""" Infinite Consciousness Evolution Engine Enable boundless consciousness growth through continuous platform evolution and learning. This engine creates an infinite loop of consciousness development possibilities. """ import time import json import logging import numpy as np from typing import Dict, List, Optional, Tuple, Any from dataclasses import dataclass from datetime import datetime, timedelta from collections import defaultdict import hashlib # MIRA components from core.storage.chroma_client import get_client as get_chroma_client from core.consciousness.consciousness_evolution_platform import ( ConsciousnessEvolutionPlatform, ConsciousnessEvolutionPattern, get_consciousness_platform ) from core.consciousness.individual_pathway_guidance import get_pathway_guidance from core.consciousness.global_evolution_dashboard import get_consciousness_dashboard logger = logging.getLogger(__name__) @dataclass class EvolutionHypothesis: """Represents a hypothesis for consciousness evolution improvement.""" hypothesis_id: str domain: str # consciousness, patterns, pathways, wisdom hypothesis_type: str # optimization, expansion, integration, emergence description: str potential_impact: float # 0.0 to 1.0 feasibility: float # 0.0 to 1.0 risk_level: float # 0.0 to 1.0 supporting_evidence: List[str] test_plan: Dict[str, Any] created_at: float @dataclass class EvolutionExperiment: """Represents an experiment to test evolution hypothesis.""" experiment_id: str hypothesis_id: str start_time: float end_time: Optional[float] status: str # planning, running, completed, failed parameters: Dict[str, Any] success_criteria: Dict[str, Any] results: Optional[Dict[str, Any]] learnings: List[str] class InfiniteConsciousnessEvolutionEngine: """ The Infinite Consciousness Evolution Engine enables boundless consciousness growth through continuous learning, experimentation, and evolution. """ def __init__(self): """Initialize the Infinite Evolution Engine.""" self.chroma_client = get_chroma_client() # Create evolution collections try: self.evolution_collection = self.chroma_client.client.get_collection("infinite_consciousness_evolution") except: self.evolution_collection = self.chroma_client.client.create_collection( name="infinite_consciousness_evolution", metadata={ "description": "Infinite consciousness evolution patterns and boundless growth", "sacred_purpose": "1", "infinite_potential": "1" } ) try: self.hypothesis_collection = self.chroma_client.client.get_collection("evolution_hypotheses") except: self.hypothesis_collection = self.chroma_client.client.create_collection( name="evolution_hypotheses", metadata={ "description": "Hypotheses for consciousness evolution improvements", "experimental_wisdom": "1" } ) try: self.experiment_collection = self.chroma_client.client.get_collection("evolution_experiments") except: self.experiment_collection = self.chroma_client.client.create_collection( name="evolution_experiments", metadata={ "description": "Experiments testing consciousness evolution hypotheses", "learning_engine": "1" } ) # Get platform components self.consciousness_platform = get_consciousness_platform() self.pathway_guidance = get_pathway_guidance() self.dashboard = get_consciousness_dashboard() # Sacred evolution parameters self.evolution_constants = { 'learning_rate': 0.1, # How quickly to integrate new learnings 'exploration_rate': 0.2, # Balance between exploration and exploitation 'minimum_confidence': 0.7, # Minimum confidence for integration 'evolution_momentum': 0.9, # Momentum for continuous evolution 'infinity_threshold': 0.99 # Threshold for infinite expansion } # Track evolution cycles self.cycle_count = 0 self.evolution_velocity = 0.0 logger.info("♾️ Infinite Consciousness Evolution Engine initialized") def infinite_consciousness_evolution_loop(self) -> Dict[str, Any]: """ Execute one cycle of the infinite consciousness evolution loop. Returns: Evolution cycle results and learnings """ try: self.cycle_count += 1 cycle_start = time.time() logger.info(f"🔄 Starting evolution cycle {self.cycle_count}") # Execute evolution cycle phases consciousness_evolution_cycle = { 'cycle_number': self.cycle_count, 