Guides / Building Search UI / UI & UX patterns / Query Suggestions

Are Query Suggestions search and indexing operations counted towards my usage?

Current plans don’t count any operations generated while building suggestions indices towards usage. All plans count searches made in the Browse section of your Algolia dashboard and any other searches made outside of the generation process towards usage. For example, any searches your users make in an autocomplete that uses Query Suggestions count towards your usage.

Whether Query Suggestions operations count towards usage varies for legacy plans (before July 1, 2020). Please reach out to your success team or the support team if you have any questions.

Are records in Query Suggestions indexes counted towards my usage?

Query Suggestions records count towards your records usage.

Is there a limit to the number of Query Suggestions index configurations I can create?

You can create up to 100 different Query Suggestion configurations per application.

Is there a limit to the number of suggestions I can create based on facet values?

When generating suggestions based on facet data, the Query Suggestions builder fetches a maximum of 1000 values per facet. That means that suggestions based on facets with high cardinality may not include all facet values. The builder also vets suggestions generated by facet values by ensuring that they fetch results and meet other configuration options.

Why do my facet-based suggestions have a popularity score of zero?

The popularity score is based on how frequently users search for a particular query in the last 30 days. Since the Query Suggestions builder generates these suggestions based on facet data alone, it can’t calculate such a metric. This is one reason to only use this suggestion generation method if you have neither Algolia Analytics nor external analytics to rely on.

Why does a suggestion I added via external analytics not have the popularity I expect?

When calculating popularity, the Query Suggestions builder considers a suggestion’s associated count. If the suggestion exists in the Algolia Analytics and the external analytics, the builder sums the count from both sources.

The Query Suggestions builder calculates the Algolia Analytics count per userToken and also include the count from normalized versions of the suggestion. Because of this, it can be difficult to predict what a suggestion’s popularity will be.

To boost or reduce a suggestion’s popularity, you need to modify the external analytic’s suggestion count.

Can I use the same source index in multiple Query Suggestions configurations?

You can create different Query Suggestions indices based on the same source index. You may want to take this approach if you want to use different ranking strategies in different scenarios, and if you use analyticsTags to create different suggestions for different use cases, for example, for mobile versus desktop users.

How can I create “localized” Query Suggestions?

You may wish to create different suggestions for users in different regions. If so, and you are already using different indices for the different regions, you can use these indices as sources for localized suggestions indices. For example, suppose you have a products_es_us index for the United States and products_es_mx, products_es_cl, etc., indices for Mexico, Chile, etc. In that case, you can use the different indices to generate separate suggestions indices based on these indices’ records and analytics.

If you have only one index that serves many different regions, you can use analyticsTags to tag searches coming from different regions. Then you can create separate Query Suggestions indices filtered by the analyticsTags.

Can I personalize Query Suggestions for individual users?

Yes, as long as you have a working Personalization implementation, you can enable personalized Query Suggestions from the dashboard. Please refer to the guide on Personalizing Query Suggestions for implementation details.

There are several ways to display suggestions. For example, you could create a standalone autocomplete that leads to a results page when users select a suggestion. For a richer UI, you could create a federated autocomplete that incorporates suggestions along with results, categories, etc. The guide on building a Query Suggestions UI helps you get started.

How do I update my suggestions?

Query Suggestions automatically updates your suggestions index once a day, every day. You can also kick off a rebuild by saving your Query Suggestions index configuration, even if you haven’t changed it. If there is already a current build underway, the Query Suggestions builder stops this build and starts a new one.

Why do edits I make to the suggestion records keep disappearing?

The Query Suggestions builder updates Query Suggestions indices once a day. If you have manually edited your suggestions in the dashboard or using an API client, the update overwrites any manual edits. If you want to customize your suggestions, either by adding new suggestions or by changing the popularity score, you can use the external analytics option instead. If you want to remove a suggestion, add it to the Banned expressions list.

Why isn’t my new Query Suggestions index appearing on the dashboard?

When you save a new Query Suggestions configuration, the Query Suggestions builder begins building the index right away. Depending on the source index’s size, the amount of Search Analytics it has, and additional sources you’ve added, this can take some time—up to a few hours. You can see the Status of a Query Suggestions index at the top of its Query Suggestions configuration page.

Can I use the Query Suggestions feature in my Magento implementation?

By default, Magento implementations rely on Magento-generated suggestions. Refer to the guide on how to implement Algolia’s Query Suggestions with a Magento implementation.

Can I use the Query Suggestions feature in my Shopify implementation?

Yes—you can set up a Query Suggestions index based on your Shopify products index’s analytics. You can then customize the Shopify front end to include suggestions from this index in the autocomplete menu.

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