Quick Start

In five minutes you’ll have a product search that handles typos, filters by stock and price, and returns relevance-ranked results.

#Prerequisites

• Sigmie installed
• Elasticsearch running on 127.0.0.1:9200

Quick sanity check:

use Sigmie\Sigmie;
 
$sigmie = Sigmie::create(hosts: ['127.0.0.1:9200']);
 
$sigmie->isConnected();   // true

#Step 1: Define a schema

NewProperties is your schema builder. Use high-level types — they wire up the right analyzers and queries underneath.

use Sigmie\Mappings\NewProperties;
 
$props = new NewProperties;
$props->title('name');         // full-text searchable title
$props->text('description');   // long-form searchable text
$props->category('category');  // exact-match category
$props->price();               // numeric, filterable by range
$props->bool('in_stock');

#Step 2: Create the index

$sigmie->newIndex('products')
    ->properties($props)
    ->create();

#Step 3: Index documents

use Sigmie\Document\Document;
 
$sigmie->collect('products', refresh: true)
    ->merge([
        new Document([
            'name' => 'Laptop Pro',
            'description' => 'High-performance laptop for professionals',
            'category' => 'electronics',
            'price' => 1299,
            'in_stock' => true,
        ]),
        new Document([
            'name' => 'Wireless Mouse',
            'description' => 'Ergonomic wireless mouse with precision tracking',
            'category' => 'accessories',
            'price' => 49,
            'in_stock' => true,
        ]),
        new Document([
            'name' => 'USB-C Cable',
            'description' => 'Fast charging and data transfer cable',
            'category' => 'accessories',
            'price' => 15,
            'in_stock' => false,
        ]),
    ]);

refresh: true makes documents immediately searchable. Omit it in production for better bulk-indexing performance.

#Step 4: Search

$results = $sigmie->newSearch('products')
    ->properties($props)
    ->queryString('laptop')
    ->get();
 
foreach ($results->hits() as $hit) {
    echo $hit['name'] . "\n";
}
// Laptop Pro

#Step 5: Tolerate typos

$results = $sigmie->newSearch('products')
    ->properties($props)
    ->queryString('lapto')                        // typo
    ->typoTolerance()                             
    ->get();
// Laptop Pro

Defaults: one typo allowed for terms of 3+ characters, two typos for 6+. Override with typoTolerance(oneTypoChars: 4, twoTypoChars: 8).

#Step 6: Filter

The filter parser reads like a sentence:

$results = $sigmie->newSearch('products')
    ->properties($props)
    ->queryString('cable')
    ->filters('in_stock:true')                    
    ->get();
// (no results — USB-C Cable is out of stock)

Combine clauses with AND, OR, and NOT:

->filters('category:"accessories" AND price:<=100')
->filters('price:100..500 AND in_stock:true')
->filters('NOT category:"books"')

#Step 7: Sort and paginate

$results = $sigmie->newSearch('products')
    ->properties($props)
    ->queryString('mouse cable')
    ->sort('_score:desc price:asc')
    ->from(0)
    ->size(10)
    ->get();

_score:desc is the default. _score:asc is not allowed — Elasticsearch always sorts relevance highest-first.

#Step 8: Weight fields

$results = $sigmie->newSearch('products')
    ->properties($props)
    ->queryString('laptop')
    ->weight(['name' => 3, 'description' => 1])   
    ->get();

A match in name now scores 3× higher than the same match in description.

#Putting it together

$results = $sigmie->newSearch('products')
    ->properties($props)
    ->queryString('wireless laptop')
    ->typoTolerance()
    ->filters('category:"electronics" AND price:100..2000 AND in_stock:true')
    ->weight(['name' => 3, 'description' => 1])
    ->sort('_score:desc price:asc')
    ->size(20)
    ->get();
 
echo "Found {$results->total()} products\n";
 
foreach ($results->hits() as $hit) {
    printf("%s — $%d\n", $hit['name'], $hit['price']);
}

#Add facets

Faceted navigation is one method away:

$props->category('brand')->facetDisjunctive();   // enable faceting
 
$results = $sigmie->newSearch('products')
    ->properties($props)
    ->queryString('laptop')
    ->facets('brand category price:100')         
    ->get();
 
$facets = $results->json('facets');
// ['brand' => ['Apple' => 5, ...], 'price' => ['min' => 999, 'max' => 2499, ...]]

See Facets for the full reference.

#Add semantic search

When you want results by meaning, not just keywords:

use Sigmie\AI\APIs\OpenAIEmbeddingsApi;
 
$sigmie->registerApi('embeddings', new OpenAIEmbeddingsApi('sk-...'));
 
$props = new NewProperties;
$props->title('name');
$props->text('description')->semantic(            
    api: 'embeddings',
    dimensions: 1536,
);
 
$results = $sigmie->newSearch('products')
    ->properties($props)
    ->semantic()
    ->queryString('portable computer for work')   // matches "laptop", "notebook"
    ->get();

See Semantic Search for embeddings setup, accuracy levels, and similarity functions.

#Where to go next

• Filter Parser — every operator and clause.
• Facets — sidebar filters with conjunctive/disjunctive logic.
• Search — every NewSearch method.
• Mappings & Properties — all field types.
• Laravel Scout — Eloquent integration.