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mikadamczyk
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Feb 10, 2026
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| ## Validation | ||
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| - [`Ibexa\Contracts\Core\Repository\Values\Content\QueryValidatorInterface`](/api/php_api/php_api_reference/classes/Ibexa-Contracts-Core-Repository-Values-Content-QueryValidatorInterface.html): Validates embedding queries and configurations are validated before reaching the search engine |
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It does not validate configuration. Maybe Validates embedding queries before they reach the search engine
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| Embeddings are numerical representations that capture the meaning of text, images, or other content. | ||
| Embeddings are generated by AI by converting words or documents into lists of numbers, instead of treating them as plain text. | ||
| Such lists, aka. vectors, can then be compared to find content with similar meaning. |
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| Such lists, aka. vectors, can then be compared to find content with similar meaning. | |
| Such lists, a.k.a. vectors, can then be compared to find content with similar meaning. |
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| !!! note "Taxonomy suggestions" | ||
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| Embedding queries have been introduced primarily to support the [Taxonomy suggestions](taxonomy.md#taxonomy-suggestions) feature but you use them in other scenarios. |
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| Embedding queries have been introduced primarily to support the [Taxonomy suggestions](taxonomy.md#taxonomy-suggestions) feature but you use them in other scenarios. | |
| Embedding queries have been introduced primarily to support the [Taxonomy suggestions](taxonomy.md#taxonomy-suggestions) feature but you can use them in other scenarios. |
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| Embedding queries have been introduced primarily to support the [Taxonomy suggestions](taxonomy.md#taxonomy-suggestions) feature but you use them in other scenarios. | ||
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| Searching with embeddings can be combined with traditional search criteria and filters, which allows the semantic search to be constrained by content type, location, permissions, or other search criteria. |
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a little bit misleading. EmbeddingQuery doesn’t support query criteria, only filter. Maybe can be combined with filters (Criterion) ...
| Searching with embeddings can be combined with traditional search criteria and filters, which allows the semantic search to be constrained by content type, location, permissions, or other search criteria. | ||
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| An embedding query is represented by the `Ibexa\Contracts\Core\Repository\Values\Content\EmbeddingQuery` value object. | ||
| The object encapsulates the vector to search for, along with configuration such as the embedding model and similarity threshold. |
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EmbeddingQuery has no model or threshold fields. It only has embedding, filter, limit/offset, aggregations, performCount.
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| ``` php | ||
| use Ibexa\Contracts\Core\Repository\Values\Content\EmbeddingQueryBuilder; | ||
| use Ibexa\Contracts\Core\Repository\Values\Content\Embedding; |
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no such thing like Ibexa\Contracts\Core\Repository\Values\Content\Embedding, maybe you were thinking about use Ibexa\Contracts\Core\Repository\Values\Content\Query\Embedding;
But Embedding is abstract and expects float[], so this snippet won’t compile
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| use Ibexa\Contracts\Core\Repository\Values\Content\Embedding; | |
| use Ibexa\Contracts\Taxonomy\Search\Query\Value\TaxonomyEmbedding |
| use Ibexa\Contracts\Core\Repository\Values\Content\Query\Aggregation; | ||
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| // Create an embedding object that represents the search input | ||
| $embedding = new Embedding('Find content similar to this text'); |
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This class accepts
/**
* @param float[] $value
*/
public function __construct(array $value)
{
$this->value = $value;
}
| ``` php | ||
| use Ibexa\Contracts\Core\Repository\Values\Content\EmbeddingQueryBuilder; | ||
| use Ibexa\Contracts\Core\Repository\Values\Content\Embedding; | ||
| use Ibexa\Contracts\Core\Repository\Values\Content\Query\Aggregation; |
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Aggregation is an interface, cannot be instantiated
| ->setLimit(10) // maximum number of results | ||
| ->setOffset(0) // result offset for pagination | ||
| ->setPerformCount(true) // optionally count total matching items | ||
| ->setAggregations([ |
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we could remove aggregations from the snippet (focus on embeddings) or:
->setAggregations([
new ContentTypeTermAggregation('count_by_type'),
])
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Describe Embeddings search API
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