users' questions through this chunklist retrieval augmented generation pattern. And while this first example might have been simple, what we can do is also pull the IBM 2025 annual report into the context here, which has 418 sections, so it's much larger, and ask the question like, "Hey, what was Red Hat's revenue growth in 2025, and how did to an overall software segment?" And so, here we're going to be iterating multiple times to figure out, "Hey, is this section relevant to the user's question?" And if not, we need to pull in more information. And so, that is how chunkless rag can work in a situation where you're using a tool like docling. But what happens when we have hundreds or hundreds of thousands of PDFs that we want to have processed? Well, this is where we can deploy docling as a REST API service using something that's known as docling serve. This allows us to scale things up and to run this as a microservice as a container or through Kubernetes. So, when we set things up here, we're going to do a pip install docling serve, and we're going to be able to serve this from a CLI with docling serve on a specific port, or once we have that server started, be able to send things to that specific endpoint with different types of options and arguments like, "Hey, do we want OCR? Do we want a specific back end? Or do we want images annotated? And so, that's how we can scale things up and allow this API endpoint to be able to handle hundreds or thousands of different document types at a time. And