MongoDB Atlas is a managed noSQL database that uses JSON-like documents with optional schemas.

The platform recently released new vector search capabilities to facilitate building AI capabilities.

Ben Flast is the director of product management at MongoDB.

He joins the show to talk about the company's developments with vector search.

This episode is hosted by Lee

Acheson. Lee Acheson is a software architect, author, and thought leader on cloud computing

and application modernization. His bestselling book, Architecting for Scale, is an essential

resource for technical teams looking to maintain high availability and manage risk in

their cloud environments. Lee is the host of his podcast, Modern Digital Business, produced for

people looking to build and grow their digital business. Listen at MDB.fm. Follow Lee at

software architecture insights.com and see all his content at leeatchison.com.

Ben, welcome to Software Engineering Daily.

Thanks. It's great to be here.

So let's start out with what is vector search.

And I know we're going to get into the discussion about embeddings and things like that.

But let's start out with just a basic of what is a vector and what is vector search.

Yeah, absolutely. So, long going to be aside,

vectors are high dimensional representations of underlying data, right? And so the way you produce a

vector is you take some piece of data, whether it's text or audio or image, and you send it

through an embedding model.

And out the other end comes a vector, which is a series of decimals or zeros and ones that

is a representation of that underlying data. And so that's what a vector is.

...