Aurelis

Aurelis Architecture

This section provides comprehensive architectural documentation for Aurelis.

System Architecture

Overview

System Overview - High-level architecture and components
Component Diagram - Detailed component relationships
Data Flow - Request/response flow and processing

Core Components

Model System - GitHub model integration architecture
Configuration System - Configuration management design
Security Architecture - Authentication and encryption
Cache Architecture - Caching strategies and implementation

Processing Pipeline

Request Processing - Request handling and routing
Code Analysis - Static analysis and quality checks
Code Generation - AI-powered code generation

Design Principles

Enterprise-Grade Quality

Aurelis follows enterprise software development principles:

Reliability: Circuit breaker patterns, automatic fallbacks
Scalability: Async processing, connection pooling
Security: Encrypted token storage, secure API communication
Maintainability: Clean architecture, comprehensive testing
Observability: Detailed logging, metrics, monitoring

GitHub Models Integration

Built exclusively for GitHub models via Azure AI Inference:

Single Token: Only GitHub token required
Native Integration: Direct Azure AI Inference API usage
Intelligent Routing: Task-based model selection
Performance Optimization: Caching, batching, rate limiting

Component Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Aurelis CLI Interface                    │
├─────────────────────────────────────────────────────────────┤
│  Commands: init | config | models | generate | analyze     │
│  Shell: Interactive mode with context preservation         │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│                 Core Orchestration Layer                   │
├─────────────────────────────────────────────────────────────┤
│  Model Orchestrator: Request routing and processing        │
│  Task Router: Intelligent model selection                  │
│  Cache Manager: Response caching and optimization          │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│                 GitHub Models Integration                  │
├─────────────────────────────────────────────────────────────┤
│  Azure AI Inference API Client                             │
│  Model Types: Codestral, GPT-4o, Cohere, Llama, Mistral  │
│  Authentication: GitHub Token via Bearer Auth              │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│                  Supporting Services                       │
├─────────────────────────────────────────────────────────────┤
│  Configuration: YAML + Environment Variables               │
│  Security: Encrypted token storage                         │
│  Logging: Structured logging with correlation IDs          │
│  Error Handling: Custom exceptions with context            │
└─────────────────────────────────────────────────────────────┘

Key Design Decisions

1. GitHub Models Exclusive

Decision: Use only GitHub models via Azure AI Inference Rationale:

Simplified authentication (single token)
Enterprise-grade reliability
Cost-effective model access
Direct GitHub integration

2. Async-First Design

Decision: Built on asyncio for all I/O operations Rationale:

Better performance for API calls
Non-blocking UI interactions
Efficient resource utilization
Scalable request processing

3. Configuration-Driven

Decision: YAML configuration with environment overrides Rationale:

Version-controlled settings
Environment-specific configurations
Easy deployment management
Secure credential handling

4. Modular Architecture

Decision: Clean separation of concerns Rationale:

Independent component testing
Easier maintenance and updates
Plugin architecture potential
Clear responsibility boundaries

Security Design

Authentication Flow

User sets GITHUB_TOKEN environment variable
Aurelis reads token from environment or config
Token encrypted and stored in secure storage
API requests use Bearer token authentication
Token validation and refresh handled automatically

Data Protection

At Rest: Configuration encrypted with system keyring
In Transit: HTTPS for all API communications
In Memory: Sensitive data cleared after use
Logging: No sensitive data in logs

Performance Architecture

Caching Strategy

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Request Hash  │───▶│  Cache Lookup   │───▶│  Model API Call │
│                 │    │                 │    │                 │
│ Prompt + Model  │    │  TTL: 1 hour    │    │  GitHub Models  │
│ + System Prompt │    │  Max: 1000 items│    │  via Azure AI   │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
                       ┌─────────────────┐
                       │ Cached Response │
                       │                 │
                       │ Content + Meta  │
                       └─────────────────┘

Rate Limiting

GitHub API Limits: Automatically respected
Exponential Backoff: On rate limit hits
Request Queuing: Prevents overwhelming APIs
Circuit Breaker: Fails fast on persistent errors

Error Handling Architecture

Exception Hierarchy

AurelisError (Base)
├── ConfigurationError
│   ├── InvalidConfigError
│   └── MissingConfigError
├── ModelError
│   ├── ModelUnavailableError
│   ├── ModelTimeoutError
│   └── ModelQuotaExceededError
├── AuthenticationError
│   ├── InvalidTokenError
│   └── TokenExpiredError
└── ProcessingError
    ├── AnalysisError
    └── GenerationError

Error Recovery

Automatic Retry: Transient failures with exponential backoff
Fallback Models: Primary model failures trigger fallback
Graceful Degradation: Partial functionality on component failure
User Guidance: Clear error messages with resolution steps

Development Environment

Local Development Setup

Local Machine
├── Python 3.8+
├── GitHub Token
├── Project Configuration (.aurelis.yaml)
└── Cache Directory (~/.aurelis/cache)