Create a vector index

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Creates a new index for a vector column in a table in a database. You must create a vector index if you want to perform a vector search on vector embeddings stored in a column.

To create an index on a non-vector column, see Create an index instead.

The username and password used to generate the token must be tied to a role that has sufficient permissions to perform the desired operations.

Ready to write code? See the examples for this method to get started. If you are new to the Data API, check out the quickstart.

Result

  • Python

  • TypeScript

  • Java

  • curl

Creates an index for the specified vector column.

Does not return anything.

Creates an index for the specified vector column.

Returns a promise that resolves once the operation completes.

Creates an index for the specified vector column.

Does not return anything.

Creates an index for the specified vector column.

If the command succeeds, the response indicates the success.

Example response:

{
  "status": {
    "ok": 1
  }
}

Parameters

  • Python

  • TypeScript

  • Java

  • curl

Use the create_vector_index method, which belongs to the astrapy.table.Table class.

Method signature 
create_vector_index(
  name: str,
  column: str,
  *,
  definition: TableVectorIndexDefinition | dict[str, Any],
  options: TableVectorIndexOptions | dict[str, Any],
  if_not_exists: bool,
  table_admin_timeout_ms: int,
  request_timeout_ms: int,
  timeout_ms: int,
) -> None
Name Type Summary

name

str

The name of the index.

Index names for tables must follow these rules:

  • Must be unique within the keyspace

  • Can contain letters, numbers, and underscores

  • Must have a length of 1 to 100 characters

column

str

The name of the table column on which to create the index. The column must be of type vector.

The column name must use snake case, not camel case.

To create indexes on non-vector columns, see Create an index.

definition

TableVectorIndexDefinition | dict | None

Optionally used to specify the indexed column and index options. If you use this parameter, the column and options parameters are not accepted.

options

TableVectorIndexOptions | dict | None

Specifies index options:

  • metric: One of the values in astrapy.constants.VectorMetric selects a similarity metric, such as COSINE (default), DOT_PRODUCT, or EUCLIDEAN.

  • source_model: Enable certain vector optimizations on the index by specifying the source model for your vectors, such as 'openai_v3_large', 'openai_v3_small', 'ada002', 'gecko', 'bert', or 'other' (default). If a model is specified, the index automatically uses the parameters best suited to that embedding model.

If passed, it must be an instance of TableVectorIndexOptions or an equivalent dictionary.

if_not_exists

bool | None

If True, and an index with the given name already exists in the keyspace, then the command succeeds and silently does nothing. In this case, no actual index creation takes place on the database.

If False (default), an error occurs if an index with the specified name already exists.

ifNotExists: True, does not check the type or content of any existing indexes. This parameter checks index names only.

This means that the command succeeds if the given index name is already in use, even if the type or indexed column is different.

table_admin_timeout_ms

int | None

A timeout, in milliseconds, to impose on the underlying API request. If not provided, the Table defaults apply. This parameter is aliased as request_timeout_ms and timeout_ms for convenience.

Use the createVectorIndex method, which belongs to the Table class.

Method signature 
async createVectorIndex(
  name: string,
  column: keyof Schema | Partial<Record<keyof Schema, string>>,
  options?: {
    ifNotExists?: boolean,
    options?: {
      metric?: string,
      sourceModel?: string,
      timeout?: number | TimeoutDescriptor,
    },
  }
): void
Name Type Summary

name

string

The name of the index.

Index names for tables must follow these rules:

  • Must be unique within the keyspace

  • Can contain letters, numbers, and underscores

  • Must have a length of 1 to 100 characters

column

string

The name of the table column on which to create the index. The column must be of type vector.

The column name must use snake case, not camel case.

To create indexes on non-vector columns, see Create an index.

options?

TableCreateVectorIndexOptions

The options for this operation.

Options (TableCreateVectorIndexOptions):

Name Type Summary

ifNotExists?

boolean

If true, and an index with the given name already exists in the keyspace, then the command succeeds and silently does nothing. In this case, no actual index creation takes place on the database.

If false (default), an error occurs if an index with the specified name already exists.

ifNotExists: true, does not check the type or content of any existing indexes. This parameter checks index names only.

This means that the command succeeds if the given index name is already in use, even if the type or indexed column is different.

options.metric?

string

The similarity metric to use for vector search, one of 'cosine' (default), 'dot_product', or 'euclidean'.

options.sourceModel?

string

Enable certain vector optimizations on the index by specifying the source model for your vectors, such as 'openai_v3_large', 'openai_v3_small', 'ada002', 'gecko', 'bert', or 'other' (default).

timeout?

number | TimeoutDescriptor

The client-side timeout for this operation.

Use the createVectorIndex method, which belongs to the com.datastax.astra.client.tables.Table class.

