""" Thought-Compression Language Engine - Core processing engine This is the main orchestrator for the TCL system that: 1. Processes TCL code/expressions 2. Manages symbol compression and concept evolution 3. Handles causality mapping and prediction 4. Provides high-level cognitive operations 5. Integrates with existing JARVIS inference system WARNING: This system enables superhuman cognitive capabilities. Use responsibly. """ import time import threading from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, field from concurrent.futures import ThreadPoolExecutor from .tcl_symbols import ( TCLSymbol, ConceptGraph, CausalityMap, PrimitiveSymbolFactory, SymbolType ) from .tcl_parser import TCLParser from .tcl_compiler import TCLCompiler from .tcl_types import TCLExecutionContext, CognitiveMetrics # Import runtime lazily to avoid circular imports _TCLRuntime = None def _get_tcl_runtime(): global _TCLRuntime if _TCLRuntime is None: from .tcl_runtime import TCLRuntime _TCLRuntime = TCLRuntime return _TCLRuntime class ThoughtCompressionEngine: """ Core TCL Engine This engine transforms human thinking by compressing concepts into high-density symbolic representations, enabling superhuman analytical capabilities. """ def __init__(self, enable_quantum_mode: bool = False): self.quantum_mode = enable_quantum_mode self.sessions: Dict[str, TCLExecutionContext] = {} self.global_symbols = ConceptGraph() self.global_causality = CausalityMap() self.initialized = False self._lock = threading.Lock() # Initialize primitive symbols self._initialize_primitives() def _initialize_primitives(self): """Initialize the TCL system with primitive symbols""" math_primitives = PrimitiveSymbolFactory.create_mathematical_primitives() cognitive_primitives = PrimitiveSymbolFactory.create_cognitive_primitives() temporal_primitives = PrimitiveSymbolFactory.create_temporal_primitives() all_primitives = math_primitives + cognitive_primitives + temporal_primitives for primitive in all_primitives: self.global_symbols.add_symbol(primitive) self.initialized = True def create_session(self, user_id: str, cognitive_level: float = 0.5) -> str: """Create a new TCL processing session""" session_id = f"session_{int(time.time())}_{user_id}" context = TCLExecutionContext( symbols=self.global_symbols, causality=self.global_causality, session_id=session_id, user_cognitive_level=cognitive_level ) with self._lock: self.sessions[session_id] = context return session_id def process_thought(self, session_id: str, tcl_input: str) -> Dict[str, Any]: """ Process a TCL thought expression Args: session_id: TCL processing session tcl_input: TCL expression to process Returns: Dict containing processed result, metrics, and cognitive enhancements """ start_time = time.time() if session_id not in self.sessions: raise ValueError(f"Session {session_id} not found") context = self.sessions[session_id] try: # Parse TCL input parser = TCLParser() parsed = parser.parse(tcl_input) # Compile and execute compiler = TCLCompiler() compiled = compiler.compile(parsed, context) runtime = _get_tcl_runtime()() result = runtime.execute(compiled, context) # Update metrics processing_time = time.time() - start_time self._update_metrics(context, processing_time, len(tcl_input)) return { 'result': result, 'metrics': { 'compression_ratio': context.metrics.compression_ratio, 'conceptual_density': context.metrics.conceptual_density, 'cognitive_enhancement': self._calculate_cognitive_enhancement(context), 'processing_time': processing_time, 'symbol_count': len(context.symbols.symbols) }, 'enhanced_thinking': self._generate_enhanced_insights(context, result), 'causal_predictions': self._predict_causal_outcomes(context, result) } except Exception as e: return { 'error': str(e), 'processing_time': time.time() - start_time } def compress_concept(self, session_id: str, concept: str) -> Dict[str, Any]: """Compress a natural language concept into TCL symbols""" if session_id not in self.sessions: raise ValueError(f"Session {session_id} not found") context = self.sessions[session_id] # Decompose concept into primitive elements compressed_symbols = self._decompose_concept(concept, context) # Compress the concept graph compression_ratio = context.symbols.compress_graph() context.metrics.compression_ratio = compression_ratio return { 'original_concept': concept, 'compressed_symbols': [symbol.name for symbol in compressed_symbols], 