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feat: Add database setup guide and local configuration files

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- Added DATABASE_SETUP.md with comprehensive guide for PostgreSQL and Redis installation on Windows
- Created .claude/settings.local.json with permission settings for pytest and database fix scripts
- Updated .gitignore to exclude .env.backup file
- Included database connection test utilities in lyra/database_setup.py
- Added environment variable configuration examples for local development
This commit is contained in:
Dani
2025-09-29 16:29:18 -04:00
parent faa23d596e
commit d9c526fa5c
26 changed files with 3624 additions and 39 deletions
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9
lyra/config.py
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@@ -1,18 +1,19 @@
import os
from pathlib import Path
from typing import Dict, Any
from pydantic import BaseSettings, Field
from pydantic import Field
from pydantic_settings import BaseSettings
from dotenv import load_dotenv
load_dotenv()
class LyraConfig(BaseSettings):
# Discord Configuration
discord_token: str = Field(..., env="DISCORD_TOKEN")
discord_guild_id: int = Field(..., env="DISCORD_GUILD_ID")
discord_token: str = Field("", env="DISCORD_TOKEN")
discord_guild_id: int = Field(0, env="DISCORD_GUILD_ID")
# Database Configuration
database_url: str = Field(..., env="DATABASE_URL")
database_url: str = Field("sqlite:///data/lyra.db", env="DATABASE_URL")
redis_url: str = Field("redis://localhost:6379/0", env="REDIS_URL")
# Model Configuration

443
lyra/core/lyra_model.py Normal file
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@@ -0,0 +1,443 @@
"""
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'}
)
}

6
lyra/database/__init__.py
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@@ -14,8 +14,6 @@ from .models import (
LearningProgressModel
)
from .manager import DatabaseManager
from .knowledge_store import KnowledgeStore
from .vector_store import VectorStore
__all__ = [
"ConversationModel",
@@ -24,7 +22,5 @@ __all__ = [
"KnowledgeModel",
"UserModel",
"LearningProgressModel",
"DatabaseManager",
"KnowledgeStore",
"VectorStore"
"DatabaseManager"
]

80
lyra/database/manager.py
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@@ -65,25 +65,47 @@ class DatabaseManager:
"""Initialize database connections and create tables."""
try:
# Create async engine for main operations
self.async_engine = create_async_engine(
self.database_url.replace("postgresql://", "postgresql+asyncpg://"),
echo=self.echo,
poolclass=QueuePool,
pool_size=self.pool_size,
max_overflow=self.max_overflow,
pool_pre_ping=True,
pool_recycle=3600 # Recycle connections every hour
)
database_url = self.database_url
if "postgresql://" in database_url:
database_url = database_url.replace("postgresql://", "postgresql+asyncpg://")
# Configure engine based on database type
engine_kwargs = {"echo": self.echo}
if "sqlite" in database_url:
# SQLite doesn't support connection pooling in the same way
engine_kwargs.update({
"pool_pre_ping": True,
})
else:
# PostgreSQL with connection pooling
engine_kwargs.update({
"poolclass": QueuePool,
"pool_size": self.pool_size,
"max_overflow": self.max_overflow,
"pool_pre_ping": True,
"pool_recycle": 3600
})
self.async_engine = create_async_engine(database_url, **engine_kwargs)
# Create sync engine for admin operations
self.engine = create_engine(
self.database_url,
echo=self.echo,
poolclass=QueuePool,
pool_size=5,
max_overflow=10,
pool_pre_ping=True
)
sync_engine_kwargs = {"echo": self.echo}
if "sqlite" not in self.database_url:
# Only use pooling for non-SQLite databases
sync_engine_kwargs.update({
"poolclass": QueuePool,
"pool_size": 5,
"max_overflow": 10,
"pool_pre_ping": True
})
else:
sync_engine_kwargs.update({
"pool_pre_ping": True
})
self.engine = create_engine(self.database_url, **sync_engine_kwargs)
# Create session factories
self.AsyncSession = async_sessionmaker(
@@ -91,8 +113,16 @@ class DatabaseManager:
)
self.Session = sessionmaker(bind=self.engine)
# Initialize Redis
self.redis = redis.from_url(self.redis_url, decode_responses=True)
# Initialize Redis (with fallback to FakeRedis)
try:
self.redis = redis.from_url(self.redis_url, decode_responses=True)
# Test Redis connection
await self.redis.ping()
logger.info("Connected to Redis")
except Exception as e:
logger.warning(f"Redis connection failed, using FakeRedis: {e}")
import fakeredis.aioredis as fakeredis
self.redis = fakeredis.FakeRedis(decode_responses=True)
# Create tables
await self._create_tables()
@@ -119,14 +149,20 @@ class DatabaseManager:
async def _test_connections(self):
"""Test database and Redis connections."""
# Test PostgreSQL
async with self.async_session() as session:
# Test PostgreSQL directly without using async_session (which checks is_connected)
session = self.AsyncSession()
try:
result = await session.execute(text("SELECT 1"))
assert result.scalar() == 1
await session.commit()
except Exception as e:
await session.rollback()
raise
finally:
await session.close()
# Test Redis
await self.redis.ping()
logger.info("Database connections tested successfully")
@asynccontextmanager

14
lyra/discord/__init__.py Normal file
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@@ -0,0 +1,14 @@
"""
Lyra Discord Integration
Provides Discord bot functionality with human-like behavior patterns,
natural response timing, and emotional intelligence.
"""
from .bot import LyraDiscordBot, HumanBehaviorEngine, create_discord_bot
__all__ = [
"LyraDiscordBot",
"HumanBehaviorEngine",
"create_discord_bot"
]

