# Aurora AI Framework - Integration Guide

## 🌟 Overview

This comprehensive guide covers integration with external systems, custom development, and best practices for extending the Aurora AI framework. With 27 integrated systems and 74 API endpoints, Aurora provides extensive integration capabilities.

## 🔌 External System Integration

### API Integration
```bash
# Basic API integration example
import requests

# Aurora AI API base URL
AURORA_API_BASE = "http://localhost:8080"

# System health check
def check_system_health():
    response = requests.get(f"{AURORA_API_BASE}/api/status")
    return response.json()

# Data validation integration
def validate_external_data(data):
    response = requests.post(
        f"{AURORA_API_BASE}/api/validation/schema",
        json={"schema_type": "json_schema", "data": data}
    )
    return response.json()

# Model training integration
def trigger_model_training(config):
    response = requests.post(
        f"{AURORA_API_BASE}/api/training/enhanced",
        json=config
    )
    return response.json()
```

### Database Integration
```python
# Database integration example
import psycopg2
import requests

class AuroraDatabaseIntegration:
    def __init__(self, db_config, aurora_api_url):
        self.db_conn = psycopg2.connect(**db_config)
        self.aurora_api = aurora_api_url
    
    def sync_data_to_aurora(self, table_name):
        """Sync database data to Aurora AI"""
        cursor = self.db_conn.cursor()
        cursor.execute(f"SELECT * FROM {table_name}")
        data = cursor.fetchall()
        
        # Validate data before sending to Aurora
        validation_response = requests.post(
            f"{self.aurora_api}/api/validation/quality",
            json={"data": data, "scope": "comprehensive"}
        )
        
        if validation_response.json().get("status") == "QUALITY_ASSESSMENT_COMPLETED":
            return {"status": "sync_ready", "data": data}
        return {"status": "validation_failed"}
```

### Message Queue Integration
```python
# RabbitMQ/Kafka integration example
import pika
import json

class AuroraMessageQueueIntegration:
    def __init__(self, queue_config, aurora_api_url):
        self.connection = pika.BlockingConnection(
            pika.ConnectionParameters(**queue_config)
        )
        self.channel = self.connection.channel()
        self.aurora_api = aurora_api_url
    
    def setup_training_queue(self):
        """Setup queue for model training requests"""
        self.channel.queue_declare(queue='aurora_training')
        
        def callback(ch, method, properties, body):
            training_request = json.loads(body)
            
            # Send to Aurora AI for training
            response = requests.post(
                f"{self.aurora_api}/api/training/enhanced",
                json=training_request
            )
            
            print(f"Training initiated: {response.json()}")
        
        self.channel.basic_consume(
            queue='aurora_training',
            on_message_callback=callback,
            auto_ack=True
        )
        
        self.channel.start_consuming()
```

## 🔧 Custom Development

### Custom Module Development
```python
# Custom Aurora module example
from core.base import BaseComponent
from typing import Dict, Any, Optional

class CustomAuroraModule(BaseComponent):
    """Custom module for Aurora AI framework"""
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        super().__init__(config)
        self.custom_config = self.config.get('custom_settings', {})
        self.initialized = False
    
    def initialize(self) -> bool:
        """Initialize custom module"""
        try:
            self.logger.info("Initializing Custom Aurora Module...")
            
            # Custom initialization logic
            if not self.validate_custom_config():
                return False
            
            self.setup_custom_components()
            self.initialized = True
            self.logger.info("Custom Module initialized successfully")
            return True
            
        except Exception as e:
            self.logger.error(f"Custom Module initialization failed: {e}")
            return False
    
    def validate_custom_config(self) -> bool:
        """Validate custom configuration"""
        required_keys = ['api_endpoint', 'auth_token', 'data_source']
        return self.validate_config(required_keys)
    
    def setup_custom_components(self):
        """Setup custom components"""
        # Custom component setup logic
        pass
    
    def process_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Process data using custom logic"""
        if not self.initialized:
            raise RuntimeError("Module not initialized")
        
        # Custom data processing logic
        processed_data = self.apply_custom_transformations(data)
        return processed_data
    
    def apply_custom_transformations(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Apply custom data transformations"""
        # Custom transformation logic
        return data
```

