Best Practices Overview

Learn production-ready patterns, optimization strategies, and enterprise deployment guidelines for Alactic AGI.

Architecture & Design

System Architecture

Scalable Design Principles

• Microservices Pattern: Separate processing, storage, and API layers
• Event-Driven Architecture: Use webhooks and message queues
• Horizontal Scaling: Design for distributed processing
• Stateless Operations: Enable seamless scaling and failover
• Circuit Breakers: Implement fault tolerance patterns

High Availability Setup

# Azure deployment example
resource:
  type: Microsoft.Web/sites
  properties:
    alwaysOn: true
    minInstances: 2
    
loadBalancer:
  enabled: true
  healthCheck:
    path: /health
    interval: 30s
    
autoscaling:
  minInstances: 2
  maxInstances: 10
  rules:
    - metric: RequestCount
      threshold: 100
      action: ScaleOut

Data Flow Design

graph LR
    A[Document Upload] --> B[Queue]
    B --> C[Processing Workers]
    C --> D[Data Store]
    D --> E[API Layer]
    E --> F[Client Applications]
    C --> G[Webhook Notifications]

Performance Optimization

Batch Processing

Process multiple documents efficiently:

from alactic import AlacticClient

client = AlacticClient(api_key="your-api-key")

# Process documents in batches
batch_result = client.process_batch(
    documents=[
        {"url": "doc1.pdf", "priority": "high"},
        {"url": "doc2.pdf", "priority": "normal"},
        {"url": "doc3.pdf", "priority": "normal"}
    ],
    batch_size=50,
    parallel_workers=5
)

Caching Strategy

import redis
import hashlib

# Initialize Redis cache
cache = redis.Redis(host='localhost', port=6379)

def process_with_cache(document_url):
    # Generate cache key
    cache_key = hashlib.sha256(document_url.encode()).hexdigest()
    
    # Check cache
    cached_result = cache.get(cache_key)
    if cached_result:
        return json.loads(cached_result)
    
    # Process document
    result = client.process_document(file_url=document_url)
    
    # Cache result (24 hour expiry)
    cache.setex(cache_key, 86400, json.dumps(result.data))
    
    return result.data

Async Processing

const Alactic = require('@alactic/sdk');
const Queue = require('bull');

// Set up processing queue
const documentQueue = new Queue('documents', {
  redis: { host: 'localhost', port: 6379 }
});

// Add documents to queue
documentQueue.add({ url: 'document.pdf' });

// Process asynchronously
documentQueue.process(async (job) => {
  const client = new Alactic({ apiKey: process.env.API_KEY });
  const result = await client.processDocument({
    fileUrl: job.data.url
  });
  return result;
});

Security Best Practices

API Key Management

from azure.keyvault.secrets import SecretClient
from azure.identity import DefaultAzureCredential

# Use Azure Key Vault
credential = DefaultAzureCredential()
client = SecretClient(
    vault_url="https://your-vault.vault.azure.net/",
    credential=credential
)

# Retrieve API key securely
api_key = client.get_secret("alactic-api-key").value

Data Encryption

# Encrypt sensitive data before processing
from cryptography.fernet import Fernet

# Generate encryption key
key = Fernet.generate_key()
cipher = Fernet(key)

# Encrypt data
encrypted_data = cipher.encrypt(document_data)

# Process encrypted data
result = client.process_document(
    data=encrypted_data,
    encryption_key=key
)

Network Security

• Use private endpoints for Azure deployments
• Implement IP whitelisting for API access
• Enable TLS 1.3 for all connections
• Use VPN or ExpressRoute for on-premises integration
• Implement DDoS protection

Error Handling & Resilience

Retry Logic

import time
from functools import wraps

def retry_with_backoff(max_retries=3, base_delay=1):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_retries - 1:
                        raise
                    delay = base_delay * (2 ** attempt)
                    print(f"Retry {attempt + 1}/{max_retries} after {delay}s")
                    time.sleep(delay)
        return wrapper
    return decorator

@retry_with_backoff(max_retries=3)
def process_document(url):
    return client.process_document(file_url=url)

Circuit Breaker Pattern

const CircuitBreaker = require('opossum');

const options = {
  timeout: 30000, // 30 seconds
  errorThresholdPercentage: 50,
  resetTimeout: 60000 // 1 minute
};

const breaker = new CircuitBreaker(async (url) => {
  return await client.processDocument({ fileUrl: url });
}, options);

// Handle circuit breaker events
breaker.on('open', () => console.log('Circuit breaker opened'));
breaker.on('halfOpen', () => console.log('Circuit breaker half-open'));
breaker.on('close', () => console.log('Circuit breaker closed'));

