Create and manage a Query Suggestions (QS) model
Create and manage a Query Suggestions (QS) model
Coveo Machine Learning (Coveo ML) Query Suggestion (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’s 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 Review query suggestion candidates).
Note
To ensure optimal performance, a QS model limits the number of possible suggestions per language to a preset maximum. The limit is enforced after the most relevant query suggestions are identified and ranked, and after any manually defined default query suggestions are applied. The enforced limit is large enough to not negatively impact the quality of the suggestions. The most relevant suggestions are always recommended to the user, regardless of the enforced limit. The limit, however, may explain why a query that appears as a candidate in your data isn’t suggested for a given user query. |
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) In the Apply filters on dataset section, you can add filters to refine the data that the model uses to make its recommendations. By narrowing down the dataset 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, such as search, click, view, or custom events.
ExampleYou 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.
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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In the Select a dimension dropdown menu, select the dimension on which you want to base the learning of the model.
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In the Select an operator dropdown menu, select the appropriate operator.
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In the Select value(s) dropdown menu, add, type, or select the appropriate value.
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You can optionally add other filters by clicking Add.
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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.
If you have the Enterprise edition, group this QS model and your other implementation resources together in a project. See Manage projects. |
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) In the Apply filters on dataset section, you can add filters to refine the data that the model uses to make its recommendations. By narrowing down the dataset 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, such as search, click, view, or custom events.
ExampleYou 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.
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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In the Select a dimension dropdown menu, select the dimension on which you want to base the learning of the model.
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In the Select an operator dropdown menu, select the appropriate operator.
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In the Select value(s) dropdown menu, add, type, or select the appropriate value.
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You can optionally add other filters by clicking Add.
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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.
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 (that is,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
(that is,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 (that is,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
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 model information).
Reference
QS models reference
Default number of suggestions: 10
If you build your search interface with the Atomic framework, you can change this behavior by using the maxWithQuery
and maxWithoutQuery
properties of the atomic-search-box-query-suggestions
component.
"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.
The following table lists the possible model statuses and their definitions:
Status | Definition | Status icon |
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Active |
The model is active and available. |
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Build in progress |
The model is currently building. |
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Inactive |
The model isn’t ready to be queried, such as when a model was recently created or the organization is offline. |
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Limited |
Build issues exist that may affect model performance. |
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No query pipeline |
The model isn’t associated with a query pipeline. |
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No case assist configuration |
The model isn’t associated with a case assist configuration. |
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Soon to be archived |
The model will soon be archived because it hasn’t been queried for an extended period of time. |
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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. |
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“Learning Interval” section
In the Learning interval section, you can modify the following:
Set the Coveo ML model training Building 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 |
Building 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 |
The more data the model has access to and learns from, the better the recommendations. As a general guide, a usage analytics dataset 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 regularly retrains on a more recent Coveo UA dataset, as determined by the Building frequency and Data period settings, to ensure that the model remains up-to-date with the most recent user behavior.
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. |
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 |