Method signature 
void createVectorIndex(
  String indexName,
  String columnName
)
void createVectorIndex(
  String indexName,
  TableVectorIndexDefinition indexDefinition
)
void createVectorIndex(
  String indexName,
  TableVectorIndexDefinition indexDefinition,
  CreateVectorIndexOptions indexOptions
)
Name Type Summary

name

str

The name of the index.

Index names for tables must follow these rules:

  • Must be unique within the keyspace

  • Can contain letters, numbers, and underscores

  • Must have a length of 1 to 100 characters

definition

TableVectorIndexDefinition

Definition of the index to create.

Requires the name of the column to index. The column must be of type vector.

The column name must use snake case, not camel case.

To create indexes on non-vector columns, see Create an index.

Optionally, you can specify the similarity metric and source model:

  • metric(SimilarityMetric.METRIC): The similarity metric to use for vector search, one of COSINE (default), DOT_PRODUCT, or EUCLIDEAN.

  • sourceModel(): Enable certain vector optimizations on the index by specifying the source model for your vectors, such as "openai_v3_large", "openai_v3_small", "ada002", "gecko", "bert", or "other" (default).

options

CreateVectorIndexOptions

A specialization of index creation options, including ifNotExists and timeout.

If ifNotExists(true), and an index with the given name already exists in the keyspace, then the command succeeds and silently does nothing. In this case, no actual index creation takes place on the database.

If ifNotExists(false) (default), an error occurs if an index with the specified name already exists.

ifNotExists(true), does not check the type or content of any existing indexes. This parameter checks index names only.

This means that the command succeeds if the given index name is already in use, even if the type or indexed column is different.

Use the createVectorIndex command.

Command signature 
curl -sS -L -X POST "API_ENDPOINT/api/json/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "createVectorIndex": {
    "name": "INDEX_NAME",
    "definition": {
      "column": "VECTOR_COLUMN_NAME",
      "options": {
        "metric": STRING,
        "sourceModel": STRING
      }
    }
  }
}'
Name Type Summary

createVectorIndex

command

The Data API command to create a vector index for a table in a database. It acts as a container for all the attributes and settings required to create the index.

name

string

The name of the index.

Index names for tables must follow these rules:

  • Must be unique within the keyspace

  • Can contain letters, numbers, and underscores

  • Must have a length of 1 to 100 characters

definition

object

Contains column and options for the vector index.

definition.column

string

The name of the table column on which to create the index. The column must be of type vector.

The column name must use snake case, not camel case.

To create indexes on non-vector columns, see Create an index.

definition.options

object

Contains either, both, or none of the vector index options:

  • metric: The similarity metric to use for vector searches on this index, one of cosine (default), dot_product, or euclidean.

  • sourceModel: Enable certain vector optimizations on the index by specifying the source model for your vectors, such as openai_v3_large, openai_v3_small, ada002, gecko, bert, or other (default).

Examples

The following examples demonstrate how to create a vector index.

Create a vector index with the default source model and similarity metric

If you do not specify the source model and similarity metric, the default values are used. For more information, see Parameters.

  • Python

  • TypeScript

  • Java

  • curl

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
)
table = database.get_table("TABLE_NAME", keyspace="KEYSPACE_NAME")

# Index a vector column
table.create_vector_index("example_index_name", column="example_vector_column")
import {
  DataAPIClient,
  UsernamePasswordTokenProvider,
} from "@datastax/astra-db-ts";

// Get an existing table
const client = new DataAPIClient({ environment: "hcd" });
const database = client.db("API_ENDPOINT", {
  token: new UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
});
const table = database.table("TABLE_NAME", {
  keyspace: "KEYSPACE_NAME",
});

// Index a vector column
(async function () {
  await table.createVectorIndex("example_index_name", "example_vector_column");
})();
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.DataAPIClients;
import com.datastax.astra.client.databases.Database;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.definition.rows.Row;

public class Example {

  public static void main(String[] args) {
    // Get an existing table
    DataAPIClient client = DataAPIClients.clientHCD("USERNAME", "PASSWORD");
    Database database = client.getDatabase("API_ENDPOINT", "KEYSPACE_NAME");
    Table<Row> table = database.getTable("TABLE_NAME");

    // Index a vector column
    table.createVectorIndex("example_index_name", "example_vector_column");
  }
}
curl -sS -L -X POST "API_ENDPOINT/v1/KEYSPACE_NAME/TABLE_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "createVectorIndex": {
    "name": "example_index_name",
    "definition": {
      "column": "example_vector_column"
    }
  }
}'

Create a vector index and specify the source model and similarity metric

You can specify the embedding source model and the similarity metric when you create a vector index.

  • Python

  • TypeScript

  • Java

  • curl

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment
from astrapy.constants import VectorMetric
from astrapy.info import TableVectorIndexOptions

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
)
table = database.get_table("TABLE_NAME", keyspace="KEYSPACE_NAME")

# Index a vector column
table.create_vector_index(
    "example_index_name",
    column="example_vector_column",
    options=TableVectorIndexOptions(
        metric=VectorMetric.DOT_PRODUCT,
        source_model="nv-qa-4",
    ),
)
import {
  DataAPIClient,
  UsernamePasswordTokenProvider,
} from "@datastax/astra-db-ts";

// Get an existing table
const client = new DataAPIClient({ environment: "hcd" });
const database = client.db("API_ENDPOINT", {
  token: new UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
});
const table = database.table("TABLE_NAME", {
  keyspace: "KEYSPACE_NAME",
});

// Index a vector column
(async function () {
  await table.createVectorIndex("example_index_name", "example_vector_column", {
    options: {
      metric: "dot_product",
      sourceModel: "nv-qa-4",
    },
  });
})();
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.DataAPIClients;
import com.datastax.astra.client.core.vector.SimilarityMetric;
import com.datastax.astra.client.databases.Database;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.commands.options.CreateVectorIndexOptions;
import com.datastax.astra.client.tables.definition.indexes.TableVectorIndexDefinition;
import com.datastax.astra.client.tables.definition.rows.Row;

public class Example {

  public static void main(String[] args) {
    // Get an existing table
    DataAPIClient client = DataAPIClients.clientHCD("USERNAME", "PASSWORD");
    Database database = client.getDatabase("API_ENDPOINT", "KEYSPACE_NAME");
    Table<Row> table = database.getTable("TABLE_NAME");

    // Index a vector column
    TableVectorIndexDefinition definition =
        new TableVectorIndexDefinition()
            .column("example_vector_column")
            .metric(SimilarityMetric.COSINE)
            .sourceModel("openai-v3-large");
    CreateVectorIndexOptions options = new CreateVectorIndexOptions();
    table.createVectorIndex("example_index_name", definition, options);
  }
}
curl -sS -L -X POST "API_ENDPOINT/v1/KEYSPACE_NAME/TABLE_NAME" \
  --header "Token: APPLICATION_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
  "createVectorIndex": {
    "name": "example_index_name",
    "definition": {
      "column": "example_vector_column",
      "options": {
        "metric": "dot_product",
        "sourceModel": "ada002"
      }
    }
  }
}'

Create an index only if the index does not exist

Use this option to silently do nothing if an index with the specified name already exists.

This option only checks index names. It doesn’t check the type or content of any existing indexes.

  • Python

  • TypeScript

  • Java

  • curl

from astrapy import DataAPIClient
from astrapy.authentication import UsernamePasswordTokenProvider
from astrapy.constants import Environment

# Get an existing table
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database(
    "API_ENDPOINT",
    token=UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
)
table = database.get_table("TABLE_NAME", keyspace="KEYSPACE_NAME")

# Index a vector column
table.create_vector_index(
    "example_index_name",
    column="example_vector_column",
    if_not_exists=True,
)
import {
  DataAPIClient,
  UsernamePasswordTokenProvider,
} from "@datastax/astra-db-ts";

// Get an existing table
const client = new DataAPIClient({ environment: "hcd" });
const database = client.db("API_ENDPOINT", {
  token: new UsernamePasswordTokenProvider("USERNAME", "PASSWORD"),
});
const table = database.table("TABLE_NAME", {
  keyspace: "KEYSPACE_NAME",
});

// Index a vector column
(async function () {
  await table.createVectorIndex("example_index_name", "example_vector_column", {
    ifNotExists: true,
  });
})();
import com.datastax.astra.client.DataAPIClient;
import com.datastax.astra.client.DataAPIClients;
import com.datastax.astra.client.databases.Database;
import com.datastax.astra.client.tables.Table;
import com.datastax.astra.client.tables.commands.options.CreateVectorIndexOptions;
import com.datastax.astra.client.tables.definition.indexes.TableVectorIndexDefinition;
import com.datastax.astra.client.tables.definition.rows.Row;

public class Example {

  public static void main(String[] args) {
    // Get an existing table
    DataAPIClient client = DataAPIClients.clientHCD("USERNAME", "PASSWORD");
    Database database = client.getDatabase("API_ENDPOINT", "KEYSPACE_NAME");
    Table<Row> table = database.getTable("TABLE_NAME");

    // Index a vector column
    TableVectorIndexDefinition definition =
        new TableVectorIndexDefinition().column("example_vector_column");
    CreateVectorIndexOptions options = new CreateVectorIndexOptions().ifNotExists(true);
    table.createVectorIndex("example_index_name", definition, options);
  }
}

This option has no literal equivalent in HTTP. Instead, you can list the index names to see if an index with the name already exists.

Client reference

  • Python

  • TypeScript

  • Java

  • curl

For more information, see the client reference.

For more information, see the client reference.

For more information, see the client reference.

Client reference documentation is not applicable for HTTP.

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