Install AlloyDB AI in AlloyDB Omni

This page shows you how to install AlloyDB Omni and integrate AlloyDB AI in it.

AlloyDB AI is a suite of features included with AlloyDB Omni that let you build enterprise generative AI applications. For more information about the ML functionality of AlloyDB, see Build generative AI applications.

AlloyDB Omni with AlloyDB AI lets you query remote ML models to work with online predictions and text embeddings generated from ML models. AlloyDB Omni with AlloyDB AI can also process vector embeddings from other content such as an image, for example, if you use the google_ml.predict_row interface and do the translation yourself in the query.

Based on where you want to install AlloyDB Omni with AlloyDB AI, select one of the following options:

Configure your AlloyDB Omni instance to query remote models

If your machine is air-gapped for security reasons, AlloyDB Omni can also query a model that is hosted on the same or any other machine also accessible from the machine where AlloyDB Omni runs.

Before you start querying remote models, enable Vertex AI online predictions in GDC.

Create a Kubernetes secret using the service account key

To create a Kubernetes secret based on the service account key downloaded in the preceding steps, run the following command:

   kubectl create secret generic SECRET_NAME \
   --from-file=PATH_TO_SERVICE_ACCOUNT_KEY/private-key.json \
   -n NAMESPACE

Replace the following:

  • SECRET_NAME: the name of the secret used when you create a DBCluster manifest to enable AlloyDB Omni to access AlloyDB AI features. For example, vertex-ai-key-alloydb.

  • PATH_TO_SERVICE_ACCOUNT_KEY: the path to the location where you downloaded the private-key.json service account key.

  • NAMESPACE: the namespace of the database cluster.

Install the AlloyDB Omni operator

Install the AlloyDB Omni operator using steps listed in Choose a database engine type and create a database cluster.

Create a database cluster with AlloyDB AI

  1. Create a database cluster with AlloyDB AI and set vertexAIKeyRef to the Kubernetes secret created in the preceding steps under the googleMLExtension field in the DBCluster manifest.

    apiVersion: v1
kind: Secret
metadata:
  name: db-pw-DBCLUSTER_NAME
  namespace: USER_PROJECT
type: Opaque
data:
  DBCLUSTER_NAME: "BASE64_PASSWORD"
---
apiVersion: DBENGINE_NAME.dbadmin.gdc.goog/v1
kind: DBCluster
metadata:
  name: DBCLUSTER_NAME
  namespace: USER_PROJECT
spec:
  primarySpec:
    adminUser:
      passwordRef:
        name: db-pw-DBCLUSTER_NAME
    features:
      googleMLExtension:
        config:
          vertexAIKeyRef: SECRET_NAME
    version: "DB_VERSION"
    resources:
      memory: DB_MEMORY
      cpu: DB_CPU
      disks:
      - name: DataDisk
        size: DB_DATA_DISK

    Replace the following variables:

    • DBCLUSTER_NAME: the name of the database cluster.
    • USER_PROJECT: the name of the user project where the database cluster will be created.
    • BASE64_PASSWORD: the base64 encoding of the database's administrator password.
    • DBENGINE_NAME: the name of the database engine. Set to alloydbomni.
    • DB_VERSION: the version of the database engine.
    • DB_MEMORY: the amount of memory allocated to the database cluster, for example 5Gi.
    • DB_CPU: the amount of CPUs allocated to the database cluster, for example 2.
    • DB_DATA_DISK: the amount of space allocated to the database cluster, for example 10 Gi.

  2. Apply the manifest.

    kubectl apply -f DB_CLUSTER_YAML

    Replace the following:

    • DB_CLUSTER_YAML: the name of this database cluster manifest file—for example, alloydb-omni-db-cluster.yaml.

Verify AlloyDB Omni with AlloyDB AI installation

To verify your installation is successful and uses model prediction, enter the following:

   CREATE EXTENSION google_ml_integration CASCADE;

   SELECT array_dims(embedding( 'textembedding-gecko@003', 'AlloyDB AI')::real[]);

The output looks similar to the following:

      array_dims
      ------------
      [1:768]
      (1 row)

In the previous query, the embedding() call generates embeddings for the input text AlloyDB AI. array_dims returns the dimensions of the array returned by embedding(). Since the pre-registered textembedding-gecko model returns an output with 768 dimensions, the output is [768].

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