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CONFIGURATION_GUIDE

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
:::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*

Wiki: Home

Nebula Wiki

AI framework for automated ML, analytics, monitoring and inference.

CONFIGURATION_GUIDE

Aurora AI Framework - Configuration Guide

🌟 Overview

📋 Current Configuration Structure

Main Configuration File

Configuration Overview from config.yaml

🔧 Core Configuration

Application Configuration

Data Pipeline Configuration

Model Configuration

API Server Configuration

Security Configuration

Monitoring Configuration

Pipeline Configuration

Logging Configuration

Modules Configuration

Error Tracking Configuration

Data Validation Configuration

Feedback Loop Configuration

Metadata Configuration

Inference Service Configuration

config/production.yaml

config/security.yaml

ssl configuration for production

Validate current configuration

Validate specific configuration section

Get configuration utilities

Configuration validation logic

.env file

Merge configuration files

Encrypt sensitive configuration

Decrypt configuration secrets

!/bin/bash

deploy_config.sh

Create backup

Validate configuration

Deploy configuration

Verify deployment

!/bin/bash

rollback_config.sh

Restore backup

Restart services

Verify rollback

Monitor configuration changes

Get configuration history

Monitor configuration performance impact

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