Generate image embeddings by using the ML.GENERATE_EMBEDDING function

Preview

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This document shows you how to create a BigQuery ML remote model that references a Vertex AI embedding foundation model. You then use that model with the ML.GENERATE_EMBEDDING function to create image embeddings by using data from a BigQuery object table.

Required roles

• To create a connection, you need membership in the following Identity and Access Management (IAM) role:

  • roles/bigquery.connectionAdmin

• To grant permissions to the connection's service account, you need the following permission:

  • resourcemanager.projects.setIamPolicy

• To create the model using BigQuery ML, you need the following IAM permissions:

  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData
  • bigquery.models.updateMetadata

• To run inference, you need the following permissions:

  • bigquery.tables.getData on the table
  • bigquery.models.getData on the model
  • bigquery.jobs.create

Before you begin

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

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

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

3. Enable the BigQuery, BigQuery Connection, and Vertex AI APIs.

   Enable the APIs

Create a dataset

Create a BigQuery dataset to store your ML model:

1. In the Google Cloud console, go to the BigQuery page.

   Go to the BigQuery page

2. In the Explorer pane, click your project name.

3. Click View actions > Create dataset.

   

4. On the Create dataset page, do the following:

   • For Dataset ID, enter bqml_tutorial.

   • For Location type, select Multi-region, and then select US (multiple regions in United States).

     The public datasets are stored in the US multi-region. For simplicity, store your dataset in the same location.

   • Leave the remaining default settings as they are, and click Create dataset.

     

Create a connection

Create a Cloud resource connection and get the connection's service account. Create the connection in the same location as the dataset you created in the previous step.

Select one of the following options:

Console

1. Go to the BigQuery page.

   Go to BigQuery

2. To create a connection, click Add, and then click Connections to external data sources.

3. In the Connection type list, select Vertex AI remote models, remote functions and BigLake (Cloud Resource).

4. In the Connection ID field, enter a name for your connection.

5. Click Create connection.

6. Click Go to connection.

7. In the Connection info pane, copy the service account ID for use in a later step.

bq

1. In a command-line environment, create a connection:

   bq mk --connection --location=REGION --project_id=PROJECT_ID \
       --connection_type=CLOUD_RESOURCE CONNECTION_ID
   

   The --project_id parameter overrides the default project.

   Replace the following:

   • REGION: your connection region
   • PROJECT_ID: your Google Cloud project ID
   • CONNECTION_ID: an ID for your connection

   When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

   Troubleshooting: If you get the following connection error, update the Google Cloud SDK:

   Flags parsing error: flag --connection_type=CLOUD_RESOURCE: value should be one of...
   

2. Retrieve and copy the service account ID for use in a later step:

   bq show --connection PROJECT_ID.REGION.CONNECTION_ID
   

   The output is similar to the following:

   name                          properties
   1234.REGION.CONNECTION_ID     {"serviceAccountId": "[e-mail]"}
   

Terraform

Append the following section into your main.tf file.

 ## This creates a cloud resource connection.
 ## Note: The cloud resource nested object has only one output only field - serviceAccountId.
 resource "google_bigquery_connection" "connection" {
    connection_id = "CONNECTION_ID"
    project = "PROJECT_ID"
    location = "REGION"
    cloud_resource {}
}        

Replace the following:

• CONNECTION_ID: an ID for your connection
• PROJECT_ID: your Google Cloud project ID
• REGION: your connection region

Give the service account access

Give your service account permission to use the connection. Failure to give permission results in an error. Select one of the following options:

Console

1. Go to the IAM & Admin page.

   Go to IAM & Admin

2. Click Grant access.

   The Add principals dialog opens.

3. In the New principals field, enter the service account ID that you copied earlier.

4. In the Select a role field, select Vertex AI, and then select Vertex AI User.

5. Click Save.

gcloud

Use the gcloud projects add-iam-policy-binding command:

gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/aiplatform.user' --condition=None

Replace the following:

• PROJECT_NUMBER: your project number
• MEMBER: the service account ID that you copied earlier

Create a model

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. Using the SQL editor, create a remote model:

   CREATE OR REPLACE MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`
   REMOTE WITH CONNECTION `PROJECT_ID.REGION.CONNECTION_ID`
   OPTIONS (ENDPOINT = 'ENDPOINT');
   

   Replace the following:

   • PROJECT_ID: your project ID
   • DATASET_ID: the ID of the dataset to contain the model
   • MODEL_NAME: the name of the model
   • REGION: the region used by the connection
   • CONNECTION_ID: the ID of your BigQuery connection

     When you view the connection details in the Google Cloud console, this is the value in the last section of the fully qualified connection ID that is shown in Connection ID, for example projects/myproject/locations/connection_location/connections/myconnection

   • ENDPOINT: the embedding LLM to use, in this case multimodalembedding@001.

Generate image embeddings

Generate image embeddings with the ML.GENERATE_EMBEDDING function by using image data from an object table:

SELECT *
FROM ML.GENERATE_EMBEDDING(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE PROJECT_ID.DATASET_ID.TABLE_NAME,
  STRUCT(FLATTEN_JSON AS flatten_json_output)
);

Replace the following:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset that contains the model.
• MODEL_NAME: the name of the remote model over a multimodalembedding@001 model.
• TABLE_NAME: the name of the object table that contains the images to embed.
• FLATTEN_JSON: a BOOL value that indicates whether to parse the embedding into a separate column. The default value is TRUE.

Example

The following example shows how to create embeddings for the images in the images object table:

SELECT *
FROM
  ML.GENERATE_EMBEDDING(
    MODEL `mydataset.embedding_model`,
    TABLE mydataset.images,
    STRUCT(TRUE AS flatten_json_output)
  );