Voy

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Voy is a WASM vector similarity search engine written in Rust. It offers fast search capabilities with a tiny gzipped size of 75KB.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is voy?

Voy is a WebAssembly (WASM) vector similarity search engine developed using Rust. It is engineered for efficiency and compactness, boasting a gzipped size of just 75KB. The engine employs a k-d tree data structure to index vectors, facilitating rapid and accurate search operations. Its tree-shakable design further contributes to bundle size optimization, making it suitable for applications where resource footprint is a critical consideration.

Best used for

Implementing efficient and lightweight vector similarity search directly within web applications.

Common actions

Implement vector search

Optimize bundle size

Build lightweight applications

Integrate search functionality

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Capabilities

Key features

WASM vector search
Rust-based
Tiny size (75KB)
Fast search (k-d tree)
Tree shakable

Target Audience

DevelopersWeb developersEngineers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

What are the typical use cases where Voy's small size and speed are most beneficial?

Voy is ideal for client-side applications, edge computing, or environments with strict bundle size constraints, such as web applications, mobile apps, or IoT devices requiring fast, localized vector search without server-side dependencies.

How does Voy's k-d tree implementation compare to other vector search algorithms like HNSW or IVF in terms of performance and accuracy?

While k-d trees offer excellent speed for lower-dimensional data and are simpler to implement, they might not scale as efficiently to very high-dimensional data as HNSW or IVF. Voy prioritizes compactness and client-side performance for moderate dimensions.

Can Voy be integrated with JavaScript frameworks like React or Vue for client-side vector search?

Yes, as a WebAssembly (WASM) module, Voy is designed to be easily integrated into JavaScript environments. Its tree-shakable nature helps optimize the final bundle size when used within modern web frameworks.

What are the limitations regarding the dimensionality or number of vectors Voy can efficiently handle?

Voy is optimized for efficiency and compactness, making it suitable for datasets up to tens or hundreds of thousands of vectors, particularly in lower to medium dimensions. For extremely large datasets or very high dimensions, server-side solutions might be more appropriate.

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Coding & Development › Frontend & UI Data & Analytics › Data Pipelines & Integration

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