Matryoshka Embeddings: Detail at Multiple Scales

What this post added

This post introduces Matryoshka embeddings, a technique that embeds multiple scales of representation within a single vector, allowing for flexible granularity to balance precision and computational cost. It details the 'funnel search' approach for accelerating similarity searches and explains the training objective modification for Matryoshka embeddings. Milvus seamlessly supports Matryoshka embedding models, with examples like OpenAI's text-embedding-3-large, Nomic's nomic-embed-text-v1, and Alibaba's gte-multilingual-base.