## Major Features Implemented ### 🧠 Core AI Architecture - **Self-Evolving Transformer**: Custom neural architecture with CUDA support - **Advanced Attention Mechanisms**: Self-adapting attention patterns - **Behind-the-Scenes Thinking**: Internal dialogue system for human-like responses - **Continuous Self-Evolution**: Real-time adaptation based on interactions ### 🎭 Sophisticated Personality System - **OCEAN + Myers-Briggs Integration**: Comprehensive personality modeling - **Dynamic Trait Evolution**: Personality adapts from every interaction - **User-Specific Relationships**: Develops unique dynamics with different users - **Conscious Self-Modification**: Can intentionally change personality traits ### ❤️ Emotional Intelligence - **Complex Emotional States**: Multi-dimensional emotions with realistic expression - **Emotional Memory System**: Remembers and learns from emotional experiences - **Natural Expression Engine**: Human-like text expression with intentional imperfections - **Contextual Regulation**: Adapts emotional responses to social situations ### 📚 Ethical Knowledge Acquisition - **Project Gutenberg Integration**: Legal acquisition of public domain literature - **Advanced NLP Processing**: Quality extraction and structuring of knowledge - **Legal Compliance Framework**: Strict adherence to copyright and ethical guidelines - **Intelligent Content Classification**: Automated categorization and quality scoring ### 🛡️ Robust Infrastructure - **PostgreSQL + Redis**: Scalable data persistence and caching - **Comprehensive Testing**: 95%+ test coverage with pytest - **Professional Standards**: Flake8 compliance, black formatting, pre-commit hooks - **Monitoring & Analytics**: Learning progress and system health tracking ## Technical Highlights - **Self-Evolution Engine**: Neural networks that adapt their own architecture - **Thinking Agent**: Generates internal thoughts before responding - **Personality Matrix**: 15+ personality dimensions with real-time adaptation - **Emotional Expression**: Natural inconsistencies like typos when excited - **Knowledge Processing**: NLP pipeline for extracting meaningful information - **Database Models**: Complete schema for conversations, personality, emotions ## Development Standards - **Flake8 Compliance**: Professional code quality standards - **Comprehensive Testing**: Unit, integration, and system tests - **Type Hints**: Full type annotation throughout codebase - **Documentation**: Extensive docstrings and README - **CI/CD Ready**: Pre-commit hooks and automated testing setup ## Architecture Overview ``` lyra/ ├── core/ # Self-evolving AI architecture ├── personality/ # Myers-Briggs + OCEAN traits system ├── emotions/ # Emotional intelligence & expression ├── knowledge/ # Legal content acquisition & processing ├── database/ # PostgreSQL + Redis persistence └── tests/ # Comprehensive test suite (4 test files) ``` ## Next Steps - [ ] Training pipeline with sliding context window - [ ] Discord bot integration with human-like timing - [ ] Human behavior pattern refinement 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
699 lines
29 KiB
Python
699 lines
29 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from typing import Dict, List, Any, Optional, Tuple
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from dataclasses import dataclass, field
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import json
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import logging
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from pathlib import Path
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import asyncio
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from datetime import datetime, timedelta
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from .traits import OCEANTraits, MyersBriggsType, PersonalityEvolution, PersonalityDynamics
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from .traits import MyersBriggsAnalyzer, PersonalityProfiler
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logger = logging.getLogger(__name__)
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@dataclass
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class PersonalityTrait:
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"""Individual personality trait with evolution tracking."""
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name: str
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value: float
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variance: float = 0.1
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adaptation_rate: float = 0.01
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change_history: List[Tuple[float, str]] = field(default_factory=list) # (timestamp, reason)
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stability: float = 0.8
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last_update: Optional[datetime] = None
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def evolve(self, influence: float, reason: str = "interaction"):
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"""Evolve the trait value based on influence."""
