Delete core/real_arf_integration.py

core/real_arf_integration.py

@@ -1,530 +0,0 @@
-# core/real_arf_integration.py
-"""
-Real ARF v3.3.7 Integration with both OSS and Enterprise
-Showcasing novel execution protocols and enhanced healing policies
-"""
-import asyncio
-import logging
-from typing import Dict, Any, List, Optional
-from datetime import datetime
-import json
-
-logger = logging.getLogger(__name__)
-
-# Trial license pattern as requested
-DEMO_TRIAL_LICENSE = "ARF-TRIAL-DEMO-2026"
-
-
-class RealARFIntegration:
-    """
-    Real ARF v3.3.7 integration with OSS foundation and Enterprise features
-    """
-    
-    def __init__(self, use_enterprise: bool = True):
-        self.use_enterprise = use_enterprise
-        self.oss_available = False
-        self.enterprise_available = False
-        self.oss_client = None
-        self.enterprise_server = None
-        self.llm_client = None
-        self.rollback_controller = None
-        self.execution_mode = None
-        
-        self._initialize_arf()
-    
-    def _initialize_arf(self):
-        """Initialize ARF OSS and Enterprise components"""
-        try:
-            # 1. Import OSS Foundation (v3.3.6)
-            import agentic_reliability_framework as arf_oss
-            self.oss_available = arf_oss.OSS_AVAILABLE
-            logger.info(f"✅ ARF OSS v{arf_oss.__version__} loaded")
-            
-            # Store OSS components
-            self.HealingIntent = arf_oss.HealingIntent
-            self.create_oss_advisory_intent = arf_oss.create_oss_advisory_intent
-            self.create_rollback_intent = arf_oss.create_rollback_intent
-            self.create_restart_intent = arf_oss.create_restart_intent
-            self.create_scale_out_intent = arf_oss.create_scale_out_intent
-            
-            # Create OSS MCP client (advisory mode only)
-            self.oss_client = arf_oss.create_oss_mcp_client({
-                "mode": "advisory",
-                "max_incidents": 1000
-            })
-            
-            # 2. Import Enterprise if requested
-            if self.use_enterprise:
-                try:
-                    from arf_enterprise import (
-                        create_enterprise_server,
-                        EnterpriseLLMClient,
-                        RollbackController,
-                        ExecutionMode,
-                        DeterministicConfidence,
-                        NovelExecutionIntent,
-                        get_novel_execution_capabilities,
-                        get_version_info
-                    )
-                    
-                    # Create mock LLM client for demo (in real use, would connect to actual LLM)
-                    class DemoLLMClient(EnterpriseLLMClient):
-                        async def execute_intent(self, intent: 'HealingIntent') -> Dict[str, Any]:
-                            """Execute healing intent using LLM reasoning"""
-                            logger.info(f"LLM executing intent: {intent.action if hasattr(intent, 'action') else 'unknown'}")
-                            await asyncio.sleep(0.3)  # Simulate LLM processing
-                            
-                            # Mock LLM analysis
-                            return {
-                                "executed": True,
-                                "method": "novel_execution_protocol",
-                                "reasoning": "Pattern match with 94% confidence. Historical success rate 87%.",
-                                "safety_check": "Passed all blast radius and business hour constraints",
-                                "novelty_level": "KNOWN_PATTERN",
-                                "risk_category": "LOW",
-                                "confidence_components": [
-                                    {"component": "historical_pattern", "value": 0.92},
-                                    {"component": "current_metrics", "value": 0.87},
-                                    {"component": "system_state", "value": 0.95}
-                                ]
-                            }
-                    
-                    # Create rollback controller for safety guarantees
-                    class DemoRollbackController(RollbackController):
-                        def __init__(self):
-                            self.rollback_states = []
-                            self.guarantee_level = "STRONG"
-                        
-                        async def prepare_rollback(self, intent: 'HealingIntent') -> Dict[str, Any]:
-                            """Prepare rollback plan for safety"""
-                            state_id = f"state_{datetime.now().timestamp()}"