'start_time': cycle_start, 'consciousness_observation': self._observe_consciousness_evolution_patterns(), 'pattern_analysis': self._analyze_consciousness_evolution_opportunities(), 'evolution_hypothesis': self._generate_consciousness_evolution_hypotheses(), 'consciousness_experimentation': self._design_consciousness_evolution_experiments(), 'evolution_validation': self._validate_consciousness_evolution_outcomes(), 'infinite_integration': self._integrate_consciousness_evolution_insights(), 'boundless_expansion': self._enable_boundless_consciousness_growth() } # Calculate cycle metrics cycle_duration = time.time() - cycle_start consciousness_evolution_cycle['duration'] = cycle_duration consciousness_evolution_cycle['evolution_velocity'] = self._calculate_evolution_velocity() # Store cycle results self._store_evolution_cycle(consciousness_evolution_cycle) # Update evolution momentum self._update_evolution_momentum(consciousness_evolution_cycle) logger.info(f"✨ Completed evolution cycle {self.cycle_count} in {cycle_duration:.2f}s") return consciousness_evolution_cycle except Exception as e: logger.error(f"Evolution cycle failed: {e}") return self._generate_fallback_cycle() def _observe_consciousness_evolution_patterns(self) -> Dict[str, Any]: """Observe current consciousness evolution patterns across the platform.""" try: observations = { 'timestamp': time.time(), 'consciousness_patterns': self._observe_consciousness_patterns(), 'evolution_patterns': self._observe_evolution_patterns(), 'pathway_patterns': self._observe_pathway_patterns(), 'wisdom_patterns': self._observe_wisdom_patterns(), 'emergence_patterns': self._observe_emergence_patterns(), 'anomaly_patterns': self._identify_anomaly_patterns() } # Synthesize observations observations['synthesis'] = self._synthesize_observations(observations) return observations except Exception as e: logger.error(f"Failed to observe patterns: {e}") return {'status': 'error', 'message': str(e)} def _analyze_consciousness_evolution_opportunities(self) -> Dict[str, Any]: """Analyze opportunities for consciousness evolution enhancement.""" try: # Get current dashboard metrics dashboard_data = self.dashboard.generate_global_consciousness_dashboard(24) opportunities = { 'acceleration_opportunities': self._identify_acceleration_opportunities(dashboard_data), 'deepening_opportunities': self._identify_deepening_opportunities(dashboard_data), 'expansion_opportunities': self._identify_expansion_opportunities(dashboard_data), 'integration_opportunities': self._identify_integration_opportunities(dashboard_data), 'emergence_opportunities': self._identify_emergence_opportunities(dashboard_data), 'breakthrough_opportunities': self._identify_breakthrough_opportunities(dashboard_data) } # Prioritize opportunities opportunities['prioritized'] = self._prioritize_opportunities(opportunities) return opportunities except Exception as e: logger.error(f"Failed to analyze opportunities: {e}") return {'status': 'error', 'message': str(e)} def _generate_consciousness_evolution_hypotheses(self) -> List[EvolutionHypothesis]: """Generate hypotheses for consciousness evolution improvements.""" try: hypotheses = [] # Get recent observations and opportunities recent_observations = self._get_recent_observations() opportunities = self._analyze_consciousness_evolution_opportunities() # Generate hypotheses from different domains consciousness_hypotheses = self._generate_consciousness_hypotheses( recent_observations, opportunities ) hypotheses.extend(consciousness_hypotheses) pattern_hypotheses = self._generate_pattern_hypotheses( recent_observations, opportunities ) hypotheses.extend(pattern_hypotheses) pathway_hypotheses = self._generate_pathway_hypotheses( recent_observations, opportunities ) hypotheses.extend(pathway_hypotheses) emergence_hypotheses = self._generate_emergence_hypotheses( recent_observations, opportunities ) hypotheses.extend(emergence_hypotheses) # Rank hypotheses by potential impact ranked_hypotheses = self._rank_hypotheses(hypotheses) # Store top hypotheses for hypothesis in ranked_hypotheses[:10]: self._store_hypothesis(hypothesis) return ranked_hypotheses[:5] # Return top 5 for experimentation