Generate multimodal embeddings

You can generate multimodal embeddings in AlloyDB for PostgreSQL using the supported Vertex AI multimodal model, multimodalembedding@001.

You can use the Vertex AI multimodal embedding models referred to in Supported models.

This page assumes that you're familiar with AlloyDB for PostgreSQL and generative AI concepts. For more information, see What are embeddings.

Before you begin

Before you use multimodal embeddings, do the following:

Integrate with Vertex AI and install the extension

  1. Configure user access to Vertex AI models.
  2. Verify that the latest version of google_ml_integration is installed.

    1. To check the installed version, run the following command:

      SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';
extversion 
------------
1.5.2
(1 row)

    2. If the extension isn't installed or if the installed version is earlier than 1.5.2, update the extension.

      CREATE EXTENSION IF NOT EXISTS google_ml_integration;
ALTER EXTENSION google_ml_integration UPDATE;

      If you experience issues when you run the preceding commands, or if the extension isn't updated to version 1.5.2 after you run the preceding commands, contact Google Cloud support.

  3. To use the AlloyDB AI query engine functionality, set the google_ml_integration.enable_ai_query_engine flag to on.

    SQL

    1. Enable the AI query engine for the current session.
      SET google_ml_integration.enable_ai_query_engine = on;
    2. Enable features for a specific database across sessions.
      ALTER DATABASE DATABASE_NAME SET google_ml_integration.enable_ai_query_engine = 'on';
    3. Enable the AI query engine for a specific user across sessions and databases.
      ALTER ROLE postgres SET google_ml_integration.enable_ai_query_engine = 'on';

    Console

    To modify the value of the google_ml_integration.enable_ai_query_engine flag, follow the steps in Configure an instance's database flags.

    gcloud

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

    You can modify the value of the google_ml_integration.enable_ai_query_engine flag. For more information, see Configure an instance's database flags. 

    gcloud alloydb instances update INSTANCE_ID \
  --database-flags google_ml_integration.enable_ai_query_engine=on \
  --region=REGION_ID \
  --cluster=CLUSTER_ID \
  --project=PROJECT_ID

Access data in Cloud Storage to generate multimodal embeddings

  • To generate multimodal embeddings, refer to content in Cloud Storage using a gs:// URI.
  • Access Cloud Storage content through your current project's Vertex AI service agent. By default, the Vertex AI service agent already has permission to access the bucket in the same project. For more information, see IAM roles and permissions index.

  • To access data in a Cloud Storage bucket in another Google Cloud project, run the following gcloud CLI command to grant the Storage Object Viewer role (roles/storage.objectViewer) to the Vertex AI service agent of your AlloyDB project.

    gcloud projects add-iam-policy-binding <ANOTHER_PROJECT_ID> \
--member="serviceAccount:service-<PROJECT_ID>@gcp-sa-aiplatform.iam.gserviceaccount.com" \
--role="roles/storage.objectViewer"

    For more information, see Set and manage IAM policies on buckets.

To generate multimodal embeddings, select one of the following schemas.

Generate multimodal embeddings

To generate text embeddings for a multimodalembedding@001 model endpoint, run the following statement:

SELECT
  ai.text_embedding(
    model_id => 'multimodalembedding@001',
    content => 'TEXT');

Replace TEXT with the text to generate the embedding for.

To generate image embeddings for a registered multimodalembedding@001 model endpoint where the image mimetype is default image/jpeg, run the following statement:

SELECT
  ai.image_embedding(
    model_id => 'multimodalembedding@001',
    image => 'IMAGE_PATH_OR_TEXT',
    mimetype => MIMETYPE');

Replace the following:

  • IMAGE_PATH_OR_TEXT with the Cloud Storage URI of the image, for example, gs://my-bucket/embeddings/flowers.jpeg, or the base64 string of the image.
  • MIMETYPE with the mimetype of the image, for example, image/jpeg. For the full list of supported mimetypes, see the Multimodal embeddings API.

To generate video embeddings for a registered multimodalembedding@001 model endpoint, run the following statement:

SELECT
  ai.video_embedding(
    model_id => 'multimodalembedding@001',
    video => 'VIDEO_URI');

Replace VIDEO_URI with the Cloud Storage URI of the target video, for example, gs://my-bucket/embeddings/supermarket-video.mp4, or the base64 string of the video. These are two-dimensional arrays that can be accessed as my_array[0][5] like syntax. For more information, see Arrays.

What's next