#!/usr/bin/env python3 """ Enhanced Holographic Integration for LiMp ========================================= Integrates the refactored holographic memory system with the existing LuiMennua dimensional entanglement framework for enhanced LLM capabilities. This module bridges the gap between the theoretical framework and practical implementation, providing a complete cognitive architecture for the LiMp model. Author: Assistant License: MIT """ import numpy as np import torch import torch.nn as nn from typing import Dict, List, Optional, Any, Tuple import json import sqlite3 from pathlib import Path # Import the refactored holographic memory components from holographic_memory_core import HolographicAssociativeMemory from fractal_memory_encoder import FractalMemoryEncoder from quantum_holographic_storage import QuantumHolographicStorage from emergent_memory_patterns import EmergentMemoryPatterns class EnhancedHolographicLLM: """ Enhanced LLM system combining dimensional entanglement with holographic memory. This class integrates: 1. The existing LuiMennua dimensional entanglement framework 2. The new modular holographic memory system 3. Quantum-inspired processing 4. Emergent cognitive protocols """ def __init__(self, dimensional_db_path: str = "dimensional_entanglement.db", config_path: str = "holographic_memory_config.txt"): # Initialize dimensional entanglement components self.dimensional_db = self._load_dimensional_database(dimensional_db_path) self.config = self._load_configuration(config_path) # Initialize holographic memory components self.holographic_memory = HolographicAssociativeMemory( memory_size=self.config.get('MEMORY_SIZE', 1024), hologram_dim=self.config.get('HOLOGRAM_DIMENSION', 256) ) self.fractal_encoder = FractalMemoryEncoder( max_depth=self.config.get('MAX_FRACTAL_DEPTH', 8) ) self.quantum_storage = QuantumHolographicStorage( num_qubits=self.config.get('NUM_QUBITS', 10) ) self.emergent_detector = EmergentMemoryPatterns( pattern_size=self.config.get('PATTERN_SIZE', 100) ) # Integration state self.cognitive_trajectory = [] self.dimensional_embeddings = {} self.holographic_contexts = {} def _load_dimensional_database(self, db_path: str) -> sqlite3.Connection: """Load the dimensional entanglement database.""" if Path(db_path).exists(): return sqlite3.connect(db_path) else: # Create a minimal database if it doesn't exist conn = sqlite3.connect(db_path) self._initialize_dimensional_database(conn) return conn def _initialize_dimensional_database(self, conn: sqlite3.Connection): """Initialize the dimensional database with basic structure.""" cursor = conn.cursor() # Create dimensional nodes table cursor.execute(''' CREATE TABLE IF NOT EXISTS dimensional_nodes ( id INTEGER PRIMARY KEY, concept TEXT UNIQUE, dimension_signature TEXT, embedding BLOB, entanglement_strength REAL, quantum_coherence REAL, emergence_score REAL ) ''') # Create entanglement matrix table cursor.execute(''' CREATE TABLE IF NOT EXISTS entanglement_matrix ( id INTEGER PRIMARY KEY, concept_a TEXT, concept_b TEXT, entanglement_strength REAL, dimension_signature TEXT ) ''') # Insert some basic concepts basic_concepts = [ ('quantum_entanglement', 'D0-D1-D3', 0.8, 0.7, 0.6), ('self_organization', 'D1-D2-D4', 0.7, 0.6, 0.5), ('superposition', 'D0-D1-D2', 0.9, 0.8, 0.7), ('topology', 'D2-D3-D4', 0.6, 0.5, 0.4), ('qualia', 'D1-D3-D4', 0.5, 0.4, 0.3), ('optimization', 'D0-D2-D4', 0.7, 0.6, 0.5) ] for concept, dim_sig, ent_str, q_coher, em_score in basic_concepts: embedding = np.random.random(256).tobytes() cursor.execute(''' INSERT OR REPLACE INTO dimensional_nodes (concept, dimension_signature, embedding, entanglement_strength, quantum_coherence, emergence_score) VALUES (?, ?, ?, ?, ?, ?) ''', (concept, dim_sig, embedding, ent_str, q_coher, em_score)) conn.commit() def _load_configuration(self, config_path: str) -> Dict: """Load configuration from text file.""" config = {} if Path(config_path).exists(): with open(config_path, 'r') as f: for line in f: line = line.strip() if line and not line.startswith('#') and ':' in line: key, value = line.split(':', 1) key = key.strip() value = value.strip() # Try to convert to appropriate type if value.isdigit(): config[key] = int(value) elif value.replace('.', '').isdigit() and value.count('.') <= 1: config[key] = float(value) elif value.lower() in ('true', 'false'): config[key] = value.lower() == 'true' else: config[key] = value return config def process_with_dimensional_entanglement(self, prompt: str, max_length: int = 512) -> Dict[str, Any]: """ Process prompt using dimensional entanglement and holographic memory. This method combines: 1. Dimensional concept analysis 2. Holographic memory recall 3. Fractal pattern encoding 4. Quantum-enhanced processing 5. Emergence