Extremely fast faceted search engine in JavaScript - lightweight, flexible, and simple to use. Created to perform fast search on json dataset (up to 100K items).
Itemsjs is being used mostly for data classification of companies, products, publications, documents, jobs or plants
The solution has been implemented by people from Amazon, Hermes, Apple, Microsoft, James Cook University, Carnegie Mellon University and more. You can find a list of real implementations - here
- Ultra-fast faceted search: Process and filter data with blazing speed.
- Simple full-text search: Intuitive and straightforward text searching.
- Relevance scoring: Rank search results based on relevance.
- Facet filtering and sorting: Filter and order results by various facets.
- Pagination
- Works on both backend and frontend
- Integration with custom full-text search engines
npm install itemsjs
const itemsjs = require('itemsjs')(data, configuration);
const items = itemsjs.search();
import itemsjs from 'itemsjs';
const searchEngine = itemsjs(data, configuration);
const items = searchEngine.search();
<!-- CDN -->
<!-- unpkg: use the latest release -->
<script src="https://unpkg.com/itemsjs@latest/dist/index.umd.js"></script>
<!-- unpkg: use a specific version -->
<script src="https://unpkg.com/itemsjs@2.1.24/dist/index.umd.js"></script>
<!-- jsdelivr: use a specific version -->
<script src="https://cdn.jsdelivr.net/npm/itemsjs@2.1.24/dist/index.umd.js"></script>
<script>
itemsjs = itemsjs(data, configuration);
itemsjs.search()
</script>
<!-- Include as ES Module -->
<script type="module">
import itemsjs from 'https://unpkg.com/itemsjs@2.1.24/dist/index.module.js';
// Initialize and use itemsjs here
const searchEngine = itemsjs(data, configuration);
searchEngine.search();
</script>
npm install itemsjs
# download json data
wget https://raw.githubusercontent.com/itemsapi/itemsapi-example-data/master/items/imdb.json -O data.json
Next, create a search.js file with the following content:
const data = require('./data.json');
const itemsjs = require('itemsjs')(data, {
sortings: {
name_asc: {
field: 'name',
order: 'asc'
}
},
aggregations: {
tags: {
title: 'Tags',
size: 10,
conjunction: false
},
actors: {
title: 'Actors',
size: 10
},
genres: {
title: 'Genres',
size: 10
}
},
searchableFields: ['name', 'tags']
});
/**
* get filtered list of movies
*/
const movies = itemsjs.search({
per_page: 1,
sort: 'name_asc',
// full text search
// query: 'forrest gump',
filters: {
tags: ['1980s']
}
})
console.log(JSON.stringify(movies, null, 2));
/**
* get list of top tags
*/
const top_tags = itemsjs.aggregation({
name: 'tags',
per_page: 10
})
console.log(JSON.stringify(top_tags, null, 2));
Run your script with Node.js:
node search.js
If native full text search is not enough then you can integrate with external full text search.
How it works:
- each item of your data needs to have
id
field. It can be also custom field but it needs to be defined. native_search_enabled
option in configuration should be disabled- index data once in your search and itemsjs
- make search in your custom search and provide
ids
data into itemsjs - done!
Examples:
The first data
argument is an array of objects.
Responsible for defining global configuration. Look for full example here - configuration
-
aggregations
filters configuration i.e. fortags
,actors
,colors
, etc. Responsible for generating facets.Each filter can have it's own configuration. You can access those as
buckets
on thesearch()
response.title
Human readable filter namesize
Number of values provided for this filter (Default:10
)sort
Values sorted bycount
(Default) orkey
for the value name. This can be also an array of keys which define the sorting priorityorder
asc
|desc
. This can be also an array of orders (ifsort
is also array)show_facet_stats
true
|false
(Default) to retrieve the min, max, avg, sum rating values from the whole filtered datasetconjunction
true
(Default) stands for an AND query (results have to fit all selected facet-values),false
for an OR query (results have to fit one of the selected facet-values)chosen_filters_on_top
true
(Default) Filters that have been selected will appear above those not selected,false
for filters displaying in the order set out bysort
andorder
regardless of selected status or nothide_zero_doc_count
true
|false
(Default) Hide filters that have 0 results returned
-
sortings
you can configure different sortings liketags_asc
,tags_desc
with options and later use it with one key. -
searchableFields
an array of searchable fields. -
native_search_enabled
if native full text search is enabled (true | false. It's enabled by default) -
isExactSearch
set totrue
if you want to always show exact search matches. See lunr stemmer and lunr stopWordFilter. -
removeStopWordFilter
set totrue
if you want to remove the stopWordFilter. See #46.
-
per_page
amount of items per page. -
page
page number - used for pagination. -
query
used for full text search. -
sort
used for sorting. one ofsortings
key -
filters
filtering items based on specific aggregations i.e. {tags: ['drama' , 'historical']} -
filter
function responsible for items filtering. The way of working is similar to js native filter function. See example -
filters_query
boolean filtering i.e. (tags:novel OR tags:80s) AND category:Western -
is_all_filtered_items
set totrue
if you want to return the whole filtered dataset.
It returns full list of filters for specific aggregation
name
aggregation nameper_page
filters per pagepage
page numberquery
used for quering filters. It's not full text searchconjunction
true
(Default) stands for an AND query,false
for an OR query
It returns similar items to item for given id
field
field name for computing similarity (i.e. tags, actors, colors)minimum
what is the minimum intersection between field of based item and similar item to show them in the resultper_page
filters per pagepage
page number
It's used in case you need to reindex the whole data
An array of objects.