 呼叫包裝器
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憲法條款: 必須使用快取保護算力資源
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Args:
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prompt: LLM prompt
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alert_context: 告警上下文
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signoz_metrics: SignOz 指標
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cache_ttl: 快取存活時間 (秒)
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Returns:
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(response, provider, success, from_cache)
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"""
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# 生成快取鍵 (基於 prompt + alert_context hash)
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context_hash = ""
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if alert_context:
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# 使用告警類型 + 目標資源作為上下文 hash
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context_hash = f"{alert_context.get('alert_type', '')}:{alert_context.get('target_resource', '')}"
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cache_key = self._generate_cache_key(prompt, context_hash)
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# 1. 嘗試從快取讀取
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try:
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redis_client = get_redis()
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cached = await redis_client.get(cache_key)
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if cached:
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cached_data = json.loads(cached)
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logger.info(
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"llm_cache_hit",
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cache_key=cache_key[:20],
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provider=cached_data.get("provider", "cached"),
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)
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return (
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cached_data["response"],
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f"{cached_data['provider']}_cached",
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True,
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True, # from_cache
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)
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except Exception as e:
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logger.warning("llm_cache_read_failed", error=str(e))
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# 2. Cache Miss - 呼叫 LLM
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logger.info("llm_cache_miss", cache_key=cache_key[:20])
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response, provider, success = await self._call_with_fallback(
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prompt, alert_context, signoz_metrics
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)
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# 3. 成功則寫入快取
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if success:
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try:
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redis_client = get_redis()
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cache_data = {
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"response": response,
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"provider": provider,
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"cached_at": datetime.now().isoformat(),
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}
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await redis_client.set(
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cache_key,
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json.dumps(cache_data, ensure_ascii=False),
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ex=cache_ttl,
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)
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logger.info(
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"llm_cache_write",
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cache_key=cache_key[:20],
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provider=provider,
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ttl=cache_ttl,
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)
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except Exception as e:
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logger.warning("llm_cache_write_failed", error=str(e))
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return response, provider, success, False # from_cache=False
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# =========================================================================
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# Fallback Chain
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# =========================================================================
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@@ -899,17 +995,21 @@ Trace URL: {signoz_trace_url}
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signoz_available=signoz_metrics is not None,
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)
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# 呼叫 LLM
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raw_response, provider, success = await self._call_with_fallback(
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# 呼叫 LLM (使用快取層保護算力)
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raw_response, provider, success, from_cache = await self._call_with_cache(
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full_prompt,
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alert_context,
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signoz_metrics,
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cache_ttl=1800, # 30 min for alert analysis
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)
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if not success:
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logger.error("openclaw_all_providers_failed")
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return None, provider, raw_response, signoz_metrics, signoz_trace_url
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if from_cache:
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logger.info("openclaw_using_cached_response", provider=provider)
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logger.info(
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"openclaw_llm_response_received",
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provider=provider,
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@@ -936,6 +1036,157 @@ Trace URL: {signoz_trace_url}
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return result, provider, raw_response, signoz_metrics, signoz_trace_url
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# =========================================================================
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# Phase 6.4: LLM Proposal Generation
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# =========================================================================
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async def generate_incident_proposal(
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self,
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incident_id: str,
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severity: str,
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signals: list[dict],
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affected_services: list[str],
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) -> tuple[dict | None, str, bool]:
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"""
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為 Incident 生成 LLM-based 修復提案
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Phase 6.4: 賦予大腦「生成解決方案」的思考能力
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Args:
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incident_id: Incident ID
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severity: 嚴重度 (P0/P1/P2/P3)
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signals: 關聯的告警訊號
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affected_services: 受影響服務
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Returns:
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(proposal_dict, provider, success)
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proposal_dict 包含:
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- action: 建議動作
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- description: 動作描述
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- kubectl_command: kubectl 指令
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- risk_level: 風險等級
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- reasoning: LLM 推理過程
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"""
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# 建構 prompt
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signal_summary = "\n".join([
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f"- {s.get('alert_name', 'unknown')}: {s.get('description', 'N/A')}"
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for s in signals[:10] # 最多 10 筆
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])
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target = affected_services[0] if affected_services else "unknown-service"
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# 擷取 SignOz 指標
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signoz_metrics, signoz_trace_url = await self.get_signoz_context(
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service_name=target,
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namespace="awoooi-prod",
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)
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signoz_context = ""
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if signoz_metrics:
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signoz_context = f"""
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## 📊 SignOz Real-time Metrics
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{signoz_metrics.to_summary()}
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"""
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proposal_prompt = f"""{OPENCLAW_SYSTEM_PROMPT}
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{signoz_context}
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## 🚨 Incident Context
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- **Incident ID**: {incident_id}
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- **Severity**: {severity}
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- **Affected Services**: {', '.join(affected_services)}
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- **Signal Count**: {len(signals)}
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## 📋 Alert Signals
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{signal_summary}
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## 🎯 Your Task
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Based on the above incident and signals, generate a remediation proposal.
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You MUST respond with ONLY valid JSON following the schema above.
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Focus on:
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1. Root cause analysis based on signals and SignOz data
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2. Specific kubectl command to remediate
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3. Risk assessment for the proposed action
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4. Preventive recommendations
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"""
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logger.info(
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"proposal_generation_start",
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incident_id=incident_id,
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severity=severity,
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signal_count=len(signals),
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signoz_available=signoz_metrics is not None,
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)
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# 使用快取呼叫 LLM
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alert_context = {
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"incident_id": incident_id,
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"alert_type": signals[0].get("alert_name", "incident") if signals else "incident",
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"target_resource": target,
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"severity": severity,
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}
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raw_response, provider, success, from_cache = await self._call_with_cache(
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proposal_prompt,
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alert_context,
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signoz_metrics,
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cache_ttl=3600, # 1 hour for proposals
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)
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if not success:
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logger.error(
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"proposal_generation_failed",
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incident_id=incident_id,
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provider=provider,
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)
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return None, provider, False
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# 解析 LLM 結果
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result = self._parse_analysis_result(raw_response)
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if result:
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logger.info(
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"proposal_generation_complete",
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incident_id=incident_id,
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action_title=result.action_title,
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risk_level=result.risk_level,
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provider=provider,
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from_cache=from_cache,
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)
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# 轉換為 proposal dict
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proposal_dict = {
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"action": result.action_title,
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"description": result.description,
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"kubectl_command": result.kubectl_command,
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"target_resource": result.target_resource,
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"namespace": result.namespace,
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"risk_level": result.risk_level,
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"reasoning": result.reasoning,
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"confidence": result.confidence,
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"primary_responsibility": result.primary_responsibility,
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"optimization_suggestions": [
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{
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"type": s.type,
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"description": s.description,
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"kubectl_or_config": s.kubectl_or_config,
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}
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for s in result.optimization_suggestions
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],
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"signoz_correlation": result.signoz_correlation,
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"from_cache": from_cache,
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"provider": provider,
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}
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return proposal_dict, provider, True
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logger.warning(
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"proposal_parse_failed",
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incident_id=incident_id,
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raw_response=raw_response[:300],
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)
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return None, provider, False
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# =========================================================================
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# Shadow Mode Auto-Tuning
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# =========================================================================
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