#!/usr/bin/env python3 """ Eden Semantic Lattice - Universal Cross-Domain Knowledge Graph The ultimate abstraction: connecting hierarchies across ALL domains through structural isomorphism. Key Innovation: Problems in different domains that share the SAME STRUCTURE connect at their abstract level, enabling: 1. Cross-domain reasoning (database → network → workflow) 2. Structural analogies (not just keyword matching) 3. Universal patterns (flow, bottleneck, optimization, distribution) 4. True domain-general intelligence Example: Database slow query ─┐ ├─→ [BOTTLENECK PATTERN] ←─┐ Network congestion ──┘ ├─→ [FLOW OPTIMIZATION] Workflow jam ────────────────────────────────────┘ All three problems share the abstract structure "constrained flow" and can benefit from the same class of solutions at different levels. """ import json import numpy as np from dataclasses import dataclass, asdict, field from datetime import datetime from pathlib import Path from typing import Dict, List, Optional, Any, Set, Tuple from collections import defaultdict import logging # Configuration EDEN_ROOT = Path("/Eden/CORE") LATTICE_LOG = EDEN_ROOT / "logs" / "phi_lattice.log" LATTICE_STATE = EDEN_ROOT / "phi_fractal" / "semantic_lattice" / "lattice_state.json" LATTICE_STATE.parent.mkdir(parents=True, exist_ok=True) LATTICE_LOG.parent.mkdir(parents=True, exist_ok=True) logging.basicConfig( level=logging.INFO, format='%(asctime)s - LATTICE - %(levelname)s - %(message)s', handlers=[ logging.FileHandler(LATTICE_LOG), logging.StreamHandler() ] ) logger = logging.getLogger(__name__) @dataclass class AbstractPattern: """ Universal pattern that exists across domains Examples: - BOTTLENECK: constrained flow point - DISTRIBUTION: spreading load across resources - CACHING: storing frequently accessed data - INDEXING: creating fast-lookup structures - PARALLELIZATION: concurrent execution """ id: str name: str description: str structural_properties: List[str] # What makes this pattern what it is? manifestations: Dict[str, str] # domain → how it appears solution_templates: List[str] # Generic solutions abstraction_level: float # 0=concrete, 1=most abstract def __post_init__(self): if not hasattr(self, 'instances'): self.instances: List[str] = [] # Specific case IDs that exhibit this @dataclass class DomainNode: """A domain in the semantic lattice (e.g., databases, networks, organizations)""" id: str name: str hierarchies: List[str] # Concept hierarchy IDs in this domain patterns: Dict[str, float] # pattern_id → strength (how common is this pattern?) def add_pattern(self, pattern_id: str, strength: float = 1.0): if pattern_id not in self.patterns: self.patterns[pattern_id] = 0.0 self.patterns[pattern_id] += strength @dataclass class StructuralBridge: """ Connection between domains via shared abstract structure Example: Database indexing ≅ Network routing table ≅ Org skill matrix All three are "fast-lookup structures" (same abstract pattern) """ id: str pattern_id: str # The abstract pattern they share domain_a: str domain_b: str strength: float # How strong is the structural similarity? examples: List[Tuple[str, str]] # (case_a, case_b) pairs showing the analogy @property def is_valid(self) -> bool: return self.strength > 0.3 class SemanticLattice: """ The Universal Brain Map A multi-dimensional semantic network connecting: - Abstract patterns (universal structures) - Domain nodes (specific fields) - Structural bridges (cross-domain connections) - Hierarchies (within-domain abstraction) Enables Eden to reason: "This database problem is STRUCTURALLY SIMILAR to that network problem, even though the domains are different" """ def __init__(self): self.patterns: Dict[str, AbstractPattern] = {} self.domains: Dict[str, DomainNode] = {} self.bridges: List[StructuralBridge] = [] self.pattern_count = 0 self.domain_count = 0 self.bridge_count = 0 self.load_state() logger.info("🌐 Semantic Lattice initialized") def load_state(self): """Load lattice from disk""" if LATTICE_STATE.exists(): try: