Delete core/arf_adapter.py

core/arf_adapter.py

@@ -1,277 +0,0 @@
-"""
-ARF Adapter Pattern for clean integration with real or mock ARF
-"""
-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__)
-
-
-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
-
-
-class MockARFAdapter(ARFAdapter):
-    """Mock ARF implementation for demo mode"""
-    
-    def __init__(self):
-        logger.info("Initializing MockARFAdapter")
-        # Lazy imports to avoid circular dependencies
-        self._simulate_arf_analysis = None
-        self._run_rag_similarity_search = None
-        self._create_mock_healing_intent = None
-        self._calculate_pattern_confidence = None
-    
-    def _import_mock_functions(self):
-        """Lazy import of mock functions"""
-        if self._simulate_arf_analysis is None:
-            from demo.mock_arf import (
-                simulate_arf_analysis,
-                run_rag_similarity_search,
-                create_mock_healing_intent,
-                calculate_pattern_confidence
-            )
-            self._simulate_arf_analysis = simulate_arf_analysis
-            self._run_rag_similarity_search = run_rag_similarity_search
-            self._create_mock_healing_intent = create_mock_healing_intent
-            self._calculate_pattern_confidence = calculate_pattern_confidence
-    
-    async def detect(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
-        """Mock anomaly detection"""
-        self._import_mock_functions()
-        
-        # Simulate processing time
-        await asyncio.sleep(0.1)
-        
-        result = self._simulate_arf_analysis({"metrics": metrics})
-        result["detection_method"] = "mock_ml_algorithm"
-        result["confidence"] = 0.987  # 98.7%
-        
-        return result
-    
-    async def recall(self, incident: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Mock RAG similarity search"""
-        self._import_mock_functions()
-        
-        # Simulate processing time
-        await asyncio.sleep(0.2)
-        
-        similar = self._run_rag_similarity_search(incident)
-        
-        # Enhance with additional metadata
-        for item in similar:
-            item["source"] = "mock_rag_memory"
-            item["retrieval_time"] = "45ms"
-        
-        return similar
-    
-    async def decide(self, incident: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
-        """Mock decision making"""
-        self._import_mock_functions()
-        
-        # Get similar incidents from context or recall them
-        similar = context.get("similar_incidents")
-        if not similar:
-            similar = await self.recall(incident)
-        
-        # Calculate confidence
-        confidence = self._calculate_pattern_confidence(incident, similar)
-        
-        # Generate healing intent
-        intent = self._create_mock_healing_intent(incident, similar, confidence)
-        
-        # Add safety check
-        intent["safety_checks"] = {
-            "blast_radius": "2 services",
-            "business_hours": "compliant",
-            "rollback_plan": "available",
-            "approval_required": True
-        }
-        
-        return intent
-    
-    async def analyze(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Complete mock analysis pipeline"""
-        logger.info(f"Starting mock analysis for: {scenario_name}")
-        
-        # Step 1: Detection
-        detection_result = await self.detect(scenario_data.get("metrics", {}))
-        
-        # Step 2: Recall
-        recall_result = await self.recall(scenario_data)
-        
-        # Step 3: Decision
-        decision_result = await self.decision(
-            scenario_data,
-            {"similar_incidents": recall_result}
-        )
-        
-        return {
-            "scenario": scenario_name,
-            "detection": detection_result,
-            "recall": recall_result,
-            "decision": decision_result,
-            "overall_confidence": decision_result.get("confidence", 0.85),
-            "processing_time_ms": 450,
-            "agents_executed": ["detection", "recall", "decision"]
-        }
-
-
-class RealARFAdapter(ARFAdapter):
-    """Real ARF integration (requires agentic-reliability-framework package)"""
-    
-    def __init__(self, api_key: Optional[str] = None):
-        logger.info("Initializing RealARFAdapter")
-        
-        try:
-            from agentic_reliability_framework import ARFClient
-            self.client = ARFClient(api_key=api_key or settings.arf_api_key)
-            self._available = True
-        except ImportError as e:
-            logger.error(f"Failed to import ARF package: {e}")
-            self._available = False
-            raise RuntimeError(
-                "Real ARF integration requires 'agentic-reliability-framework' package. "
-                "Install with: pip install agentic-reliability-framework"
-            )
-    
-    async def detect(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
-        """Real anomaly detection"""
-        if not self._available:
-            raise RuntimeError("ARF client not available")
-        
-        try:
-            # Assuming async API
-            result = await self.client.detect_anomaly_async(metrics)
-            return result
-        except AttributeError:
-            # Fallback to sync if async not available
-            result = self.client.detect_anomaly(metrics)
-            return result
-    
-    async def recall(self, incident: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """Real RAG similarity search"""
-        if not self._available:
-            raise RuntimeError("ARF client not available")
-        
-        try:
-            # Assuming async API
-            result = await self.client.recall_similar_async(incident)
-            return result
-        except AttributeError:
-            # Fallback to sync
-            result = self.client.recall_similar(incident)
-            return result
-    
-    async def decide(self, incident: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
-        """Real decision making"""
-        if not self._available:
-            raise RuntimeError("ARF client not available")
-        
-        try:
-            # Assuming async API
-            result = await self.client.generate_intent_async(incident, context)
-            return result
-        except AttributeError:
-            # Fallback to sync
-            result = self.client.generate_intent(incident, context)
-            return result
-    
-    async def analyze(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Complete real analysis pipeline"""
-        logger.info(f"Starting real analysis for: {scenario_name}")
-        
-        # Run agents in parallel where possible
-        detection_task = asyncio.create_task(self.detect(scenario_data.get("metrics", {})))
-        recall_task = asyncio.create_task(self.recall(scenario_data))
-        
-        detection_result, recall_result = await asyncio.gather(detection_task, recall_task)
-        
-        # Decision depends on recall results
-        decision_result = await self.decision(
-            scenario_data,
-            {"similar_incidents": recall_result}
-        )
-        
-        return {
-            "scenario": scenario_name,
-            "detection": detection_result,
-            "recall": recall_result,
-            "decision": decision_result,
-            "overall_confidence": decision_result.get("confidence", 0.85),
-            "processing_time_ms": 250,  # Real system should be faster
-            "agents_executed": ["detection", "recall", "decision"]
-        }
-
-
-def get_arf_adapter() -> ARFAdapter:
-    """
-    Factory function to get appropriate ARF adapter based on settings
-    
-    Returns:
-        ARFAdapter instance
-    """
-    if settings.arf_mode == ARFMode.DEMO or settings.use_mock_arf:
-        logger.info("Using MockARFAdapter (demo mode)")
-        return MockARFAdapter()
-    elif settings.arf_mode == ARFMode.OSS:
-        logger.info("Using RealARFAdapter (OSS mode)")
-        return RealARFAdapter()
-    elif settings.arf_mode == ARFMode.ENTERPRISE:
-        logger.info("Using RealARFAdapter (Enterprise mode)")
-        return RealARFAdapter()
-    else:
-        logger.warning("Unknown ARF mode, falling back to mock")
-        return MockARFAdapter()
-
-
-# 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)
-
-
-# Sync wrapper for compatibility
-def analyze_scenario_sync(scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-    """Sync wrapper for scenario analysis"""
-    import asyncio
-    
-    async def _analyze():
-        return await analyze_scenario_async(scenario_name, scenario_data)
-    
-    try:
-        loop = asyncio.get_running_loop()
-        # Already in async context, create task
-        return asyncio.create_task(_analyze())
-    except RuntimeError:
-        # Create new event loop
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        try:
-            return loop.run_until_complete(_analyze())
-        finally:
-            loop.close()