SurrealDB 3.0 and Building Event-Driven AI Applications with Tobie Morgan Hitchcock

Modern application development often involves juggling multiple types of databases to handle diverse data models. The lack of unification can lead to complex architectures with attendant security concerns and fragmented development workflows. SurrealDB is an open source, multimodal database developed in Rust and integrates functionalities of many databases, including relational, document, graph, time series, search, and vector databases. It supports both schema less and schema full data models and has a SQL like query language. The project has rapidly grown in popularity, and version three point o was just released with a focus on enabling AI powered analysis of unstructured data directly within the database, along with tooling for building event driven applications. Toby Morgan Hitchcock is the CEO and cofounder of SurrealDB. He joins the podcast with Kevin Ball to talk about SurrealDB, handling multimodal data, unstructured data processing, building event driven AI applications, coupling databases with AI models, and more. Kevin Ball or Kate Ball is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He cofounded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI in action discussion group through Latent Space. Check out the show notes to follow Keball on Twitter or LinkedIn, or visit his website, keball.llc. Toby, welcome to the show. Thank you. Great to be here. Thanks for having me. Yeah. I'm excited to get to learn about what you're doing. So let's maybe start with you. Do you wanna give yourself a brief introduction and then talk a little about Surreal and what brought us here? Yeah. So, Toby, CEO and cofounder of SurrealDB. SurrealDB is a new multimodal database that is designed to simplify the infrastructure and the development process for developers and organizations where they might typically have been using multiple different databases to achieve what you can do with just a single database in CerealDB. It's designed for AI native applications, so we're being used a lot in knowledge graphs and graph rag. But generally speaking, anywhere that you need to use time series data, document data, key value access to that same data, and then graph, and be able to bring all those together in a simple single SQL like query language called SurrealQL. That's where SurrealDB really shines. So let's dive into that multimodel concept a little bit because I think that's interesting, and it's sort of a trend that we've seen Postgres adopting JSON types and letting you do document style things. So when you say multimodel, I heard a lot of different models in there, but what do you mean by that, and what's the driving vision there? Yeah. So in SuruDB, how we store data is effectively like a a document database, so similar to something like MongoDB or a WranglerDB. But the query language that we have built, it's very similar to ANSI SQL, but it has some differences. And this query language enables you to store and …