enhanced_holographic_integration.py

#!/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()