Package Data | |
---|---|
Maintainer Username: | websecret |
Package Create Date: | 2016-03-23 |
Package Last Update: | 2017-06-21 |
Language: | PHP |
License: | MIT |
Last Refreshed: | 2024-12-26 15:23:26 |
Package Statistics | |
---|---|
Total Downloads: | 341 |
Monthly Downloads: | 0 |
Daily Downloads: | 0 |
Total Stars: | 4 |
Total Watchers: | 7 |
Total Forks: | 1 |
Total Open Issues: | 1 |
Laravel Eloquent models with Elasticsearch 2
First, you'll need to require the package with Composer:
composer require websecret/laravel-search
Aftwards, run composer update
from your command line.
Then, update config/app.php
by adding an entry for the service provider.
'providers' => [
// ...
Websecret\LaravelSearchable\SearchableServiceProvider::class,
];
Finally, from the command line again, run php artisan vendor:publish --provider=Websecret\LaravelSearchable\SearchableServiceProvider
to publish
the default configuration file.
Your models should implement Searchable's interface and use it's trait. You should
also define a protected property $searchable
with any model-specific configurations
(see Configuration below for details):
use Websecret\LaravelSearchable\SearchableTrait;
use Websecret\LaravelSearchable\SearchableInterface;
class Article extends Model implements SearchableInterface
{
use SearchableTrait;
protected $searchable = [
//'index' => 'domain_name',
//'type' => 'articles',
'fields' => [
'title' => [
'weight' => 3,
],
'content',
'category.title' => [
'title' => 'category',
'weight' => 1,
],
],
//"fuzziness" => "AUTO",
//"prefix_length"=> 2,
//"max_expansions"=> 100,
];
}
Use search
scope to find models. Result collection will be sorted by score.
$articles = Article::where('is_active', 1)->search('apple')->get();
Models auto indexing on updated
, created
and deleted
events.
You can use $article->searchIndex();
and $article->searchDelete();
to manually index or delete from index. Use Article::searchDeleteAll()
to clear all index by specified model.
Fuzzy matching treats two words that are “fuzzily” similar as if they were the same word.
Of course, the impact that a single edit has on a string depends on the length of the string. Two edits to the word hat can produce mad, so allowing two edits on a string of length 3 is overkill. The fuzziness parameter can be set to AUTO, which results in the following maximum edit distances:
0
for strings of one or two characters1
for strings of three, four, or five characters2
for strings of more than five charactersOf course, you may find that an edit distance of 2 is still overkill, and returns results that don’t appear to be related. You may get better results, and better performance, with a maximum fuzziness of 1.
The number of initial characters which will not be “fuzzified”. This helps to reduce the number of terms which must be examined.
The maximum number of terms that the fuzzy query will expand to.