Memoriva System Design

Scalable RAG-based AI Study Assistant Infrastructure

System Architecture Overview

High-Level System Design

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RAG System Architecture

RAG Pipeline Scalability

Document Ingestion

  • • Async processing with queue system
  • • Parallel document chunking
  • • Batch embedding generation
  • • Progressive indexing

Vector Search Optimization

  • • Hierarchical navigable small world (HNSW)
  • • Approximate nearest neighbor search
  • • Index partitioning by user/session
  • • Caching frequent queries

Performance Metrics

// RAG Performance Targets
const ragMetrics = {
  // Document Processing
  ingestion: {
    throughput: "100 docs/min",
    latency: "< 30s per doc",
    chunkSize: "512-1024 tokens",
    overlap: "50 tokens"
  },
  
  // Vector Search
  retrieval: {
    queryLatency: "< 200ms",
    accuracy: "> 90%",
    recall: "> 85%",
    indexSize: "10M+ vectors"
  },
  
  // Generation
  synthesis: {
    responseTime: "< 3s",
    contextLength: "4k tokens",
    relevanceScore: "> 0.8",
    coherence: "> 90%"
  }
}

Scalability & Performance Architecture

Horizontal Scaling Strategy

Auto Scaling Components

  • • ECS services with target tracking
  • • Queue-based worker scaling
  • • Database read replicas
  • • CDN edge caching

Load Distribution

  • • Application Load Balancer
  • • Session-aware routing
  • • Geographic distribution
  • • Circuit breaker patterns

Queue-Based Processing Architecture

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Data Architecture & Storage Strategy

Multi-tier Storage Strategy

Hot Data (Redis)

  • • Active user sessions
  • • Recent query results
  • • Embedding cache
  • • Processing job status

Warm Data (PostgreSQL)

  • • User accounts & sessions
  • • Study session metadata
  • • RAG query history
  • • Analytics data

Cold Data (S3)

  • • Original documents
  • • Processed images
  • • Backup embeddings
  • • Audit logs

Vector Database Architecture

// Vector Database Scaling Strategy
const vectorDBArchitecture = {
  // Indexing Strategy
  indexing: {
    algorithm: "HNSW",
    dimensions: 1536,
    efConstruction: 200,
    maxConnections: 16,
    partitioning: "user-based"
  },
  
  // Sharding Strategy
  sharding: {
    strategy: "hash-based",
    shardKey: "userId",
    replication: 3,
    consistency: "eventual"
  },
  
  // Performance Optimization
  optimization: {
    batchSize: 1000,
    parallelQueries: 10,
    cacheSize: "1GB",
    compressionRatio: 0.7
  },
  
  // Backup & Recovery
  backup: {
    frequency: "daily",
    retention: "30-days",
    crossRegion: true,
    pointInTime: true
  }
}

AI Service Integration & Reliability

Multi-Provider Strategy

Primary Services

  • • OpenAI GPT-4 for complex reasoning
  • • OpenAI text-embedding-3-small
  • • DeepSeek for cost-effective processing
  • • Fallback to local models

Reliability Patterns

  • • Circuit breaker for API failures
  • • Exponential backoff with jitter
  • • Request timeout and retries
  • • Graceful degradation

Cost Optimization

// AI Service Cost Management
const costOptimization = {
  // Request Routing
  routing: {
    simple: "DeepSeek API",
    complex: "OpenAI GPT-4",
    embeddings: "OpenAI text-embedding-3-small",
    fallback: "Local models"
  },
  
  // Caching Strategy
  caching: {
    embeddings: "30-day TTL",
    responses: "7-day TTL",
    hitRate: "> 70%",
    storage: "Redis + S3"
  },
  
  // Rate Limiting
  rateLimiting: {
    perUser: "100 req/hour",
    perSession: "20 req/hour",
    burst: "10 req/min",
    priority: "premium users"
  },
  
  // Monitoring
  monitoring: {
    costPerRequest: "< $0.01",
    monthlyBudget: "$1000",
    alerts: "80% threshold",
    optimization: "weekly review"
  }
}

Security & Privacy Architecture

Data Protection Layers

Data Encryption

  • • TLS 1.3 for data in transit
  • • AES-256 for data at rest
  • • End-to-end encryption for sensitive data
  • • Key rotation every 90 days

Access Control

  • • OAuth 2.0 with PKCE
  • • JWT with short expiration
  • • Role-based access control (RBAC)
  • • Multi-factor authentication

Privacy Compliance

  • • GDPR compliance framework
  • • Data anonymization
  • • Right to be forgotten
  • • Audit trail logging

Security Monitoring

// Security Monitoring Stack
const securityMonitoring = {
  // Threat Detection
  detection: {
    tool: "AWS GuardDuty",
    alerts: "real-time",
    ml: "anomaly detection",
    integration: "SIEM"
  },
  
  // Vulnerability Management
  vulnerability: {
    scanning: "daily",
    dependencies: "Snyk",
    containers: "Trivy",
    compliance: "SOC 2"
  },
  
  // Incident Response
  incident: {
    playbooks: "automated",
    escalation: "PagerDuty",
    forensics: "CloudTrail",
    recovery: "< 4h RTO"
  },
  
  // Compliance
  compliance: {
    framework: "GDPR + SOC 2",
    audits: "quarterly",
    certifications: "ISO 27001",
    reporting: "automated"
  }
}

Deployment & Infrastructure Architecture

Container Orchestration

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Infrastructure as Code

  • • Terraform for AWS resources
  • • Docker Compose for local development
  • • ECS task definitions
  • • Environment-specific configurations

Monitoring & Observability

  • • CloudWatch for metrics and logs
  • • X-Ray for distributed tracing
  • • Custom dashboards for RAG metrics
  • • Automated alerting and escalation
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