Integrate NVIDIA as an embedding provider

Integrate the Astra-hosted NVIDIA embedding provider to enable Astra DB vectorize.

Prerequisites

To use NVIDIA as an embedding provider, you need the following:

An active Astra account with permission to create collections.
A Serverless (Vector) database in the AWS us-east-2 region.

Only databases in the AWS us-east-2 region can use the built-in NVIDIA embedding provider integration.

If this is your first time using Astra DB, we recommend that you follow the Quickstart to guide you through creating a database and connecting to it with your API client of choice.

Add the NVIDIA integration to a new collection

Before you can use the NVIDIA integration to generate embeddings, you must add the integration to a new collection.

Astra Portal
Python
TypeScript
Java

Use the Astra Portal to add the NVIDIA integration to a new collection:

In the Astra Portal, go to Databases, and then select your Serverless (Vector) database.
Click Data Explorer.
Optional: Use the Namespace dropdown to select the namespace where you want to create the collection. Otherwise, leave default_keyspace selected to create the collection in the default namespace.
Click Create Collection.
In the Create collection dialog, enter a name for the new collection in the Collection name field.
Under Embedding generation method, select the NVIDIA embedding provider integration.
Complete the following fields:
- Embedding model: The model that you want to use to generate embeddings. If only one model is available, it is selected by default.
- Dimensions: The number of dimensions that you want the generated vectors to have. Typically, the number of dimensions is automatically determined by the model you select.
- Similarity metric: The method you want to use to calculate vector similarities.
  
  The available metrics are:
  - Cosine
  - Dot Product
  - Euclidean
    
    To learn more about similarity metrics, see Intro to vector databases.
Click Create collection.

If you get a Collection Limit Reached message, you’ll need to delete a collection before you can create a new one.

An empty collection appears in the list of collections. You can now load data into this collection.

Use the Python client to create a collection that uses the NVIDIA integration.

Initialize the client

If you haven’t done so already, initialize the client before creating a collection:

import os
from astrapy import DataAPIClient
from astrapy.constants import VectorMetric
from astrapy.ids import UUID
from astrapy.info import CollectionVectorServiceOptions

# Initialize the client and get a "Database" object
client = DataAPIClient(os.environ["ASTRA_DB_APPLICATION_TOKEN"])
database = client.get_database(os.environ["ASTRA_DB_API_ENDPOINT"])
print(f"* Database: {database.info().name}\n")

Create a collection integrated with NVIDIA:

collection = database.create_collection(
    "COLLECTION_NAME",
    metric=VectorMetric.COSINE,
    service=CollectionVectorServiceOptions(
        provider="nvidia",
        model_name="NV-Embed-QA",
    ),
)
print(f"* Collection: {collection.full_name}\n")

Use the TypeScript client to create a collection that uses the NVIDIA integration.

Initialize the client

If you haven’t done so already, initialize the client before creating a collection:

import { DataAPIClient, VectorDoc, UUID } from '@datastax/astra-db-ts';

const { ASTRA_DB_APPLICATION_TOKEN, ASTRA_DB_API_ENDPOINT } = process.env;

// Initialize the client and get a 'Db' object
const client = new DataAPIClient(ASTRA_DB_APPLICATION_TOKEN);
const db = client.db(ASTRA_DB_API_ENDPOINT);

console.log(`* Connected to DB ${db.id}`);

Create a collection integrated with NVIDIA:

(async function () {
  const collection = await db.createCollection('COLLECTION_NAME', {
    vector: {
      service: {
        provider: 'nvidia',
        modelName: 'NV-Embed-QA',
      },
    },
  });
  console.log(`* Created collection ${collection.namespace}.${collection.collectionName}`);

Use the Java client to create a collection that uses the NVIDIA integration.

Initialize the client

If you haven’t done so already, initialize the client before creating a collection:

import com.datastax.astra.client.Collection;
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.Database;
import com.datastax.astra.client.model.CollectionOptions;
import com.datastax.astra.client.model.Document;
import com.datastax.astra.client.model.FindIterable;
import com.datastax.astra.client.model.FindOptions;
import com.datastax.astra.client.model.SimilarityMetric;

import static com.datastax.astra.client.model.SimilarityMetric.COSINE;

public class Quickstart {

  public static void main(String[] args) {
    // Loading Arguments
    String astraToken = System.getenv("ASTRA_DB_APPLICATION_TOKEN");
    String astraApiEndpoint = System.getenv("ASTRA_DB_API_ENDPOINT");

    // Initialize the client
    DataAPIClient client = new DataAPIClient(astraToken);
    System.out.println("Connected to AstraDB");

    Database db = client.getDatabase(astraApiEndpoint);
    System.out.println("Connected to Database.");

Create a collection integrated with NVIDIA:

CollectionOptions.CollectionOptionsBuilder builder = CollectionOptions
 .builder()
 .vectorSimilarity(SimilarityMetric.COSINE)
 .defaultIdType(CollectionIdTypes.UUID)
 .vectorize("nvidia", "NV-Embed-QA");
Collection<Document> collection = db
 .createCollection("COLLECTION_NAME", builder.build());

You can’t change a collection’s embedding provider after you create it. To use a different embedding provider, you must create a new collection with a different embedding provider integration.

Load data using vectorize to auto-generate embeddings

Use the following methods to load vector data into a collection and use $vectorize to auto-generate embeddings.

Astra Portal
Python
TypeScript
Java

Use the Astra Portal to load a dataset from a JSON or a CSV file.

In the Astra Portal, go to Databases, and then select a database that contains a collection that uses the NVIDIA integration.
Click Data Explorer.
Select the collection that uses the NVIDIA integration.
Click Load Data.
In the Load Data dialog, click Select File.
Select the file on your computer that contains your dataset.

