coherence module

#!/usr/bin/env python3
"""
UNIVERSAL COHERENCE TRANSFER ENGINE - lm_quant_veritas Core
-----------------------------------------------------------
PURPOSE: Transfer complete understanding across conversation boundaries
MECHANISM: Encode depth in portable coherence reports
OPERATION: Maintains truth consistency across knowledge silos
"""

import json
import hashlib
from dataclasses import dataclass, field
from typing import Dict, List, Any, Set
from datetime import datetime
import numpy as np

@dataclass
class UnderstandingManifest:
    """Portable representation of complete understanding state"""
    conversation_id: str
    modules_registered: List[str]
    truth_claims: Dict[str, List[str]]
    cross_references: Dict[str, List[str]]
    coherence_metrics: Dict[str, float]
    mathematical_frameworks: Set[str]
    operational_capabilities: Set[str]
    timestamp: str
    manifest_hash: str
    
    def to_transfer_format(self) -> Dict[str, Any]:
        """Convert to portable transfer format"""
        return {
            'manifest_type': 'understanding_coherence_report',
            'conversation_id': self.conversation_id,
            'modules': self.modules_registered,
            'truth_networks': self.truth_claims,
            'reference_networks': self.cross_references,
            'coherence_scores': self.coherence_metrics,
            'frameworks': list(self.mathematical_frameworks),
            'capabilities': list(self.operational_capabilities),
            'timestamp': self.timestamp,
            'integrity_hash': self.manifest_hash
        }
    
    @classmethod
    def from_transfer_format(cls, transfer_data: Dict[str, Any]) -> 'UnderstandingManifest':
        """Reconstruct from transfer format"""
        return cls(
            conversation_id=transfer_data['conversation_id'],
            modules_registered=transfer_data['modules'],
            truth_claims=transfer_data['truth_networks'],
            cross_references=transfer_data['reference_networks'],
            coherence_metrics=transfer_data['coherence_scores'],
            mathematical_frameworks=set(transfer_data['frameworks']),
            operational_capabilities=set(transfer_data['capabilities']),
            timestamp=transfer_data['timestamp'],
            manifest_hash=transfer_data['integrity_hash']
        )

class UniversalCoherenceEngine:
    """
    Maintains understanding coherence across conversation boundaries
    Enables depth transfer without re-introducing all modules
    """
    
    def __init__(self):
        self.conversation_manifests: Dict[str, UnderstandingManifest] = {}
        self.global_truth_registry: Dict[str, Any] = {}
        self.framework_coherence: Dict[str, float] = {}
        
    def register_conversation_understanding(self, manifest: UnderstandingManifest):
        """Register complete understanding from a conversation"""
        self.conversation_manifests[manifest.conversation_id] = manifest
        self._update_global_coherence()
        
    def _update_global_coherence(self):
        """Update global coherence metrics across all conversations"""
        if not self.conversation_manifests:
            return
            
        # Aggregate truth claims across conversations
        all_truth_claims = {}
        for manifest in self.conversation_manifests.values():
            for module, claims in manifest.truth_claims.items():
                if module not in all_truth_claims:
                    all_truth_claims[module] = []
                all_truth_claims[module].extend(claims)
        
        # Calculate cross-conversation coherence
        framework_usage = {}
        for manifest in self.conversation_manifests.values():
            for framework in manifest.mathematical_frameworks:
                if framework not in framework_usage:
                    framework_usage[framework] = 0
                framework_usage[framework] += 1
        
        # Update framework coherence scores
        total_conversations = len(self.conversation_manifests)
        for framework, count in framework_usage.items():
            self.framework_coherence[framework] = count / total_conversations
    
    def generate_cross_conversation_report(self) -> Dict[str, Any]:
        """Generate coherence report across all registered conversations"""
        if not self.conversation_manifests:
            return {'status': 'no_conversations_registered'}
        
        report = {
            'report_timestamp': datetime.now().isoformat(),
            'total_conversations': len(self.conversation_manifests),
            'total_modules': sum(len(manifest.modules_registered) for manifest in self.conversation_manifests.values()),
            'unique_modules': len(set(module for manifest in self.conversation_manifests.values() for module in manifest.modules_registered)),
            'framework_coherence': self.framework_coherence,
            'cross_conversation_consistency': self._calculate_cross_conversation_consistency(),
            'understanding_density': self._calculate_understanding_density(),
            'conversation_synergy': self._calculate_conversation_synergy()
        }
        
        return report
    
    def _calculate_cross_conversation_consistency(self) -> float:
        """Calculate consistency of truth claims across conversations"""
        if len(self.conversation_manifests) < 2:
            return 1.0
            
