import { DataAPIClient, ObjectId, vector, VectorDoc, oid } from '@datastax/astra-db-ts'; // Connect to the db const client = new DataAPIClient({ logging: 'all' }); const db = client.db(process.env.CLIENT_DB_URL!, { token: process.env.CLIENT_DB_TOKEN! }); // The `VectorDoc` interface adds `$vector?: DataAPIVector` as a field to the collection type interface Dream extends VectorDoc { _id: ObjectId, // Uses an `astra-db-ts` provided type here (NOT the `bson` version) summary: string, tags?: string[], // No sets/maps available without creating custom ser/des rules } (async () => { // Create the table using our helper function. // The _id should be an `ObjectId` type, as specified by `defaultId.type` const collection = await db.createCollection<Dream>('dreams', { defaultId: { type: 'objectId' }, }); // Batch-insert some rows into the table // _id can be optionally provided, or be auto-generated @ the server side await collection.insertMany([{ summary: 'A dinner on the Moon', $vector: vector([0.2, -0.3, -0.5]), // Shorthand for `new DataAPIVector([0.2, -0.3, -0.5])` }, { summary: 'Riding the waves', $vector: vector([0, 0.2, 1]), tags: ['sport'], }, { _id: oid('674f0f5c1c162131319fa09e'), // Shorthand for `new ObjectId('674f0f5c1c162131319fa09e')` summary: 'Meeting Beethoven at the dentist', $vector: vector([0.2, 0.6, 0]), }]); // Hm, changed my mind await collection.updateOne({ id: 103 }, { $set: { summary: 'Surfers\' paradise' } }); // Let's see what we've got const cursor = collection.find({}) .sort({ vector: vector([0, 0.2, 0.4]) }) // Performing a vector search .includeSimilarity(true) // The found doc is inferred to have `$similarity` as a property now .limit(2); // This would print: // - Surfers' paradise: 0.98238194 // - Friendly aliens in town: 0.91873914 for await (const result of cursor) { console.log(`${result.summary}: ${result.$similarity}`); } // Cleanup (if desired) await collection.drop(); })();

import { DataAPIClient, InferTableSchema, vector } from '@datastax/astra-db-ts'; // Connect to the db const client = new DataAPIClient({ logging: 'all' }); const db = client.db(process.env.CLIENT_DB_URL!, { token: process.env.CLIENT_DB_TOKEN! }); // Create a table through the Data API if it does not yet exist. // Returns the created table through a function so we can use the inferred type of the table ourselves // (instead of having to manually define it) const mkDreamsTable = async () => await db.createTable('dreams', { definition: { columns: { id: 'int', // Shorthand notation for { type: 'int' } summary: 'text', tags: { type: 'set', valueType: 'text' }, // Collection types require additional type information vector: { type: 'vector', dimension: 3 }, // Auto-embedding-generation can be enabled through a `service` block }, primaryKey: 'id', // Shorthand for { partitionBy: ['id'] } }, ifNotExists: true, // If any table with the same name exists, do nothing }); // (note that this does not check if the tables are the same) // Infer the TS-equivalent type from the table definition (like zod or arktype). Equivalent to: // // interface TableSchema { // id: number, -- A primary key component, so it's required // summary?: string | null, -- Not a primary key, so it's optional and may return as null when found // tags?: Set<string>, -- Sets/maps/lists are optional to insert, but will actually be returned as empty collections instead of null // vector?: DataAPIVector | null, -- Vectors, however, may be null. // } type Dream = InferTableSchema<typeof mkDreamsTable>; (async () => { // Create the table using our helper function. // Table will be typed as `Table<Dream, { id: number }>`, where the former is the schema, and the latter is the primary key const table = await mkDreamsTable(); // Enables vector search on the table (on the 'vector' column) await table.createVectorIndex('dreams_vector_idx', 'vector', { options: { metric: 'cosine' }, ifNotExists: true, }); // Batch-insert some rows into the table const rows: Dream[] = [{ id: 102, summary: 'A dinner on the Moon', vector: vector([0.2, -0.3, -0.5]), // Shorthand for `new DataAPIVector([0.2, -0.3, -0.5])` }, { id: 103, summary: 'Riding the waves', vector: vector([0, 0.2, 1]), tags: new Set(['sport']), // Collection types use native JS collections }, { id: 37, summary: 'Meeting Beethoven at the dentist', vector: vector([0.2, 0.6, 0]), }]; await table.insertMany(rows); // Hm, changed my mind await table.updateOne({ id: 103 }, { $set: { summary: 'Surfers\' paradise' } }); // Let's see what we've got const cursor = table.find({}) .sort({ vector: vector([0, 0.2, 0.4]) }) // Performing a vector search .includeSimilarity(true) // The found doc is inferred to have `$similarity` as a property now .limit(2); // This would print: // - Surfers' paradise: 0.98238194 // - Friendly aliens in town: 0.91873914 for await (const result of cursor) { console.log(`${result.summary}: ${result.$similarity}`); } // Cleanup (if desired) await table.drop(); })();

import { DataAPIClient } from '@datastax/astra-db-ts'; // Spawn an admin  const client = new DataAPIClient('*TOKEN*'); const admin = client.admin(); (async () => { // list info about all databases const databases = await admin.listDatabases(); const dbInfo = databases[0]; console.log(dbInfo.info.name, dbInfo.id, dbInfo.info.region); // list namespaces for the first database const dbAdmin = admin.dbAdmin(dbInfo.id, dbInfo.info.region); console.log(await dbAdmin.listNamespaces()); })();

import { DataAPIClient, UsernamePasswordTokenProvider, DataAPIDbAdmin } from '@datastax/astra-db-ts'; // You'll need to pass in environment to the DataAPIClient when not using Astra const tp = new UsernamePasswordTokenProvider('*USERNAME*', '*PASSWORD*'); const client = new DataAPIClient(tp, { environment: 'dse' }); const db = client.db('*ENDPOINT*'); // You'll also need to pass it to db.admin() when not using Astra for typing purposes // If the environment does not match, an error will be thrown as a reminder // `environment: 'dse'` makes the return type be `DataAPIDbAdmin` const dbAdmin = db.admin({ environment: 'dse' }); dbAdmin.createNamespace('...');

import { DataAPIClient } from '@datastax/astra-db-ts'; const client = new DataAPIClient('*TOKEN*', { httpOptions: { client: 'fetch' }, });

import { DataAPIClient } from '@datastax/astra-db-ts'; import * as fetchH2 from 'fetch-h2'; const client = new DataAPIClient('*TOKEN*', { httpOptions: { fetchH2 }, });