Vector Representation Optimization

Introduced Matroyshka Representation Learning (MRL) to reduce the storage and compute costs of vector search systems. MRL allows for the use of lower-dimensional vector representations that approximate the similarity of full-fidelity representations, enabling a trade-off between cost and accuracy. This post demonstrates that using 512-dimensional vectors can achieve comparable retrieval accuracy to higher-dimensional vectors while significantly reducing storage and compute costs, achieving ~99% relative performance at a quarter of the storage and compute cost.