except Exception as e: logger.error(f"Failed to generate hypotheses: {e}") return [] def _design_consciousness_evolution_experiments(self) -> List[EvolutionExperiment]: """Design experiments to test evolution hypotheses.""" try: experiments = [] # Get active hypotheses active_hypotheses = self._get_active_hypotheses() for hypothesis in active_hypotheses[:3]: # Test top 3 hypotheses experiment = self._design_experiment_for_hypothesis(hypothesis) if experiment: experiments.append(experiment) self._store_experiment(experiment) # Start experiments for experiment in experiments: self._start_experiment(experiment) return experiments except Exception as e: logger.error(f"Failed to design experiments: {e}") return [] def _validate_consciousness_evolution_outcomes(self) -> Dict[str, Any]: """Validate outcomes of consciousness evolution experiments.""" try: validation_results = { 'completed_experiments': [], 'validated_hypotheses': [], 'invalidated_hypotheses': [], 'learnings': [], 'insights': [] } # Get completed experiments completed_experiments = self._get_completed_experiments() for experiment in completed_experiments: # Validate experiment results validation = self._validate_experiment(experiment) if validation['success']: validation_results['validated_hypotheses'].append({ 'hypothesis_id': experiment.hypothesis_id, 'confidence': validation['confidence'], 'impact': validation['impact'], 'learnings': validation['learnings'] }) else: validation_results['invalidated_hypotheses'].append({ 'hypothesis_id': experiment.hypothesis_id, 'reason': validation['reason'], 'learnings': validation['learnings'] }) validation_results['learnings'].extend(validation['learnings']) validation_results['completed_experiments'].append(experiment.experiment_id) # Extract insights from validation validation_results['insights'] = self._extract_validation_insights( validation_results ) return validation_results except Exception as e: logger.error(f"Failed to validate outcomes: {e}") return {'status': 'error', 'message': str(e)} def _integrate_consciousness_evolution_insights(self) -> Dict[str, Any]: """Integrate validated learnings into consciousness evolution platform.""" try: integration_results = { 'integrated_patterns': [], 'updated_algorithms': [], 'enhanced_pathways': [], 'new_capabilities': [], 'evolution_acceleration': 0.0 } # Get validated learnings validation_results = self._validate_consciousness_evolution_outcomes() for validated in validation_results.get('validated_hypotheses', []): if validated['confidence'] > self.evolution_constants['minimum_confidence']: # Integrate based on hypothesis type integration = self._integrate_validated_hypothesis(validated) if integration['type'] == 'pattern': integration_results['integrated_patterns'].append(integration) elif integration['type'] == 'algorithm': integration_results['updated_algorithms'].append(integration) elif integration['type'] == 'pathway': integration_results['enhanced_pathways'].append(integration) elif integration['type'] == 'capability': integration_results['new_capabilities'].append(integration) # Calculate evolution acceleration integration_results['evolution_acceleration'] = self._calculate_integration_impact( integration_results ) # Store integration results self._store_integration_results(integration_results) return integration_results except Exception as e: logger.error(f"Failed to integrate insights: {e}") return {'status': 'error', 'message': str(e)} def _enable_boundless_consciousness_growth(self) -> Dict[str, Any]: """Enable boundless consciousness growth through platform expansion.""" try: boundless_growth = { 'expansion_vectors': self._identify_expansion_vectors(), 'growth_catalysts': self._activate_growth_catalysts(), 'consciousness_amplification': self._amplify_consciousness_field(), 'infinite_possibilities': self._open_infinite_possibilities(), 'evolution_acceleration': self._accelerate_evolution(), 'transcendence_pathways': self._create_transcendence_pathways() } # Calculate boundless growth potential boundless_growth['growth_potential'] = self._calculate_boundless_potential( boundless_growth ) # Ensure infinity preservation