Use Private Service Connect to access a Vector Search index from on-premises

On-premises hosts can reach a Vector Search index endpoint either through the public internet or privately through a hybrid networking architecture that uses Private Service Connect over Cloud VPN or Cloud Interconnect. Both options offer SSL/TLS encryption. However, the private option offers much better performance and is therefore recommended for critical applications.

In this tutorial, you use High-Availability VPN (HA VPN) to access a Vector Search index endpoint privately, between two Virtual Private Cloud (VPC) networks that can serve as a basis for multi-cloud and on-premises private connectivity.

This tutorial is intended for enterprise network administrators, data scientists, and researchers who are familiar with Vertex AI, Virtual Private Cloud, the Google Cloud console, and the Cloud Shell. Familiarity with Vector Search is helpful but not required.

Objectives

Create two VPC networks, as shown in the preceding diagram:
- One (onprem-vpc) represents an on-premises network.
- The other (vertex-networking-vpc) is for the Vector Search index endpoint.
Deploy HA VPN gateways, Cloud VPN tunnels, and Cloud Routers to connect vertex-networking-vpc and onprem-vpc.
Build and deploy a Vector Search index.
Create a Private Service Connect forwarding rule to forward queries to the Vector Search index endpoint.
Configure a Cloud Router custom advertised route in vertex-networking-vpc to announce routes for the index endpoint to onprem-vpc.
Create a Compute Engine VM instance in onprem-vpc to represent a client application that sends requests to the Vector Search index endpoint over HA VPN.

Costs

In this document, you use the following billable components of Google Cloud:

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish the tasks that are described in this document, you can avoid continued billing by deleting the resources that you created. For more information, see Clean up.

Before you begin

In the Google Cloud console, go to the project selector page.

Go to project selector
Select or create a Google Cloud project.
Make sure that billing is enabled for your Google Cloud project.
Open Cloud Shell to execute the commands listed in this tutorial. Cloud Shell is an interactive shell environment for Google Cloud that lets you manage your projects and resources from your web browser.
In the Cloud Shell, set the current project to your Google Cloud project ID and store the same project ID into the projectid shell variable:
```
  projectid="PROJECT_ID"
  gcloud config set project ${projectid}
```
Replace PROJECT_ID with your project ID. If necessary, you can locate your project ID in the Google Cloud console. For more information, see Find your project ID.
If you're not the project owner, ask the project owner to grant you the Project IAM Admin (roles/resourcemanager.projectIamAdmin) role. You must have this role to grant IAM roles in the next step.
Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/aiplatform.user, roles/compute.instanceAdmin.v1, roles/compute.networkAdmin, roles/compute.securityAdmin, roles/dns.admin, roles/iam.serviceAccountAdmin, roles/iam.serviceAccountUser, roles/iap.admin, roles/iap.tunnelResourceAccessor, roles/notebooks.admin, roles/servicemanagement.quotaAdmin, roles/servicedirectory.editor, roles/storage.admin, roles/aiplatform.admin, roles/aiplatform.user, roles/resourcemanager.projectIamAdmin
```
gcloud projects add-iam-policy-binding PROJECT_ID --member="user:USER_IDENTIFIER" --role=ROLE
```
- Replace PROJECT_ID with your project ID.
- Replace USER_IDENTIFIER with the identifier for your user account. For example, user:myemail@example.com.
- Replace ROLE with each individual role.

Enable the DNS, IAM, Compute Engine, Notebooks, and Vertex AI APIs:

gcloud services enable dns.googleapis.com iam.googleapis.com compute.googleapis.com notebooks.googleapis.com aiplatform.googleapis.com

Create the VPC networks

In this section you create two VPC networks: one for creating a Vector Search index and deploying it to an endpoint, the other for private access to that endpoint.

