Architecture

Understanding Engram's distributed architecture and design principles

Architecture

Engram is built on a modern, distributed architecture designed for scale, reliability, and performance. This document provides an in-depth look at the system's components and design decisions.

System Overview

Engram follows a layered architecture pattern with clear separation of concerns:

┌─────────────────────────────────────────────────────────┐
│                    Application Layer                    │
│  SDKs, Web Interface, CLI Tools, Third-party Tools     │
└─────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────┐
│                      API Gateway                       │
│   Authentication, Rate Limiting, Request Routing       │
└─────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────┐
│                    Processing Layer                     │
│     Memory Manager, Search Engine, ML Pipeline          │
└─────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────┐
│                     Storage Layer                      │
│   Vector DB, Metadata DB, Object Storage, Cache        │
└─────────────────────────────────────────────────────────┘

Core Components

API Gateway

The API Gateway serves as the entry point for all client requests:

  • Authentication & Authorization: JWT-based authentication with role-based access control
  • Rate Limiting: Configurable rate limits per user/organization
  • Request Routing: Intelligent routing to appropriate service instances
  • Load Balancing: Distributes traffic across multiple service instances
  • Monitoring: Request logging, metrics collection, and health checks

Memory Manager

The Memory Manager handles all memory-related operations:

  • Storage Operations: Create, read, update, delete memories
  • Metadata Management: Manages structured metadata and relationships
  • Validation: Input validation and data sanitization
  • Lifecycle Management: Automatic expiration and cleanup policies

Search Engine

The Search Engine provides advanced search capabilities:

  • Vector Search: Semantic similarity search using embeddings
  • Hybrid Search: Combines vector and traditional text search
  • Filtering: Advanced filtering on metadata and attributes
  • Ranking: Custom ranking algorithms for relevance scoring

ML Pipeline

The ML Pipeline handles AI-related processing:

  • Embedding Generation: Converts text to vector embeddings
  • Content Analysis: Extracts metadata and insights from content
  • Classification: Automatic categorization and tagging
  • Similarity Computation: Real-time similarity calculations

Storage Architecture

Vector Database

Engram uses Qdrant for vector storage and search:

  • High-Performance: Optimized for similarity search operations
  • Scalability: Horizontal scaling with automatic sharding
  • Persistence: Durable storage with WAL and snapshots
  • Memory Management: Intelligent caching and memory optimization

Metadata Database

PostgreSQL stores structured metadata:

  • ACID Compliance: Ensures data consistency and reliability
  • JSON Support: Native JSON columns for flexible metadata
  • Indexing: Optimized indexes for fast metadata queries
  • Relationships: Supports complex relationships between memories

Object Storage

Large content and files are stored in object storage:

  • Scalability: Virtually unlimited storage capacity
  • Durability: High durability with redundancy and versioning
  • Cost Efficiency: Tiered storage for cost optimization
  • CDN Integration: Global content delivery network

Caching Layer

Redis provides high-performance caching:

  • Query Caching: Frequently accessed search results
  • Session Storage: User sessions and temporary data
  • Rate Limiting: Distributed rate limiting state
  • Real-time Features: Pub/sub for real-time notifications

Scalability Design

Horizontal Scaling

Engram is designed to scale horizontally:

  • Stateless Services: All services are stateless for easy scaling
  • Load Balancing: Automatic load distribution across instances
  • Auto-scaling: Dynamic scaling based on demand
  • Geographic Distribution: Multi-region deployment support

Data Partitioning

Data is partitioned for optimal performance:

  • User-based Partitioning: Data partitioned by user/organization
  • Time-based Partitioning: Historical data archived to cold storage
  • Content-based Partitioning: Large content stored separately
  • Consistent Hashing: Efficient data distribution

Performance Optimization

Multiple optimization strategies ensure high performance:

  • Connection Pooling: Efficient database connection management
  • Batch Processing: Bulk operations for efficiency
  • Asynchronous Processing: Non-blocking operations where possible
  • Caching Strategy: Multi-level caching for faster access

Security Architecture

Authentication & Authorization

  • API Keys: Secure API key management with rotation
  • JWT Tokens: Stateless authentication with configurable expiration
  • RBAC: Role-based access control with fine-grained permissions
  • OAuth Integration: Support for external identity providers

Data Security

  • Encryption at Rest: All data encrypted using AES-256
  • Encryption in Transit: TLS 1.3 for all network communication
  • Key Management: Hardware security modules for key protection
  • Audit Logging: Comprehensive audit trails for compliance

Network Security

  • VPC Isolation: Network isolation in cloud environments
  • Firewall Rules: Strict ingress/egress controls
  • DDoS Protection: Built-in DDoS mitigation
  • Intrusion Detection: Real-time threat monitoring

Deployment Architecture

Container Orchestration

Engram runs on Kubernetes for orchestration:

  • Service Mesh: Istio for service-to-service communication
  • Auto-scaling: HPA and VPA for automatic scaling
  • Rolling Updates: Zero-downtime deployments
  • Health Checks: Comprehensive health monitoring

Infrastructure as Code

  • Terraform: Infrastructure provisioning and management
  • Helm Charts: Kubernetes application packaging
  • CI/CD Pipelines: Automated testing and deployment
  • GitOps: Git-based deployment workflows

Monitoring & Observability

  • Metrics: Prometheus for metrics collection
  • Logging: Centralized logging with ELK stack
  • Tracing: Distributed tracing with Jaeger
  • Alerting: PagerDuty integration for incident response

Data Flow

Write Path

  1. Client sends memory storage request
  2. API Gateway authenticates and validates request
  3. Memory Manager processes and stores metadata
  4. ML Pipeline generates embeddings asynchronously
  5. Vector database stores embeddings
  6. Response returned to client

Read Path

  1. Client sends search request
  2. API Gateway routes request to Search Engine
  3. Query vector generated from search terms
  4. Vector database performs similarity search
  5. Results enriched with metadata
  6. Ranked results returned to client

Performance Characteristics

Latency

  • Memory Storage: < 100ms p99
  • Simple Search: < 50ms p99
  • Complex Search: < 200ms p99
  • Metadata Queries: < 10ms p99

Throughput

  • Write Operations: 10,000+ ops/second
  • Search Operations: 50,000+ ops/second
  • Concurrent Users: 100,000+ users
  • Data Volume: Petabyte-scale storage

Availability

  • Uptime: 99.99% SLA
  • RPO: < 1 minute
  • RTO: < 5 minutes
  • Multi-AZ: Cross-zone redundancy

Future Roadmap

Performance Improvements

  • GPU acceleration for embedding generation
  • Advanced vector compression techniques
  • Improved caching strategies
  • Query optimization engine

New Capabilities

  • Multi-modal embeddings (text, images, audio)
  • Real-time streaming ingestion
  • Advanced analytics and insights
  • Federated search across instances

Developer Experience

  • GraphQL API support
  • Enhanced SDKs with offline capabilities
  • Visual query builder
  • Advanced debugging tools