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February 03, 2025
Pinning is available for Vertex AI Search for commerce. Pinning is a serving control for that lets you specify an exact position in search results for a certain item to appear.
The pinning control is created by adding a rule to the search or browse condition, which is the action field pin_action in the Retail API. A pin value from 1 to 120 can be applied to determine the fixed position for results matching the defined conditions. This feature is not supported for recommendations.
For more about pinning, see Pinning controls.
January 10, 2025
Vertex AI Search for commerce: Renamed in the console and documentation
Vertex AI Search for retail is renamed as Vertex AI Search for commerce. The Google Cloud console and the documentation at cloud.google.com have been updated to reflect the rename. In the console, look for Search for Commerce.
September 27, 2024
Vertex AI Search for retail: Tile navigation
As part of Search for retail's Guided search package, tile navigation allows tiles to appear for each of the most likely to be used dynamic facets across a search page. The objective is to increase filter usage to narrow search faster.
For more information, see Tile navigation.
March 29, 2024
Vertex AI Retail Search: Search analytics v2 improvements
- Enhanced dashboard experience: Leverages Looker for a more interactive and informative analysis of your search and browse performance.
- Detailed metrics: Gain granular insights with per-search/per-browse metrics, along with metrics tied to search/browse visits.
- Full funnel reporting: Analyze page-views, add-to-cart events, purchases, and revenue to understand the entire customer conversion journey.
- Flexible analysis: Filter data by date ranges and device types to tailor your analysis.
December 15, 2023
Retail API: Export analytics metrics to BigQuery
You can export Retail analytics metrics into BigQuery. Exporting analytics metrics allows you to retain metrics and write SQL for your own analysis.
For more information, see Export your analytics metrics into BigQuery.
December 12, 2023
Retail Search: Retail Search with LLM public preview
Retail Search with LLM is in public preview.
Retail Search with LLM improves ranking by improving AI-driven grading of how relevant each product is for a specific query.
Prior to this upgrade, these relevance grades were generated by an older generation of AI that produced imperfect scoring. This sometimes caused low-relevance products to be highly ranked in search results.
With this upgrade, Retail Search uses state-of-the-art AI techniques to do the following:
- Develop a Giant Relevance LLM that can accurately grade product/query relevance in any retail category in any supported language.
- Distill a smaller LLM that is specific to one retailer from the Giant Relevance LLM. This smaller LLM contains the knowledge needed to grade query/product relevance for a specific retailer's catalog and their unique query stream.
- Use the smaller, retail-specific LLM to accurately grade products in real-time.
- Allow downstream AIs in Retail Search that consume these more accurate query/product relevance grades to correctly rank lower-relevance products as lower in search results.
- Lead to increases in revenues and visits for retailers by focusing early search results on higher-relevance products.
Who has access to Retail Search with LLM in the public preview phase?
You must meet the access criteria to be considered for the Public Preview. You need to have:
- A fully onboarded Retail project. This means the project is:
- Fully onboarded onto Retail Search
- Has stable usage. It is being used for production, has no sudden ramps up/down, and has no off-label (unsupported) usage
- Correctly onboarded with no major data quality issues
- Sufficient search volumes:
- Have >5M searches/day (counting search only, no browse) served by Retail Search for each of the past 30 days
If you have multiple projects, you can choose only one project to use for the public preview.
November 02, 2023
Retail API: Configure logging
You can configure which service logs are written to Cloud Logging. Logging configuration provides a way to set the severity levels at which to write logs, turn logging on or off, and override default logging settings for specific services. For information on how to change Logging configurations, see Configure Logging.
February 06, 2023
Retail Search catalog support for Korean, Polish, and Turkish is now generally available (GA). For a list of all languages supported by the Retail Search catalog, see the FAQ.
January 12, 2023
Browse search is generally available using Retail Search. Typically, browsing products using site navigation produces results that are all of equal relevance or sorted by best-selling items. Retail Search leverages AI to optimize how browse results are sorted by considering popularity, buyability, and personalization. See About text search and browse search with Retail Search.
October 27, 2022
Recording Google Analytics 4 user events to the Retail API is available in GA. If you have integrated Google Analytics 4 for your user events, you can record the user event data in Google Analytics 4 format directly to the Retail API.
To use this feature, see the Record user events with Google Analytics 4 documentation.
A/B experiment traffic monitoring for Retail Search is available in private preview. See the documentation for A/B experiment monitoring.
A/B experiments compare key metrics between the Retail API and your existing search implementation. After setting up an experiment and its traffic splitting, you can monitor experiment traffic using the Retail console. In the console, you create variant arms that map to each experiment group that you created for the A/B experiment. This allows you to check whether the actual traffic matches the intended traffic split of your experiment. Traffic monitoring can help you determine if differences in traffic are due to a quality gap between services or an incorrect experiment setup.
To use A/B experiment traffic monitoring in private preview, contact Retail Support.
October 12, 2022
Recommendations AI now provides a revenue per session optimization objective for the Others You May Like and Frequently Bought Together model types.
This objective works differently for each model type, but always optimizes for revenue by recommending items that have a higher probability of being added to carts.
For more about the revenue per session optimization objective, see the Revenue per session documentation.
September 23, 2022
Recommendations AI now provides a Page-Level Optimization model. This extends Recommendations AI from optimizing for a single recommendation panel at a time to optimizing for an entire page with multiple panels. When creating a Page-Level Optimization model, you specify existing serving configurations that this model can use as candidates for each recommendation panel. Page-Level Optimization model then automates the decision process for coordinating model combinations and layouts by automatically selecting the contents for each panel and determining the panel order on your page.
For more information about the Page-Level Optimization model, see the Page-Level Optimization documentation. For how to create this model, see Create models.
September 15, 2022
Bulk importing of historical Google Analytics 4 user events with BigQuery is generally available. You can use this feature to import user events to the Retail API if you have integrated Google Analytics 4 with BigQuery and use Enhanced Ecommerce.
See the new documentation: Import Google Analytics 4 user events with BigQuery
August 03, 2022
Serving controls can now be imported from and exported to files. This allows you to move serving controls between projects and do bulk edits and additions of serving controls within a project. This feature is available in Preview.
See the new documentation:
April 05, 2022
Retail Search is generally available.
For available features, see Features and capabilities.
For an overview of the steps to take to implement Retail Search, see Implementing the Retail API. To begin setting up Retail Search, go to Before you begin.
January 15, 2021
Recommendations AI is now generally available.
This product has migrated to the Retail API from the Recommendations Engine API.
The previous API (service endpoint https://recommendationengine.googleapis.com) and its documentation set remain available, but they will no longer be updated. If you used the previous API while it was in beta, we recommend migrating your recommendations to the Retail API (service endpoint https://retail.googleapis.com).
See the new documentation: