Create a vector index (HTTP)
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For details about additional languages, see: |
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Tables with the Data API are currently in public preview. Development is ongoing, and the features and functionality are subject to change. Hyper-Converged Database (HCD), and the use of such, is subject to the DataStax Preview Terms. |
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 (HTTP) 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.
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Ready to write code? See the examples for this method to get started. |
Result
Creates an index for the specified vector column.
If the command succeeds, the response indicates the success.
Example response:
{
"status": {
"ok": 1
}
}Signature
Use the createVectorIndex command.
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
}
},
"options": {
"ifNotExists": BOOLEAN
}
}
}'Parameters
| Name | Type | Summary |
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The name of the index. Index names for tables must follow these rules:
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The name of the vector column on which to create the index. To create indexes on non-vector columns, see Create an index (HTTP). |
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Optional. The similarity metric to use for vector search. Can be one of: For an example, see Create a vector index and specify the source model and similarity metric. Default: |
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Optional. The model used to generate the embeddings that the indexed column stores. This enables certain vector optimizations on the index. Can be one of: For an example, see Create a vector index and specify the source model and similarity metric. Default: |
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Optional. Whether the command should silently succeed even if an index with the given name already exists in the keyspace and no new index was created. This option only checks index names. It does not check index definitions. Default: false |
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.
curl -sS -L -X POST "API_ENDPOINT/v1/KEYSPACE_NAME/TABLE_NAME" \
--header "Token: APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"createVectorIndex": {
"name": "INDEX_NAME",
"definition": {
"column": "VECTOR_COLUMN_NAME"
}
}
}'Create a vector index and specify the source model and similarity metric
When you create a vector index, you can specify the embedding source model, the similarity metric, or both.
curl -sS -L -X POST "API_ENDPOINT/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": "dot_product",
"sourceModel": "nv-qa-4"
}
}
}
}'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.
curl -sS -L -X POST "API_ENDPOINT/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": {
"ifNotExists": true
}
}
}'