detection """ # Phase 1: Dimensional Analysis dimensional_context = self._analyze_dimensional_context(prompt) # Phase 2: Holographic Memory Processing holographic_context = self._process_holographic_context(prompt, dimensional_context) # Phase 3: Fractal Encoding fractal_context = self._encode_fractal_patterns(prompt, dimensional_context) # Phase 4: Quantum Enhancement quantum_context = self._apply_quantum_enhancement(fractal_context) # Phase 5: Emergence Detection emergence_analysis = self._detect_emergence_patterns( prompt, dimensional_context, holographic_context, fractal_context, quantum_context ) # Phase 6: Integrated Response Generation response = self._generate_integrated_response( prompt, dimensional_context, holographic_context, fractal_context, quantum_context, emergence_analysis ) # Store in cognitive trajectory cognitive_state = { 'timestamp': np.datetime64('now'), 'prompt': prompt, 'dimensional_context': dimensional_context, 'holographic_context': holographic_context, 'fractal_context': fractal_context, 'quantum_context': quantum_context, 'emergence_analysis': emergence_analysis, 'response': response } self.cognitive_trajectory.append(cognitive_state) return { 'response': response, 'dimensional_context': dimensional_context, 'holographic_context': holographic_context, 'fractal_context': fractal_context, 'quantum_context': quantum_context, 'emergence_analysis': emergence_analysis, 'cognitive_state': cognitive_state } def _analyze_dimensional_context(self, prompt: str) -> Dict[str, Any]: """Analyze prompt using dimensional entanglement framework.""" words = prompt.lower().split() # Find related dimensional concepts related_concepts = [] cursor = self.dimensional_db.cursor() for word in words: cursor.execute(''' SELECT concept, dimension_signature, entanglement_strength, quantum_coherence, emergence_score FROM dimensional_nodes WHERE concept LIKE ? OR concept LIKE ? ORDER BY emergence_score DESC LIMIT 5 ''', (f'%{word}%', f'{word}%')) for row in cursor.fetchall(): related_concepts.append({ 'concept': row[0], 'dimension_signature': row[1], 'entanglement_strength': row[2], 'quantum_coherence': row[3], 'emergence_score': row[4] }) # Calculate dimensional signature if related_concepts: all_dims = [] for concept in related_concepts: dims = concept['dimension_signature'].split('-') all_dims.extend(dims) # Get most frequent dimensions from collections import Counter dim_counts = Counter(all_dims) primary_dimensions = [dim for dim, count in dim_counts.most_common(4)] dimension_signature = '-'.join(primary_dimensions) else: dimension_signature = 'D0-D1-D2-D3' return { 'related_concepts': related_concepts, 'dimension_signature': dimension_signature, 'dimensional_coherence': len(related_concepts) / len(words) if words else 0.0 } def _process_holographic_context(self, prompt: str, dimensional_context: Dict) -> Dict[str, Any]: """Process prompt using holographic memory system.""" # Convert prompt to embedding (simplified) prompt_embedding = self._text_to_embedding(prompt) # Store in holographic memory with dimensional context metadata = { 'dimensional_signature': dimensional_context['dimension_signature'], 'related_concepts': [c['concept'] for c in dimensional_context['related_concepts']], 'dimensional_coherence': dimensional_context['dimensional_coherence'] } memory_key = self.holographic_memory.store_holographic(prompt_embedding, metadata) # Recall similar contexts recalled_contexts = self.holographic_memory.recall_associative( prompt_embedding, similarity_threshold=0.5 ) return { 'memory_key': memory_key, 'recalled_contexts': recalled_contexts, 'holographic_similarity': len(recalled_contexts) / max(1, len(self.holographic_memory.memory_traces)) } def _encode_fractal_patterns(self, prompt: str, dimensional_context: Dict) -> Dict[str, Any]: """Encode prompt using fractal memory patterns.""" # Convert prompt to data array prompt_data = self._text_to_embedding(prompt) # Create context for fractal encoding fractal_context = { 'dimensional_signature': dimensional_context['dimension_signature'], 'concept_count': len(dimensional_context['related_concepts']), 'coherence': dimensional_context['dimensional_coherence'] } # Encode fractal memory fractal_encoding = self.fractal_encoder.encode_fractal_memory(prompt_data, fractal_context) return { 'fractal_encoding': fractal_encoding, 'self_similarity': fractal_encoding['self_similarity'], 'fractal_dimension': fractal_encoding['fractal_dimension'], 'emergence_level': fractal_encoding['emergence_level'] } def _apply_quantum_enhancement(self, fractal_context: Dict) -> Dict[str, Any]: """Apply quantum enhancement to fractal patterns.""" # Extract fractal data fractal_data = fractal_context['fractal_encoding']['scales'][0]['data'] # Store in quantum