RAGNA Studio Backend

RAGNA Studio Backend is the API-Server built with NestJS, featuring Retrieval-Augmented Generation (RAG), Named Entity Recognition (NER), multi-provider AI integrations, multi-AI-Agent capabilities with function calling, TextToImage and much more (see frontend for full feature list). This project provides a robust foundation for building AI applications with document processing, chat interfaces, and intelligent assistants.

⚠️ Important Notes

• Not a Turnkey Solution: This API Server is part of a larger ecosystem and requires additional microservices to function fully
• Not Production Ready: This software is provided for educational and development purposes
• API Keys Required: You'll need API keys from AI providers to use AI features
• Resource Intensive: AI operations require significant computational resources

🏗️ RAGNA Studio Ecosystem

This backend is part of the complete RAGNA Studio platform:

• RAGNA Studio Backend (this repository) - NestJs API backend
• RAGNA Studio Frontend - Modern Vue3-based user interface
• RAGNA SDK - TypeScript SDK for easy integration into your own software

Microservices

• RAG Server: Handles document parsing and embedding generation
• NER Server: Performs Named Entity Recognition on text data
• WebSocket Gateway: Manages real-time communication for chat features
• WebScraper Server: Fetches and processes web content

For the full experience, you'll want to set up both the backend, the frontend and all the microservices. The SDK provides a convenient way to integrate RAGNA Studio's AI capabilities into your own applications with type-safe API calls and built-in error handling.

🌟 Features

Core Architecture

• NestJS Framework: Efficient, scalable, and high-performance async API endpoints
• Modular Design: Maintainable architecture that scales with your needs
• TypeScript: Full type safety throughout the codebase
• Prisma ORM: Type-safe database interactions with PostgreSQL
• Redis Integration: Caching and session management
• BullMQ: Background job processing for heavy operations
• WebSocket Support: Real-time communication capabilities
• OpenAPI Documentation: Auto-generated API documentation

AI & ML Capabilities

• RAG (Retrieval-Augmented Generation): Advanced document retrieval and generation
• Multi-Provider AI Support: OpenAI, Anthropic, Google, Mistral, and more
• Named Entity Recognition (NER): Extract and process entities from text
• Document Processing: Support for various file formats (PDF, DOCX, etc.)
• Embedding Generation: Vector embeddings for semantic search
• Chat Interfaces: Real-time chat with AI assistants

Security & Performance

• Role-Based Access Control (RBAC): Granular permission management
• JWT Authentication: Secure token-based authentication with rotation
• Session Management: Secure user session handling
• Input Sanitization: Protection against malicious inputs
• CORS Configuration: Cross-origin resource sharing controls
• Rate Limiting: API abuse protection
• Comprehensive Logging: Structured logging with debug traces

Developer Experience

• Clean Architecture: Object-oriented design with dependency injection
• Separation of Concerns: Modular code organization
• TypeScript SDK: Pre-built, type-safe API client
• Code Quality Tools: Prettier, ESLint, and comprehensive testing
• Environment Configuration: Easy setup for different environments
• Docker Support: Containerized development and deployment

🚀 Quick Start

Prerequisites

• Node.js 22+ and npm/yarn
• Docker & Docker Compose (recommended for development)
• PostgreSQL 17+ (if not using Docker)
• Redis 7+ (if not using Docker)
• Minimum 8GB RAM (for AI model operations)

Installation

1. Clone the repository

   git clone https://github.com/hopkins385/ragna-studio-backend.git
   cd ragna-studio-backend

2. Install dependencies

   npm install

3. Environment Setup

   # Copy the example environment file
   cp .env.example .env
   
   # Edit the .env file with your configuration
   nano .env

4. Database Setup

   # Start PostgreSQL and Redis with Docker
   docker-compose up -d postgres redis
   
   # Run database migrations
   npm run migrate
   
   # Seed the database (optional)
   npm run seed:init

5. Start the Development Server

   npm run dev

Docker Development

For a complete development environment with all services:

# Start all services
docker-compose -f docker-compose.dev.yml up

# Or run in detached mode
docker-compose -f docker-compose.dev.yml up -d

📖 Documentation

• API Documentation: Available at /swagger when running the server (dev-only)
• Database Schema: Check prisma/schema.prisma for the complete data model
• Environment Variables: See .env.example for all configuration options

🛠️ Development

Project Structure

src/
├── modules/           # Feature modules (auth, chat, documents, etc.)
├── common/           # Shared utilities, guards, decorators
├── config/           # Configuration files
└── filter/           # Exception filters

prisma/
├── schema.prisma     # Database schema
├── migrations/       # Database migrations
└── seed/            # Database seeders

Key Modules

• Auth: User authentication and authorization
• Chat: Real-time chat functionality with AI assistants
• AI Models: Integration with various AI providers
• Assistants: Configurable AI assistants with tools
• Collections: Document collections and knowledge bases

🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with NestJS
• Database ORM by Prisma
• AI integrations powered by Vercel AI SDK
• And many other amazing open-source projects

---

Built with ❤️ and Appreciation by Sven Stadhouders

Website • GitHub