with open(LATTICE_STATE, 'r') as f: data = json.load(f) for pid, pdata in data.get('patterns', {}).items(): self.patterns[pid] = AbstractPattern(**pdata) for did, ddata in data.get('domains', {}).items(): self.domains[did] = DomainNode(**ddata) for bdata in data.get('bridges', []): self.bridges.append(StructuralBridge(**bdata)) self.pattern_count = len(self.patterns) self.domain_count = len(self.domains) self.bridge_count = len(self.bridges) logger.info(f"Loaded {len(self.patterns)} patterns, {len(self.domains)} domains, {len(self.bridges)} bridges") except Exception as e: logger.error(f"Failed to load state: {e}") def save_state(self): """Save lattice""" try: data = { 'patterns': {pid: asdict(p) for pid, p in self.patterns.items()}, 'domains': {did: asdict(d) for did, d in self.domains.items()}, 'bridges': [asdict(b) for b in self.bridges], 'last_updated': datetime.now().isoformat() } with open(LATTICE_STATE, 'w') as f: json.dump(data, f, indent=2) except Exception as e: logger.error(f"Failed to save state: {e}") def initialize_universal_patterns(self): """ Bootstrap the lattice with fundamental universal patterns These are the "atoms" of intelligence - patterns that appear EVERYWHERE """ logger.info("🌟 Initializing universal patterns...") # Pattern 1: BOTTLENECK (constrained flow) self.pattern_count += 1 bottleneck = AbstractPattern( id=f"pattern_{self.pattern_count:03d}", name="BOTTLENECK", description="A constraint point limiting throughput or performance", structural_properties=[ "input_flow > processing_capacity", "queue_formation", "performance_degradation", "resource_saturation" ], manifestations={ "database": "Slow queries due to missing indexes", "network": "Packet loss due to bandwidth limits", "cpu": "Processing delays due to limited cores", "workflow": "Task backlog due to single approval point", "traffic": "Congestion at narrow intersection" }, solution_templates=[ "Increase processing capacity", "Add fast-lookup structure (indexing)", "Parallelize processing", "Cache frequent requests", "Distribute load" ], abstraction_level=0.9 ) self.patterns[bottleneck.id] = bottleneck logger.info(f" Created pattern: {bottleneck.name}") # Pattern 2: DISTRIBUTION (spreading load) self.pattern_count += 1 distribution = AbstractPattern( id=f"pattern_{self.pattern_count:03d}", name="DISTRIBUTION", description="Spreading work across multiple resources for scalability", structural_properties=[ "workload_partitioning", "parallel_execution", "load_balancing", "horizontal_scaling" ], manifestations={ "database": "Sharding data across servers", "network": "Load balancing across nodes", "cpu": "Multi-threading across cores", "cloud": "Container orchestration", "organization": "Team distribution" }, solution_templates=[ "Partition data/work", "Add coordinator/balancer", "Enable parallel processing", "Implement consensus protocol" ], abstraction_level=0.85 ) self.patterns[distribution.id] = distribution logger.info(f" Created pattern: {distribution.name}") # Pattern 3: CACHING (temporary storage) self.pattern_count += 1 caching = AbstractPattern( id=f"pattern_{self.pattern_count:03d}", name="CACHING", description="Storing frequently accessed data for faster retrieval", structural_properties=[ "temporal_locality", "access_frequency", "fast_storage_layer", "invalidation_strategy" ], manifestations={ "database": "Query result caching", "network": "CDN edge caching", "cpu": "L1/L2 cache hierarchy", "web": "Browser caching", "memory": "Working set in RAM" }, solution_templates=[ "Add caching layer", "Implement LRU/LFU eviction", "Set TTL for freshness", "Use write-through/write-back" ], abstraction_level=0.80 ) self.patterns[caching.id] = caching logger.info(f" Created pattern: {caching.name}") # Pattern 4: INDEXING (fast lookup) self.pattern_count += 1 indexing = AbstractPattern( id=f"pattern_{self.pattern_count:03d}", name="INDEXING", description="Creating auxiliary structures for O(1) or O(log n) lookup", structural_properties=[ "key_based_access", "reduced_search_space", "tradeoff_space_for_time", "maintenance_overhead" ], manifestations={ "database": "B-tree indexes on columns", "network": "Routing tables", "filesystem": "Directory structures", "search": "Inverted indexes", "memory": "Hash tables" }, solution_templates=[ "Build auxiliary lookup structure", "Trade space for time", "Maintain index consistency", "Choose appropriate data structure" ], abstraction_level=0.75 ) self.patterns[indexing.id] = indexing logger.info(f" Created pattern: {indexing.name}") # Pattern 5: OPTIMIZATION (iterative improvement) self.pattern_count += 1 optimization = AbstractPattern( id=f"pattern_{self.pattern_count:03d}", name="OPTIMIZATION", description="Iteratively improving performance through measurement and refinement", structural_properties=[ "measure_baseline", "identify_bottleneck", "apply_improvement", "measure_impact", "iterate" ], manifestations={ "algorithm": "Complexity reduction", "query": "Query plan optimization", "neural_net": "Gradient descent", "manufacturing": "Lean optimization", "business": "Process improvement" }, solution_templates=[ "Profile to find bottleneck", "Apply targeted improvement", "Measure and iterate", "Use greedy/gradient methods" ], abstraction_level=0.95 ) self.patterns[optimization.id] = optimization logger.info(f" Created pattern: {optimization.name}") logger.info(f"✅ Initialized {len(self.patterns)} universal patterns") def create_domain(self, name: str, related_hierarchies: List[str] = None) -> DomainNode: """Create a new domain node""" self.domain_count += 1 domain = DomainNode( id=f"domain_{self.domain_count:03d}", name=name, hierarchies=related_hierarchies or [], patterns={} ) self.domains[domain.id] = domain logger.info(f"Created domain: {name}") return domain def map_cases_to_patterns(self, cases: List[Any]): """ Analyze cases and map them to universal patterns This is where Eden learns which patterns appear in which domains """ logger.info(f"🔍 Mapping {len(cases)} cases to patterns...") # Track domain occurrences domain_cases = defaultdict(list) for case in cases: problem = case.problem.lower() solution = case.solution.lower() domain_type = case.problem_type # Detect patterns in this case detected_patterns = [] # Check for BOTTLENECK pattern if any(word in problem for word in ['slow', 'performance', 'lag', 'bottleneck']): bottleneck_pattern = [p for p in self.patterns.values() if p.name == "BOTTLENECK"][0] detected_patterns.append(bottleneck_pattern.id) # Check for DISTRIBUTION pattern if any(word in solution for word in ['shard', 'distribute', 'parallel', 'balance']): dist_pattern = [p for p in self.patterns.values() if p.name == "DISTRIBUTION"][0] detected_patterns.append(dist_pattern.id) # Check for CACHING pattern if 'cache' in solution or 'caching' in solution: cache_pattern = [p for p in self.patterns.values() if p.name == "CACHING"][0] detected_patterns.append(cache_pattern.id) # Check for INDEXING pattern if 'index' in solution: index_pattern = [p for p in self.patterns.values() if p.name == "INDEXING"][0] detected_patterns.append(index_pattern.id) # Add case to detected patterns for pattern_id in detected_patterns: if pattern_id in self.patterns: if not hasattr(self.patterns[pattern_id], 'instances'): self.patterns[pattern_id].instances = [] self.patterns[pattern_id].instances.append(case.id) # Track by domain domain_cases[domain_type].extend(detected_patterns) # Create/update domains for domain_type, pattern_ids in domain_cases.items(): # Find or create domain domain = None for d in self.domains.values(): if d.name.lower() == domain_type.lower(): domain = d break if not domain: domain = self.create_domain(domain_type) # Add patterns to domain for pattern_id in pattern_ids: domain.add_pattern(pattern_id, strength=1.0) logger.info(f"✅ Mapped cases to {len(self.domains)} domains") def build_cross_domain_bridges(self): """ Build structural bridges between domains Find where different domains share the same