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

### Data Pipeline Configuration
```yaml
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
```yaml
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
```yaml
api_server:
  host: 0.0.0.0
  port: 8080
  debug: false
```

### Security Configuration
```yaml
security:
  enable_authentication: false
  encryption_key: "L_8Hfm33ainlgyoN0t_3YsGjw-ujM15X8_VsrKrKr5U="
  api_keys:
    internal: "internal_api_key"
    external: "external_api_key"
```

### Monitoring Configuration
```yaml
monitoring:
  log_interval: 5
  drift_detection: true
  alerting: true
  alert_threshold: 0.8
```

### Pipeline Configuration
```yaml
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
```yaml
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
```yaml
modules:
  enabled:
    - monitoring
    - alerting
    - data_validation
    - error_tracker
  disabled:
    - emotional_core
    - eternal_art
```

### Error Tracking Configuration
```yaml
error_tracking:
  error_db_path: "data/errors.db"
  max_errors: 10000
  alert_threshold: 5
```

### Data Validation Configuration
```yaml
data_validation:
  validation_rules: {}
  schema: {}
  quality_thresholds:
    minimum_score: 0.7
```

### Feedback Loop Configuration
```yaml
feedback_loop:
  feedback_db_path: "data/feedback.db"
  retrain_threshold: 100
  feedback_quality_threshold: 0.8
```

### Metadata Configuration
```yaml
metadata:
  author: "Aurora Development Team"
  last_updated: "2025-05-06"
```

### Inference Service Configuration
```yaml
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
```yaml
# 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
```yaml
# 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
```yaml
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
```yaml
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
```yaml
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
```yaml
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
```bash
# 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
```python
# 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
```bash
# .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
```bash
# 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
```bash
# 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
```yaml
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
```yaml
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
```yaml
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
```bash
#!/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
```bash
#!/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
```bash
# 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
```python
# 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*