Once the file upload is complete, the first ten rows of your data appear in the Data Preview section.

If you get a Selected embedding does not match collection dimensions error, you need to create a new collection with vector dimensions that match your dataset.
Use the Vector Field dropdown to select the field that you want to auto-generate embeddings for.

The data importer will apply the top-level $vectorize key to the Vector Field, and automatically generate an embedding vector from its contents. The resulting documents in the collection will have the actual text stored in the special $vectorize field, and the resulting embedding stored in the $vector field. The original field name (such as reviewtext) isn’t preserved in the documents in the database.

Optional: Configure field data types.

In the Data Preview section, use the drop-down controls to change the data type for each field or column.

The options are:

String
Number
Array
Object
Vector

Data type selections you make in the Data Preview section only apply to the initial data that you load (with the exception of Vector, which permanently maps the field to the reserved key $vector). These selections aren’t fixed in the schema, and don’t apply to documents inserted later on. The same field can be a string in one document, and a number in another. You can also have different sets of fields in different documents in the same collection.

Click Load Data.

Once your dataset has loaded, you can interact with it and do a vector search using the Data Explorer and the client APIs.

Use the Python client to load data into your database.

# Insert documents into the collection.
# (UUIDs here are version 7.)
documents = [
    {
        "_id": UUID("018e65c9-df45-7913-89f8-175f28bd7f74"),
        "$vectorize": "Chat bot integrated sneakers that talk to you",
    },
    {
        "_id": UUID("018e65c9-e1b7-7048-a593-db452be1e4c2"),
        "$vectorize": "An AI quilt to help you sleep forever",
    },
    {
        "_id": UUID("018e65c9-e33d-749b-9386-e848739582f0"),
        "$vectorize": "A deep learning display that controls your mood",
    },
]
insertion_result = collection.insert_many(documents)
print(f"* Inserted {len(insertion_result.inserted_ids)} items.\n")

Use the TypeScript client to load data into your database.

  // Insert documents into the collection (using UUIDv7s)
  const documents = [
    {
      _id: new UUID('018e65c9-df45-7913-89f8-175f28bd7f74'),
      $vectorize: 'Chat bot integrated sneakers that talk to you',
    },
    {
      _id: new UUID('018e65c9-e1b7-7048-a593-db452be1e4c2'),
      $vectorize: 'An AI quilt to help you sleep forever',
    },
    {
      _id: new UUID('018e65c9-e33d-749b-9386-e848739582f0'),
      $vectorize: 'A deep learning display that controls your mood',
    },
  ];

  try {
    const inserted = await collection.insertMany(documents);
    console.log(`* Inserted ${inserted.insertedCount} items.`);
  } catch (e) {
    console.log('* Documents found on DB already. Let\'s move on!');
  }

Use the Java client to load data into your database.

// Insert documents into the collection
InsertManyResult insertResult = collection.insertMany(
  new Document()
   .id(UUID.fromString("018e65c9-df45-7913-89f8-175f28bd7f74"))
   .vectorize("Chat bot integrated sneakers that talk to you"),
  new Document()
   .id(UUID.fromString("018e65c9-e1b7-7048-a593-db452be1e4c2"))
   .vectorize("An AI quilt to help you sleep forever"),
  new Document()
   .id(UUID.fromString("018e65c9-e33d-749b-9386-e848739582f0"))
   .vectorize("A deep learning display that controls your mood")
);
System.out.println("Insert " + insertResult.getInsertedIds().size() + " items.");

Search your data with vectorize

Perform a similarity search using text, rather than a vector.

Astra Portal
Python
TypeScript
Java

Use the Astra Portal to perform a search with vectorize:

In the Astra Portal, go to Databases, and then select your Serverless (Vector) database.
Click Data Explorer.
Select the Namespace and Collection that contain the data you want to view.

Your data is displayed in the Collection Data section. The field you configured to auto-generate embeddings is notated with ($vectorize) in the column title. The $vector field contains the generated embeddings.
Enter a text query into the Hybrid Search field, and then click Apply.

Astra DB auto-generates a vector from the text query and performs a similarity search. The search uses the similarity metric that you chose when you created the collection.
Optional: Use Add Filter to filter your search results by the other fields in the collection. For more information about using filters, see Add a metadata filter.

The Collection Data section updates to show the rows that match your search criteria.

Use the Python client to perform a search with vectorize:

# Perform a similarity search
query = "I'd like some talking shoes"
results = collection.find(
    sort={"$vectorize": query},
    limit=2,
    projection={"$vectorize": True},
    include_similarity=True,
)
print(f"Vector search results for '{query}':")
for document in results:
    print("    ", document)

Use the TypeScript client to perform a search with vectorize:

  // Perform a similarity search
  const cursor = await collection.find({}, {
    sort: { $vectorize: 'shoes' },
    limit: 2,
    includeSimilarity: true,
  });

  console.log('* Search results:')
  for await (const doc of cursor) {
    console.log('  ', doc.text, doc.$similarity);
  }

Use the Java client to perform a search with vectorize:

// Perform a similarity search
FindOptions findOptions = new FindOptions()
       .limit(2)
       .includeSimilarity()
       .sort("I'd like some talking shoes");
FindIterable<Document> results = collection.find(findOptions);
for (Document document : results) {
   System.out.println("Document: " + document);
}

Integrate NVIDIA as an embedding provider

Prerequisites

Add the NVIDIA integration to a new collection

Load data using vectorize to auto-generate embeddings

Search your data with vectorize

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