vectorsearch-rs

A tiny, fast in-memory vector store with cosine-similarity search, written in Rust and exposed to Python via pyo3. It does one thing: keep a pile of equal-length vectors, each tagged with an integer id, and return the top_k most similar ids to a query, with a parallel search path for large stores.

No index building, no config, no server. Just insert and search.

Install

pip install vectorsearch-rs

To build from source you need a Rust toolchain and maturin:

pip install maturin
maturin develop --release   # builds and installs into the active venv

Usage

from vectorsearch_rs import Vectorstore, cosine_similarity

# Every vector must have this many dimensions.
store = Vectorstore(vector_size=3)

store.insert([1.0, 2.0, 3.0], 1)
store.insert([1.0, 2.0, 4.0], 2)
store.insert([9.0, 0.0, 0.0], 3)

# Metadata lives on the Python side, keyed by the same ids.
meta = {1: {"title": "first"}, 2: {"title": "second"}, 3: {"title": "third"}}

# search returns a list of (score, id), most similar first.
for score, id in store.search([1.0, 2.0, 3.5], top_k=2):
    print(score, id, meta[id])

# For large stores, spread the scan across threads (defaults to all cores).
store.parallel_search([1.0, 2.0, 3.5], top_k=2, n_threads=8)

len(store)                                 # -> 3
cosine_similarity([1.0, 0.0], [1.0, 0.0])  # -> 1.0

You can also seed the store at construction time:

store = Vectorstore(3, [([1.0, 2.0, 3.0], 1), ([0.0, 1.0, 0.0], 2)])

To run the benchmarking script, you need to install the dev dependencies (numpy and scikit-learn)

Behaviour notes

  • Inserting or querying a vector whose length differs from vector_size raises ValueError.
  • Cosine similarity of a zero-magnitude vector is defined as 0.0 (rather than NaN), so zero vectors simply never match.
  • search is single-threaded; parallel_search fans the scan out across threads and merges the per-thread top-k. Both return identical results.

License

MIT — see LICENSE.

Project links

Data verified by PyPI on Aug 9, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for m.weber from gravatar.com m.weber

Credits

Author: dev-weber

GitHub Statistics

Data verified by PyPI on Aug 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.9

Tags

vector vectorstore embeddings similarity cosine search nearest-neighbor

Classifiers

Report project as malware

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