Struct BatchSemanticEmbeddingRequest

pub struct BatchSemanticEmbeddingRequest {
    pub model: String,
    pub hosting: Option<Hosting>,
    pub prompts: Vec<Prompt>,
    pub representation: EmbeddingRepresentation,
    pub compress_to_size: Option<i32>,
    pub normalize: Option<bool>,
    pub contextual_control_threshold: Option<f64>,
    pub control_log_additive: Option<bool>,
}

Fields

model: String

Name of the model to use. A model name refers to a model’s architecture (number of parameters among others). The most recent version of the model is always used. The model output contains information as to the model version. To create semantic embeddings, please use luminous-base.

hosting: Option<Hosting>

Possible values: [aleph-alpha, None] Optional parameter that specifies which datacenters may process the request. You can either set the parameter to “aleph-alpha” or omit it (defaulting to null). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximum availability. Setting it to “aleph-alpha” allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.

prompts: Vec<Prompt>

This field is used to send prompts to the model. A prompt can either be a text prompt or a multimodal prompt. A text prompt is a string of text. A multimodal prompt is an array of prompt items. It can be a combination of text, images, and token ID arrays.

representation: EmbeddingRepresentation

Type of embedding representation to embed the prompt with.

compress_to_size: Option<i32>

The default behavior is to return the full embedding with 5120 dimensions. With this parameter you can compress the returned embedding to 128 dimensions. The compression is expected to result in a small drop in accuracy performance (4-6%), with the benefit of being much smaller, which makes comparing these embeddings much faster for use cases where speed is critical. With the compressed embedding can also perform better if you are embedding really short texts or documents.

normalize: Option<bool>

Return normalized embeddings. This can be used to save on additional compute when applying a cosine similarity metric.

contextual_control_threshold: Option<f64>

If set to null, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-null value, we apply the control parameters to similar tokens as well. Controls that have been applied to one token will then be applied to all other tokens that have at least the similarity score defined by this parameter. The similarity score is the cosine similarity of token embeddings.

control_log_additive: Option<bool>

true: apply controls on prompt items by adding the log(control_factor) to attention scores. false: apply controls on prompt items by (attention_scores - -attention_scores.min(-1)) * control_factor

Implementations

impl BatchSemanticEmbeddingRequest

pub fn hosting(self, hosting: Hosting) -> Self

pub fn compress_to_size(self, compress_to_size: i32) -> Self

pub fn normalize(self, normalize: bool) -> Self

pub fn contextual_control_threshold( self, contextual_control_threshold: f64, ) -> Self

pub fn control_log_additive(self, control_log_additive: bool) -> Self

Trait Implementations

impl Debug for BatchSemanticEmbeddingRequest

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for BatchSemanticEmbeddingRequest

fn default() -> BatchSemanticEmbeddingRequest

Returns the “default value” for a type.

impl Serialize for BatchSemanticEmbeddingRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.