Create and manage a Query Suggestions (QS) model
Create and manage a Query Suggestions (QS) model
Coveo Machine Learning (Coveo ML) Query Suggestions (QS) models provide end-users with relevant query completion suggestions.
To take advantage of Coveo ML QS, members with the required privileges must first create their QS models.
Prerequisites
Ensure that your Coveo organization collects usage analytics data for the search hub on which you want to activate Query Suggestions (QS) (see Coveo Usage Analytics overview).
QS models are based on Coveo usage analytics data, so if no data is available, there will be no suggestions. If you recently started collecting usage analytics data, suggestions will improve as more data becomes available each time the model is retrained.
When your search interface has low traffic, you may wonder if there is enough usage analytics data to return relevant query suggestions. You can review which queries logged by Coveo Usage Analytics (Coveo UA) match minimal QS requirements (see Reviewing Coveo Machine Learning query suggestion candidates).
Create a QS model
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Depending on whether models have already been created in your Coveo organization:
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If your Coveo organization doesn’t contain any models, on the Models (platform-ca | platform-eu | platform-au) page, click the Query Suggestions card.
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If your Coveo organization already contains models, on the Models (platform-ca | platform-eu | platform-au) page, click Add model, and then click the Query Suggestions card.
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Click Next.
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(Optional) In the Learning interval section, you can change the default and recommended Data period and Building frequency.
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Click Next.
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(Optional) Under Apply filters on data set, add filters to refine the data that the model uses to make its recommendations.
The Data volume preview section shows the impact of filters on the data that’s available to train the model. This section includes data gathered only during the last seven days and doesn’t consider the selections from the Learning interval section.
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Click Next.
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In the Name your model input, enter a meaningful display name for the model, and then click Start building.
NoteOn the Models (platform-ca | platform-eu | platform-au) page, under the Status column, in the model row, the value is most probably Inactive.
The model value will change to Active when the model creation is complete (typically within 30 minutes, depending on the amount of usage analytics data to process). The model can only return recommendations when its status is Active.
For more information on Coveo ML model statuses, see the Status column reference.
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(Optional) If you want to specify advanced parameters for your model, you can use the Advanced tab of the model configuration.
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You can then associate the model with a pipeline to take advantage of the model in a search interface and test your model to ensure that it behaves as expected.
Make sure to follow the model association leading practices before associating your model with your production query pipeline.
Edit a QS model
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On the Models (platform-ca | platform-eu | platform-au) page, click the model you want to edit, and then click Edit in the Action bar.
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On the subpage that opens, select the Configuration tab.
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Under Name, you can optionally edit the model’s display name.
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(Optional) In the Learning interval section, you can change the default and recommended Data period and Building frequency.
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(Optional) Under Apply filters on data set, add filters to refine the data that the model uses to make its recommendations.
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Click Save.
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On the Models (platform-ca | platform-eu | platform-au) page, under the Status column, in the model row, the value is most probably Updating.
NoteThe model value will change to Active when the model edition is complete (typically within 30 minutes, depending on the amount of usage analytics data to process). The model can only return recommendations when its status is Active.
For more information on Coveo ML model statuses, see the Status column reference.
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You can then associate the model with a pipeline to take advantage of the model in a search interface and test your model to ensure that it behaves as expected.
Make sure to follow the model association leading practices before associating your model with your production query pipeline.
Create a QS model with JSON
Advanced users who have the required privileges may want to create a model using a JSON configuration.
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Access the Models (platform-ca | platform-eu | platform-au) page.
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On the right side of the page, click
, and then select Add model with JSON.