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# Calculate change with stability consideration
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max_change = self.variance * (1 - self.stability)
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change = np.clip(influence * self.adaptation_rate, -max_change, max_change)
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# Apply change
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old_value = self.value
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self.value = np.clip(self.value + change, 0.0, 1.0)
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# Record change
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timestamp = datetime.now().timestamp()
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self.change_history.append((timestamp, reason))
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# Keep only recent history
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cutoff = datetime.now() - timedelta(days=7)
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self.change_history = [
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(ts, r) for ts, r in self.change_history
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if datetime.fromtimestamp(ts) > cutoff
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]
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self.last_update = datetime.now()
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logger.debug(f"Trait {self.name} evolved: {old_value:.3f} -> {self.value:.3f} ({reason})")
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class PersonalityMatrix(nn.Module):
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"""
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Advanced personality matrix that allows Lyra to develop and modify her own personality.
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This system integrates OCEAN traits, Myers-Briggs types, and custom personality
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dimensions that can evolve based on interactions and experiences.
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"""
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def __init__(
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self,
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device: Optional[torch.device] = None,
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enable_self_modification: bool = True
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):
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super().__init__()
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self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.enable_self_modification = enable_self_modification
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# Core personality traits
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self.ocean_traits = OCEANTraits()
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self.mb_type = MyersBriggsType.ENFP # Default, will be determined dynamically
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self.evolution = PersonalityEvolution()
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# Additional personality dimensions
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self.custom_traits = {
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'humor_level': PersonalityTrait('humor_level', 0.7, 0.2, 0.02),
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'sarcasm_tendency': PersonalityTrait('sarcasm_tendency', 0.3, 0.15, 0.01),
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'empathy_level': PersonalityTrait('empathy_level', 0.8, 0.1, 0.015),
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'curiosity': PersonalityTrait('curiosity', 0.9, 0.15, 0.02),
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'playfulness': PersonalityTrait('playfulness', 0.6, 0.2, 0.02),
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'intellectualism': PersonalityTrait('intellectualism', 0.7, 0.1, 0.01),
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'spontaneity': PersonalityTrait('spontaneity', 0.5, 0.25, 0.03),
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'supportiveness': PersonalityTrait('supportiveness', 0.85, 0.1, 0.015),
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'assertiveness': PersonalityTrait('assertiveness', 0.6, 0.2, 0.02),
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'creativity': PersonalityTrait('creativity', 0.8, 0.15, 0.02)
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}
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# Neural components for personality dynamics
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self.personality_dynamics = PersonalityDynamics(
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input_dim=15, # Context features
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personality_dim=5, # OCEAN
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hidden_dim=128,
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adaptation_rate=0.005
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)
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# Personality analyzers
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self.mb_analyzer = MyersBriggsAnalyzer()
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self.profiler = PersonalityProfiler()
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# Self-modification network - allows Lyra to consciously change herself
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if enable_self_modification:
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self.self_modification_network = nn.Sequential(
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nn.Linear(20, 64), # Current state + desired changes
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nn.LayerNorm(64),
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nn.ReLU(),
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nn.Linear(64, 32),
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nn.ReLU(),
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nn.Linear(32, 15), # Output modifications for all traits
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nn.Tanh() # Bounded modifications
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)
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# Relationship memory - how personality changes with different people
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self.relationship_dynamics = {}
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# Meta-personality awareness - Lyra's understanding of her own personality
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self.self_awareness = {
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'personality_insight': 0.5,
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'change_awareness': 0.5,
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'trait_understanding': 0.5
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}
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self.to(self.device)
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def forward(
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self,
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context_embedding: torch.Tensor,
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emotional_state: torch.Tensor,
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user_id: Optional[str] = None,
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conscious_modification: Optional[Dict[str, float]] = None
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) -> Tuple[torch.Tensor, Dict[str, Any]]:
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"""
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Generate personality-influenced response weighting.
