MiniSearch is a tiny but powerful in-memory fulltext search engine written in
JavaScript. It is respectful of resources, and it can comfortably run both in
Node and in the browser.
Try out the demo application.
Find the complete documentation and API reference
here,
and more background about MiniSearch, including a comparison with other
similar libraries, in this blog
post.
MiniSearch addresses use cases where full-text search features are needed
(e.g. prefix search, fuzzy search, ranking, boosting of fields…), but the data
to be indexed can fit locally in the process memory. While you won't index the
whole Internet with it, there are surprisingly many use cases that are served
well by MiniSearch. By storing the index in local memory, MiniSearch can
work offline, and can process queries quickly, without network latency.
A prominent use-case is real time search "as you type" in web and mobile applications, where keeping the index on the client enables fast and reactive UIs, removing the need to make requests to a search server.
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Memory-efficient index, designed to support memory-constrained use cases like mobile browsers.
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Exact match, prefix search, fuzzy match, field boosting
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Auto-suggestion engine, for auto-completion of search queries
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Documents can be added and removed from the index at any time
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Zero external dependencies
MiniSearch strives to expose a simple API that provides the building blocks to
build custom solutions, while keeping a small and well tested codebase.
With npm:
npm install --save minisearch
With yarn:
yarn add minisearch
Then require or import it in your project:
// If you are using import:
import MiniSearch from 'minisearch'
// If you are using require:
const MiniSearch = require('minisearch')Alternatively, if you prefer to use a <script> tag, you can require MiniSearch
from a CDN:
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/index.min.js"></script>In this case, MiniSearch will appear as a global variable in your project.
Finally, if you want to manually build the library, clone the repository and run
yarn build (or yarn build-minified for a minified version + source maps).
The compiled source will be created in the dist folder (UMD, ES6 and ES2015
module versions are provided).
// A collection of documents for our examples
const documents = [
{
id: 1,
title: 'Moby Dick',
text: 'Call me Ishmael. Some years ago...',
category: 'fiction'
},
{
id: 2,
title: 'Zen and the Art of Motorcycle Maintenance',
text: 'I can see by my watch...',
category: 'fiction'
},
{
id: 3,
title: 'Neuromancer',
text: 'The sky above the port was...',
category: 'fiction'
},
{
id: 4,
title: 'Zen and the Art of Archery',
text: 'At first sight it must seem...',
category: 'non-fiction'
},
// ...and more
]
let miniSearch = new MiniSearch({
fields: ['title', 'text'], // fields to index for full-text search
storeFields: ['title', 'category'] // fields to return with search results
})
// Index all documents
miniSearch.addAll(documents)
// Search with default options
let results = miniSearch.search('zen art motorcycle')
// => [
// { id: 2, title: 'Zen and the Art of Motorcycle Maintenance', category: 'fiction', score: 2.77258, match: { ... } },
// { id: 4, title: 'Zen and the Art of Archery', category: 'non-fiction', score: 1.38629, match: { ... } }
// ]MiniSearch supports several options for more advanced search behavior:
// Search only specific fields
miniSearch.search('zen', { fields: ['title'] })
// Boost some fields (here "title")
miniSearch.search('zen', { boost: { title: 2 } })
// Prefix search (so that 'moto' will match 'motorcycle')
miniSearch.search('moto', { prefix: true })
// Search within a specific category
miniSearch.search('zen', {
filter: (result) => result.category === 'fiction'
})
// Fuzzy search, in this example, with a max edit distance of 0.2 * term length,
// rounded to nearest integer. The mispelled 'ismael' will match 'ishmael'.
miniSearch.search('ismael', { fuzzy: 0.2 })
// You can set the default search options upon initialization
miniSearch = new MiniSearch({
fields: ['title', 'text'],
searchOptions: {
boost: { title: 2 },
fuzzy: 0.2
}
})
miniSearch.addAll(documents)
// It will now by default perform fuzzy search and boost "title":
miniSearch.search('zen and motorcycles')MiniSearch can suggest search queries given an incomplete query:
miniSearch.autoSuggest('zen ar')
// => [ { suggestion: 'zen archery art', terms: [ 'zen', 'archery', 'art' ], score: 1.73332 },
// { suggestion: 'zen art', terms: [ 'zen', 'art' ], score: 1.21313 } ]The autoSuggest method takes the same options as the search method, so you
can get suggestions for misspelled words using fuzzy search:
miniSearch.autoSuggest('neromancer', { fuzzy: 0.2 })
// => [ { suggestion: 'neuromancer', terms: [ 'neuromancer' ], score: 1.03998 } ]Suggestions are ranked by the relevance of the documents that would be returned by that search.
