pyarrowspace
Python bindings for arrowspace-rs.
arrowspace is a graph-based analytics library for vector spaces, supported by a graph representation and a key-value store. The main use-cases targeted are: AI search capabilities as advanced vector similarity, graph characterisation analysis and search, indexing of high-dimensional vectors. Design principles described in this article.
For labs and tests please see tests/
Installation
Core library
The core library ships as a compiled Rust extension. It only requires numpy, pyarrow, pandas, and scikit-learn:
pip install arrowspace
Optional extras
Additional capabilities are available as optional extras:
| Extra | Packages included | Use case |
|---|---|---|
embeddings |
datasets, sentence-transformers, transformers[torch]<5.0, tsdae |
Generating embeddings from text via HuggingFace models; required for test_1_quora_questions.py and all tests above |
benchmarks |
beir, nltk |
Running BEIR/MS-MARCO benchmark suites and NLP preprocessing |
viz |
matplotlib, seaborn, tqdm |
Plotting results and progress bars in test scripts |
full |
all of the above | Full development and research environment |
Install with one or more extras:
# Required to run test_1_quora_questions.py and above
pip install arrowspace[embeddings]
# Benchmarking + visualisation
pip install arrowspace[benchmarks,viz]
# Everything (for running the full test suite)
pip install arrowspace[full]
Note:
tests/test_0_*.pyonly require the core install. All tests numberedtest_1and above require at minimumarrowspace[embeddings].
Build from source
If you have Cargo installed, you can compile and install locally using maturin:
pip install maturin[patchelf]
# quick development build
maturin develop
# optimised release build (recommended for large datasets)
maturin develop --release
Tests
test_0_*.py scripts only require the core install:
python tests/test_0_0.py
test_1 and above require the embeddings extra (pip install arrowspace[embeddings]):
python tests/test_1_quora_questions.py
Higher-numbered tests (test_3 and above) additionally require benchmarks and viz:
pip install arrowspace[full]
python tests/test_3_beir.py
Some tests require downloading a dataset separately or fine-tuning embeddings on a given dataset.
Simplest Example
from arrowspace import ArrowSpaceBuilder
import numpy as np
items: np.array = np.array(
[[0.1, 0.2, 0.3], [0.0, 0.5, 0.1], [0.9, 0.1, 0.0]],
dtype = np.float64
)
graph_params: dict = {
"eps": 1.0,
"k": 6,
"topk": 3,
"p": 2.0,
"sigma": 1.0,
}
# Create an ArrowSpace instance, returning the computed
# signal graph and lambdas
aspace, gl = ArrowSpaceBuilder().build(graph_params, items)
# Search comparable items
# defaults: k = nitems, alpha = 0.9, beta = 0.1
query: np.array = np.array(
[0.05, 0.2, 0.25],
dtype = np.float64
)
tau: float = 1.0
hits: list = aspace.search(query, gl, tau)
# Search returns a list of `(index, score`) tuples, where
# expected value from the code above show the first index
# having the top score, i.e., being nearest.
print(hits)
# [ (0, 0.989743318610787), (1, 0.7565344158360029), (2, 0.22151940739207396) ]
2 maintainers
Metadata
2 maintainers
Release files for arrowspace 0.30.0
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Built distributions (wheels)
Total release size: 80.7 MB
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Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 15, 2026.
Transparency log