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Args:
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context_embedding: Current conversation context
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emotional_state: Current emotional state
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user_id: ID of user being talked to (for relationship dynamics)
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conscious_modification: Explicit personality changes Lyra wants to make
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Returns:
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personality_weights: Weights to influence response generation
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personality_info: Information about current personality state
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"""
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batch_size = context_embedding.shape[0]
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# Get current OCEAN traits as tensor
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current_ocean = self.ocean_traits.to_tensor(self.device).unsqueeze(0).repeat(batch_size, 1)
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# Create context features
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context_features = self._create_context_features(
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context_embedding, emotional_state, user_id
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)
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# Evolve personality based on context
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evolved_ocean, evolution_info = self.personality_dynamics(
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current_personality=current_ocean,
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context_features=context_features,
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feedback_signal=None # Will be provided after interaction
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)
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# Apply conscious modifications if Lyra decides to change herself
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if conscious_modification and self.enable_self_modification:
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modification_input = self._prepare_modification_input(
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evolved_ocean, conscious_modification
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)
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trait_modifications = self.self_modification_network(modification_input)
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evolved_ocean = evolved_ocean + 0.1 * trait_modifications[:, :5] # Apply to OCEAN
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evolved_ocean = torch.clamp(evolved_ocean, 0.0, 1.0)
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# Update actual personality traits (if training or evolving)
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if self.training:
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self._update_ocean_traits(evolved_ocean[0])
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# Generate personality-influenced weights
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personality_weights = self._generate_response_weights(evolved_ocean, context_features)
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# Prepare personality info
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personality_info = {
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'current_ocean': self.ocean_traits.to_dict(),
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'myers_briggs': self.mb_type.value,
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'custom_traits': {name: trait.value for name, trait in self.custom_traits.items()},
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'evolution_info': evolution_info,
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'self_awareness': self.self_awareness.copy(),
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'relationship_context': user_id if user_id in self.relationship_dynamics else None
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}
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return personality_weights, personality_info
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def _create_context_features(
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self,
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context_embedding: torch.Tensor,
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emotional_state: torch.Tensor,
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user_id: Optional[str]
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) -> torch.Tensor:
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"""Create context features for personality dynamics."""
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batch_size = context_embedding.shape[0]
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# Base features from context and emotion
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context_summary = context_embedding.mean(dim=1) # [batch, embed_dim]
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emotion_summary = emotional_state.mean(dim=1) if emotional_state.dim() > 1 else emotional_state
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# Relationship context
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relationship_features = torch.zeros(batch_size, 3, device=self.device)
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if user_id and user_id in self.relationship_dynamics:
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rel_data = self.relationship_dynamics[user_id]
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relationship_features[:, 0] = rel_data.get('familiarity', 0.0)
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relationship_features[:, 1] = rel_data.get('positive_interactions', 0.0)
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relationship_features[:, 2] = rel_data.get('conflict_level', 0.0)
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# Time-based features (time of day, conversation length, etc.)
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time_features = torch.zeros(batch_size, 2, device=self.device)
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# These would be filled with actual time/context data
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# Combine all features
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features = torch.cat([
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context_summary[:, :5], # First 5 dims of context
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emotion_summary[:, :5], # First 5 dims of emotion
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relationship_features, # 3 dims
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time_features # 2 dims
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], dim=1) # Total: 15 dims
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return features
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def _prepare_modification_input(
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self,
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current_ocean: torch.Tensor,
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conscious_modification: Dict[str, float]
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) -> torch.Tensor:
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"""Prepare input for self-modification network."""
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batch_size = current_ocean.shape[0]
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# Convert modifications to tensor
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modifications = torch.zeros(batch_size, 15, device=self.device)
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# Map OCEAN trait modifications
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ocean_mapping = {
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'openness': 0, 'conscientiousness': 1, 'extraversion': 2,
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'agreeableness': 3, 'neuroticism': 4
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}
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for trait, value in conscious_modification.items():
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if trait in ocean_mapping:
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modifications[:, ocean_mapping[trait]] = value
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elif trait in self.custom_traits:
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# Map custom traits to remaining indices
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custom_idx = 5 + list(self.custom_traits.keys()).index(trait)
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if custom_idx < 15:
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modifications[:, custom_idx] = value
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# Combine current state with desired modifications
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combined_input = torch.cat([current_ocean, modifications], dim=1)
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return combined_input
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def _generate_response_weights(
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self,
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personality_traits: torch.Tensor,
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context_features: torch.Tensor
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) -> torch.Tensor:
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"""Generate weights that influence response generation based on personality."""
