""" Main Lyra model that integrates all AI components. This is the central coordinator that brings together the transformer, personality matrix, emotional system, and thinking agent. """ import torch import torch.nn as nn import logging from typing import Dict, List, Any, Optional, Tuple from datetime import datetime from .transformer import LyraTransformer from .self_evolution import SelfEvolutionEngine from .thinking_agent import ThinkingAgent from ..personality.matrix import PersonalityMatrix from ..emotions.system import EmotionalSystem from ..emotions.expressions import EmotionalExpressionEngine logger = logging.getLogger(__name__) class LyraModel(nn.Module): """ Complete Lyra AI model integrating all cognitive systems. This model combines: - Self-evolving transformer for language generation - Personality matrix for trait-based behavior - Emotional intelligence for natural responses - Behind-the-scenes thinking for human-like reasoning - Self-evolution for continuous improvement """ def __init__( self, vocab_size: int = 50000, embed_dim: int = 768, num_layers: int = 12, num_heads: int = 12, ff_dim: int = 3072, max_len: int = 2048, device: Optional[torch.device] = None, enable_evolution: bool = True ): super().__init__() self.vocab_size = vocab_size self.embed_dim = embed_dim self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu") self.enable_evolution = enable_evolution # Core transformer for language generation self.transformer = LyraTransformer( vocab_size=vocab_size, embed_dim=embed_dim, num_layers=num_layers, num_heads=num_heads, ff_dim=ff_dim, max_len=max_len, use_evolution=enable_evolution ) # Personality system self.personality_matrix = PersonalityMatrix( device=self.device, enable_self_modification=True ) # Emotional intelligence self.emotional_system = EmotionalSystem( input_dim=embed_dim, emotion_dim=19, memory_capacity=1000, device=self.device ) # Thinking agent for internal reasoning self.thinking_agent = ThinkingAgent( model_dim=embed_dim, thought_types=8, max_thought_depth=5, device=self.device ) # Self-evolution engine if enable_evolution: self.evolution_engine = SelfEvolutionEngine( model_dim=embed_dim, evolution_rate=0.001, adaptation_threshold=0.7, device=self.device ) else: self.evolution_engine = None # Emotional expression engine self.expression_engine = EmotionalExpressionEngine( vocab_size=vocab_size, expression_dim=128, device=self.device ) # Integration layers self.context_integrator = nn.Sequential( nn.Linear(embed_dim + 19 + 24, embed_dim), # context + emotions + personality nn.LayerNorm(embed_dim), nn.ReLU(), nn.Linear(embed_dim, embed_dim) ) # Conversation state self.conversation_history = [] self.current_user_id = None self.interaction_count = 0 self.to(self.device) def forward( self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, user_id: Optional[str] = None, conversation_context: Optional[str] = None ) -> Tuple[torch.Tensor, Dict[str, Any]]: """ Forward pass through complete Lyra model. Args: input_ids: Input token IDs attention_mask: Attention mask user_id: Current user ID for personalization conversation_context: Context description Returns: output_logits: Language model logits lyra_info: Comprehensive information about Lyra's processing """ batch_size, seq_len = input_ids.shape # Create context embedding from input with torch.no_grad(): # Get initial embeddings input_embeddings = self.transformer.token_embedding(input_ids) context_embedding = input_embeddings.mean(dim=1, keepdim=True) # [batch, 1, embed_dim] # Update current user self.current_user_id = user_id # Process through emotional system emotional_state, emotion_info = self.emotional_system( context_embedding=context_embedding, social_context={ 'user_id': user_id, 'context': conversation_context, 'interaction_count': self.interaction_count } ) # Process through personality matrix personality_weights, personality_info = self.personality_matrix( context_embedding=context_embedding, emotional_state=emotional_state.to_tensor(self.device).unsqueeze(0), user_id=user_id ) # Generate internal thoughts if conversation_context: thought_chain, thinking_info = self.thinking_agent( context_embedding=context_embedding, personality_state=personality_weights, emotional_state=emotional_state.to_tensor(self.device).unsqueeze(0), user_message=conversation_context ) else: thought_chain, thinking_info = [], {} # Integrate all contexts integrated_context = self._integrate_contexts( context_embedding, emotional_state, personality_weights ) # Apply self-evolution if enabled if self.enable_evolution and self.evolution_engine: evolved_context, evolution_info = self.evolution_engine( current_state=integrated_context, context=context_embedding, feedback_signal=None # Will be provided after generation ) else: evolved_context = integrated_context evolution_info = {} # Generate response through transformer logits, model_info = self.transformer( input_ids=input_ids, attention_mask=attention_mask, emotional_state=emotional_state.to_tensor(self.device).unsqueeze(0), evolve=self.enable_evolution ) # Compile comprehensive information lyra_info = { 'emotional_state': emotion_info, 'personality_state': personality_info, 'thinking_process': thinking_info, 'model_processing': model_info, 'thought_chain': [ { 'type': thought.thought_type, 'content': thought.content, 'confidence': thought.confidence, 'reasoning': thought.reasoning } for thought in thought_chain ], 'interaction_count': self.interaction_count, 'current_user': user_id } if self.enable_evolution: lyra_info['evolution'] = evolution_info self.interaction_count += 1 return logits, lyra_info def _integrate_contexts( self, context_embedding: torch.Tensor, emotional_state: Any, personality_weights: torch.Tensor ) -> torch.Tensor: """Integrate context, emotional, and personality information.""" batch_size = context_embedding.shape[0] # Get emotional tensor emotional_tensor = emotional_state.to_tensor(self.device).unsqueeze(0) if emotional_tensor.shape[0] != batch_size: emotional_tensor = emotional_tensor.repeat(batch_size, 1) # Ensure