Put unstructured data to work with Redis
A vector database is a type of database that stores data in the form of vectors or mathematical representations of data points. This transformation of unstructured data into numeric representations (vectors) captures the meaning and context that complement natural language processing and computer vision.
Vector Search (VS) is the process of finding data points that are similar to a given query vector in a vector database. Popular VS uses go well beyond keyword matching and filtering to include recommendation systems, image and video search, natural language processing, and anomaly detection. (Need a deep dive? This should do it.)
Users expect search functionality in every application and website they encounter. Yet more than 80% of business data is unsearchable and stored across multiple formats. The time has come for organizations to reimagine the ways to make all kinds of data discoverable and exceed user expectations with powerful features to fuel the next generation of AI applications.
Every organization that stores non-textual data – and that’s just about everyone – can benefit from improving search functionality across unstructured data. That day is here.