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

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.