MUVERA

• MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings:

MUVERA (MUlti-VEctor Retrieval Algorithm), a retrieval mechanism which reduces multi-vector similarity search to single-vector similarity search. This enables the usage of off-the-shelf MIPS solvers for multi-vector retrieval. MUVERA asymmetrically generates Fixed Dimensional Encodings (FDEs) of queries and documents, which are vectors whose inner product approximates multi-vector similarity. We prove that FDEs give high-quality ϵ-approximations, thus providing the first single-vector proxy for multi-vector similarity with theoretical guarantees. Empirically, we find that FDEs achieve the same recall as prior state-of-the-art heuristics while retrieving 2-5× fewer candidates. Compared to prior state of the art implementations, MUVERA achieves consistently good end-to-end recall and latency across a diverse set of the BEIR retrieval datasets, achieving an average of 10% improved recall with 90% lower latency.

• sionic-ai/muvera-py
  • Python Implementation of MUVERA (Multi-Vector Retrieval via Fixed Dimensional Encodings)
• MUVERA Making multi-vector retrieval as fast as single-vector search
  • 2025-06-25
  • Google Research announcement of MUVERA and FDEs
• MUVERA Making Multivectors More Performant
  • 2025-09-05
• MUVERA - Qdrant Course