Struct EmbeddingsBuilder 

pub struct EmbeddingsBuilder<M: EmbeddingModel, T: Embed> { /* private fields */ }

Builder for creating embeddings from one or more documents of type T. Note: T can be any type that implements the Embed trait.

Using the builder is preferred over using EmbeddingModel::embed_text directly as it will batch the documents in a single request to the model provider.

Example

use std::env;

use rig::{
    embeddings::EmbeddingsBuilder,
    providers::openai::{Client, TEXT_EMBEDDING_ADA_002},
};
use serde::{Deserialize, Serialize};

// Create OpenAI client
let openai_api_key = env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY not set");
let openai_client = Client::new(&openai_api_key);

let model = openai_client.embedding_model(TEXT_EMBEDDING_ADA_002);

let embeddings = EmbeddingsBuilder::new(model.clone())
    .documents(vec![
        "1. *flurbo* (noun): A green alien that lives on cold planets.".to_string(),
        "2. *flurbo* (noun): A fictional digital currency that originated in the animated series Rick and Morty.".to_string()
        "1. *glarb-glarb* (noun): An ancient tool used by the ancestors of the inhabitants of planet Jiro to farm the land.".to_string(),
        "2. *glarb-glarb* (noun): A fictional creature found in the distant, swampy marshlands of the planet Glibbo in the Andromeda galaxy.".to_string()
        "1. *linlingdong* (noun): A term used by inhabitants of the sombrero galaxy to describe humans.".to_string(),
        "2. *linlingdong* (noun): A rare, mystical instrument crafted by the ancient monks of the Nebulon Mountain Ranges on the planet Quarm.".to_string()
    ])?
    .build()
    .await?;

Implementations

impl<M: EmbeddingModel, T: Embed> EmbeddingsBuilder<M, T>

pub fn new(model: M) -> Self

Create a new embedding builder with the given embedding model

pub fn document(self, document: T) -> Result<Self, EmbedError>

Add a document to be embedded to the builder. document must implement the Embed trait.

pub fn documents( self, documents: impl IntoIterator<Item = T>, ) -> Result<Self, EmbedError>

Add multiple documents to be embedded to the builder. documents must be iterable with items that implement the Embed trait.

impl<M: EmbeddingModel, T: Embed + Send> EmbeddingsBuilder<M, T>

pub async fn build( self, ) -> Result<Vec<(T, OneOrMany<Embedding>)>, EmbeddingError>

Generate embeddings for all documents in the builder. Returns a vector of tuples, where the first element is the document and the second element is the embeddings (either one embedding or many).