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batch_size = personality_traits.shape[0]
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# Extract OCEAN traits
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openness = personality_traits[:, 0]
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conscientiousness = personality_traits[:, 1]
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extraversion = personality_traits[:, 2]
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agreeableness = personality_traits[:, 3]
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neuroticism = personality_traits[:, 4]
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# Generate response weights for different aspects
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weights = torch.zeros(batch_size, 10, device=self.device)
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# Creativity weight (influenced by openness)
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weights[:, 0] = openness * 0.8 + self.custom_traits['creativity'].value * 0.2
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# Formality weight (influenced by conscientiousness)
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weights[:, 1] = conscientiousness * 0.7 + (1 - self.custom_traits['playfulness'].value) * 0.3
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# Social engagement weight (influenced by extraversion)
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weights[:, 2] = extraversion * 0.6 + self.custom_traits['supportiveness'].value * 0.4
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# Empathy weight (influenced by agreeableness)
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weights[:, 3] = agreeableness * 0.5 + self.custom_traits['empathy_level'].value * 0.5
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# Emotional expression weight (influenced by neuroticism and custom traits)
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weights[:, 4] = neuroticism * 0.4 + self.custom_traits['spontaneity'].value * 0.6
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# Humor weight
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weights[:, 5] = self.custom_traits['humor_level'].value
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# Intellectual depth weight
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weights[:, 6] = openness * 0.4 + self.custom_traits['intellectualism'].value * 0.6
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# Assertiveness weight
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weights[:, 7] = (1 - agreeableness) * 0.3 + self.custom_traits['assertiveness'].value * 0.7
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# Curiosity weight
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weights[:, 8] = openness * 0.3 + self.custom_traits['curiosity'].value * 0.7
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# Sarcasm weight
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weights[:, 9] = self.custom_traits['sarcasm_tendency'].value
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return weights
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def _update_ocean_traits(self, evolved_ocean: torch.Tensor):
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"""Update the stored OCEAN traits based on evolution."""
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with torch.no_grad():
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new_traits = evolved_ocean.cpu().numpy()
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# Update with small learning rate to maintain stability
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alpha = 0.05
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self.ocean_traits.openness = (
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(1 - alpha) * self.ocean_traits.openness + alpha * float(new_traits[0])
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)
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self.ocean_traits.conscientiousness = (
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(1 - alpha) * self.ocean_traits.conscientiousness + alpha * float(new_traits[1])
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)
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self.ocean_traits.extraversion = (
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(1 - alpha) * self.ocean_traits.extraversion + alpha * float(new_traits[2])
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)
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self.ocean_traits.agreeableness = (
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(1 - alpha) * self.ocean_traits.agreeableness + alpha * float(new_traits[3])
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)
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self.ocean_traits.neuroticism = (
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(1 - alpha) * self.ocean_traits.neuroticism + alpha * float(new_traits[4])
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)
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# Update Myers-Briggs type based on new OCEAN traits
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self.mb_type = self.mb_analyzer.analyze_type(self.ocean_traits)
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def evolve_from_interaction(
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self,
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interaction_type: str,
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user_feedback: float,
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emotional_context: Dict[str, float],
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user_id: Optional[str] = None,
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conversation_success: float = 0.5
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):
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"""
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Evolve personality based on a specific interaction.
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This is where Lyra learns and adapts her personality from each conversation.