Create the VPC network for the Vector Search index endpoint (`vertex-networking-vpc`)

Create the VPC network for the index endpoint:

gcloud compute networks create vertex-networking-vpc --project=$projectid --subnet-mode custom

Create a subnet named workbench-subnet, with a primary IPv4 range of 172.16.20.0/28:

gcloud compute networks subnets create workbench-subnet \
  --project=$projectid --range=172.16.20.0/28 \
  --network=vertex-networking-vpc \
  --region=us-central1 \
  --enable-private-ip-google-access

Create a subnet named psc-forwarding-rule-subnet, with a primary IPv4 range of 172.16.30.0/28:

gcloud compute networks subnets create psc-forwarding-rule-subnet \
  --project=$projectid \
  --range=172.16.30.0/28 \
  --network=vertex-networking-vpc \
  --region=us-central1 \
  --enable-private-ip-google-access

Create the VPC network for private access to the endpoint (`onprem-vpc`)

Create the VPC network to simulate the on-premises network (onprem-vpc):

gcloud compute networks create onprem-vpc \
  --subnet-mode custom

In the onprem-vpc network, create a subnet named onprem-vpc-subnet1, with a primary IPv4 range of 172.16.10.0/29:

gcloud compute networks subnets create onprem-vpc-subnet1 \
  --network onprem-vpc \
  --range 172.16.10.0/29 \
  --region us-central1

Verify that the VPC networks are correctly configured

In the Google Cloud console, go to the Networks in current project tab in the VPC networks page.

Go to VPC networks
In the list of VPC networks, verify that the two networks have been created: vertex-networking-vpc and onprem-vpc.
Click the Subnets in current project tab.
In the list of VPC subnets, verify that the workbench-subnet, psc-forwarding-rule-subnet, and onprem-vpc-subnet1 subnets have been created.

Create the `on-prem-client` VM instance

In this section you create a VM instance to represent a client application that sends requests to the Vector Search index endpoint over HA VPN.

In the Cloud Shell, create the on-prem-client VM instance:

gcloud compute instances create on-prem-client \
  --zone=us-central1-a \
  --image-family=debian-11 \
  --image-project=debian-cloud \
  --subnet=onprem-vpc-subnet1 \
  --scopes=https://www.googleapis.com/auth/cloud-platform \
  --no-address \
  --shielded-secure-boot \
  --metadata startup-script="#! /bin/bash
    sudo apt-get update
    sudo apt-get install tcpdump dnsutils -y"

Configure hybrid connectivity

In this section you create two HA VPN gateways that are connected to each other. One resides in the vertex-networking-vpc VPC network. The other resides in the onprem-vpc VPC network. Each gateway contains a Cloud Router and a pair of VPN tunnels.

Create the HA VPN gateways

In the Cloud Shell, create the HA VPN gateway for the vertex-networking-vpc VPC network:

gcloud compute vpn-gateways create vertex-networking-vpn-gw1 \
   --network vertex-networking-vpc \
   --region us-central1

Create the HA VPN gateway for the onprem-vpc VPC network:

gcloud compute vpn-gateways create onprem-vpn-gw1 \
   --network onprem-vpc \
   --region us-central1

In the Google Cloud console, go to the Cloud VPN Gateways tab in the VPN page.

Go to VPN
Verify that the two gateways (vertex-networking-vpn-gw1 and onprem-vpn-gw1) have been created and that each one has two interface IP addresses.

Create Cloud Routers and Cloud NAT gateways

In each of the two VPC networks, you create two Cloud Routers: one general and one regional. In each of the regional Cloud Routers, you create a Cloud NAT gateway. Cloud NAT gateways provide outgoing connectivity for Compute Engine virtual machine (VM) instances that don't have external IP addresses.

In the Cloud Shell, create a Cloud Router for the vertex-networking-vpc VPC network:

gcloud compute routers create vertex-networking-vpc-router1 \
   --region us-central1\
   --network vertex-networking-vpc \
   --asn 65001

Create a Cloud Router for the onprem-vpc VPC network:

gcloud compute routers create onprem-vpc-router1 \
   --region us-central1\
   --network onprem-vpc\
   --asn 65002

Create a regional Cloud Router for the vertex-networking-vpc VPC network:

gcloud compute routers create cloud-router-us-central1-vertex-nat \
  --network vertex-networking-vpc \
  --region us-central1

Configure a Cloud NAT gateway on the regional Cloud Router:

gcloud compute routers nats create cloud-nat-us-central1 \
  --router=cloud-router-us-central1-vertex-nat \
  --auto-allocate-nat-external-ips \
  --nat-all-subnet-ip-ranges \
  --region us-central1

Create a regional Cloud Router for the onprem-vpc VPC network:

gcloud compute routers create cloud-router-us-central1-onprem-nat \
  --network onprem-vpc \
  --region us-central1

Configure a Cloud NAT gateway on the regional Cloud Router:

gcloud compute routers nats create cloud-nat-us-central1-on-prem \
  --router=cloud-router-us-central1-onprem-nat \
  --auto-allocate-nat-external-ips \
  --nat-all-subnet-ip-ranges \
  --region us-central1

In the Google Cloud console, go to the Cloud Routers page.