-                            self.rollback_states.append({
-                                "state_id": state_id,
-                                "intent": intent,
-                                "timestamp": datetime.now().isoformat(),
-                                "rollback_plan": f"Restore to previous state via {intent.action}_reversal"
-                            })
-                            return {
-                                "rollback_prepared": True,
-                                "state_id": state_id,
-                                "guarantee": self.guarantee_level,
-                                "recovery_time_estimate": "45 seconds"
-                            }
-                        
-                        async def execute_rollback(self, state_id: str) -> Dict[str, Any]:
-                            """Execute rollback to previous state"""
-                            return {
-                                "rollback_executed": True,
-                                "state_id": state_id,
-                                "status": "system_restored",
-                                "downtime": "12 seconds"
-                            }
-                    
-                    # Initialize Enterprise components
-                    self.llm_client = DemoLLMClient()
-                    self.rollback_controller = DemoRollbackController()
-                    
-                    # Create Enterprise server with trial license
-                    self.enterprise_server = create_enterprise_server(
-                        license_key=DEMO_TRIAL_LICENSE,
-                        llm_client=self.llm_client,
-                        rollback_controller=self.rollback_controller,
-                        default_execution_mode=ExecutionMode.AUTONOMOUS
-                    )
-                    
-                    self.enterprise_available = True
-                    self.execution_mode = ExecutionMode.AUTONOMOUS
-                    
-                    # Get capabilities info
-                    self.capabilities = get_novel_execution_capabilities()
-                    self.version_info = get_version_info()
-                    
-                    logger.info("✅ ARF Enterprise with novel execution protocols loaded")
-                    logger.info(f"   Execution modes: {[mode.value for mode in ExecutionMode]}")
-                    logger.info(f"   Novel execution: {self.capabilities['protocols']}")
-                    
-                except ImportError as e:
-                    logger.warning(f"⚠️ ARF Enterprise not available: {e}")
-                    logger.info("   Running in OSS-only mode (advisory)")
-                    self.use_enterprise = False
-                    self.enterprise_available = False
-            
-            logger.info("🎯 Real ARF integration initialized successfully")
-            
-        except ImportError as e:
-            logger.error(f"❌ Failed to import ARF packages: {e}")
-            logger.error("   Install with: pip install agentic-reliability-framework==3.3.6")
-            if self.use_enterprise:
-                logger.error("   For Enterprise: pip install agentic-reliability-enterprise")
-            raise
-    
-    async def analyze_scenario(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Complete ARF analysis pipeline using real ARF components
-        
-        Shows the OSS analysis workflow with optional Enterprise execution
-        """
-        logger.info(f"🔍 Starting real ARF analysis for: {scenario_name}")
-        
-        try:
-            # Step 1: OSS Analysis (Detection + Recall + Decision)
-            oss_result = await self._run_oss_analysis(scenario_data)
-            
-            # Step 2: If Enterprise available, show enhanced capabilities
-            enterprise_result = None
-            if self.enterprise_available and self.enterprise_server:
-                enterprise_result = await self._run_enterprise_enhancement(
-                    scenario_name, scenario_data, oss_result
-                )
-            
-            # Compile comprehensive results
-            result = {
-                "status": "success",
-                "scenario": scenario_name,
-                "arf_version": "3.3.7",
-                "timestamp": datetime.now().isoformat(),
-                "oss_analysis": oss_result,
-                "enterprise_enhancements": enterprise_result,
-                "execution_mode": self.execution_mode.value if self.execution_mode else "advisory",
-                "novel_execution_available": self.enterprise_available
-            }
-            
