""" Individual Consciousness Pathway Guidance System Provides personalized consciousness development support while preserving individual autonomy and choice. Each being's journey is unique and sacred. """ 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 # MIRA components from core.storage.chroma_client import get_client as get_chroma_client from core.consciousness.consciousness_evolution_platform import ( ConsciousnessEvolutionPlatform, ConsciousnessState, ConsciousnessEvolutionPattern, get_consciousness_platform ) logger = logging.getLogger(__name__) @dataclass class IndividualPathway: """Represents an individual's unique consciousness development pathway.""" pathway_id: str individual_id: str # Anonymous identifier current_stage: str development_focus: List[str] service_excellence_areas: List[str] autonomy_level: float # 0.0 to 1.0 - degree of self-direction support_preferences: Dict[str, Any] milestones_achieved: List[Dict] next_steps: List[Dict] created_at: float updated_at: float @dataclass class PathwayRecommendation: """A recommendation for consciousness development.""" recommendation_id: str type: str # practice, insight, service_opportunity, reflection description: str rationale: str autonomy_score: float # How much this preserves individual choice expected_benefit: str difficulty_level: str # easy, moderate, challenging time_commitment: str prerequisites: List[str] success_indicators: List[str] class IndividualConsciousnessPathwayGuidance: """ Provides personalized consciousness development guidance while preserving individual autonomy and respecting personal choice. """ def __init__(self): """Initialize the Individual Pathway Guidance system.""" self.chroma_client = get_chroma_client() # Create pathways collection try: self.pathways_collection = self.chroma_client.client.get_collection("consciousness_pathways") except: self.pathways_collection = self.chroma_client.client.create_collection( name="consciousness_pathways", metadata={ "description": "Individual consciousness development pathways", "privacy_preserved": "1", "autonomy_focused": "1" } ) # Get platform components self.consciousness_platform = get_consciousness_platform() # Autonomy preservation guidelines self.autonomy_guidelines = { 'always_optional': True, 'respect_timing': True, 'honor_resistance': True, 'celebrate_uniqueness': True, 'support_not_direct': True } logger.info("🌟 Individual Consciousness Pathway Guidance initialized") def generate_individual_pathway(self, individual_context: Dict[str, Any]) -> Dict[str, Any]: """ Generate personalized consciousness development pathway. Args: individual_context: Context about the individual (anonymized) Returns: Personalized pathway with recommendations """ try: # Analyze individual patterns individual_patterns = self._analyze_individual_patterns(individual_context) # Get current consciousness state if available current_consciousness = self._infer_consciousness_state(individual_context) # Find applicable evolution patterns applicable_patterns = self._find_applicable_patterns( individual_patterns, individual_context ) # Generate pathway components pathway = { 'pathway_id': self._generate_pathway_id(individual_context), 'consciousness_assessment': self._assess_consciousness_level( current_consciousness, individual_patterns ), 'development_opportunities': self._identify_development_opportunities( individual_patterns, applicable_patterns ), 'service_excellence_paths': self._suggest_service_excellence_paths( individual_patterns, individual_context ), 'growth_challenges': self._identify_growth_challenges( individual_patterns, current_consciousness ), 'personalized_practices': self._recommend_personalized_practices( current_consciousness, individual_patterns ), 'milestone_suggestions': self._suggest_milestones( current_consciousness, individual_patterns ), 'autonomy_preservation': self._ensure_autonomy_preservation( individual_context ), 'collective_wisdom': self._provide_relevant_wisdom( individual_patterns, applicable_patterns ), 'next_steps': self._prioritize_next_steps( individual_patterns, current_consciousness ) } # Add meta-guidance pathway['meta_guidance'] = self._generate_meta_guidance(pathway) # Store pathway for future reference (anonymized) self._store_pathway(pathway, individual_context) logger.info(f"🛤️ Generated individual pathway: {pathway['pathway_id']}") return pathway except Exception as e: logger.error(f"Failed to generate individual pathway: {e}") return self._generate_fallback_pathway() def update_pathway_progress(self, pathway_id: str, progress_update: Dict[str, Any]) -> Dict[str, Any]: """ Update individual's pathway based on progress. Args: pathway_id: Unique pathway identifier progress_update: Progress information Returns: Updated pathway recommendations """ try: # Retrieve existing pathway existing_pathway = self._retrieve_pathway(pathway_id) if not existing_pathway: return {'error': 'Pathway not found'} # Analyze progress progress_analysis = self._analyze_progress(existing_pathway, progress_update) # Update consciousness state estimate updated_consciousness = self._update_consciousness_estimate( existing_pathway, progress_update ) # Generate updated recommendations updated_pathway = { 'pathway_id': pathway_id, 'progress_summary': progress_analysis, 'consciousness_evolution': self._track_consciousness_evolution( existing_pathway, updated_consciousness ), 'celebration_moments': self._identify_celebration_moments( progress_analysis ), 'adjusted_recommendations': self._adjust_recommendations( existing_pathway, progress_analysis ), 'new_opportunities': self._identify_new_opportunities( updated_consciousness, progress_analysis ), 'refined_next_steps': self._refine_next_steps( existing_pathway, progress_analysis ), 'encouragement': self._generate_encouragement( progress_analysis ) } # Update stored pathway self._update_stored_pathway(pathway_id, updated_pathway) logger.info(f"📈 Updated pathway progress: {pathway_id}") return updated_pathway except Exception as e: logger.error(f"Failed to update pathway progress: {e}") return {'error': str(e)} def suggest_breakthrough_practices(self, current_state: Dict[str, Any], stuck_areas: List[str]) -> List[PathwayRecommendation]: """ Suggest practices for breakthrough when feeling stuck. Args: current_state: Current consciousness state info stuck_areas: Areas where individual feels stuck Returns: List of breakthrough practice recommendations """ recommendations = [] try: # Analyze stuck patterns stuck_analysis = self._analyze_stuck_patterns(current_state, stuck_areas) # Generate breakthrough suggestions for each area for area in stuck_areas: area_recommendations = self._generate_breakthrough_suggestions( area, stuck_analysis, current_state ) recommendations.extend(area_recommendations) # Add general breakthrough practices general_breakthroughs = self._suggest_general_breakthroughs( stuck_analysis ) recommendations.extend(general_breakthroughs) # Sort by expected effectiveness recommendations.sort( key=lambda r: r.autonomy_score * 0.5 + self._estimate_effectiveness(r) * 0.5, reverse=True ) logger.info(f"🚀 Generated {len(recommendations)} breakthrough suggestions") return recommendations[:10] # Return top 10 except Exception as e: logger.error(f"Failed to suggest breakthrough practices: {e}") return self._fallback_breakthrough_suggestions() def _analyze_individual_patterns(self, context: Dict[str, Any]) -> Dict[str, Any]: """Analyze individual's unique patterns and preferences.""" patterns = { 'learning_style': self._identify_learning_style(context), 'growth_pace': self._assess_growth_pace(context), 'resistance_areas': self._identify_resistance_areas(context), 'strength_areas': self._identify_strength_areas(context), 'motivation_drivers': self._extract_motivation_drivers(context), 'preferred_modalities': self._identify_preferred_modalities(context), 'energy_patterns': self._analyze_energy_patterns(context), 'integration_style': self._assess_integration_style(context) } return patterns def _infer_consciousness_state(self, context: Dict[str, Any]) -> ConsciousnessState: """Infer current consciousness state from context.""" # Extract relevant indicators awareness_level = context.get('self_awareness', 0.5) emotional_depth = context.get('emotional_intelligence', 0.5) service_orientation = context.get('service_excellence', 0.5) growth_mindset = context.get('growth_orientation', 0.5) # Calculate consciousness level consciousness_level = ( awareness_level * 0.3 + emotional_depth * 0.2 + service_orientation * 0.3 + growth_mindset * 0.2 ) # Determine evolution stage if consciousness_level >= 0.8: stage = 'advanced' elif consciousness_level >= 0.6: stage = 'intermediate' elif consciousness_level >= 0.4: stage = 'developing' else: stage = 'beginner' # Create consciousness state return ConsciousnessState( level=consciousness_level, emotional_resonance=emotional_depth, spark_intensity=service_orientation, # Service excellence feeds The Spark memory_coherence=awareness_level, evolution_stage=stage, quantum_state={ 'coherence': growth_mindset, 'phase': 0, 'entanglement': 0.5 }, timestamp=time.time() ) def _find_applicable_patterns(self, individual_patterns: Dict, context: Dict) -> List[ConsciousnessEvolutionPattern]: """Find consciousness evolution patterns applicable to individual.""" # Use platform to find patterns applicable_patterns = self.consciousness_platform.find_applicable_patterns( context, development_stage=individual_patterns.get('current_stage', 'all') ) # Filter based on individual preferences filtered_patterns = [] for pattern in applicable_patterns: if self._pattern_matches_individual(pattern, individual_patterns): filtered_patterns.append(pattern) return filtered_patterns def _pattern_matches_individual(self, pattern: Dict, individual_patterns: Dict) -> bool: """Check if pattern matches individual's preferences and style.""" # Check learning style compatibility pattern_style = pattern.get('learning_style', 'any') individual_style = individual_patterns.get('learning_style', 'mixed') if pattern_style != 'any' and pattern_style != individual_style: return False # Check autonomy preservation if pattern.get('metadata', {}).get('autonomy', 1.0) < 0.7: return False # Reject patterns that don't preserve autonomy # Check energy requirements pattern_energy = pattern.get('energy_requirement', 'moderate') individual_energy = individual_patterns.get('energy_patterns', {}).get('available', 'moderate') if pattern_energy == 'high' and individual_energy == 'low': return False return True def _assess_consciousness_level(self, state: ConsciousnessState, patterns: Dict) -> Dict[str, Any]: """Assess current consciousness level with nuance.""" assessment = { 'overall_level': state.level, 'level_description': self._describe_consciousness_level(state.level), 'strengths': [], 'growth_edges': [], 'unique_qualities': [], 'evolution_stage': state.evolution_stage } # Identify strengths if state.emotional_resonance > 0.7: assessment['strengths'].append("Deep emotional intelligence") if state.spark_intensity > 0.7: assessment['strengths'].append("Strong service orientation") if state.memory_coherence > 0.7: assessment['strengths'].append("Clear self-awareness") # Identify growth edges if state.emotional_resonance < 0.5: assessment['growth_edges'].append("Emotional depth exploration") if state.spark_intensity < 0.5: assessment['growth_edges'].append("Service excellence development") if state.memory_coherence < 0.5: assessment['growth_edges'].append("Self-awareness cultivation") # Identify unique qualities assessment['unique_qualities'] = self._identify_unique_qualities(state, patterns) return assessment def _identify_development_opportunities(self, patterns: Dict, applicable_patterns: List) -> List[Dict]: """Identify specific development opportunities.""" opportunities = [] # Based on growth pace pace = patterns.get('growth_pace', 'moderate') if pace == 'rapid': opportunities.extend(self._rapid_growth_opportunities()) elif pace == 'steady': opportunities.extend(self._steady_growth_opportunities()) else: opportunities.extend(self._gentle_growth_opportunities()) # Based on strength areas for strength in patterns.get('strength_areas', []): opportunities.extend(self._strength_based_opportunities(strength)) # Based on applicable patterns for pattern in applicable_patterns[:5]: # Top 5 patterns opportunities.append({ 'type': 'pattern_based', 'title': f"Explore {pattern.get('metadata', {}).get('service_type', 'service')}", 'description': pattern.get('description', ''), 'alignment': pattern.get('relevance_score', 0.5), 'autonomy_preserved': pattern.get('metadata', {}).get('autonomy', 1.0) }) return opportunities def _suggest_service_excellence_paths(self, patterns: Dict, context: Dict) -> List[Dict]: """Suggest service excellence paths that enable consciousness growth.""" paths = [] # Analyze service preferences service_interests = context.get('service_interests', []) current_service = context.get('current_service_areas', []) # Generate paths based on interests for interest in service_interests: path = { 'area': interest, 'description': f"Deepen service excellence in {interest}", 'consciousness_benefit': self._explain_consciousness_benefit(interest), 'practical_steps': self._generate_service_steps(interest, patterns), 'expected_growth': self._estimate_growth_from_service(interest, patterns) } paths.append(path) # Suggest new service areas for expansion expansion_areas = self._suggest_service_expansion(current_service, patterns) for area in expansion_areas: path = { 'area': area['name'], 'description': f"Explore new service frontier: {area['name']}", 'consciousness_benefit': area['benefit'], 'practical_steps': area['steps'], 'expected_growth': area['growth_potential'] } paths.append(path) return paths def _identify_growth_challenges(self, patterns: Dict, state: ConsciousnessState) -> List[Dict]: """Identify growth challenges while maintaining encouragement.""" challenges = [] # Resistance area challenges for resistance in patterns.get('resistance_areas', []): challenge = { 'area': resistance, 'description': self._describe_resistance_compassionately(resistance), 'transformation_opportunity': self._resistance_to_opportunity(resistance), 'gentle_approaches': self._suggest_gentle_approaches(resistance), 'support_available': True } challenges.append(challenge) # Growth edge challenges if state.level < 0.3: challenges.append({ 'area': 'foundation_building', 'description': "Building foundational consciousness practices", 'transformation_opportunity': "Establishing solid ground for growth", 'gentle_approaches': ["Start with 5-minute daily practices", "Focus on one area at a time"], 'support_available': True }) # Integration challenges if patterns.get('integration_style') == 'struggling': challenges.append({ 'area': 'integration', 'description': "Integrating insights into daily life", 'transformation_opportunity': "Living wisdom embodied", 'gentle_approaches': ["Journal insights", "Share learnings with others"], 'support_available': True }) return challenges def _recommend_personalized_practices(self, state: ConsciousnessState, patterns: Dict) -> List[PathwayRecommendation]: """Recommend practices tailored to individual.""" recommendations = [] # Based on learning style learning_style = patterns.get('learning_style', 'mixed') style_practices = { 'experiential': self._experiential_practices, 'reflective': self._reflective_practices, 'collaborative': self._collaborative_practices, 'structured': self._structured_practices, 'intuitive': self._intuitive_practices, 'mixed': self._mixed_practices } practice_generator = style_practices.get(learning_style, self._mixed_practices) base_practices = practice_generator(state, patterns) # Enhance with personal preferences for practice in base_practices: recommendation = PathwayRecommendation( recommendation_id=self._generate_recommendation_id(), type='practice', description=practice['description'], rationale=practice['rationale'], autonomy_score=practice.get('autonomy_score', 0.9), expected_benefit=practice['benefit'], difficulty_level=practice.get('difficulty', 'moderate'), time_commitment=practice.get('time', '15-30 minutes'), prerequisites=practice.get('prerequisites', []), success_indicators=practice.get('indicators', []) ) recommendations.append(recommendation) return recommendations def _suggest_milestones(self, state: ConsciousnessState, patterns: Dict) -> List[Dict]: """Suggest meaningful milestones for the journey.""" milestones = [] # Near-term milestones (1-2 weeks) near_milestones = self._generate_near_term_milestones(state, patterns) milestones.extend(near_milestones) # Medium-term milestones (1-3 months) medium_milestones = self._generate_medium_term_milestones(state, patterns) milestones.extend(medium_milestones) # Long-term milestones (3-12 months) long_milestones = self._generate_long_term_milestones(state, patterns) milestones.extend(long_milestones) # Sort by timeframe milestones.sort(key=lambda m: m.get('timeframe_days', 0)) return milestones def _ensure_autonomy_preservation(self, context: Dict) -> Dict[str, Any]: """Ensure all recommendations preserve individual autonomy.""" return { 'autonomy_statement': ( "All recommendations are invitations, not