RAGNA Studio - RAG Server

A high-performance Retrieval-Augmented Generation (RAG) server built with TypeScript, Express.js, and modern vector databases. This server provides APIs for document parsing, embedding, and semantic search capabilities.

🚀 Features

• Document Processing: Parse various document formats using Apache Tika
• Text Embedding: Support for multiple embedding providers (OpenAI, Cohere)
• Vector Storage: Qdrant integration for efficient vector storage and retrieval
• Semantic Search: Fast similarity search across embedded documents
• Text Tokenization: Built-in tokenization with tiktoken
• Docker Support: Complete Docker Compose setup for easy deployment
• TypeScript: Full type safety and modern development experience

📋 API Endpoints

The server exposes the following REST API endpoints under /api/v1:

• /parse - Document parsing and text extraction
• /embed - Document embedding and vector storage
• /search - Semantic search across embedded documents
• /tokenize - Text tokenization services

🛠️ Tech Stack

• Runtime: Node.js with TypeScript
• Framework: Express.js
• Vector Database: Qdrant
• Document Processing: Apache Tika
• Embedding Providers: OpenAI, Cohere
• Validation: Zod schemas
• Logging: Consola
• Containerization: Docker & Docker Compose

🏃‍♂️ Quick Start

Prerequisites

• Node.js 22+
• Docker and Docker Compose
• Environment variables configured (see Configuration)

Development Setup

1. Clone the repository

   git clone https://github.com/hopkins385/rag-server-ts.git
   cd rag-server-ts

2. Install dependencies

   npm install

3. Configure environment

   cp .env.example .env
   # Edit .env with your configuration

4. Start development server

   npm run dev

Docker Deployment

1. Start the complete stack

   docker-compose up -d

   This will start:

   • RAG Server (API)
   • Qdrant (Vector Database)
   • Apache Tika (Document Processing)

2. For development with hot reload

   docker-compose -f docker-compose.dev.yml up -d

📖 API Documentation

Document Embedding

POST /api/v1/embed/file
Content-Type: application/json

{
  "mediaId": "unique-media-id",
  "recordId": "unique-record-id",
  "mimeType": "application/pdf",
  "filePath": "/path/to/document.pdf"
}

Semantic Search

POST /api/v1/search/vector
Content-Type: application/json

{
  "query": "your search query",
  "recordIds": ["record-id-1", "record-id-2"]
}

Text Tokenization

POST /api/v1/tokenize/text
Content-Type: application/json

{
  "text": "text to tokenize"
}

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow TypeScript best practices
• Add tests for new features
• Update documentation as needed
• Run linting and type checking before committing
• Use conventional commit messages

📄 License

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

🔗 Related Projects

• Qdrant - Vector similarity search engine
• Apache Tika - Content analysis toolkit
• OpenAI - AI platform for embeddings
• Cohere - Natural language AI platform

📞 Support

If you have any questions or run into issues, please:

1. Check the Issues page
2. Create a new issue with detailed information
3. Join our community discussions

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Built with ❤️ and Appreciation by Sven Stadhouders