Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Sentence Transformers is a Python library for using and training embedding and reranker models for applications like retrieval augmented generation, semantic search, and more. With the v6.0 update, it gains a fourth model type: MultiVectorEncoder, for ColBERT-style late interaction retrieval. Any PyLate checkpoint and any Stanford-NLP ColBERT checkpoint loads straight into it, and colpali-engine models for visual document retrieval can be used too, through the same familiar API you already use for dense, sparse, and reranker models.

Where a regular embedding model compresses a whole text into one vector, a multi-vector model keeps one vector per token and scores query against document with the MaxSim operator. That preserves token-level matching information that a single vector has to average away, which usually means stronger retrieval at the cost of a bigger index. It's also the state of the art for visual document retrieval, where a text query is matched against page images directly, with no OCR step in between.

In this blogpost, we'll show you how to use these models: loading the various checkpoint formats, encoding and scoring, plugging them into a search stack, running them on page images, and keeping the index affordable. Everything below runs on a plain pip install -U sentence-transformers.

This blog is about using multi-vector models. If you want to learn how to train them, see the companion Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers blogpost.

Table of Contents

What are Multi-Vector Models?

A multi-vector model (also called a late-interaction or ColBERT-style model, after the ColBERT paper) skips that compression. It runs the same transformer, but instead of pooling the token embeddings into one vector, it projects each token embedding down to a small dimension (classically 128) and keeps all of them. A 9-token document becomes a 9x128 matrix, not a 1x128 vector.