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Fast and tiny fuzzy-search utility

Package Exports

  • fast-fuzzy

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Readme

fast-fuzzy Build Status npm

Fast fuzzy-search utility

methodology

fast-fuzzy is a tiny, lightning-quick on-line fuzzy-searching utility. The ranking algorithm is a modification of levenshtein distance proposed by Peter H. Sellers (paper). fast-fuzzy also use the damerau-levenshtein distance by default, which, compared to normal levenshtein, punishes transpositions less.

Inputs are normalized before search. Normalization consists of standard utf8-normalization, followed by optionally taking the lowercase of a string, optionally removing non-word characters, and optionally flattening/trimming whitespace.

Inputs are scored from 0 to 1, where a higher score indicates a closer match. When searching, results are returned in descending order of score. Ties are broken by favoring the candidate whose length is closest to the length of the search term. This causes matches which are closer to exact full string matches to be effectively ranked higher in the case of a tie.

exports

name description signature
fuzzy fuzzy ranking algorithm; returns match strength (term, candidate, options?) => score
search for one-off searches; returns a sorted array of matches (term, candidates, options?) => matches
Searcher for searching the same set of candidates multiple times; caches normalization and key selection N/A

Searcher methods

name description signature
constructor supply the options and initial list of candidates (candidates?, options?) => searcher
add add new candidates to the list (...candidates) => void
search perform a search against the instance's candidates (term, options?) => matches*

* allows overriding the threshold, returnMatchData, and useDamerau options

options

Searcher and search both take an options object for configuring behavior.

option type description default
keySelector Function selects the string to search (if candidates are objects) (_) => _
threshold Number the minimum score that can be returned .6
ignoreCase Bool normalize case by calling toLower on input and pattern true
ignoreSymbols Bool strip non-word symbols* from input true
normalizeWhitespace Bool normalize and trim whitespace true
returnMatchData Bool return match data** false
useDamerau Bool use damerau-levenshtein distance true

* `~!@#$%^&*()-=_+{}[]\|\;':",./<>?

** in the form {item, key, score, match: {index, length}}

fuzzy accepts a subset of these options (excluding keySelector and threshold) with the same defaults.

examples

You can call fuzzy directly to get a match score for a single string

const {fuzzy} = require("fast-fuzzy");

fuzzy("hello", "hello world"); //returns 1
fuzzy("word", "hello world"); //returns .75

//pass in custom options
fuzzy("hello world", "hello  world"); //returns 1
fuzzy("hello world", "hello  world", {normalizeWhitespace: false}); //returns .90909090...

Use search to search a list of strings or objects

const {search} = require("fast-fuzzy");

search("abc", ["def", "bcd", "cde", "abc"]); //returns ["abc", "bcd"]

//pass in a keySelector to search for objects
search("abc", [{name: "def"}, {name: "bcd"}, {name: "cde"}, {name: "abc"}], {keySelector: (obj) => obj.name});
//returns [{name: "abc"}, {name: "bcd"}]

//pass returnMatchData to receive the matchData for each result
search("abc", ["def", "bcd", "cde", "abc"], {returnMatchData: true});
/* returns [{
    item: 'abc', key: 'abc', score: 1,
    match: {index: 0, length: 3},
}, { 
    item: 'bcd', key: 'bcd', score: 0.6666666666666667,
    match: {index: 0, length: 2},
}] */

Use Searcher in much the same way as search

const {Searcher} = require("fast-fuzzy");

const searcher = new Searcher(["def", "bcd", "cde", "abc"]);
searcher.search("abc"); //returns ["abc", "bcd"]

//options are passed in on construction
const anotherSearcher = new Searcher([{name: "thing1"}, {name: "thing2"}], {keySelector: (obj) => obj.name});

//some options can be overridden per call
searcher.search("abc", {returnMatchData: true});
/* returns [{
    item: 'abc', key: 'abc', score: 1,
    match: {index: 0, length: 3},
}, { 
    item: 'bcd', key: 'bcd', score: 0.6666666666666667,
    match: {index: 0, length: 2},
}] */