If you're new to AI Applications, consider following the Get started with custom recommendations tutorial to create a sample app.
Set up a Google Cloud project, turn on AI Applications, and set up access control for your project. You can use an existing Google Cloud project if you have one already.
Actions
- Review Before you begin and confirm that you have completed the steps.
Prepare your data for importing to Vertex AI Search.
For custom recommendations, you must supply structured data. This is data provided with a specific schema. For example, you can provide data in a BigQuery table or as JSON files in Cloud Storage.
Actions
Review the information about supported data and the relationship between apps and data stores in About apps and data stores.
Prepare your data according to the requirements in Prepare data for ingestion.
Create a data store and then import your data into it.
How you import your data depends on where you're importing it from. For example, if your data is in Cloud Storage, you can import it using the console or the API by providing the bucket location of your data.
Actions
- Follow the instructions for your data source in Create a custom recommendations data store.
Create your custom recommendations app and connect it to your new data store.
Actions
You can update field settings to make them filterable and filter your recommendations results using those fields.
Actions
Set specific fields as filterable to allow Vertex AI Search to use those fields for filtering recommendations. See Configure field settings.
You can preview your recommendations to check if your recommendations are appearing as expected.
Actions
To preview your recommendations, use the AI Applications console or the API.
Console. Use the console Preview page to preview your recommendations. See the Console instructions for the kind of data that your app uses in Get recommendations.
API. If you're integrating API calls into your application, make API calls to preview your recommendations. See the REST instructions for the kind of data that your app uses in Get recommendations.
When you are happy with the previews from your recommendations app, share it with your users by deploying it to your website.
Actions
To deploy your recommendations app, integrate API calls into your server or applications. For more information about making API calls, see the REST instructions for the kind of data that your app uses in Get recommendations.
For client library resources, see AI Applications client libraries.
To get personalized recommendation results, you can update the user events in your recommendation app. For more information see About user events.
Actions
You can maintain your app to ensure that latest and necessary data is available in your data store.