rotorvec 0.1.1

Vector index using Clifford-rotor block-diagonal quantization (PlanarQuant / RotorQuant / IsoQuant)

rotorvec

Vector index using block-diagonal Clifford-rotor quantization. Three rotation variants, one API: Planar2 (2D Givens), Rotor3 (Cl(3,0) sandwich, default), Iso4 (4D quaternion).

A block-diagonal cousin of turbovec (TurboQuant): same compression and bit-plane layout, but the dense d×d random rotation is replaced by small per-block rotors — O(d) work, ~d parameters, no BLAS dependency.

See the main repo README for the full algorithm description, attribution, and benchmarks.

Install

pip install rotorvec

Wheels are built for Linux (x86_64, aarch64), macOS (x86_64, aarch64), and Windows (x64), abi3-py310 (one binary per platform covers Python 3.10–3.13).

Usage

import numpy as np
from rotorvec import RotorQuantIndex, IdMapIndex

# Default: Cl(3,0) rotors, 4-bit codes
index = RotorQuantIndex(dim=1536, bits=4)

# Or pick a variant explicitly
planar = RotorQuantIndex(dim=1536, bits=4, rotation="planar2")
iso    = RotorQuantIndex(dim=1536, bits=4, rotation="iso4")

vectors = np.random.randn(10_000, 1536).astype(np.float32)
queries = np.random.randn(8, 1536).astype(np.float32)

index.add(vectors)
scores, indices = index.search(queries, k=10)   # (8, 10) float32 / int64

index.write("index.rv")
loaded = RotorQuantIndex.load("index.rv")

Stable external ids:

ids = np.array([1001, 1002, 1003], dtype=np.uint64)
m = IdMapIndex(dim=1536, bits=4, rotation="planar2")
m.add_with_ids(vectors[:3], ids)
scores, returned_ids = m.search(queries, k=3)   # ids are uint64
m.remove(1002)

Accepted rotation values: "planar2", "rotor3" (default), "iso4" (case-insensitive; aliases "givens", "clifford", "quaternion" also work).

Development

git clone https://github.com/smarthi/rotorvec
cd rotorvec/rotorvec-python
uv venv --python 3.10
uv pip install -e .
uv run pytest tests/

License

MIT — see LICENSE.