Error Logging

import logging
from pythonjsonlogger import jsonlogger

# Configure structured logging
logger = logging.getLogger()
logHandler = logging.StreamHandler()
formatter = jsonlogger.JsonFormatter()
logHandler.setFormatter(formatter)
logger.addHandler(logHandler)
logger.setLevel(logging.INFO)

def process_with_logging(document_url):
    try:
        logger.info("Processing started", extra={
            "document_url": document_url,
            "timestamp": datetime.now().isoformat()
        })
        
        result = client.process_document(file_url=document_url)
        
        logger.info("Processing completed", extra={
            "document_url": document_url,
            "processing_time_ms": result.metadata.processing_time,
            "success": True
        })
        
        return result
    except Exception as e:
        logger.error("Processing failed", extra={
            "document_url": document_url,
            "error": str(e),
            "error_type": type(e).__name__
        })
        raise

Monitoring & Observability

Application Insights Integration

from opencensus.ext.azure import metrics_exporter
from opencensus.stats import aggregation as aggregation_module
from opencensus.stats import measure as measure_module
from opencensus.stats import stats as stats_module
from opencensus.stats import view as view_module

# Configure metrics
stats = stats_module.stats
view_manager = stats.view_manager
exporter = metrics_exporter.new_metrics_exporter(
    connection_string='InstrumentationKey=your-key'
)

# Create custom metrics
processing_measure = measure_module.MeasureInt(
    "documents_processed",
    "Number of documents processed",
    "documents"
)

# Track metrics
def process_with_metrics(document_url):
    mmap = stats.stats_recorder.new_measurement_map()
    mmap.measure_int_put(processing_measure, 1)
    mmap.record()
    
    return client.process_document(file_url=document_url)

Health Checks

const express = require('express');
const app = express();

app.get('/health', async (req, res) => {
  const health = {
    status: 'healthy',
    timestamp: new Date().toISOString(),
    checks: {}
  };
  
  // Check API connectivity
  try {
    await client.ping();
    health.checks.api = 'healthy';
  } catch (error) {
    health.checks.api = 'unhealthy';
    health.status = 'unhealthy';
  }
  
  // Check database
  try {
    await db.ping();
    health.checks.database = 'healthy';
  } catch (error) {
    health.checks.database = 'unhealthy';
    health.status = 'unhealthy';
  }
  
  const statusCode = health.status === 'healthy' ? 200 : 503;
  res.status(statusCode).json(health);
});

Cost Optimization

Request Optimization

• Batch similar documents together
• Use appropriate processing tiers
• Implement result caching
• Schedule non-urgent processing during off-peak hours
• Use webhook callbacks instead of polling

Resource Management

# Optimize based on document type
def process_optimized(document):
    # Simple documents use basic processing
    if document.type in ['txt', 'csv']:
        return client.process_document(
            file_url=document.url,
            tier='basic'
        )
    
    # Complex documents use advanced processing
    elif document.type in ['pdf', 'docx']:
        return client.process_document(
            file_url=document.url,
            tier='advanced'
        )

Testing Strategies

Unit Testing

import unittest
from unittest.mock import Mock, patch

class TestDocumentProcessing(unittest.TestCase):
    def setUp(self):
        self.client = AlacticClient(api_key="test-key")
    
    @patch('alactic.AlacticClient.process_document')
    def test_successful_processing(self, mock_process):
        # Mock successful response
        mock_process.return_value = Mock(
            success=True,
            data={"extracted": "content"}
        )
        
        result = self.client.process_document(
            file_url="test.pdf"
        )
        
        self.assertTrue(result.success)
        self.assertIn("extracted", result.data)

Integration Testing

def test_end_to_end_processing():
    # Upload test document
    upload_result = client.upload_document(
        file_path="test_data/sample.pdf"
    )
    
    # Process document
    process_result = client.process_document(
        document_id=upload_result.document_id
    )
    
    # Verify results
    assert process_result.success
    assert len(process_result.data) > 0
    
    # Cleanup
    client.delete_document(upload_result.document_id)

Deployment Strategies

Blue-Green Deployment

# Azure App Service deployment slots
production:
  slot: production
  url: https://app.alactic.io
  
staging:
  slot: staging
  url: https://app-staging.alactic.io

# Swap slots after validation
az webapp deployment slot swap \
  --name app \
  --resource-group rg \
  --slot staging \
  --target-slot production

Canary Deployment

# Traffic split configuration
traffic:
  stable: 90%
  canary: 10%
  
monitoring:
  metrics:
    - error_rate
    - latency_p99
    - success_rate
  threshold:
    error_rate: 1%
    latency_p99: 2000ms

Next Steps

• Security Guidelines - Comprehensive security practices
• Performance Tuning - Advanced optimization techniques
• Monitoring Guide - Set up comprehensive monitoring
• Disaster Recovery - Business continuity planning
• Cost Management - Optimize your spending

Support

Need guidance on best practices?

• Email: support@alacticai.com
• Enterprise Support
• Architecture Review