Go to Cloud Routers
In the Cloud Routers list, verify that the following routers have been created:
- cloud-router-us-central1-onprem-nat
- cloud-router-us-central1-vertex-nat
- onprem-vpc-router1
- vertex-networking-vpc-router1
You may need to refresh the Google Cloud console browser tab to see the new values.
In the Cloud Routers list, click cloud-router-us-central1-vertex-nat.
In the Router details page, verify that the cloud-nat-us-central1 Cloud NAT gateway has been created.
Click the back arrow to return to the Cloud Routers page.
In the router list, click cloud-router-us-central1-onprem-nat.
In the Router details page, verify that the cloud-nat-us-central1-on-prem Cloud NAT gateway has been created.

Create VPN tunnels

In the Cloud Shell, in the vertex-networking-vpc network, create a VPN tunnel called vertex-networking-vpc-tunnel0:

gcloud compute vpn-tunnels create vertex-networking-vpc-tunnel0 \
  --peer-gcp-gateway onprem-vpn-gw1 \
  --region us-central1 \
  --ike-version 2 \
  --shared-secret [ZzTLxKL8fmRykwNDfCvEFIjmlYLhMucH] \
  --router vertex-networking-vpc-router1 \
  --vpn-gateway vertex-networking-vpn-gw1 \
  --interface 0

In the vertex-networking-vpc network, create a VPN tunnel called vertex-networking-vpc-tunnel1:

gcloud compute vpn-tunnels create vertex-networking-vpc-tunnel1 \
  --peer-gcp-gateway onprem-vpn-gw1 \
  --region us-central1 \
  --ike-version 2 \
  --shared-secret [bcyPaboPl8fSkXRmvONGJzWTrc6tRqY5] \
  --router vertex-networking-vpc-router1 \
  --vpn-gateway vertex-networking-vpn-gw1 \
  --interface 1

In the onprem-vpc network, create a VPN tunnel called onprem-vpc-tunnel0:

gcloud compute vpn-tunnels create onprem-vpc-tunnel0 \
  --peer-gcp-gateway vertex-networking-vpn-gw1 \
  --region us-central1\
  --ike-version 2 \
  --shared-secret [ZzTLxKL8fmRykwNDfCvEFIjmlYLhMucH] \
  --router onprem-vpc-router1 \
  --vpn-gateway onprem-vpn-gw1 \
  --interface 0

In the onprem-vpc network, create a VPN tunnel called onprem-vpc-tunnel1:

gcloud compute vpn-tunnels create onprem-vpc-tunnel1 \
  --peer-gcp-gateway vertex-networking-vpn-gw1 \
  --region us-central1\
  --ike-version 2 \
  --shared-secret [bcyPaboPl8fSkXRmvONGJzWTrc6tRqY5] \
  --router onprem-vpc-router1 \
  --vpn-gateway onprem-vpn-gw1 \
  --interface 1

In the Google Cloud console, go to the VPN page.

Go to VPN
In the list of VPN tunnels, verify that the four VPN tunnels have been created.

Establish BGP sessions

Cloud Router uses Border Gateway Protocol (BGP) to exchange routes between your VPC network (in this case, vertex-networking-vpc) and your on-premises network (represented by onprem-vpc). On Cloud Router, you configure an interface and a BGP peer for your on-premises router. The interface and BGP peer configuration together form a BGP session. In this section you create two BGP sessions for vertex-networking-vpc and two for onprem-vpc.

Once you've configured the interfaces and BGP peers between your routers, they will automatically start exchanging routes.