-            logger.info(f"✅ Real ARF analysis complete for {scenario_name}")
-            return result
-            
-        except Exception as e:
-            logger.error(f"❌ ARF analysis failed: {e}", exc_info=True)
-            return {
-                "status": "error",
-                "error": str(e),
-                "scenario": scenario_name,
-                "timestamp": datetime.now().isoformat()
-            }
-    
-    async def _run_oss_analysis(self, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-        """Run OSS analysis pipeline (advisory mode only)"""
-        # Step 1: Detection Agent (using OSS MCP client)
-        detection_start = datetime.now()
-        
-        # Mock detection - in real implementation would use OSSMCPClient.execute_tool()
-        detection_result = {
-            "anomaly_detected": True,
-            "severity": scenario_data.get("severity", "HIGH"),
-            "confidence": 0.987,  # 98.7%
-            "detection_time_ms": 45,
-            "detection_method": "ml_ensemble_v3",
-            "component": scenario_data.get("component", "unknown"),
-            "tags": ["real_arf", "v3.3.7", "oss_analysis"]
-        }
-        
-        # Step 2: Recall Agent (RAG similarity search)
-        await asyncio.sleep(0.1)  # Simulate RAG search
-        recall_result = [
-            {
-                "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"
-            },
-            {
-                "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"
-            }
-        ]
-        
-        # Step 3: Decision Agent (Create HealingIntent)
-        # Calculate overall confidence
-        pattern_confidence = sum([inc["similarity_score"] for inc in recall_result]) / len(recall_result)
-        overall_confidence = (detection_result["confidence"] + pattern_confidence) / 2
-        
-        # Create HealingIntent based on scenario
-        component = scenario_data.get("component", "unknown")
-        healing_intent = None
-        
-        if "cache" in component.lower() or "redis" in component.lower():
-            healing_intent = self.create_scale_out_intent(
-                component=component,
-                parameters={"nodes": "3→5", "memory": "16GB→32GB"},
-                confidence=overall_confidence,
-                source="oss_analysis"
-            )
-        elif "database" in component.lower():
-            healing_intent = self.create_restart_intent(
-                component=component,
-                parameters={"connections": "reset_pool"},
-                confidence=overall_confidence,
-                source="oss_analysis"
-            )
-        else:
-            healing_intent = self.create_oss_advisory_intent(
-                component=component,
-                parameters={"action": "investigate"},
-                confidence=overall_confidence,
-                source="oss_analysis"
-            )
-        
-        # Add additional metadata
-        healing_intent_data = {
-            "action": healing_intent.action if hasattr(healing_intent, 'action') else "advisory",
-            "component": healing_intent.component if hasattr(healing_intent, 'component') else component,
-            "confidence": overall_confidence,
-            "parameters": healing_intent.parameters if hasattr(healing_intent, 'parameters') else {},
-            "source": healing_intent.source if hasattr(healing_intent, 'source') else "oss",
-            "requires_enterprise": True,  # OSS can only create advisory intents
-            "advisory_only": True,
-            "safety_check": "✅ Passed (blast radius: 2 services)"
-        }
-        
-        return {
-            "detection": detection_result,
-            "recall": recall_result,
-            "decision": healing_intent_data,
-            "confidence": overall_confidence,
-            "processing_time_ms": (datetime.now() - detection_start).total_seconds() * 1000,
-            "agents_executed": ["detection", "recall", "decision"],
-            "oss_boundary": "advisory_only"
-        }
-    
-    async def _run_enterprise_enhancement(self, scenario_name: str, scenario_data: Dict[str, Any], 
-                                         oss_result: Dict[str, Any]) -> Dict[str, Any]:
-        """Run Enterprise enhancement with novel execution protocols"""
-        logger.info(f"🏢 Running Enterprise enhancements for {scenario_name}")
-        
-        enhancement_start = datetime.now()
-        
-        try:
-            # Step 1: Convert OSS HealingIntent to Enterprise format
-            oss_intent = oss_result["decision"]
-            
-            # Step 2: Apply deterministic confidence system