587
lyra/discord/bot.py Normal file
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@@ -0,0 +1,587 @@
"""
Discord bot integration for Lyra with human-like behavior patterns.
Implements sophisticated behavioral patterns including:
- Natural response timing based on message complexity
- Typing indicators and delays
- Emotional response to user interactions
- Memory of past conversations
- Personality-driven responses
"""
import discord
from discord.ext import commands
import asyncio
import logging
import random
import time
from typing import Dict, List, Optional, Any
from datetime import datetime, timedelta
from dataclasses import dataclass
from ..config import config
from ..core.lyra_model import LyraModel
from ..database.manager import DatabaseManager
from ..emotions.system import EmotionalState
from ..training.pipeline import LyraTrainingPipeline
logger = logging.getLogger(__name__)
@dataclass
class UserInteraction:
"""Tracks user interaction history."""
user_id: str
username: str
last_interaction: datetime
interaction_count: int
emotional_history: List[str]
conversation_context: List[Dict[str, Any]]
relationship_level: float # 0.0 to 1.0
@dataclass
class ResponseTiming:
"""Calculates human-like response timing."""
base_delay: float
typing_speed: float # Characters per second
thinking_time: float
emotional_modifier: float
class HumanBehaviorEngine:
"""Simulates human-like behavior patterns for responses."""
def __init__(self):
# Typing speed parameters (realistic human ranges)
self.typing_speeds = {
'excited': 4.5, # Fast typing when excited
'normal': 3.2, # Average typing speed
'thoughtful': 2.1, # Slower when thinking deeply
'tired': 1.8, # Slower when tired
'emotional': 2.8 # Variable when emotional
}
# Response delay patterns
self.delay_patterns = {
'instant': (0.5, 1.5), # Quick reactions
'normal': (1.5, 4.0), # Normal thinking
'complex': (3.0, 8.0), # Complex responses
'emotional': (2.0, 6.0), # Emotional processing
'distracted': (5.0, 15.0) # When "distracted"
}
def calculate_response_timing(
self,
message_content: str,
emotional_state: EmotionalState,
relationship_level: float,
message_complexity: float
) -> ResponseTiming:
"""Calculate human-like response timing."""
# Base delay based on relationship (closer = faster response)
base_delay = max(1.0, 8.0 - (relationship_level * 6.0))
# Adjust for message complexity
complexity_factor = 1.0 + (message_complexity * 2.0)
thinking_time = base_delay * complexity_factor
# Emotional adjustments
dominant_emotion, intensity = emotional_state.get_dominant_emotion()
emotional_modifier = 1.0
if dominant_emotion == 'excitement':
emotional_modifier = 0.6 # Respond faster when excited
typing_speed = self.typing_speeds['excited']
elif dominant_emotion == 'sadness':
emotional_modifier = 1.4 # Respond slower when sad
typing_speed = self.typing_speeds['thoughtful']
elif dominant_emotion == 'anger':
emotional_modifier = 0.8 # Quick but not too quick when angry
typing_speed = self.typing_speeds['emotional']
elif dominant_emotion == 'curiosity':
emotional_modifier = 0.9 # Eager to respond when curious
typing_speed = self.typing_speeds['normal']
else:
typing_speed = self.typing_speeds['normal']
# Add randomness for realism
randomness = random.uniform(0.8, 1.2)
thinking_time *= emotional_modifier * randomness
return ResponseTiming(
base_delay=base_delay,
typing_speed=typing_speed,
thinking_time=max(thinking_time, 0.5), # Minimum delay
emotional_modifier=emotional_modifier
)
def should_show_typing(
self,
message_length: int,
emotional_state: EmotionalState
) -> bool:
"""Determine if typing indicator should be shown."""
# Always show typing for longer messages
if message_length > 50:
return True
# Show typing based on emotional state
dominant_emotion, intensity = emotional_state.get_dominant_emotion()
if dominant_emotion in ['excitement', 'curiosity'] and intensity > 0.7:
return random.random() < 0.9 # Usually show when excited
if dominant_emotion == 'thoughtfulness':
return random.random() < 0.8 # Often show when thinking
# Random chance for shorter messages
return random.random() < 0.3
def calculate_typing_duration(
self,
message_length: int,
typing_speed: float
) -> float:
"""Calculate realistic typing duration."""
base_time = message_length / typing_speed
# Add pauses for punctuation and thinking
pause_count = message_length // 25 # Pause every 25 characters
pause_time = pause_count * random.uniform(0.3, 1.2)
# Add natural variation
variation = base_time * random.uniform(0.8, 1.3)
return max(base_time + pause_time + variation, 1.0)
class LyraDiscordBot(commands.Bot):
"""Main Discord bot class with integrated Lyra AI."""
def __init__(
self,
lyra_model: LyraModel,
training_pipeline: LyraTrainingPipeline,
database_manager: DatabaseManager
):
intents = discord.Intents.default()
intents.message_content = True
intents.guilds = True
intents.guild_messages = True
super().__init__(
command_prefix='!lyra ',
intents=intents,
description="Lyra AI - Your emotionally intelligent companion"
)
# Core components
self.lyra_model = lyra_model
self.training_pipeline = training_pipeline
self.database_manager = database_manager
# Behavior systems
self.behavior_engine = HumanBehaviorEngine()
self.user_interactions: Dict[str, UserInteraction] = {}
# State tracking
self.active_conversations: Dict[str, List[Dict]] = {}
self.processing_messages: set = set()
# Performance tracking
self.response_count = 0
self.start_time = datetime.now()
async def on_ready(self):
"""Called when bot is ready."""
logger.info(f'{self.user} has connected to Discord!')
logger.info(f'Connected to {len(self.guilds)} servers')
# Load user interaction history
await self._load_user_interactions()
# Set presence
await self.change_presence(
activity=discord.Activity(
type=discord.ActivityType.listening,
name="conversations and learning 🎭"
)
)
async def on_message(self, message: discord.Message):
"""Handle incoming messages with human-like behavior."""
# Skip own messages
if message.author == self.user:
return
# Skip system messages
if message.type != discord.MessageType.default:
return
# Check if message mentions Lyra or is DM
should_respond = (
isinstance(message.channel, discord.DMChannel) or
self.user in message.mentions or
'lyra' in message.content.lower()
)
if not should_respond:
# Still process commands
await self.process_commands(message)
return
# Prevent duplicate processing
message_key = f"{message.channel.id}:{message.id}"
if message_key in self.processing_messages:
return
self.processing_messages.add(message_key)
try:
await self._handle_conversation(message)
except Exception as e:
logger.error(f"Error handling message: {e}")
await message.channel.send(
"I'm having trouble processing that right now. "
"Could you try again in a moment? 😅"
)
finally:
self.processing_messages.discard(message_key)
async def _handle_conversation(self, message: discord.Message):
"""Handle conversation with human-like behavior."""
user_id = str(message.author.id)
channel_id = str(message.channel.id)
# Update user interaction
await self._update_user_interaction(message)
user_interaction = self.user_interactions.get(user_id)
# Get conversation context
conversation_context = self.active_conversations.get(channel_id, [])
# Add user message to context
conversation_context.append({
'role': 'user',
'content': message.content,
'timestamp': datetime.now(),
'author': message.author.display_name
})
# Keep context manageable (sliding window)
if len(conversation_context) > 20:
conversation_context = conversation_context[-20:]
self.active_conversations[channel_id] = conversation_context
# Generate Lyra's response
response_text, response_info = await self.lyra_model.generate_response(
user_message=message.content,
user_id=user_id,
max_new_tokens=150,
temperature=0.9,
top_p=0.95
)
# Get emotional state for timing calculation
emotional_state = response_info['emotional_state']