### Custom API Endpoints
```python
# Custom API endpoint integration
from flask import Flask, request, jsonify
import requests

app = Flask(__name__)
AURORA_API = "http://localhost:8080"

@app.route('/custom/predict', methods=['POST'])
def custom_prediction():
    """Custom prediction endpoint using Aurora AI"""
    data = request.get_json()
    
    # Validate input data
    validation_response = requests.post(
        f"{AURORA_API}/api/validation/schema",
        json={"data": data, "schema_type": "prediction_input"}
    )
    
    if validation_response.json().get("status") != "SCHEMA_VALIDATION_COMPLETED":
        return jsonify({"error": "Invalid input data"}), 400
    
    # Get prediction from Aurora
    prediction_response = requests.post(
        f"{AURORA_API}/api/inference/batch",
        json={"data": [data], "model_id": "MDL-001"}
    )
    
    return jsonify(prediction_response.json())

@app.route('/custom/train', methods=['POST'])
def custom_training():
    """Custom training endpoint using Aurora AI"""
    config = request.get_json()
    
    # Trigger training in Aurora
    training_response = requests.post(
        f"{AURORA_API}/api/training/enhanced",
        json=config
    )
    
    return jsonify(training_response.json())
```

## 🎯 Integration Best Practices

### 1. Error Handling
```python
# Robust error handling for Aurora integration
import requests
from typing import Optional
import time

class AuroraIntegrationClient:
    def __init__(self, base_url: str, max_retries: int = 3):
        self.base_url = base_url
        self.max_retries = max_retries
    
    def make_request(self, endpoint: str, method: str = "GET", 
                    data: Optional[dict] = None) -> Optional[dict]:
        """Make request with retry logic"""
        url = f"{self.base_url}{endpoint}"
        
        for attempt in range(self.max_retries):
            try:
                if method == "GET":
                    response = requests.get(url, timeout=30)
                elif method == "POST":
                    response = requests.post(url, json=data, timeout=30)
                
                if response.status_code == 200:
                    return response.json()
                elif response.status_code == 500:
                    time.sleep(2 ** attempt)  # Exponential backoff
                    continue
                else:
                    return {"error": f"HTTP {response.status_code}"}
                    
            except requests.exceptions.RequestException as e:
                if attempt == self.max_retries - 1:
                    return {"error": f"Request failed: {str(e)}"}
                time.sleep(2 ** attempt)
        
        return None
```

### 2. Configuration Management
```python
# Environment-specific configuration
import os
from typing import Dict

class AuroraConfig:
    def __init__(self):
        self.environment = os.getenv('AURORA_ENV', 'development')
        self.config = self._load_config()
    
    def _load_config(self) -> Dict:
        configs = {
            'development': {
                'api_url': 'http://localhost:8080',
                'timeout': 30,
                'retry_attempts': 3
            },
            'staging': {
                'api_url': 'https://staging.aurora.ai',
                'timeout': 60,
                'retry_attempts': 5
            },
            'production': {
                'api_url': 'https://api.aurora.ai',
                'timeout': 120,
                'retry_attempts': 5
            }
        }
        return configs.get(self.environment, configs['development'])
    
    def get(self, key: str, default=None):
        return self.config.get(key, default)
```

### 3. Monitoring Integration
```python
# Monitoring and logging integration
import logging
from datetime import datetime

class AuroraMonitoring:
    def __init__(self, aurora_api_url: str):
        self.api_url = aurora_api_url
        self.logger = logging.getLogger(__name__)
    
    def log_api_call(self, endpoint: str, method: str, 
                    response_code: int, duration: float):
        """Log API call metrics"""
        log_data = {
            'timestamp': datetime.now().isoformat(),
            'endpoint': endpoint,
            'method': method,
            'response_code': response_code,
            'duration': duration
        }
        
        # Send to Aurora monitoring
        try:
            requests.post(
                f"{self.api_url}/api/logs/system",
                json=log_data
            )
        except Exception as e:
            self.logger.error(f"Failed to log to Aurora: {e}")
    
    def check_system_health(self):
        """Check Aurora system health"""
        try:
            response = requests.get(f"{self.api_url}/api/status")
            return response.json().get('status') == 'SUCCESS'
        except Exception as e:
            self.logger.error(f"Health check failed: {e}")
            return False
```