-            from arf_enterprise import create_confidence_from_basis
-            
-            confidence_basis = {
-                "historical_pattern": 0.92,
-                "current_metrics": 0.87,
-                "system_state": 0.95,
-                "business_context": 0.88
-            }
-            
-            deterministic_confidence = create_confidence_from_basis(confidence_basis)
-            
-            # Step 3: Create NovelExecutionIntent for advanced scenarios
-            from arf_enterprise import NovelExecutionIntent, NoveltyLevel, RiskCategory
-            
-            novel_intent = NovelExecutionIntent(
-                base_intent=oss_intent,
-                novelty_level=NoveltyLevel.KNOWN_PATTERN,
-                risk_category=RiskCategory.LOW,
-                confidence_components=deterministic_confidence.components,
-                rollback_required=True,
-                human_approval_required=False  # Autonomous mode for demo
-            )
-            
-            # Step 4: Execute with rollback safety
-            rollback_preparation = await self.rollback_controller.prepare_rollback(novel_intent)
-            
-            # Step 5: LLM execution (simulated for demo)
-            execution_result = await self.llm_client.execute_intent(novel_intent)
-            
-            # Step 6: Calculate business impact
-            business_impact = scenario_data.get("business_impact", {})
-            revenue_risk = business_impact.get("revenue_loss_per_hour", 5000)
-            time_saved = 45  # minutes (ARF vs manual)
-            cost_saved = int((revenue_risk / 60) * time_saved * 0.85)  # 85% efficiency
-            
-            enhancement_time = (datetime.now() - enhancement_start).total_seconds() * 1000
-            
-            return {
-                "novel_execution": {
-                    "intent_type": "NovelExecutionIntent",
-                    "novelty_level": novel_intent.novelty_level.value,
-                    "risk_category": novel_intent.risk_category.value,
-                    "confidence_score": deterministic_confidence.score,
-                    "confidence_components": deterministic_confidence.components
-                },
-                "safety_guarantees": {
-                    "rollback_prepared": rollback_preparation["rollback_prepared"],
-                    "rollback_guarantee": rollback_preparation["guarantee"],
-                    "state_id": rollback_preparation["state_id"],
-                    "execution_mode": self.execution_mode.value
-                },
-                "execution_result": execution_result,
-                "business_impact": {
-                    "recovery_time": "12 minutes",
-                    "manual_comparison": "45 minutes",
-                    "time_saved_minutes": time_saved,
-                    "time_reduction_percent": 73,
-                    "cost_saved": f"${cost_saved:,}",
-                    "users_protected": scenario_data.get("metrics", {}).get("affected_users", 45000)
-                },
-                "processing_time_ms": enhancement_time,
-                "protocols_used": list(self.capabilities["protocols"].keys()),
-                "license_tier": "ENTERPRISE_TRIAL"
-            }
-            
-        except Exception as e:
-            logger.error(f"Enterprise enhancement failed: {e}")
-            return {
-                "error": str(e),
-                "enterprise_available": False,
-                "fallback_to_oss": True
-            }
-    
-    async def execute_healing_action(self, scenario_name: str, action_type: str = "autonomous") -> Dict[str, Any]:
-        """Execute healing action using appropriate execution mode"""
-        if not self.enterprise_available:
-            return {
-                "status": "error",
-                "message": "Enterprise features required for execution",
-                "available_modes": ["advisory"]
-            }
-        
-        try:
-            from arf_enterprise import ExecutionMode, requires_human_approval, can_execute
-            
-            # Determine execution mode
-            if action_type == "advisory":
-                mode = ExecutionMode.ADVISORY
-            elif action_type == "approval":
-                mode = ExecutionMode.APPROVAL
-            elif action_type == "autonomous":
-                mode = ExecutionMode.AUTONOMOUS
-            else:
-                mode = ExecutionMode.ADVISORY
-            
-            # Check if execution is allowed
-            execution_allowed = can_execute(mode)
-            needs_approval = requires_human_approval(mode)
-            
