This blog will delve into the nuances of combining the prowess of DataStax Astra with the power of Ray and is a companion to this demo on GitHub. We’ll explore the step-by-step procedure, the pitfalls to avoid, and the advantages this dynamic duo brings to the table. Whether you’re a data engineer, a developer looking to optimize your workflows, or just a tech enthusiast curious about the latest in data solutions, this guide promises insights aplenty. Soon, you’ll be able to use Cassandra 5 in place of AstraDB in this demo — but for a quick start, AstraDB is a great way to get started with a vector-search-compliant Cassandra database!
Introduction
Vector search is a technology that works by turning data that we are interested in into numerical representations of locations in a coordinate system. A database that holds and operates on vectors is called a vector store. This functionality is coming to Cassandra 5.0, which will be released soon. To preview this functionality, we can make use of DataStax Astra. Similar items have their vector locations close to each other in this space. That way, we can take some items and find items similar to them. In this case, we have bits of text that are embedded. Embedding takes text into a machine-learning model that returns vectors that represent the data. You can almost think about embedding and translating data from real text into vectors.