Integrate with Vertex AI

This page details how to set up the integration between AlloyDB for PostgreSQL and Vertex AI, letting you apply queries to cloud-stored large language models (LLMs) to your data.

These instructions are specific to using AlloyDB, and not AlloyDB Omni. To instead integrate a local installation of AlloyDB Omni using Vertex AI, see Install AlloyDB Omni with AlloyDB AI.

For more information about using ML models with AlloyDB, see Build generative AI applications using AlloyDB AI.

For more information about Vertex AI, see Introduction to Vertex AI.

Before you begin

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  3. Make sure that billing is enabled for your Google Cloud project.

  4. Enable the Vertex AI API.

    Enable the API

  5. Install the Google Cloud CLI.

  6. To initialize the gcloud CLI, run the following command: 

    gcloud init

  7. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Go to project selector

  8. Make sure that billing is enabled for your Google Cloud project.

  9. Enable the Vertex AI API.

    Enable the API

  10. Install the Google Cloud CLI.

  11. To initialize the gcloud CLI, run the following command: 

    gcloud init

Enable database integration with Vertex AI

To enable database integration with Vertex AI, follow these steps to grant the AlloyDB service agent Identity and Access Management (IAM) permissions to access Vertex AI:

  1. Add Vertex AI permissions to the AlloyDB service agent for the project where the AlloyDB database's cluster is located:

    Console

    1. Get the project number of the project that has AlloyDB clusters or instances by following the instructions in Identifying projects.

    2. In the Google Cloud console, go to the IAM page.

      Go to IAM

    3. Select the project that has Vertex AI endpoints.

    4. Enable Include Google-provided role grants.

    5. Click Add.

    6. In the New principals field, enter the following:

      service-PROJECT_NUMBER@gcp-sa-alloydb.iam.gserviceaccount.com

      Replace PROJECT_NUMBER with the project number.

    7. In the Role field, enter Vertex AI User.

    8. Click Save.

    gcloud

    1. To use the gcloud CLI, you can install and initialize the Google Cloud CLI, or you can use Cloud Shell.

    gcloud projects add-iam-policy-binding PROJECT_ID \
--member="serviceAccount:service-PROJECT_NUMBER@gcp-sa-alloydb.iam.gserviceaccount.com" \
--role="roles/aiplatform.user"

    Replace the following:

    • PROJECT_ID: The ID of the project that has the Vertex AI endpoint.
    • PROJECT_NUMBER: The project number of the project that has AlloyDB clusters or instances.

  2. Verify if the google_ml_integration is installed in the database that contains the data that you want to run predictions on:

    1. Connect a psql client to the cluster's primary instance, as described in Connect a psql client to an instance.

    2. At the psql command prompt, connect to the database: 

      \c DB_NAME

      Replace DB_NAME with the name of the database on which you want to install the extension.

    3. Verify the google_ml_integration extension version 1.4.2 or later is installed: 

      SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';

    4. Optional. Install the latest version of the extension manually: 

      CREATE EXTENSION google_ml_integration VERSION '1.4.2';

    5. Optional. If you're using an extension older than 1.4.2, then update the extension to the latest version: 

      ALTER EXTENSION google_ml_integration UPDATE TO '1.4.2'

What's next