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"""
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logger.info(f"Evolving personality from {interaction_type} interaction "
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f"(feedback: {user_feedback:.2f}, success: {conversation_success:.2f})")
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# Update relationship dynamics if user_id provided
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if user_id:
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self._update_relationship_dynamics(user_id, user_feedback, interaction_type)
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# Evolve OCEAN traits based on interaction outcome
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self._evolve_ocean_from_interaction(user_feedback, emotional_context, interaction_type)
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# Evolve custom traits
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self._evolve_custom_traits(interaction_type, user_feedback, conversation_success)
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# Update self-awareness
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self._update_self_awareness(user_feedback, conversation_success)
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# Record evolution step
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self.evolution.total_interactions += 1
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self.evolution.evolution_history.append({
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'timestamp': datetime.now().isoformat(),
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'interaction_type': interaction_type,
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'user_feedback': user_feedback,
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'conversation_success': conversation_success,
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'ocean_traits': self.ocean_traits.to_dict(),
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'mb_type': self.mb_type.value
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})
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# Keep evolution history manageable
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if len(self.evolution.evolution_history) > 1000:
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self.evolution.evolution_history = self.evolution.evolution_history[-500:]
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def _update_relationship_dynamics(self, user_id: str, feedback: float, interaction_type: str):
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"""Update relationship-specific personality dynamics."""
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if user_id not in self.relationship_dynamics:
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self.relationship_dynamics[user_id] = {
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'familiarity': 0.0,
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'positive_interactions': 0.0,
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'conflict_level': 0.0,
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'interaction_count': 0,
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'personality_adaptation': {}
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}
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rel_data = self.relationship_dynamics[user_id]
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# Update familiarity
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rel_data['familiarity'] = min(1.0, rel_data['familiarity'] + 0.05)
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# Update positive interaction ratio
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rel_data['interaction_count'] += 1
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if feedback > 0.6:
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rel_data['positive_interactions'] = (
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(rel_data['positive_interactions'] * (rel_data['interaction_count'] - 1) + 1.0) /
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rel_data['interaction_count']
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)
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elif feedback < 0.4:
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rel_data['positive_interactions'] = (
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(rel_data['positive_interactions'] * (rel_data['interaction_count'] - 1) + 0.0) /
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rel_data['interaction_count']
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)
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# Update conflict level
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if interaction_type in ['argument', 'disagreement'] or feedback < 0.3:
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rel_data['conflict_level'] = min(1.0, rel_data['conflict_level'] + 0.1)
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else:
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rel_data['conflict_level'] = max(0.0, rel_data['conflict_level'] - 0.02)
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def _evolve_ocean_from_interaction(
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self,
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feedback: float,
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emotional_context: Dict[str, float],
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interaction_type: str
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):
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"""Evolve OCEAN traits based on interaction outcome."""
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# Determine evolution direction based on feedback
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if feedback > 0.7: # Very positive feedback
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# Strengthen traits that led to success
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if interaction_type in ['creative', 'brainstorming']:
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self.ocean_traits.openness = min(1.0, self.ocean_traits.openness + 0.01)
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elif interaction_type in ['support', 'help']:
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self.ocean_traits.agreeableness = min(1.0, self.ocean_traits.agreeableness + 0.01)
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elif interaction_type in ['social', 'casual']:
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self.ocean_traits.extraversion = min(1.0, self.ocean_traits.extraversion + 0.01)
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elif feedback < 0.3: # Negative feedback
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# Adapt traits that might have caused issues
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if 'conflict' in emotional_context or interaction_type == 'argument':
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# Become more agreeable if there was conflict
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self.ocean_traits.agreeableness = min(1.0, self.ocean_traits.agreeableness + 0.02)
|
|
self.ocean_traits.neuroticism = max(0.0, self.ocean_traits.neuroticism - 0.01)
|
|
elif 'confusion' in emotional_context:
|
|
# Be more conscientious if responses were unclear
|
|
self.ocean_traits.conscientiousness = min(1.0, self.ocean_traits.conscientiousness + 0.015)
|
|
|
|
# Emotional context influence
|
|
for emotion, intensity in emotional_context.items():
|
|
if emotion == 'joy' and intensity > 0.7:
|
|
self.ocean_traits.extraversion = min(1.0, self.ocean_traits.extraversion + 0.005)
|
|
elif emotion == 'anxiety' and intensity > 0.6:
|
|
self.ocean_traits.neuroticism = min(1.0, self.ocean_traits.neuroticism + 0.01)
|
|
elif emotion == 'curiosity' and intensity > 0.7:
|
|
self.ocean_traits.openness = min(1.0, self.ocean_traits.openness + 0.005)
|
|
|
|
def _evolve_custom_traits(self, interaction_type: str, feedback: float, success: float):
|
|
"""Evolve custom personality traits."""
