Embeddings API (TypeScript)

Methods, examples, and parameters for client.embeddings.

Module Overview

Generate vector embeddings for search, retrieval, and ranking pipelines.

Available Methods

client.embeddings.create() - Generate one or many embeddings.
client.embeddings.batch() - Submit large asynchronous embedding jobs.
client.embeddings.retrieve() - Get a previously created batch result.

Examples

TypeScript

import { TheRouter.ai } from "@therouter/sdk";

const client = new TheRouter.ai({ apiKey: process.env.THEROUTER_API_KEY! });

const vectors = await client.embeddings.create({
  model: "openai/text-embedding-3-large",
  input: ["routing", "guardrails"],
});

console.log(vectors.data[0].embedding.length);

embeddings-response.json

{
  "id": "req_01HXYZ123",
  "module": "embeddings",
  "status": "ok"
}

Parameter Types

Name	Type	Required	Description
model	string	Required	Embedding model identifier.
input	string \| string[]	Required	Text input or list of inputs.
dimensions	integer		Optional output dimension count.

SDK parity

Method signatures are aligned across SDKs so migration between TypeScript and Python stays predictable.