Awesome
backlunr
is a solution to bring Backbone Collections together with the browser fulltext search engine Lunr.js.
INFO: all examples are written in coffee-script
Install
To use backlunr you first have to load the dependencies:
Then add the backlunr.js
script to your page.
Usage
Config
To use the lunr.js
fulltext search within a collection you have to load backlunr.js
and use Backbone.Collection.Lunr
instead of Backbone.Collection
The only thing you have to define are the fields to index.
Config Example:
class User extends Backbone.Model
class Users extends Backbone.Collection.Lunr
model: User
lunroptions:
fields: [
{ name: "firstname", boost: 10 }
{ name: "lastname", boost: 5 }
{ name: "email" }
{ name: "address" }
]
lunroptions
fields
( Array | Function ): An array of field definitions or a method called on init/reset which has to return the field definitions arrayfields[ n ]
( Object ): The field definitionfields[ n ].name
( String - required ): The field name within your model attributefields[ n ].boost
( Number - optional: default=1 ): A boost factor for results in this field.
Attension: Currently Lunr.js does not support an indexing of numeric values. To use numeric values backlunr converts all fields to strings.
search
To search within your collection you just have to call .search( "[ term ]" )
.
Search Example:
# add some users
usersCollection = new Users [
firstname: "Fritz", lastname: "Maier", "email": "fmaier@maier.com", "address": "Teststreet 123"
,
firstname: "Hans", lastname: "Schubert", "email": "hschubert@maier.com", "address": "Checkway 987"
]
result = usersCollection.search( "Fritz" )
# [ User( Fritz ) ]
# returns an array of matching models sorted by the result scoring
result.toJSON()
# [ {firstname: "Fritz", lastname: "Maier", "email": "fmaier@maier.com", "address": "Teststreet 123"} ]
# the single special method `toJSON` of the array returns all models converted by `model.toJSON()`.
Methods
Collection.search( term [, raw ] )
.search()
is the only method added to the collection by Backlunr.
arguments:
term
( String - required ): the search termraw
( Boolean - optional: default=false ): Return the raw lunr result including thescore
and modelscid
asref
retuns ( Array ):
An array of matching models.
If you define raw=true
you will receive an array with object like { score: 0.789, ref: "c123" }
toJSON()
Ifraw=false
you can use the specialtoJSON
method to transform the aray of models to an array of model attributes.
Ideas
- Create a solution to search in multiple collections by hooking them to a global search module. If a search has been done the result will return all matching models in al hooked collection including the type ( Collection name )
Work in progress
backlunr
is work in progress. Your ideas, suggestions etc. are very welcome.
Development
If you want to contribute you have to install the dependencies and gulp as task runner:
$ npm install
$ npm install -g gulp
After that it's possible to build the code using
$ gulp
Changelog
0.3.0
- added option to grab the contenct from sub elements
- use coffee 1.x for compile
0.2.5
- Fixed handling of tags/arrays with spaces
- updated to current gulp version
0.2.4
- Readme typo fix #2. Thanks to Cody Nguyen
- restructured code
- Updated test to Lunr.js
v0.6.0
, jqueryv2.1.4
, backbonev1.2.3
, mochav2.3.4
and expectv0.3.1
. *Backlunr test already working fine
0.2.3
- Fixed Issue #1 to init the collection on search if nor reset has been fired
- Added gulp compile
- Updated test to Lunr.js
v0.5.8
, jqueryv2.1.3
, mochav1.21.5
and expectv0.3.1
. *Backlunr test already working fine
0.2.2
- Updated test to Lunr.js
v0.5.3
, jqueryv2.1.1
, mochav1.19.0
and expectv0.3.1
. Backlunr test already working fine
0.2.1
- Updated test to Lunr.js
v0.4.0
, jqueryv2.0.3
, mochav1.12.0
and expectv0.2.0
. Backlunr test already working fine but 15% faster. - Compiled scripts with new coffee version
v1.6.3
0.2.0
- Added minified version
- Added support for numeric values by converting every field-value to a string
0.1.0
- Initial version
Todo
- restructure to use it standalone or with browserify, AMD, ...
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License
(The MIT License)
Copyright (c) 2010 TCS <dev (at) tcs.de>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.