Struct TogetherEmbedProvider 

pub struct TogetherEmbedProvider { /* private fields */ }

Embedding provider backed by the Together AI embeddings API.

Default model: togethercomputer/m2-bert-80M-32k-retrieval (768 dims). Together uses an OpenAI-compatible request/response shape.

Implementations

impl TogetherEmbedProvider

pub fn new(api_key: impl Into<String>) -> Self

Create a new Together provider with the default BERT retrieval model.

pub fn with_model( api_key: impl Into<String>, model: impl Into<String>, dimensions: usize, ) -> Self

Create with an explicit model and dimension override.

pub fn with_base_url(self, base_url: impl Into<String>) -> Self

Override the API base URL.

pub fn model(&self) -> &str

Returns the configured model name.

pub fn build_payload(&self, texts: &[String]) -> Value

Build the request payload for the Together API.

Trait Implementations

impl EmbeddingProvider for TogetherEmbedProvider

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

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

Compute embedding vector for a single text.

fn dimension(&self) -> usize

Dimension of the embedding vectors produced by this provider.

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

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

Compute embeddings for a batch of texts.