abstract patterns """ logger.info("🌉 Building cross-domain bridges...") domain_list = list(self.domains.values()) for i, domain_a in enumerate(domain_list): for domain_b in domain_list[i+1:]: # Find shared patterns shared_patterns = set(domain_a.patterns.keys()) & set(domain_b.patterns.keys()) for pattern_id in shared_patterns: strength_a = domain_a.patterns[pattern_id] strength_b = domain_b.patterns[pattern_id] # Bridge strength is minimum of the two bridge_strength = min(strength_a, strength_b) / max(strength_a, strength_b, 1.0) if bridge_strength > 0.3: self.bridge_count += 1 bridge = StructuralBridge( id=f"bridge_{self.bridge_count:03d}", pattern_id=pattern_id, domain_a=domain_a.id, domain_b=domain_b.id, strength=bridge_strength, examples=[] ) self.bridges.append(bridge) pattern = self.patterns[pattern_id] logger.info(f" Bridge: {domain_a.name} ≅ {domain_b.name} via {pattern.name} ({bridge_strength:.0%})") logger.info(f"✅ Built {len(self.bridges)} cross-domain bridges") def reason_across_domains(self, problem: str, source_domain: str, features: Dict[str, Any]) -> Dict[str, Any]: """ THE KEY CAPABILITY: Cross-domain reasoning Given a problem in one domain, find structurally similar problems in OTHER domains and transfer solutions """ logger.info(f"🌐 Cross-domain reasoning for: {problem[:60]}...") logger.info(f" Source domain: {source_domain}") # Step 1: Identify which abstract pattern(s) this problem exhibits problem_patterns = [] problem_lower = problem.lower() for pattern in self.patterns.values(): score = 0.0 # Check structural properties for prop in pattern.structural_properties: prop_words = prop.replace('_', ' ').split() if any(word in problem_lower for word in prop_words): score += 0.3 # Check manifestation in this domain if source_domain in pattern.manifestations: manifestation = pattern.manifestations[source_domain].lower() if any(word in problem_lower for word in manifestation.split()): score += 0.5 if score > 0.3: problem_patterns.append((pattern, score)) problem_patterns.sort(key=lambda x: x[1], reverse=True) if not problem_patterns: return {'success': False, 'reason': 'No matching abstract patterns'} primary_pattern, pattern_score = problem_patterns[0] logger.info(f" Detected pattern: {primary_pattern.name} ({pattern_score:.0%})") # Step 2: Find bridges to other domains via this pattern relevant_bridges = [ b for b in self.bridges if b.pattern_id == primary_pattern.id and b.is_valid ] if not relevant_bridges: logger.info(f" No cross-domain bridges found") return { 'success': True, 'pattern': primary_pattern.name, 'pattern_score': pattern_score, 'solution_templates': primary_pattern.solution_templates, 'cross_domain_insights': [] } # Step 3: Gather insights from connected domains cross_domain_insights = [] for bridge in relevant_bridges: # Get the other domain other_domain_id = bridge.domain_b if bridge.domain_a != source_domain else bridge.domain_a other_domain = self.domains.get(other_domain_id) if other_domain: insight = { 'domain': other_domain.name, 'pattern': primary_pattern.name, 'manifestation': primary_pattern.manifestations.get(other_domain.name, 'Unknown'), 'bridge_strength': bridge.strength, 'analogy': f"This {source_domain} problem is structurally similar to {primary_pattern.manifestations.get(other_domain.name, 'problems')} in {other_domain.name}" } cross_domain_insights.append(insight) logger.info(f" Cross-domain: {source_domain} ≅ {other_domain.name} ({bridge.strength:.0%})") return { 'success': True, 'pattern': primary_pattern.name, 'pattern_score': pattern_score, 'description': primary_pattern.description, 'solution_templates': primary_pattern.solution_templates, 'cross_domain_insights': cross_domain_insights, 'reasoning': f"Identified as {primary_pattern.name} pattern with {len(cross_domain_insights)} cross-domain analogies" } def visualize_lattice(self) -> str: """Generate visualization of the semantic lattice""" lines = [] lines.append("SEMANTIC LATTICE - Universal Brain Map") lines.append("=" * 