Set up Vector Search with Private Service Connect

Private Service Connect allows private consumption of services across VPC networks that belong to different groups, teams, projects, or organizations. You can publish and consume services using IP addresses that you define and that are internal to your VPC network, and for Vector Search endpoints to perform vector similarity searches.

Enabling Private Service Connect on a Vector Search endpoint is suited for use cases that:

1. Require low latency and a secure connection to Vector Search serving backends.
2. Have limited IP space for exclusive VPC peering reservation.
3. Need to access the serving backends from multiple user VPC networks.

To learn more about setting up Private Service Connect, see the Private Service Connect Overview from the Virtual Private Cloud (VPC) documentation.

Create the index endpoint

To create an endpoint with Private Service Connect enabled, you must define it when you create the endpoint. This is similar to creating other endpoints in Vertex AI.

REST

PROJECT: The service project where you are creating Vertex AI resources.

VPC_PROJECT: The project where your client VPC lives. For standalone VPC setup, this is the same as $PROJECT. For Shared VPC setup, this is be the VPC host project.

PROJECT=<your-service-project>
VPC_PROJECT=<your-vpc-project>
REGION=us-central1
VERTEX_ENDPOINT=$REGION-aiplatform.googleapis.com
curl -H "Content-Type: application/json" \
  -H "Authorization: Bearer `gcloud auth print-access-token`" \
  https://$VERTEX_ENDPOINT/v1/projects/$PROJECT/locations/$REGION/indexEndpoints \
  -d '{displayName: "<your-index-endpoint-name>", privateServiceConnectConfig:
  { enablePrivateServiceConnect: true, projectAllowlist: ["'$VPC_PROJECT'", "'$PROJECT'"] }}'

Terraform

The following sample uses the vertex_ai_index_endpoint Terraform resource to create an index endpoint.

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands.

resource "google_vertex_ai_index_endpoint" "default" {
  display_name = "sample-endpoint"
  description  = "A sample index endpoint with Private Service Connect enabled"
  region       = "us-central1"
  private_service_connect_config {
    enable_private_service_connect = true
    project_allowlist = [
      data.google_project.project.project_id,
    ]
  }
}

data "google_project" "project" {}

# Cloud Storage bucket name must be unique
resource "random_id" "default" {
  byte_length = 8
}

# Create a Cloud Storage bucket
resource "google_storage_bucket" "bucket" {
  name                        = "vertex-ai-index-bucket-${random_id.default.hex}"
  location                    = "us-central1"
  uniform_bucket_level_access = true
}

# Create index content
resource "google_storage_bucket_object" "data" {
  name    = "contents/data.json"
  bucket  = google_storage_bucket.bucket.name
  content = <<EOF
{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
EOF
}

resource "google_vertex_ai_index" "default" {
  region       = "us-central1"
  display_name = "sample-index-batch-update"
  description  = "A sample index for batch update"
  labels = {
    foo = "bar"
  }

  metadata {
    contents_delta_uri = "gs://${google_storage_bucket.bucket.name}/contents"
    config {
      dimensions                  = 2
      approximate_neighbors_count = 150
      distance_measure_type       = "DOT_PRODUCT_DISTANCE"
      algorithm_config {
        tree_ah_config {
          leaf_node_embedding_count    = 500
          leaf_nodes_to_search_percent = 7
        }
      }
    }
  }
  index_update_method = "BATCH_UPDATE"

  timeouts {
    create = "2h"
    update = "1h"
  }
}

Python

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

def vector_search_create_index_endpoint_private_service_connect(
    project: str, location: str, display_name: str, project_allowlist: list[str]
) -> aiplatform.MatchingEngineIndexEndpoint:
    """Create a vector search index endpoint with Private Service Connect enabled.

    Args:
        project (str): Required. Project ID
        location (str): Required. The region name
        display_name (str): Required. The index endpoint display name
        project_allowlist (list[str]): Required. A list of projects from which
            the forwarding rule will be able to target the service attachment.

    Returns:
        aiplatform.MatchingEngineIndexEndpoint - The created index endpoint.
    """
    # Initialize the Vertex AI client
    aiplatform.init(project=project, location=location)

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

    return index_endpoint

About index deployment options

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 at least 2 or greater, and must be adequately provisioned for workload size. To be adequately provisioned we recommend adding additional replicas if CPU/Memory usage consistency operates 60%.

You can deploy your index with automatic or manual service connectivity.