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In the Add a Model With JSON panel that appears, select Model configuration, and then modify the model placeholder configuration:
{ "engineId": "querysuggest", "modelName": "<MODEL_NAME>", "modelDisplayName": "<MODEL_DISPLAY_NAME>", "exportPeriod": "<EXPORT_PERIOD>", "intervalTime": <INTERVAL_TIME>, "intervalUnit": "<INTERVAL_UNIT>" }
Where:
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modelName
(string, required) is the name of the model, which must be unique in your Coveo organization. -
modelDisplayName
(string) is the name of the model appearing on the Models (platform-ca | platform-eu | platform-au) page. -
exportPeriod
(ISO-8601 string, required) is the period defining the age of the usage analytics data used to build the model. Must be in the ISO8601 period format (e.g.,PyYmMwWdDThHmMsS
).NoteUnless an
exportOffset
is specified, theexportPeriod
uses the moment when the model was generated as a base. -
intervalTime
(integer, required) is the number ofintervalUnit
(e.g.,DAY
,WEEK
, orMONTH
) between each update of the model. Must be between1
and30
inclusively. -
intervalUnit
(string enum, required) is the duration unit of the interval between each update of the model. SeeintervalTime
. Accepted values are:DAY
,WEEK
, andMONTH
.
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Click Add Model.
The model now appears on the Models (platform-ca | platform-eu | platform-au) page.
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Notes
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Edit a QS model JSON configuration
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Access the Models (platform-ca | platform-eu | platform-au) page.
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Click the desired model, and then click More > Edit JSON in the Action bar.
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In the Edit a Model JSON Configuration panel that appears, modify the existing model configuration:
{ "modelDisplayName": "<MODEL_DISPLAY_NAME>", "exportPeriod": "<EXPORT_PERIOD>", "intervalTime": <INTERVAL_TIME>, "intervalUnit": "<INTERVAL_UNIT>", "commonFilter": "<COMMON_FILTER>", "customEventFilter": "<CUSTOM_EVENT_FILTER>", "exportOffset": "<EXPORT_OFF_SET>", "searchEventFilter": "<SEARCH_EVENT_FILTER>", "viewEventFilter": "<VIEW_EVENT_FILTER>", "extraConfig": [<ADVANCED_ML_PARAMETERS>], }
Where:
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modelDisplayName
(string) is the name of the model appearing on the Models (platform-ca | platform-eu | platform-au) page. -
exportPeriod
(ISO-8601 string, required) is the period defining the age of the usage analytics data used to build the model. Must be in the ISO8601 period format (i.e.,PyYmMwWdDThHmMsS
).NoteUnless an
exportOffset
is specified, theexportPeriod
uses the moment when the model was generated as a base. -
intervalTime
(integer, required) is the number ofintervalUnit
(i.e.,DAY
,WEEK
, orMONTH
) between each update of the model. Must be between1
and30
inclusively. -
intervalUnit
(string enum, required) is the duration unit of the interval between each update of the model. SeeintervalTime
. Accepted values are:DAY
,WEEK
, andMONTH
. -
commonFilter
(string) is the filter to apply to the common event dimensions (shared by all event types) in the export. Multiple filter parameters are joined with theAND
operator. -
customEventFilter
(string) is the filter to apply to the custom event dimensions in the export. Multiple filter parameters are joined with theAND
operator. -
exportOffset
(ISO-8601 string) is the offset of the usage analytics data used to build the model. Must be in the ISO8601 period format (i.e.,PyYmMwWdDThHmMsS
). The default value isPT0S
, meaning that all events are considered when building a model (theexportPeriod
is based on the moment the model was generated).ExampleYou want to ignore events that occur on the current day, so you set the
exportOffset
value toP1D
. -
searchEventFilter
(string) is the filter to apply to the click and search event dimensions in the export. Multiple filter parameters are joined with theAND
operator. -
viewEventFilter
(string) is the filter to apply to the view event dimensions (shared by all event types) in the export. Multiple filter parameters are joined with theAND
operator. -
extraConfig
(array of string) are additional advanced parameters used to tailor the model to your use case.
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Click Save to apply your changes.
Delete a QS model
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You must Dissociate a model from all its associated query pipelines before deleting it. Models aren’t automatically dissociated from pipelines when they’re deleted. |
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On the Models (platform-ca | platform-eu | platform-au) page, click the ML model that you want to delete, and then click More > Delete in the Action bar.
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In the Delete a Model panel that appears, click Delete model.