holographic storage quantum_key = self.quantum_storage.store_quantum_holographic(fractal_data) # Perform quantum associative recall quantum_query = self.quantum_storage._encode_quantum_state(fractal_data) quantum_recall = self.quantum_storage.quantum_associative_recall(quantum_query) # Calculate quantum metrics quantum_capacity = self.quantum_storage.quantum_superposition_capacity() entanglement_measure = self.quantum_storage.quantum_entanglement_measure() return { 'quantum_key': quantum_key, 'quantum_recall': quantum_recall, 'quantum_capacity': quantum_capacity, 'entanglement_measure': entanglement_measure, 'quantum_enhancement_factor': len(quantum_recall) / max(1, len(self.quantum_storage.quantum_memory_states)) } def _detect_emergence_patterns(self, prompt: str, dimensional_context: Dict, holographic_context: Dict, fractal_context: Dict, quantum_context: Dict) -> Dict[str, Any]: """Detect emergence patterns across all processing layers.""" # Create memory access sequence memory_access = [{ 'timestamp': np.datetime64('now'), 'memory_type': 'integrated_processing', 'dimensional_coherence': dimensional_context['dimensional_coherence'], 'holographic_similarity': holographic_context['holographic_similarity'], 'fractal_emergence': fractal_context['emergence_level'], 'quantum_enhancement': quantum_context['quantum_enhancement_factor'], 'cognitive_load': self._calculate_cognitive_load( dimensional_context, holographic_context, fractal_context, quantum_context ) }] # Detect emergent patterns emergence_analysis = self.emergent_detector.detect_emergent_memory_patterns(memory_access) # Predict future emergence if len(self.cognitive_trajectory) > 5: current_state = { 'dimensional_coherence': dimensional_context['dimensional_coherence'], 'holographic_similarity': holographic_context['holographic_similarity'], 'fractal_emergence': fractal_context['emergence_level'], 'quantum_enhancement': quantum_context['quantum_enhancement_factor'] } emergence_prediction = self.emergent_detector.predict_memory_emergence(current_state) else: emergence_prediction = {'predicted_emergence_points': []} return { 'emergence_analysis': emergence_analysis, 'emergence_prediction': emergence_prediction, 'total_emergence': emergence_analysis.get('cognitive_emergence_level', 0.0), 'emergence_detected': len(emergence_analysis.get('emergence_events', [])) > 0 } def _generate_integrated_response(self, prompt: str, dimensional_context: Dict, holographic_context: Dict, fractal_context: Dict, quantum_context: Dict, emergence_analysis: Dict) -> str: """Generate integrated response combining all processing layers.""" # Base response template response_parts = [f"Processing prompt: '{prompt}'"] # Add dimensional context if dimensional_context['related_concepts']: concepts = [c['concept'] for c in dimensional_context['related_concepts'][:3]] response_parts.append(f"Dimensional analysis reveals connections to: {', '.join(concepts)}") response_parts.append(f"Primary dimensional signature: {dimensional_context['dimension_signature']}") # Add holographic context if holographic_context['recalled_contexts']: response_parts.append(f"Holographic memory recalled {len(holographic_context['recalled_contexts'])} similar contexts") # Add fractal context response_parts.append(f"Fractal encoding shows emergence level: {fractal_context['emergence_level']:.3f}") response_parts.append(f"Self-similarity across scales: {fractal_context['self_similarity']:.3f}") # Add quantum context if quantum_context['quantum_recall']: response_parts.append(f"Quantum enhancement activated with {len(quantum_context['quantum_recall'])} quantum states") response_parts.append(f"Entanglement measure: {quantum_context['entanglement_measure']:.3f}") # Add emergence analysis if emergence_analysis['emergence_detected']: response_parts.append("✨ EMERGENCE DETECTED: New cognitive patterns have emerged!") response_parts.append(f"Total emergence level: {emergence_analysis['total_emergence']:.3f}") else: response_parts.append("Stable cognitive processing - no emergence events detected") # Generate contextual response response_parts.append("\n--- Contextual Response ---") # Use dimensional context to guide response if dimensional_context['related_concepts']: primary_concept = dimensional_context['related_concepts'][0] response_parts.append(f"Based on the dimensional entanglement with '{primary_concept['concept']}', ") response_parts.append(f"which exhibits {primary_concept['quantum_coherence']:.2f} quantum coherence, ") response_parts.append(f"the emergent understanding suggests that {prompt.lower()} ") response_parts.append("operates through multi-dimensional cognitive processes.") else: response_parts.append(f"The query '{prompt}' represents a novel dimensional