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

This tutorial shows you how to create a remote model that's based on the open-source text embedding model Qwen3-Embedding-0.6B , and then how to use that model with the ML.GENERATE_EMBEDDING function to embed movie reviews from the bigquery-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 in this document. To see the exact permissions that are required, expand the Required permissionssection:

Required permissions

Create a dataset: bigquery.datasets.create
Create, delegate, and use a connection: bigquery.connections.*
Set the default connection: bigquery.config.*
Set service account permissions: resourcemanager.projects.getIamPolicy and resourcemanager.projects.setIamPolicy
Deploy and undeploy a Vertex AI model:
- aiplatform.endpoints.deploy
- aiplatform.endpoints.undeploy
Create a model and run inference:
- bigquery.jobs.create
- bigquery.models.create
- bigquery.models.getData
- bigquery.models.updateData
- bigquery.models.updateMetadata

You might also be able to get these permissions with custom roles or other predefined 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 the pricing calculator .

New Google Cloud users might be eligible for a free trial .

For more information about BigQuery pricing, see BigQuery pricing in the BigQuery documentation.

Open models that you deploy to Vertex AI are charged per machine-hour. This means billing starts as soon as the endpoint is fully set up, and continues until you un-deploy it. For more information about Vertex AI pricing, see the Vertex AI pricing page.

Before you begin

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 the resourcemanager.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
Verify that billing is enabled for your Google Cloud project .
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 the serviceusage.services.enable permission. Learn how to grant roles .

Enable the APIs

Create a dataset

Create a BigQuery dataset to store your ML model.

Console

In the Google Cloud console, go to the BigQuerypage.

Go to the BigQuery page
In the Explorerpane, click your project name.
Click View actions > Create dataset
On the Create datasetpage, do the following:
- For Dataset ID, enter bqml_tutorial .
- For Location type, select Multi-region, and then select US (multiple regions in United States).
- Leave the remaining default settings as they are, and click Create dataset.

bq

To create a new dataset, use the bq mk command with the --location flag. For a full list of possible parameters, see the bq mk --dataset command reference.

Create a dataset named bqml_tutorial with the data location set to US and a description of BigQuery ML tutorial dataset :
```
bq --location=US mk -d \
 --description "BigQuery ML tutorial dataset." \
 bqml_tutorial
```
Instead of using the --dataset flag, the command uses the -d shortcut. If you omit -d and --dataset , the command defaults to creating a dataset.
Confirm that the dataset was created:
```
bq  
ls
```

API

Call the datasets.insert method with a defined dataset resource .

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

BigQuery DataFrames

Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

  import 
  
 google.cloud.bigquery 
 bqclient 
 = 
 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 AI model:

In the Google Cloud console, go to the BigQuerypage.

Go to BigQuery
In the query editor, run the following statement:

 CREATE 
  
 OR 
  
 REPLACE 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 qwen3_embedding_model 
 ` 
  
 REMOTE 
  
 WITH 
  
 CONNECTION 
  
 DEFAULT 
  
 OPTIONS 
  
 ( 
  
 HUGGING_FACE_MODEL_ID 
  
 = 
  
 'Qwen/Qwen3-Embedding-0.6B' 
 );

The query takes up to 20 minutes to complete, after which the qwen3_embedding_model model appears in the bqml_tutorial dataset in the Explorerpane. Because the query uses a CREATE MODEL statement to create a model, there are no query results.

Perform text embedding

Perform text embedding on IMDB movie reviews by using the remote model and the ML.GENERATE_EMBEDDING function:

In the Google Cloud console, go to the BigQuerypage.

Go to BigQuery
In the query editor, enter the following statement to perform text embedding on five movie reviews:
```
 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_EMBEDDING 
 ( 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 qwen3_embedding_model 
 ` 
 , 
  
 ( 
  
 SELECT 
  
 review 
  
 AS 
  
 content 
 , 
  
 * 
  
 FROM 
  
 ` 
 bigquery 
 - 
 public 
 - 
 data 
 . 
 imdb 
 . 
 reviews 
 ` 
  
 LIMIT 
  
 5 
  
 ) 
  
 ); 
```
The results include the following columns:
- ml_generate_embedding_result : an array of double to represent the generated embeddings.
- ml_generate_embedding_status : the API response status for the corresponding row. If the operation was successful, this value is empty.
- content : the input text from which to extract embeddings.
- All of the columns from the bigquery-public-data.imdb.reviews table.

Undeploy model

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

 ALTER 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 qwen3_embedding_model 
 ` 
 SET 
  
 OPTIONS 
  
 ( 
 deploy_model 
  
 = 
  
 false 
 );

For more information, see Automatic or immediate open model undeployment .

Clean up

In the Google Cloud console, go to the Manage resources page.
Go to Manage resources
In the project list, select the project that you want to delete, and then click Delete .
In the dialog, type the project ID, and then click Shut down to delete the project.

What's next

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