npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@laboralphy/did-you-mean

v2.2.3

Published

This tool suggest a list of most closest words to a given entry.

Downloads

29

Readme

O876 Levenshtein

Description

Did you encounter this situation ?

Enter a city name > PARSI
Invalid name. Did you mean "PARIS" ?

When a user is excepted to input a name which must be part of a set of entities, it's often a good idea to display a list of suggested valid entity names so should the user make a mistype, they may efficiently correct themselves.

This is what this library does.

Example

console.log(suggest("PARSI", ["PARIS", "BORDEAUX", "LILLE", .... ]));
console.log(suggest("BRDEAUX", ["PARIS", "BORDEAUX", "LILLE", .... ]));
console.log(suggest("LILE", ["PARIS", "BORDEAUX", "LILLE", .... ]));

Will display :

["PARIS"]

["BORDEAUX"]

["LILLE"]

Usage

npm install @laboralphy/did-you-mean

In code :

const {suggest} = require('@laboralphy/did-you-mean'};

console.log(suggest("PARSI", ["PARIS", "BORDEAUX", "LILLE", .... ]));
// prints ["PARIS"]

Options

The third (optionnal) parameters is a configuration object that holds two elements : count and relevance

  • count : is a number that limits the maximum number of suggested words. Default value is 1.
  • relevance : is a number that limits the maximum number of character differences between types word and suggested words. Default value is Infinity

Getting more than one suggestion

// this will returns the three closest words
suggest("...typed word...", [...list of valid words...], {count: 3});

// this will returns the three closest words that have at most a character-relevance of 2
suggest("...typed word...", [...list of valid words...], {count: 3, relevance: 2});

// this will returns all words the have a character-relevance of 2 or less
suggest("...typed word...", [...list of valid words...], {count: Infinity, relevance: 2});