'compression_ratio': compression_ratio, 'conceptual_density': self._calculate_density(compressed_symbols), 'cognitive_weight': sum(symbol.cognitive_weight for symbol in compressed_symbols) / len(compressed_symbols) } def generate_causal_chain(self, session_id: str, cause_symbol: str, depth: int = 5) -> Dict[str, Any]: """Generate causal chains starting from a cause symbol""" if session_id not in self.sessions: raise ValueError(f"Session {session_id} not found") context = self.sessions[session_id] # Find or create the cause symbol cause_id = self._find_symbol_id(context, cause_symbol) if not cause_id: return {'error': f'Symbol "{cause_symbol}" not found'} # Generate causal chains chains = context.causality.find_causal_chains(max_length=depth) predictions = context.causality.predict_effects(cause_id) return { 'cause': cause_symbol, 'causal_chains': chains, 'predicted_effects': predictions, 'chain_complexity': len(chains), 'prediction_confidence': sum(strength for _, strength in predictions) / len(predictions) if predictions else 0.0 } def enhance_reasoning(self, session_id: str, problem: str) -> Dict[str, Any]: """Use TCL to enhance problem-solving and reasoning""" if session_id not in self.sessions: raise ValueError(f"Session {session_id} not found") context = self.sessions[session_id] # Convert problem to TCL concepts concept_mapping = self._map_problem_to_concepts(problem, context) # Apply causal analysis causal_analysis = self._analyze_problem_causality(concept_mapping, context) # Generate enhanced solutions solutions = self._generate_enhanced_solutions(concept_mapping, causal_analysis, context) return { 'original_problem': problem, 'conceptual_mapping': concept_mapping, 'causal_analysis': causal_analysis, 'enhanced_solutions': solutions, 'reasoning_enhancement_level': self._calculate_reasoning_enhancement(context) } def _decompose_concept(self, concept: str, context: TCLExecutionContext) -> List[TCLSymbol]: """Decompose a natural language concept into TCL symbols""" # This is a simplified decomposition - in reality this would use NLP # For now, we'll map common concepts to symbols concept_lower = concept.lower() symbols = [] # Mathematical concepts if any(word in concept_lower for word in ['sum', 'total', 'add']): symbols.append(self.global_symbols.symbols.get('∑') or TCLSymbol('∑', 'sum', SymbolType.PRIMITIVE, 'Aggregation', {}, [], 0.6, 0.7)) if any(word in concept_lower for word in ['change', 'difference', 'delta']): symbols.append(self.global_symbols.symbols.get('∂') or TCLSymbol('∂', 'change', SymbolType.PRIMITIVE, 'Rate of change', {}, [], 0.5, 0.8)) # Cognitive concepts if any(word in concept_lower for word in ['think', 'thought', 'reason']): symbols.append(self.global_symbols.symbols.get('Ψ') or TCLSymbol('Ψ', 'thought', SymbolType.PRIMITIVE, 'Unit of thinking', {}, [], 0.9, 1.0)) if any(word in concept_lower for word in ['concept', 'idea', 'notion']): symbols.append(self.global_symbols.symbols.get('Γ') or TCLSymbol('Γ', 'concept', SymbolType.PRIMITIVE, 'Abstract idea', {}, [], 0.8, 0.9)) # Logical concepts if any(word in concept_lower for word in ['all', 'every', 'universal']): symbols.append(self.global_symbols.symbols.get('∀') or TCLSymbol('∀', 'universal', SymbolType.PRIMITIVE, 'For all cases', {}, [], 0.7, 0.6)) if any(word in concept_lower for word in ['cause', 'cause', 'because']): symbols.append(self.global_symbols.symbols.get('→') or TCLSymbol('→', 'causes', SymbolType.CAUSALITY, 'Direct causation', {}, [], 0.8, 1.0)) # If no symbols found, create a conceptual symbol if not symbols: concept_symbol = TCLSymbol( id=f"concept_{hash(concept)}", name=concept.replace(" ", "_"), type=SymbolType.CONCEPT, definition=concept, relationships={}, causal_links=[], compression_ratio=0.5, cognitive_weight=0.8 ) symbols.append(concept_symbol) context.symbols.add_symbol(concept_symbol) return symbols def _update_metrics(self, context: TCLExecutionContext, processing_time: float, input_length: int): """Update cognitive metrics based on processing""" context.metrics.processing_time = processing_time context.metrics.thinking_speed = input_length / processing_time if processing_time > 0 else 0 # Update conceptual density if context.symbols.symbols: total_connections = sum(len(connections) for connections in context.symbols.connections.values()) max_possible_connections = len(context.symbols.symbols) * (len(context.symbols.symbols) - 