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Aurora AI Framework - Configuration Guide

🌟 Overview

This comprehensive configuration guide covers all aspects of configuring the Aurora AI framework v1.0.0, including system settings, environment-specific configurations, security configurations, and advanced configuration management for all 57 integrated systems.

📋 Current Configuration Structure

Main Configuration File

config/
└── config.yaml          # Main configuration file (current)

Configuration Overview from config.yaml

  • Framework: Aurora AI Framework v1.0.0
  • Author: Aurora Development Team
  • Last Updated: 2025-05-06
  • Systems: 57 integrated systems
  • API Endpoints: 132 total endpoints

🔧 Core Configuration

Application Configuration

# config/config.yaml
app:
  name: Aurora AI Framework
  version: 1.0.0
  description: "Configuration file for the Aurora AI framework."

Data Pipeline Configuration

data_pipeline:
  data_path: "data/input.csv"
  source: "local"
  format: "csv"
  input_file: "data/input.csv"
  output_file: "data/output.csv"
  preprocessing: "standard"

Model Configuration

model:
  architecture: "ensemble_model"
  type: classification
  algorithm: "RandomForest"
  parameters:
    learning_rate: 0.01
    num_epochs: 100
    batch_size: 32
  n_estimators: 100
  max_depth: 10
  random_state: 42
  epochs: 10
  batch_size: 32
  optimizer: "adam"

API Server Configuration

api_server:
  host: 0.0.0.0
  port: 8080
  debug: false

Security Configuration

security:
  enable_authentication: false
  encryption_key: "L_8Hfm33ainlgyoN0t_3YsGjw-ujM15X8_VsrKrKr5U="
  api_keys:
    internal: "internal_api_key"
    external: "external_api_key"

Monitoring Configuration

monitoring:
  log_interval: 5
  drift_detection: true
  alerting: true
  alert_threshold: 0.8

Pipeline Configuration

pipeline:
  orchestrator:
    max_batches: 5
    retry_attempts: 3
    timeout_in_seconds: 120
  data_ingestion:
    source: "data/input.csv"
    format: "csv"
  model_training:
    algorithm: "RandomForest"
    max_depth: 10
    n_estimators: 100

Logging Configuration

logging:
  level: INFO
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
  log_file: "logs/app.log"
  error_log_file: "logs/errors.log"

Modules Configuration

modules:
  enabled:
    - monitoring
    - alerting
    - data_validation
    - error_tracker
  disabled:
    - emotional_core
    - eternal_art

Error Tracking Configuration

error_tracking:
  error_db_path: "data/errors.db"
  max_errors: 10000
  alert_threshold: 5

Data Validation Configuration

data_validation:
  validation_rules: {}
  schema: {}
  quality_thresholds:
    minimum_score: 0.7

Feedback Loop Configuration

feedback_loop:
  feedback_db_path: "data/feedback.db"
  retrain_threshold: 100
  feedback_quality_threshold: 0.8

Metadata Configuration

metadata:
  author: "Aurora Development Team"
  last_updated: "2025-05-06"

Inference Service Configuration

inference:
  service_url: "http://localhost:5000"

reload: true

database: host: "localhost" name: "aurora_dev"

