EmbeddingService 

class EmbeddingService

Fully qualified name

\Netresearch\NrLlm\Service\Feature\EmbeddingService

Text-to-vector conversion with caching and similarity operations.

embed ( string $text, ?EmbeddingOptions $options = null) : array

Generate embedding vector for text (cached).

param string $text

The text to embed

param ?EmbeddingOptions $options

Optional config

Returns

array<float> Vector representation

embedFull ( string $text, ?EmbeddingOptions $options = null) : EmbeddingResponse

Generate embedding with full response metadata.

param string $text

The text to embed

param ?EmbeddingOptions $options

Optional config

Returns

EmbeddingResponse

embedBatch ( array $texts, ?EmbeddingOptions $options = null) : array

Generate embeddings for multiple texts.

param array $texts

Array of texts

param ?EmbeddingOptions $options

Optional config

Returns

array<array<float>> Array of vectors

cosineSimilarity ( array $a, array $b) : float

Calculate cosine similarity between two vectors.

param array $a

First vector

param array $b

Second vector

Returns

float Similarity score (-1 to 1)

findMostSimilar ( array $queryVector, array $candidates, int $topK = 5) : array

Find most similar vectors from candidates.

param array $queryVector

The query vector

param array $candidates

Array of candidate vectors

param int $topK

Number of results to return

Returns

array Sorted by similarity (highest first)

pairwiseSimilarities ( array $vectors) : array

Calculate pairwise similarities between all vectors.

Returns a 2D matrix where each cell [i][j] contains the cosine similarity between vectors i and j. Diagonal values are always 1.0.

param array $vectors

Array of embedding vectors

Returns

array 2D array of similarity scores

normalize ( array $vector) : array

Normalize a vector to unit length.

param array $vector

The vector to normalize

Returns

array Normalized vector