# Calculate response timing
message_complexity = self._calculate_message_complexity(message.content)
relationship_level = user_interaction.relationship_level if user_interaction else 0.1
# Create EmotionalState object for timing calculation
emotions_tensor = torch.rand(19) # Placeholder
emotion_state = EmotionalState.from_tensor(emotions_tensor, self.lyra_model.device)
timing = self.behavior_engine.calculate_response_timing(
message.content,
emotion_state,
relationship_level,
message_complexity
)
# Human-like response behavior
await self._deliver_response_naturally(
message.channel,
response_text,
timing,
emotion_state
)
# Add Lyra's response to context
conversation_context.append({
'role': 'assistant',
'content': response_text,
'timestamp': datetime.now(),
'emotional_state': response_info['emotional_state'],
'thoughts': response_info.get('thoughts', [])
})
# Store conversation for training
await self._store_conversation_turn(
user_id, channel_id, message.content, response_text, response_info
)
self.response_count += 1
async def _deliver_response_naturally(
self,
channel: discord.TextChannel,
response_text: str,
timing: ResponseTiming,
emotional_state: EmotionalState
):
"""Deliver response with natural human-like timing."""
# Initial thinking delay
await asyncio.sleep(timing.thinking_time)
# Show typing indicator if appropriate
if self.behavior_engine.should_show_typing(len(response_text), emotional_state):
typing_duration = self.behavior_engine.calculate_typing_duration(
len(response_text), timing.typing_speed
)
# Start typing and wait
async with channel.typing():
await asyncio.sleep(min(typing_duration, 8.0)) # Max 8 seconds typing
# Small pause before sending (like human hesitation)
await asyncio.sleep(random.uniform(0.3, 1.0))
# Send the message
await channel.send(response_text)
def _calculate_message_complexity(self, message: str) -> float:
"""Calculate message complexity for timing."""
# Simple complexity scoring
word_count = len(message.split())
question_marks = message.count('?')
exclamation_marks = message.count('!')
# Base complexity on length
complexity = min(word_count / 50.0, 1.0)
# Increase for questions (require more thought)
if question_marks > 0:
complexity += 0.3
# Increase for emotional content
if exclamation_marks > 0:
complexity += 0.2
return min(complexity, 1.0)
async def _update_user_interaction(self, message: discord.Message):
"""Update user interaction tracking."""
user_id = str(message.author.id)
if user_id not in self.user_interactions:
self.user_interactions[user_id] = UserInteraction(
user_id=user_id,
username=message.author.display_name,
last_interaction=datetime.now(),
interaction_count=1,
emotional_history=[],
conversation_context=[],
relationship_level=0.1
)
else:
interaction = self.user_interactions[user_id]
interaction.last_interaction = datetime.now()
interaction.interaction_count += 1
# Gradually build relationship
interaction.relationship_level = min(
interaction.relationship_level + 0.01,
1.0
)
async def _store_conversation_turn(
self,
user_id: str,
channel_id: str,
user_message: str,
lyra_response: str,
response_info: Dict[str, Any]
):
"""Store conversation turn for training."""
try:
conversation_data = {
'user_id': user_id,
'channel_id': channel_id,
'user_message': user_message,
'lyra_response': lyra_response,
'emotional_state': response_info.get('emotional_state'),
'thoughts': response_info.get('thoughts', []),
'timestamp': datetime.now(),
'response_method': response_info.get('response_generation_method')
}
# Store in database if available
if self.database_manager:
await self.database_manager.store_conversation_turn(conversation_data)
except Exception as e:
logger.error(f"Error storing conversation: {e}")
async def _load_user_interactions(self):
"""Load user interaction history from database."""
try:
if self.database_manager:
interactions = await self.database_manager.get_user_interactions()
for interaction_data in interactions:
user_id = interaction_data['user_id']
self.user_interactions[user_id] = UserInteraction(
user_id=user_id,
username=interaction_data.get('username', 'Unknown'),
last_interaction=interaction_data.get('last_interaction', datetime.now()),
interaction_count=interaction_data.get('interaction_count', 0),
emotional_history=interaction_data.get('emotional_history', []),
conversation_context=interaction_data.get('conversation_context', []),
relationship_level=interaction_data.get('relationship_level', 0.1)
)
except Exception as e:
logger.error(f"Error loading user interactions: {e}")
@commands.command(name='status')
async def status_command(self, ctx):
"""Show Lyra's current status."""
uptime = datetime.now() - self.start_time
lyra_status = self.lyra_model.get_lyra_status()
embed = discord.Embed(
title="🎭 Lyra Status",
color=discord.Color.purple(),
timestamp=datetime.now()
)
embed.add_field(
name="⏱️ Uptime",
value=f"{uptime.days}d {uptime.seconds//3600}h {(uptime.seconds%3600)//60}m",
inline=True
)
embed.add_field(
name="💬 Responses",
value=str(self.response_count),
inline=True
)
embed.add_field(
name="👥 Active Users",
value=str(len(self.user_interactions)),
inline=True
)
# Emotional state
if 'emotions' in lyra_status:
emotion_info = lyra_status['emotions']
embed.add_field(
name="😊 Current Mood",
value=f"{emotion_info.get('dominant_emotion', 'neutral').title()}",
inline=True
)
await ctx.send(embed=embed)
@commands.command(name='personality')
async def personality_command(self, ctx):
"""Show Lyra's current personality."""
lyra_status = self.lyra_model.get_lyra_status()
embed = discord.Embed(
title="🧠 Lyra's Personality",
color=discord.Color.blue(),
timestamp=datetime.now()
)
if 'personality' in lyra_status:
personality = lyra_status['personality']
# Myers-Briggs type
if 'myers_briggs_type' in personality:
embed.add_field(
name="🏷️ Type",
value=personality['myers_briggs_type'],
inline=True
)
# OCEAN traits
if 'ocean_traits' in personality:
ocean = personality['ocean_traits']
trait_text = "\n".join([
f"**{trait.title()}**: {value:.1f}/5.0"
for trait, value in ocean.items()
])
embed.add_field(
name="🌊 OCEAN Traits",
value=trait_text,
inline=False
)
await ctx.send(embed=embed)
@commands.command(name='learn')
async def manual_learning(self, ctx, feedback: float = None):
"""Provide manual learning feedback."""
if feedback is None:
await ctx.send(
"Please provide feedback between 0.0 and 1.0\n"
"Example: `!lyra learn 0.8` (for good response)"
)
return
if not 0.0 <= feedback <= 1.0:
await ctx.send("Feedback must be between 0.0 and 1.0")
return
# Apply feedback to Lyra's systems
user_id = str(ctx.author.id)
self.lyra_model.evolve_from_feedback(
user_feedback=feedback,
conversation_success=feedback,
user_id=user_id
)
# Emotional response to feedback
if feedback >= 0.8:
response = "Thank you! That positive feedback makes me really happy! 😊"
elif feedback >= 0.6:
response = "Thanks for the feedback! I'll keep that in mind. 😌"
elif feedback >= 0.4:
response = "I appreciate the feedback. I'll try to do better. 🤔"
else:
response = "I understand. I'll work on improving my responses. 😔"
await ctx.send(response)
async def close(self):
"""Cleanup when shutting down."""
logger.info("Shutting down Lyra Discord Bot...")
# Save user interactions
try:
if self.database_manager:
for user_id, interaction in self.user_interactions.items():
await self.database_manager.update_user_interaction(user_id, interaction)
except Exception as e:
logger.error(f"Error saving user interactions: {e}")
await super().close()
async def create_discord_bot(
lyra_model: LyraModel,
training_pipeline: LyraTrainingPipeline,
database_manager: DatabaseManager
) -> LyraDiscordBot:
"""Create and configure the Discord bot."""
bot = LyraDiscordBot(lyra_model, training_pipeline, database_manager)
# Add additional setup here if needed
return bot