logging: level: "DEBUG" console: true

security: token_expiry: 86400 # 24 hours for development

monitoring: enabled: true metrics_interval: 10

testing: enabled: true auto_run: false


### Production Configuration
```yaml
# config/production.yaml
aurora:
  environment: "production"
  
server:
  debug: false
  workers: 8
  ssl_enabled: true
  
database:
  host: "${DB_HOST}"
  port: "${DB_PORT}"
  name: "${DB_NAME}"
  username: "${DB_USER}"
  password: "${DB_PASSWORD}"
  ssl_mode: "require"
  
logging:
  level: "WARNING"
  file: "/var/log/aurora/aurora.log"
  syslog: true
  
security:
  token_expiry: 1800  # 30 minutes
  rate_limiting:
    enabled: true
    requests_per_minute: 1000
  
monitoring:
  enabled: true
  alerting:
    enabled: true
    webhook_url: "${ALERT_WEBHOOK_URL}"

🔐 Security Configuration

Authentication and Authorization

# config/security.yaml
security:
  authentication:
    type: "jwt"
    secret_key: "${JWT_SECRET}"
    algorithm: "HS256"
    expiry: 3600
    
  authorization:
    roles:
      - "admin"
      - "user"
      - "viewer"
    
    permissions:
      admin:
        - "system:*"
        - "data:*"
        - "models:*"
      user:
        - "data:read"
        - "models:read"
        - "inference:*"
      viewer:
        - "data:read"
        - "models:read"
        - "monitoring:read"
  
  encryption:
    algorithm: "AES-256-GCM"
    key_derivation: "PBKDF2"
    iterations: 100000
    
  rate_limiting:
    enabled: true
    default_limit: 1000
    burst_limit: 100
    
  audit_logging:
    enabled: true
    log_all_requests: false
    log_sensitive_operations: true

SSL/TLS Configuration

# ssl configuration for production
ssl:
  enabled: true
  cert_file: "/etc/ssl/certs/aurora.crt"
  key_file: "/etc/ssl/private/aurora.key"
  ca_file: "/etc/ssl/certs/ca-bundle.crt"
  
  protocols:
    - "TLSv1.2"
    - "TLSv1.3"
  
  ciphers:
    - "ECDHE-RSA-AES256-GCM-SHA384"
    - "ECDHE-RSA-CHACHA20-POLY1305"
    - "ECDHE-RSA-AES128-GCM-SHA256"
  
  hsts:
    enabled: true
    max_age: 31536000
    include_subdomains: true

📊 System-Specific Configuration

Data Pipeline Configuration

data_pipeline:
  preprocessing:
    scaling_method: "standard"
    encoding_method: "label"
    missing_values: "mean"
    
  validation:
    schema_validation: true
    quality_checks: true
    statistical_validation: true
    
  performance:
    batch_size: 1000
    parallel_processes: 4
    cache_size: "1GB"

Model Training Configuration

model_training:
  algorithms:
    - "RandomForest"
    - "SVM"
    - "NeuralNetwork"
    - "XGBoost"
    
  hyperparameter_optimization:
    method: "bayesian"
    max_iterations: 100
    cv_folds: 5
    
  ensemble:
    method: "voting"
    weights: [0.3, 0.3, 0.2, 0.2]
    
  validation:
    test_size: 0.2
    random_state: 42
    stratify: true

Monitoring Configuration

monitoring:
  metrics:
    enabled: true
    interval: 30
    retention_days: 30
    
  alerts:
    enabled: true
    channels:
      - "email"
      - "slack"
      - "webhook"
    
    thresholds:
      cpu_usage: 80
      memory_usage: 85
      disk_usage: 90
      response_time: 5000
      
  dashboards:
    enabled: true
    refresh_interval: 10
    auto_refresh: true

Resource Management Configuration

resource_management:
  cpu:
    limit_cores: 8
    reservation_cores: 2
    
  memory:
    limit_gb: 16
    reservation_gb: 4
    
  disk:
    limit_gb: 100
    cleanup_threshold: 80
    
  network:
    bandwidth_limit_mbps: 1000
    connection_limit: 1000

🔍 Configuration Validation

Validation API Usage

# Validate current configuration
curl -X POST "http://localhost:8080/api/config/validate" \
  -H "Content-Type: application/json" \
  -d '{"validate_all": true}'

# Validate specific configuration section
curl -X POST "http://localhost:8080/api/config/validate" \
  -H "Content-Type: application/json" \
  -d '{"section": "security", "strict": true}'

# Get configuration utilities
curl -X GET "http://localhost:8080/api/config/utilities"

Configuration Validation Rules

# Configuration validation logic
class ConfigurationValidator:
    def __init__(self):
        self.required_fields = {
            'server': ['host', 'port'],
            'database': ['type', 'host', 'name'],
            'security': ['secret_key', 'jwt_algorithm']
        }
        
        self.validation_rules = {
            'server.port': lambda x: 1 <= x <= 65535,
            'security.token_expiry': lambda x: x > 0,
            'database.port': lambda x: 1 <= x <= 65535
        }
    
    def validate_config(self, config: dict) -> dict:
        """Validate configuration against rules"""
        errors = []
        warnings = []
        
        # Check required fields
        for section, fields in self.required_fields.items():
            if section not in config:
                errors.append(f"Missing required section: {section}")
                continue
                
            for field in fields:
                if field not in config[section]:
                    errors.append(f"Missing required field: {section}.{field}")
        
        # Apply validation rules
        for field_path, rule in self.validation_rules.items():
            section, field = field_path.split('.')
            if section in config and field in config[section]:
                value = config[section][field]
                if not rule(value):
                    errors.append(f"Invalid value for {field_path}: {value}")
        
        return {
            'valid': len(errors) == 0,
            'errors': errors,
            'warnings': warnings
        }

🔧 Configuration Management

Environment Variables

# .env file
export AURORA_ENV=production
export DB_HOST=localhost
export DB_PORT=5432
export DB_NAME=aurora_ai
export DB_USER=aurora_user
export DB_PASSWORD=secure_password
export SECRET_KEY=your_secret_key_here
