TurboQuant/pyproject.toml at main · bojobh609/TurboQuant · GitHub

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[build-system]
requires = ["setuptools>=68.0", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "turboquant"
version = "0.3.0"
description = "Near-optimal vector quantization for AI — 5x compression, 95%+ recall, zero preprocessing"
readme = "README.md"
license = {text = "Apache-2.0"}
requires-python = ">=3.10"
authors = [
    {name = "Firmamento Technologies", email = "info@firmamento.tech"},
    {name = "Luca Di Domenico"},
]
keywords = [
    "vector-quantization", "faiss", "approximate-nearest-neighbor",
    "embedding-compression", "kv-cache", "llm-inference",
    "turboquant", "polarquant", "similarity-search",
    "information-retrieval", "machine-learning", "deep-learning",
]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Developers",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: Apache Software License",
    "Operating System :: OS Independent",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Scientific/Engineering :: Information Analysis",
]
dependencies = [
    "numpy>=1.24",
    "scipy>=1.10",
]

[project.optional-dependencies]
dev = ["pytest>=7.0", "pytest-benchmark"]
bench = ["faiss-cpu>=1.7", "sentence-transformers>=2.0"]
all = ["faiss-cpu>=1.7", "sentence-transformers>=2.0", "pytest>=7.0"]

[project.urls]
Homepage = "https://github.com/Firmamento-Technologies/TurboQuant"
Documentation = "https://github.com/Firmamento-Technologies/TurboQuant#readme"
Repository = "https://github.com/Firmamento-Technologies/TurboQuant"
Issues = "https://github.com/Firmamento-Technologies/TurboQuant/issues"
Paper = "https://arxiv.org/abs/2504.19874"

[tool.setuptools.packages.find]
include = ["turboquant*"]

[tool.pytest.ini_options]
testpaths = ["tests"]