👋 Meet the Creator
Michael Lynn
Principal Developer Advocate at MongoDB
I’m passionate about **helping developers** build better applications, faster. As a **Developer Advocate at MongoDB**, I’ve spent years teaching developers how to harness the power of **document databases** and now **vector search**.
🚀 Why MongoDB-RAG?
MongoDB-RAG simplifies AI-powered search by combining vector search with retrieval.
Before MongoDB-RAG, developers had to:
✅ Configure vector indexes
✅ Handle chunking strategies
✅ Manage embeddings
MongoDB-RAG makes it easy.
🔥 What Makes MongoDB-RAG Special?
MongoDB-RAG helps developers:
✔ Ingest documents (Markdown, JSON, text files)
✔ Chunk and vectorize with OpenAI embeddings
✔ Search intelligently using MongoDB’s native vector search
✔ Deploy fast with just a few CLI commands
⚠ Disclaimer
MongoDB-RAG is not an officially supported MongoDB product.
This is an independent, open-source project and does not come with official MongoDB support.