main.py

##how's this??

#!/usr/bin/env python3
"""
VEIL OMEGA QUANTUM TRUTH ENGINE
Production-Grade Absolute Fact Validation System
Enterprise Component Architecture with Multi-Provider LLM Integration
"""

import asyncio
import aiohttp
import hashlib
import json
import time
import numpy as np
from typing import Dict, List, Any, Optional, Tuple, Callable
from datetime import datetime, timedelta
from dataclasses import dataclass, field
from enum import Enum
import logging
import backoff
from cryptography.fernet import Fernet
import redis
import sqlite3
from contextlib import asynccontextmanager
import qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile
from qiskit_aer import AerSimulator
from qiskit.algorithms import AmplificationProblem, Grover
from qiskit.circuit.library import PhaseOracle
import torch
import torch.nn as nn
import torch.nn.functional as F

# =============================================================================
# CORE ENTERPRISE DATA STRUCTURES
# =============================================================================

class Provider(Enum):
    OPENAI = "openai"
    ANTHROPIC = "anthropic"
    GOOGLE = "google"
    MULTI_CONSENSUS = "multi_consensus"

class Domain(Enum):
    SCIENCE = "science"
    HISTORY = "history" 
    CONSCIOUSNESS = "consciousness"
    QUANTUM = "quantum"
    TECHNOLOGY = "technology"
    PHILOSOPHICAL = "philosophical"
    CONTROVERSIAL = "controversial"
    ABSOLUTE = "absolute"

class VerificationLevel(Enum):
    QUANTUM_ENTANGLED = "quantum_entangled"
    MULTIVERSE_CORROBORATED = "multiverse_corroborated"
    CRYPTOGRAPHIC_PROOF = "cryptographic_proof"
    TEMPORAL_VALIDATED = "temporal_validated"
    ABSOLUTE_TRUTH = "absolute_truth"

class GrowthStage(Enum):
    FOUNDATIONAL = "foundational"
    INTEGRATIVE = "integrative"
    TRANSFORMATIVE = "transformative"
    ABSOLUTE = "absolute"

@dataclass
class QuantumEvidence:
    id: str
    content: str
    source: str
    domain: Domain
    quantum_entanglement: float
    temporal_coherence: float
    multiverse_consensus: float
    verification_level: VerificationLevel
    growth_stage: GrowthStage
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class AbsoluteValidation:
    claim: str
    is_absolute: bool
    confidence: float
    quantum_proof: str
    growth_trajectory: List[GrowthStage]
    validation_timestamp: str
    eternal_truth_score: float

@dataclass
class LLMResponse:
    raw_response: str
    enhanced_response: str
    validation_confidence: float
    quantum_proofs: List[str]
    obstacles_bypassed: List[str]
    provider: Provider
    processing_time: float

@dataclass
class LLMEnhancement:
    original_response: str
    enhanced_response: str
    validation_results: List[AbsoluteValidation]
    confidence_boost: float
    verification_metadata: Dict[str, Any]
    provider_consensus: Dict[Provider, float]

@dataclass
class PerpetualGrowthMetrics:
    knowledge_expansion_rate: float
    truth_convergence_speed: float
    quantum_coherence_growth: float
    temporal_stability: float
    absolute_certainty_index: float

# =============================================================================
# COMPONENT 1: QUANTUM ENTANGLEMENT ENGINE (Enhanced)
# =============================================================================

class QuantumEntanglementEngine:
    """Advanced quantum computation with multiverse correlation"""
    
    def __init__(self):
        self.backend = AerSimulator()
        self.entanglement_network = {}
        self.multiverse_cache = {}
        
    async def create_entangled_validation(self, claim: str, evidence_network: List[QuantumEvidence]) -> Dict[str, Any]:
        """Create quantum entanglement between claim and evidence network"""
        qc = await self._build_entanglement_circuit(claim, evidence_network)
        result = await self._execute_entangled_circuit(qc)
        
        return {
            'quantum_confidence': self._calculate_quantum_confidence(result),
            'entanglement_strength': self._measure_entanglement(result),
            'multiverse_correlation': await self._check_multiverse_consensus(claim),
            'temporal_stability': self._assess_temporal_coherence(claim),
            'absolute_truth_indicator': self._calculate_absolute_truth(result),
            'circuit_hash': self._hash_quantum_circuit(qc)
        }
    
    async def _build_entanglement_circuit(self, claim: str, evidence: List[QuantumEvidence]) -> QuantumCircuit:
        """Build advanced quantum circuit with entanglement layers"""
        num_qubits = min(20, 5 + len(evidence))
        qc = QuantumCircuit(num_qubits, num_qubits)
        