• Deploy with Private Service Connect automation: Set up a service connection policy and deploy your indexes. Setting up a service connection policy lets you deploy to a specific network without creating a compute address and forwarding rule each time.
• Deploy with manual connection: Deploy your index and manually create a compute address and forwarding rule. You might choose this option if you need to use multiple IP addresses.

Deploy with Private Service Connect automation

You can set up a service connection policy so that you don't have to manually create a compute address and forwarding rule after each index deployment.

1. First, create a service connection policy that specifies the network, service class, and region to deploy indexes to. This is a one-time setup. If you've already done this, skip to the next procedure.
2. Deploy the index.

Limitations

Automation allows only one IP address per project per network. If you need to use multiple IP addresses, see Manually deploy the index.

Create a service connection policy

You must be a network administrator to create a service connection policy for automating index deployment.

To automate index deployment, follow these steps:

1. Create your service connection policy.

   • PROJECT: The service project where you are creating Vertex AI resources.
   • VPC_PROJECT: The project where your client VPC lives. For single VPC setup, this will be the same as $PROJECT. For Shared VPC setup, this is the VPC host project.
   • NETWORK_NAME: The name of the network to deploy to, in the format projects//global/networks/.
   • REGION: The network region.
   • PSC_SUBNETS: The Private Service Connect subnet to use.

   gcloud network-connectivity service-connection-policies create <policy_name> \
   --project=<vpc_project> --network=<network_name> # in the format projects/<project_id>/global/networks/<network_name> \
   --service-class=gcp-vertexai --region=<region> --subnets=<psc subnets>
   

2. View your service connection policy.

   gcloud network-connectivity service-connection-policies list --project=<vpc_project> -–region=<region>
   

Deploy the index

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.
• NETWORK_1: The Compute Engine network name this index endpoint will be deployed to.
• NETWORK_2: (Optional) An additional Compute Engine network name to deploy this endpoint to.

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 \
    --psc-automation-configs=project-id=PROJECT_ID,network=NETWORK_1 \
    --psc-automation-configs=project-id=PROJECT_ID,network=NETWORK_2

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 `
    --psc-automation-configs=project-id=PROJECT_ID,network=NETWORK_1 `
    --psc-automation-configs=project-id=PROJECT_ID,network=NETWORK_2

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 ^
    --psc-automation-configs=project-id=PROJECT_ID,network=NETWORK_1 ^
    --psc-automation-configs=project-id=PROJECT_ID,network=NETWORK_2

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.
• NETWORK_1: The Compute Engine network name this index endpoint will be deployed to.
• NETWORK_2: (Optional) An additional Compute Engine network name to deploy this endpoint to.
• 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"
   "psc_automation_configs": [
          {"project_id": "PROJECT_ID", "network": "NETWORK_1"},
          {"project_id": "PROJECT_ID", "network": "NETWORK_2" }]
 }
}

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"
 }
}

Python

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

def vector_search_deploy_psc_automation_index(
    project: str,
    location: str,
    index_name: str,
    index_endpoint_name: str,
    deployed_index_id: str,
    psc_automation_configs: Sequence[Tuple[str, str]],
) -> None:
    """Deploy a vector search index to an index endpoint using PSC automation.

    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.
        psc_automation_config (Sequence[Tuple[str, str]]): Required. A list of
          (project_id, network) pairs where PSC endpoints will be setup for the
          deployed index. Example:
          [("123", "{projects/123/global/networks/my-network1"),
          ("123", "{projects/123/global/networks/my-network2")]
    """
    # 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 with PSC automation enabled.
    index_endpoint.deploy_index(
        index=index,
        deployed_index_id=deployed_index_id,
        psc_automation_configs=psc_automation_configs,
    )

Delete service connection policy

If you need to delete the service connection policy, run the following command:

gcloud network-connectivity service-connection-policies delete --project=<vpc_project> –-region=<region> <policy_name>

Deploy with manual connection

Deploy the index and create a forwarding rule in your VPC project.

Deploy the index

Now that the index is ready, in this step, you deploy the index to the endpoint you created with Private Service Connect enabled.

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

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"
 }
}

Terraform

The following sample uses the vertex_ai_index_endpoint_deployed_index Terraform resource to create a deployed index endpoint.

