Crate fast_hnsw 

hnsw

A pure-Rust implementation of Hierarchical Navigable Small World (HNSW) approximate nearest-neighbour search, following the algorithm from:

Malkov & Yashunin, “Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs”, IEEE TPAMI 2018.

Quick start

use hnsw::{Builder, Hnsw, SearchResult};
use hnsw::distance::Euclidean;

// Build an index.
let mut index: Hnsw<Euclidean> = Builder::new()
    .m(16)
    .ef_construction(200)
    .seed(42)
    .build(Euclidean);

// Insert vectors.
for i in 0..100_u32 {
    index.insert(vec![i as f32, (i * i) as f32]);
}

// Query: find 5 nearest neighbours with ef=50.
let results: Vec<SearchResult> = index.search(&[10.0, 101.0], 5, 50);
assert_eq!(results[0].id, 10);

Distance metrics

Type	Description
distance::Euclidean	True L2 distance
distance::SquaredEuclidean	L2² (faster, same NN order)
distance::Cosine	1 − cosine similarity
distance::DotProduct	1 − dot product
distance::Manhattan	L1 / taxicab distance

Custom metrics are easy to add by implementing the distance::Distance trait.

Feature flags

(none yet — this crate is dependency-light by design)

Re-exports

pub use builder::Builder;

pub use hnsw::Config;

pub use hnsw::Hnsw;

pub use hnsw::IndexStats;

pub use hnsw::PruneStrategy;

pub use hnsw::SearchResult;

pub use labeled::LabeledIndex;

pub use paired::PairedIndex;

Modules

builder

Ergonomic builder for Hnsw.

distance

hnsw

Core HNSW index — optimised implementation.

labeled

Single-embedding index with typed per-vector payload.

paired

Dual-embedding index: two HNSW graphs over the same items.

payload

Typed payload stored alongside each vector in a [LabeledIndex] or [PairedIndex].

persist

Binary file format and memory-mapped loading for Hnsw indexes.