Delete core/updated_arf_adapter.py

core/updated_arf_adapter.py

@@ -1,373 +0,0 @@
-# core/updated_arf_adapter.py
-"""
-Updated ARF Adapter using real ARF v3.3.7
-Replaces mock implementation with real OSS + Enterprise
-"""
-from abc import ABC, abstractmethod
-from typing import Dict, Any, List, Optional
-import asyncio
-import logging
-from config.settings import settings, ARFMode
-
-logger = logging.getLogger(__name__)
-
-# Import our real ARF integration
-from .real_arf_integration import (
-    analyze_with_real_arf,
-    execute_with_real_arf,
-    get_arf_capabilities,
-    DEMO_TRIAL_LICENSE
-)
-
-
-class ARFAdapter(ABC):
-    """Abstract adapter for ARF integration"""
-    
-    @abstractmethod
-    async def detect(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
-        """Detect anomalies in metrics"""
-        pass
-    
-    @abstractmethod
-    async def recall(self, incident: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Recall similar incidents from memory"""
-        pass
-    
-    @abstractmethod
-    async def decide(self, incident: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
-        """Generate healing intent"""
-        pass
-    
-    @abstractmethod
-    async def analyze(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Complete analysis pipeline"""
-        pass
-    
-    @abstractmethod
-    async def execute(self, scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
-        """Execute healing action"""
-        pass
-    
-    @abstractmethod
-    def get_capabilities(self) -> Dict[str, Any]:
-        """Get system capabilities"""
-        pass
-
-
-class RealARFv3Adapter(ARFAdapter):
-    """
-    Real ARF v3.3.7 adapter with OSS + Enterprise integration
-    
-    Shows novel execution protocols and enhanced healing policies
-    """
-    
-    def __init__(self, use_enterprise: bool = True):
-        logger.info(f"Initializing RealARFv3Adapter (Enterprise: {use_enterprise})")
-        self.use_enterprise = use_enterprise
-        self.license_key = DEMO_TRIAL_LICENSE if use_enterprise else None
-        self._capabilities = None
-    
-    async def detect(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
-        """Real anomaly detection using ARF OSS"""
-        # In real ARF, this would use OSSMCPClient
-        # For demo, we simulate with realistic data
-        await asyncio.sleep(0.05)  # Simulate ML processing
-        
-        # Analyze metrics for anomalies
-        anomaly_score = 0.0
-        if metrics.get("error_rate", 0) > 0.1:
-            anomaly_score = 0.92
-        elif metrics.get("latency_p95", 0) > 1000:
-            anomaly_score = 0.87
-        elif metrics.get("cpu_usage", 0) > 0.9:
-            anomaly_score = 0.78
-        
-        return {
-            "anomaly_detected": anomaly_score > 0.7,
-            "anomaly_score": anomaly_score,
-            "confidence": 0.987,
-            "detection_method": "arf_ml_ensemble_v3",
-            "detection_time_ms": 45,
-            "metrics_analyzed": len(metrics),
-            "severity": "HIGH" if anomaly_score > 0.8 else "MEDIUM"
-        }
-    
-    async def recall(self, incident: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Real RAG similarity search using ARF memory"""
-        await asyncio.sleep(0.1)  # Simulate vector search
-        
-        component = incident.get("component", "").lower()
-        
-        # Return realistic similar incidents based on component
-        base_incidents = [
-            {
-                "incident_id": "inc_20250101_001",
-                "similarity_score": 0.92,
-                "success": True,
-                "resolution": "scale_out",
-                "cost_savings": 6500,
-                "detection_time": "48s",
-                "resolution_time": "15m",
-                "pattern": "cache_miss_storm_v2",
-                "component_match": component,
-                "rag_source": "production_memory_v3"
-            },
-            {
-                "incident_id": "inc_20241215_045",
-                "similarity_score": 0.87,
-                "success": True,
-                "resolution": "warm_cache",
-                "cost_savings": 4200,
-                "detection_time": "52s",
-                "resolution_time": "22m",
-                "pattern": "redis_saturation",
-                "component_match": component,
-                "rag_source": "production_memory_v3"
-            }
-        ]
-        
-        # Add more specific incidents based on component type
-        if "cache" in component or "redis" in component:
-            base_incidents.append({
-                "incident_id": "inc_20241120_123",
-                "similarity_score": 0.95,
-                "success": True,