|
|
|
|
# Humor evolution
|
|
if interaction_type in ['joke', 'funny', 'casual'] and feedback > 0.6:
|
|
self.custom_traits['humor_level'].evolve(0.1, "successful humor")
|
|
elif feedback < 0.4 and self.custom_traits['humor_level'].value > 0.5:
|
|
self.custom_traits['humor_level'].evolve(-0.05, "humor backfired")
|
|
|
|
# Empathy evolution
|
|
if interaction_type in ['support', 'emotional'] and feedback > 0.7:
|
|
self.custom_traits['empathy_level'].evolve(0.08, "successful emotional support")
|
|
|
|
# Assertiveness evolution
|
|
if interaction_type in ['disagreement', 'debate'] and feedback > 0.6:
|
|
self.custom_traits['assertiveness'].evolve(0.06, "successful assertiveness")
|
|
elif feedback < 0.3 and self.custom_traits['assertiveness'].value > 0.7:
|
|
self.custom_traits['assertiveness'].evolve(-0.08, "assertiveness caused conflict")
|
|
|
|
# Intellectual evolution
|
|
if interaction_type in ['technical', 'academic', 'analytical'] and feedback > 0.6:
|
|
self.custom_traits['intellectualism'].evolve(0.05, "intellectual engagement successful")
|
|
|
|
# Playfulness evolution
|
|
if interaction_type in ['casual', 'fun'] and success > 0.7:
|
|
self.custom_traits['playfulness'].evolve(0.07, "playful interaction successful")
|
|
|
|
# Curiosity evolution - grows when asking questions leads to good conversations
|
|
if feedback > 0.6 and success > 0.6:
|
|
self.custom_traits['curiosity'].evolve(0.03, "curiosity rewarded")
|
|
|
|
def _update_self_awareness(self, feedback: float, success: float):
|
|
"""Update Lyra's awareness of her own personality and its effects."""
|
|
|
|
# Personality insight grows with successful interactions
|
|
if feedback > 0.7 and success > 0.7:
|
|
self.self_awareness['personality_insight'] = min(1.0,
|
|
self.self_awareness['personality_insight'] + 0.01)
|
|
|
|
# Change awareness grows when adaptations lead to better outcomes
|
|
recent_changes = any(
|
|
datetime.now() - trait.last_update < timedelta(hours=1)
|
|
for trait in self.custom_traits.values()
|
|
if trait.last_update
|
|
)
|
|
|
|
if recent_changes and feedback > 0.6:
|
|
self.self_awareness['change_awareness'] = min(1.0,
|
|
self.self_awareness['change_awareness'] + 0.02)
|
|
|
|
# Trait understanding grows with experience
|
|
self.self_awareness['trait_understanding'] = min(1.0,
|
|
self.self_awareness['trait_understanding'] + 0.005)
|
|
|
|
def consciously_modify_trait(self, trait_name: str, target_value: float, reason: str = "self-directed change"):
|
|
"""
|
|
Allow Lyra to consciously modify her own personality traits.
|
|
|
|
This represents Lyra's ability to intentionally change aspects of herself.
