Summary

At this stage of the book, you should have a pretty good understanding of the fundamentals of vector search, including vector representation, how vectors are organized in an HNSW graph, and the method to calculate similarity between vectors. In addition, we have seen how to set up your Elastic Cloud environment as well as your Elasticsearch mapping to run Vector Search queries and leverage the k-nearest neighbors algorithm.

Now, you are equipped with the fundamental knowledge to explore all the subsequent chapters. We’ll discover vector search domains of applications in various code examples and fields such as observability and security.

In the following chapter, we will go a step further – we’ll not only learn how to host a model and generate vectors within Elasticsearch, as opposed to handling it externally, but also explore the intricacies of managing it at different scales and optimizing a deployment from a resource standpoint.