Generate text embeddings by using an open model and the AI.GENERATE_EMBEDDING function

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This tutorial shows you how to create aremote modelthat's based on theopen-source text embedding modelQwen3-Embedding-0.6B,and then how to use that model with theAI.GENERATE_EMBEDDING functionto embed movie reviews from thebigquery-public-data.imdb.reviews public table.

Required permissions

To run this tutorial, you need the following Identity and Access Management (IAM)roles:

• Create and use BigQuery datasets, connections, and models:BigQuery Admin (roles/bigquery.admin).
• Grant permissions to the connection's service account: Project IAM Admin(roles/resourcemanager.projectIamAdmin).
• Deploy and undeploy models in Vertex AI: Vertex AI Administrator(roles/aiplatform.admin).

These predefined roles contain the permissions required to perform the tasks inthis document. To see the exact permissions that are required, expand theRequired permissions section:

You might also be able to get these permissions withcustom roles or otherpredefined roles.

Costs

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

• BigQuery ML: You incur costs for the data that you process in BigQuery.
• Vertex AI: You incur costs for calls to the Vertex AI model that's represented by the remote model.

To generate a cost estimate based on your projected usage, use thepricing calculator.

For more information about BigQuery pricing, seeBigQuery pricing inthe BigQuery documentation.

Open models that you deploy to Vertex AI are charged permachine-hour. This means billing starts as soon as the endpoint is fully setup, and continues until you un-deploy it.For more information about Vertex AI pricing, see theVertex AI pricingpage.

Before you begin

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

   Roles required to select or create a project

   • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
   • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.
   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. Verify that billing is enabled for your Google Cloud project.

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

   Roles required to enable APIs

   To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

   Enable the APIs

Create a dataset

Create a BigQuery dataset to store your ML model.

{"datasetReference":{"datasetId":"bqml_tutorial"}}

importgoogle.cloud.bigquerybqclient=google.cloud.bigquery.Client()bqclient.create_dataset("bqml_tutorial",exists_ok=True)

Create the remote model

Create a remote model that represents a hosted Vertex AImodel:

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

   Go to BigQuery

2. In the query editor, run the following statement:

CREATEORREPLACEMODEL`bqml_tutorial.qwen3_embedding_model`REMOTEWITHCONNECTIONDEFAULTOPTIONS(HUGGING_FACE_MODEL_ID='Qwen/Qwen3-Embedding-0.6B');

The query takes up to 20 minutes to complete, after which theqwen3_embedding_model model appears in thebqml_tutorial dataset in theExplorer pane. Because the query uses aCREATE MODEL statement to create amodel, there are no query results.

Perform text embedding

Perform text embedding onIMDB movie reviews byusing the remote model and theAI.GENERATE_EMBEDDING function:

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

   Go to BigQuery

2. In the query editor, enter the following statement to perform text embedding on five movie reviews:

   SELECT*FROMAI.GENERATE_EMBEDDING(MODEL`bqml_tutorial.qwen3_embedding_model`,(SELECTreviewAScontent,*FROM`bigquery-public-data.imdb.reviews`LIMIT5));

   The results include the following columns:

   • embedding: an array of double to represent the generated embeddings.
   • status: the API response status for the correspondingrow. If the operation was successful, this value is empty.
   • content: the input text from which to extract embeddings.
   • All of the columns from thebigquery-public-data.imdb.reviews table.

Undeploy model

If you choose not todelete your project as recommended, you mustundeploy the Qwen3 embedding model in Vertex AI to avoidcontinued billing for it. BigQuery automatically undeploys themodel after a specified period of idleness (6.5 hours by default).Alternatively, you can immediately undeploy the model by using theALTER MODEL statement,as shown in the following example:

ALTERMODEL`bqml_tutorial.qwen3_embedding_model`SETOPTIONS(deploy_model=false);

For more information, seeAutomatic or immediate open model undeployment.

Clean up

Caution: Deleting a project has the following effects:
• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as anappspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Google Cloud console, go to theManage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then clickDelete.
3. In the dialog, type the project ID, and then clickShut down to delete the project.

What's next

• Learn how touse text embeddings for semantic search and retrieval-augmented generation (RAG).