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Cheat Sheet for Node.js with Momento Vector Index

If you need to get going quickly with Node.js and Momento Vector Index, this page contains the basic API calls you'll need.

Import libraries and instantiate a vector index client

This code sets up the class with the necessary imports, the class definition, and the PreviewVectorIndexClient instantiation that each of the other functions will need to call.

import {CredentialProvider, PreviewVectorIndexClient, VectorIndexConfigurations} from "@gomomento/sdk";

const client = new PreviewVectorIndexClient({
configuration: VectorIndexConfigurations.Laptop.latest(),
credentialProvider: CredentialProvider.fromEnvironmentVariable({
environmentVariableName: 'MOMENTO_API_KEY',

The following examples assume that you have already instantiated a PreviewVectorIndexClient as shown above.

Create a new index in Momento Vector Index

Use this snippet to create a new index in your account. The similarityMetric parameter is optional and defaults to VectorSimilarityMetric.COSINE_SIMILARITY.

const indexName = "my-index";
// The number of dimensions in your vectors
const numDimensions = 2;
const similarityMetric = VectorSimilarityMetric.COSINE_SIMILARITY;

const createResponse = await client.createIndex(indexName, numDimensions, similarityMetric);
if (createResponse instanceof CreateVectorIndex.Success) {
console.log('Index created successfully!');
} else {
console.log(`Error creating index: ${createResponse.toString()}`);

Get list of existing indexes in your account

In this example, we list the indexes in your account.

const listResponse = await client.listIndexes();
if (listResponse instanceof ListVectorIndexes.Success) {
} else {
console.log(`Error listing indexes: ${listResponse.toString()}`);

Write a batch of items to the index

A simple example of doing an upsertItemBatch operation. This operation will insert the items if they don't exist, or replace them if they do.

const indexName = "my-index";
const upsertResponse = await client.upsertItemBatch(indexName, [
id: 'example_item_1',
vector: [1.0, 2.0],
metadata: {key1: 'value1'},
id: 'example_item_2',
vector: [3.0, 4.0],
metadata: {key2: 'value2'},
if (upsertResponse instanceof VectorUpsertItemBatch.Success) {
console.log('Successfully upserted items');
} else {
console.log(`Error adding items: ${upsertResponse.toString()}`);

Searching the index

This is an example of a search operation to get the top-k items from the index matching the queryVector. The metadataFields parameter is optional and can be used to specify which metadata fields to return in the response.

Here we use a queryVector of [1.0, 2.0] and ask for the top 2 results.

const indexName = "my-index";
const queryVector = [1.0, 2.0];
const searchResponse = await, queryVector, {
topK: 2,
metadataFields: ALL_VECTOR_METADATA,
if (searchResponse instanceof VectorSearch.Success) {
console.log(`Search succeeded with ${searchResponse.hits().length} results`);
} else {
console.log(`Error searching items: ${searchResponse.toString()}`);

Deleting items from the index

An example of deleting the items from an index using deleteItemBatch.

const indexName = "my-index";
const itemsToDelete = [

const deleteResponse = await client.deleteItemBatch(indexName, itemsToDelete);
if (deleteResponse instanceof VectorDeleteItemBatch.Success) {
console.log('Successfully deleted items');
} else {
console.log(`Error deleting items: ${deleteResponse.toString()}`);

Deleting an index

An example of deleting an index using deleteIndex.

const indexName = "my-index";
const deleteIndexResponse = await client.deleteIndex(indexName);
if (deleteIndexResponse instanceof DeleteVectorIndex.Success) {
console.log("Index 'test-index' deleted");
} else {
console.log(`Error deleting index: ${deleteIndexResponse.toString()}`);