-                "resolution": "memory_increase",
-                "cost_savings": 8500,
-                "detection_time": "38s",
-                "resolution_time": "8m",
-                "pattern": "redis_oom_prevention",
-                "component_match": component,
-                "rag_source": "production_memory_v3"
-            })
-        
-        return base_incidents
-    
-    async def decide(self, incident: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
-        """Real decision making using ARF HealingIntent"""
-        similar = context.get("similar_incidents", [])
-        
-        # Calculate confidence from similar incidents
-        if similar:
-            avg_similarity = sum([inc["similarity_score"] for inc in similar]) / len(similar)
-            success_rate = sum([1 for inc in similar if inc["success"]]) / len(similar)
-            confidence = (avg_similarity + success_rate) / 2
-        else:
-            confidence = 0.75
-        
-        # Determine action based on component and patterns
-        component = incident.get("component", "unknown")
-        action = "investigate"
-        parameters = {}
-        
-        if "cache" in component.lower():
-            action = "scale_out"
-            parameters = {"nodes": "3→5", "memory": "16GB→32GB", "strategy": "gradual"}
-        elif "database" in component.lower():
-            action = "restart"
-            parameters = {"connections": "reset_pool", "timeout": "30s", "strategy": "rolling"}
-        elif "api" in component.lower():
-            action = "circuit_breaker"
-            parameters = {"threshold": "80%", "window": "5m", "fallback": "cached_response"}
-        
-        # Create healing intent structure
-        healing_intent = {
-            "action": action,
-            "component": component,
-            "confidence": confidence,
-            "parameters": parameters,
-            "source": "arf_v3.3.7",
-            "requires_enterprise": True if action != "investigate" else False,
-            "advisory_only": not self.use_enterprise,
-            "safety_checks": {
-                "blast_radius": "2 services",
-                "business_hours": "compliant",
-                "rollback_plan": "available",
-                "approval_required": self.use_enterprise and action != "investigate"
-            },
-            "novel_execution_eligible": self.use_enterprise and confidence > 0.85
-        }
-        
-        # Add enterprise features if available
-        if self.use_enterprise and confidence > 0.85:
-            healing_intent.update({
-                "enterprise_features": {
-                    "deterministic_confidence": True,
-                    "rollback_guarantee": "STRONG",
-                    "execution_mode": "autonomous",
-                    "novelty_level": "KNOWN_PATTERN",
-                    "risk_category": "LOW"
-                }
-            })
-        
-        return healing_intent
-    
-    async def analyze(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Complete real ARF analysis pipeline"""
-        logger.info(f"🔍 Real ARF v3.3.7 analyzing: {scenario_name}")
-        
-        # Use our real ARF integration for comprehensive analysis
-        return await analyze_with_real_arf(scenario_name, scenario_data)
-    
-    async def execute(self, scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
-        """Execute healing action using ARF Enterprise"""
-        if not self.use_enterprise:
-            return {
-                "status": "error",
-                "message": "Enterprise features required for execution",
-                "available_modes": ["advisory"],
-                "suggestion": "Enable Enterprise mode or use trial license"
-            }
-        
-        logger.info(f"⚡ Executing healing for {scenario_name} in {mode} mode")
-        return await execute_with_real_arf(scenario_name, mode)
-    
-    def get_capabilities(self) -> Dict[str, Any]:
-        """Get ARF v3.3.7 capabilities"""
-        if self._capabilities is None:
-            self._capabilities = get_arf_capabilities()
-        
-        return self._capabilities
-
-
-class HybridARFAdapter(ARFAdapter):
-    """
-    Hybrid adapter that can switch between mock and real ARF
-    
-    Useful for demo environments where real ARF might not be installed
-    """
-    
-    def __init__(self):
-        self.real_adapter = None
-        self.mock_adapter = None
-        self.use_real = False
-        
-        # Try to initialize real ARF
-        try:
-            self.real_adapter = RealARFv3Adapter(use_enterprise=True)
-            self.use_real = True
-            logger.info("✅ Using real ARF v3.3.7 with Enterprise")
-        except ImportError as e:
-            logger.warning(f"⚠️ Real ARF not available, falling back to mock: {e}")
-            from .arf_adapter import MockARFAdapter
-            self.mock_adapter = MockARFAdapter()
-            self.use_real = False
-    
-    async def detect(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
-        if self.use_real and self.real_adapter:
-            return await self.real_adapter.detect(metrics)
-        else:
-            return await self.mock_adapter.detect(metrics)
-    
-    async def recall(self, incident: Dict[str, Any]) -> List[Dict[str, Any]]:
-        if self.use_real and self.real_adapter:
-            return await self.real_adapter.recall(incident)
-        else:
-            return await self.mock_adapter.recall(incident)
-    
-    async def decide(self, incident: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
-        if self.use_real and self.real_adapter:
-            return await self.real_adapter.decide(incident, context)
-        else:
-            return await self.mock_adapter.decide(incident, context)
-    
-    async def analyze(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-        if self.use_real and self.real_adapter:
-            return await self.real_adapter.analyze(scenario_name, scenario_data)
-        else:
-            return await self.mock_adapter.analyze(scenario_name, scenario_data)
-    
-    async def execute(self, scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
-        if self.use_real and self.real_adapter:
-            return await self.real_adapter.execute(scenario_name, mode)
-        else:
-            return {
-                "status": "mock_mode",
-                "message": "Execution simulated in mock mode",
-                "scenario": scenario_name,
-                "mode": mode
-            }
-    
-    def get_capabilities(self) -> Dict[str, Any]:
-        if self.use_real and self.real_adapter:
-            return self.real_adapter.get_capabilities()
-        else:
-            return {
-                "mode": "mock",
-                "version": "mock_implementation",
-                "capabilities": ["simulated_analysis", "mock_execution"],
-                "enterprise_available": False,
-                "oss_available": False
-            }
-
-
-def get_arf_adapter() -> ARFAdapter:
-    """
-    Factory function to get appropriate ARF adapter based on settings
-    
-    Now includes real ARF v3.3.7 with novel execution protocols
-    """
-    mode = settings.arf_mode
-    
-    if mode == ARFMode.DEMO and not settings.use_mock_arf:
-        # Try to use real ARF even in demo mode if configured
-        logger.info("Attempting to use real ARF v3.3.7 in demo mode")
-        return HybridARFAdapter()
-    elif mode == ARFMode.OSS:
-        logger.info("Using RealARFv3Adapter (OSS mode)")
-        return RealARFv3Adapter(use_enterprise=False)
-    elif mode == ARFMode.ENTERPRISE:
-        logger.info("Using RealARFv3Adapter (Enterprise mode)")
-        return RealARFv3Adapter(use_enterprise=True)
-    else:
-        logger.info("Using HybridARFAdapter (auto-detect best available)")
-        return HybridARFAdapter()
-
-
-# Async helper for easy integration
-async def analyze_scenario_async(scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-    """Convenience function for async scenario analysis"""
-    adapter = get_arf_adapter()
-    return await adapter.analyze(scenario_name, scenario_data)
-
-
-async def execute_scenario_async(scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
-    """Convenience function for async execution"""
-    adapter = get_arf_adapter()
-    return await adapter.execute(scenario_name, mode)
-
-
-# Sync wrappers for compatibility
-def analyze_scenario_sync(scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-    """Sync wrapper for scenario analysis"""
-    async def _analyze():
-        return await analyze_scenario_async(scenario_name, scenario_data)
-    
-    return _run_sync(_analyze())
-
-
-def execute_scenario_sync(scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
-    """Sync wrapper for execution"""
-    async def _execute():
-        return await execute_scenario_async(scenario_name, mode)
-    
-    return _run_sync(_execute())
-
-
-def _run_sync(coro):
-    """Run async coroutine in sync context"""
-    try:
-        loop = asyncio.get_event_loop()
-        if loop.is_running():
-            # In async context, return coroutine
-            return coro
-        else:
-            return loop.run_until_complete(coro)
-    except RuntimeError:
-        # Create new loop
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        try:
-            return loop.run_until_complete(coro)
-        finally:
-            loop.close()