Js Search: client-side search library

Js Search enables efficient client-side searches of JavaScript and JSON objects.
It is ES5 compatible and does not require jQuery or any other third-party libraries.

Js Search began as a lightweight implementation of Lunr JS, offering runtime performance
improvements and a smaller file size. It has since expanded to include a rich feature set- supporting stemming,
stop-words, and TF-IDF ranking.

Here are some JS Perf benchmarks comparing the two search libraries. (Thanks to olivernn
for tweaking the Lunr side for a better comparison!)

* Initial building of search index
* Running a search

If you're looking for a simpler, web-worker optimized JS search utility check out js-worker-search.

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You can install using either Bower or NPM like so:

``shell npm install js-search bower install js-search`

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At a high level you configure Js Search by telling it which fields it should index for searching and then add the objects to be searched.

For example, a simple use of JS Search would be as follows:

`javascript import * as JsSearch from 'js-search';

var theGreatGatsby = { isbn: '9781597226769', title: 'The Great Gatsby', author: { name: 'F. Scott Fitzgerald' }, tags: ['book', 'inspirational'] }; var theDaVinciCode = { isbn: '0307474275', title: 'The DaVinci Code', author: { name: 'Dan Brown' }, tags: ['book', 'mystery'] }; var angelsAndDemons = { isbn: '074349346X', title: 'Angels & Demons', author: { name: 'Dan Brown', }, tags: ['book', 'mystery'] };

var search = new JsSearch.Search('isbn'); search.addIndex('title'); search.addIndex(['author', 'name']); search.addIndex('tags')

search.addDocuments([theGreatGatsby, theDaVinciCode, angelsAndDemons]);

search.search('The'); // [theGreatGatsby, theDaVinciCode] search.search('scott'); // [theGreatGatsby] search.search('dan'); // [angelsAndDemons, theDaVinciCode] search.search('mystery') // [angelsAndDemons, theDaVinciCode]`

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Tokenization is the process of breaking text (e.g. sentences) into smaller, searchable tokens (e.g. words or parts of words). Js Search provides a basic tokenizer that should work well for English but you can provide your own like so:

`javascript search.tokenizer = { tokenize( text / string / ) { // Convert text to an Array of strings and return the Array } };`

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Stemming is the process of reducing search tokens to their root (or "stem") so that searches for different forms of a word will still yield results. For example "search", "searching" and "searched" can all be reduced to the stem "search".

Js Search does not implement its own stemming library but it does support stemming through the use of third-party libraries.

To enable stemming, use the StemmingTokenizer like so:

`javascript var stemmer = require('porter-stemmer').stemmer;

search.tokenizer = new JsSearch.StemmingTokenizer( stemmer, // Function should accept a string param and return a string new JsSearch.SimpleTokenizer());`

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Stop words are very common (e.g. a, an, and, the, of) and are often not semantically meaningful. By default Js Search does not filter these words, but filtering can be enabled by using theStopWordsTokenizer like so:

`javascript search.tokenizer = new JsSearch.StopWordsTokenizer( new JsSearch.SimpleTokenizer());`

By default Js Search uses a slightly modified version of the Google History stop words listed on www.ranks.nl/stopwords. You can modify this list of stop words by adding or removing values from theJsSearch.StopWordsMap object like so:

`javascript JsSearch.StopWordsMap.the = false; // Do not treat "the" as a stop word JsSearch.StopWordsMap.bob = true; // Treat "bob" as a stop word`

Note that stop words are lower case and so using a case-sensitive sanitizer may prevent some stop words from being removed.

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There are two search indices packaged with js-search.

Term frequency–inverse document frequency (or TF-IDF) is a numeric statistic intended to reflect how important a word (or words) are to a document within a corpus. The TF-IDF value increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus. This helps to adjust for the fact that some words (e.g. and, or, the) appear more frequently than others.

By default Js Search supports TF-IDF ranking but this can be disabled for performance reasons if it is not required. You can specify an alternateISearchIndeximplementation in order to disable TF-IDF, like so:

`javascript // default search.searchIndex = new JsSearch.TfIdfSearchIndex();

// Search index capable of returning results matching a set of tokens // but without any meaningful rank or order. search.searchIndex = new JsSearch.UnorderedSearchIndex();`

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There are three index strategies packaged with js-search.

PrefixIndexStrategyindexes for prefix searches. (e.g. the term "cat" is indexed as "c", "ca", and "cat" allowing prefix search lookups).

AllSubstringsIndexStrategy indexes for all substrings. In other word "c", "ca", "cat", "a", "at", and "t" all match "cat".

ExactWordIndexStrategy indexes for exact word matches. For example "bob" will match "bob jones" (but "bo" will not).

By default Js Search supports prefix indexing but this is configurable. You can specify an alternateIIndexStrategyimplementation in order to disable prefix indexing, like so:

`javascript // default search.indexStrategy = new JsSearch.PrefixIndexStrategy();

// this index strategy is built for all substrings matches. search.indexStrategy = new JsSearch.AllSubstringsIndexStrategy();

// this index strategy is built for exact word matches. search.indexStrategy = new JsSearch.ExactWordIndexStrategy();``