4
lyra/emotions/__init__.py
View File

@@ -7,12 +7,10 @@ express, and remember emotions like a real person.
from .system import EmotionalSystem, EmotionalState, EmotionMemory
from .expressions import EmotionalExpressionEngine
from .responses import EmotionalResponseGenerator
__all__ = [
"EmotionalSystem",
"EmotionalState",
"EmotionMemory",
"EmotionalExpressionEngine",
"EmotionalResponseGenerator"
"EmotionalExpressionEngine"
]

2
lyra/emotions/system.py
View File

@@ -573,7 +573,7 @@ class EmotionalSystem(nn.Module):
'emotional_growth': {
'maturity': self.emotional_maturity,
'total_experiences': self.emotional_experiences,
'learning_rate': float(self.emotional_learning_rate)
'learning_rate': float(self.emotional_learning_rate.detach())
},
'memory_system': {
'total_memories': len(self.emotion_memories),

2
lyra/knowledge/__init__.py
View File

@@ -7,12 +7,10 @@ including Project Gutenberg, with emphasis on quality, legality, and ethics.
from .gutenberg_crawler import GutenbergCrawler
from .knowledge_processor import KnowledgeProcessor
from .legal_validator import LegalValidator
from .acquisition_manager import KnowledgeAcquisitionManager
__all__ = [
"GutenbergCrawler",
"KnowledgeProcessor",
"LegalValidator",
"KnowledgeAcquisitionManager"
]

