TypeScript client reference

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Use TypeScript to interact with collections and documents in vector-enabled Astra DB databases.


Before you develop a TypeScript client, ensure you’ve met the prerequisites. You will need:

  • Node.js v16.20.2 or higher. Download and install Node.js.

  • An Astra DB account and an active vector-enabled database created in Astra Portal.

  • An Astra DB application token associated with a Database Administrator role that you create in Astra Portal.

Initialize and install

Perform the following steps to install the astra-db-ts client and the tsx package.

  1. Verify that Node is version 14 or higher.

    node --version
  2. Install the tsx package globally

    npm install -g tsx
  3. Install the astra-db-ts package

    npm install @datastax/astra-db-ts@latest

Create a vector-enabled Astra DB database

To create a vector-enabled Astra DB database:

  1. Sign into your Astra Portal account.

  2. Follow the New Experience prompts to create a vector database.

In Astra Portal, take note of the values for your vector database: ASTRA_DB_REGION and ASTRA_DB_ID, plus the generated ASTRA_DB_APPLICATION_TOKEN.

When you generate the application token in Astra Portal, associate the Database Administrator role with the token.

Environment variables

The TypeScript code will reference environment variables. You can use a package like dotenv to load environment variables from a .env file, or set them manually in your terminal.

  1. To set the values as environment variables, export them. Examples:

    • Linux or macOS

    • Windows

    • Google Colab

    export ASTRA_DB_API_ENDPOINT="<Astra DB API endpoint>"
    set ASTRA_DB_API_ENDPOINT=<Astra DB API endpoint>
    import os
    os.environ["ASTRA_DB_APPLICATION_TOKEN"] = "AstraCS:..."
    os.environ["ASTRA_DB_API_ENDPOINT"] = "<Astra DB API endpoint>"

Set up your TypeScript client for similarity searches

Add the following code in your TypeScript source file. In this example, we’ll use the filename index.ts.

  1. Import the astra-db-ts library and initialize the client.

    import { AstraDB } from "@datastax/astra-db-ts";
    async function main() {
      // Create a collection
      await db.createCollection(
          "vector": {
            "dimension": 5,
            "metric": "cosine"
      const col = await db.collection("vector_test");
      // ...
  2. Use the API clauses that will load data into your vector database:

      // ...
      const documents = [
              "_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],
      const results = await col.insertMany(documents);
      // ...
  3. Add code that will perform a similarity search. Use the API to find documents that are most similar to the specified vector, based on a similarity metric. This metric produces a similarity score (such as 0.99444735), which shows how close each document is to that vector.

      // ...
      interface Document {
        _id: string;
        text: string;
        $vector: number[];
      const options = {
        sort: {
            "$vector": [0.15, 0.1, 0.1, 0.35, 0.55],
        limit: 5
      const document_list = await col.find({}, options).toArray();
      document_list.forEach((doc: Document) => console.log(doc));

A similarity score returned from a vector-enabled Astra DB database can range from 0 to 1. For example:

  • A value of 0 indicates that the vectors are diametrically opposed.

  • A value of 0.5 suggests the vectors are orthogonal (or perpendicular) and have no match.

  • A value of 1 indicates that the vectors are identical in direction.

Run your client

Run the code you defined above:

tsx client.ts


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