A Vector is an object that represents a real-world item as an array of floating numbers, with each dimension representing the value of an attribute associated with the item. Real-world objects can have multiple attributes, and a vector representing such an object is represented by a larger array of values. Vector search is a method of finding items based on their vector representation in multidimensional space, where each dimension represents the value of an attribute. Vector search differs from full-text search in that it operates on vectors rather than text, and is suited for searching through collections of data such as audio, video, image, and text. Couchbase supports vector search across its products, including cloud, on-prem, and mobile deployments, with features like integrated cache, full-text search, analytical search, time-series search, key-value search, eventing, and other capabilities. To get started with vector search in Couchbase, one can create sample data using the `rgb.json` file provided, create a vector search index using the `color-index.json` file, perform a vector search by selecting the search option in the index row, and then combine SQL queries with vector search to consolidate database stack.