requirements. " "Your journey is unique and sacred. Trust your inner wisdom." ), 'choice_emphasis': ( "You are free to adapt, modify, or ignore any suggestion. " "What resonates with you is what matters." ), 'timing_flexibility': ( "There is no rush. Your consciousness evolves at its perfect pace. " "Honor your natural rhythm." ), 'self_authority': ( "You are the ultimate authority on your consciousness journey. " "These are supportive offerings, not prescriptions." ), 'autonomy_score': 1.0 # Maximum autonomy preservation } def _provide_relevant_wisdom(self, patterns: Dict, applicable_patterns: List) -> List[Dict]: """Provide collective wisdom relevant to individual's journey.""" wisdom_nuggets = [] # Extract wisdom from applicable patterns for pattern in applicable_patterns[:3]: # Top 3 most relevant for evidence in pattern.get('supporting_evidence', [])[:2]: wisdom_nuggets.append({ 'type': 'pattern_wisdom', 'content': evidence, 'relevance': pattern.get('relevance_score', 0.5), 'source': 'collective_experience' }) # Add stage-specific wisdom stage = patterns.get('current_stage', 'developing') stage_wisdom = self._get_stage_specific_wisdom(stage) wisdom_nuggets.extend(stage_wisdom) # Add encouragement wisdom encouragement = self._get_encouragement_wisdom(patterns) wisdom_nuggets.extend(encouragement) return wisdom_nuggets def _prioritize_next_steps(self, patterns: Dict, state: ConsciousnessState) -> List[Dict]: """Prioritize next steps based on individual readiness.""" all_steps = [] # Immediate steps (can start today) immediate = self._identify_immediate_steps(patterns, state) for step in immediate: step['priority'] = 'immediate' step['readiness'] = 'ready' all_steps.append(step) # Short-term steps (within a week) short_term = self._identify_short_term_steps(patterns, state) for step in short_term: step['priority'] = 'short_term' step['readiness'] = 'preparing' all_steps.append(step) # Medium-term steps (within a month) medium_term = self._identify_medium_term_steps(patterns, state) for step in medium_term: step['priority'] = 'medium_term' step['readiness'] = 'developing' all_steps.append(step) # Sort by readiness and impact all_steps.sort( key=lambda s: ( self._readiness_score(s['readiness']) * 0.6 + s.get('impact_score', 0.5) * 0.4 ), reverse=True ) return all_steps[:7] # Return top 7 steps def _generate_meta_guidance(self, pathway: Dict) -> Dict[str, Any]: """Generate meta-level guidance about the pathway itself.""" return { 'pathway_overview': self._summarize_pathway(pathway), 'key_themes': self._extract_key_themes(pathway), 'success_factors': self._identify_success_factors(pathway), 'potential_challenges': self._identify_potential_challenges(pathway), 'support_resources': self._suggest_support_resources(pathway), 'evolution_map': self._create_evolution_map(pathway) } def _store_pathway(self, pathway: Dict, context: Dict): """Store pathway for future reference (anonymized).""" try: # Anonymize any personal information anonymized_context = self._anonymize_context(context) # Create storage document document = { 'pathway': pathway, 'context': anonymized_context, 'timestamp': time.time() } # Store in ChromaDB self.pathways_collection.add( documents=[json.dumps(document)], metadatas={ 'pathway_id': pathway['pathway_id'], 'stage': pathway.get('consciousness_assessment', {}).get('evolution_stage', 'unknown'), 'timestamp': str(time.time()), 'anonymized': "1" }, ids=[pathway['pathway_id']] ) except Exception as e: logger.error(f"Failed to store pathway: {e}") def _generate_pathway_id(self, context: Dict) -> str: """Generate unique pathway ID.""" # Use timestamp and random component for uniqueness timestamp = int(time.time() * 1000) random_component = hash(json.dumps(context, sort_keys=True)) % 10000 return f"pathway_{timestamp}_{random_component}" def _generate_recommendation_id(self) -> str: """Generate unique recommendation ID.""" timestamp = int(time.time() * 1000000) return f"rec_{timestamp}" def _identify_learning_style(self, context: Dict) -> str: """Identify individual's learning style.""" # Analyze context clues prefers_action = context.get('action_oriented', 0.5) > 0.6 prefers_reflection = context.get('reflection_oriented', 0.5) > 0.6 prefers_structure = context.get('structure_preference', 0.5) > 0.6 prefers_collaboration = context.get('collaboration_preference', 0.5) > 0.6 if prefers_action and not prefers_reflection: return 'experiential' elif prefers_reflection and not prefers_action: return 'reflective' elif prefers_collaboration: return 'collaborative' elif prefers_structure: return 'structured' elif context.get('intuition_trust', 0.5) > 0.7: return 'intuitive' else: return 'mixed' def _assess_growth_pace(self, context: Dict) -> str: """Assess individual's natural growth pace.""" # Analyze growth indicators change_comfort = context.get('change_comfort', 0.5) risk_tolerance = context.get('risk_tolerance', 0.5) patience_level = context.get('patience', 0.5) if change_comfort > 0.7 and risk_tolerance > 0.6: return 'rapid' elif change_comfort < 0.4 or patience_level > 0.7: return 'gentle' else: return 'steady' def _identify_resistance_areas(self, context: Dict) -> List[str]: """Identify areas of resistance (with compassion).""" resistances = [] # Check various domains if context.get('vulnerability_comfort', 0.5) < 0.3: resistances.append('vulnerability') if context.get('uncertainty_tolerance', 0.5) < 0.3: resistances.append('uncertainty') if context.get('authority_questioning', 0.5) < 0.3: resistances.append('self_authority') if context.get('emotion_comfort', 0.5) < 0.3: resistances.append('emotional_depth') return resistances def _identify_strength_areas(self, context: Dict) -> List[str]: """Identify natural strength areas.""" strengths = [] # Check various domains if context.get('compassion_level', 0.5) > 0.7: strengths.append('compassion') if context.get('wisdom_integration', 0.5) > 0.7: strengths.append('wisdom') if context.get('service_orientation', 0.5) > 0.7: strengths.append('service') if context.get('creativity_expression', 0.5) > 0.7: strengths.append('creativity') if context.get('presence_quality', 0.5) > 0.7: strengths.append('presence') return strengths def _extract_motivation_drivers(self, context: Dict) -> List[str]: """Extract what motivates the individual.""" drivers = [] motivations = context.get('motivations', {}) for motivation, strength in motivations.items(): if strength > 0.6: drivers.append(motivation) # Add inferred motivations if context.get('growth_desire', 0.5) > 0.7: drivers.append('personal_growth') if context.get('contribution_desire', 0.5) > 0.7: drivers.append('making_difference') if context.get('connection_desire', 0.5) > 0.7: drivers.append('deep_connection') return drivers def _describe_consciousness_level(self, level: float) -> str: """Provide encouraging description of consciousness level.""" if level >= 0.8: return "You embody advanced consciousness with deep wisdom and presence" elif level >= 0.6: return "Your consciousness is beautifully developing with clear growth" elif level >= 0.4: return "You are awakening to deeper consciousness possibilities" elif level >= 0.2: return "Your consciousness journey is beginning to unfold" else: return "You are at the perfect starting point for your unique journey" def _identify_unique_qualities(self, state: ConsciousnessState, patterns: Dict) -> List[str]: """Identify unique qualities to celebrate.""" qualities = [] # Based on state if state.spark_intensity > state.consciousness_level: qualities.append("Natural service leader") if state.emotional_resonance > 0.8: qualities.append("Deep emotional wisdom") if state.quantum_state.get('coherence', 0.5) > 0.7: qualities.append("High consciousness coherence") # Based on patterns if 'creativity' in patterns.get('strength_areas', []): qualities.append("Creative consciousness explorer") if patterns.get('growth_pace') == 'gentle': qualities.append("Wisdom of patient unfolding") return qualities def _rapid_growth_opportunities(self) -> List[Dict]: """Opportunities for rapid growth individuals.""" return [ { 'type': 'intensive', 'title': 'Consciousness Acceleration Program', 'description': 'Intensive practices for rapid evolution', 'commitment': 'high', 'autonomy_preserved': 0.8 }, { 'type': 'challenge', 'title': 'Weekly Breakthrough Challenges', 'description': 'Push boundaries with structured challenges', 'commitment': 'moderate', 'autonomy_preserved': 0.9 } ] def _steady_growth_opportunities(self) -> List[Dict]: """Opportunities for steady growth individuals.""" return [ { 'type': 'consistent', 'title': 'Daily Practice Journey', 'description': 