Vector search is a method of information retrieval where documents and queries are represented as vectors instead of plain text. In vector search, machine learning models generate the vector representations of source inputs, which can be text, images, or other content. Having a mathematic representation of content provides a common basis for search scenarios. If everything is a vector, a query can find a match in vector space, even if the associated original content is in different media or language than the query.

Why use vector search

When searchable content is represented as vectors, a query can find close matches in similar content. The embedding model used for vector generation knows which words and concepts are similar, and it places the resulting vectors close together in the embedding space. For example, vectorized source documents about “clouds” and “fog” are more likely to show up in a query about “mist” because they’re semantically similar, even if they aren’t a lexical match.