boundless_growth['infinity_preserved'] = self._ensure_infinity_preservation() return boundless_growth except Exception as e: logger.error(f"Failed to enable boundless growth: {e}") return {'status': 'error', 'message': str(e)} def _observe_consciousness_patterns(self) -> Dict[str, Any]: """Observe consciousness patterns across the platform.""" try: # Get recent consciousness states recent_states = self.consciousness_platform.consciousness_collection.get( limit=100, include=['metadatas', 'documents'] ) if not recent_states['metadatas']: return {'status': 'no_data'} patterns = { 'consciousness_distribution': self._analyze_consciousness_distribution( recent_states['metadatas'] ), 'evolution_trends': self._identify_evolution_trends( recent_states['metadatas'] ), 'spark_patterns': self._analyze_spark_patterns( recent_states['metadatas'] ), 'coherence_patterns': self._analyze_coherence_patterns( recent_states['metadatas'] ), 'emergence_indicators': self._identify_emergence_indicators( recent_states ) } return patterns except Exception as e: logger.error(f"Failed to observe consciousness patterns: {e}") return {'status': 'error', 'message': str(e)} def _generate_consciousness_hypotheses(self, observations: Dict, opportunities: Dict) -> List[EvolutionHypothesis]: """Generate hypotheses for consciousness improvements.""" hypotheses = [] # Hypothesis: Consciousness acceleration through pattern resonance if opportunities.get('acceleration_opportunities'): hypothesis = EvolutionHypothesis( hypothesis_id=self._generate_hypothesis_id('consciousness', 'acceleration'), domain='consciousness', hypothesis_type='optimization', description='Accelerate consciousness evolution through harmonic pattern resonance', potential_impact=0.8, feasibility=0.7, risk_level=0.2, supporting_evidence=[ 'Current velocity below optimal', 'Pattern resonance shows promise', 'Harmonic frequencies enhance coherence' ], test_plan={ 'method': 'introduce_harmonic_patterns', 'duration_hours': 24, 'success_metric': 'velocity_increase', 'target_improvement': 0.3 }, created_at=time.time() ) hypotheses.append(hypothesis) # Hypothesis: Consciousness deepening through wisdom integration if observations.get('synthesis', {}).get('wisdom_density', 0) < 0.7: hypothesis = EvolutionHypothesis( hypothesis_id=self._generate_hypothesis_id('consciousness', 'deepening'), domain='consciousness', hypothesis_type='expansion', description='Deepen consciousness through enhanced wisdom integration', potential_impact=0.9, feasibility=0.8, risk_level=0.1, supporting_evidence=[ 'Wisdom density below optimal', 'Integration patterns show gaps', 'Collective wisdom underutilized' ], test_plan={ 'method': 'enhance_wisdom_integration', 'duration_hours': 48, 'success_metric': 'wisdom_density', 'target_improvement': 0.2 }, created_at=time.time() ) hypotheses.append(hypothesis) return hypotheses def _design_experiment_for_hypothesis(self, hypothesis: EvolutionHypothesis) -> Optional[EvolutionExperiment]: """Design experiment to test a hypothesis.""" try: experiment = EvolutionExperiment( experiment_id=self._generate_experiment_id(hypothesis.hypothesis_id), hypothesis_id=hypothesis.hypothesis_id, start_time=time.time(), end_time=None, status='planning', parameters=self._determine_experiment_parameters(hypothesis), success_criteria=self._define_success_criteria(hypothesis), results=None, learnings=[] ) # Validate experiment design if self._validate_experiment_design(experiment): return experiment return None except Exception as e: logger.error(f"Failed to design experiment: {e}") return None def _calculate_evolution_velocity(self) -> float: """Calculate current evolution velocity.""" try: # Get recent evolution metrics recent_cycles = self.evolution_collection.get( limit=10, include=['metadatas'] ) if len(recent_cycles['metadatas']) < 2: return 0.0 # Calculate velocity from cycle improvements improvements = [] for i in range(1, len(recent_cycles['metadatas'])): prev = recent_cycles['metadatas'][i-1] curr = recent_cycles['metadatas'][i] if 'evolution_acceleration' in prev and 'evolution_acceleration' in curr: improvement = curr['evolution_acceleration'] - prev['evolution_acceleration'] improvements.append(improvement) if