Search

Introduction
Full-Text Search
- Adding Full-Text Indexes
- Running Full-Text Queries
Semantic / Vector Search
Reranking Results
Laravel Scout
- Database Engine
- Third-Party Engines
Combining Techniques

Introduction

Almost every application needs search. Whether your users are searching a knowledge base for relevant articles, exploring a product catalog, or asking natural-language questions against a corpus of documents, Laravel provides built-in tools to handle each of these scenarios — and you often don't need any external services to get there.

Most applications will find that the built-in database-powered options provided by Laravel are more than sufficient — external search services are only necessary when you need features like typo tolerance, faceted filtering, or geo-search at massive scale.

Full-Text Search

When you need keyword relevance ranking — where the database scores and sorts results based on how well they match the search terms — Laravel's whereFullText query builder method leverages native full-text indexes on MariaDB, MySQL, and PostgreSQL. Full-text search understands word boundaries and stemming, so a search for "running" can match records containing "run". No external service is required.

Semantic / Vector Search

For AI-powered semantic search that matches results by meaning rather than exact keywords, the whereVectorSimilarTo query builder method uses vector embeddings stored in PostgreSQL with the pgvector extension. For example, a search for "best wineries in Napa Valley" can surface an article titled "Top Vineyards to Visit" — even though the words don't overlap. Vector search requires PostgreSQL with the pgvector extension and the Laravel AI SDK.

Reranking

Laravel's AI SDK provides reranking capabilities that use AI models to reorder any set of results by semantic relevance to a query. Reranking is especially powerful as a second stage after a fast initial retrieval step like full-text search — giving you both speed and semantic accuracy.

Laravel Scout Search

For applications that want a Searchable trait that automatically keeps search indexes in sync with Eloquent models, Laravel Scout offers both a built-in database engine and drivers for third-party services like Algolia, Meilisearch, and Typesense.

Full-Text Search

While LIKE queries work well for simple substring matching, they don't understand language. A LIKE search for "running" won't find a record containing "run", and results aren't ranked by relevance — they're simply returned in whatever order the database finds them. Full-text search solves both of these problems by using specialized indexes that understand word boundaries, stemming, and relevance scoring, allowing the database to return the most relevant results first.

Fast full-text search is built into MariaDB, MySQL, and PostgreSQL — no external search service is required. You only need to add a full-text index to the columns you want to search, and then use the whereFullText query builder method to search against them.

WARNING

Full-text search is currently supported by MariaDB, MySQL, and PostgreSQL.

Adding Full-Text Indexes

To use full-text search, first add a full-text index to the columns you want to search. You may add the index to a single column, or pass an array of columns to create a composite index that searches across multiple fields at once:

php

Schema::create('articles', function (Blueprint $table) {
    $table->id();
    $table->string('title');
    $table->text('body');
    $table->timestamps();

    $table->fullText(['title', 'body']);
});

On PostgreSQL, you may specify a language configuration for the index, which controls how words are stemmed:

php

$table->fullText('body')->language('english');

For more information on creating indexes, consult the migration documentation.

Running Full-Text Queries

Once the index is in place, use the whereFullText query builder method to search against it. Laravel will generate the appropriate SQL for your database driver — for example, MATCH(...) AGAINST(...) on MariaDB and MySQL, and to_tsvector(...) @@ plainto_tsquery(...) on PostgreSQL:

php

$articles = Article::whereFullText('body', 'web developer')->get();

When using MariaDB and MySQL, results are automatically ordered by relevance score. On PostgreSQL, whereFullText filters matching records but does not order them by relevance — if you need automatic relevance ordering on PostgreSQL, consider using Scout's database engine, which handles this for you.

If you created a composite full-text index across multiple columns, you may search against all of them by passing the same array of columns to whereFullText:

php

$articles = Article::whereFullText(
    ['title', 'body'], 'web developer'
)->get();

The orWhereFullText method may be used to add a full-text search clause as an "or" condition. For complete details, consult the query builder documentation.

Semantic / Vector Search

Full-text search relies on matching keywords — the words in the query must appear (in some form) in the data. Semantic search takes a fundamentally different approach: it uses AI-generated vector embeddings to represent the meaning of text as arrays of numbers, and then finds results whose meaning is most similar to the query. For example, a search for "best wineries in Napa Valley" can surface an article titled "Top Vineyards to Visit" — even though the words don't overlap at all.