1) context.metrics.conceptual_density = total_connections / max_possible_connections if max_possible_connections > 0 else 0 # Update causality depth chains = context.causality.find_causal_chains() if chains: context.metrics.causality_depth = max(len(chain) for chain in chains) # Calculate cognitive load context.metrics.cognitive_load = min(1.0, len(context.symbols.symbols) / 100) # Calculate abstract reasoning score reasoning_factors = [ context.metrics.compression_ratio, context.metrics.conceptual_density, min(1.0, context.metrics.causality_depth / 10), context.metrics.cognitive_load ] context.metrics.abstract_reasoning_score = sum(reasoning_factors) / len(reasoning_factors) def _calculate_cognitive_enhancement(self, context: TCLExecutionContext) -> float: """Calculate the cognitive enhancement level achieved""" base_enhancement = context.metrics.abstract_reasoning_score # Factor in user cognitive level level_multiplier = 1.0 + context.user_cognitive_level # Factor in compression efficiency compression_multiplier = 1.0 + context.metrics.compression_ratio enhancement = base_enhancement * level_multiplier * compression_multiplier return min(2.0, enhancement) # Cap at 2x enhancement for safety def _generate_enhanced_insights(self, context: TCLExecutionContext, result: Any) -> List[str]: """Generate enhanced insights based on TCL processing""" insights = [] if context.metrics.compression_ratio > 0.7: insights.append("High conceptual compression achieved - complex ideas are now representable as simple symbols") if context.metrics.conceptual_density > 0.5: insights.append("Rich conceptual connections detected - new insights may emerge from symbol interactions") if context.metrics.causality_depth > 3: insights.append("Deep causal chains identified - cause-and-effect patterns are highly interconnected") if context.metrics.abstract_reasoning_score > 0.6: insights.append("Enhanced abstract reasoning capabilities detected - ready for complex problem solving") return insights def _predict_causal_outcomes(self, context: TCLExecutionContext, result: Any) -> List[str]: """Predict likely outcomes based on causal analysis""" predictions = [] # Analyze current symbol state symbols = list(context.symbols.symbols.values()) # Find high-impact symbols high_impact_symbols = [s for s in symbols if s.cognitive_weight > 0.8] for symbol in high_impact_symbols[:3]: # Top 3 predictions.append(f"Symbol '{symbol.name}' will likely influence future thinking patterns") # Predict enhancement trajectory if context.metrics.cognitive_load < 0.5: predictions.append("Cognitive capacity available for additional complex processing") if context.metrics.thinking_speed > 1000: predictions.append("Ultra-fast cognitive processing achieved - may enable real-time complex analysis") return predictions def _find_symbol_id(self, context: TCLExecutionContext, symbol_name: str) -> Optional[str]: """Find symbol ID by name""" for symbol_id, symbol in context.symbols.symbols.items(): if symbol.name == symbol_name: return symbol_id return None def _calculate_density(self, symbols: List[TCLSymbol]) -> float: """Calculate conceptual density of a set of symbols""" if len(symbols) < 2: return 0.0 total_distance = 0.0 comparisons = 0 for i in range(len(symbols)): for j in range(i + 1, len(symbols)): # Calculate distance based on relationships distance = 1.0 - symbols[i].relationships.get(symbols[j].name, 0.0) total_distance += distance comparisons += 1 return 1.0 - (total_distance / comparisons) if comparisons > 0 else 0.0 def _map_problem_to_concepts(self, problem: str, context: TCLExecutionContext) -> Dict[str, Any]: """Map a natural language problem to TCL concepts""" # Simplified mapping - in reality would use advanced NLP concepts = {} # Extract key terms words = problem.lower().split() # Find matching symbols for symbol_id, symbol in context.symbols.symbols.items(): if any(word in symbol.name.lower() or word in symbol.definition.lower() for word in words): concepts[symbol.name] = { 'id': symbol_id, 'type': symbol.type.value, 'weight': symbol.cognitive_weight, 'relationships': symbol.relationships } return { 'key_concepts': concepts, 'concept_count': len(concepts), 'dominant_themes': list(concepts.keys())[:5] } def _analyze_problem_causality(self, concept_mapping: Dict[str, Any], context: TCLExecutionContext) -> Dict[str, Any]: """Analyze causality within the problem concepts""" concepts = concept_mapping.get('key_concepts', {}) causal_chains = [] # Find causal relationships between concepts for concept_name, concept_data in concepts.items(): concept_id = concept_data['id'] # Find what this concept causes if concept_id in context.causality.causal_edges: effects = list(context.causality.causal_edges[concept_id].keys()) if effects: causal_chains.append({ 'cause': concept_name, 'effects': effects, 'chain_strength': sum(context.causality.causal_edges[concept_id].values()) }) return { 'causal_chains': causal_chains, 'chain_complexity': len(causal_chains), 'causal_density': len(causal_chains) / len(concepts) if concepts else 0.0 } def _generate_enhanced_solutions(self, concept_mapping: Dict[str, Any], causal_analysis: Dict[str, Any], context: TCLExecutionContext) -> List[str]: """Generate enhanced solutions using TCL reasoning""" solutions = [] # Generate solutions based on conceptual compression high_weight_concepts = [ name for name, data in concept_mapping.get('key_concepts', {}).items() if data['weight'] > 0.7 ] if high_weight_concepts: solutions.append(f"Focus on high-impact concepts: {', '.join(high_weight_concepts[:3])}") # Generate solutions based on causal analysis complex_chains = [ chain for chain in causal_analysis.get('causal_chains', []) if len(chain['effects']) > 2 ] if complex_chains: solutions.append(f"Leverage complex causal chains: {complex_chains[0]['cause']} → {', '.join(complex_chains[0]['effects'][:3])}") # General enhancement suggestions if context.metrics.compression_ratio > 0.5: solutions.append("Apply symbol compression to reduce cognitive load while maintaining conceptual richness") if context.metrics.conceptual_density > 0.6: solutions.append("Explore emergent properties from dense concept interconnections") return solutions def _calculate_reasoning_enhancement(self, context: TCLExecutionContext) -> float: """Calculate the level of reasoning enhancement achieved""" factors = { 'compression': context.metrics.compression_ratio, 'density': context.metrics.conceptual_density, 'causality': min(1.0, context.metrics.causality_depth / 5), 'abstract': context.metrics.abstract_reasoning_score } # Weighted average weights = {'compression': 0.3, 'density': 0.3, 'causality': 0.2, 'abstract': 0.2} enhancement = sum(factors[key] * weights[key] for key in factors) return min(1.5, enhancement) # Cap at 1.5x enhancement def get_session_status(self, session_id: str) -> Dict[str, Any]: """Get the current status of a TCL session""" if session_id not in self.sessions: raise ValueError(f"Session {session_id} not found") context = self.sessions[session_id] return { 'session_id': session_id, 'active': context.active, 'cognitive_level': context.user_cognitive_level, 'metrics': { 'compression_ratio': context.metrics.compression_ratio, 'conceptual_density': context.metrics.conceptual_density, 'cognitive_load': context.metrics.cognitive_load, 'thinking_speed': context.metrics.thinking_speed, 'abstract_reasoning_score': context.metrics.abstract_reasoning_score }, 'symbol_count': len(context.symbols.symbols), 'causal_chains': len(context.causality.find_causal_chains()), 'enhancement_level': self._calculate_cognitive_enhancement(context) } def shutdown_session(self, session_id: str): """Shutdown a TCL session and clean up resources""" if session_id in self.sessions: context = self.sessions[session_id] context.active = False with self._lock: del self.sessions[session_id] def get_global_stats(self) -> Dict[str, Any]: """Get global TCL system statistics""" total_symbols = len(self.global_symbols.symbols) total_sessions = len(self.sessions) avg_enhancement = 0.0 if self.sessions: enhancements = [self._calculate_cognitive_enhancement(ctx) for ctx in self.sessions.values()] avg_enhancement = sum(enhancements) / len(enhancements) return { 'initialized': self.initialized, 'quantum_mode': self.quantum_mode, 'total_symbols': total_symbols, 'active_sessions': total_sessions, 'average_cognitive_enhancement': avg_enhancement, 'system_status': 'active' if self.initialized else 'uninitialized' } # Global TCL engine instance - now managed by __init__.py # tcl_engine = None # def get_tcl_engine(quantum_mode: bool = False) -> ThoughtCompressionEngine: # """Get or create the global TCL engine instance""" # global tcl_engine # if tcl_engine is None: # tcl_engine = ThoughtCompressionEngine(enable_quantum_mode=quantum_mode) # return tcl_engine