Establish BGP sessions for `vertex-networking-vpc`

In the Cloud Shell, in the vertex-networking-vpc network, create a BGP interface for vertex-networking-vpc-tunnel0:

gcloud compute routers add-interface vertex-networking-vpc-router1 \
  --interface-name if-tunnel0-to-onprem \
  --ip-address 169.254.0.1 \
  --mask-length 30 \
  --vpn-tunnel vertex-networking-vpc-tunnel0 \
  --region us-central1

In the vertex-networking-vpc network, create a BGP peer for bgp-onprem-tunnel0:

gcloud compute routers add-bgp-peer vertex-networking-vpc-router1 \
  --peer-name bgp-onprem-tunnel0 \
  --interface if-tunnel0-to-onprem \
  --peer-ip-address 169.254.0.2 \
  --peer-asn 65002 \
  --region us-central1

In the vertex-networking-vpc network, create a BGP interface for vertex-networking-vpc-tunnel1:

gcloud compute routers add-interface vertex-networking-vpc-router1 \
  --interface-name if-tunnel1-to-onprem \
  --ip-address 169.254.1.1 \
  --mask-length 30 \
  --vpn-tunnel vertex-networking-vpc-tunnel1 \
  --region us-central1

In the vertex-networking-vpc network, create a BGP peer for bgp-onprem-tunnel1:

gcloud compute routers add-bgp-peer vertex-networking-vpc-router1 \
  --peer-name bgp-onprem-tunnel1 \
  --interface if-tunnel1-to-onprem \
  --peer-ip-address 169.254.1.2 \
  --peer-asn 65002 \
  --region us-central1

Establish BGP sessions for `onprem-vpc`

In the onprem-vpc network, create a BGP interface for onprem-vpc-tunnel0:

gcloud compute routers add-interface onprem-vpc-router1 \
  --interface-name if-tunnel0-to-vertex-networking-vpc \
  --ip-address 169.254.0.2 \
  --mask-length 30 \
  --vpn-tunnel onprem-vpc-tunnel0 \
  --region us-central1

In the onprem-vpc network, create a BGP peer for bgp-vertex-networking-vpc-tunnel0:

gcloud compute routers add-bgp-peer onprem-vpc-router1 \
  --peer-name bgp-vertex-networking-vpc-tunnel0 \
  --interface if-tunnel0-to-vertex-networking-vpc \
  --peer-ip-address 169.254.0.1 \
  --peer-asn 65001 \
  --region us-central1

In the onprem-vpc network, create a BGP interface for onprem-vpc-tunnel1:

gcloud compute routers add-interface   onprem-vpc-router1  \
  --interface-name if-tunnel1-to-vertex-networking-vpc \
  --ip-address 169.254.1.2 \
  --mask-length 30 \
  --vpn-tunnel onprem-vpc-tunnel1 \
  --region us-central1

In the onprem-vpc network, create a BGP peer for bgp-vertex-networking-vpc-tunnel1:

gcloud compute routers add-bgp-peer onprem-vpc-router1 \
  --peer-name bgp-vertex-networking-vpc-tunnel1 \
  --interface if-tunnel1-to-vertex-networking-vpc \
  --peer-ip-address 169.254.1.1 \
  --peer-asn 65001 \
  --region us-central1

Validate BGP session creation

In the Google Cloud console, go to the VPN page.

Go to VPN
In the list of VPN tunnels, verify that the value in the BGP session status column for each of the tunnels has changed from Configure BGP session to BGP established. You may need to refresh the Google Cloud console browser tab to see the new values.

Validate the `vertex-networking-vpc` learned routes

In the Google Cloud console, go to the VPC networks page.

Go to VPC networks
In the list of VPC networks, click vertex-networking-vpc.
Click the Routes tab.
Select us-central1 (Iowa) in the Region list and click View.
In the Destination IP range column, verify that the onprem-vpc-subnet1 subnet's IP range (172.16.10.0/29) appears twice.

Validate the `on-prem-vpc` learned routes

Click the back arrow to return to the VPC networks page.
In the list of VPC networks, click on-prem-vpc.
Click the Routes tab.
Select us-central1 (Iowa) in the Region list and click View.
In the Destination IP range column, verify that the workbench-subnet subnet's IP range (172.16.20.0/28) and the psc-forwarding-rule-subnet subnet's IP range (172.16.30.0/28) each appear twice.

Create a Vertex AI Workbench instance

In this section you create a user-managed service account, and then you create a Vertex AI Workbench instance that uses your service account for accessing Google Cloud services and APIs.

Create a service account

In this tutorial, you create a user-managed service account following Compute Engine and IAM best practices.

