Deploy and manage public index endpoints

There are a few steps required before you can query an index:

  1. Create an IndexEndpoint if needed, or reuse an existing IndexEndpoint.
  2. Get the IndexEndpoint ID.
  3. Deploy the index to the IndexEndpoint.

Create an IndexEndpoint

gcloud

Before using any of the command data below, make the following replacements:

  • INDEX_ENDPOINT_NAME: Display name of the index endpoint.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai index-endpoints create \
    --display-name=INDEX_ENDPOINT_NAME \
    --public-endpoint-enabled \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

gcloud ai index-endpoints create `
    --display-name=INDEX_ENDPOINT_NAME `
    --public-endpoint-enabled `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

gcloud ai index-endpoints create ^
    --display-name=INDEX_ENDPOINT_NAME ^
    --public-endpoint-enabled ^
    --region=LOCATION ^
    --project=PROJECT_ID

REST

Before using any of the request data, make the following replacements:

  • INDEX_ENDPOINT_NAME: Display name of the index endpoint.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.
  • PROJECT_NUMBER: Your project's automatically generated project number.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints

Request JSON body:

{
 "display_name": "INDEX_ENDPOINT_NAME",
 "publicEndpointEnabled": "true"
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateIndexEndpointOperationMetadata",
    "genericMetadata": {
      "createTime": "2022-01-13T04:09:56.641107Z",
      "updateTime": "2022-01-13T04:09:56.641107Z"
    }
  }
}
You can poll for the status of the operation until the response includes "done": true.

Python

To learn how to install or update the Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

def vector_search_create_index_endpoint(
    project: str, location: str, display_name: str
) -> None:
    """Create a vector search index endpoint.

    Args:
        project (str): Required. Project ID
        location (str): Required. The region name
        display_name (str): Required. The index endpoint display name
    """
    # Initialize the Vertex AI client
    aiplatform.init(project=project, location=location)

    # Create Index Endpoint
    index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(
        display_name=display_name,
        public_endpoint_enabled=True,
        description="Matching Engine Index Endpoint",
    )

    print(index_endpoint.name)

Console

Use these instructions to create an index endpoint.

  1. In the Vertex AI section of the Google Cloud console, go to the Deploy and Use section. Select Vector Search

    Go to Vector Search

  2. A list of your active indexes is displayed.
  3. On the top of the page, select the Index endpoints tab. Your index endpoints are displayed.
  4. Click Create new index endpoint. The Create a new index endpoint panel opens.
  5. Enter a display name for the index endpoint.
  6. In the Region field, select a region from the drop-down.
  7. In the Access field, select Standard.
  8. Click Create.

Deploy an index to an endpoint

gcloud

This example uses the gcloud ai index-endpoints deploy-index command.

Before using any of the command data below, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • DEPLOYED_INDEX_ENDPOINT_NAME: Display name of the deployed index endpoint.
  • INDEX_ID: The ID of the index.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai index-endpoints deploy-index INDEX_ENDPOINT_ID \
    --deployed-index-id=DEPLOYED_INDEX_ID \
    --display-name=DEPLOYED_INDEX_ENDPOINT_NAME \
    --index=INDEX_ID \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

gcloud ai index-endpoints deploy-index INDEX_ENDPOINT_ID `
    --deployed-index-id=DEPLOYED_INDEX_ID `
    --display-name=DEPLOYED_INDEX_ENDPOINT_NAME `
    --index=INDEX_ID `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

gcloud ai index-endpoints deploy-index INDEX_ENDPOINT_ID ^
    --deployed-index-id=DEPLOYED_INDEX_ID ^
    --display-name=DEPLOYED_INDEX_ENDPOINT_NAME ^
    --index=INDEX_ID ^
    --region=LOCATION ^
    --project=PROJECT_ID

REST

Before using any of the request data, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • DEPLOYED_INDEX_ENDPOINT_NAME: Display name of the deployed index endpoint.
  • INDEX_ID: The ID of the index.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.
  • PROJECT_NUMBER: Your project's automatically generated project number.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex

Request JSON body:

{
 "deployedIndex": {
   "id": "DEPLOYED_INDEX_ID",
   "index": "projects/PROJECT_ID/locations/LOCATION/indexes/INDEX_ID",
   "displayName": "DEPLOYED_INDEX_ENDPOINT_NAME"
 }
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
 "name": "projects/PROJECT_NUMBER/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
 "metadata": {
   "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployIndexOperationMetadata",
   "genericMetadata": {
     "createTime": "2022-10-19T17:53:16.502088Z",
     "updateTime": "2022-10-19T17:53:16.502088Z"
   },
   "deployedIndexId": "DEPLOYED_INDEX_ID"
 }
}

Python

To learn how to install or update the Python, see Install the Vertex AI SDK for Python. For more information, see the Python API reference documentation.

def vector_search_deploy_index(
    project: str,
    location: str,
    index_name: str,
    index_endpoint_name: str,
    deployed_index_id: str,
) -> None:
    """Deploy a vector search index to a vector search index endpoint.

