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arXiv · 2501.17788

WARP: An Efficient Engine for Multi-Vector Retrieval

Jan Luca Scheerer·Matei Zaharia·Christopher Potts·Gustavo Alonso·Omar Khattab
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Abstract

Multi-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARP$_\text{SELECT}$ for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage reduction process for efficient score aggregation. Combined with highly-optimized C++ kernels, our system reduces end-to-end latency compared to XTR's reference implementation by 41x, and achieves a 3x speedup over the ColBERTv2/PLAID engine, while preserving retrieval quality.

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Jan Luca Scheerer, Matei Zaharia, Christopher Potts, Gustavo Alonso, Omar Khattab. 2025-01-29. WARP: An Efficient Engine for Multi-Vector Retrieval. https://doi.org/10.1145/3726302.3729904

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