Sometimes, you might need to filter auto suggestions to, say, only a specific
category. You can do so by providing a filter option:
miniSearch.autoSuggest('zen ar', {
filter: (result) => result.category === 'fiction'
})
// => [ { suggestion: 'zen art', terms: [ 'zen', 'art' ], score: 1.21313 } ]By default, documents are assumed to be plain key-value objects with field names
as keys and field values as simple values. In order to support custom field
extraction logic (for example for nested fields, or non-string field values that
need processing before tokenization), a custom field extractor function can be
passed as the extractField option:
// Assuming that our documents look like:
const documents = [
{ id: 1, title: 'Moby Dick', author: { name: 'Herman Melville' }, pubDate: new Date(1851, 9, 18) },
{ id: 2, title: 'Zen and the Art of Motorcycle Maintenance', author: { name: 'Robert Pirsig' }, pubDate: new Date(1974, 3, 1) },
{ id: 3, title: 'Neuromancer', author: { name: 'William Gibson' }, pubDate: new Date(1984, 6, 1) },
{ id: 4, title: 'Zen in the Art of Archery', author: { name: 'Eugen Herrigel' }, pubDate: new Date(1948, 0, 1) },
// ...and more
]
// We can support nested fields (author.name) and date fields (pubDate) with a
// custom `extractField` function:
let miniSearch = new MiniSearch({
fields: ['title', 'author.name', 'pubYear'],
extractField: (document, fieldName) => {
// If field name is 'pubYear', extract just the year from 'pubDate'
if (fieldName === 'pubYear') {
const pubDate = document['pubDate']
return pubDate && pubDate.getFullYear().toString()
}
// Access nested fields
return fieldName.split('.').reduce((doc, key) => doc && doc[key], document)
}
})The default field extractor can be obtained by calling
MiniSearch.getDefault('extractField').
By default, documents are tokenized by splitting on Unicode space or punctuation
characters. The tokenization logic can be easily changed by passing a custom
tokenizer function as the tokenize option:
// Tokenize splitting by hyphen
let miniSearch = new MiniSearch({
fields: ['title', 'text'],
tokenize: (string, _fieldName) => string.split('-')
})Upon search, the same tokenization is used by default, but it is possible to
pass a tokenize search option in case a different search-time tokenization is
necessary:
// Tokenize splitting by hyphen
let miniSearch = new MiniSearch({
fields: ['title', 'text'],
tokenize: (string) => string.split('-'), // indexing tokenizer
searchOptions: {
tokenize: (string) => string.split(/[\s-]+/) // search query tokenizer
}
})The default tokenizer can be obtained by calling
MiniSearch.getDefault('tokenize').
Terms are downcased by default. No stemming is performed, and no stop-word list
is applied. To customize how the terms are processed upon indexing, for example
to normalize them, filter them, or to apply stemming, the processTerm option
can be used. The processTerm function should return the processed term as a
string, or a falsy value if the term should be discarded:
let stopWords = new Set(['and', 'or', 'to', 'in', 'a', 'the', /* ...and more */ ])
// Perform custom term processing (here discarding stop words and downcasing)
let miniSearch = new MiniSearch({
fields: ['title', 'text'],
processTerm: (term, _fieldName) =>
stopWords.has(term) ? null : term.toLowerCase()
})By default, the same processing is applied to search queries. In order to apply
a different processing to search queries, supply a processTerm search option:
let miniSearch = new MiniSearch({
fields: ['title', 'text'],
processTerm: (term) =>
stopWords.has(term) ? null : term.toLowerCase(), // index term processing
searchOptions: {
processTerm: (term) => term.toLowerCase() // search query processing
}
})The default term processor can be obtained by calling
MiniSearch.getDefault('processTerm').
Refer to the API documentation for details about configuration options and methods.
MiniSearch natively supports all modern browsers implementing JavaScript
standards, but requires a polyfill when used in Internet Explorer, as it makes
use functions like Object.entries, Array.includes, and Array.from, which
are standard but not available on older browsers. The package
core-js is one such polyfill that can
be used to provide those functions.
Contributions to MiniSearch are welcome! Please read the contributions
guidelines.
Reading the design
document is
also useful to understand the project goals and the technical implementation.