The [`AI.GENERATE_TABLE` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-table.md.txt) lets you extract structured attributes from unstructured text into typed table columns in BigQuery. In this tutorial, you create a [remote model](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model.md.txt) over a Gemini model and extract sentiment and keywords from a public dataset of movie reviews. ## Objectives - Create a dataset and a Cloud resource connection. - Grant permissions to the connection's service account. - Create a BigQuery ML remote model. - Generate structured data from unstructured text by using the `AI.GENERATE_TABLE` function. ## Costs In this document, you use the following billable components of Google Cloud: - **BigQuery**: You incur costs for the data that you process in BigQuery. - **Gemini Enterprise Agent Platform**: You incur costs for calls to the Agent Platform model. To generate a cost estimate based on your projected usage, use the [pricing calculator](https://docs.cloud.google.com/products/calculator). New Google Cloud users might be eligible for a [free trial](https://docs.cloud.google.com/free).
For more information, see [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and [Agent Platform pricing](https://cloud.google.com/vertex-ai/generative-ai/pricing). ## Before you begin ### Console 1. Make sure that you have the following role or roles on the project: **BigQuery Admin** , **Project IAM Admin** #### Check for the roles 1. In the Google Cloud console, go to the **IAM** page. [Go to IAM](https://console.cloud.google.com/projectselector/iam-admin/iam?supportedpurview=project) 2. Select the project. 3. In the **Principal** column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator. 4. For all rows that specify or include you, check the **Role** column to see whether the list of roles includes the required roles. #### Grant the roles 1. In the Google Cloud console, go to the **IAM** page. [Go to IAM](https://console.cloud.google.com/projectselector/iam-admin/iam?supportedpurview=project) 2. Select the project. 3. Click **Grant access**. 4. In the **New principals** field, enter your user identifier. This is typically the email address for a Google Account. 5. Click **Select a role**, then search for the role. 6. To grant additional roles, click **Add another role** and add each additional role. 7. Click **Save**.
### gcloud 1. Grant roles to your user account. Run the following command once for each of the following IAM roles: ` roles/bigquery.admin, roles/resourcemanager.projectIamAdmin ` ```bash gcloud projects add-iam-policy-binding PROJECT_ID --member="user:USER_IDENTIFIER" --role=ROLE ``` Replace the following: - `PROJECT_ID`: Your project ID. - `USER_IDENTIFIER`: The identifier for your user account. For example, `myemail@example.com`. - `ROLE`: The IAM role that you grant to your user account.
## Create a dataset To create a BigQuery dataset, select one of the following options: ### Console 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the left pane, click **Explorer**: ![Highlighted button for the Explorer pane.](https://docs.cloud.google.com/static/bigquery/images/explorer-tab.png) If you don't see the left pane, click **Expand left pane** to open the pane. 3. In **Explorer** , expand your project, and then click **Datasets**. 4. On the **Datasets** page, click **Create dataset**. 5. In the **Create dataset** pane, do the following: - For **Dataset ID** , enter `bqml_tutorial`. - For **Data location** , select **US**. Leave the remaining default settings as they are. 6. Click **Create dataset**. ### bq To create a new dataset, use the [`bq mk --dataset` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference.md.txt#mk-dataset). 1. Create a dataset named `bqml_tutorial` with the data location set to `US`: ``` bq mk --dataset \ --location=US \ --description "BigQuery ML tutorial dataset." \ bqml_tutorial ``` 2. Confirm that the dataset was created: ```bash bq ls ``` ### API Call the [`datasets.insert`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/datasets/insert.md.txt) method with a defined [dataset resource](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/datasets.md.txt):
```json { "datasetReference": { "datasetId": "bqml_tutorial" } } ``` ## Create a connection Create a [Cloud resource connection](https://docs.cloud.google.com/bigquery/docs/create-cloud-resource-connection.md.txt) in the `US` multiregion, where you created the dataset. Then get the connection's service account. Select one of the following options:
### Console 1. Go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the left pane, click **Explorer**: ![Highlighted button for the Explorer pane.](https://docs.cloud.google.com/static/bigquery/images/explorer-tab.png) If you don't see the left pane, click **Expand left pane** to open the pane. 3. In the **Explorer** pane, expand your project name, and then click **Connections**. 4. On the **Connections** page, click **Create connection**. 5. For **Connection type** , choose **Vertex AI remote models, remote functions, BigLake and Spanner (Cloud Resource)**. 6. In the **Connection ID** field, enter a name for your connection. 7. For **Location type**, select a location for your connection. The connection should be colocated with your other resources such as datasets. 8. Click **Create connection**. 9. Click **Go to connection**. 10. In the **Connection info** pane, copy the service account ID for use in a later step. ### SQL Use the [`CREATE CONNECTION` statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language.md.txt#create_connection_statement): 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the query editor, enter the following statement: ```googlesql CREATE CONNECTION [IF NOT EXISTS] `CONNECTION_NAME` OPTIONS ( connection_type = "CLOUD_RESOURCE", friendly_name = "FRIENDLY_NAME", description = "DESCRIPTION" ); ``` Replace the following: - `CONNECTION_NAME`: the name of the connection in either the `PROJECT_ID.LOCATION.CONNECTION_ID`, `LOCATION.CONNECTION_ID`, or `CONNECTION_ID` format. If the project or location are omitted, then they are inferred from the project and location where the statement is run. - `FRIENDLY_NAME` (optional): a descriptive name for the connection. - `DESCRIPTION` (optional): a description of the connection.
