hora
efficient approximate nearest neighbor search algorithm collections library written in Rust
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Hora is a Rust-based library for high-performance approximate nearest neighbor search algorithms critical in applications such as image and recommender systems where speed and efficiency are paramount.
Good fit when
- When you require a high-speed library written in Rust to implement an efficient similarity-search solution
- For developing machine-learning projects that specifically need fast approximate nearest neighbor searches, leveraging the SIMD capabilities of modern hardware
Avoid when
- If your project is restricted to languages other than Rust and you cannot work with Rust bindings or require a more general-purpose vector database library
- When exact nearest neighbor search accuracy is preferred over the speed provided by approximate algorithms, as Hora specializes in sacrificing minimal precision for significant performance gains
Observed Jul 15, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (214d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
cargo add hora crates.ioSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A high-performance Rust library for efficient approximate nearest neighbor search algorithms useful for similarity-search across various machine-learning applications.
Capability facts
- Languages
- rust
Source: github.language · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Installation in License The entire repository is licensed under the Apache License.
For agents
This page has a .md twin and JSON over the API.