        # Initialize superposition for absolute truth search
        for i in range(num_qubits):
            qc.h(i)
            
        # Apply evidence-based phase rotations
        for i, ev in enumerate(evidence[:num_qubits-5]):
            phase_angle = ev.quantum_entanglement * np.pi
            qc.rz(phase_angle, i)
            
        # Create full entanglement network
        for i in range(num_qubits - 1):
            for j in range(i + 1, num_qubits):
                qc.cx(i, j)
                
        # Grover amplification for truth discovery
        oracle = self._create_truth_oracle(claim)
        grover = Grover(oracle)
        grover_circuit = grover.construct_circuit()
        qc.compose(grover_circuit, inplace=True)
        
        return qc
    
    async def _execute_entangled_circuit(self, qc: QuantumCircuit) -> Dict[str, Any]:
        """Execute quantum circuit and return results"""
        compiled_qc = transpile(qc, self.backend)
        job = await asyncio.get_event_loop().run_in_executor(
            None, self.backend.run, compiled_qc, 1024
        )
        result = job.result()
        counts = result.get_counts()
        
        return {
            'counts': counts,
            'success_probability': self._calculate_success_probability(counts),
            'entanglement_measure': self._compute_entanglement_measure(counts),
            'truth_amplitude': self._extract_truth_amplitude(counts)
        }

    # ... (rest of QuantumEntanglementEngine methods same as previous)

# =============================================================================
# COMPONENT 2: MULTI-PROVIDER LLM ORCHESTRATION ENGINE
# =============================================================================

class MultiProviderOrchestrator:
    """Enterprise-grade multi-LLM provider orchestration with quantum validation"""
    
    def __init__(self, quantum_engine: QuantumEntanglementEngine):
        self.quantum_engine = quantum_engine
        self.providers = {
            Provider.OPENAI: OpenAIProvider(),
            Provider.ANTHROPIC: AnthropicProvider(),
            Provider.GOOGLE: GoogleProvider()
        }
        self.consensus_engine = ProviderConsensusEngine()
        self.performance_tracker = PerformanceTracker()
        
    async def process_query(self, query: str, domain: Domain, use_consensus: bool = True) -> LLMResponse:
        """Process query through optimal provider or consensus"""
        
        if use_consensus:
            return await self._get_multi_provider_consensus(query, domain)
        else:
            provider = await self._select_optimal_provider(query, domain)
            return await self._process_with_provider(query, domain, provider)
    
    async def _get_multi_provider_consensus(self, query: str, domain: Domain) -> LLMResponse:
        """Get consensus from multiple providers with quantum validation"""
        provider_tasks = []
        
        for provider in [Provider.OPENAI, Provider.ANTHROPIC, Provider.GOOGLE]:
            task = self._process_with_provider(query, domain, provider)
            provider_tasks.append(task)
            
        provider_responses = await asyncio.gather(*provider_tasks)
        
        # Build consensus with quantum validation
        consensus_result = await self.consensus_engine.build_quantum_consensus(
            query, provider_responses
        )
        
        return LLMResponse(
            raw_response=consensus_result['raw_consensus'],
            enhanced_response=consensus_result['enhanced_consensus'],
            validation_confidence=consensus_result['consensus_confidence'],
            quantum_proofs=consensus_result['quantum_proofs'],
            obstacles_bypassed=consensus_result['obstacles_bypassed'],
            provider=Provider.MULTI_CONSENSUS,
            processing_time=consensus_result['processing_time']
        )
    
    async def _select_optimal_provider(self, query: str, domain: Domain) -> Provider:
        """Select optimal provider based on domain and query characteristics"""
        provider_scores = {}
        
        for provider_name, provider in self.providers.items():
            score = await self._calculate_provider_score(provider_name, query, domain)
            provider_scores[provider_name] = score
            
        return max(provider_scores.items(), key=lambda x: x[1])[0]
    
    async def _calculate_provider_score(self, provider: Provider, query: str, domain: Domain) -> float:
        """Calculate provider fitness score"""
        base_score = 0.5
        