3. Click **Run**.
For more information about how to run queries, see [Run an interactive query](https://docs.cloud.google.com/bigquery/docs/running-queries.md.txt#queries). ### bq 1. In a command-line environment, create a connection: ```bash 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](https://docs.cloud.google.com/bigquery/docs/locations.md.txt#supported_locations) - `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](https://docs.cloud.google.com/sdk/docs/quickstart): ``` 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: ```bash bq show --connection PROJECT_ID.REGION.CONNECTION_ID ``` The output is similar to the following: ``` name properties 1234.REGION.CONNECTION_ID {"serviceAccountId": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"} ``` ### Python Before trying this sample, follow the Python setup instructions in the [Vertex AI quickstart using client libraries](https://docs.cloud.google.com/vertex-ai/docs/start/client-libraries). For more information, see the [Vertex AI Python API reference documentation](https://docs.cloud.google.com/python/docs/reference/aiplatform/latest). To authenticate to Vertex AI, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication.md.txt#client-libs). ```python import google.api_core.exceptions from google.cloud import bigquery_connection_v1 client = bigquery_connection_v1.ConnectionServiceClient() def create_connection( project_id: str, location: str, connection_id: str, ): """Creates a BigQuery connection to a Cloud Resource. Cloud Resource connection creates a service account which can then be granted access to other Google Cloud resources for federated queries. Args: project_id: The Google Cloud project ID. location: The location of the connection (for example, "us-central1"). connection_id: The ID of the connection to create. """ parent = client.common_location_path(project_id, location) connection = bigquery_connection_v1.Connection( friendly_name="Example Connection", description="A sample connection for a Cloud Resource.", cloud_resource=bigquery_connection_v1.CloudResourceProperties(), ) try: created_connection = client.create_connection( parent=parent, connection_id=connection_id, connection=connection ) print(f"Successfully created connection: {created_connection.name}") print(f"Friendly name: {created_connection.friendly_name}") print( f"Service Account: {created_connection.cloud_resource.service_account_id}" ) except google.api_core.exceptions.AlreadyExists: print(f"Connection with ID '{connection_id}' already exists.") print("Please use a different connection ID.") except Exception as e: print(f"An unexpected error occurred while creating the connection: {e}") ``` ### Node.js Before trying this sample, follow the Node.js setup instructions in the [Vertex AI quickstart using client libraries](https://docs.cloud.google.com/vertex-ai/docs/start/client-libraries). For more information, see the [Vertex AI Node.js API reference documentation](https://docs.cloud.google.com/nodejs/docs/reference/aiplatform/latest). To authenticate to Vertex AI, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication.md.txt#client-libs). ```javascript const {ConnectionServiceClient} = require('@google-cloud/bigquery-connection').v1; const {status} = require('@grpc/grpc-js'); const client = new ConnectionServiceClient(); /** * Creates a new BigQuery connection to a Cloud Resource. * * A Cloud Resource connection creates a service account that can be granted access * to other Google Cloud resources. * * @param {string} projectId The Google Cloud project ID. for example, 'example-project-id' * @param {string} location The location of the project to create the connection in. for example, 'us-central1' * @param {string} connectionId The ID of the connection to create. for example, 'example-connection-id' */ async function createConnection(projectId, location, connectionId) { const parent = client.locationPath(projectId, location); const connection = { friendlyName: 'Example Connection', description: 'A sample connection for a Cloud Resource', // The service account for this cloudResource will be created by the API. // Its ID will be available in the response. cloudResource: {}, }; const request = { parent, connectionId, connection, }; try { const [response] = await client.createConnection(request); console.log(`Successfully created connection: ${response.name}`); console.log(`Friendly name: ${response.friendlyName}`); console.log(`Service Account: ${response.cloudResource.serviceAccountId}`); } catch (err) { if (err.code === status.ALREADY_EXISTS) { console.log(`Connection '${connectionId}' already exists.