rapid-fuzzy

Fuzzy search and string similarity for JavaScript and TypeScript, powered by Rust. Native in Node.js, WebAssembly in browsers, Deno, Bun and Cloudflare Workers, with one API.

Documentation · Playground · API reference · Benchmarks

Features

• Fast on large lists: a persistent FuzzyIndex answers a query over 10,000 items in about 0.2 ms, about 180x faster than fuse.js and 1.6x faster than fuzzysort with prepared targets (benchmarks). Large searches use every CPU core in Node.js.
• Runs everywhere: a native addon (napi-rs) for Node.js on macOS, Linux and Windows, and a WebAssembly build for browsers, Deno, Bun and Cloudflare Workers, picked by your bundler or runtime on its own.
• Search that feels like fzf: the nucleo matcher from the Helix editor, with smart case, diacritic folding, ^prefix, suffix$, 'exact and !exclude terms in any order, match positions for highlighting, and opt-in typo tolerance (maxTypos).
• Objects with weighted keys: search records across nested keys, and choose how key scores combine (scoreMode) and whether terms may match different keys (matchMode).
• String similarity built in: Levenshtein, Damerau-Levenshtein, Jaro-Winkler, Sørensen-Dice, Hamming, Indel and RapidFuzz-style token ratios, with batch and typed-array variants.
• Strictly typed, no runtime dependencies: complete TypeScript declarations, with key paths checked against your item type.

Install

npm install rapid-fuzzy

Node.js 22 or later. Prebuilt binaries are installed for macOS, Linux (glibc and musl) and Windows on x64 and arm64. See the runtime guides for Node.js and SSR frameworks, browsers and bundlers, Cloudflare Workers and Deno and Bun.

Quick start

import { search, closest, FuzzyIndex } from 'rapid-fuzzy';

search('typscript', ['TypeScript', 'JavaScript', 'Python']);
// → [{ item: 'TypeScript', score: 0.86, index: 0, positions: [] }]

closest('tsc', ['TypeScript', 'JavaScript', 'Python']); // → 'TypeScript'

// Searching the same items many times? Build an index once:
// it is about 10x faster per query on 1,000-10,000 items.
const index = new FuzzyIndex(['TypeScript', 'JavaScript', 'Python']);
index.search('typscript', { maxResults: 5 });

Search objects by weighted keys:

import { searchObjects } from 'rapid-fuzzy';

const users = [
  { name: 'John Smith', city: 'Tokyo' },
  { name: 'Jane Doe', city: 'Osaka' },
];

searchObjects('john tokyo', users, {
  keys: [{ name: 'name', weight: 2 }, 'city'],
  matchMode: 'crossKey', // terms may match different keys
});

Compare strings:

import { levenshtein, jaroWinkler, tokenSortRatio, levenshteinMany } from 'rapid-fuzzy';

levenshtein('kitten', 'sitting'); // → 3
jaroWinkler('martha', 'marhta'); // → 0.961
tokenSortRatio('new york mets', 'mets new york'); // → 1
levenshteinMany('kitten', ['sitting', 'kittens', 'kitchen']); // → [3, 1, 2]

Highlight matches:

import { search, highlight } from 'rapid-fuzzy';

const [match] = search('fzy', ['fuzzy'], { includePositions: true });
highlight(match.item, match.positions, '<b>', '</b>'); // → '<b>f</b>uz<b>zy</b>'

Documentation

The documentation site covers everything in detail:

• Guides: fuzzy search and query syntax, how matching and scoring work, object search, persistent indexes, highlighting, string similarity, choosing an algorithm, React, Vue and Svelte, Unicode and non-Latin scripts, performance and untrusted input and errors and edge cases.
• API reference, generated from the type declarations.
• Troubleshooting for native binding, WebAssembly and SSR errors.
• llms.txt for AI assistants.

This is the beginning of the README. Read the rest at the original. Read the full README ↗