    Args:
        project (str): Required. Project ID
        location (str): Required. The region name
        index_name (str): Required. The index to update. A fully-qualified index
          resource name or a index ID.  Example:
          "projects/123/locations/us-central1/indexes/my_index_id" or
          "my_index_id".
        index_endpoint_name (str): Required. Index endpoint to deploy the index to.
        deployed_index_id (str): Required. The user specified ID of the DeployedIndex.
    """
    # Initialize the Vertex AI client
    aiplatform.init(project=project, location=location)

    # Create the index instance from an existing index
    index = aiplatform.MatchingEngineIndex(index_name=index_name)

    # Create the index endpoint instance from an existing endpoint.
    index_endpoint = aiplatform.MatchingEngineIndexEndpoint(
        index_endpoint_name=index_endpoint_name
    )

    # Deploy Index to Endpoint
    index_endpoint = index_endpoint.deploy_index(
        index=index, deployed_index_id=deployed_index_id
    )

    print(index_endpoint.deployed_indexes)

Console

Use these instructions to deploy your index to an endpoint.

  1. In the Vertex AI section of the Google Cloud console, go to the Deploy and Use section. Select Vector Search

    Go to Vector Search

  2. A list of your active indexes is displayed.
  3. Select the name of the index you want to deploy. The index details page opens.
  4. From the index details page, click Deploy to endpoint. The index deployment panel opens.
  5. Enter a display name - this name acts as an ID and can't be updated.
  6. From the Endpoint drop-down, select the endpoint you want to deploy this index to. Note: The endpoint is unavailable if the index is already deployed to it.
  7. Optional: In the Machine type field, select either standard or high-memory.
  8. Optional. Select Enable autoscaling to automatically resize the number of nodes based on the demands of your workloads. The default number of replicas is 2 if autoscaling is disabled.
  9. Click Deploy to deploy your index to the endpoint. Note: It takes around 30 minutes to be deployed.

Get the index domain name

After the index is deployed, you need the domain name to be able to use for an online query. The value is available under publicEndpointDomainName.

curl -H "Content-Type: application/json" -H "Authorization: Bearer `gcloud auth print-access-token`"  ${ENDPOINT}/v1/projects/${PROJECT_ID}/locations/${REGION}/indexEndpoints/${INDEX_ENDPOINT_ID}

Example Response

{
  "name": "projects/181224308459/locations/us-central1/indexEndpoints/3370566089086861312",
  "displayName": "public-endpoint-test1",
  "deployedIndexes": [
    {
      "id": "test_index_public1",
      "index": "projects/181224308459/locations/us-central1/indexes/7733428228102029312",
      "displayName": "test_index_public1",
      "createTime": "2023-02-08T23:19:58.026843Z",
      "indexSyncTime": "2023-02-09T05:26:19.309417Z",
      "automaticResources": {
        "minReplicaCount": 2,
        "maxReplicaCount": 2
      },
      "deploymentGroup": "default"
    }
  ],
  "etag": "AMEw9yNkXQcSke8iqW9SYxfhj_hT9GCwPt1XlxVwJRSCxiXOYnG4CKrZM_X0oH-XN8tR",
  "createTime": "2023-02-08T22:44:20.285382Z",
  "updateTime": "2023-02-08T22:44:26.515162Z",
  "publicEndpointDomainName": "1957880287.us-central1-181224308459.vdb.vertexai.goog"
}

Enable autoscaling

Vector Search supports autoscaling, which can automatically resize the number of nodes based on the demands of your workloads. When demand is high, nodes are added to the node pool, which won't exceed the maximum size you designate. When demand is low, the node pool scales back down to a minimum size that you designate. You can check the actual nodes in use and the changes by monitoring the current replicas.

To enable autoscaling, specify the maxReplicaCount and minReplicaCount when you deploy your index:

gcloud

The following example uses the gcloud ai index-endpoints deploy-index command.