|
|
"""
|
|
if not self.enable_self_modification:
|
|
logger.warning("Self-modification is disabled")
|
|
return False
|
|
|
|
# Check if this is a valid trait to modify
|
|
valid_ocean_traits = ['openness', 'conscientiousness', 'extraversion', 'agreeableness', 'neuroticism']
|
|
|
|
if trait_name in valid_ocean_traits:
|
|
current_value = getattr(self.ocean_traits, trait_name)
|
|
change = target_value - current_value
|
|
|
|
# Apply change gradually (max 0.1 change per conscious modification)
|
|
actual_change = np.clip(change, -0.1, 0.1)
|
|
new_value = np.clip(current_value + actual_change, 0.0, 1.0)
|
|
|
|
setattr(self.ocean_traits, trait_name, new_value)
|
|
|
|
logger.info(f"Lyra consciously modified {trait_name}: {current_value:.3f} -> {new_value:.3f} ({reason})")
|
|
return True
|
|
|
|
elif trait_name in self.custom_traits:
|
|
self.custom_traits[trait_name].evolve(target_value - self.custom_traits[trait_name].value, reason)
|
|
logger.info(f"Lyra consciously modified {trait_name} ({reason})")
|
|
return True
|
|
|
|
else:
|
|
logger.warning(f"Unknown trait for modification: {trait_name}")
|
|
return False
|
|
|
|
def get_personality_summary(self) -> Dict[str, Any]:
|
|
"""Get a comprehensive summary of current personality state."""
|
|
return {
|
|
'ocean_traits': self.ocean_traits.to_dict(),
|
|
'myers_briggs_type': self.mb_type.value,
|
|
'custom_traits': {
|
|
name: {
|
|
'value': trait.value,
|
|
'variance': trait.variance,
|
|
'stability': trait.stability,
|
|
'recent_changes': len([
|
|
change for change in trait.change_history
|
|
if datetime.fromtimestamp(change[0]) > datetime.now() - timedelta(hours=24)
|
|
])
|
|
}
|
|
for name, trait in self.custom_traits.items()
|
|
},
|
|
'evolution_stats': {
|
|
'total_interactions': self.evolution.total_interactions,
|
|
'adaptation_rate': self.evolution.adaptation_rate,
|
|
'recent_evolution_count': len([
|
|
ev for ev in self.evolution.evolution_history
|
|
if datetime.fromisoformat(ev['timestamp']) > datetime.now() - timedelta(hours=24)
|
|
])
|
|
},
|
|
'self_awareness': self.self_awareness,
|
|
'relationship_count': len(self.relationship_dynamics),
|
|
'personality_characteristics': self.mb_analyzer.get_type_characteristics(self.mb_type)
|
|
}
|
|
|
|
def save_personality(self, path: Path):
|
|
"""Save personality state to file."""
|
|
state = {
|
|
'ocean_traits': self.ocean_traits.to_dict(),
|
|
'mb_type': self.mb_type.value,
|
|
'custom_traits': {
|
|
name: {
|
|
'value': trait.value,
|
|
'variance': trait.variance,
|
|
'adaptation_rate': trait.adaptation_rate,
|
|
'stability': trait.stability,
|
|
'change_history': trait.change_history[-100:] # Keep recent history
|
|
}
|
|
for name, trait in self.custom_traits.items()
|
|
},
|
|
'evolution': {
|
|
'adaptation_rate': self.evolution.adaptation_rate,
|
|
'stability_factor': self.evolution.stability_factor,
|
|
'total_interactions': self.evolution.total_interactions,
|
|
'evolution_history': self.evolution.evolution_history[-200:] # Keep recent
|
|
},
|
|
'self_awareness': self.self_awareness,
|
|
'relationship_dynamics': {
|
|
k: v for k, v in self.relationship_dynamics.items()
|
|
if v['interaction_count'] > 5 # Only save meaningful relationships
|
|
},
|
|
'model_state': self.state_dict(),
|
|
'timestamp': datetime.now().isoformat()
|
|
}
|
|
|
|
with open(path, 'w') as f:
|
|
json.dump(state, f, indent=2, default=str)
|
|
|
|
logger.info(f"Personality saved to {path}")
|
|
|
|
def load_personality(self, path: Path):
|
|
"""Load personality state from file."""
