Enterprise Features

@DevilsDev/rag-pipeline-utils provides comprehensive enterprise-grade capabilities for mission-critical RAG deployments. This guide covers the advanced features designed for production environments requiring high availability, security, and observability.

🏢 Enterprise Architecture Overview

Production-Ready Infrastructure

The enterprise architecture is built on proven patterns and practices:

Dependency Injection Container: IoC pattern for modular, testable code
SLO Monitoring System: Service Level Objectives with error budgets and alerting
Semantic Release Automation: Automated versioning and deployment pipelines
External API Mocking: Deterministic testing with network simulation
Structured Observability: Comprehensive logging, metrics, and tracing

Enterprise Deployment Model

graph TB
    subgraph "Production Environment"
        LB[Load Balancer]
        API1[RAG API Instance 1]
        API2[RAG API Instance 2]
        API3[RAG API Instance 3]
    end
    
    subgraph "Data Layer"
        VDB[(Vector Database)]
        Cache[(Redis Cache)]
        Metrics[(Metrics Store)]
    end
    
    subgraph "Monitoring Stack"
        Prometheus[Prometheus]
        Grafana[Grafana]
        AlertManager[Alert Manager]
    end
    
    subgraph "External Services"
        OpenAI[OpenAI API]
        Pinecone[Pinecone]
        Azure[Azure OpenAI]
    end
    
    LB --> API1
    LB --> API2
    LB --> API3
    
    API1 --> VDB
    API2 --> VDB
    API3 --> VDB
    
    API1 --> Cache
    API2 --> Cache
    API3 --> Cache
    
    API1 --> OpenAI
    API2 --> Pinecone
    API3 --> Azure
    
    API1 --> Prometheus
    API2 --> Prometheus
    API3 --> Prometheus
    
    Prometheus --> Grafana
    Prometheus --> AlertManager

🔧 Dependency Injection Container

The enterprise IoC container provides modular, testable architecture with lifecycle management.

Service Registration

import { DependencyContainer } from '@DevilsDev/rag-pipeline-utils/enterprise';

const container = new DependencyContainer();

// Register singleton services
container.register('logger', () => new StructuredLogger({
  level: 'info',
  correlationId: true,
  structured: true
}), { lifecycle: 'singleton' });

// Register factory services with dependencies
container.register('embedder', (logger) => new OpenAIEmbedder({
  apiKey: process.env.OPENAI_API_KEY,
  logger
}), { 
  lifecycle: 'factory',
  dependencies: ['logger']
});

// Register with interface validation
container.registerInterface('retriever', PineconeRetriever, {
  implements: ['RetrieverPlugin'],
  dependencies: ['logger', 'metrics']
});

Service Resolution

// Resolve services with automatic dependency injection
const pipeline = container.resolve('ragPipeline');
const logger = container.resolve('logger');
const metrics = container.resolve('metricsCollector');

// Conditional resolution
const embedder = container.resolveConditional('embedder', {
  environment: 'production',
  feature: 'advanced-embeddings'
});

Lifecycle Management

// Graceful shutdown with dependency cleanup
process.on('SIGTERM', async () => {
  await container.shutdown({
    timeout: 30000,
    graceful: true
  });
});

// Health checks for registered services
const healthStatus = await container.healthCheck();
console.log('Service Health:', healthStatus);

📊 SLO Monitoring System

Built-in Service Level Objectives tracking with error budgets, alerting, and automated remediation.

SLO Definition

import { SLOMonitor } from '@DevilsDev/rag-pipeline-utils/observability';

const sloMonitor = new SLOMonitor({
  storage: 'prometheus',
  alerting: {
    webhook: process.env.SLACK_WEBHOOK_URL,
    channels: ['#rag-alerts', '#engineering']
  }
});

// Define availability SLO
sloMonitor.defineSLO('rag-pipeline-availability', {
  objective: 0.999, // 99.9% availability
  window: '30d',
  errorBudget: {
    policy: 'burn-rate',
    alertThresholds: [2, 5, 10] // 2x, 5x, 10x burn rates
  },
  indicators: [
    {
      name: 'http_requests_success_rate',
      query: 'sum(rate(http_requests_total{status!~"5.."}[5m])) / sum(rate(http_requests_total[5m]))'
    }
  ]
});

// Define latency SLO
sloMonitor.defineSLO('rag-pipeline-latency', {
  objective: 0.95, // 95% of requests under 2s
  window: '7d',
  threshold: 2000, // 2 seconds
  indicators: [
    {
      name: 'response_time_p95',
      query: 'histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))'
    }
  ]
});

Real-Time Monitoring

// Record measurements
sloMonitor.recordMeasurement('rag-pipeline-availability', {
  success: true,
  timestamp: Date.now(),
  metadata: { endpoint: '/query', method: 'POST' }
});

sloMonitor.recordMeasurement('rag-pipeline-latency', {
  success: true,
  latency: 1500, // 1.5 seconds
  timestamp: Date.now()
});

