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MongoDB-RAG

MongoDB-RAG is a library designed to integrate Retrieval-Augmented Generation (RAG) with MongoDB Atlas Vector Search.

🚀 Features​

  • Fast Vector Search with MongoDB Atlas
  • OpenAI and DeepSeek Embeddings Support
  • Batch Processing for Large Datasets
  • Advanced Indexing via MongoDB Aggregation
  • CLI for Scaffolding RAG Apps

📦 Installation​

npm install mongodb-rag

📚 Quick Start with CLI​

You can generate a fully working RAG-enabled app with MongoDB Atlas Vector Search using:

npx mongodb-rag create-rag-app my-rag-app

This will:

  • Scaffold a new CRUD RAG app with Express and MongoDB Atlas.
  • Set up environment variables for embedding providers.
  • Create API routes for ingestion, search, and deletion.

Then, navigate into your project and run:

cd my-rag-app
npm install
npm run dev

📚 Usage Example​

import MongoRAG from 'mongodb-rag';

const rag = new MongoRAG({
  mongoUrl: 'mongodb+srv://your-db-url',
  database: 'mydb',
  collection: 'mycollection',
  embedding: {
    provider: process.env.EMBEDDING_PROVIDER || 'openai',
    apiKey: process.env.EMBEDDING_API_KEY,
    model: process.env.EMBEDDING_MODEL || 'text-embedding-3-small'
  }
});

await rag.connect();
const results = await rag.search('What is vector search?');
console.log(results);