Create real_arf_integration.py

core/real_arf_integration.py

@@ -0,0 +1,530 @@
+# 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()