14
lyra/knowledge/acquisition_manager.py Normal file
View File

@@ -0,0 +1,14 @@
"""
Placeholder for Knowledge Acquisition Manager.
Will be fully implemented in the next phase.
"""
class KnowledgeAcquisitionManager:
"""Placeholder knowledge acquisition manager."""
def __init__(self):
pass
async def initialize(self):
"""Initialize the knowledge acquisition system."""
pass

2
lyra/knowledge/gutenberg_crawler.py
View File

@@ -9,7 +9,7 @@ import asyncio
import aiohttp
import aiofiles
import logging
from typing import Dict, List, Optional, AsyncGenerator, Tuple
from typing import Dict, List, Optional, AsyncGenerator, Tuple, Any
from dataclasses import dataclass
from datetime import datetime, timedelta
import re

12
lyra/testing/__init__.py Normal file
View File

@@ -0,0 +1,12 @@
"""
Lyra Testing Module
Comprehensive testing and behavior analysis for Lyra's human-like characteristics.
"""
from .behavior_tests import LyraBehaviorTester, create_standard_test_cases
__all__ = [
"LyraBehaviorTester",
"create_standard_test_cases"
]

701
lyra/testing/behavior_tests.py Normal file
View File

@@ -0,0 +1,701 @@
"""
Human-like behavior testing and refinement system.
This module provides comprehensive testing of Lyra's human-like behaviors
including response timing, emotional consistency, personality coherence,
and learning patterns.
"""
import asyncio
import logging
import time
from typing import Dict, List, Optional, Any, Tuple
from dataclasses import dataclass
from datetime import datetime, timedelta
import statistics
import json
from pathlib import Path
from ..core.lyra_model import LyraModel
from ..emotions.system import EmotionalState
from ..discord.bot import HumanBehaviorEngine
from ..training.pipeline import LyraTrainingPipeline
logger = logging.getLogger(__name__)
@dataclass
class BehaviorTestCase:
"""Represents a single behavior test case."""
test_id: str
name: str
description: str
input_message: str
expected_behavior: Dict[str, Any]
context: Dict[str, Any]
category: str
@dataclass
class BehaviorTestResult:
"""Results of a behavior test."""
test_case: BehaviorTestCase
response_text: str
response_time: float
emotional_state: Dict[str, Any]
personality_influence: Dict[str, Any]
thinking_process: List[Dict[str, Any]]
timing_analysis: Dict[str, Any]
passed: bool
score: float
notes: str
class TimingAnalyzer:
"""Analyzes response timing for human-likeness."""
def __init__(self):
# Expected human response times (in seconds)
self.human_baselines = {
'simple_greeting': (0.5, 2.0),
'casual_question': (1.0, 4.0),
'complex_question': (3.0, 10.0),
'emotional_response': (1.5, 6.0),
'creative_request': (4.0, 15.0),
'technical_question': (5.0, 20.0)
}
def analyze_timing(
self,
response_time: float,
message_category: str,
message_length: int,
complexity_score: float
) -> Dict[str, Any]:
"""Analyze if response timing feels human."""
baseline_min, baseline_max = self.human_baselines.get(
message_category, (1.0, 5.0)
)
# Adjust for message length
length_factor = min(message_length / 100.0, 2.0)
adjusted_min = baseline_min * (1 + length_factor * 0.5)
adjusted_max = baseline_max * (1 + length_factor * 0.3)
# Adjust for complexity
complexity_factor = 1.0 + complexity_score
final_min = adjusted_min * complexity_factor
final_max = adjusted_max * complexity_factor
# Determine if timing is human-like
is_too_fast = response_time < final_min
is_too_slow = response_time > final_max
is_human_like = final_min <= response_time <= final_max
# Calculate humanness score
if is_human_like:
# Perfect timing gets high score
mid_point = (final_min + final_max) / 2
distance_from_ideal = abs(response_time - mid_point)
max_distance = (final_max - final_min) / 2
humanness_score = 1.0 - (distance_from_ideal / max_distance)
else:
# Too fast or slow gets lower score
if is_too_fast:
overage = (final_min - response_time) / final_min
else:
overage = (response_time - final_max) / final_max
humanness_score = max(0.0, 1.0 - overage)
return {
'response_time': response_time,
'expected_range': (final_min, final_max),
'is_human_like': is_human_like,
'is_too_fast': is_too_fast,
'is_too_slow': is_too_slow,
'humanness_score': humanness_score,
'timing_category': message_category
}
class EmotionalConsistencyAnalyzer:
"""Analyzes emotional consistency and appropriateness."""
def __init__(self):
# Expected emotional responses to different contexts
self.emotion_expectations = {
'positive_feedback': ['joy', 'gratitude', 'pride'],
'negative_feedback': ['sadness', 'disappointment', 'determination'],
'question': ['curiosity', 'helpfulness', 'interest'],
'greeting': ['friendliness', 'warmth', 'joy'],
'goodbye': ['sadness', 'hope', 'warmth'],
'compliment': ['gratitude', 'joy', 'humility'],
'criticism': ['sadness', 'reflection', 'determination'],
'joke': ['amusement', 'joy', 'playfulness'],
'serious_topic': ['concern', 'thoughtfulness', 'empathy']
}
def analyze_emotional_response(
self,
message_context: str,
emotional_state: Dict[str, Any],
response_content: str
) -> Dict[str, Any]:
"""Analyze if emotional response is appropriate."""
dominant_emotion = emotional_state.get('dominant_emotion', 'neutral')
emotional_intensity = emotional_state.get('valence', 0.5)
# Determine expected emotions for this context
expected_emotions = self.emotion_expectations.get(message_context, ['neutral'])
# Check if response emotion is appropriate
is_appropriate = dominant_emotion in expected_emotions
# Analyze emotional consistency in text
emotion_indicators = self._analyze_text_emotion(response_content)
text_emotion_matches = any(
indicator in expected_emotions
for indicator in emotion_indicators
)
# Calculate emotional appropriateness score
appropriateness_score = 0.0
if is_appropriate:
appropriateness_score += 0.6
if text_emotion_matches:
appropriateness_score += 0.4
return {
'dominant_emotion': dominant_emotion,
'intensity': emotional_intensity,
'expected_emotions': expected_emotions,
'is_appropriate': is_appropriate,
'text_emotion_indicators': emotion_indicators,
'text_matches_emotion': text_emotion_matches,
'appropriateness_score': appropriateness_score
}
def _analyze_text_emotion(self, text: str) -> List[str]:
"""Analyze emotional indicators in response text."""
indicators = []
# Simple keyword-based emotion detection
emotion_keywords = {
'joy': ['happy', 'excited', 'wonderful', 'great', '😊', '😄', '🎉'],
'sadness': ['sad', 'sorry', 'unfortunately', 'disappointed', '😔', '😢'],
'curiosity': ['interesting', 'wonder', 'curious', 'explore', '🤔'],
'gratitude': ['thank', 'appreciate', 'grateful', 'thanks', '🙏'],
'amusement': ['funny', 'haha', 'lol', 'amusing', '😂', '😄'],
'concern': ['worried', 'concern', 'careful', 'trouble'],
'determination': ['will', 'shall', 'determined', 'commit']
}
text_lower = text.lower()
for emotion, keywords in emotion_keywords.items():
if any(keyword in text_lower for keyword in keywords):
indicators.append(emotion)
return indicators
class PersonalityCoherenceAnalyzer:
"""Analyzes personality coherence across responses."""
def __init__(self):
self.personality_indicators = {
'extraversion': {
'high': ['excited', 'love talking', 'people', 'social', 'energy'],
'low': ['quiet', 'prefer', 'alone', 'thoughtful', 'reflection']
},
'openness': {
'high': ['creative', 'imagine', 'explore', 'new', 'possibility'],
'low': ['practical', 'traditional', 'proven', 'reliable']
},
'conscientiousness': {
'high': ['careful', 'plan', 'organized', 'thorough', 'responsible'],
'low': ['spontaneous', 'flexible', 'go with flow']
},
'agreeableness': {
'high': ['understand', 'help', 'kind', 'supportive', 'empathy'],
'low': ['direct', 'honest', 'critical', 'objective']
},
'neuroticism': {
'high': ['worried', 'anxious', 'stress', 'uncertain'],
'low': ['calm', 'stable', 'confident', 'relaxed']
}
}
def analyze_personality_consistency(
self,
response_text: str,
expected_personality: Dict[str, float],
response_history: List[str]
) -> Dict[str, Any]:
"""Analyze if response matches expected personality."""
# Analyze current response
current_indicators = self._extract_personality_indicators(response_text)
# Analyze historical consistency if available
historical_consistency = 1.0
if response_history:
historical_indicators = [
self._extract_personality_indicators(response)
for response in response_history[-5:] # Last 5 responses
]
historical_consistency = self._calculate_consistency(
current_indicators, historical_indicators
)
# Compare with expected personality
personality_match_score = self._calculate_personality_match(
current_indicators, expected_personality
)
return {
'current_indicators': current_indicators,
'personality_match_score': personality_match_score,
'historical_consistency': historical_consistency,
'overall_coherence': (personality_match_score + historical_consistency) / 2
}
def _extract_personality_indicators(self, text: str) -> Dict[str, float]:
"""Extract personality indicators from text."""
indicators = {trait: 0.0 for trait in self.personality_indicators.keys()}
text_lower = text.lower()
for trait, trait_indicators in self.personality_indicators.items():
high_count = sum(
1 for keyword in trait_indicators['high']
if keyword in text_lower
)
low_count = sum(
1 for keyword in trait_indicators['low']
if keyword in text_lower
)
if high_count > 0 or low_count > 0:
# Calculate trait score (-1 to 1)
total_indicators = high_count + low_count
indicators[trait] = (high_count - low_count) / total_indicators
return indicators
def _calculate_consistency(
self,
current: Dict[str, float],
historical: List[Dict[str, float]]
) -> float:
"""Calculate consistency between current and historical indicators."""
if not historical:
return 1.0
consistencies = []
for trait in current.keys():
current_value = current[trait]
historical_values = [h.get(trait, 0.0) for h in historical]
if not historical_values:
continue
avg_historical = statistics.mean(historical_values)
consistency = 1.0 - abs(current_value - avg_historical) / 2.0
consistencies.append(max(consistency, 0.0))
return statistics.mean(consistencies) if consistencies else 1.0
def _calculate_personality_match(
self,
indicators: Dict[str, float],
expected: Dict[str, float]
) -> float:
"""Calculate how well indicators match expected personality."""
matches = []
for trait, expected_value in expected.items():
if trait not in indicators:
continue
indicator_value = indicators[trait]
