System Architecture
This page provides an overview of Qwello's system architecture, explaining how the different components work together to deliver the platform's capabilities.
Last updated
This page provides an overview of Qwello's system architecture, explaining how the different components work together to deliver the platform's capabilities.
Last updated
Qwello implements a modern, scalable architecture designed for performance, reliability, and extensibility. The system comprises several key components that work together to deliver a seamless document processing and knowledge discovery experience.
The Qwello frontend is built with React and TypeScript, providing a responsive and intuitive user interface:
React Framework: Component-based architecture for efficient UI development
TypeScript Integration: Enhanced type safety and developer experience
Responsive Design: Optimized for various screen sizes and devices
Interactive Visualization: Powered by vis-network.js for knowledge graph exploration
Real-Time Updates: WebSocket integration for processing status updates
Qwello's backend is built on NestJS, a progressive Node.js framework:
Modular Architecture: Organized into functional modules for maintainability
WebSocket Integration: Real-time communication for live updates and notifications
Authentication & Authorization: Secure user management with JWT-based authentication
RESTful API: Well-defined endpoints for frontend communication
Qwello integrates with multiple AI models through Cloudflare's AI platform:
Model Selection: Appropriate models for different processing stages
Fallback Mechanisms: Automatic switching to alternative models when needed
Intelligent Routing: Automatic selection of optimal models based on document characteristics
Context Management: Efficient handling of context for large documents
Qwello uses a combination of storage solutions for different data types:
Knowledge Graph Storage: MongoDB for flexible, scalable knowledge graph storage
Document Storage: AWS S3 for secure, durable document storage
Caching Layer: Redis for high-performance caching and session management
Metadata Management: Comprehensive tracking of document properties and processing status
Qwello implements a sophisticated job processing system for handling document processing tasks:
Queue Management: Redis-based job queuing for reliable, scalable processing
Worker Pool: Distributed workers for parallel document processing
Load Balancing: Intelligent distribution of processing tasks
Resource Management: Dynamic scaling based on processing demand
Qwello's backend is organized into functional modules:
┌───────────────────────────────────────────────────────────────────────────────┐
│ Service Architecture │
└───────────────────────────────────────────────────────────────────────────────┘
│
▼
┌───────────────────────────────────────────────────────────────────────────────┐
│ Core Services │
├─────────────┬──────────────┬───────────────┬───────────────┬──────────────────┤
│ │ │ │ │ │
│ Auth │ Document │ Processing │ Knowledge │ User │
│ Service │ Service │ Service │ Service │ Management │
│ │ │ │ │ │
└─────────────┴──────────────┴───────────────┴───────────────┴──────────────────┘The system uses event-driven patterns for loose coupling and scalability:
This modular structure enables:
Separation of Concerns: Each module handles a specific aspect of functionality
Code Reusability: Common components can be shared across modules
Maintainability: Changes to one module don't affect others
Testability: Modules can be tested independently
Scalability: Modules can be deployed separately if needed
The following diagram illustrates the flow of data through the PDF processing pipeline:
The following diagram illustrates the flow of data during knowledge graph querying:
Qwello's architecture is designed for scalability and performance:
Stateless Backend: Multiple backend instances can run in parallel
Database Sharding: MongoDB can be sharded for larger datasets
Load Balancing: Requests can be distributed across multiple instances
CDN Integration: Content delivery networks for global performance optimization
Resource Optimization: Dynamic allocation of CPU and memory resources
Performance Monitoring: Continuous monitoring and optimization of resource usage
Capacity Planning: Predictive scaling based on usage patterns
Cost Optimization: Efficient resource allocation to minimize operational costs
Parallel Processing: Multiple workers process document pages simultaneously
Caching: Redis caching for frequently accessed data
Efficient Storage: Optimized storage formats for knowledge graphs
Lazy Loading: On-demand loading of graph components in the frontend
Image Optimization: Efficient image processing for PDF pages
Comprehensive security measures across all system layers:
Network Security: Secure communication protocols and network isolation
Application Security: Input validation, output encoding, and secure coding practices
Data Security: Encryption at rest and in transit, secure key management
Infrastructure Security: Secure deployment and configuration management
Robust identity and access management:
Multi-Provider Authentication: Support for various identity providers
Role-Based Access Control: Granular permissions based on user roles
JWT Token Management: Secure token-based authentication with proper expiration
Session Management: Secure session handling and timeout policies
Comprehensive data protection measures:
Encryption Standards: Industry-standard encryption for all sensitive data
Privacy Controls: User control over data sharing and retention
Compliance: Adherence to data protection regulations (GDPR, CCPA)
Audit Logging: Comprehensive logging of all data access and modifications
This architecture provides a solid foundation for Qwello's document processing and knowledge graph capabilities, enabling efficient, reliable, and secure operation at scale.
