Sentence Transformers adds multi-vector retrieval in v6.0

Multi-vector embedding models represent an evolution in retrieval architecture that sits between two extremes: dense embeddings, which compress text into a single vector for speed and storage efficiency, and cross-encoders, which jointly encode both query and document for maximum accuracy but sacrifice the ability to precompute representations. Late interaction retrieves the benefits of both by encoding documents once offline (preserving the indexing efficiency of bi-encoders) while deferring the detailed comparison until query time via the MaxSim operator, which scores by taking each query token's best match against any document token and summing those maxima. This design choice has real consequences for different retrieval scenarios. On multi-requirement queries, where a user specifies several attributes or criteria, a dense model must compress all requirements into a single vector, causing each to compete for representation space; a late-interaction model lets each requirement find its own supporting token. On exact-match cases—product codes, surnames, function names—where a single rare entity determines relevance, dense models average that entity into the wider context; multi-vector models preserve the token unaveraged. The Sentence Transformers integration directly addresses a prior ecosystem gap: while the library supported dense and sparse models, late interaction was the responsibility of separate projects like PyLate (built by LightOn) and colpali-engine. Consolidating these capabilities into the v6.0 core removes the barrier to adoption, allowing users familiar with Sentence Transformers' API to load PyLate checkpoints, Stanford-NLP ColBERT variants, and visual document retrieval models through a single entry point.