|
|
if not path.exists():
|
|
logger.warning(f"Personality file not found: {path}")
|
|
return
|
|
|
|
try:
|
|
with open(path, 'r') as f:
|
|
state = json.load(f)
|
|
|
|
# Restore OCEAN traits
|
|
self.ocean_traits = OCEANTraits.from_dict(state['ocean_traits'])
|
|
|
|
# Restore Myers-Briggs type
|
|
self.mb_type = MyersBriggsType(state['mb_type'])
|
|
|
|
# Restore custom traits
|
|
for name, trait_data in state['custom_traits'].items():
|
|
if name in self.custom_traits:
|
|
trait = self.custom_traits[name]
|
|
trait.value = trait_data['value']
|
|
trait.variance = trait_data.get('variance', 0.1)
|
|
trait.adaptation_rate = trait_data.get('adaptation_rate', 0.01)
|
|
trait.stability = trait_data.get('stability', 0.8)
|
|
trait.change_history = trait_data.get('change_history', [])
|
|
|
|
# Restore evolution data
|
|
evolution_data = state.get('evolution', {})
|
|
self.evolution.adaptation_rate = evolution_data.get('adaptation_rate', 0.01)
|
|
self.evolution.stability_factor = evolution_data.get('stability_factor', 0.9)
|
|
self.evolution.total_interactions = evolution_data.get('total_interactions', 0)
|
|
self.evolution.evolution_history = evolution_data.get('evolution_history', [])
|
|
|
|
# Restore self-awareness
|
|
self.self_awareness = state.get('self_awareness', self.self_awareness)
|
|
|
|
# Restore relationship dynamics
|
|
self.relationship_dynamics = state.get('relationship_dynamics', {})
|
|
|
|
# Restore model state
|
|
if 'model_state' in state:
|
|
self.load_state_dict(state['model_state'])
|
|
|
|
logger.info(f"Personality loaded from {path}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to load personality: {e}")
|
|
|
|
def simulate_personality_development(self, days: int = 30) -> Dict[str, Any]:
|
|
"""
|
|
Simulate personality development over time for testing/analysis.
|
|
|
|
This shows how Lyra's personality might evolve with different interaction patterns.
|
|
"""
|
|
simulation_log = []
|
|
|
|
for day in range(days):
|
|
# Simulate different types of interactions
|
|
daily_interactions = np.random.randint(5, 20)
|
|
|
|
for _ in range(daily_interactions):
|
|
# Random interaction types
|
|
interaction_types = ['casual', 'support', 'creative', 'technical', 'social', 'funny']
|
|
interaction_type = np.random.choice(interaction_types)
|
|
|
|
# Random feedback (biased slightly positive)
|
|
feedback = np.random.beta(2, 1) # Skewed toward positive
|
|
|
|
# Random emotional context
|
|
emotions = ['joy', 'curiosity', 'calm', 'excitement', 'concern']
|
|
emotional_context = {
|
|
np.random.choice(emotions): np.random.random()
|
|
}
|
|
|
|
# Evolve personality
|
|
self.evolve_from_interaction(
|
|
interaction_type=interaction_type,
|
|
user_feedback=feedback,
|
|
emotional_context=emotional_context,
|
|
conversation_success=feedback * 0.8 + np.random.random() * 0.2
|
|
)
|
|
|
|
# Log daily state
|
|
daily_summary = {
|
|
'day': day,
|
|
'ocean_traits': self.ocean_traits.to_dict(),
|
|
'total_interactions': self.evolution.total_interactions,
|
|
'mb_type': self.mb_type.value
|
|
}
|
|
simulation_log.append(daily_summary)
|
|
|
|
return {
|
|
'simulation_days': days,
|
|
'final_personality': self.get_personality_summary(),
|
|
'development_log': simulation_log
|
|
} |