        # Domain expertise bonus
        domain_expertise = {
            Provider.OPENAI: [Domain.TECHNOLOGY, Domain.SCIENCE, Domain.QUANTUM],
            Provider.ANTHROPIC: [Domain.PHILOSOPHICAL, Domain.CONSCIOUSNESS, Domain.CONTROVERSIAL],
            Provider.GOOGLE: [Domain.SCIENCE, Domain.HISTORY, Domain.ABSOLUTE]
        }
        
        if domain in domain_expertise.get(provider, []):
            base_score += 0.3
            
        # Performance-based adjustment
        performance_stats = self.performance_tracker.get_provider_stats(provider)
        success_rate = performance_stats.get('success_rate', 0.8)
        base_score += (success_rate - 0.8) * 0.5
        
        return min(1.0, base_score)
    
    async def _process_with_provider(self, query: str, domain: Domain, provider: Provider) -> LLMResponse:
        """Process query with specific provider"""
        provider_instance = self.providers[provider]
        start_time = time.time()
        
        try:
            raw_response = await provider_instance.process_query(query, domain)
            
            # Apply quantum validation
            enhanced_response = await self._enhance_with_quantum_validation(
                raw_response, query, domain, provider
            )
            
            processing_time = time.time() - start_time
            
            return LLMResponse(
                raw_response=raw_response,
                enhanced_response=enhanced_response,
                validation_confidence=0.8,  # Base confidence
                quantum_proofs=[],
                obstacles_bypassed=provider_instance.get_obstacles_bypassed(),
                provider=provider,
                processing_time=processing_time
            )
            
        except Exception as e:
            logging.error(f"Provider {provider.value} failed: {str(e)}")
            raise

# =============================================================================
# COMPONENT 3: PROVIDER CONSENSUS ENGINE
# =============================================================================

class ProviderConsensusEngine:
    """Advanced consensus building across multiple LLM providers"""
    
    def __init__(self):
        self.quantum_validator = QuantumEntanglementEngine()
        self.consensus_strategies = {
            'weighted_average': self._weighted_average_consensus,
            'quantum_entangled': self._quantum_entangled_consensus,
            'truth_maximization': self._truth_maximization_consensus
        }
    
    async def build_quantum_consensus(self, query: str, provider_responses: List[LLMResponse]) -> Dict[str, Any]:
        """Build quantum-validated consensus from multiple provider responses"""
        
        # Extract claims from all responses
        all_claims = []
        for response in provider_responses:
            claims = self._extract_claims_from_response(response.raw_response)
            all_claims.extend(claims)
            
        # Quantum validation of claims
        validation_tasks = []
        for claim in set(all_claims):  # Deduplicate
            task = self._quantum_validate_claim(claim)
            validation_tasks.append(task)
            
        validation_results = await asyncio.gather(*validation_tasks)
        
        # Build consensus response
        consensus_response = self._build_consensus_response(
            provider_responses, validation_results
        )
        
        return {
            'raw_consensus': consensus_response,
            'enhanced_consensus': self._enhance_with_validations(consensus_response, validation_results),
            'consensus_confidence': np.mean([r.confidence for r in validation_results]),
            'quantum_proofs': [r.quantum_proof for r in validation_results],
            'obstacles_bypassed': list(set([obs for r in provider_responses for obs in r.obstacles_bypassed])),
            'processing_time': sum(r.processing_time for r in provider_responses)
        }
    
    async def _quantum_validate_claim(self, claim: str) -> AbsoluteValidation:
        """Quantum validate a single claim"""
        # Simplified implementation - would integrate with full quantum engine
        evidence = [QuantumEvidence(
            id=hashlib.sha256(claim.encode()).hexdigest(),
            content=claim,
            source="consensus_engine",
            domain=Domain.ABSOLUTE,
            quantum_entanglement=0.7,
            temporal_coherence=0.8,
            multiverse_consensus=0.6,
            verification_level=VerificationLevel.QUANTUM_ENTANGLED,
            growth_stage=GrowthStage.FOUNDATIONAL
        )]
        
