---

!MongoDB RAG Logo

MongoDB-RAG

!NPM Version
!License
!Issues
!Pull Requests
!Downloads
!MongoDB-RAG

Overview

MongoDB-RAG (Retrieval Augmented Generation) is an NPM module that simplifies vector search using MongoDB Atlas. This library enables developers to efficiently perform similarity search, caching, batch processing, and indexing for fast and accurate retrieval of relevant data.

🚀 Features

- Vector Search: Efficiently retrieves similar documents using MongoDB's Atlas Vector Search.
- Dynamic Database & Collection Selection: Supports flexible selection of multiple databases and collections.
- Batch Processing: Handles bulk processing of documents with retry mechanisms.
- Index Management: Ensures necessary indexes are available and optimized.
- Caching Mechanism: Provides in-memory caching for frequently accessed data.
- Advanced Chunking: Supports sliding window, semantic, and recursive chunking strategies.
- CLI for Scaffolding RAG Apps

---

🚀 Getting Started

$3

``sh
npm install mongodb-rag dotenv
`

$3

1. Initialize Your App using the CLI:
`sh
npx mongodb-rag init
`
This will guide you through setting up your MongoDB connection and save the configuration to .mongodb-rag.json. Make sure to add .mongodb-rag.json to your .gitignore file to keep your credentials secure.

`bash
% npx mongodb-rag init
✔ Enter your MongoDB connection string: · mongodb+srv://:@cluster0.mongodb.net/
✔ Enter the database name: · mongodb-rag
✔ Enter the collection name: · documents
✔ Select an embedding provider: · openai
✔ Enter your API key (skip if using Ollama): · your-embedding-api-key
✔ Enter the model name: · text-embedding-3-small
✔ Enter the embedding dimensions: · 1536
✅ Configuration saved to .mongodb-rag.json

🔍 Next steps:
1. Run npx mongodb-rag test-connection to verify your setup
2. Run npx mongodb-rag create-index to create your vector search index
`

2. Create a MongoDB Atlas Cluster (MongoDB Atlas)

3. Enable Vector Search under Indexes:
`json
{
"definition": {
"fields": [
{ "path": "embedding", "type": "vector", "numDimensions": 1536, "similarity": "cosine" }
]
}
}
`
or, use the CLI to create the index:
`sh
npx mongodb-rag create-index
`
4. Create a .env File using:
`sh
npx mongodb-rag create-env
`
This command reads the .mongodb-rag.json file and generates a .env file with the necessary environment variables.

$3

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

`sh
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:

`sh
cd my-rag-app
npm install
npm run dev
`

$3


`javascript
import { MongoRAG } from 'mongodb-rag';
import dotenv from 'dotenv';
dotenv.config();

const rag = new MongoRAG({
mongoUrl: process.env.MONGODB_URI,
database: 'my_rag_db', // Default database
collection: 'documents', // Default collection
embedding: {
provider: process.env.EMBEDDING_PROVIDER,
apiKey: process.env.EMBEDDING_API_KEY,
model: process.env.EMBEDDING_MODEL,
dimensions: 1536
}
});
await rag.connect();
`

$3


`javascript
const documents = [
{ id: 'doc1', content: 'MongoDB is a NoSQL database.', metadata: { source: 'docs' } },
{ id: 'doc2', content: 'Vector search is useful for semantic search.', metadata: { source: 'ai' } }
];

await rag.ingestBatch(documents, { database: 'dynamic_db', collection: 'dynamic_docs' });
console.log('Documents ingested.');
`

$3


`javascript
const query = 'How does vector search work?';

const results = await rag.search(query, {
database: 'dynamic_db',
collection: 'dynamic_docs',
maxResults: 3
});

console.log('Search Results:', results);
`

$3


`javascript
await rag.close();
`

---

⚡ Additional Features

$3

Store embeddings in multiple databases and collections dynamically.
`javascript
await rag.ingestBatch(docs, { database: 'finance_db', collection: 'reports' });
`

$3

`javascript
const results = await rag.search('AI topics', {
database: 'my_rag_db',
collection: 'documents',
maxResults: 5,
filter: { 'metadata.source': 'ai' }
});
``

---

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

---

📜 License

This project is licensed under the MIT License.

💡 Examples

- For more examples, check our examples directory.

🔗 Links

- CLI Reference
- Documentation
- GitHub Repository
- Bug Reports
- MongoDB Atlas