One database, three jobs: MongoDB's GenAI cookbook

What it does

This repository is a curated collection of Jupyter notebooks, Python and JavaScript apps, and self-paced workshops. It demonstrates patterns for Retrieval-Augmented Generation, AI agents, and related evaluations, all wired to MongoDB Atlas as the backing data layer. Think of it as a vendor-specific cookbook rather than a standalone framework.

The interesting bit

The examples pitch MongoDB as a triple-duty backend: vector database, operational store, and memory provider for agents. The underlying argument is that keeping embeddings, metadata, and conversation history in one system beats juggling a separate vector store, relational database, and cache.

Key highlights

• Jupyter notebooks covering RAG, agentic applications, and evaluation techniques
• Standalone apps in both JavaScript and Python
• Self-paced hands-on workshops
• Partner contributions from AI vendors
• MIT licensed

Caveats

• Every example requires a live MongoDB Atlas cluster; no offline or local-only path is described
• The patterns are tightly coupled to MongoDB, so adapting them to other vector stores means rewriting the data layer

Verdict

Worth a look if you are already committed to MongoDB Atlas and want practical, copy-pasteable patterns for RAG or agent workflows. If you are shopping for database-agnostic architecture or comparing vector stores, this will feel more like product documentation than an engineering reference.