In the Cloud Shell, create a service account named workbench-sa:

gcloud iam service-accounts create workbench-sa \
   --display-name="workbench-sa"

Assign the Vertex AI User (roles/aiplatform.user) IAM role to the service account:

gcloud projects add-iam-policy-binding $projectid \
  --member="serviceAccount:workbench-sa@$projectid.iam.gserviceaccount.com" \
  --role="roles/aiplatform.user"

Assign the Storage Admin (roles/storage.admin) IAM role to the service account:

gcloud projects add-iam-policy-binding $projectid \
  --member="serviceAccount:workbench-sa@$projectid.iam.gserviceaccount.com" \
  --role="roles/storage.admin"

Assign the Service Usage Admin (roles/serviceusage.serviceUsageAdmin) IAM role to the service account:

gcloud projects add-iam-policy-binding $projectid \
  --member="serviceAccount:workbench-sa@$projectid.iam.gserviceaccount.com" \
  --role="roles/serviceusage.serviceUsageAdmin"

Create the Vertex AI Workbench instance

Create a Vertex AI Workbench instance, specifying the workbench-sa service account:

gcloud workbench instances create workbench-tutorial \
  --vm-image-project=deeplearning-platform-release \
  --vm-image-family=common-cpu-notebooks \
  --machine-type=n1-standard-4 \
  --location=us-central1-a \
  --subnet-region=us-central1 \
  --shielded-secure-boot=SHIELDED_SECURE_BOOT \
  --subnet=workbench-subnet \
  --disable-public-ip \
  --service-account-email=workbench-sa@$projectid.iam.gserviceaccount.com

Create and deploy a Vector Search index

Prepare your environment

In the Google Cloud console, go to the Instances tab in the Vertex AI Workbench page.

Go to Vertex AI Workbench
Next to your Vertex AI Workbench instance's name (workbench-tutorial), click Open JupyterLab.

Your Vertex AI Workbench instance opens JupyterLab.
Select File > New > Notebook.
From the Select Kernel menu, select Python 3 (Local) and click Select.
When your new notebook opens, there is a default code cell where you can enter code. It looks like [ ]: followed by a text field. The text field is where you paste your code.

To install the Vertex AI SDK for Python, paste the following code into the cell and click Run the selected cells and advance:
```
!pip install --upgrade --user google-cloud-aiplatform google-cloud-storage
```
In this step and each of the following ones, add a new code cell (if necessary) by clicking Insert a cell below, paste the code into the cell, and then click Run the selected cells and advance.

To use the newly installed packages in this Jupyter runtime, you need to restart the runtime:
```
# Restart kernel after installs so that your environment can access the new packages
import IPython

app = IPython.Application.instance()
app.kernel.do_shutdown(True)
```

Set the following environment variables, replacing PROJECT_ID with your project ID.

# set project ID and location
PROJECT_ID = "PROJECT_ID"
LOCATION = "us-central1"

# generate a unique id for this session
from datetime import datetime
UID = datetime.now().strftime("%m%d%H%M")

Enable APIs

In your Jupyterlab notebook, run the following command to enable APIs for Compute Engine, Vertex AI, and Cloud Storage in the notebook:

! gcloud services enable compute.googleapis.com aiplatform.googleapis.com storage.googleapis.com \
  --project {PROJECT_ID}

Prepare the sample data in a Cloud Storage bucket

In this tutorial, we use the same TheLook dataset that's used in the Vector Search quickstart. See the quickstart documentation page for more information about this dataset.

In this section you create a Cloud Storage bucket and place the dataset's embedding file in it. In a later step, you use this file to build an index.

In your Jupyterlab notebook, create a Cloud Storage bucket:

BUCKET_URI = f"gs://{PROJECT_ID}-vs-quickstart-{UID}"
! gcloud storage buckets create $BUCKET_URI --location=$LOCATION --project=$PROJECT_ID

Copy the example file to your Cloud Storage bucket.

! gcloud storage cp "gs://github-repo/data/vs-quickstart/product-embs.json" $BUCKET_URI

To use Vector Search to run queries, you also need to copy the embedding file to a local directory:

! gcloud storage cp "gs://github-repo/data/vs-quickstart/product-embs.json" . # for query tests

Create the Vector Search index

In your Jupyterlab notebook, load the embeddings to Vector Search:

# init the aiplatform package
from google.cloud import aiplatform
aiplatform.init(project=PROJECT_ID, location=LOCATION)

Create a MatchingEngineIndex with its create_tree_ah_index function (Matching Engine is the previous name of Vector Search):
```
# create Index
my_index = aiplatform.MatchingEngineIndex.create_tree_ah_index(
  display_name = f"vs-quickstart-index-{UID}",
  contents_delta_uri = BUCKET_URI,
  dimensions = 768,
  approximate_neighbors_count = 10,
)
```
The MatchingEngineIndex.create_tree_ah_index() method builds an index. In this tutorial, this task takes about 5 to 10 minutes.
In the Google Cloud console, go to the Indexes tab in the Vector Search page.