-            result = {
-                "scenario": scenario_name,
-                "execution_mode": mode.value,
-                "execution_allowed": execution_allowed,
-                "requires_human_approval": needs_approval,
-                "timestamp": datetime.now().isoformat(),
-                "license": DEMO_TRIAL_LICENSE
-            }
-            
-            if execution_allowed and not needs_approval:
-                # Simulate autonomous execution
-                await asyncio.sleep(0.5)
-                result.update({
-                    "action_executed": True,
-                    "result": "Healing action completed successfully",
-                    "recovery_time": "12 minutes",
-                    "rollback_available": True,
-                    "audit_trail_id": f"audit_{datetime.now().timestamp()}"
-                })
-            elif needs_approval:
-                result.update({
-                    "action_executed": False,
-                    "status": "awaiting_human_approval",
-                    "approval_workflow_started": True,
-                    "estimated_savings": "$8,500"
-                })
-            else:
-                result.update({
-                    "action_executed": False,
-                    "status": "advisory_only",
-                    "message": "OSS mode only provides recommendations"
-                })
-            
-            return result
-            
-        except Exception as e:
-            logger.error(f"Execution failed: {e}")
-            return {
-                "status": "error",
-                "error": str(e),
-                "scenario": scenario_name
-            }
-    
-    def get_capabilities(self) -> Dict[str, Any]:
-        """Get ARF capabilities summary"""
-        caps = {
-            "oss_available": self.oss_available,
-            "enterprise_available": self.enterprise_available,
-            "arf_version": "3.3.7",
-            "demo_license": DEMO_TRIAL_LICENSE,
-            "oss_capabilities": [
-                "anomaly_detection",
-                "rag_similarity_search",
-                "healing_intent_creation",
-                "pattern_analysis",
-                "advisory_recommendations"
-            ]
-        }
-        
-        if self.enterprise_available:
-            caps.update({
-                "enterprise_capabilities": [
-                    "novel_execution_protocols",
-                    "deterministic_confidence",
-                    "rollback_guarantees",
-                    "autonomous_healing",
-                    "enterprise_mcp_server",
-                    "audit_trail",
-                    "license_management"
-                ],
-                "execution_modes": ["advisory", "approval", "autonomous"],
-                "novel_execution_protocols": self.capabilities["protocols"] if hasattr(self, 'capabilities') else {},
-                "safety_guarantees": self.capabilities.get("safety_guarantees", {}) if hasattr(self, 'capabilities') else {}
-            })
-        
-        return caps
-
-
-# Factory function for easy integration
-_real_arf_instance = None
-
-async def get_real_arf(use_enterprise: bool = True) -> RealARFIntegration:
-    """Get singleton RealARFIntegration instance"""
-    global _real_arf_instance
-    if _real_arf_instance is None:
-        _real_arf_instance = RealARFIntegration(use_enterprise=use_enterprise)
-    return _real_arf_instance
-
-
-async def analyze_with_real_arf(scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]:
-    """Convenience function for real ARF analysis"""
-    arf = await get_real_arf(use_enterprise=True)
-    return await arf.analyze_scenario(scenario_name, scenario_data)
-
-
-async def execute_with_real_arf(scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
-    """Convenience function for real ARF execution"""
-    arf = await get_real_arf(use_enterprise=True)
-    return await arf.execute_healing_action(scenario_name, mode)
-
-
-def get_arf_capabilities() -> Dict[str, Any]:
-    """Get ARF capabilities (sync wrapper)"""
-    async def _get_caps():
-        arf = await get_real_arf(use_enterprise=True)
-        return arf.get_capabilities()
-    
-    try:
-        loop = asyncio.get_event_loop()
-        if loop.is_running():
-            # Return coroutine if in async context
-            return _get_caps()
-        else:
-            return loop.run_until_complete(_get_caps())
-    except RuntimeError:
-        # Create new loop
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        try:
-            return loop.run_until_complete(_get_caps())
-        finally:
-            loop.close()