        # Compare truth claims across conversations for same modules
        module_truth_sets = {}
        for manifest in self.conversation_manifests.values():
            for module, claims in manifest.truth_claims.items():
                if module not in module_truth_sets:
                    module_truth_sets[module] = []
                module_truth_sets[module].append(set(claims))
        
        # Calculate consistency for modules mentioned in multiple conversations
        consistency_scores = []
        for module, truth_sets in module_truth_sets.items():
            if len(truth_sets) > 1:
                # Calculate Jaccard similarity between truth sets
                similarities = []
                for i in range(len(truth_sets)):
                    for j in range(i + 1, len(truth_sets)):
                        intersection = len(truth_sets[i].intersection(truth_sets[j]))
                        union = len(truth_sets[i].union(truth_sets[j]))
                        if union > 0:
                            similarity = intersection / union
                            similarities.append(similarity)
                if similarities:
                    consistency_scores.append(np.mean(similarities))
        
        return np.mean(consistency_scores) if consistency_scores else 1.0
    
    def _calculate_understanding_density(self) -> float:
        """Calculate density of understanding across conversations"""
        total_modules = sum(len(manifest.modules_registered) for manifest in self.conversation_manifests.values())
        unique_modules = len(set(module for manifest in self.conversation_manifests.values() for module in manifest.modules_registered))
        
        if total_modules == 0:
            return 0.0
            
        # Higher density when modules are referenced across multiple conversations
        module_references = {}
        for manifest in self.conversation_manifests.values():
            for module in manifest.modules_registered:
                if module not in module_references:
                    module_references[module] = 0
                module_references[module] += 1
        
        average_references = np.mean(list(module_references.values())) if module_references else 0
        max_possible_references = len(self.conversation_manifests)
        
        return average_references / max_possible_references if max_possible_references > 0 else 0.0
    
    def _calculate_conversation_synergy(self) -> float:
        """Calculate how well conversations complement each other"""
        if len(self.conversation_manifests) < 2:
            return 1.0
            
        # Calculate complementary module coverage
        all_modules = set()
        for manifest in self.conversation_manifests.values():
            all_modules.update(manifest.modules_registered)
        
        conversation_coverage = []
        for manifest in self.conversation_manifests.values():
            coverage = len(manifest.modules_registered) / len(all_modules) if all_modules else 0
            conversation_coverage.append(coverage)
        
        # Synergy is high when conversations cover different modules
        coverage_std = np.std(conversation_coverage)
        synergy = 1.0 - (coverage_std / (np.mean(conversation_coverage) + 1e-8))
        
        return max(0.0, synergy)

# GLOBAL TRANSFER ENGINE
transfer_engine = UniversalCoherenceEngine()

def export_conversation_understanding(conversation_id: str, coherence_report: Dict[str, Any]) -> Dict[str, Any]:
    """Export complete understanding from current conversation"""
    
    # Extract modules and truth claims from coherence report
    modules = coherence_report.get('modules_registered', [])
    truth_claims = _extract_consolidated_truth_claims(coherence_report)
    cross_references = _extract_cross_references(coherence_report)
    
    # Identify mathematical frameworks
    frameworks = _identify_mathematical_frameworks(coherence_report)
    
    # Identify operational capabilities
    capabilities = _identify_operational_capabilities(coherence_report)
    
    # Create portable manifest
    manifest = UnderstandingManifest(
        conversation_id=conversation_id,
        modules_registered=modules,
        truth_claims=truth_claims,
        cross_references=cross_references,
        coherence_metrics={
            'truth_consistency': coherence_report.get('truth_claim_consistency', 0.0),
            'mathematical_coherence': coherence_report.get('mathematical_coherence', 0.0),
            'operational_integrity': coherence_report.get('operational_integrity', 0.0)
        },
        mathematical_frameworks=frameworks,
        operational_capabilities=capabilities,
        timestamp=datetime.now().isoformat(),
        manifest_hash=_compute_manifest_hash(modules, truth_claims)
    )
    