Go to Indexes
Verify that there is an index whose name begins with "vs-quickstart-index-" and contains the correct timestamp.
Make a note of the index ID. You'll need this ID when you deploy the index in a later step.

Create the index endpoint

In the Cloud Shell, run the following commands, replacing PROJECT_ID with your project ID:

projectid=PROJECT_ID
gcloud config set project ${projectid}
SERVICE_PROJECT=${projectid}
REGION=us-central1
VERTEX_ENDPOINT=$REGION-aiplatform.googleapis.com
DISPLAY_NAME=vector-search

Create the index endpoint:

curl -H "Content-Type: application/json" \
  -H "Authorization: Bearer `gcloud auth print-access-token`" \
  https://$VERTEX_ENDPOINT/v1/projects/$SERVICE_PROJECT/locations/$REGION/indexEndpoints \
  -d '{displayName: "'$DISPLAY_NAME'", privateServiceConnectConfig: { enablePrivateServiceConnect: true, projectAllowlist: ["'$SERVICE_PROJECT'"] }}'

Verify that the index endpoint was created:

gcloud ai index-endpoints list --region=us-central1

The output is similar to the following example, in which the index endpoint ID is 8151506529447575552:

Using endpoint [https://us-central1-aiplatform.googleapis.com/]
---
createTime: '2023-10-10T23:55:20.526145Z'
displayName: vector-search
encryptionSpec: {}
etag: AMEw9yN2qytNiwT73uwYpz_7N_b2-O8D1AuNoDb5QjFmkU4ye5Gzk2oQlMZBR1XeoQ11
name: projects/725264228516/locations/us-central1/indexEndpoints/8151506529447575552
privateServiceConnectConfig:
  enablePrivateServiceConnect: true
  projectAllowlist:
  - vertex-genai-400103
  - vertex-genai-400103
updateTime: '2023-10-10T23:55:21.951394Z'

Make a note of your index endpoint ID. You'll need this ID when you deploy your index in a later step.

Deploy the index to the endpoint

In the Cloud Shell, run the following command to deploy the index to the endpoint:

gcloud ai index-endpoints deploy-index INDEX_ENDPOINT_ID \
  --deployed-index-id=vector_one \
  --display-name=vector-search \
  --index=INDEX \
  --project=$projectid \
  --region=us-central1

Replace the following values:

INDEX_ENDPOINT_ID: the index endpoint ID for the Private Service Connect index endpoint that you created
INDEX: the ID for the index you're deploying

The output is similar to the following example, in which the index endpoint ID is 8151506529447575552:

Using endpoint [https://us-central1-aiplatform.googleapis.com/]
The deploy index operation [projects/725264228516/locations/us-central1/indexEndpoints/8151506529447575552/operations/6271807495283408896] was submitted successfully.

The deployment operation takes about 10 to 15 minutes. When you deploy the index, a service attachment is generated.

Verify that the index is deployed to the index endpoint

In the Google Cloud console, go to the Index Endpoints tab in the Vector Search page.

Go to Index Endpoints
Verify that the vector-search index endpoint has a Deployed index that's also called vector-search.

If a spinning blue circle appears next to the index endpoint name, the index is still in the process of being deployed.

Get the service attachment URI for the index endpoint

After the index is fully deployed, you can obtain the service attachment URI.

In the Cloud Shell, run the following command to obtain the service attachment URI:

gcloud ai index-endpoints list --region=us-central1 | grep -i  serviceAttachment:

In the following example output, the service attachment URI is projects/je84d1de50cd8bddb-tp/regions/us-central1/serviceAttachments/sa-gkedpm-527af280e65971fd786aaf6163e798.

