Embeddings

Embeddings is a wrapper around various embeddings providers, making your function implementations vendor-agnostic.

Embeddings Features:

• Built-in timeout mechanism for better control when a provider takes too long.
• Automatic retry with configurable back-off for errors.
• Use different LLM's with different configurations for different functions.

Basic Usage

Use createEmbedding to create an embedding instance for a supported provider, then call it with the text you want to embed:

import { createEmbedding } from "llm-exe";

const embeddings = createEmbedding("openai.embedding.v1", {
  model: "text-embedding-3-small",
});

const embedding = await embeddings.call("The text you want to embed");
const vector = embedding.getEmbedding();
// Returns a number[] representing the embedding vector

Parameters

createEmbedding(provider, options) accepts:

Parameter	Type	Description
provider	EmbeddingProviderKey	The embedding provider key (see supported providers below)
options	object	Provider-specific options including model

The returned object has a .call(input) method that returns a promise. The resolved result has a .getEmbedding() method that returns the embedding vector as number[]. For a batch, pass an index — .getEmbedding(1) returns the vector for the second input.

Input

.call(input) accepts an EmbeddingInput:

Input	Type	Description
Single text	string	Embeds one string, returning one vector
Text batch	string[]	Embeds each string in one request, returning one vector per entry. Not supported by Amazon Titan (amazon.embedding.v1), which takes a single string per call — see Amazon
Multimodal batch	EmbeddingContentItem[]	Interleaved text and image content. Supported only by amazon:cohere.embedding.v1 — see Cohere → Multimodal Input

A single string works with every provider; string[] batching does not apply to Amazon Titan, which embeds one string per request. Passing content items to a text-only provider throws an embedding.unsupported_input error rather than sending an object into a field the provider expects to be a string.

Supported Embedding Providers

Provider	Key	Details
OpenAI (and OpenAI-compatible)	openai.embedding.v1	OpenAI Embeddings
Amazon Titan	amazon.embedding.v1	Amazon Embeddings
Cohere (via Bedrock)	amazon:cohere.embedding.v1	Cohere Embeddings

The openai.embedding.v1 provider accepts a baseUrl option, so it can also be used with any OpenAI-compatible embeddings endpoint (Baseten, Together AI, vLLM, TEI, local servers, etc.). See OpenAI Embeddings → OpenAI-Compatible Endpoints.

Adding Custom Providers

Custom embedding providers are not currently supported. If you need an embedding provider that isn't listed above, please open an issue.