Trait EmbeddingsClient 

pub trait EmbeddingsClient: ProviderClient + Clone {
    type EmbeddingModel: EmbeddingModel;

    // Required methods
    fn embedding_model(&self, model: &str) -> Self::EmbeddingModel;
    fn embedding_model_with_ndims(
        &self,
        model: &str,
        ndims: usize,
    ) -> Self::EmbeddingModel;

    // Provided methods
    fn embeddings<D: Embed>(
        &self,
        model: &str,
    ) -> EmbeddingsBuilder<Self::EmbeddingModel, D> { ... }
    fn embeddings_with_ndims<D: Embed>(
        &self,
        model: &str,
        ndims: usize,
    ) -> EmbeddingsBuilder<Self::EmbeddingModel, D> { ... }
}

A provider client with embedding capabilities. Clone is required for conversions between client types.

Required Associated Types

type EmbeddingModel: EmbeddingModel

The type of EmbeddingModel used by the Client

Required Methods

fn embedding_model(&self, model: &str) -> Self::EmbeddingModel

Create an embedding model with the given name. Note: default embedding dimension of 0 will be used if model is not known. If this is the case, it’s better to use function embedding_model_with_ndims

Example

use rig::prelude::*;
use rig::providers::openai::{Client, self};

// Initialize the OpenAI client
let openai = Client::new("your-open-ai-api-key");

let embedding_model = openai.embedding_model(openai::TEXT_EMBEDDING_3_LARGE);

fn embedding_model_with_ndims( &self, model: &str, ndims: usize, ) -> Self::EmbeddingModel

Create an embedding model with the given name and the number of dimensions in the embedding generated by the model.

Example with OpenAI

use rig::prelude::*;
use rig::providers::openai::{Client, self};

// Initialize the OpenAI client
let openai = Client::new("your-open-ai-api-key");

let embedding_model = openai.embedding_model("model-unknown-to-rig", 3072);

Provided Methods

fn embeddings<D: Embed>( &self, model: &str, ) -> EmbeddingsBuilder<Self::EmbeddingModel, D>

Create an embedding builder with the given embedding model.

Example with OpenAI

use rig::prelude::*;
use rig::providers::openai::{Client, self};

// Initialize the OpenAI client
let openai = Client::new("your-open-ai-api-key");

let embeddings = openai.embeddings(openai::TEXT_EMBEDDING_3_LARGE)
    .simple_document("doc0", "Hello, world!")
    .simple_document("doc1", "Goodbye, world!")
    .build()
    .await
    .expect("Failed to embed documents");

fn embeddings_with_ndims<D: Embed>( &self, model: &str, ndims: usize, ) -> EmbeddingsBuilder<Self::EmbeddingModel, D>

Create an embedding builder with the given name and the number of dimensions in the embedding generated by the model.

Example with OpenAI

use rig::prelude::*;
use rig::providers::openai::{Client, self};

// Initialize the OpenAI client
let openai = Client::new("your-open-ai-api-key");

let embeddings = openai.embeddings_with_ndims(openai::TEXT_EMBEDDING_3_LARGE, 3072)
    .simple_document("doc0", "Hello, world!")
    .simple_document("doc1", "Goodbye, world!")
    .build()
    .await
    .expect("Failed to embed documents");

Dyn Compatibility

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors

impl EmbeddingsClient for rig::providers::azure::Client

type EmbeddingModel = EmbeddingModel

impl EmbeddingsClient for rig::providers::cohere::client::Client

type EmbeddingModel = EmbeddingModel

impl EmbeddingsClient for rig::providers::gemini::client::Client

type EmbeddingModel = EmbeddingModel

impl EmbeddingsClient for rig::providers::mistral::client::Client

type EmbeddingModel = EmbeddingModel

impl EmbeddingsClient for rig::providers::ollama::Client

type EmbeddingModel = EmbeddingModel

impl EmbeddingsClient for rig::providers::openai::client::Client

type EmbeddingModel = EmbeddingModel

impl EmbeddingsClient for rig::providers::together::client::Client

type EmbeddingModel = EmbeddingModel

impl EmbeddingsClient for rig::providers::voyageai::Client

Although the models have default embedding dimensions, there are additional alternatives for increasing and decreasing the dimensions to your requirements. See Voyage AI’s documentation: https://docs.voyageai.com/docs/embeddings

type EmbeddingModel = EmbeddingModel