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

embedForConfiguration(string $text, LlmConfiguration $configuration, ?EmbeddingOptions $options = null): array

Generate embedding vector for text against a specific LLM configuration, so the configuration's provider/model drive the call and per-configuration budgets and cost attribution apply.

param string $text

The text to embed

param LlmConfiguration $configuration

The configuration record to resolve provider/model from

param ?EmbeddingOptions $options

Optional config

Returns

array<float> Vector representation

embedBatchForConfiguration(array $texts, LlmConfiguration $configuration, ?EmbeddingOptions $options = null): array

Generate embeddings for multiple texts against a specific LLM configuration in a single provider call.

param array $texts

Array of texts

param LlmConfiguration $configuration

The configuration record to resolve provider/model from

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