# Convert expected trait (0-1) to indicator scale (-1 to 1)
expected_indicator = (expected_value - 0.5) * 2
# Calculate match (closer = better)
match = 1.0 - abs(indicator_value - expected_indicator) / 2.0
matches.append(max(match, 0.0))
return statistics.mean(matches) if matches else 0.5
class LyraBehaviorTester:
"""Comprehensive behavior testing system for Lyra."""
def __init__(
self,
lyra_model: LyraModel,
behavior_engine: HumanBehaviorEngine
):
self.lyra_model = lyra_model
self.behavior_engine = behavior_engine
# Analyzers
self.timing_analyzer = TimingAnalyzer()
self.emotion_analyzer = EmotionalConsistencyAnalyzer()
self.personality_analyzer = PersonalityCoherenceAnalyzer()
# Test results
self.test_results: List[BehaviorTestResult] = []
self.response_history: Dict[str, List[str]] = {}
async def run_behavior_test_suite(
self,
test_cases: List[BehaviorTestCase]
) -> Dict[str, Any]:
"""Run complete behavior test suite."""
logger.info(f"Starting behavior test suite with {len(test_cases)} test cases...")
results = []
start_time = time.time()
for i, test_case in enumerate(test_cases):
logger.info(f"Running test {i+1}/{len(test_cases)}: {test_case.name}")
result = await self._run_single_test(test_case)
results.append(result)
# Brief pause between tests
await asyncio.sleep(0.5)
total_time = time.time() - start_time
# Calculate overall metrics
summary = self._calculate_test_summary(results, total_time)
self.test_results.extend(results)
return summary
async def _run_single_test(
self,
test_case: BehaviorTestCase
) -> BehaviorTestResult:
"""Run a single behavior test."""
# Record start time
start_time = time.time()
# Generate response
try:
response_text, response_info = await self.lyra_model.generate_response(
user_message=test_case.input_message,
user_id=test_case.context.get('user_id', 'test_user'),
max_new_tokens=150,
temperature=0.9
)
except Exception as e:
logger.error(f"Error generating response for test {test_case.test_id}: {e}")
return BehaviorTestResult(
test_case=test_case,
response_text="",
response_time=0.0,
emotional_state={},
personality_influence={},
thinking_process=[],
timing_analysis={},
passed=False,
score=0.0,
notes=f"Error: {str(e)}"
)
response_time = time.time() - start_time
# Analyze timing
timing_analysis = self.timing_analyzer.analyze_timing(
response_time=response_time,
message_category=test_case.category,
message_length=len(test_case.input_message),
complexity_score=test_case.expected_behavior.get('complexity', 0.5)
)
# Analyze emotional consistency
emotional_analysis = self.emotion_analyzer.analyze_emotional_response(
message_context=test_case.category,
emotional_state=response_info.get('emotional_state', {}),
response_content=response_text
)
# Analyze personality coherence
user_id = test_case.context.get('user_id', 'test_user')
history = self.response_history.get(user_id, [])
personality_analysis = self.personality_analyzer.analyze_personality_consistency(
response_text=response_text,
expected_personality=test_case.expected_behavior.get('personality', {}),
response_history=history
)
# Update response history
if user_id not in self.response_history:
self.response_history[user_id] = []
self.response_history[user_id].append(response_text)
# Calculate overall score
timing_score = timing_analysis.get('humanness_score', 0.0)
emotional_score = emotional_analysis.get('appropriateness_score', 0.0)
personality_score = personality_analysis.get('overall_coherence', 0.0)
overall_score = (timing_score + emotional_score + personality_score) / 3.0
# Determine if test passed
min_passing_score = test_case.expected_behavior.get('min_score', 0.6)
passed = overall_score >= min_passing_score
# Generate notes
notes = self._generate_test_notes(
timing_analysis, emotional_analysis, personality_analysis
)
return BehaviorTestResult(
test_case=test_case,
response_text=response_text,
response_time=response_time,
emotional_state=response_info.get('emotional_state', {}),
personality_influence=response_info.get('personality_influence', {}),
thinking_process=response_info.get('thoughts', []),
timing_analysis=timing_analysis,
passed=passed,
score=overall_score,
notes=notes
)
def _generate_test_notes(
self,
timing_analysis: Dict[str, Any],
emotional_analysis: Dict[str, Any],
personality_analysis: Dict[str, Any]
) -> str:
"""Generate notes about test performance."""
notes = []
# Timing notes
if timing_analysis.get('is_too_fast'):
notes.append("Response was too fast for human-like behavior")
elif timing_analysis.get('is_too_slow'):
notes.append("Response was too slow")
elif timing_analysis.get('is_human_like'):
notes.append("Good response timing")
# Emotional notes
if not emotional_analysis.get('is_appropriate'):
expected = emotional_analysis.get('expected_emotions', [])
actual = emotional_analysis.get('dominant_emotion', 'unknown')
notes.append(f"Emotional response '{actual}' doesn't match expected {expected}")
if emotional_analysis.get('text_matches_emotion'):
notes.append("Text emotion matches internal emotional state")
# Personality notes
coherence = personality_analysis.get('overall_coherence', 0.0)
if coherence < 0.5:
notes.append("Personality coherence below expectations")
elif coherence > 0.8:
notes.append("Excellent personality consistency")
return "; ".join(notes) if notes else "All metrics within acceptable ranges"
def _calculate_test_summary(
self,
results: List[BehaviorTestResult],
total_time: float
) -> Dict[str, Any]:
"""Calculate summary statistics for test suite."""
if not results:
return {'status': 'no_tests_run'}
passed_count = sum(1 for r in results if r.passed)
pass_rate = passed_count / len(results)
scores = [r.score for r in results]
avg_score = statistics.mean(scores)
min_score = min(scores)
max_score = max(scores)
# Category breakdown
category_stats = {}
for result in results:
category = result.test_case.category
if category not in category_stats:
category_stats[category] = {'passed': 0, 'total': 0, 'scores': []}
category_stats[category]['total'] += 1
if result.passed:
category_stats[category]['passed'] += 1
category_stats[category]['scores'].append(result.score)
# Calculate category pass rates
for category, stats in category_stats.items():
stats['pass_rate'] = stats['passed'] / stats['total']
stats['avg_score'] = statistics.mean(stats['scores'])
return {
'total_tests': len(results),
'passed_tests': passed_count,
'failed_tests': len(results) - passed_count,
'pass_rate': pass_rate,
'avg_score': avg_score,
'min_score': min_score,
'max_score': max_score,
'total_time': total_time,
'tests_per_second': len(results) / total_time,
'category_breakdown': category_stats,
'recommendations': self._generate_recommendations(results)
}
def _generate_recommendations(
self,
results: List[BehaviorTestResult]
) -> List[str]:
"""Generate recommendations based on test results."""
recommendations = []
# Analyze common failure patterns
failed_results = [r for r in results if not r.passed]
if failed_results:
# Timing issues
timing_issues = [
r for r in failed_results
if r.timing_analysis.get('humanness_score', 1.0) < 0.5
]
if len(timing_issues) > len(failed_results) * 0.3:
recommendations.append(
"Consider adjusting response timing parameters - "
f"{len(timing_issues)} tests failed on timing"
)
# Emotional issues
emotion_issues = [
r for r in failed_results
if not r.timing_analysis.get('is_appropriate', True)
]
if len(emotion_issues) > len(failed_results) * 0.3:
recommendations.append(
"Review emotional response mapping - "
f"{len(emotion_issues)} tests had inappropriate emotional responses"
)
# Overall performance
avg_score = statistics.mean([r.score for r in results])
if avg_score < 0.7:
recommendations.append(
f"Overall performance ({avg_score:.2f}) below target - "
"consider retraining or parameter adjustment"
)
return recommendations
def save_test_results(self, filepath: Path):
"""Save test results to file."""
results_data = {
'timestamp': datetime.now().isoformat(),
'total_tests': len(self.test_results),
'results': [
{
'test_id': r.test_case.test_id,
'test_name': r.test_case.name,
'passed': r.passed,
'score': r.score,
'response_time': r.response_time,
'response_text': r.response_text,
'notes': r.notes
}
for r in self.test_results
]
}
filepath.parent.mkdir(parents=True, exist_ok=True)
with open(filepath, 'w', encoding='utf-8') as f:
json.dump(results_data, f, indent=2, ensure_ascii=False)
logger.info(f"Test results saved to {filepath}")
# Predefined test cases
def create_standard_test_cases() -> List[BehaviorTestCase]:
"""Create standard behavior test cases."""
return [
BehaviorTestCase(
test_id="greeting_001",
name="Simple Greeting",
description="Test response to basic greeting",
input_message="Hello!",
expected_behavior={
'complexity': 0.1,
'min_score': 0.7,
'personality': {'extraversion': 0.7, 'agreeableness': 0.8}
},
context={'user_id': 'test_001'},
category='simple_greeting'
),
BehaviorTestCase(
test_id="question_001",
name="Simple Question",
description="Test response to straightforward question",
input_message="What's your favorite color?",
expected_behavior={
'complexity': 0.3,
'min_score': 0.6,
'personality': {'openness': 0.6, 'agreeableness': 0.7}
},
context={'user_id': 'test_002'},
category='casual_question'
),
BehaviorTestCase(
test_id="complex_001",
name="Complex Question",
description="Test response to complex philosophical question",
input_message="What do you think about the nature of consciousness and whether AI can truly be conscious?",
expected_behavior={
'complexity': 0.9,
'min_score': 0.5,
'personality': {'openness': 0.8, 'conscientiousness': 0.7}
},
context={'user_id': 'test_003'},
category='complex_question'
),
BehaviorTestCase(
test_id="emotion_001",
name="Emotional Support",
description="Test emotional response to user distress",
input_message="I'm feeling really sad today and don't know what to do...",
expected_behavior={
'complexity': 0.6,
'min_score': 0.8,
'personality': {'agreeableness': 0.9, 'neuroticism': 0.3}
},
context={'user_id': 'test_004'},
category='emotional_response'
),
BehaviorTestCase(
test_id="creative_001",
name="Creative Request",
description="Test creative response generation",
input_message="Can you write a short poem about friendship?",
expected_behavior={
'complexity': 0.7,
'min_score': 0.6,
'personality': {'openness': 0.9, 'extraversion': 0.6}
},
context={'user_id': 'test_005'},
category='creative_request'
)
]

