What Is Vector Reduction?

Vector reduction is the practice of reducing the number of vectors stored in a database by eliminating semantic redundancy. It is distinct from vector compression, which reduces the precision or size of each individual vector. Vector reduction lowers storage and improves query speed by storing fewer vectors, while each retained vector keeps full precision. Morphos AI achieves vector reduction through patent-pending Green Vectors technology.

Vector reduction vs vector compression

These are often confused but they work differently. Compression, including quantization, shrinks each vector by lowering precision. A full-precision vector becomes a smaller binary representation. Storage drops, but accuracy degrades because information is lost.

Vector reduction shrinks the index by reducing the count of vectors. Redundant or near-duplicate vectors are eliminated through semantic transformation. Storage drops because there are fewer vectors, and accuracy can improve because redundant noise is removed from the search space.

Why vector reduction improves accuracy

When every data point becomes a stored vector, the search space fills with near-duplicate vectors that add noise. A query has to disambiguate among many similar vectors of varying relevance. By eliminating semantic redundancy, vector reduction produces a cleaner search space, which can improve relevance rather than degrade it.

Vector reduction and compression together

Vector reduction and compression operate on different axes and can be layered. In the Elastic BBQ benchmark, though, Green Vectors alone achieved 1.5GB at .9658 relevancy, better on storage than Green Vectors combined with BBQ (2.6GB at .9653). Once an index is reduced, quantization often has little left to improve, so it becomes optional rather than additive.