Lower-Cost Vector Retrieval with Voyage AI’s Model Options

What this post added

This post introduces Matroyshka Representation Learning (MRL) as a technique to reduce vector dimensionality for cost and performance optimization in vector search. It explains the concept of MRL, demonstrates its application with Voyage AI's models (voyage-3-large, voyage-3.5, voyage-3.5-lite) by showing cosine similarity scores across different dimensions, and presents experimental results (NDCG@10, MRR@10, storage costs) comparing vector search indexes with varying MRL dimensions (256, 512, 1024, 2048). The findings suggest that 512-dimensional vectors offer a good balance of accuracy and cost-efficiency.