The basic workflow for vector search is: generate an embedding (a numeric array) for each piece of content and store it alongside your data, then at search time, generate an embedding for the user's query and find the stored embeddings that are closest to it in vector space.

NOTE

Vector search requires a PostgreSQL database with the pgvector extension and the Laravel AI SDK. All Laravel Cloud Serverless Postgres databases already include pgvector.

Generating Embeddings

An embedding is a high-dimensional numeric array (typically hundreds or thousands of numbers) that represents the semantic meaning of a piece of text. You may generate embeddings for a string using the toEmbeddings method available on Laravel's Stringable class:

php

use Illuminate\Support\Str;

$embedding = Str::of('Napa Valley has great wine.')->toEmbeddings();

To generate embeddings for multiple inputs at once — which is more efficient than generating them one at a time since it requires only a single API call to the embedding provider — use the Embeddings class:

php

use Laravel\Ai\Embeddings;

$response = Embeddings::for([
    'Napa Valley has great wine.',
    'Laravel is a PHP framework.',
])->generate();

$response->embeddings; // [[0.123, 0.456, ...], [0.789, 0.012, ...]]

For more details on configuring embedding providers, customizing dimensions, and caching, consult the AI SDK documentation.

Storing and Indexing Vectors

To store vector embeddings, define a vector column in your migration, specifying the number of dimensions that matches your embedding provider's output (for example, 1536 for OpenAI's text-embedding-3-small model). You should also call index on the column to create an HNSW (Hierarchical Navigable Small World) index, which dramatically speeds up similarity searches on large datasets:

php

Schema::ensureVectorExtensionExists();

Schema::create('documents', function (Blueprint $table) {
    $table->id();
    $table->string('title');
    $table->text('content');
    $table->vector('embedding', dimensions: 1536)->index();
    $table->timestamps();
});

The Schema::ensureVectorExtensionExists method ensures the pgvector extension is enabled on your PostgreSQL database before creating the table.

On your Eloquent model, cast the vector column to an array so that Laravel automatically handles the conversion between PHP arrays and the database's vector format:

php

protected function casts(): array
{
    return [
        'embedding' => 'array',
    ];
}

For more details on vector columns and indexes, consult the migration documentation.

Querying by Similarity

Once you have stored embeddings for your content, you can search for similar records using the whereVectorSimilarTo method. This method compares the given embedding against the stored vectors using cosine similarity, filters out results below the minSimilarity threshold, and automatically orders the results by relevance — with the most similar records first. The threshold should be a value between 0.0 and 1.0, where 1.0 means the vectors are identical:

php

$documents = Document::query()
    ->whereVectorSimilarTo('embedding', $queryEmbedding, minSimilarity: 0.4)
    ->limit(10)
    ->get();

As a convenience, when a plain string is given instead of an embedding array, Laravel will automatically generate the embedding for you using your configured embedding provider. This means you can pass the user's search query directly without manually converting it to an embedding first:

php

$documents = Document::query()
    ->whereVectorSimilarTo('embedding', 'best wineries in Napa Valley')
    ->limit(10)
    ->get();

For lower-level control over vector queries, the whereVectorDistanceLessThan, selectVectorDistance, and orderByVectorDistance methods are also available. These methods let you work directly with distance values rather than similarity scores, select the computed distance as a column in your results, or manually control the ordering. For complete details, consult the query builder documentation and the AI SDK documentation.

Reranking Results

Reranking is a technique where an AI model reorders a set of results by how semantically relevant each result is to a given query. Unlike vector search, which requires you to pre-compute and store embeddings, reranking works on any collection of text — it takes the raw content and the query as input and returns the items sorted by relevance.

Reranking is especially powerful as a second stage after a fast initial retrieval step. For example, you might use full-text search to quickly narrow thousands of records down to the top 50 candidates, and then use reranking to put the most relevant results at the top. This "retrieve then rerank" pattern gives you both speed and semantic accuracy.

You may rerank an array of strings using the Reranking class:

php

use Laravel\Ai\Reranking;

$response = Reranking::of([
    'Django is a Python web framework.',
    'Laravel is a PHP web application framework.',
    'React is a JavaScript library for building user interfaces.',
])->rerank('PHP frameworks');

$response->first()->document; // "Laravel is a PHP web application framework."

Laravel collections also have a rerank macro that accepts a field name (or closure) and a query, making it easy to rerank Eloquent results:

php

$articles = Article::all()
    ->rerank('body', 'Laravel tutorials');

For complete details on configuring reranking providers and available options, consult the AI SDK documentation.

