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

Multi-vector models bridge the gap between simple dense retrieval and complex cross-encoders by preserving token-level granularity during the search process. This shift enables significantly higher precision in document ranking without the extreme latency costs typically associated with reranking systems.