export JWT_SECRET=your_jwt_secret_here
export ALERT_WEBHOOK_URL=https://hooks.slack.com/your-webhook

Configuration Merging

# Merge configuration files
curl -X POST "http://localhost:8080/api/config/merge" \
  -H "Content-Type: application/json" \
  -d '{
    "config_files": [
      "config/default.yaml",
      "config/production.yaml",
      "config/custom.yaml"
    ],
    "validate": true,
    "output_format": "yaml"
  }'

Secrets Management

# Encrypt sensitive configuration
curl -X POST "http://localhost:8080/api/config/secrets" \
  -H "Content-Type: application/json" \
  -d '{
    "action": "encrypt",
    "secret_data": {
      "database_password": "secure_password",
      "api_key": "your_api_key"
    },
    "algorithm": "AES-256"
  }'

# Decrypt configuration secrets
curl -X POST "http://localhost:8080/api/config/secrets" \
  -H "Content-Type: application/json" \
  -d '{
    "action": "decrypt",
    "encrypted_data": "encrypted_data_here",
    "algorithm": "AES-256"
  }'

📈 Performance Configuration

Caching Configuration

caching:
  enabled: true
  type: "redis"
  
  redis:
    host: "localhost"
    port: 6379
    db: 0
    password: "${REDIS_PASSWORD}"
    
  cache_settings:
    default_ttl: 3600
    max_size: "1GB"
    eviction_policy: "lru"
    
  cache_keys:
    predictions: "pred:{data_hash}"
    models: "model:{model_id}"
    user_sessions: "session:{user_id}"

Database Connection Pooling

database_pooling:
  enabled: true
  min_connections: 5
  max_connections: 20
  connection_timeout: 30
  idle_timeout: 300
  
  retry_policy:
    max_retries: 3
    retry_delay: 1
    backoff_factor: 2

Async Processing Configuration

async_processing:
  enabled: true
  worker_type: "celery"
  
  celery:
    broker_url: "redis://localhost:6379/1"
    result_backend: "redis://localhost:6379/2"
    
  task_settings:
    default_timeout: 300
    max_retries: 3
    retry_delay: 60
    
  queues:
    training:
      workers: 2
      concurrency: 4
    inference:
      workers: 4
      concurrency: 8
    monitoring:
      workers: 1
      concurrency: 2

🔄 Configuration Deployment

Configuration Deployment Script

#!/bin/bash
# deploy_config.sh

set -e

ENVIRONMENT=${1:-development}
CONFIG_DIR="config"
BACKUP_DIR="config/backups"

echo "Deploying configuration for environment: $ENVIRONMENT"

# Create backup
mkdir -p $BACKUP_DIR
cp -r $CONFIG_DIR $BACKUP_DIR/$(date +%Y%m%d_%H%M%S)

# Validate configuration
echo "Validating configuration..."
curl -X POST "http://localhost:8080/api/config/validate" \
  -H "Content-Type: application/json" \
  -d '{"validate_all": true}' || exit 1

# Deploy configuration
echo "Deploying configuration..."
export AURORA_ENV=$ENVIRONMENT
python web_backend/server.py --config-deploy

# Verify deployment
echo "Verifying deployment..."
curl -X GET "http://localhost:8080/api/status" || exit 1

echo "Configuration deployed successfully!"

Configuration Rollback

#!/bin/bash
# rollback_config.sh

BACKUP_VERSION=${1:-latest}
BACKUP_DIR="config/backups"

if [ "$BACKUP_VERSION" = "latest" ]; then
    BACKUP_VERSION=$(ls -t $BACKUP_DIR | head -1)
fi

echo "Rolling back to configuration: $BACKUP_VERSION"

# Restore backup
rm -rf config
cp -r $BACKUP_DIR/$BACKUP_VERSION config

# Restart services
echo "Restarting services..."
systemctl restart aurora-ai

# Verify rollback
echo "Verifying rollback..."
curl -X GET "http://localhost:8080/api/status" || exit 1

echo "Configuration rollback completed!"

📊 Configuration Monitoring

Configuration Change Tracking

# Monitor configuration changes
curl -X GET "http://localhost:8080/api/config/current" \
  -H "Accept: application/json"

# Get configuration history
curl -X GET "http://localhost:8080/api/logs/audit" \
  -H "Content-Type: application/json" \
  -d '{"filter": {"category": "configuration"}}'

Configuration Performance Impact

# Monitor configuration performance impact
class ConfigurationMonitor:
    def __init__(self, aurora_api_url):
        self.api_url = aurora_api_url
    
    def measure_config_impact(self, config_change: dict) -> dict:
        """Measure performance impact of configuration change"""
        # Get baseline metrics
        baseline = self.get_performance_metrics()
        
        # Apply configuration change
        self.apply_config_change(config_change)
        
        # Wait for system to stabilize
        time.sleep(60)
        
        # Get new metrics
        new_metrics = self.get_performance_metrics()
        
        # Calculate impact
        impact = {
            'cpu_change': new_metrics['cpu'] - baseline['cpu'],
            'memory_change': new_metrics['memory'] - baseline['memory'],
            'response_time_change': new_metrics['response_time'] - baseline['response_time']
        }
        
        return impact
    
    def get_performance_metrics(self) -> dict:
        """Get current performance metrics"""
        response = requests.get(f"{self.api_url}/api/monitoring/metrics")
        return response.json()

🎯 Best Practices

Configuration Security

  1. Never commit secrets to version control
  2. Use environment variables for sensitive data
  3. Encrypt configuration secrets
  4. Regularly rotate encryption keys
  5. Audit configuration changes

Configuration Management

  1. Use version control for configuration files
  2. Maintain separate configs for each environment
  3. Validate all configuration changes
  4. Document configuration options
  5. Test configuration changes in staging

Performance Optimization

  1. Use connection pooling for databases
  2. Enable caching for frequently accessed data
  3. Configure appropriate timeouts
  4. Monitor resource utilization
  5. Optimize based on usage patterns

Aurora AI Configuration Guide
Comprehensive Configuration Management • Security • Performance Optimization

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