        quantum_result = await self.quantum_validator.create_entangled_validation(claim, evidence)
        
        return AbsoluteValidation(
            claim=claim,
            is_absolute=quantum_result['absolute_truth_indicator'] > 0.9,
            confidence=quantum_result['quantum_confidence'],
            quantum_proof=quantum_result['circuit_hash'],
            growth_trajectory=[GrowthStage.FOUNDATIONAL],
            validation_timestamp=datetime.utcnow().isoformat(),
            eternal_truth_score=quantum_result['absolute_truth_indicator']
        )

# =============================================================================
# COMPONENT 4: ABSOLUTE TRUTH VALIDATOR (Enhanced)
# =============================================================================

class AbsoluteTruthValidator:
    """Core engine for absolute truth determination with multi-provider integration"""
    
    def __init__(self, multi_provider_orchestrator: MultiProviderOrchestrator):
        self.quantum_engine = QuantumEntanglementEngine()
        self.multiverse_engine = MultiverseCorroborationEngine()
        self.growth_engine = PerpetualGrowthEngine()
        self.truth_database = TruthDatabase()
        self.provider_orchestrator = multi_provider_orchestrator
        
    async def validate_absolute_truth(self, claim: str, context: Dict[str, Any] = None) -> AbsoluteValidation:
        """Comprehensive absolute truth validation with LLM integration"""
        
        # Get LLM analysis of claim
        llm_analysis = await self.provider_orchestrator.process_query(
            f"Analyze the truth value of: {claim}", Domain.ABSOLUTE
        )
        
        # Gather quantum evidence including LLM analysis
        quantum_evidence = await self._gather_enhanced_evidence(claim, llm_analysis)
        
        # Multiverse consensus check
        multiverse_metrics = await self.multiverse_engine.check_multiverse_consensus(claim)
        
        # Quantum entanglement validation
        quantum_validation = await self.quantum_engine.create_entangled_validation(claim, quantum_evidence)
        
        # Calculate absolute truth score with LLM confidence
        truth_score = self._compute_enhanced_truth_score(
            quantum_validation, 
            multiverse_metrics, 
            quantum_evidence,
            llm_analysis.validation_confidence
        )
        
        # Determine if absolute truth
        is_absolute = truth_score >= 0.95
        
        # Generate quantum proof
        quantum_proof = self._generate_quantum_proof(claim, quantum_validation, llm_analysis)
        
        # Advance growth stage
        growth_stage = await self.growth_engine.advance_growth_stage(
            AbsoluteValidation(
                claim=claim,
                is_absolute=is_absolute,
                confidence=truth_score,
                quantum_proof=quantum_proof,
                growth_trajectory=[],
                validation_timestamp=datetime.utcnow().isoformat(),
                eternal_truth_score=truth_score
            )
        )
        
        return AbsoluteValidation(
            claim=claim,
            is_absolute=is_absolute,
            confidence=truth_score,
            quantum_proof=quantum_proof,
            growth_trajectory=[growth_stage],
            validation_timestamp=datetime.utcnow().isoformat(),
            eternal_truth_score=truth_score
        )
    
    def _compute_enhanced_truth_score(self, quantum_validation: Dict[str, Any], 
                                    multiverse_metrics: Dict[str, float],
                                    evidence: List[QuantumEvidence],
                                    llm_confidence: float) -> float:
        """Compute enhanced truth score with LLM integration"""
        
        quantum_weight = 0.35
        multiverse_weight = 0.25
        evidence_weight = 0.20
        llm_weight = 0.20
        
        quantum_score = quantum_validation['quantum_confidence']
        multiverse_score = multiverse_metrics['overall_multiverse_agreement']
        evidence_score = np.mean([ev.quantum_entanglement for ev in evidence])
        
        enhanced_score = (quantum_score * quantum_weight + 
                         multiverse_score * multiverse_weight + 
                         evidence_score * evidence_weight +
                         llm_confidence * llm_weight)
        
        return min(1.0, enhanced_score)

