Node.js driver quickstart
This quickstart provides an end-to-end workflow for how to use the Node.js driver to connect to your database. For demonstration purposes, it also shows how to use the driver to run a vector search with Cassandra Query Language (CQL) statements.
Prerequisites
You need the following items to complete this quickstart:
-
A running DSE cluster
-
A current Node.js LTS version with
npm
Install the cassandra-driver package
Install the cassandra-driver package using npm to connect your Node.js application to your database.
npm install cassandra-driverImport libraries and connect to the database
Import the necessary libraries and establish a connection to your database.
The following code configures the Node.js driver with authentication details, then establishes a connection to the database through an asynchronous run function:
const cassandra = require('cassandra-driver');
const authProvider = new cassandra.auth.PlainTextAuthProvider('token', process.env['DB_APPLICATION_TOKEN']);
const client = new cassandra.Client({ cloud, authProvider });
async function run() {
await client.connect();
// ...
}Create a table and vector-compatible Storage Attached Index (SAI)
Create a table named vector_test in the default_keyspace of your database, with an integer id as the primary key, a text field, and a 5-dimensional float vector.
This example code also establishes a custom index on the vector column for efficient similarity searches using the dot product.
const keyspace = 'cycling';
const v_dimension = 5;
await client.execute(`
CREATE TABLE IF NOT EXISTS ${keyspace}.vector_test (id INT PRIMARY KEY,
text TEXT, vector VECTOR<FLOAT,${v_dimension}>);
`);
await client.execute(`
CREATE CUSTOM INDEX IF NOT EXISTS idx_vector_test
ON ${keyspace}.vector_test
(vector) USING 'StorageAttachedIndex' WITH OPTIONS =
{'similarity_function' : 'cosine'};
`);Load data
Insert a few documents with embeddings into the collection.
const text_blocks = [
{ id: 1, text: 'ChatGPT integrated sneakers that talk to you', vector: [0.1, 0.15, 0.3, 0.12, 0.05] },
{ id: 2, text: 'An AI quilt to help you sleep forever', vector: [0.45, 0.09, 0.01, 0.2, 0.11] },
{ id: 3, text: 'A deep learning display that controls your mood', vector: [0.1, 0.05, 0.08, 0.3, 0.6] },
];
for (let block of text_blocks) {
const {id, text, vector} = block;
await client.execute(
`INSERT INTO ${keyspace}.vector_test (id, text, vector) VALUES (${id}, '${text}', [${vector}])`
);
}Perform a similarity search
Find documents that are close to a specific vector embedding.
const ann_query = `
SELECT id, text, similarity_cosine(vector, [0.15, 0.1, 0.1, 0.35, 0.55]) as sim
FROM ${keyspace}.vector_test
ORDER BY vector ANN OF [0.15, 0.1, 0.1, 0.35, 0.55] LIMIT 2
`;
const result = await client.execute(ann_query);
result.rows.forEach(row => {
console.log(`[${row.id}] "${row.text}" (sim: ${row.sim.toFixed(4)})`);
});
await client.shutdown();
}
run().catch(console.error);The Node.js driver is now connected to your database, a set of vector embeddings has been loaded, and a similarity search to find vectors that are close to the one in your query has been performed.