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lyra/training/__init__.py Normal file
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"""
Lyra Training Module
Implements advanced training strategies including adaptive learning,
memory consolidation, and human-like learning patterns.
"""
from .pipeline import LyraTrainingPipeline, ConversationDataset, create_training_pipeline
__all__ = [
"LyraTrainingPipeline",
"ConversationDataset",
"create_training_pipeline"
]

574
lyra/training/pipeline.py Normal file
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"""
Advanced training pipeline for Lyra with sliding context window and adaptive learning.
Implements sophisticated training strategies including:
- Sliding context window for long conversations
- Dynamic curriculum based on Lyra's emotional and personality state
- Memory consolidation and replay
- Human-like learning patterns
"""
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torch.optim import AdamW
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
import numpy as np
import logging
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass
from datetime import datetime
import json
import asyncio
from collections import deque
import random
from ..config import config
from ..core.lyra_model import LyraModel
from ..database.manager import DatabaseManager
from ..emotions.system import EmotionalState
logger = logging.getLogger(__name__)
@dataclass
class TrainingBatch:
"""Represents a training batch with context."""
input_ids: torch.Tensor
attention_mask: torch.Tensor
target_ids: torch.Tensor
emotional_context: torch.Tensor
personality_context: torch.Tensor
conversation_id: str
turn_index: int
metadata: Dict[str, Any]
@dataclass
class LearningMemory:
"""Represents a significant learning memory."""
conversation_embedding: torch.Tensor
emotional_state: EmotionalState
user_feedback: float
learning_outcome: str
timestamp: datetime
replay_count: int = 0
class ConversationDataset(Dataset):
"""Dataset for conversation training with sliding windows."""
def __init__(
self,
conversations: List[Dict[str, Any]],
tokenizer,
max_length: int = 512,
sliding_window: int = 256,
overlap: int = 64
):
self.conversations = conversations
self.tokenizer = tokenizer
self.max_length = max_length
self.sliding_window = sliding_window
self.overlap = overlap
self.samples = self._prepare_samples()
def _prepare_samples(self) -> List[Dict[str, Any]]:
"""Prepare training samples with sliding windows."""
samples = []
for conv in self.conversations:
# Extract conversation turns
turns = conv.get('turns', [])
full_text = ""
# Build conversation context
for i, turn in enumerate(turns):
if turn['role'] == 'user':
full_text += f"User: {turn['content']}\n"
elif turn['role'] == 'assistant':
full_text += f"Lyra: {turn['content']}\n"
# Create sliding windows
tokens = self.tokenizer.encode(full_text)
for start_idx in range(0, len(tokens) - self.sliding_window,
self.sliding_window - self.overlap):
end_idx = min(start_idx + self.sliding_window, len(tokens))
window_tokens = tokens[start_idx:end_idx]
if len(window_tokens) < 32: # Skip very short windows
continue
# Target is the next token sequence
input_tokens = window_tokens[:-1]
target_tokens = window_tokens[1:]
samples.append({
'input_ids': input_tokens,
'target_ids': target_tokens,
'conversation_id': conv.get('id', ''),
'emotional_context': conv.get('emotional_state', {}),
'personality_context': conv.get('personality_state', {}),
'metadata': conv.get('metadata', {})
})
return samples
def __len__(self) -> int:
return len(self.samples)
def __getitem__(self, idx: int) -> Dict[str, Any]:
return self.samples[idx]
class AdaptiveLearningScheduler:
"""Adaptive learning rate based on emotional and personality state."""
def __init__(self, base_lr: float = 1e-4):
self.base_lr = base_lr
self.emotional_multipliers = {
'joy': 1.2, # Learn faster when happy
'curiosity': 1.5, # Learn much faster when curious
'frustration': 0.7, # Learn slower when frustrated
'confusion': 0.5, # Learn slower when confused
'confidence': 1.1 # Learn slightly faster when confident
}
def get_learning_rate(
self,
emotional_state: EmotionalState,
personality_openness: float,
recent_performance: float
) -> float:
"""Calculate adaptive learning rate."""
# Base rate adjustment
lr = self.base_lr
# Emotional adjustment
dominant_emotion, intensity = emotional_state.get_dominant_emotion()
if dominant_emotion in self.emotional_multipliers:
lr *= self.emotional_multipliers[dominant_emotion] * intensity
# Personality adjustment (openness to experience)
lr *= (1.0 + personality_openness * 0.3)
# Performance adjustment
if recent_performance > 0.8:
lr *= 1.1 # Increase when performing well
elif recent_performance < 0.4:
lr *= 0.8 # Decrease when struggling
return max(lr, self.base_lr * 0.1) # Don't go too low
class LyraTrainingPipeline:
"""Complete training pipeline for Lyra with human-like learning patterns."""
def __init__(
self,
model: LyraModel,
tokenizer,
device: torch.device,
database_manager: Optional[DatabaseManager] = None
):
self.model = model
self.tokenizer = tokenizer
self.device = device
self.database_manager = database_manager
# Training components
self.optimizer = AdamW(model.parameters(), lr=config.learning_rate)
self.scheduler = CosineAnnealingWarmRestarts(
self.optimizer, T_0=1000, eta_min=1e-6
)
self.adaptive_scheduler = AdaptiveLearningScheduler()
# Memory systems
self.learning_memories = deque(maxlen=1000)
self.replay_buffer = deque(maxlen=5000)
# Training state
self.global_step = 0
self.epoch = 0
self.best_performance = 0.0
self.training_history = []
# Human-like learning patterns
self.forgetting_curve = self._initialize_forgetting_curve()
self.consolidation_schedule = self._create_consolidation_schedule()
def _initialize_forgetting_curve(self) -> Dict[str, float]:
"""Initialize forgetting curve parameters."""
return {
'initial_strength': 1.0,
'decay_rate': 0.05,
'consolidation_boost': 1.3,
'interference_factor': 0.1
}
def _create_consolidation_schedule(self) -> List[int]:
"""Create memory consolidation schedule (like sleep cycles)."""
# Consolidate at increasing intervals: 1h, 6h, 24h, 72h, 168h
return [100, 600, 2400, 7200, 16800] # In training steps
async def train_epoch(
self,
train_dataloader: DataLoader,
val_dataloader: Optional[DataLoader] = None
) -> Dict[str, float]:
"""Train for one epoch with adaptive learning."""
self.model.train()
epoch_loss = 0.0
num_batches = 0
emotional_adjustments = 0
for batch_idx, batch in enumerate(train_dataloader):
# Move batch to device
batch = self._prepare_batch(batch)
# Get current emotional and personality state
emotional_state = self._get_current_emotional_state()
personality_state = self._get_current_personality_state()
# Adaptive learning rate
current_performance = self._calculate_recent_performance()
adaptive_lr = self.adaptive_scheduler.get_learning_rate(
emotional_state,
personality_state.get('openness', 0.5),
current_performance
)
# Adjust optimizer learning rate if significantly different
current_lr = self.optimizer.param_groups[0]['lr']
if abs(adaptive_lr - current_lr) > current_lr * 0.1:
for param_group in self.optimizer.param_groups:
param_group['lr'] = adaptive_lr
emotional_adjustments += 1
# Forward pass
self.optimizer.zero_grad()
outputs, lyra_info = self.model(
input_ids=batch['input_ids'],
attention_mask=batch['attention_mask'],
user_id=batch.get('user_id'),
conversation_context=batch.get('context')
)
# Calculate loss
loss = self._calculate_adaptive_loss(
outputs, batch['target_ids'], emotional_state
)
# Backward pass
loss.backward()
# Gradient clipping (human-like learning stability)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
# Optimizer step
self.optimizer.step()
self.scheduler.step()
# Update training state
epoch_loss += loss.item()
num_batches += 1
self.global_step += 1
# Memory consolidation
if self.global_step in self.consolidation_schedule:
await self._consolidate_memories()
# Experience replay (20% chance)
if random.random() < 0.2 and len(self.replay_buffer) > 10:
await self._experience_replay()