Laravel Scout

The search techniques described above are all query builder methods that you call directly in your code. Laravel Scout takes a different approach: it provides a Searchable trait that you add to your Eloquent models, and Scout automatically keeps your search indexes in sync as records are created, updated, and deleted. This is particularly convenient when you want your models to always be searchable without manually managing index updates.

Database Engine

Scout's built-in database engine performs full-text and LIKE searches against your existing database — no external service or extra infrastructure required. Simply add the Searchable trait to your model and define a toSearchableArray method that returns the columns you want to be searchable.

You may use PHP attributes to control the search strategy for each column. SearchUsingFullText will use your database's full-text index, SearchUsingPrefix will only match from the beginning of the string (example%), and any columns without an attribute use a default LIKE strategy with wildcards on both sides (%example%):

php

<?php

namespace App\Models;

use Illuminate\Database\Eloquent\Model;
use Laravel\Scout\Attributes\SearchUsingFullText;
use Laravel\Scout\Attributes\SearchUsingPrefix;
use Laravel\Scout\Searchable;

class Article extends Model
{
    use Searchable;

    #[SearchUsingPrefix(['id'])]
    #[SearchUsingFullText(['title', 'body'])]
    public function toSearchableArray(): array
    {
        return [
            'id' => $this->id,
            'title' => $this->title,
            'body' => $this->body,
        ];
    }
}

WARNING

Before specifying that a column should use full-text query constraints, ensure that the column has been assigned a full-text index.

Once the trait is added, you may search your model using Scout's search method. Scout's database engine will automatically order results by relevance, even on PostgreSQL:

php

$articles = Article::search('Laravel')->get();

The database engine is a great choice when your search needs are moderate and you want the convenience of Scout's automatic index syncing without deploying an external service. It handles the most common search use cases well, including filtering, pagination, and soft-deleted record handling. For complete details, consult the Scout documentation.

Third-Party Engines

Scout also supports third-party search engines such as Algolia, Meilisearch, and Typesense. These dedicated search services offer advanced features like typo tolerance, faceted filtering, geo-search, and custom ranking rules — features that become important at very large scale or when you need a highly polished search-as-you-type experience.

Since Scout provides a unified API across all of its drivers, switching from the database engine to a third-party engine later requires minimal code changes. You may start with the database engine and migrate to a third-party service only if your application's needs outgrow what the database can provide.

For complete details on configuring third-party engines, consult the Scout documentation.

NOTE

Many applications never need an external search engine. The built-in techniques described on this page cover the vast majority of use cases.

Combining Techniques

The search techniques described on this page are not mutually exclusive — combining them often produces the best results. Here are two common patterns that demonstrate how these tools work together.

Full-Text Retrieval + Reranking

Use full-text search to quickly narrow a large dataset down to a candidate set, then apply reranking to sort those candidates by semantic relevance. This gives you the speed of database-native full-text search with the accuracy of AI-powered relevance scoring:

php

$articles = Article::query()
    ->whereFullText('body', $request->input('query'))
    ->limit(50)
    ->get()
    ->rerank('body', $request->input('query'), limit: 10);

Vector Search + Traditional Filters

Combine vector similarity with standard where clauses to scope semantic search to a subset of records. This is useful when you want meaning-based search but need to restrict results by ownership, category, or any other attribute:

php

$documents = Document::query()
    ->where('team_id', $user->team_id)
    ->whereVectorSimilarTo('embedding', $request->input('query'))
    ->limit(10)
    ->get();

Search ​

Introduction ​

Full-Text Search ​

Semantic / Vector Search ​

Reranking ​

Laravel Scout Search ​

Full-Text Search ​

Adding Full-Text Indexes ​

Running Full-Text Queries ​

Semantic / Vector Search ​

Generating Embeddings ​

Storing and Indexing Vectors ​

Querying by Similarity ​

Reranking Results ​

Laravel Scout ​

Database Engine ​

Third-Party Engines ​

Combining Techniques ​

Search

Introduction

Full-Text Search

Semantic / Vector Search

Reranking

Laravel Scout Search

Full-Text Search

Adding Full-Text Indexes

Running Full-Text Queries

Semantic / Vector Search

Generating Embeddings

Storing and Indexing Vectors

Querying by Similarity

Reranking Results

Laravel Scout

Database Engine

Third-Party Engines

Combining Techniques