fuzzify

A tiny lightweight library for Fuzzy Search.

Why

I made this library as a result of learning about Levenshtein Distance algorithm to calculate minimum number of single-character edits (insertions, deletions or substitutions) required to transform one word to another by Vladimir Levenshtein.

Note

Note: The library is at a very early stage, if you want to help improve it, please open an issue.

Installation

with yarn

yarn add fuzzify

with npm

npm install fuzzify

Usage

import Fuzzy from "fuzzify";

const countries = [
  "Australia",
  "France",
  "Germany",
  "Hungary",
  "Iceland",
  "India",
  "Israel",
  "Italy",
  "Japan",
  "Malawi",
  "Malaysia",
  "Maldives",
];
const fuzzy = new Fuzzy(countries);

const query = "ala";
const results = fuzzy.search(query);

console.log("RESULTS", results);

The search API gives approximate matched strings with the passed query in the below format.

Attributes	Description
text	The target string against which query is matched
distance	The minimum number of edits (Insertion / Deletion / Substitutions) required to transform the query to target text.

[
  {
    text: "Malawi",
    distance: 3,
  },
  {
    text: "Malaysia",
    distance: 5,
  },
  {
    text: "Australia",
    distance: 6,
  },
  {
    text: "Italy",
    distance: 3,
  },
  {
    text: "Japan",
    distance: 3,
  },
  {
    text: "Iceland",
    distance: 5,
  },
  {
    text: "Maldives",
    distance: 6,
  },
  {
    text: "Israel",
    distance: 5,
  },
  {
    text: "India",
    distance: 4,
  },
  {
    text: "France",
    distance: 5,
  },
  {
    text: "Germany",
    distance: 6,
  },
  {
    text: "Hungary",
    distance: 6,
  },
];

Options

includeMatches

includeMatches - Determines whether the indices at which characters matche should be returned in the response. Each match element consists of two indices -

1. The index of query string where match is found.
2. The index of target string where a match is found.

Example 👇

query = "ala", target string = "Australia"
matches: [
  [0, 5],
  [1, 6],
  [2, 8],
],

In the above example 👇 matches are found

1. character a at 0th index in ala matches with characater a at 5th index in Australia
2. character l at 1st index in ala matches with characater a at 6th index in Australia
3. character a at 2nd index in ala matches with characater a at 8th index in Australia

The complete response would be 👇

[
  {
    text: "Malawi",
    distance: 3,
    matches: [
      [0, 1],
      [1, 2],
      [2, 3],
    ],
  },
  {
    text: "Malaysia",
    distance: 5,
    matches: [
      [0, 1],
      [1, 2],
      [2, 7],
    ],
  },
  {
    text: "Australia",
    distance: 6,
    matches: [
      [0, 5],
      [1, 6],
      [2, 8],
    ],
  },
  {
    text: "Italy",
    distance: 3,
    matches: [
      [0, 2],
      [1, 3],
    ],
  },
  {
    text: "Japan",
    distance: 3,
    matches: [
      [0, 1],
      [2, 3],
    ],
  },
  {
    text: "Iceland",
    distance: 5,
    matches: [
      [1, 3],
      [2, 4],
    ],
  },
  {
    text: "Maldives",
    distance: 6,
    matches: [
      [0, 1],
      [1, 2],
    ],
  },
  {
    text: "Israel",
    distance: 5,
    matches: [
      [0, 3],
      [1, 5],
    ],
  },
  {
    text: "India",
    distance: 4,
    matches: [[2, 4]],
  },
  {
    text: "France",
    distance: 5,
    matches: [[2, 2]],
  },
  {
    text: "Germany",
    distance: 6,
    matches: [[2, 4]],
  },
  {
    text: "Hungary",
    distance: 6,
    matches: [[2, 4]],
  },
];

Live Demo

You can check the demo here.

Set up

Install packages:

yarn

Start development playground:

yarn start

Build command:

yarn build

Contributing

Please open an issue so we can start discussing. Any help to improve the library is most welcome :).