We're thrilled to announce the launch of MongoDB-RAG.com, a comprehensive marketing landing page showcasing the power and flexibility of our MongoDB-RAG npm library. This new site not only provides detailed information about the library's capabilities but also features an interactive demonstration that lets visitors experience Retrieval Augmented Generation (RAG) with MongoDB Vector Search firsthand.

What's New on MongoDB-RAG.com​

The new landing page serves as a central hub for everything related to the MongoDB-RAG library, with several exciting features:

🚀 What is the MongoDB-RAG Playground?​

The MongoDB-RAG Playground is an interactive UI built directly into the mongodb-rag library. Designed to provide developers with a quick and easy way to explore vector search, the Playground helps users visualize their documents, manage indexes, and run search queries—all without writing extra code.

📖 TL;DR​

Ever wished your documentation could just answer questions directly instead of forcing users to sift through endless pages? That’s exactly what we built with the MongoDB-RAG Documentation Assistant. In this article, I’ll walk you through the why, what, and how of building a chatbot that retrieves precise, relevant information from MongoDB-RAG’s own documentation.

🤔 Why Build a Documentation Assistant?​

Traditional documentation search is useful, but it often leaves users with more questions than answers. Developers need to read through entire pages, infer context, and piece together solutions. Instead, we wanted something:

Since releasing MongoDB-RAG earlier this year, I've received a consistent stream of questions from developers about best practices for building production-ready AI applications. While the library makes RAG implementation much simpler, many developers are looking for end-to-end guidance on the entire development journey.

That's why I'm excited to announce our new MongoDB-RAG Workshop - a comprehensive, hands-on guide to building intelligent applications with MongoDB Atlas Vector Search.

Over the past year, I've spent a lot of time talking to developers about Retrieval-Augmented Generation (RAG) and how MongoDB Atlas Vector Search enables efficient vector-based retrieval. While the power of MongoDB as a vector database is undeniable, I noticed a recurring theme: developers wanted a simpler way to implement RAG applications.

That's what inspired me to create MongoDB-RAG, an npm library that abstracts away the complexity of embedding generation, vector search, and document retrieval giving developers a plug-and-play way to build RAG-powered applications with MongoDB.

Today, I want to introduce the library and share some of the exciting new features we've recently added to make RAG with MongoDB even more intuitive and performant.