Before using any of the command data below, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • DEPLOYED_INDEX_NAME: Display name of the deployed index.
  • INDEX_ID: The ID of the index.
  • MIN_REPLICA_COUNT: Minimum number of machine replicas the deployed index will be always deployed on. If specified, the value must be equal to or larger than 1.
  • MAX_REPLICA_COUNT: Maximum number of machine replicas the deployed index could be deployed on.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai index-endpoints deploy-index INDEX_ENDPOINT_ID \
    --deployed-index-id=DEPLOYED_INDEX_ID \
    --display-name=DEPLOYED_INDEX_NAME \
    --index=INDEX_ID \
    --min-replica-count=MIN_REPLICA_COUNT \
    --max-replica-count=MAX_REPLICA_COUNT \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

gcloud ai index-endpoints deploy-index INDEX_ENDPOINT_ID `
    --deployed-index-id=DEPLOYED_INDEX_ID `
    --display-name=DEPLOYED_INDEX_NAME `
    --index=INDEX_ID `
    --min-replica-count=MIN_REPLICA_COUNT `
    --max-replica-count=MAX_REPLICA_COUNT `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

gcloud ai index-endpoints deploy-index INDEX_ENDPOINT_ID ^
    --deployed-index-id=DEPLOYED_INDEX_ID ^
    --display-name=DEPLOYED_INDEX_NAME ^
    --index=INDEX_ID ^
    --min-replica-count=MIN_REPLICA_COUNT ^
    --max-replica-count=MAX_REPLICA_COUNT ^
    --region=LOCATION ^
    --project=PROJECT_ID

REST

Before using any of the request data, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • DEPLOYED_INDEX_NAME: Display name of the deployed index.
  • INDEX_ID: The ID of the index.
  • MIN_REPLICA_COUNT: Minimum number of machine replicas the deployed index will be always deployed on. If specified, the value must be equal to or larger than 1.
  • MAX_REPLICA_COUNT: Maximum number of machine replicas the deployed index could be deployed on.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.
  • PROJECT_NUMBER: Your project's automatically generated project number.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex

Request JSON body:

{
 "deployedIndex": {
   "id": "DEPLOYED_INDEX_ID",
   "index": "projects/PROJECT_NUMBER/locations/LOCATION/indexes/INDEX_ID",
   "displayName": "DEPLOYED_INDEX_NAME",
   "automaticResources": {
     "minReplicaCount": MIN_REPLICA_COUNT,
     "maxReplicaCount": MAX_REPLICA_COUNT
   }
 }
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
 "name": "projects/PROJECT_NUMBER/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
 "metadata": {
   "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployIndexOperationMetadata",
   "genericMetadata": {
     "createTime": "2023-10-19T17:53:16.502088Z",
     "updateTime": "2023-10-19T17:53:16.502088Z"
   },
   "deployedIndexId": "DEPLOYED_INDEX_ID"
 }
}

Console

You can only enable autoscaling from the console during index deployment.

  1. In the Vertex AI section of the Google Cloud console, go to the Deploy and Use section. Select Vector Search

    Go to Vector Search

  2. A list of your active indexes is displayed.
  3. Select the name of the index you want to deploy. The index details page opens.
  4. From the index details page, click Deploy to endpoint. The index deployment panel opens.
  5. Enter a display name - this name acts as an ID and can't be updated.
  6. From the Endpoint drop-down, select the endpoint you want to deploy this index to. Note: The endpoint is unavailable if the index is already deployed to it.
  7. Optional: In the Machine type field, select either standard or high-memory.
  8. Optional. Select Enable autoscaling to automatically resize the number of nodes based on the demands of your workloads. The default number of replicas is 2 if autoscaling is disabled.
  • If both minReplicaCount and maxReplicaCount are not set, they are set to 2 by default.
  • If only maxReplicaCount is set, minReplicaCount is set to 2 by default.
  • If only minReplicaCount is set, maxReplicaCount is set to equal minReplicaCount.

Mutate a DeployedIndex

You can use MutateDeployedIndex API to update the deployment resources (for example, minReplicaCount and maxReplicaCount) of an already deployed index.

  • Users aren't allowed to change the machineType after the index is deployed.
  • If maxReplicaCount isn't specified in the request, the DeployedIndex keeps using the existing maxReplicaCount.

gcloud

The following example uses the gcloud ai index-endpoints mutate-deployed-index command.

