MuVERA
A Rust-accelerated Python implementation of Multi-Vector Retrieval via Fixed Dimensional Encoding Algorithm.
Converts multi-vector embeddings (point clouds) into fixed-dimensional single vectors, enabling the use of existing single-vector search infrastructure (MIPS, ANN, etc.) as-is.
Why this library?
The original MuVERA algorithm is described in a research paper and implemented in C++ within Google's graph-mining repository. While a Python reference exists, it exposes low-level config objects and separate functions for queries vs. documents, making it cumbersome to integrate into real workflows.
This library wraps the full algorithm behind a single Muvera class with a minimal, intuitive interface — initialize once, then call encode_documents() and encode_queries(). No config dataclasses, no encoding-type enums, no manual seed juggling. Just NumPy arrays in, NumPy arrays out.
Performance-critical inner loops (Gray code partitioning, scatter-add, empty partition filling) are implemented in Rust via PyO3, with automatic fallback to pure Python if the native extension is unavailable.
Installation
pip install muvera-python
Development install (requires Rust toolchain):
git clone https://github.com/craftsangjae/muvera-python.git
cd muvera-python
pip install maturin
maturin develop --release
pip install -e ".[dev]"
Quick Start
import numpy as np
from muvera import Muvera
# Initialize encoder
encoder = Muvera(
num_repetitions=10,
num_simhash_projections=4,
dimension=128,
seed=42,
)
# Encode documents (batch of variable-length point clouds)
documents = [np.random.randn(80, 128).astype(np.float32) for _ in range(100)]
doc_fdes = encoder.encode_documents(documents) # (100, output_dimension)
# Encode queries (batch)
queries = [np.random.randn(32, 128).astype(np.float32) for _ in range(10)]
query_fdes = encoder.encode_queries(queries) # (10, output_dimension)
# Compute similarity (dot product)
scores = query_fdes @ doc_fdes.T # (10, 100)
Parameters
| Parameter | Default | Description |
|---|---|---|
num_repetitions |
20 | Number of FDE repetitions. Higher values improve accuracy but increase output dimension |
num_simhash_projections |
5 | Number of SimHash projections. Number of partitions = 2^n |
dimension |
16 | Input embedding dimension |
projection_type |
"identity" |
"identity" or "ams_sketch" |
projection_dimension |
None | Projected dimension when using AMS Sketch |
fill_empty_partitions |
True | Whether to fill empty partitions with the nearest vector |
final_projection_dimension |
None | Final dimension reduction via Count Sketch |
seed |
42 | Random seed for reproducibility |
Benchmark
Rust Acceleration
Encoding speed comparison between Rust-accelerated and pure-Python backends. Measured on a single ARM64 core (Python 3.9, NumPy 2.0). Pareto-optimal configs from the MuVERA paper, dim=128:
Single document encoding (128 vectors):
| Config | Rust | Python | Speedup |
|---|---|---|---|
| R=20, k=3, d_proj=8 | 0.64 ms | 5.14 ms | 8.1x |
| R=20, k=4, d_proj=8 | 0.65 ms | 5.72 ms | 8.9x |
| R=20, k=5, d_proj=8 | 0.69 ms | 11.51 ms | 16.7x |
| R=20, k=5, d_proj=16 | 0.74 ms | 12.00 ms | 16.2x |
Batch document encoding (100 docs, ~128 vectors each):
| Config | Rust | Python | Speedup |
|---|---|---|---|
| R=20, k=3, d_proj=8 | 24.23 ms | 22.35 ms | 0.9x |
| R=20, k=4, d_proj=8 | 20.66 ms | 22.47 ms | 1.1x |
| R=20, k=5, d_proj=8 | 17.89 ms | 32.07 ms | 1.8x |
| R=20, k=5, d_proj=16 | 23.42 ms | 57.66 ms | 2.5x |
Single document encoding sees 8-17x speedup where Rust eliminates Python loop overhead in Gray code partitioning and scatter-add. Batch encoding gains are more modest (1-2.5x) since NumPy vectorized operations already handle the bulk of computation. The largest gains appear with higher num_simhash_projections (k=5) where partition count (2^k=32) creates more Python-level iteration.
Retrieval Quality
End-to-end retrieval on NanoFiQA2018 (4598 documents, 50 queries) using raphaelsty/neural-cherche-colbert (dim=128):
=====================================================================================
RESULTS
(zeta-alpha-ai/NanoFiQA2018)
=====================================================================================
Retriever | Index (s) | Query (ms) | Recall@25
-------------------------------------------------------------------------------------
ColBERT (Native MaxSim) | 240.04 | 836.94 | 0.8400
ColBERT + Muvera FDE | 77.29 | 69.48 | 0.7600
=====================================================================================
FDE achieves 90% of native MaxSim recall while being 12x faster at query time. See examples/colbert_nanobeir.py to reproduce.
Acknowledgments
This library was inspired by sionic-ai/muvera-py, the first Python implementation of the MuVERA algorithm. Their faithful port of the C++ reference made it possible to validate correctness and understand the algorithm deeply. This project builds on that foundation with a simplified API designed for easier integration.
References
1 maintainer
Metadata
1 maintainer
Release files for muvera-python 0.2.0
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