@nlptools/distance

Complete string distance and similarity algorithms package with WebAssembly and JavaScript implementations

This package provides comprehensive text similarity and distance algorithms, combining the high-performance WebAssembly implementation from @nlptools/distance-wasm with additional JavaScript-based algorithms for maximum compatibility and performance.

Features

• ⚡ Dual Implementation: WebAssembly for performance + JavaScript for compatibility
• 🧮 Comprehensive Algorithms: 30+ string similarity and distance algorithms
• 🎯 Multiple Categories: Edit-based, sequence-based, token-based, and naive algorithms
• 📝 TypeScript First: Full type safety with comprehensive API
• 🔧 Universal Interface: Single compare function for all algorithms
• 📊 Normalized Results: Consistent 0-1 similarity scores across algorithms
• 🚀 Auto-optimization: Automatically chooses the fastest implementation available

Installation

# Install with npm
npm install @nlptools/distance

# Install with yarn
yarn add @nlptools/distance

# Install with pnpm
pnpm add @nlptools/distance

Usage

Basic Setup

import * as distance from "@nlptools/distance";

// All algorithms are available as named functions
console.log(distance.levenshtein("kitten", "sitting")); // 3
console.log(distance.jaro("hello", "hallo")); // 0.8666666666666667
console.log(distance.cosine("abc", "bcd")); // 0.6666666666666666

Distance vs Similarity

Most algorithms have both distance and normalized versions:

// Distance algorithms (lower is more similar)
const dist = distance.levenshtein("cat", "bat"); // 1

// Similarity algorithms (higher is more similar, 0-1 range)
const sim = distance.levenshtein_normalized("cat", "bat"); // 0.6666666666666666

Available Algorithms

This package includes all algorithms from @nlptools/distance-wasm plus additional JavaScript implementations:

Edit Distance Algorithms

• levenshtein - Classic edit distance
• fastest_levenshtein - High-performance Levenshtein distance (fastest-levenshtein)
• damerau_levenshtein - Edit distance with transpositions
• myers_levenshtein - Myers bit-parallel algorithm for edit distance
• jaro - Jaro similarity
• jarowinkler - Jaro-Winkler similarity
• hamming - Hamming distance for equal-length strings
• sift4_simple - SIFT4 algorithm

Sequence-based Algorithms

• lcs_seq - Longest common subsequence
• lcs_str - Longest common substring
• ratcliff_obershelp - Gestalt pattern matching
• smith_waterman - Local sequence alignment

Token-based Algorithms

• jaccard - Jaccard similarity
• cosine - Cosine similarity
• sorensen - Sørensen-Dice coefficient
• tversky - Tversky index
• overlap - Overlap coefficient

Bigram Algorithms

• jaccard_bigram - Jaccard similarity on character bigrams
• cosine_bigram - Cosine similarity on character bigrams

Naive Algorithms

• prefix - Prefix similarity
• suffix - Suffix similarity
• length - Length-based similarity

Universal Compare Function

const result = distance.compare("hello", "hallo", "jaro");
console.log(result); // 0.8666666666666667

// Use fastest-levenshtein for optimal performance
console.log(distance.fastest_levenshtein("fast", "faster")); // 2

Performance

The package automatically selects the fastest implementation available:

• WebAssembly algorithms: 10-100x faster than pure JavaScript
• Auto-detection: Seamlessly switches between WASM and JS implementations

References

This package incorporates and builds upon the following excellent open source projects:

• textdistance.rs - Core Rust implementation via @nlptools/distance-wasm
• fastest-levenshtein - High-performance Levenshtein implementation

License

• MIT © Demo Macro