## 🔐 Security Integration

### Authentication
```python
# JWT authentication for Aurora API
import jwt
import requests
from datetime import datetime, timedelta

class AuroraAuthClient:
    def __init__(self, api_url: str, secret_key: str):
        self.api_url = api_url
        self.secret_key = secret_key
        self.token = None
    
    def generate_token(self, user_id: str, permissions: list) -> str:
        """Generate JWT token for Aurora API"""
        payload = {
            'user_id': user_id,
            'permissions': permissions,
            'exp': datetime.utcnow() + timedelta(hours=1),
            'iat': datetime.utcnow()
        }
        return jwt.encode(payload, self.secret_key, algorithm='HS256')
    
    def authenticate(self, user_id: str, permissions: list):
        """Authenticate with Aurora API"""
        self.token = self.generate_token(user_id, permissions)
        return self.token
    
    def make_authenticated_request(self, endpoint: str, method: str = "GET", 
                                  data: Optional[dict] = None):
        """Make authenticated request to Aurora API"""
        headers = {'Authorization': f'Bearer {self.token}'}
        
        if method == "GET":
            return requests.get(f"{self.api_url}{endpoint}", headers=headers)
        elif method == "POST":
            return requests.post(f"{self.api_url}{endpoint}", 
                               json=data, headers=headers)
```

### Data Encryption
```python
# Data encryption integration
import requests
import json

class AuroraSecurityIntegration:
    def __init__(self, api_url: str):
        self.api_url = api_url
    
    def encrypt_sensitive_data(self, data: dict) -> dict:
        """Encrypt sensitive data using Aurora security"""
        response = requests.post(
            f"{self.api_url}/api/security/encrypt",
            json={
                'action': 'encrypt',
                'data': json.dumps(data),
                'algorithm': 'AES-256'
            }
        )
        
        if response.status_code == 200:
            return response.json()
        return {'error': 'Encryption failed'}
    
    def decrypt_sensitive_data(self, encrypted_data: str) -> dict:
        """Decrypt sensitive data using Aurora security"""
        response = requests.post(
            f"{self.api_url}/api/security/encrypt",
            json={
                'action': 'decrypt',
                'data': encrypted_data,
                'algorithm': 'AES-256'
            }
        )
        
        if response.status_code == 200:
            return response.json()
        return {'error': 'Decryption failed'}
```

## 📊 Performance Integration

### Caching Strategy
```python
# Redis caching integration
import redis
import json
from typing import Optional

class AuroraCacheIntegration:
    def __init__(self, redis_host: str, redis_port: int, aurora_api_url: str):
        self.redis_client = redis.Redis(host=redis_host, port=redis_port)
        self.aurora_api = aurora_api_url
        self.cache_ttl = 3600  # 1 hour
    
    def get_cached_prediction(self, data_hash: str) -> Optional[dict]:
        """Get cached prediction from Redis"""
        cached_result = self.redis_client.get(f"prediction:{data_hash}")
        if cached_result:
            return json.loads(cached_result)
        return None
    
    def cache_prediction(self, data_hash: str, prediction: dict):
        """Cache prediction result"""
        self.redis_client.setex(
            f"prediction:{data_hash}",
            self.cache_ttl,
            json.dumps(prediction)
        )
    
    def get_prediction_with_cache(self, data: dict) -> dict:
        """Get prediction with caching"""
        data_hash = hash(json.dumps(data, sort_keys=True))
        
        # Check cache first
        cached_result = self.get_cached_prediction(data_hash)
        if cached_result:
            return cached_result
        
        # Get from Aurora AI
        response = requests.post(
            f"{self.aurora_api}/api/inference/batch",
            json={"data": [data]}
        )
        
        if response.status_code == 200:
            result = response.json()
            # Cache the result
            self.cache_prediction(data_hash, result)
            return result
        
        return {'error': 'Prediction failed'}
```

## 🔄 CI/CD Integration

### GitHub Actions Integration
```yaml
# .github/workflows/aurora-integration.yml
name: Aurora AI Integration

on:
  push:
    branches: [ main, develop ]
  pull_request:
    branches: [ main ]

jobs:
  test-aurora-integration:
    runs-on: ubuntu-latest
    
    steps:
    - uses: actions/checkout@v2
    
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.9
    
    - name: Install dependencies
      run: |
        pip install -r requirements.txt
        pip install pytest requests
    
    - name: Start Aurora AI
      run: |
        python web_backend/server.py &
        sleep 10
    
    - name: Run integration tests
      run: |
        python -c "
        import requests
        import time
        
        # Wait for Aurora to be ready
        for _ in range(30):
            try:
                response = requests.get('http://localhost:8080/api/status')
                if response.status_code == 200:
                    break
            except:
                time.sleep(1)
        
        # Run integration tests
        pytest tests/integration/
        "
    
    - name: Test Aurora endpoints
      run: |
        python -c "
        import requests
        
        # Test key endpoints
        endpoints = [
            '/api/status',
            '/api/training/status',
            '/api/security/status',
            '/api/monitoring/advanced'
        ]
        
        for endpoint in endpoints:
            response = requests.get(f'http://localhost:8080{endpoint}')
            assert response.status_code == 200, f'Endpoint {endpoint} failed'
        
        print('All Aurora endpoints working correctly')
        "
```

