Struct SentenceTransformerProvider 

pub struct SentenceTransformerProvider { /* private fields */ }

Sentence-transformer embedding provider for documentation.

Optimised for natural language paragraphs rather than code. Pre-processes markdown, splits into sentences, embeds each, and averages (mean pooling).

Implementations

impl SentenceTransformerProvider

pub const DEFAULT_MAX_TOKENS: usize = 512

Default max tokens for documentation (longer context than code).

pub fn new(model_path: impl Into<PathBuf>) -> Self

Create a new sentence-transformer provider.

pub fn with_config( model_path: impl Into<PathBuf>, max_tokens: usize, split_sentences: bool, ) -> Self

Create with custom max tokens and optional sentence splitting.

pub fn split_sentences(&self) -> bool

Whether sentence splitting is enabled.

pub fn max_tokens(&self) -> usize

Max token length.

Trait Implementations

impl EmbeddingProvider for SentenceTransformerProvider

fn embed<'life0, 'life1, 'async_trait>( &'life0 self, text: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<Vec<f32>, EmbeddingError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Embed a single text chunk into a vector.

fn embed_batch<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, texts: &'life1 [&'life2 str], ) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<f32>>, EmbeddingError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Embed a batch of text chunks.

fn dimensions(&self) -> usize

Dimensionality of the output vectors.

fn model_name(&self) -> &str

Name of the embedding model (for metadata tracking).