Managing Coveo Machine Learning Query Suggestions Models in a Query Pipeline

Coveo™ Machine Learning (Coveo ML) is a service that leverages usage analytics data (see Coveo Machine Learning). Query Suggestions is a Coveo ML feature that optimizes query completion suggestions that can be proposed to users as they type queries in a search box (see Query Suggestions Feature).

You configure a Query Suggestions model from the Coveo Cloud administration console for a query pipeline defined in your Coveo Organization. You add a machine learning model built from a usage analytics data set recorded over a given period, and set the frequency at which the model is retrained to maintain its freshness (see Coveo Machine Learning Models). The default values for available parameters are typically appropriate for in most cases, but the following procedure details why or when you may want to change them.

For a Salesforce community search page, you create a model that looks at the last 3 months of usage analytics data and schedule to retrain the model every week to keep it fresh.

A Coveo ML Query Suggestions model automatically includes sub-models for search hubs, interfaces, and languages to ensure the query suggestions are relevant to the context. This means that you do not need to create separate models for each tab, interface, or language.

A user typing a query in English in the Knowledge Articles interface (tab) of the Community hub gets search box query completion suggestions optimized for this context combination. The suggestions may be different if the same query is typed in another interface, or obviously in another language on the same hub. If you also have an internal Agents hub, this means that the agent queries cannot affect the Community hub suggestions and vice versa.