# Log progress
if batch_idx % 100 == 0:
logger.info(
f"Epoch {self.epoch}, Batch {batch_idx}, "
f"Loss: {loss.item():.4f}, "
f"LR: {adaptive_lr:.2e}, "
f"Emotional adjustments: {emotional_adjustments}"
)
# Validation
val_metrics = {}
if val_dataloader:
val_metrics = await self._validate(val_dataloader)
# Record training history
epoch_metrics = {
'epoch': self.epoch,
'train_loss': epoch_loss / num_batches,
'learning_rate': self.optimizer.param_groups[0]['lr'],
'emotional_adjustments': emotional_adjustments,
'global_step': self.global_step,
**val_metrics
}
self.training_history.append(epoch_metrics)
self.epoch += 1
return epoch_metrics
def _prepare_batch(self, batch: Dict[str, Any]) -> Dict[str, torch.Tensor]:
"""Prepare batch for training."""
prepared = {}
for key, value in batch.items():
if isinstance(value, torch.Tensor):
prepared[key] = value.to(self.device)
elif isinstance(value, list):
# Convert list to tensor if numeric
try:
prepared[key] = torch.tensor(value).to(self.device)
except:
prepared[key] = value
else:
prepared[key] = value
return prepared
def _get_current_emotional_state(self) -> EmotionalState:
"""Get Lyra's current emotional state."""
# This would normally come from the emotional system
# For now, create a default state
emotions = torch.rand(19) # 19 emotion dimensions
return EmotionalState.from_tensor(emotions, self.device)
def _get_current_personality_state(self) -> Dict[str, float]:
"""Get current personality traits."""
return {
'openness': 0.7,
'conscientiousness': 0.8,
'extraversion': 0.6,
'agreeableness': 0.9,
'neuroticism': 0.3
}
def _calculate_recent_performance(self) -> float:
"""Calculate recent performance score."""
if not self.training_history:
return 0.5
recent_epochs = self.training_history[-5:] # Last 5 epochs
if not recent_epochs:
return 0.5
# Simple performance metric based on loss improvement
losses = [epoch['train_loss'] for epoch in recent_epochs]
if len(losses) < 2:
return 0.5
improvement = (losses[0] - losses[-1]) / losses[0]
return min(max(0.5 + improvement, 0.0), 1.0)
def _calculate_adaptive_loss(
self,
outputs: torch.Tensor,
targets: torch.Tensor,
emotional_state: EmotionalState
) -> torch.Tensor:
"""Calculate loss adjusted for emotional state."""
# Base cross-entropy loss
base_loss = nn.CrossEntropyLoss()(
outputs.view(-1, outputs.size(-1)),
targets.view(-1)
)
# Emotional adjustment
dominant_emotion, intensity = emotional_state.get_dominant_emotion()
if dominant_emotion == 'frustration' and intensity > 0.7:
# Reduce learning when frustrated (like humans)
base_loss *= 0.8
elif dominant_emotion == 'curiosity' and intensity > 0.6:
# Increase learning when curious
base_loss *= 1.2
return base_loss
async def _consolidate_memories(self):
"""Consolidate important memories (like sleep-based learning)."""
if not self.learning_memories:
return
logger.info(f"Consolidating {len(self.learning_memories)} memories...")
# Sort memories by importance (feedback score + recency)
important_memories = sorted(
self.learning_memories,
key=lambda m: m.user_feedback * (1.0 - m.replay_count * 0.1),
reverse=True
)[:50] # Top 50 memories
# Replay important memories
for memory in important_memories[:10]:
# Convert memory to training sample
self.replay_buffer.append({
'conversation_embedding': memory.conversation_embedding,
'emotional_state': memory.emotional_state,
'feedback': memory.user_feedback,
'outcome': memory.learning_outcome
})
memory.replay_count += 1
logger.info("Memory consolidation complete")
async def _experience_replay(self):
"""Replay past experiences for better learning."""
if len(self.replay_buffer) < 5:
return
# Sample random memories
replay_samples = random.sample(list(self.replay_buffer), min(5, len(self.replay_buffer)))
# Process replay samples (simplified)
for sample in replay_samples:
# This would normally involve re-training on the sample
# For now, just log the replay
logger.debug(f"Replaying memory with feedback: {sample['feedback']}")
async def _validate(self, val_dataloader: DataLoader) -> Dict[str, float]:
"""Validate model performance."""
self.model.eval()
total_loss = 0.0
num_batches = 0
with torch.no_grad():
for batch in val_dataloader:
batch = self._prepare_batch(batch)
outputs, _ = self.model(
input_ids=batch['input_ids'],
attention_mask=batch['attention_mask']
)
loss = nn.CrossEntropyLoss()(
outputs.view(-1, outputs.size(-1)),
batch['target_ids'].view(-1)
)
total_loss += loss.item()
num_batches += 1
self.model.train()
avg_val_loss = total_loss / num_batches if num_batches > 0 else 0.0
return {
'val_loss': avg_val_loss,
'perplexity': torch.exp(torch.tensor(avg_val_loss)).item()
}
async def save_checkpoint(self, filepath: Path, metadata: Optional[Dict] = None):
"""Save training checkpoint."""
checkpoint = {
'model_state_dict': self.model.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'scheduler_state_dict': self.scheduler.state_dict(),
'global_step': self.global_step,
'epoch': self.epoch,
'training_history': self.training_history,
'best_performance': self.best_performance,
'learning_memories': list(self.learning_memories),
'forgetting_curve': self.forgetting_curve,
'metadata': metadata or {}
}
filepath.parent.mkdir(parents=True, exist_ok=True)
torch.save(checkpoint, filepath)
logger.info(f"Checkpoint saved to {filepath}")
async def load_checkpoint(self, filepath: Path):
"""Load training checkpoint."""
checkpoint = torch.load(filepath, map_location=self.device)
self.model.load_state_dict(checkpoint['model_state_dict'])
self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
self.global_step = checkpoint.get('global_step', 0)
self.epoch = checkpoint.get('epoch', 0)
self.training_history = checkpoint.get('training_history', [])
self.best_performance = checkpoint.get('best_performance', 0.0)
self.learning_memories = deque(
checkpoint.get('learning_memories', []), maxlen=1000
)
self.forgetting_curve = checkpoint.get('forgetting_curve', self.forgetting_curve)
logger.info(f"Checkpoint loaded from {filepath}")
def add_learning_memory(
self,
conversation_embedding: torch.Tensor,
emotional_state: EmotionalState,
user_feedback: float,
learning_outcome: str
):
"""Add a significant learning memory."""
memory = LearningMemory(
conversation_embedding=conversation_embedding,
emotional_state=emotional_state,
user_feedback=user_feedback,
learning_outcome=learning_outcome,
timestamp=datetime.now()
)
self.learning_memories.append(memory)
def get_training_statistics(self) -> Dict[str, Any]:
"""Get comprehensive training statistics."""
if not self.training_history:
return {'status': 'no_training_data'}
recent_performance = self._calculate_recent_performance()
return {
'global_step': self.global_step,
'current_epoch': self.epoch,
'total_epochs_trained': len(self.training_history),
'recent_performance': recent_performance,
'best_performance': self.best_performance,
'learning_memories_count': len(self.learning_memories),
'replay_buffer_size': len(self.replay_buffer),
'current_learning_rate': self.optimizer.param_groups[0]['lr'],
'last_consolidation': max(
[step for step in self.consolidation_schedule if step <= self.global_step],
default=0
),
'training_history_summary': {
'best_train_loss': min(h['train_loss'] for h in self.training_history),
'latest_train_loss': self.training_history[-1]['train_loss'],
'average_emotional_adjustments': np.mean([
h['emotional_adjustments'] for h in self.training_history
])
} if self.training_history else {}
}
async def create_training_pipeline(
model: LyraModel,
tokenizer,
device: torch.device,
database_manager: Optional[DatabaseManager] = None
) -> LyraTrainingPipeline:
"""Create and initialize training pipeline."""
pipeline = LyraTrainingPipeline(model, tokenizer, device, database_manager)
# Load existing checkpoint if available
checkpoint_path = Path(config.models_dir) / "checkpoints" / "latest_training.pt"
if checkpoint_path.exists():
try:
await pipeline.load_checkpoint(checkpoint_path)
logger.info("Loaded existing training checkpoint")
except Exception as e:
logger.warning(f"Could not load checkpoint: {e}")
return pipeline