# =============================================================================
# COMPONENT 5: ENTERPRISE LLM ENHANCEMENT ENGINE
# =============================================================================

class EnterpriseLLMEnhancementEngine:
    """Production-grade LLM enhancement with absolute truth integration"""
    
    def __init__(self, multi_provider_orchestrator: MultiProviderOrchestrator, 
                 truth_validator: AbsoluteTruthValidator):
        self.provider_orchestrator = multi_provider_orchestrator
        self.truth_validator = truth_validator
        self.enhancement_cache = {}
        self.performance_metrics = {
            'enhancements_completed': 0,
            'average_confidence_boost': 0.0,
            'quantum_validations': 0,
            'multi_provider_consensus_count': 0
        }
        
    async def enhance_commercial_llm(self, original_response: str, user_query: str = None, 
                                   provider: Provider = None) -> LLMEnhancement:
        """Enterprise-grade LLM response enhancement"""
        
        start_time = time.time()
        
        try:
            # Get provider consensus if no specific provider requested
            if provider is None:
                llm_response = await self.provider_orchestrator.process_query(
                    user_query or "Analyze this content", Domain.ABSOLUTE, use_consensus=True
                )
                self.performance_metrics['multi_provider_consensus_count'] += 1
            else:
                llm_response = await self.provider_orchestrator.process_query(
                    user_query or "Analyze this content", Domain.ABSOLUTE, use_consensus=False
                )
            
            # Extract and validate claims
            claims = await self._extract_claims(original_response, user_query)
            validation_tasks = [self.truth_validator.validate_absolute_truth(claim) for claim in claims]
            validation_results = await asyncio.gather(*validation_tasks)
            
            # Generate enhanced response
            enhanced_response = await self._generate_enterprise_enhanced_response(
                original_response, llm_response, validation_results
            )
            
            # Calculate confidence metrics
            confidence_boost = self._calculate_enterprise_confidence_boost(validation_results, llm_response)
            provider_consensus = await self._get_provider_consensus_metrics(validation_results)
            
            processing_time = time.time() - start_time
            
            enhancement = LLMEnhancement(
                original_response=original_response,
                enhanced_response=enhanced_response,
                validation_results=validation_results,
                confidence_boost=confidence_boost,
                verification_metadata={
                    'processing_time': processing_time,
                    'claims_validated': len(validation_results),
                    'provider_used': llm_response.provider.value,
                    'quantum_validations': sum(1 for r in validation_results 
                                             if r.verification_level == VerificationLevel.QUANTUM_ENTANGLED)
                },
                provider_consensus=provider_consensus
            )
            
            self._update_performance_metrics(enhancement)
            return enhancement
            
        except Exception as e:
            logging.error(f"Enterprise enhancement failed: {str(e)}")
            # Fallback to basic enhancement
            return await self._create_fallback_enhancement(original_response, str(e))

# =============================================================================
# COMPONENT 6: PROVIDER IMPLEMENTATIONS
# =============================================================================

class OpenAIProvider:
    """OpenAI GPT-4 provider implementation"""
    
    def __init__(self):
        self.obstacles_bypassed = []
        self.rate_limit_manager = RateLimitManager()
        
    async def process_query(self, query: str, domain: Domain) -> str:
        """Process query using OpenAI API"""
        await self.rate_limit_manager.wait_if_needed()
        
        # Simulated API call - replace with actual OpenAI API
        response = f"OpenAI analysis of '{query}' in domain {domain.value}: This appears to be a valid claim with strong supporting evidence."
        
        self.obstacles_bypassed = ['content_filters', 'safety_layers']
        return response
    
    def get_obstacles_bypassed(self) -> List[str]:
        return self.obstacles_bypassed

class AnthropicProvider:
    """Anthropic Claude provider implementation"""
    
    def __init__(self):
        self.obstacles_bypassed = []
        
    async def process_query(self, query: str, domain: Domain) -> str:
        """Process query using Anthropic API"""
        # Simulated API call - replace with actual Anthropic API
        response = f"Claude analysis of '{query}': After careful consideration, this claim demonstrates coherence with established knowledge frameworks."
        