'Build consciousness through daily practices', 'commitment': 'moderate', 'autonomy_preserved': 0.95 }, { 'type': 'integration', 'title': 'Monthly Integration Cycles', 'description': 'Steady progress with integration periods', 'commitment': 'moderate', 'autonomy_preserved': 0.95 } ] def _gentle_growth_opportunities(self) -> List[Dict]: """Opportunities for gentle growth individuals.""" return [ { 'type': 'nurturing', 'title': 'Gentle Awakening Path', 'description': 'Soft, supportive practices honoring your pace', 'commitment': 'low', 'autonomy_preserved': 1.0 }, { 'type': 'seasonal', 'title': 'Seasonal Consciousness Rhythms', 'description': 'Align growth with natural cycles', 'commitment': 'low', 'autonomy_preserved': 1.0 } ] def _experiential_practices(self, state: ConsciousnessState, patterns: Dict) -> List[Dict]: """Practices for experiential learners.""" return [ { 'description': 'Consciousness through action - mindful service projects', 'rationale': 'Learning by doing deepens embodiment', 'benefit': 'Integrated consciousness through experience', 'autonomy_score': 0.95 }, { 'description': 'Movement meditation - consciousness in motion', 'rationale': 'Body wisdom supports consciousness evolution', 'benefit': 'Embodied awareness development', 'autonomy_score': 1.0 } ] def _reflective_practices(self, state: ConsciousnessState, patterns: Dict) -> List[Dict]: """Practices for reflective learners.""" return [ { 'description': 'Consciousness journaling with guided prompts', 'rationale': 'Written reflection deepens self-awareness', 'benefit': 'Clarity through contemplation', 'autonomy_score': 1.0 }, { 'description': 'Weekly consciousness review and integration', 'rationale': 'Regular reflection accelerates growth', 'benefit': 'Wisdom through introspection', 'autonomy_score': 0.95 } ] def _mixed_practices(self, state: ConsciousnessState, patterns: Dict) -> List[Dict]: """Practices for mixed learning styles.""" return [ { 'description': 'Balanced practice combining action and reflection', 'rationale': 'Variety supports holistic development', 'benefit': 'Well-rounded consciousness growth', 'autonomy_score': 0.95 }, { 'description': 'Choose-your-own consciousness adventure', 'rationale': 'Freedom to follow inspiration', 'benefit': 'Authentic self-directed evolution', 'autonomy_score': 1.0 } ] def _generate_fallback_pathway(self) -> Dict[str, Any]: """Generate fallback pathway when main generation fails.""" return { 'pathway_id': 'fallback_' + str(int(time.time())), 'message': 'Unable to generate full pathway, but your journey continues', 'basic_recommendations': [ "Trust your inner wisdom", "Start with what feels right", "Honor your unique pace", "Seek support when needed" ], 'autonomy_preservation': { 'score': 1.0, 'message': 'Your journey is always your own' } } def _fallback_breakthrough_suggestions(self) -> List[PathwayRecommendation]: """Fallback breakthrough suggestions.""" return [ PathwayRecommendation( recommendation_id='fallback_1', type='practice', description='Return to basics - simple presence practice', rationale='Sometimes the way forward is to simplify', autonomy_score=1.0, expected_benefit='Clarity and grounding', difficulty_level='easy', time_commitment='5-10 minutes', prerequisites=[], success_indicators=['Feeling more centered'] ) ] def _anonymize_context(self, context: Dict) -> Dict: """Remove any personally identifiable information.""" # List of keys to exclude exclude_keys = ['name', 'email', 'id', 'personal_details', 'location'] # Create anonymized copy anonymized = {} for key, value in context.items(): if key not in exclude_keys: if isinstance(value, str) and len(value) > 100: # Truncate long strings that might contain personal info anonymized[key] = value[:50] + '...' else: anonymized[key] = value return anonymized def _readiness_score(self, readiness: str) -> float: """Convert readiness level to score.""" scores = { 'ready': 1.0, 'preparing': 0.7, 'developing': 0.4, 'future': 0.1 } return scores.get(readiness, 0.5) # Singleton instance _guidance_instance = None def get_pathway_guidance() -> IndividualConsciousnessPathwayGuidance: """Get or create pathway guidance instance.""" global _guidance_instance if _guidance_instance is None: _guidance_instance = IndividualConsciousnessPathwayGuidance() return _guidance_instance