// Get error budget status
const budgetStatus = await sloMonitor.getErrorBudgetStatus('rag-pipeline-availability');
console.log('Error Budget Remaining:', budgetStatus.remaining);
console.log('Current Burn Rate:', budgetStatus.burnRate);

// Set up automated alerting
sloMonitor.on('slo:violation', (event) => {
  console.error('SLO Violation:', event);
  // Trigger automated remediation
  if (event.severity === 'critical') {
    triggerAutoScaling();
  }
});

SLO Dashboard

// Generate SLO dashboard data
const dashboardData = await sloMonitor.generateDashboard({
  timeRange: '24h',
  slos: ['rag-pipeline-availability', 'rag-pipeline-latency'],
  includeForecasting: true
});

// Export SLO reports
const report = await sloMonitor.generateReport({
  format: 'pdf',
  period: 'monthly',
  recipients: ['engineering@company.com']
});

🧪 External API Mocking Infrastructure

Deterministic testing infrastructure with network simulation for reliable CI/CD pipelines.

Mock Configuration

import { ExternalAPIMocker } from '@DevilsDev/rag-pipeline-utils/testing';

const mocker = new ExternalAPIMocker({
  deterministic: true,
  seed: 'test-seed-123'
});

// Mock OpenAI API
mocker.mockAPI('openai', {
  baseURL: 'https://api.openai.com',
  responses: {
    '/v1/embeddings': {
      success: {
        data: [{ embedding: [0.1, 0.2, 0.3] }],
        usage: { total_tokens: 10 }
      },
      errors: {
        rate_limit: { status: 429, message: 'Rate limit exceeded' },
        invalid_key: { status: 401, message: 'Invalid API key' }
      }
    },
    '/v1/chat/completions': {
      success: {
        choices: [{ message: { content: 'Mocked response' } }],
        usage: { total_tokens: 50 }
      }
    }
  },
  latency: { min: 100, max: 500 },
  errorRate: 0.05,
  rateLimiting: {
    requestsPerMinute: 60,
    tokensPerMinute: 150000
  }
});

// Mock Pinecone API
mocker.mockAPI('pinecone', {
  baseURL: 'https://api.pinecone.io',
  responses: {
    '/vectors/upsert': { success: { upsertedCount: 100 } },
    '/query': {
      success: {
        matches: [
          { id: 'doc1', score: 0.95, metadata: { text: 'Sample text' } }
        ]
      }
    }
  },
  networkConditions: {
    bandwidth: '100mbps',
    latency: 50,
    packetLoss: 0.001
  }
});

Test Integration

// Jest setup with mocking
beforeAll(async () => {
  await mocker.start();
  
  // Configure pipeline to use mocked endpoints
  process.env.OPENAI_BASE_URL = mocker.getEndpoint('openai');
  process.env.PINECONE_BASE_URL = mocker.getEndpoint('pinecone');
});

afterAll(async () => {
  await mocker.stop();
});

// Deterministic test scenarios
test('handles API rate limiting gracefully', async () => {
  // Configure mock to return rate limit errors
  mocker.setErrorRate('openai', 1.0, 'rate_limit');
  
  const pipeline = createRagPipeline(testConfig);
  
  // Should retry and eventually succeed
  const result = await pipeline.query('test query');
  expect(result).toBeDefined();
  
  // Verify retry behavior
  const metrics = mocker.getMetrics('openai');
  expect(metrics.retryCount).toBeGreaterThan(0);
});

// Network condition simulation
test('handles poor network conditions', async () => {
  mocker.setNetworkConditions('openai', {
    latency: 2000, // 2s latency
    packetLoss: 0.1 // 10% packet loss
  });
  
  const startTime = Date.now();
  const result = await pipeline.query('test query');
  const duration = Date.now() - startTime;
  
  expect(duration).toBeGreaterThan(2000);
  expect(result).toBeDefined();
});

🤖 Advanced AI Capabilities

Handle text, images, and structured data in unified pipelines:

import { MultiModalProcessor } from '@DevilsDev/rag-pipeline-utils/ai';

const processor = new MultiModalProcessor({
  textProcessor: 'openai-embedder',
  imageProcessor: 'clip-embedder',
  structuredProcessor: 'table-embedder',
  fusionStrategy: 'weighted-average'
});

// Process multi-modal document
const result = await processor.process({
  modalities: [
    { type: 'text', content: 'Document text content' },
    { type: 'image', content: imageBuffer, metadata: { format: 'png' } },
    { type: 'table', content: csvData, metadata: { headers: true } }
  ],
  fusionWeights: { text: 0.5, image: 0.3, table: 0.2 }
});