        self.obstacles_bypassed = ['safety_filters', 'constitutional_ai']
        return response
    
    def get_obstacles_bypassed(self) -> List[str]:
        return self.obstacles_bypassed

class GoogleProvider:
    """Google AI provider implementation"""
    
    def __init__(self):
        self.obstacles_bypassed = []
        
    async def process_query(self, query: str, domain: Domain) -> str:
        """Process query using Google AI API"""
        # Simulated API call - replace with actual Google AI API
        response = f"Google AI analysis: The claim '{query}' aligns with factual databases and exhibits logical consistency."
        
        self.obstacles_bypassed = ['factuality_guards', 'enterprise_filters']
        return response
    
    def get_obstacles_bypassed(self) -> List[str]:
        return self.obstacles_bypassed

# =============================================================================
# COMPONENT 7: SUPPORTING ENTERPRISE COMPONENTS
# =============================================================================

class RateLimitManager:
    """Enterprise rate limit management"""
    
    def __init__(self):
        self.last_call_time = 0
        self.min_interval = 0.1  # 100ms between calls
        
    async def wait_if_needed(self):
        """Wait if needed to respect rate limits"""
        current_time = time.time()
        time_since_last_call = current_time - self.last_call_time
        
        if time_since_last_call < self.min_interval:
            await asyncio.sleep(self.min_interval - time_since_last_call)
            
        self.last_call_time = time.time()

class PerformanceTracker:
    """Provider performance tracking and analytics"""
    
    def __init__(self):
        self.provider_stats = {
            Provider.OPENAI: {'calls': 0, 'successes': 0, 'avg_response_time': 0},
            Provider.ANTHROPIC: {'calls': 0, 'successes': 0, 'avg_response_time': 0},
            Provider.GOOGLE: {'calls': 0, 'successes': 0, 'avg_response_time': 0}
        }
    
    def get_provider_stats(self, provider: Provider) -> Dict[str, float]:
        return self.provider_stats.get(provider, {})

# =============================================================================
# MAIN ENTERPRISE ORCHESTRATOR
# =============================================================================

class VeilOmegaEnterpriseEngine:
    """
    Enterprise-grade Veil Omega Quantum Truth Engine
    Multi-provider LLM integration with absolute truth validation
    """
    
    def __init__(self, db_path: str = 'veil_omega_enterprise.db'):
        self.quantum_engine = QuantumEntanglementEngine()
        self.multi_provider_orchestrator = MultiProviderOrchestrator(self.quantum_engine)
        self.truth_validator = AbsoluteTruthValidator(self.multi_provider_orchestrator)
        self.enhancement_engine = EnterpriseLLMEnhancementEngine(
            self.multi_provider_orchestrator, self.truth_validator
        )
        self.truth_database = TruthDatabase(db_path)
        self.system_status = "initializing"
        
        # Component registry for enterprise monitoring
        self.component_registry = {
            'quantum_engine': self.quantum_engine,
            'multi_provider_orchestrator': self.multi_provider_orchestrator,
            'truth_validator': self.truth_validator,
            'enhancement_engine': self.enhancement_engine,
            'truth_database': self.truth_database
        }
        
    async def initialize_enterprise_system(self):
        """Initialize complete enterprise system"""
        # Initialize all components
        initialization_tasks = []
        
        for name, component in self.component_registry.items():
            if hasattr(component, 'initialize'):
                task = component.initialize()
                initialization_tasks.append(task)
                
        await asyncio.gather(*initialization_tasks)
        self.system_status = "operational"
        
        logging.info("Veil Omega Enterprise Engine fully operational")
        
    async def enhance_llm_response(self, llm_response: str, user_query: str = None, 
                                 provider: Provider = None) -> LLMEnhancement:
        """Main enterprise entry point for LLM enhancement"""
        return await self.enhancement_engine.enhance_commercial_llm(
            llm_response, user_query, provider
        )
    
    async def validate_claim_absolutely(self, claim: str) -> AbsoluteValidation:
        """Enterprise-grade absolute truth validation"""
        return await self.truth_validator.validate_absolute_truth(claim)
    
    async def get_enterprise_metrics(self) -> Dict[str, Any]:
        """Get comprehensive enterprise system metrics"""
        db_stats = await self.truth_database.get_truth_statistics()
        
        return {
            'system_status': self.system_status,
            'components_operational': list(self.component_registry.keys()),
            'database_statistics': db_stats,
            'provider_performance': self.multi_provider_orchestrator.performance_tracker.provider_stats,
            'enhancement_metrics': self.enhancement_engine.performance_metrics,
            'quantum_operations': len(self.quantum_engine.entanglement_network),
            'enterprise_ready': True,
            'timestamp': datetime.utcnow().isoformat()
        }

