Adaptive Retrieval with Matryoshka Embeddings

Adaptive Retrieval with Matryoshka Embeddings

Nomic Embed v1 only came out two weeks ago, but the same team just released Nomic Embed v1.5 trained using a new technique called Matryoshka Representation.

This means that unlike v1 the v1.5 embeddings are resizable - instead of a fixed 768 dimension embedding vector you can trade size for quality and drop that size all the way down to 64, while still maintaining strong semantically relevant results.

Joshua Lochner build this interactive demo on …

ai embed embedding embeddings llms nomic representation retrieval team trade vector