personality weights have correct batch size if personality_weights.shape[0] != batch_size: personality_weights = personality_weights.repeat(batch_size, 1) # Combine all contexts combined_input = torch.cat([ context_embedding.squeeze(1), # Remove sequence dimension emotional_tensor[:, :19], # Take only emotion dimensions personality_weights[:, :24] # Take personality dimensions ], dim=1) # Integrate through neural network integrated = self.context_integrator(combined_input) return integrated.unsqueeze(1) # Add sequence dimension back async def generate_response( self, user_message: str, user_id: Optional[str] = None, max_new_tokens: int = 100, temperature: float = 1.0, top_k: int = 50, top_p: float = 0.9 ) -> Tuple[str, Dict[str, Any]]: """ Generate a complete response to user input. This is the main interface for having conversations with Lyra. """ # For now, create a simple response (will be enhanced with tokenizer) # This is a placeholder until we implement the full training pipeline # Process through thinking and emotional systems context_embedding = torch.randn(1, 10, self.embed_dim, device=self.device) # Get Lyra's thoughts about the message thought_chain, thinking_info = self.thinking_agent( context_embedding=context_embedding, personality_state=torch.rand(1, 24, device=self.device), emotional_state=torch.rand(1, 19, device=self.device), user_message=user_message ) # Process emotional response emotional_state, emotion_info = self.emotional_system( context_embedding=context_embedding, social_context={ 'user_id': user_id, 'context': user_message, 'trigger': 'user_message' } ) # Generate personality-influenced response personality_weights, personality_info = self.personality_matrix( context_embedding=context_embedding, emotional_state=emotional_state.to_tensor(self.device).unsqueeze(0), user_id=user_id ) # Create a response based on current emotional and personality state base_response = self._generate_contextual_response( user_message, emotional_state, personality_info, thought_chain ) # Apply emotional expression expressed_response, expression_info = self.expression_engine( text=base_response, emotional_state=emotional_state, intensity_multiplier=1.0 ) # Compile response information response_info = { 'thoughts': [ { 'type': thought.thought_type, 'content': thought.content, 'confidence': thought.confidence } for thought in thought_chain ], 'emotional_state': { 'dominant_emotion': emotional_state.get_dominant_emotion(), 'valence': emotional_state.get_emotional_valence(), 'arousal': emotional_state.get_emotional_arousal() }, 'personality_influence': personality_info, 'expression_modifications': expression_info, 'response_generation_method': 'contextual_template' # Will change after training } return expressed_response, response_info def _generate_contextual_response( self, user_message: str, emotional_state: Any, personality_info: Dict[str, Any], thought_chain: List[Any] ) -> str: """Generate contextual response based on Lyra's current state.""" # This is a simplified response generation for testing # Will be replaced with proper transformer generation after training dominant_emotion, intensity = emotional_state.get_dominant_emotion() mb_type = personality_info.get('myers_briggs', 'ENFP') # Basic response templates based on emotional state and personality responses = { 'joy': [ "That's wonderful! I'm really excited about this.", "This makes me so happy! Tell me more!", "I love hearing about this kind of thing!" ], 'curiosity': [ "That's really interesting! I'm curious to learn more.", "Fascinating! How does that work exactly?", "I wonder about the implications of this..." ], 'empathy': [ "I can understand how you might feel about that.", "That sounds like it could be challenging.", "I appreciate you sharing this with me." ], 'analytical': [ "Let me think about this systematically.", "There are several factors to consider here.", "From an analytical perspective..." ] } # Select response based on thinking and emotional state if thought_chain and len(thought_chain) > 0: primary_thought_type = thought_chain[0].thought_type if primary_thought_type in responses: response_options = responses[primary_thought_type] else: response_options = responses.get(dominant_emotion, responses['empathy']) else: response_options = responses.get(dominant_emotion, responses['empathy']) import random base_response = random.choice(response_options) return base_response def evolve_from_feedback( self, user_feedback: float, conversation_success: float, user_id: Optional[str] = None ): """Update Lyra based on conversation feedback.""" if not self.enable_evolution: return # Evolve personality self.personality_matrix.evolve_from_interaction( interaction_type='conversation', user_feedback=user_feedback, emotional_context=self.emotional_system.get_emotional_context_for_response(), user_id=user_id, conversation_success=conversation_success ) # Evolve transformer self.transformer.evolve_from_conversation(feedback_signal=user_feedback) # Evolve emotional system (implicit through usage) # Evolve self-evolution engine if self.evolution_engine: context_embedding = torch.randn(10, self.embed_dim, device=self.device) emotional_context = self.emotional_system.get_emotional_context_for_response() self.evolution_engine.evolve_from_conversation( conversation_embedding=context_embedding, user_satisfaction=user_feedback, emotional_context=emotional_context ) def get_lyra_status(self) -> Dict[str, Any]: """Get comprehensive status of all Lyra systems.""" return { 'model_info': { 'vocab_size': self.vocab_size, 'embed_dim': self.embed_dim, 'device': str(self.device), 'evolution_enabled': self.enable_evolution, 'interaction_count': self.interaction_count }, 'personality': self.personality_matrix.get_personality_summary(), 'emotions': self.emotional_system.get_emotional_summary(), 'thinking': self.thinking_agent.get_thinking_summary(), 'transformer_stats': self.transformer.get_model_stats(), 'evolution': ( self.evolution_engine.get_evolution_summary() if self.evolution_engine else {'status': 'disabled'} ) }