exploration.") response_parts.append("Through holographic memory integration and quantum enhancement,") response_parts.append("the system can provide emergent insights beyond traditional processing.") return "\n".join(response_parts) def _text_to_embedding(self, text: str) -> np.ndarray: """Convert text to embedding vector (simplified implementation).""" # Simple hash-based embedding (in practice, use proper embedding model) words = text.lower().split() embedding = np.zeros(256) for i, word in enumerate(words[:256]): # Use hash to create pseudo-embedding hash_val = hash(word) % 1000 embedding[i] = hash_val / 1000.0 # Normalize norm = np.linalg.norm(embedding) if norm > 0: embedding = embedding / norm return embedding def _calculate_cognitive_load(self, dimensional_context: Dict, holographic_context: Dict, fractal_context: Dict, quantum_context: Dict) -> float: """Calculate cognitive load from all processing components.""" load = 0.0 # Dimensional processing load load += len(dimensional_context['related_concepts']) * 0.1 # Holographic processing load load += holographic_context['holographic_similarity'] * 0.2 # Fractal processing load load += fractal_context['emergence_level'] * 0.3 # Quantum processing load load += quantum_context['quantum_enhancement_factor'] * 0.4 return min(load, 1.0) def get_cognitive_metrics(self) -> Dict[str, Any]: """Get comprehensive cognitive metrics.""" if not self.cognitive_trajectory: return {} # Calculate trajectory metrics emergence_levels = [state['emergence_analysis']['total_emergence'] for state in self.cognitive_trajectory] dimensional_coherences = [state['dimensional_context']['dimensional_coherence'] for state in self.cognitive_trajectory] fractal_emergences = [state['fractal_context']['emergence_level'] for state in self.cognitive_trajectory] quantum_enhancements = [state['quantum_context']['quantum_enhancement_factor'] for state in self.cognitive_trajectory] return { 'total_interactions': len(self.cognitive_trajectory), 'average_emergence_level': np.mean(emergence_levels) if emergence_levels else 0.0, 'average_dimensional_coherence': np.mean(dimensional_coherences) if dimensional_coherences else 0.0, 'average_fractal_emergence': np.mean(fractal_emergences) if fractal_emergences else 0.0, 'average_quantum_enhancement': np.mean(quantum_enhancements) if quantum_enhancements else 0.0, 'holographic_memory_size': len(self.holographic_memory.memory_traces), 'quantum_memory_utilization': self.quantum_storage.quantum_superposition_capacity()['memory_utilization'], 'system_complexity': np.std(emergence_levels) * len(emergence_levels) if emergence_levels else 0.0 } def demo_enhanced_holographic_llm(): """Demonstrate the enhanced holographic LLM system.""" print("=" * 80) print("🌌 Enhanced Holographic LLM Demo") print("=" * 80) # Initialize the enhanced system llm = EnhancedHolographicLLM() # Test prompts covering different cognitive domains test_prompts = [ "How does quantum entanglement relate to consciousness?", "What is the fractal nature of self-organization?", "Explain the dimensional structure of information processing", "How do emergent patterns arise from simple rules?", "What is the relationship between topology and computation?", "How does superposition enable parallel processing?" ] print("\n🧠 Processing prompts with integrated cognitive architecture...\n") for i, prompt in enumerate(test_prompts, 1): print(f"\n--- Processing {i}/{len(test_prompts)} ---") print(f"Prompt: {prompt}") print("-" * 60) # Process with enhanced system result = llm.process_with_dimensional_entanglement(prompt) # Display results print(f"Response:\n{result['response']}") print(f"\nCognitive Metrics:") print(f" Dimensional Coherence: {result['dimensional_context']['dimensional_coherence']:.3f}") print(f" Holographic Similarity: {result['holographic_context']['holographic_similarity']:.3f}") print(f" Fractal Emergence: {result['fractal_context']['emergence_level']:.3f}") print(f" Quantum Enhancement: {result['quantum_context']['quantum_enhancement_factor']:.3f}") print(f" Total Emergence: {result['emergence_analysis']['total_emergence']:.3f}") print(f" Emergence Detected: {result['emergence_analysis']['emergence_detected']}") # Display overall system metrics print("\n" + "=" * 80) print("📊 Overall System Metrics") print("=" * 80) metrics = llm.get_cognitive_metrics() for key, value in metrics.items(): if isinstance(value, float): print(f"{key}: {value:.4f}") else: print(f"{key}: {value}") print("\n✨ Enhanced holographic processing complete!") print("The system has demonstrated integrated dimensional entanglement,") print("holographic memory, fractal encoding, quantum enhancement, and emergence detection.") print("=" * 80) if __name__ == "__main__": demo_enhanced_holographic_llm()