`); } else { console.error(`Error creating connection: ${err.message}`); } } } ``` ### Java Before trying this sample, follow the Java setup instructions in the [Vertex AI quickstart using client libraries](https://docs.cloud.google.com/vertex-ai/docs/start/client-libraries). For more information, see the [Vertex AI Java API reference documentation](https://docs.cloud.google.com/java/docs/reference/google-cloud-aiplatform/latest/com.google.cloud.aiplatform.v1). To authenticate to Vertex AI, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication.md.txt#client-libs). ```java import com.google.api.gax.rpc.AlreadyExistsException; import com.google.api.gax.rpc.ApiException; import com.google.cloud.bigquery.connection.v1.CloudResourceProperties; import com.google.cloud.bigquery.connection.v1.Connection; import com.google.cloud.bigquery.connection.v1.CreateConnectionRequest; import com.google.cloud.bigquery.connection.v1.LocationName; import com.google.cloud.bigqueryconnection.v1.ConnectionServiceClient; import java.io.IOException; public class CreateConnection { /** * Creates a BigQuery connection to a Cloud Resource. A Cloud Resource connection provisions a * service account that can be granted access to other Google Cloud resources for federated * queries. * * @param projectId The Google Cloud project ID (for example, "example-project-id"). * @param location The geographic location of the connection (for example, "us-central1"). * @param connectionId The ID of the connection to create (for example, "example-connection-id"). */ public static void createConnection(String projectId, String location, String connectionId) { LocationName parent = LocationName.of(projectId, location); // CloudResourceProperties provisions a managed Google Cloud service account on creation. CloudResourceProperties cloudResourceProperties = CloudResourceProperties.newBuilder().build(); Connection connection = Connection.newBuilder() .setFriendlyName("Example Connection") .setDescription("Cloud Resource connection for federated queries.") .setCloudResource(cloudResourceProperties) .build(); CreateConnectionRequest request = CreateConnectionRequest.newBuilder() .setParent(parent.toString()) .setConnectionId(connectionId) .setConnection(connection) .build(); try (ConnectionServiceClient client = ConnectionServiceClient.create()) { Connection response = client.createConnection(request); System.out.printf("Successfully created connection: %s%n", response.getName()); System.out.printf("Friendly name: %s%n", response.getFriendlyName()); System.out.printf( "Service Account: %s%n", response.getCloudResource().getServiceAccountId()); } catch (AlreadyExistsException err) { System.err.printf( "error: connection '%s' already exists in location '%s' for project '%s': %s%n", connectionId, location, projectId, err.getMessage()); } catch (ApiException | IOException err) { System.err.printf( "error: failed to create connection '%s' in location '%s' for project '%s': %s%n", connectionId, location, projectId, err.getMessage()); } } ``` ### Terraform Use the [`google_bigquery_connection`](https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/bigquery_connection) resource. > [!NOTE] > **Note:** To create BigQuery objects using Terraform, you must enable the [Cloud Resource Manager API](https://docs.cloud.google.com/resource-manager/reference/rest.md.txt). To authenticate to BigQuery, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](https://docs.cloud.google.com/bigquery/docs/authentication.md.txt#client-libs). The following example creates a Cloud resource connection named `my_cloud_resource_connection` in the `US` region: ```terraform # This queries the provider for project information. data "google_project" "default" {} # This creates a cloud resource connection in the US region named my_cloud_resource_connection. # Note: The cloud resource nested object has only one output field - serviceAccountId. resource "google_bigquery_connection" "default" { connection_id = "my_cloud_resource_connection" project = data.google_project.default.project_id location = "US" cloud_resource {} } ``` To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections. ## Prepare Cloud Shell 1. Launch [Cloud Shell](https://shell.cloud.google.com/). 2. Set the default Google Cloud project where you want to apply your Terraform configurations. You only need to run this command once per project, and you can run it in any directory. ``` export GOOGLE_CLOUD_PROJECT=PROJECT_ID ``` Environment variables are overridden if you set explicit values in the Terraform configuration file. ## Prepare the directory Each Terraform configuration file must have its own directory (also called a *root module*). 1. In [Cloud Shell](https://shell.cloud.google.com/), create a directory and a new file within that directory. The filename must have the `.tf` extension---for example `main.tf`. In this tutorial, the file is referred to as `main.tf`. ``` mkdir DIRECTORY && cd DIRECTORY && touch main.tf ``` 2. If you are following a tutorial, you can copy the sample code in each section or step. Copy the sample code into the newly created `main.tf`. Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution. 3. Review and modify the sample parameters to apply to your environment. 4. Save your changes. 5. Initialize Terraform. You only need to do this once per directory. ``` terraform init ``` Optionally, to use the latest Google provider version, include the `-upgrade` option: ``` terraform init -upgrade ``` ## Apply the changes 1. Review the configuration and verify that the resources that Terraform is going to create or update match your expectations: ``` terraform plan ``` Make corrections to the configuration as necessary. 