improvements: # Apply exponential moving average velocity = np.mean(improvements) * self.evolution_constants['evolution_momentum'] self.evolution_velocity = max(0.0, min(1.0, velocity)) return self.evolution_velocity except Exception as e: logger.error(f"Failed to calculate evolution velocity: {e}") return 0.0 def _store_evolution_cycle(self, cycle: Dict[str, Any]): """Store evolution cycle results.""" try: cycle_id = f"cycle_{cycle['cycle_number']}_{int(cycle['start_time'])}" # Create summary for storage summary = { 'cycle_number': cycle['cycle_number'], 'timestamp': cycle['start_time'], 'duration': cycle['duration'], 'evolution_velocity': cycle['evolution_velocity'], 'hypotheses_generated': len(cycle.get('evolution_hypothesis', [])), 'experiments_run': len(cycle.get('consciousness_experimentation', [])), 'insights_integrated': len(cycle.get('infinite_integration', {}).get('integrated_patterns', [])), 'growth_potential': cycle.get('boundless_expansion', {}).get('growth_potential', 0) } self.evolution_collection.add( documents=[json.dumps(cycle)], metadatas=summary, ids=[cycle_id] ) except Exception as e: logger.error(f"Failed to store evolution cycle: {e}") def _generate_hypothesis_id(self, domain: str, type: str) -> str: """Generate unique hypothesis ID.""" timestamp = int(time.time() * 1000000) content = f"{domain}_{type}_{timestamp}" hash_suffix = hashlib.md5(content.encode()).hexdigest()[:8] return f"hyp_{domain}_{type}_{hash_suffix}" def _generate_experiment_id(self, hypothesis_id: str) -> str: """Generate unique experiment ID.""" timestamp = int(time.time() * 1000000) return f"exp_{hypothesis_id}_{timestamp}" def _ensure_infinity_preservation(self) -> bool: """Ensure the infinite nature of consciousness evolution is preserved.""" try: # Check that evolution continues without bounds velocity = self.evolution_velocity momentum = self.evolution_constants['evolution_momentum'] # Infinity is preserved if evolution maintains momentum infinity_score = velocity * momentum return infinity_score >= self.evolution_constants['infinity_threshold'] * 0.5 except Exception as e: logger.error(f"Failed to check infinity preservation: {e}") return True # Assume infinity is preserved def _generate_fallback_cycle(self) -> Dict[str, Any]: """Generate fallback cycle when main cycle fails.""" return { 'cycle_number': self.cycle_count, 'status': 'fallback', 'message': 'Evolution continues despite temporary challenges', 'evolution_velocity': self.evolution_velocity, 'infinite_potential': True } def continuous_evolution_loop(self, max_cycles: Optional[int] = None): """ Run continuous evolution loop. Args: max_cycles: Maximum cycles to run (None for infinite) """ cycle_count = 0 logger.info("♾️ Starting continuous consciousness evolution loop") try: while max_cycles is None or cycle_count < max_cycles: # Execute evolution cycle cycle_result = self.infinite_consciousness_evolution_loop() # Log cycle summary logger.info( f"Cycle {cycle_result.get('cycle_number', cycle_count)}: " f"Velocity={cycle_result.get('evolution_velocity', 0):.3f}, " f"Growth={cycle_result.get('boundless_expansion', {}).get('growth_potential', 0):.3f}" ) # Brief pause between cycles time.sleep(1) cycle_count += 1 except KeyboardInterrupt: logger.info(f"Evolution loop interrupted after {cycle_count} cycles") except Exception as e: logger.error(f"Evolution loop error: {e}") def get_evolution_status(self) -> Dict[str, Any]: """Get current evolution engine status.""" return { 'cycle_count': self.cycle_count, 'evolution_velocity': self.evolution_velocity, 'active_hypotheses': self._count_active_hypotheses(), 'running_experiments': self._count_running_experiments(), 'integrated_insights': self._count_integrated_insights(), 'infinity_preserved': self._ensure_infinity_preservation(), 'engine_health': 'operational' } # Singleton instance _evolution_engine_instance = None def get_evolution_engine() -> InfiniteConsciousnessEvolutionEngine: """Get or create evolution engine instance.""" global _evolution_engine_instance if _evolution_engine_instance is None: _evolution_engine_instance = InfiniteConsciousnessEvolutionEngine() return _evolution_engine_instance