Struct EmbeddingModel 

pub struct EmbeddingModel<T = Client> { /* private fields */ }

Implementations

impl<T> EmbeddingModel<T>

pub fn new( client: Client<GeminiExt, T>, model: impl Into<String>, ndims: usize, ) -> EmbeddingModel<T>

pub fn with_model( client: Client<GeminiExt, T>, model: &str, ndims: usize, ) -> EmbeddingModel<T>

Trait Implementations

impl<T> Clone for EmbeddingModel<T>

where T: Clone,

fn clone(&self) -> EmbeddingModel<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T> EmbeddingModel for EmbeddingModel<T>

where T: Clone + HttpClientExt + 'static,

async fn embed_texts( &self, documents: impl IntoIterator<Item = String> + WasmCompatSend, ) -> Result<Vec<Embedding>, EmbeddingError>

https://ai.google.dev/api/embeddings#batch_embed_contents-SHELL

const MAX_DOCUMENTS: usize = 1024

The maximum number of documents that can be embedded in a single request.

type Client = Client<GeminiExt, T>

Provider client type used to construct this embedding model.

fn make( client: &<EmbeddingModel<T> as EmbeddingModel>::Client, model: impl Into<String>, dims: Option<usize>, ) -> EmbeddingModel<T>

Construct a model handle from a provider client, model identifier, and optional dimensions.

fn ndims(&self) -> usize

The number of dimensions in the embedding vector.

fn embed_text( &self, text: &str, ) -> impl Future<Output = Result<Embedding, EmbeddingError>> + WasmCompatSend

Embed a single text document.

fn embed_texts_with_usage( &self, texts: impl IntoIterator<Item = String> + WasmCompatSend, ) -> impl Future<Output = Result<EmbeddingResponse, EmbeddingError>> + WasmCompatSend

Embed multiple text documents in a single request and return token usage.

fn embed_text_with_usage( &self, text: &str, ) -> impl Future<Output = Result<EmbeddingResponse, EmbeddingError>> + WasmCompatSend

Embed a single text document and return token usage.