core/real_arf_integration.py

# 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()