Deploy and manage index endpoints in a VPC network

Deploying an index to an endpoint includes the following three tasks:

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 within your VPC network

If you are deploying an Index to an existing IndexEndpoint, you can skip this step.

Before you use an index to serve online vector matching queries, you must deploy the Index to an IndexEndpoint within your VPC Network Peering network. The first step is to create an IndexEndpoint. You can deploy more than one index to an IndexEndpoint that shares the same VPC network.

gcloud

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

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

• INDEX_ENDPOINT_NAME: Display name of the index endpoint.
• VPC_NETWORK_NAME: The Google Compute Engine network name to which the index endpoint should be peered.
• 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

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

gcloud ai index-endpoints create \
    --display-name=INDEX_ENDPOINT_NAME \
    --network=VPC_NETWORK_NAME \
    --region=LOCATION \
    --project=PROJECT_ID

Windows (PowerShell)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

gcloud ai index-endpoints create `
    --display-name=INDEX_ENDPOINT_NAME `
    --network=VPC_NETWORK_NAME `
    --region=LOCATION `
    --project=PROJECT_ID

Windows (cmd.exe)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

gcloud ai index-endpoints create ^
    --display-name=INDEX_ENDPOINT_NAME ^
    --network=VPC_NETWORK_NAME ^
    --region=LOCATION ^
    --project=PROJECT_ID

You should receive a response similar to the following:

The Google Cloud CLI tool might take a few minutes to create the IndexEndpoint.

REST

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

• INDEX_ENDPOINT_NAME: Display name of the index endpoint.
• VPC_NETWORK_NAME: The Google Compute Engine network name to which the index endpoint should be peered.
• 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",
  "network": "VPC_NETWORK_NAME"
}

To send your request, expand one of these options:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints" | Select-Object -Expand Content

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.

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 Private.
8. Enter your peered VPC network details. Enter the full name of the Compute Engine network to which the job should be peered. The format should be projects/{project_num}/global/networks/{network_id}
9. Click Create.

Deploy an index

Important: Initial deployment to an endpoint typically takes between 20 and 30 minutes.

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

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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

You should receive a response similar to the following:

The Google Cloud CLI tool might take a few minutes to create the IndexEndpoint.

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:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex
"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex
" | Select-Object -Expand Content

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"
 }
}

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.

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

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex
"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:deployIndex
" | Select-Object -Expand Content

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.

Note: If your DeployedIndex uses fewer than two replicas per shard, then it is excluded from the Vertex AI Service Level Agreement. For your DeployedIndex to be covered by the SLA, you must set minReplicaCount to 2 or greater.

Mutate a DeployedIndex

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

• Users are not allowed to change the machineType after the index is deployed.
• If maxReplicaCount is not specified in the request, the DeployedIndex will keep 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

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:mutateDeployedIndex"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:mutateDeployedIndex" | Select-Object -Expand Content

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"
}
}

Note: If your DeployedIndex uses fewer than two replicas per shard, then it is excluded from the Vertex AI Service Level Agreement. For your DeployIndex to be covered by the SLA, you must set minReplicaCount to 2 or greater.

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

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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

Windows (PowerShell)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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

Windows (cmd.exe)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Execute the following command:

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints" | Select-Object -Expand Content

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, 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

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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

Windows (PowerShell)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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

Windows (cmd.exe)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:undeployIndex"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID:undeployIndex" | Select-Object -Expand Content

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.

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 endpoint you want to undeploy.
5. Click the options menu that is in the same row as the index endpoint and select Undeploy.
6. A confirmation screen opens. Click Undeploy. Note: It can take up to 30 minutes to be undeployed.

Note: It takes 10 to 20 minutes for Google backend jobs to clean up the deployment. You can't reuse the DeployedIndex ID until after the deployment is cleaned up.

Delete an IndexEndpoint

Before you delete an IndexEndpoint, you must undeploy all indexes deploy 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

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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

Windows (PowerShell)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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

Windows (cmd.exe)

Note: Ensure you have initialized the Google Cloud CLI with authentication and a project by running either gcloud init; or gcloud auth login and gcloud config set project.

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:

curl (Linux, macOS, or Cloud Shell)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Execute the following command:

curl -X DELETE \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID"

PowerShell (Windows)

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/indexEndpoints/INDEX_ENDPOINT_ID" | Select-Object -Expand Content

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