Vicinity is a lightweight vector store designed to simplify nearest neighbor search by offering a unified and intuitive interface. It addresses the challenge of comparing various nearest neighbor packages and methods, each with distinct interfaces and limitations, by providing a consistent user experience across different backends. This approach is intended to make it easier for users to experiment with various indexing methods and distance metrics without having to learn a new interface for each backend.
The tool supports multiple backends and allows users to evaluate their performance, including metrics such as queries per second and recall. Vicinity enables users to initialize an instance with their vectors and associated items, select a backend type and distance metric, and perform nearest neighbor queries. It supports both top-k search and threshold-based search, as well as querying with single or multiple query vectors. The interface is described as simple and intuitive, aiming to streamline experimentation and performance evaluation for different use cases.
Vicinity is delivered as a Python package that can be installed via pip. No information is provided about pricing, licensing, or the specific intended audience beyond those interested in nearest neighbor search and vector storage.
In the Infrastructure & Backend space, Vicinity takes a focused approach. It simplifies nearest neighbor search and evaluation across different vector store backends. It is built as an open-source project for machine learning engineers and data scientists. Vicinity is open source under the MIT license. Vicinity is available on the command line, and it can be self-hosted.
It is developed by Minish, and the product first shipped in 2024. Development happens publicly on GitHub with 345 stars and 5 commits in the last 90 days. PulseGate's similarity index finds few close equivalents — Vicinity occupies a relatively distinct niche. Key capabilities include nearest neighbor search, multiple backends, and unified interface.
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