Before using any of the command data below, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • MIN_REPLICA_COUNT: Minimum number of machine replicas the deployed index will be always deployed on. If specified, the value must be equal to or larger than 1.
  • MAX_REPLICA_COUNT: Maximum number of machine replicas the deployed index could be deployed on.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai index-endpoints mutate-deployed-index INDEX_ENDPOINT_ID \
    --deployed-index-id=DEPLOYED_INDEX_ID \
    --min-replica-count=MIN_REPLICA_COUNT \
    --max-replica-count=MAX_REPLICA_COUNT \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

gcloud ai index-endpoints mutate-deployed-index INDEX_ENDPOINT_ID `
    --deployed-index-id=DEPLOYED_INDEX_ID `
    --min-replica-count=MIN_REPLICA_COUNT `
    --max-replica-count=MAX_REPLICA_COUNT `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

gcloud ai index-endpoints mutate-deployed-index INDEX_ENDPOINT_ID ^
    --deployed-index-id=DEPLOYED_INDEX_ID ^
    --min-replica-count=MIN_REPLICA_COUNT ^
    --max-replica-count=MAX_REPLICA_COUNT ^
    --region=LOCATION ^
    --project=PROJECT_ID

REST

Before using any of the request data, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • MIN_REPLICA_COUNT: Minimum number of machine replicas the deployed index will be always deployed on. If specified, the value must be equal to or larger than 1.
  • MAX_REPLICA_COUNT: Maximum number of machine replicas the deployed index could be deployed on.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.
  • PROJECT_NUMBER: Your project's automatically generated project number.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:mutateDeployedIndex

Request JSON body:

{
"deployedIndex": {
  "id": "DEPLOYED_INDEX_ID",
  "index": "projects/PROJECT_ID/locations/LOCATION/indexes/INDEX_ID",
  "displayName": "DEPLOYED_INDEX_NAME"
}
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
"name": "projects/PROJECT_NUMBER/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
"metadata": {
  "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployIndexOperationMetadata",
  "genericMetadata": {
    "createTime": "2020-10-19T17:53:16.502088Z",
    "updateTime": "2020-10-19T17:53:16.502088Z"
  },
  "deployedIndexId": "DEPLOYED_INDEX_ID"
}
}

Deployment settings that impact performance

The following deployment settings can affect latency, availability, and cost when using Vector Search. This guidance applies to most cases. However, always experiment with your configurations to make sure that they work for your use case.

Setting Performance impact
Machine type

The hardware selection has a direct interaction with the shard size selected. Depending on shard choices you specified at index creation time, each machine type offers a tradeoff between performance and cost.

Reference the pricing page to determine the hardware available and pricing. In general, performance increases in the following order:

  • E2 standard
  • E2 highmem
  • N1 standard
  • N2D standard
Minimum replica count

minReplicaCount reserves a minimum capacity for availability and latency to ensure that the system doesn't have cold start issues when traffic scales up quickly from low levels.

If you have workloads that drop to low levels and then quickly increase to higher levels, consider setting minReplicaCount to a number that can accommodate the initial bursts of traffic.

Maximum replica count maxReplicaCount primarily lets you control usage cost. You can choose to prevent increasing costs beyond a certain threshold, with the tradeoff of allowing increased latency and reducing availability.

List IndexEndpoints

To list your IndexEndpoint resources and view the information of any associated DeployedIndex instances, run the following code:

gcloud

The following example uses the gcloud ai index-endpoints list command.

Before using any of the command data below, make the following replacements:

  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai index-endpoints list \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

gcloud ai index-endpoints list `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

gcloud ai index-endpoints list ^
    --region=LOCATION ^
    --project=PROJECT_ID

REST

Before using any of the request data, make the following replacements:

  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.
  • PROJECT_NUMBER: Your project's automatically generated project number.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
 "indexEndpoints": [
   {
     "name": "projects/PROJECT_NUMBER/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID",
     "displayName": "INDEX_ENDPOINT_DISPLAY_NAME",
     "deployedIndexes": [
       {
         "id": "DEPLOYED_INDEX_ID",
         "index": "projects/PROJECT_NUMBER/locations/LOCATION/indexes/INDEX_ID",
         "displayName": "DEPLOYED_INDEX_DISPLAY_NAME",
         "createTime": "2021-06-04T02:23:40.178286Z",
         "privateEndpoints": {
           "matchGrpcAddress": "GRPC_ADDRESS"
         },
         "indexSyncTime": "2022-01-13T04:22:00.151916Z",
         "automaticResources": {
           "minReplicaCount": 2,
           "maxReplicaCount": 10
         }
       }
     ],
     "etag": "AMEw9yP367UitPkLo-khZ1OQvqIK8Q0vLAzZVF7QjdZ5O3l7Zow-mzBo2l6xmiuuMljV",
     "createTime": "2021-03-17T04:47:28.460373Z",
     "updateTime": "2021-06-04T02:23:40.930513Z",
     "network": "VPC_NETWORK_NAME"
   }
 ]
}

Console

Use these instructions to view a list of your index endpoints.

