JavaScript (Node.js) Quick Start Guide for /v1/embeddings API

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Last updated February 09, 2026

The Cohere Embed Multilingual (cohere-embed-multilingual) model generates vector embeddings (lists of numbers) for provided text inputs. These embeddings can be used in various applications, such as search, classification, and clustering. This guide describes how to access the /v1/embeddings API using JavaScript.

Prerequisites

Before making requests, provision access to the model of your choice.

  1. Attach an inference addon to an app of yours:

    # If you don't have an app yet, you can create one with:
heroku create $APP_NAME # specify the name you want for your app (or skip this step to use an existing app you have)

# Create and attach one of our chat models to your app, $APP_NAME:
heroku addons:create heroku-inference:standard -a $APP_NAME --as INFERENCE

  2. Install the necessary axios package:

    npm install axios

JavaScript Example Code

const axios = require('axios');

// Assert that environment variables are set
const EMBEDDING_URL = process.env.EMBEDDING_URL;
const EMBEDDING_KEY = process.env.EMBEDDING_KEY;

if (!EMBEDDING_URL || !EMBEDDING_KEY) {
    console.error("Missing required environment variables.");
    console.log("Set them up using the following commands:");
    console.log("export EMBEDDING_URL=$(heroku config:get -a $APP_NAME EMBEDDING_URL)");
    console.log("export EMBEDDING_KEY=$(heroku config:get -a $APP_NAME EMBEDDING_KEY)");
    process.exit(1);
}

async function parseEmbeddingOutput(response) {
    if (response.status === 200) {
        console.log("Embeddings:", response.data.data);
    } else {
        console.log(`Request failed: ${response.status}, ${response.statusText}`);
    }
}

async function generateEmbeddings(payload) {
    try {
        const response = await axios.post(`${EMBEDDING_URL}/v1/embeddings`, payload, {
            headers: {
                'Authorization': `Bearer ${EMBEDDING_KEY}`,
                'Content-Type': 'application/json'
            }
        });
        await parseEmbeddingOutput(response);
    } catch (error) {
        console.error("Error generating embeddings:", error.message);
    }
}

// Example payload
const payload = {
    model:"cohere-embed-multilingual",
    input: ["Hello, I am a blob of text.", "How's the weather in Portland?"],
    input_type: "search_document",
    truncate: "END",
    encoding_format: "float"
};

// Generate embeddings
generateEmbeddings(payload);