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

Sentence Transformers v6.0 introduces the MultiVectorEncoder model type, bringing ColBERT-style late-interaction retrieval to the library alongside dense, sparse, and reranker models. Unlike conventional dense embeddings that represent an entire document with one vector, multi-vector models retain token-level vectors and use the MaxSim operator to align each query token with its best matching document token, improving performance on exact terms, multi-part queries, long documents, and semantic paraphrases while requiring substantially larger indexes. The update supports PyLate, Stanford-NLP ColBERT, and many ColPali visual-document checkpoints through a unified API for encoding queries and documents, scoring, semantic search, reranking, evaluation, and interpretability. The article describes integration with indexes such as fast-plaid, Qdrant, Weaviate, Vespa, Milvus, and others, while recommending retrieve-and-rerank workflows or token pooling to reduce storage and compute costs. It also demonstrates retrieval across page images without OCR, as well as audio and video, explains inference optimizations such as Flash Attention and OpenVINO, and reports benchmark results showing that multi-vector retrieval often improves average quality over comparable dense models but is not universally superior across all datasets.

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