RAG Starter Kit

A plug-and-play Retrieval-Augmented Generation (RAG) application built with Node.js, React, Pinecone, and OpenAI.

🚀 Features

• Document Upload & Processing: Support for PDF, TXT, DOCX files
• Vector Database: Pinecone integration for efficient similarity search
• AI-Powered Chat: OpenAI GPT integration with context-aware responses
• Modern UI: React frontend with Tailwind CSS
• Real-time Streaming: Live response generation
• Conversation History: Persistent chat sessions
• Document Management: Upload, view, and delete documents
• Metadata Filtering: Filter by document type and source

🛠️ Tech Stack

• Backend: Node.js, Express, Pinecone SDK
• Frontend: React, Tailwind CSS, Axios
• AI: OpenAI GPT-4/3.5-turbo
• Vector Database: Pinecone
• File Processing: Multer, PDF-parse, mammoth

📋 Prerequisites

• Node.js (v18 or higher)
• npm or yarn
• Pinecone account and API key
• OpenAI API key

⚡ Quick Start

1. Clone and Setup

   git clone <your-repo-url>
   cd rag-starter-kit
   npm run setup

2. Environment Configuration

   cp server/env.example server/.env

   Edit server/.env with your API keys:

   OPENAI_API_KEY=your_openai_api_key
   PINECONE_API_KEY=your_pinecone_api_key
   PINECONE_ENVIRONMENT=your_pinecone_environment
   PINECONE_INDEX_NAME=rag-starter-kit
   

3. Start the Application

   npm run dev

4. Access the Application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:5001

📁 Project Structure

rag-starter-kit/
├── server/                 # Node.js backend
│   ├── controllers/        # API route handlers
│   ├── services/          # Business logic
│   ├── utils/             # Helper functions
│   └── index.js           # Server entry point
├── client/                # React frontend
│   ├── src/
│   │   ├── components/    # React components
│   │   ├── services/      # API calls
│   │   └── utils/         # Frontend utilities
│   └── public/            # Static assets
├── uploads/               # Document storage
└── package.json           # Root package.json

🔧 Configuration

Pinecone Setup

1. Create a Pinecone account at https://www.pinecone.io/
2. Create a new index with:
   • Dimensions: 1536 (for OpenAI embeddings)
   • Metric: cosine
   • Pod type: starter (free tier)

OpenAI Setup

1. Get your API key from https://platform.openai.com/
2. Ensure you have credits for API usage

📖 Usage

1. Upload Documents: Drag and drop or select files to upload
2. Process Documents: Wait for documents to be processed and indexed
3. Start Chatting: Ask questions about your documents
4. View History: Access previous conversations
5. Manage Documents: View, search, or delete uploaded documents

🔒 Environment Variables

Variable	Description	Required
OPENAI_API_KEY	OpenAI API key	Yes
PINECONE_API_KEY	Pinecone API key	Yes
PINECONE_ENVIRONMENT	Pinecone environment	Yes
PINECONE_INDEX_NAME	Pinecone index name	Yes
PORT	Server port (default: 5001)	No
NODE_ENV	Environment (development/production)	No

🚀 Deployment

Local Development

npm run dev

Production Build

npm run build
cd server
npm start

Docker Deployment

docker build -t rag-starter-kit .
docker run -p 3000:3000 -p 5001:5001 rag-starter-kit

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details

🆘 Support

• Create an issue for bugs or feature requests
• Check the documentation in the /docs folder
• Review the example configurations

🔄 Updates

Stay updated with the latest features and improvements by checking the releases page.