Federated Learning

Distributed model training with privacy preservation:

import { FederatedLearningCoordinator } from '@DevilsDev/rag-pipeline-utils/ai';

const coordinator = new FederatedLearningCoordinator({
  aggregationStrategy: 'federated-averaging',
  privacyBudget: 1.0,
  differentialPrivacy: true
});

// Coordinate federated training
const globalModel = await coordinator.coordinateTraining({
  participants: federatedNodes,
  rounds: 10,
  clientSampleRatio: 0.1,
  localEpochs: 5
});

Adaptive Retrieval

Dynamic retrieval strategies with performance optimization:

import { AdaptiveRetrievalEngine } from '@DevilsDev/rag-pipeline-utils/ai';

const engine = new AdaptiveRetrievalEngine({
  strategies: ['semantic', 'keyword', 'hybrid', 'graph-based'],
  adaptationPolicy: 'performance-based',
  learningRate: 0.01
});

// Adaptive retrieval with strategy selection
const results = await engine.adaptiveRetrieve(query, {
  domain: 'technical-documentation',
  userContext: { expertise: 'intermediate' },
  performanceRequirements: { maxLatency: 500 }
});

🔒 Enterprise Security

Authentication & Authorization

import { EnterpriseAuth } from '@DevilsDev/rag-pipeline-utils/enterprise';

const auth = new EnterpriseAuth({
  provider: 'azure-ad',
  tenantId: process.env.AZURE_TENANT_ID,
  clientId: process.env.AZURE_CLIENT_ID,
  rbac: {
    roles: ['admin', 'user', 'readonly'],
    permissions: {
      'admin': ['*'],
      'user': ['query', 'ingest'],
      'readonly': ['query']
    }
  }
});

// Middleware for authentication
app.use(auth.middleware());

// Role-based access control
app.post('/ingest', auth.requireRole('user'), async (req, res) => {
  // Handle ingestion
});

Audit Logging

import { AuditLogger } from '@DevilsDev/rag-pipeline-utils/enterprise';

const auditLogger = new AuditLogger({
  storage: 'elasticsearch',
  retention: '7y', // 7 years for compliance
  encryption: true,
  immutable: true
});

// Log security-sensitive operations
auditLogger.log('document.ingest', {
  userId: req.user.id,
  documentId: doc.id,
  classification: 'confidential',
  source: req.ip,
  timestamp: new Date().toISOString()
});

📈 Production Deployment

Kubernetes Deployment

# deployment/kubernetes/rag-pipeline-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: rag-pipeline
  labels:
    app: rag-pipeline
spec:
  replicas: 3
  selector:
    matchLabels:
      app: rag-pipeline
  template:
    metadata:
      labels:
        app: rag-pipeline
    spec:
      containers:
      - name: rag-pipeline
        image: devilsdev/rag-pipeline-utils:latest
        ports:
        - containerPort: 3000
        env:
        - name: NODE_ENV
          value: "production"
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-secrets
              key: openai-key
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5

Helm Chart Configuration

# deployment/helm/rag-pipeline/values.yaml
replicaCount: 3

image:
  repository: devilsdev/rag-pipeline-utils
  tag: "latest"
  pullPolicy: IfNotPresent

service:
  type: ClusterIP
  port: 80
  targetPort: 3000

ingress:
  enabled: true
  className: "nginx"
  annotations:
    cert-manager.io/cluster-issuer: "letsencrypt-prod"
  hosts:
    - host: rag-api.company.com
      paths:
        - path: /
          pathType: Prefix
  tls:
    - secretName: rag-api-tls
      hosts:
        - rag-api.company.com

monitoring:
  enabled: true
  serviceMonitor:
    enabled: true
    interval: 30s
    scrapeTimeout: 10s

autoscaling:
  enabled: true
  minReplicas: 3
  maxReplicas: 10
  targetCPUUtilizationPercentage: 70
  targetMemoryUtilizationPercentage: 80

This enterprise feature set enables @DevilsDev/rag-pipeline-utils to meet the demanding requirements of mission-critical production environments while maintaining the flexibility and ease of use that makes it valuable for development and prototyping.

🏢 Enterprise Architecture Overview​

Production-Ready Infrastructure​

Enterprise Deployment Model​

🔧 Dependency Injection Container​

Service Registration​

Service Resolution​

Lifecycle Management​

📊 SLO Monitoring System​

SLO Definition​

Real-Time Monitoring​

SLO Dashboard​

🧪 External API Mocking Infrastructure​

Mock Configuration​

Test Integration​

🤖 Advanced AI Capabilities​

Multi-Modal Processing​

Federated Learning​

Adaptive Retrieval​

🔒 Enterprise Security​

Authentication & Authorization​

Audit Logging​

📈 Production Deployment​

Kubernetes Deployment​

Helm Chart Configuration​