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@sarfarajey/fuzzy-match

v1.0.0

Published

Token-based fuzzy string matching with Levenshtein distance — zero dependencies

Readme

@sarfarajey/fuzzy-match

Token-based fuzzy string matching with Levenshtein distance fallback. Zero dependencies.

Designed for entity deduplication and voice/text input matching — mapping raw user input to a known list of canonical candidates.

Install

npm install @sarfarajey/fuzzy-match

Usage

import { findBestMatchId, scoreTerm } from '@sarfarajey/fuzzy-match';

const candidates = [
  { id: 'acme', label: 'Acme Corp', aliases: ['Acme', 'ACME Corporation'] },
  { id: 'globex', label: 'Globex', aliases: ['Globex Corp', 'GlobEx'] },
];

findBestMatchId('acme corp', candidates);     // → 'acme'
findBestMatchId('globex corporation', candidates); // → 'globex'
findBestMatchId('xyz unknown', candidates);   // → null  (below threshold)

// With custom threshold (0–100, default 58)
findBestMatchId('acmee', candidates, 70);     // → 'acme' (typo tolerance)
findBestMatchId('acmee', candidates, 95);     // → null  (strict)

// Score a single term pair
scoreTerm('acme', 'Acme Corp');   // → 86  (substring match)
scoreTerm('acme', 'Acme');        // → 100 (exact after normalization)
scoreTerm('akme', 'Acme');        // → 75  (Levenshtein)

API

findBestMatchId(input, candidates, threshold?)

Returns the id of the best-matching candidate, or null if no candidate meets the threshold.

| Param | Type | Default | Description | |-------|------|---------|-------------| | input | string | — | Raw input to match | | candidates | MatchCandidate[] | — | Known entity list | | threshold | number | 58 | Minimum score (0–100) |

scoreTerm(input, term)

Score a single input string against a single candidate term. Returns 0–100.

MatchCandidate

interface MatchCandidate {
  id: string;       // returned on match
  label: string;    // primary display label
  aliases: string[]; // alternate names / abbreviations
}

Scoring

| Condition | Score | |-----------|-------| | Exact match (after normalization) | 100 | | Substring containment (either direction) | 86 | | Levenshtein similarity | 0–85 |

Normalization lowercases input, strips non-alphanumeric characters (except spaces), and collapses whitespace.

License

MIT