2. Apply the Terraform configuration by running the following command and entering `yes` at the prompt: ``` terraform apply ``` Wait until Terraform displays the "Apply complete!" message. 3. [Open your Google Cloud project](https://console.cloud.google.com/) to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them. > [!NOTE] > **Note:** Terraform samples typically assume that the required APIs are enabled in your Google Cloud project. ## Grant permissions to the connection's service account Grant the connection's service account the Agent Platform User (`roles/aiplatform.user`) role. You must grant this role in the same project you created or selected in the [Before you begin](https://docs.cloud.google.com/bigquery/docs/generate-table.md.txt#before_you_begin) section. Granting the role in a different project results in the error `bqcx-1234567890-xxxx@gcp-sa-bigquery-condel.iam.gserviceaccount.com does not have the permission to access resource`. To grant the role, follow these steps: 1. Go to the **IAM \& Admin** page. [Go to IAM \& Admin](https://console.cloud.google.com/project/_/iam-admin) 2. Click **Grant Access**. 3. In the **New principals** field, enter the service account ID that you copied earlier. 4. In the **Select a role** field, search for the **Agent Platform User role**. 5. Click **Save**. ## Create the remote model Use the [`CREATE MODEL`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model.md.txt) statement to create a remote model that represents a hosted Agent Platform model: 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the query editor, run the following statement: ```googlesql CREATE OR REPLACE MODEL `bqml_tutorial.gemini_model` REMOTE WITH CONNECTION `LOCATION.CONNECTION_ID` OPTIONS (ENDPOINT = 'gemini-3.5-flash-lite'); ``` Replace the following: - `LOCATION`: the connection location - `CONNECTION_ID`: the ID of your BigQuery connection When you [view the connection details](https://docs.cloud.google.com/bigquery/docs/working-with-connections.md.txt#view-connections) 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*` The query takes several seconds to complete, after which the model `gemini_model` appears in the `bqml_tutorial` dataset. There are no query results. ## Generate structured data Extract structured sentiment and keywords from movie reviews by using the [`AI.GENERATE_TABLE` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-table.md.txt) and the remote model: 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the query editor, run the following statement to extract sentiment and keywords from five movie reviews: ```googlesql SELECT movie_id, sentiment, keywords FROM AI.GENERATE_TABLE( MODEL `bqml_tutorial.gemini_model`, ( SELECT CONCAT( """Analyze the following movie review. Extract a sentiment (positive or negative) and up to 3 keywords: """, review) AS prompt, movie_id FROM `bigquery-public-data.imdb.reviews` LIMIT 5 ), STRUCT( "sentiment STRING, keywords ARRAY" AS output_schema)); ``` The output is similar to the following: ``` +---+---+---+ | movie_id | sentiment | keywords | +---+---+---+ | tt0369053 | positive | ["cinematography", "performances", "comedy"] | | tt0328400 | negative | ["disregarded", "disappointing", "poorly"] | | ... | ... | ... | +---+---+---+ ``` ## Clean up To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources. ### Delete the project > [!CAUTION] > **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 an `appspot.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. Delete a Google Cloud project: ``` gcloud projects delete PROJECT_ID ```
### Delete individual resources If you want to reuse the project, then delete the resources that you created for the tutorial: ### Console 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. Delete the `bqml_tutorial` dataset. Deleting the dataset also deletes the remote model. 1. In the **Explorer** pane, expand your project and click **Datasets**. 2. In the **Datasets** list, click the `bqml_tutorial` dataset. 3. In the details pane, click **Delete**. 4. In the **Delete dataset** dialog, click **Delete**. 3. Delete the connection. 1. In the **Explorer** pane, expand your project and click **Connections**. 2. In the **Connections** list, click the connection that you created. 3. In the details pane, click **Delete**. 4. In the **Delete connection** dialog, enter `delete` to confirm deletion. 5. Click **Delete**. ### gcloud 1. Delete the `bqml_tutorial` dataset and the remote model: ```bash bq rm --dataset --recursive bqml_tutorial ``` 2. Delete the connection: ```bash bq rm --connection PROJECT_ID.LOCATION.CONNECTION_ID ``` Replace the following: - `PROJECT_ID`: your Google Cloud project ID - `LOCATION`: the connection location - `CONNECTION_ID`: the connection ID ## What's next - Learn more about the [`AI.GENERATE_TABLE` function](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-table.md.txt). - Learn more about [generative AI in BigQuery](https://docs.cloud.google.com/bigquery/docs/generative-ai-overview.md.txt). - Learn how to [choose a text generation function](https://docs.cloud.google.com/bigquery/docs/choose-text-generation-function.md.txt).