## 📱 Mobile App Integration

### React Native Integration
```javascript
// Aurora AI integration for React Native
import axios from 'axios';

class AuroraAIService {
  constructor(baseURL = 'http://localhost:8080') {
    this.api = axios.create({
      baseURL,
      timeout: 30000,
      headers: {
        'Content-Type': 'application/json',
      },
    });
  }

  async predict(data) {
    try {
      const response = await this.api.post('/api/inference/batch', {
        data: [data],
        model_id: 'MDL-001',
      });
      return response.data;
    } catch (error) {
      throw new Error(`Prediction failed: ${error.message}`);
    }
  }

  async validateData(data) {
    try {
      const response = await this.api.post('/api/validation/schema', {
        schema_type: 'json_schema',
        data,
      });
      return response.data;
    } catch (error) {
      throw new Error(`Validation failed: ${error.message}`);
    }
  }

  async getSystemStatus() {
    try {
      const response = await this.api.get('/api/status');
      return response.data;
    } catch (error) {
      throw new Error(`Status check failed: ${error.message}`);
    }
  }
}

// Usage example
const aurora = new AuroraAIService();

const makePrediction = async (inputData) => {
  try {
    // Validate input first
    const validation = await aurora.validateData(inputData);
    if (validation.status !== 'SCHEMA_VALIDATION_COMPLETED') {
      throw new Error('Invalid input data');
    }

    // Make prediction
    const prediction = await aurora.predict(inputData);
    return prediction;
  } catch (error) {
    console.error('Prediction error:', error);
    throw error;
  }
};
```

## 🌐 Web Application Integration

### Frontend Integration
```javascript
// Aurora AI integration for web applications
class AuroraWebIntegration {
  constructor(apiURL, authToken) {
    this.apiURL = apiURL;
    this.authToken = authToken;
  }

  async makeAuthenticatedRequest(endpoint, method = 'GET', data = null) {
    const config = {
      method,
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${this.authToken}`,
      },
    };

    if (data && method !== 'GET') {
      config.body = JSON.stringify(data);
    }

    const response = await fetch(`${this.apiURL}${endpoint}`, config);
    return response.json();
  }

  async trainModel(config) {
    return this.makeAuthenticatedRequest('/api/training/enhanced', 'POST', config);
  }

  async getModelMetrics() {
    return this.makeAuthenticatedRequest('/api/monitoring/analytics');
  }

  async optimizeSystem() {
    return this.makeAuthenticatedRequest('/api/optimization/analyze', 'POST', {
      scope: 'full_system',
      depth: 'comprehensive',
    });
  }
}
```

## 📞 Support and Troubleshooting

### Common Integration Issues
1. **Connection Timeouts**: Increase timeout values for large operations
2. **Authentication Failures**: Verify JWT tokens and API keys
3. **Data Format Issues**: Use schema validation before sending data
4. **Rate Limiting**: Implement request throttling and caching
5. **System Unavailability**: Implement health checks and retry logic

### Debug Mode
```python
# Debug mode for Aurora integration
class AuroraDebugClient:
    def __init__(self, api_url: str):
        self.api_url = api_url
        self.debug = True
    
    def debug_request(self, endpoint: str, method: str, data: dict = None):
        """Debug request with detailed logging"""
        if self.debug:
            print(f"Making {method} request to {endpoint}")
            if data:
                print(f"Request data: {json.dumps(data, indent=2)}")
        
        start_time = time.time()
        response = requests.request(method, f"{self.api_url}{endpoint}", json=data)
        duration = time.time() - start_time
        
        if self.debug:
            print(f"Response status: {response.status_code}")
            print(f"Response time: {duration:.2f}s")
            print(f"Response data: {json.dumps(response.json(), indent=2)}")
        
        return response
```

---

**Aurora AI Integration Guide**  
*Enterprise Integration • Custom Development • Best Practices*