  1. In the Vertex AI section of the Google Cloud console, go to the Deploy and Use section. Select Vector Search

    Go to Vector Search

  2. On the top of the page, select the Index endpoint tab.
  3. All of the existing index endpoints are displayed.

For more information, see the reference documentation for IndexEndpoint.

Undeploy an index

To undeploy an index from endpoint, run the following code:

gcloud

The following example uses the gcloud ai index-endpoints undeploy-index command.

Before using any of the command data below, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai index-endpoints undeploy-index INDEX_ENDPOINT_ID \
    --deployed-index-id=DEPLOYED_INDEX_ID \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

gcloud ai index-endpoints undeploy-index INDEX_ENDPOINT_ID `
    --deployed-index-id=DEPLOYED_INDEX_ID `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

gcloud ai index-endpoints undeploy-index INDEX_ENDPOINT_ID ^
    --deployed-index-id=DEPLOYED_INDEX_ID ^
    --region=LOCATION ^
    --project=PROJECT_ID

REST

Before using any of the request data, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • DEPLOYED_INDEX_ID: A user specified string to uniquely identify the deployed index. It must start with a letter and contain only letters, numbers or underscores. See DeployedIndex.id for format guidelines.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.
  • PROJECT_NUMBER: Your project's automatically generated project number.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:undeployIndex

Request JSON body:

{
 "deployed_index_id": "DEPLOYED_INDEX_ID"
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
 "name": "projects/PROJECT_NUMBER/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
 "metadata": {
   "@type": "type.googleapis.com/google.cloud.aiplatform.v1.UndeployIndexOperationMetadata",
   "genericMetadata": {
     "createTime": "2022-01-13T04:09:56.641107Z",
     "updateTime": "2022-01-13T04:09:56.641107Z"
   }
 }
}

Console

Use these instructions to undeploy an index from an endpoint.

  1. In the Vertex AI section of the Google Cloud console, go to the Deploy and Use section. Select Vector Search

    Go to Vector Search

  2. A list of your active indexes is displayed.
  3. Select the index you want to undeploy. The index details page opens.
  4. Under the Deployed indexes section, identify the index version you want to undeploy.
  5. Click the options menu that is in the same row as the index and select Undeploy.
  6. A confirmation screen opens. Click Undeploy. Note: It can take up to 30 minutes to be undeployed.

Delete an IndexEndpoint

Before you delete an IndexEndpoint, you must undeploy the all indexes that are deployed to the endpoint.

gcloud

The following example uses the gcloud ai index-endpoints delete command.

Before using any of the command data below, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.

Execute the following command:

Linux, macOS, or Cloud Shell

gcloud ai index-endpoints delete INDEX_ENDPOINT_ID \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

gcloud ai index-endpoints delete INDEX_ENDPOINT_ID `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

gcloud ai index-endpoints delete INDEX_ENDPOINT_ID ^
    --region=LOCATION ^
    --project=PROJECT_ID

REST

Before using any of the request data, make the following replacements:

  • INDEX_ENDPOINT_ID: The ID of the index endpoint.
  • LOCATION: The region where you are using Vertex AI.
  • PROJECT_ID: Your Google Cloud project ID.
  • PROJECT_NUMBER: Your project's automatically generated project number.

HTTP method and URL:

DELETE https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
 "name": "projects/PROJECT_NUMBER/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID/operations/OPERATION_ID",
 "metadata": {
   "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeleteOperationMetadata",
   "genericMetadata": {
     "createTime": "2022-01-13T04:36:19.142203Z",
     "updateTime": "2022-01-13T04:36:19.142203Z"
   }
 },
 "done": true,
 "response": {
   "@type": "type.googleapis.com/google.protobuf.Empty"
 }
}

Console

Use these instructions to delete an index endpoint.

  1. In the Vertex AI section of the Google Cloud console, go to the Deploy and Use section. Select Vector Search

    Go to Vector Search

  2. On the top of the page, select the Index endpoint tab.
  3. All of the existing index endpoints are displayed.
  4. Click the options menu that is in the same row as the index you want to delete and select Delete.
  5. A confirmation screen opens. Click Delete. Your index endpoint is now deleted.