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

levenshtein('kitten', 'sitting');     // 3
jaroWinkler('MARTHA', 'MARHTA');      // 0.961
sorensenDice('night', 'nacht');       // 0.25

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

// Find matches sorted by relevance (scores normalized to 0.0-1.0)
const results = search('typscript', [
  'TypeScript',
  'JavaScript',
  'Python',
  'TypeSpec',
]);
// → [{ item: 'TypeScript', score: 0.85, index: 0, positions: [] }, ...]

// With options: filter by minimum score and limit results
search('app', items, { maxResults: 5, minScore: 0.3 });

// Backward compatible: pass a number for maxResults
search('app', items, 5);

// Get matched character positions for highlighting
const [match] = search('hlo', ['hello world'], { includePositions: true });
// → { item: 'hello world', score: 0.75, index: 0, positions: [0, 2, 4] }

// Case-sensitive matching (default: smart case)
search('Type', items, { isCaseSensitive: true });

// Find the single best match
closest('tsc', ['TypeScript', 'JavaScript', 'Python']);
// → 'TypeScript'

// With minimum score threshold (returns null if no match is good enough)
closest('xyz', items, 0.5);
// → null

import { searchObjects } from 'rapid-fuzzy';

const users = [
  { name: 'John Smith', email: 'john@example.com' },
  { name: 'Jane Doe', email: 'jane@example.com' },
  { name: 'Bob Johnson', email: 'bob@test.com' },
];

// Search across multiple keys
const results = searchObjects('john', users, {
  keys: ['name', 'email'],
});
// → [{ item: { name: 'John Smith', ... }, score: 0.95, keyScores: [0.98, 0.85], index: 0 }]

// Weighted keys — prioritize name matches over email
searchObjects('john', users, {
  keys: [
    { name: 'name', weight: 2.0 },
    { name: 'email', weight: 1.0 },
  ],
});

// Nested key paths
searchObjects('new york', items, { keys: ['address.city'] });

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

const results = search('fzy', ['fuzzy'], { includePositions: true });
const { item, positions } = results[0];

// String markers
highlight(item, positions, '<b>', '</b>');
// → '<b>f</b>u<b>zy</b>'

// Callback (React, JSX, custom DOM)
highlight(item, positions, (matched) => `<mark>${matched}</mark>`);

// Raw ranges for custom rendering
highlightRanges(item, positions);
// → [{ start: 0, end: 1, matched: true }, { start: 1, end: 2, matched: false }, ...]

import {
  tokenSortRatio,
  tokenSetRatio,
  partialRatio,
  weightedRatio,
} from 'rapid-fuzzy';

// Token Sort: order-independent comparison
tokenSortRatio('New York Mets', 'Mets New York'); // 1.0

// Token Set: handles extra/missing tokens
tokenSetRatio('Great Gatsby', 'The Great Gatsby by Fitzgerald'); // ~0.85

// Partial: best substring match
partialRatio('hello', 'hello world'); // 1.0

// Weighted: best score across all methods
weightedRatio('John Smith', 'Smith, John'); // 1.0

import { levenshteinBatch, levenshteinMany } from 'rapid-fuzzy';

// Compute distances for multiple pairs at once
levenshteinBatch([
  ['kitten', 'sitting'],
  ['hello', 'help'],
  ['foo', 'bar'],
]);
// → [3, 2, 3]

// Compare one string against many candidates
levenshteinMany('kitten', ['sitting', 'kittens', 'kitchen']);
// → [3, 1, 2]