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands.

provider "google" {
  region = "us-central1"
}

resource "google_vertex_ai_index_endpoint_deployed_index" "default" {
  depends_on        = [google_vertex_ai_index_endpoint.default]
  index_endpoint    = google_vertex_ai_index_endpoint.default.id
  index             = google_vertex_ai_index.default.id
  deployed_index_id = "deployed_index_for_psc"
}

resource "google_vertex_ai_index_endpoint" "default" {
  display_name = "sample-endpoint"
  description  = "A sample index endpoint with Private Service Connect enabled"
  region       = "us-central1"
  private_service_connect_config {
    enable_private_service_connect = true
    project_allowlist = [
      data.google_project.project.project_id,
    ]
  }
}

data "google_project" "project" {}

# Cloud Storage bucket name must be unique
resource "random_id" "default" {
  byte_length = 8
}

# Create a Cloud Storage bucket
resource "google_storage_bucket" "bucket" {
  name                        = "vertex-ai-index-bucket-${random_id.default.hex}"
  location                    = "us-central1"
  uniform_bucket_level_access = true
}

# Create index content
resource "google_storage_bucket_object" "data" {
  name    = "contents/data.json"
  bucket  = google_storage_bucket.bucket.name
  content = <<EOF
{"id": "42", "embedding": [0.5, 1.0], "restricts": [{"namespace": "class", "allow": ["cat", "pet"]},{"namespace": "category", "allow": ["feline"]}]}
{"id": "43", "embedding": [0.6, 1.0], "restricts": [{"namespace": "class", "allow": ["dog", "pet"]},{"namespace": "category", "allow": ["canine"]}]}
EOF
}

resource "google_vertex_ai_index" "default" {
  region       = "us-central1"
  display_name = "sample-index-batch-update"
  description  = "A sample index for batch update"
  labels = {
    foo = "bar"
  }

  metadata {
    contents_delta_uri = "gs://${google_storage_bucket.bucket.name}/contents"
    config {
      dimensions                  = 2
      approximate_neighbors_count = 150
      distance_measure_type       = "DOT_PRODUCT_DISTANCE"
      algorithm_config {
        tree_ah_config {
          leaf_node_embedding_count    = 500
          leaf_nodes_to_search_percent = 7
        }
      }
    }
  }
  index_update_method = "BATCH_UPDATE"

  timeouts {
    create = "2h"
    update = "1h"
  }
}

Python

To learn how to install or update the Vertex AI SDK for 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.

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.

Create a forwarding rule in the VPC project

After index deployment is done, the index endpoint returns a service attachment URI instead of an IP address. You need to create a compute address, as well as a forwarding rule in the VPC project targeting the service attachment using the created compute address. To create a compute address, use the following example:

gcloud compute addresses create ${ADDRESS_NAME:?} \
    --region=${REGION:?} \
    --subnet=${SUBNET_NAME:?} \
    --project=${VPC_PROJECT:?}

To create a forwarding rule targeting the service attachment URI using the created compute address, use the following example:

SERVICE_ATTACHMENT_URI=`gcloud ai index-endpoints describe {INDEX_ENDPOINT_ID}
--format="value(deployedIndexes.privateEndpoints.serviceAttachment)"`

gcloud compute forwarding-rules create ${ENDPOINT_NAME:?} \
    --network=${NETWORK_NAME:?} \
    --address=${ADDRESS_NAME:?} \
    --target-service-attachment=${SERVICE_ATTACHMENT_URI:?} \
    --project=${VPC_PROJECT:?} \
    --region=${REGION:?}

(Optional) Create DNS record for the IP address

If you want to connect and load without memorizing the actual IP address, you can create a DNS record. This step is optional.

DNS_NAME_SUFFIX=matchingengine.vertexai.goog. # Don't forget the "." in the end.
DNS_NAME=${INDEX_ENDPOINT_ID:?}.${REGION:?}.${DNS_NAME_SUFFIX:?}

gcloud dns managed-zones create ${DNS_ZONE_NAME:?} \
    --dns-name=${DNS_NAME_SUFFIX:?} \
    --visibility=private \
    --project=${VPC_PROJECT:?} \
    --region=${REGION:?}

gcloud dns record-sets create ${DNS_NAME:?} \
    --rrdatas=${IP_ADDRESS:?} \
    --type=A --ttl=60 \
    --zone=${DNS_ZONE_NAME:?} \
    --project=${VPC_PROJECT:?} \
    --region=${REGION:?}

Send queries to the index endpoint

Now that you've created an endpoint with Private Service Connect and created the index, you can begin running queries.

To query your index, see Query indexes to get nearest neighbors.

What's next

• Learn how to Update and rebuild your index
• Learn how to Monitor the index endpoint