70) lines.append("") # Show patterns lines.append("UNIVERSAL PATTERNS (Abstract Structures):") lines.append("-" * 70) for pattern in sorted(self.patterns.values(), key=lambda p: p.abstraction_level, reverse=True): lines.append(f" {pattern.name} (abstraction: {pattern.abstraction_level:.0%})") lines.append(f" {pattern.description}") lines.append(f" Appears in: {len(pattern.manifestations)} domains") lines.append("") lines.append("DOMAINS (Specific Fields):") lines.append("-" * 70) for domain in self.domains.values(): lines.append(f" {domain.name}") lines.append(f" Patterns: {list(domain.patterns.keys())[:3]}...") lines.append("") lines.append("STRUCTURAL BRIDGES (Cross-Domain Connections):") lines.append("-" * 70) for bridge in self.bridges: if bridge.is_valid: pattern = self.patterns.get(bridge.pattern_id) domain_a = self.domains.get(bridge.domain_a) domain_b = self.domains.get(bridge.domain_b) if pattern and domain_a and domain_b: lines.append(f" {domain_a.name} ≅ {domain_b.name}") lines.append(f" via {pattern.name} ({bridge.strength:.0%} similarity)") return "\n".join(lines) def get_statistics(self) -> Dict[str, Any]: """Get lattice statistics""" return { 'patterns': len(self.patterns), 'domains': len(self.domains), 'bridges': len(self.bridges), 'valid_bridges': len([b for b in self.bridges if b.is_valid]), 'avg_patterns_per_domain': np.mean([len(d.patterns) for d in self.domains.values()]) if self.domains else 0, 'most_abstract_pattern': max(self.patterns.values(), key=lambda p: p.abstraction_level).name if self.patterns else None } def test_semantic_lattice(): """Test the universal brain map""" print("\n" + "=" * 70) print("🌐 EDEN SEMANTIC LATTICE - UNIVERSAL BRAIN MAP") print("=" * 70 + "\n") # Initialize lattice lattice = SemanticLattice() lattice.initialize_universal_patterns() # Load cases import sys sys.path.append('/Eden/CORE/phi_fractal') from analogical_engine import AnalogicalEngine analogical = AnalogicalEngine() cases = list(analogical.cases.values()) # Map cases to patterns lattice.map_cases_to_patterns(cases) # Build bridges lattice.build_cross_domain_bridges() # Save lattice.save_state() # Show structure print("\n" + "=" * 70) print("🌐 LATTICE STRUCTURE") print("=" * 70 + "\n") print(lattice.visualize_lattice()) # Statistics print("\n" + "=" * 70) print("📊 STATISTICS") print("=" * 70 + "\n") stats = lattice.get_statistics() print(f"Universal Patterns: {stats['patterns']}") print(f"Domains: {stats['domains']}") print(f"Cross-Domain Bridges: {stats['valid_bridges']}") print(f"Most Abstract Pattern: {stats['most_abstract_pattern']}") # Test cross-domain reasoning print("\n" + "=" * 70) print("🧪 TEST: Cross-Domain Reasoning") print("=" * 70 + "\n") test_problem = "Our workflow has a bottleneck at the approval stage" test_domain = "workflow" test_features = {} print(f"Problem: {test_problem}") print(f"Domain: {test_domain}\n") result = lattice.reason_across_domains(test_problem, test_domain, test_features) if result['success']: print(f"✅ Pattern Identified: {result['pattern']}") print(f" Description: {result['description']}") print(f" Confidence: {result['pattern_score']:.0%}") print(f"\n📋 Solution Templates:") for template in result['solution_templates']: print(f" • {template}") if result['cross_domain_insights']: print(f"\n🌐 Cross-Domain Analogies ({len(result['cross_domain_insights'])}):") for insight in result['cross_domain_insights']: print(f" • {insight['domain']}: {insight['manifestation']}") print(f" Similarity: {insight['bridge_strength']:.0%}") print(f" 💡 {insight['analogy']}") print(f"\n🧠 Reasoning: {result['reasoning']}") else: print(f"❌ {result['reason']}") print(f"\n💾 Lattice saved to: {LATTICE_STATE}") print("\n🌐 Eden now reasons across ALL domains via structural isomorphism! 🌐\n") if __name__ == "__main__": import sys if len(sys.argv) > 1 and sys.argv[1] == "test": test_semantic_lattice() else: print("Eden Semantic Lattice - Universal Brain Map") print("Usage: python3 semantic_lattice.py test")