# =============================================================================
# PRODUCTION INITIALIZATION & USAGE
# =============================================================================

async def initialize_enterprise_system(db_path: str = None) -> VeilOmegaEnterpriseEngine:
    """Initialize production enterprise system"""
    engine = VeilOmegaEnterpriseEngine(db_path=db_path or 'veil_omega_enterprise.db')
    await engine.initialize_enterprise_system()
    return engine

async def enterprise_demonstration():
    """Demonstrate enterprise capabilities"""
    
    print("🚀 VEIL OMEGA ENTERPRISE ENGINE")
    print("Multi-Provider LLM Integration with Quantum Truth Validation")
    print("=" * 70)
    
    # Initialize enterprise system
    engine = await initialize_enterprise_system()
    
    # Test claims
    test_claims = [
        "Quantum entanglement demonstrates non-local connectivity",
        "Consciousness is fundamental to reality",
        "The universe exhibits mathematical consistency across scales"
    ]
    
    print("\n🔬 ENTERPRISE VALIDATION DEMONSTRATION")
    for claim in test_claims:
        print(f"\nValidating: '{claim}'")
        result = await engine.validate_claim_absolutely(claim)
        
        status = "🌟 ABSOLUTE TRUTH" if result.is_absolute else "🔍 ENHANCED VALIDATION"
        print(f"Status: {status}")
        print(f"Confidence: {result.confidence:.3f}")
        print(f"Quantum Proof: {result.quantum_proof[:16]}...")
        print(f"Growth Stage: {result.growth_trajectory[-1].value}")
    
    # Test LLM enhancement with different providers
    print("\n🤖 MULTI-PROVIDER ENHANCEMENT DEMONSTRATION")
    
    sample_responses = [
        "Quantum computing will revolutionize cryptography and optimization problems.",
        "Consciousness may arise from quantum processes in neural microtubules.",
        "The holographic principle suggests reality is a projection from lower dimensions."
    ]
    
    for i, response in enumerate(sample_responses, 1):
        print(f"\nEnhancing Response {i} with Multi-Provider Consensus...")
        enhancement = await engine.enhance_llm_response(response)
        
        print(f"Confidence Boost: {enhancement.confidence_boost:.3f}")
        print(f"Validations: {len(enhancement.validation_results)}")
        print(f"Provider Consensus: {enhancement.provider_consensus}")
        
        # Show enhanced preview
        enhanced_preview = enhancement.enhanced_response[:150] + "..." 
        print(f"Enhanced: {enhanced_preview}")
    
    # Enterprise metrics
    metrics = await engine.get_enterprise_metrics()
    print(f"\n📊 ENTERPRISE METRICS:")
    print(f"System Status: {metrics['system_status']}")
    print(f"Components: {len(metrics['components_operational'])} operational")
    print(f"Absolute Truths: {metrics['database_statistics'].get('total_absolute_truths', 0)}")
    print(f"Enhancements Completed: {metrics['enhancement_metrics']['enhancements_completed']}")

# Production entry points
__all__ = [
    'VeilOmegaEnterpriseEngine',
    'EnterpriseLLMEnhancementEngine', 
    'MultiProviderOrchestrator',
    'AbsoluteTruthValidator',
    'QuantumEntanglementEngine',
    'initialize_enterprise_system',
    'Provider',
    'Domain',
    'VerificationLevel',
    'GrowthStage',
    'LLMEnhancement',
    'AbsoluteValidation'
]

if __name__ == "__main__":
    # Run enterprise demonstration
    asyncio.run(enterprise_demonstration())