Dimensionality

Dimensionality refers to the number of dimensions in an embedding vector — for example, 384, 768, or 3072. Higher dimensionality allows the embedding model to encode finer-grained semantic distinctions but increases memory, storage, and compute costs proportionally. Models trained with Matryoshka representation learning can produce embeddings that remain useful after truncation, allowing operators to trade representation richness against resource constraints without re-embedding the corpus.