Review active model information
On the Models (platform-ca | platform-eu | platform-au) page, click the desired model (must be Active), and then click Open in the Action bar (see Reviewing Coveo Machine Learning model information).
Reference
QS models reference
Default number of suggestions: 10
The JavaScript Search Framework’s Omnibox
component overrides this value at query time with its own default of 5 (see numberOfSuggestions
option).
Therefore, in a JavaScript Search Framework-powered search interface, a maximum of 5 (not 10) query suggestions is recommended by default.
"Status" column
On the Models (platform-ca | platform-eu | platform-au) page of the Administration Console, the Status column indicates the current state of your Coveo ML models.
When inspecting the Status column, a given model is either in the Active or Inactive state. Additional information can be displayed depending on the model’s current state.

The following table lists the possible model statuses, their definitions, and their status colors as shown in the Administration Console:
Status | Definition | Status color |
---|---|---|
Active |
The model is active and available. |
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Inactive |
The model isn’t available. |
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Update in queue |
Waiting to process a scheduled update or configuration change. |
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Updating |
The model is being rebuilt based on a new configuration. |
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Waiting |
The model is in the building queue. |
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Building |
The model is currently being processed. |
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Degraded |
The model is active, but has some limitations. Additional information is available in the Error section of a model (see Review Coveo Machine Learning model information). |
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Failed |
The model couldn’t be built with the requested configuration. Additional information is available in the Error section of a model (see Review Coveo Machine Learning model information). See Edit a QS model for information on how to edit a model configuration. |
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Update failed |
The model couldn’t be updated with the requested configuration. |
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Error |
An error prevented the model from being built successfully. |
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Archived |
The model was archived because it hasn’t been queried for at least 30 days. |
“Learning Interval” section
In this section, you can modify the following:
The more data the model has access to and learns from, the better the recommendations. As a general guide, a usage analytics data set of 10,000 queries or more typically allows a Coveo ML model to provide very relevant recommendations. You can look at your Coveo Usage Analytics (Coveo UA) data to evaluate the volume of queries on your search hub, and ensure that your Coveo ML models are configured with a training Data Period that corresponds to at least 10,000 queries. When your search hub serves a very high volume of queries, you can consider reducing the data period so that the model learns only more recent user behavior and be more responsive to trends.
A Coveo ML model is regularly retrained on a more recent Coveo UA data set, to ensure that recent user behavior is learned and that the model freshness maintained.
Set the Coveo ML model training Frequency based on the Data Period value. Less frequent for a larger Data Period and more frequent for a smaller Data Period as recommended in the following table.
Data Period |
Frequency |
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---|---|---|---|
Daily |
Weekly |
Monthly |
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1 month |
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3 months (Recommended) |
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6 months |
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Note
If you’re testing the model in a sandbox environment in which very little analytics data is available to train the model, you can activate the Test configuration mode advanced option to ensure the model provides recommendations. |
“Apply filters on data set” section
The Apply filters on data set section allows you to refine the data that the model uses to make its recommendations. By narrowing down the data set that a model uses, you can better customize relevancy for specific user groups and use cases. You can apply filters on all events, or on every event that belongs to a specific category (i.e., search, click, view, or custom events).
You want your QS model to return queries that pertain to a specific user group, so you add a data filter to ensure that only a specific set of analytics are used by the model for training purposes.
To add a filter
Under Apply filters on data set:
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In the Select a dimension drop-down menu, select the dimension on which you want to base the learning of the model.
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In the Select an operator drop-down menu, select the appropriate operator.
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In the Select value(s) drop-down menu, add, type, or select the appropriate value.
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You can optionally add other filters by clicking Add.
Required privileges
By default, members with the required privileges can view and edit elements of the Models (platform-ca | platform-eu | platform-au) page.
The following table indicates the privileges required to use elements of the Models page and associated panels (see Manage privileges and Privilege reference).
Action | Service - Domain | Required access level |
---|---|---|
View models |
Machine Learning - Models |
View |
Edit models |
Organization - Organization |
View |
Machine Learning - Models |
Edit |