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[].

Supported Embedding Providers

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

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.