System Architecture

The Four Sided Triangle framework implements a sophisticated multi-modal processing system inspired by biological systems, particularly the glycolytic cycle. This document provides a detailed overview of the system’s architecture and its components.

High-Level Overview

The system is structured as a pipeline of specialized models, each handling specific aspects of information processing. The architecture consists of several key layers:

1. Frontend Layer: Modern web interface for user interaction
2. API Layer: RESTful interface for system communication
3. Pipeline Layer: Orchestrates the flow of information
4. Model Layer: Contains specialized AI models
5. Storage Layer: Manages data persistence and caching

Component Details

Frontend Layer

The frontend is built using modern web technologies:

• React.js for UI components
• Redux for state management
• Material-UI for component styling
• WebSocket for real-time updates

API Layer

The REST API provides:

• Authentication and authorization
• Request validation
• Rate limiting
• Response formatting
• Error handling
• Monitoring endpoints

Pipeline Layer

The pipeline orchestrator:

• Manages the flow of information between models
• Handles error recovery and retries
• Provides monitoring and logging
• Implements caching strategies
• Manages resource allocation

Model Layer

Specialized models include:

• SciBert: Scientific text understanding
• BART-MNLI: Natural language inference
• Custom Models: Domain-specific processing
• Verification Models: Output validation

Storage Layer

Data management includes:

• Document storage
• Model cache
• Processing results
• System metrics
• User data

Data Flow

1. Input Processing:
   • Request validation
   • Data normalization
   • Format conversion
2. Pipeline Processing:
   • Model selection
   • Sequential processing
   • Parallel processing where applicable
   • Result aggregation
3. Output Generation:
   • Result formatting
   • Response validation
   • Client delivery

System Integration

Internal Communication

Components communicate through:

• REST API calls
• Message queues
• WebSocket connections
• Shared memory (where applicable)

External Integration

The system can be integrated with:

• External APIs
• Database systems
• Message brokers
• Monitoring systems

Security Architecture

Security measures include:

• API authentication
• Request validation
• Rate limiting
• Input sanitization
• Output validation
• Audit logging

Deployment Architecture

The system supports:

• Docker containerization
• Kubernetes orchestration
• Cloud deployment
• On-premises installation

Scaling Strategies

The architecture supports scaling through:

• Horizontal scaling of API servers
• Model parallelization
• Load balancing
• Caching layers

Monitoring and Logging

The system implements:

• Performance metrics
• Error tracking
• Resource utilization monitoring
• Request/response logging
• Model performance analytics

Configuration Management

Configuration is managed through:

• Environment variables
• Configuration files
• API endpoints
• Database settings

Future Considerations

The architecture is designed to accommodate:

• New model integration
• Additional processing stages
• Enhanced monitoring
• Improved scaling capabilities
• Extended API functionality

Technical Specifications

Technology Stack

• Backend: Python, FastAPI
• Frontend: React, TypeScript
• Database: PostgreSQL
• Cache: Redis
• Message Queue: RabbitMQ
• Container: Docker
• Orchestration: Kubernetes

Performance Characteristics

• Request latency targets
• Throughput capabilities
• Resource utilization
• Scaling limits
• Cache hit rates

Development Guidelines

When extending the architecture:

1. Maintain modularity
2. Follow existing patterns
3. Document changes
4. Update tests
5. Consider backward compatibility