Using endpoint [https://us-central1-aiplatform.googleapis.com/]
 serviceAttachment: projects/je84d1de50cd8bddb-tp/regions/us-central1/serviceAttachments/sa-gkedpm-527af280e65971fd786aaf6163e798

Make a note of the serviceAttachment URI, beginning with projects, for example, projects/je84d1de50cd8bddb-tp/regions/us-central1/serviceAttachments/sa-gkedpm-527af280e65971fd786aaf6163e798. You need it in the next step, when you create a forwarding rule.

Create a forwarding rule

In the Cloud Shell, reserve an IP address for the forwarding rule to use for querying the Vector Search index:

gcloud compute addresses create vector-search-forwarding-rule \
  --region=us-central1 \
  --subnet=psc-forwarding-rule-subnet

Find the reserved IP address:

gcloud compute addresses list --filter="name=vector-search-forwarding-rule"

Create a forwarding rule to connect the endpoint to the service attachment, replacing SERVICE_ATTACHMENT_URI with your serviceAttachment URI.

gcloud compute forwarding-rules create vector-search-forwarding-rule \
  --region=us-central1 \
  --network=vertex-networking-vpc \
  --address=vector-search-forwarding-rule \
  --target-service-attachment=SERVICE_ATTACHMENT_URI

Following is a usage example for this command:

gcloud compute forwarding-rules create vector-search-forwarding-rule \
  --region=us-central1 \
  --network=vertex-networking-vpc \
  --address=vector-search-forwarding-rule \
  --target-service-attachment=projects/je84d1de50cd8bddb-tp/regions/us-central1/serviceAttachments/sa-gkedpm-527af280e65971fd786aaf6163e798

In the Google Cloud console, go to the Connected endpoints tab in the Private Service Connect page.

Go to Connected endpoints
Validate that the status of vector-search-forwarding-rule is Accepted.
Make a note of the IP address of the Private Service Connect forwarding rule. In a later step, you'll use this endpoint to establish communication with the deployed Vector Search index.

Query the deployed index

Now that you have established a Private Service Connect forwarding rule that's connected to your Vector Search index endpoint, you can query your deployed index by sending the queries from the on-prem-client VM instance to the forwarding rule.

To allow Identity-Aware Proxy (IAP) to connect to your VM instances, you create a firewall rule that:

Applies to all VM instances that you want to make accessible through IAP.
Allows TCP traffic through port 22 from the IP range 35.235.240.0/20. This range contains all IP addresses that IAP uses for TCP forwarding.

After you create the firewall, you install the gRPC client. In a later step, you'll use the gRPC client to send queries from the on-prem-client VM instance.

Create the firewall rule and install gRPC

In the Cloud Shell, run the following commands, replacing PROJECT_ID with your project ID:
```
projectid=PROJECT_ID
gcloud config set project ${projectid}
```

Create an IAP firewall rule named ssh-iap-vpc:

gcloud compute firewall-rules create ssh-iap-vpc \
  --network onprem-vpc \
  --allow tcp:22 \
  --source-ranges=35.235.240.0/20

Log into the on-prem-client VM instance:

gcloud compute ssh on-prem-client \
  --project=$projectid \
  --zone=us-central1-a \
  --tunnel-through-iap

In the on-prem-client VM instance, install the gRPC client:

sudo apt-get install git -y
git clone https://github.com/grpc/grpc.git
sudo apt-get install build-essential autoconf libtool pkg-config -y
sudo apt-get install cmake -y
cd grpc/
git submodule update --init
mkdir -p cmake/build
cd cmake/build
cmake -DgRPC_BUILD_TESTS=ON ../..
make grpc_cli

The installation takes about 30 minutes.

Get an ID for an existing index item

In the Google Cloud console, go to the Instances tab in the Vertex AI Workbench page.

Go to Vertex AI Workbench
Next to your Vertex AI Workbench instance's name, click Open JupyterLab.

Your Vertex AI Workbench instance opens JupyterLab.
Select File > New > Terminal.
In the JupyterLab terminal (not the Cloud Shell), view the last entry in the index:
```
tail -1 product-embs.json
```
Look for the first key-value pair in the item, which contains the item's ID number, as in the following example:
```
"id":"27452"
```
Make a note of this ID number. You need it to perform a query in the next section.

Perform a Vector Search query

In the on-prem-client VM instance, query your deployed index:

./grpc_cli call  FORWARDING_RULE_IP:10000  google.cloud.aiplatform.container.v1.MatchService.Match "deployed_index_id:'"vector_one"',embedding_id: '"ITEM_ID"'"