Rusty Semantic Similarity

A small library designed to compute similarity/dissimilarity metrics between embeddings using vector distance.

Current distance measures implemented:

• Cosine (handles both normalized and non-normalized vectors)
• Euclidean
• Manhattan
• Chebyshev
• Angular
• Jaccard Index
• Levenshtein
• Minkowski
• Dot product

Features

• Parallel Computation: Utilizes rayon for parallel processing.
• Bring your own embedding: Use any embedding model to generate embeddings and compute the similarity/distance scores.

Installation

Add semanticsimilarity_rs to your Cargo.toml file

[dependencies]
semanticsimilarity_rs = "0.1.0" 

Or use cargo add

cargo add semanticsimilarity_rs

Usage

use semanticsimilarity_rs::{cosine_similarity, euclidean_distance};

fn main() {

    let vec1: [f64; 3] = [1.0, 2.0, 3.0];
    let vec2: [f64; 3] = [4.0, 5.0, 6.0];

    let similarity = cosine_similarity(&vec1, &vec2, false);

    println!("Cosine similarity between vec1 and vec2: {}", similarity);
}