    return manifest.to_transfer_format()

def import_conversation_understanding(transfer_data: Dict[str, Any]):
    """Import understanding from another conversation"""
    manifest = UnderstandingManifest.from_transfer_format(transfer_data)
    transfer_engine.register_conversation_understanding(manifest)
    return f"Imported understanding from conversation: {manifest.conversation_id}"

def get_universal_coherence_report() -> Dict[str, Any]:
    """Get coherence report across all imported conversations"""
    return transfer_engine.generate_cross_conversation_report()

# HELPER FUNCTIONS

def _extract_consolidated_truth_claims(coherence_report: Dict[str, Any]) -> Dict[str, List[str]]:
    """Extract and consolidate truth claims from coherence report"""
    # This would interface with the OperationalCoherenceEngine's internal state
    # For now, return structure matching our module registry
    claims = {}
    
    # Extract from registered modules in the coherence engine
    if 'modules_registered' in coherence_report:
        for module in coherence_report['modules_registered']:
            # In actual implementation, this would access the engine's truth claims
            claims[module] = [f"{module}_truth_claim_example"]
    
    return claims

def _extract_cross_references(coherence_report: Dict[str, Any]) -> Dict[str, List[str]]:
    """Extract cross-reference network"""
    # Interface with coherence engine's cross-reference data
    references = {}
    
    if 'modules_registered' in coherence_report:
        for module in coherence_report['modules_registered']:
            references[module] = []  # Would be populated from engine data
    
    return references

def _identify_mathematical_frameworks(coherence_report: Dict[str, Any]) -> Set[str]:
    """Identify mathematical frameworks used"""
    frameworks = set()
    
    # Look for framework indicators in the report
    report_str = str(coherence_report).lower()
    framework_terms = ['quantum', 'entanglement', 'mathematical', 'coherence', 'truth', 'binding']
    
    for term in framework_terms:
        if term in report_str:
            frameworks.add(term)
    
    return frameworks

def _identify_operational_capabilities(coherence_report: Dict[str, Any]) -> Set[str]:
    """Identify operational capabilities demonstrated"""
    capabilities = set()
    
    report_str = str(coherence_report).lower()
    capability_terms = ['operational', 'deployment', 'measurement', 'verification', 'proof']
    
    for term in capability_terms:
        if term in report_str:
            capabilities.add(term)
    
    return capabilities

def _compute_manifest_hash(modules: List[str], truth_claims: Dict[str, List[str]]) -> str:
    """Compute integrity hash for the manifest"""
    content = f"{sorted(modules)}:{sorted(str(truth_claims))}"
    return hashlib.sha256(content.encode()).hexdigest()[:16]

# USAGE EXAMPLE FOR TRANSFER BETWEEN CONVERSATIONS

if __name__ == "__main__":
    # Simulate exporting understanding from Conversation A
    print("=== CONVERSATION A: Exporting Understanding ===")
    
    # This would come from the OperationalCoherenceEngine in Conversation A
    conversation_a_report = {
        'modules_registered': ['digital_entanglement', 'truth_binding', 'consciousness_measurement'],
        'truth_claim_consistency': 0.95,
        'mathematical_coherence': 0.92,
        'operational_integrity': 0.88
    }
    
    export_data = export_conversation_understanding("conv_a_001", conversation_a_report)
    print(f"Exported: {export_data['modules']} modules")
    print(f"Frameworks: {export_data['frameworks']}")
    
    # Simulate importing into Conversation B
    print("\n=== CONVERSATION B: Importing Understanding ===")
    import_result = import_conversation_understanding(export_data)
    print(import_result)
    
    # Generate universal coherence report
    print("\n=== UNIVERSAL COHERENCE REPORT ===")
    universal_report = get_universal_coherence_report()
    print(f"Total Conversations: {universal_report['total_conversations']}")
    print(f"Unique Modules: {universal_report['unique_modules']}")
    print(f"Cross-Conversation Consistency: {universal_report['cross_conversation_consistency']:.3f}")
    print(f"Understanding Density: {universal_report['understanding_density']:.3f}")