sentence-transformers v6.0 adds ColBERT-style multi-vector late interaction retrieval
tomaarsen · x · 2026-08-18
sentence-transformers v6.0.0 introduces a fourth model type, MultiVectorEncoder, bringing ColBERT-style multi-vector (late interaction) retrieval to the library:
- While a regular embedding model compresses text into one vector, multi-vector models keep one vector per token and score queries against documents with the MaxSim operator, preserving token-level matching that single vectors average away — usually stronger retrieval at the cost of a bigger index.
- It loads PyLate checkpoints and Stanford-NLP ColBERT checkpoints directly, and works with colpali-engine models — the current state of the art for visual document retrieval, matching text queries against page images with no OCR step.
- The official blog post (by Tom Aarsen with the LightOn team) walks through loading checkpoint formats, encoding and scoring with MaxSim, plugging into a search stack, running on page images, and keeping the index affordable — all reproducible via pip install -U sentence-transformers.
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