What must be ensured about the dimensions of the vectors used in a vector index?

The requirement that the dimensions of the vectors used in a vector index must match is crucial for effective vector retrieval and similarity computations. When performing operations like similarity searches or clustering, using vectors of different dimensions would lead to incompatible calculations, such as dot products or distance measures, which rely on the vectors being in the same vector space.

Matching dimensions ensure that each vector can be compared directly with others, allowing for relevant outcomes based on defined metrics such as cosine similarity or Euclidean distance. This consistency enables efficient indexing and searching within the data set, ultimately leading to improved performance in applications that make use of vector indexing.

In contrast, the other options suggest scenarios that would compromise the integrity and functionality of vector searches. Variations in size could lead to inaccuracies, uniform increases are not a requirement, and differing dimensions based on data types would create inconsistencies that undermine the vector search process.