arrowspace 0.26.0

Python bindings for ArrowSpace (Rust) providing graph-based similarity search, signal graphs, and spectral methods for vector data.

pyarrowspace

Python bindings for arrowspace-rs.

arrowspace is a database for vectors 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

From PyPi:

pip install arrowspace

or any other way of installing a Python library.

If you have cargo installed, to compile the libraries involved (from crates.io):

pip install maturin[patchelf]
maturin develop

Tests

Simple test:

python tests/test_0.py

Test with public QA dataset:

python tests/test_1_quora_questions.py

There are other tests but they require downloadin a dataset separately or fine-tuning the embeddings on a given dataset. Give it a try and let me know!

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) ]