Create and manage a Query Suggestions (QS) model

This is for:

Developer System Administrator

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).

Create a QS model

  1. Depending on whether models have already been created in your Coveo organization:

  2. Click Next.

  3. (Optional) In the Learning interval section, you can change the default and recommended Data period and Building frequency.

  4. Click Next.

  5. (Optional) Under Apply filters on dataset, add filters to refine the data that the model uses to make its recommendations.

    Tip

    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.

  6. Click Next.

  7. In the Name your model input, enter a meaningful display name for the model, and then click Start building.

    Note

    On 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.

  8. (Optional) If you want to specify advanced parameters for your model, you can use the Advanced tab of the model configuration.

  9. 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.

    Important

    Make sure to follow the model association leading practices before associating your model with your production query pipeline.
Tip

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

  1. 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.

  2. On the subpage that opens, select the Configuration tab.

  3. Under Name, you can optionally edit the model’s display name.

  4. (Optional) In the Learning interval section, you can change the default and recommended Data period and Building frequency.

  5. (Optional) Under Apply filters on dataset, add filters to refine the data that the model uses to make its recommendations.

    Tip

    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.

  6. Click Save.

  7. On the Models (platform-ca | platform-eu | platform-au) page, under the Status column, in the model row, the value is most probably Updating.

    Note

    The 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.

  8. 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.

    Important

    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.

  1. Access the Models (platform-ca | platform-eu | platform-au) page.

  2. On the right side of the page, click menu-button, and then select Add model with JSON.

  3. 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:

    • 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).

      Note

      Unless an exportOffset is specified, the exportPeriod uses the moment when the model was generated as a base.

    • intervalTime (integer, required) is the number of intervalUnit (e.g., DAY, WEEK, or MONTH) between each update of the model. Must be between 1 and 30 inclusively.

    • intervalUnit (string enum, required) is the duration unit of the interval between each update of the model. See intervalTime. Accepted values are: DAY, WEEK, and MONTH.

  4. Click Add Model.

    The model now appears on the Models (platform-ca | platform-eu | platform-au) page.

Notes

  • The default configuration contains all the required parameters with placeholder values.

  • You can add other advanced model parameters.

  • When using an existing configuration as a starting point, make sure to change the modelDisplayName as it must be unique in a Coveo organization.

Edit a QS model JSON configuration

  1. Access the Models (platform-ca | platform-eu | platform-au) page.

  2. Click the desired model, and then click More > Edit JSON in the Action bar.

  3. 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:

    • 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).

      Note

      Unless an exportOffset is specified, the exportPeriod uses the moment when the model was generated as a base.

    • intervalTime (integer, required) is the number of intervalUnit (i.e., DAY, WEEK, or MONTH) between each update of the model. Must be between 1 and 30 inclusively.

    • intervalUnit (string enum, required) is the duration unit of the interval between each update of the model. See intervalTime. Accepted values are: DAY, WEEK, and MONTH.

    • 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 the AND operator.

    • customEventFilter (string) is the filter to apply to the custom event dimensions in the export. Multiple filter parameters are joined with the AND 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 is PT0S, meaning that all events are considered when building a model (the exportPeriod is based on the moment the model was generated).

      Example

      You want to ignore events that occur on the current day, so you set the exportOffset value to P1D.

    • searchEventFilter (string) is the filter to apply to the click and search event dimensions in the export. Multiple filter parameters are joined with the AND 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 the AND operator.

    • extraConfig (array of string) are additional advanced parameters used to tailor the model to your use case.

  4. Click Save to apply your changes.

Delete a QS model

Important

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.

  1. 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.

  2. 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

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

Active

The model is active and available.

ML status check

Build in progress

The model is currently building.

ML status building

Inactive

The model isn’t ready to be queried, such as when a model was recently created or the organization is offline.
Click See more details for additional information (see Review model information).

ML status warning

Limited

Build issues exist that may affect model performance.
Click See more details for additional information (see Review model information).

ML status warning

No query pipeline

The model isn’t associated with a query pipeline.
Click Associate with a query pipeline to go to the Query Pipelines page.

ML status warning

No case assist configuration

The model isn’t associated with a case assist configuration.
Click Associate with a case assist configuration to go to the Case Assist page.

ML status warning

Soon to be archived

The model will soon be archived because it hasn’t been queried for an extended period of time.
Click Delete to remove the model.
Learn more about archived models.

ML status warning

Error

An error prevented the model from being built successfully.
If it’s a temporary system error, check back soon. Otherwise, click See more details for additional information (see Review model information).

ML status error

Archived

The model was archived because it hasn’t been queried for at least 60 days.
Click Delete to remove the model.
Learn more about archived models.

n/a

“Learning Interval” section

In this section, you can modify the following:

  • Frequency: The rate at which the model is retrained.

  • Data period: The usage analytics data time interval on which the model will be based.

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 is regularly retrained on a more recent Coveo UA dataset, 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

Daily

Weekly

Monthly

1 month

check

check

3 months (Recommended)

check

6 months

check
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 dataset” section

The Apply filters on dataset section allows you 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 (i.e., search, click, view, or custom events).

Example

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 dataset:

  1. In the Select a dimension dropdown menu, select the dimension on which you want to base the learning of the model.

  2. In the Select an operator dropdown menu, select the appropriate operator.

  3. In the Select value(s) dropdown menu, add, type, or select the appropriate value.

  4. 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
Organization - Organization
Search - Query pipelines

View

Edit models

Organization - Organization
Search - Query pipelines

View

Machine Learning - Models

Edit