Configure query pipelines and machine learning

After you create your IPX interface, we highly recommend that you configure query pipelines and Coveo Machine Learning (ML) for your IPX interface for enhanced relevance.

A query pipeline can be associated with one or more Coveo ML models. A pipeline can also contain custom relevance tuning rules (thesaurus, featured results, stop words, etc.), which allows you to modify incoming search requests as required.

IPX supports the following Coveo ML models:

Note

Each of the Coveo ML models listed here are optional, but we highly recommend configuring all of them for an optimal search experience.

You’ll likely want to configure two dedicated pipelines for your IPX interface:

  • A main query pipeline that’s used to process manual search requests from your IPX search interface. Your ART, QS, and Smart Snippet models will be associated to this query pipeline.

  • A recommendation query pipeline that’s used solely for the CR model.

    Note

    A recommendation query pipeline isn’t required if you don’t plan on using a Coveo ML CR model.

Step 1: Create the main query pipeline

  1. On the Query Pipelines (platform-ca | platform-eu | platform-au) page, click Add pipeline.

  2. In the Add a Query Pipeline panel that opens, select the Configuration tab.

  3. Enter a Pipeline name (for example, IPX_Workplace_Main_Pipeline).

  4. (Optional) Enter a Description for the query pipeline to help Administration Console users understand its purpose.

  5. (Optional) Select a Use case to categorize your query pipeline.

  6. Under Condition, create one of the following query pipeline conditions, where you replace <SEARCH_HUB> with the search hub value of your IPX interface.

    Note

    The In-Product Experiences (platform-ca | platform-eu | platform-au) page shows the search hub value for each of your IPX configurations.

    In-Product Experiences page

    The condition you create depends on if you plan on using a Coveo ML Content Recommendation (CR) model to allow your IPX interface to suggest content related to the current web page:

    • Search Hub is <SEARCH_HUB> and Recommendation is empty: Create this condition if you plan on using Coveo ML content recommendations.

      Note

      A CR model can’t be associated with a pipeline that contains other types of machine learning models. Since the main query pipeline is used for manual search requests and will contain other model types, such as ART and QS models, adding Recommendation is empty to the condition ensures that the main query pipeline won’t be used for content recommendations. You’ll create a dedicated query pipeline for CR when enabling recommendations for your IPX interface.

    • Search Hub is <SEARCH_HUB>: Create this condition if you don’t plan on using Coveo ML content recommendations.

      Examples
      • If your IPX search hub value is IPX_Workplace, and you plan on configuring Coveo ML content recommendations, the condition for your main query pipeline should be:

        Condition with recommendation is empty
      • If your IPX search hub value is IPX_Workplace, and you don’t plan on configuring Coveo ML content recommendations, the condition for your main query pipeline should be:

        Condition with no recommendations
  7. Click Add Pipeline.

Note

Assuming each pipeline in your organization (except the default one) has a unique condition based on a distinct search hub value, all manual search requests originating from your IPX interface will now be routed to your new pipeline.

Step 2: Configure Coveo ML ART, QS, and Smart Snippets

Configuring Coveo ML Automatic Relevance Tuning (ART) and Query Suggestions (QS) can significantly improve relevance for end users performing manual queries in your IPX search interface. Smart Snippets provide users with answers to their manual queries directly on the results page by displaying a snippet of the most relevant result item.

Note

Smart Snippets aren’t supported in classic IPX search interfaces. Classic IPX interface configurations appear with a Classic badge on the In-Product Experiences (platform-ca | platform-eu | platform-au) page.

Classic badge

Step 2A: Create ART, QS, and Smart Snippet models

Step 2B: Associate the ART, QS, and Smart Snippet models

Associate the ART, QS, and Smart Snippet models with the main query pipeline that you created for your IPX.

  1. Associate the ART model with your main query pipeline.

  2. Associate the QS model with your main query pipeline.

  3. Associate the Smart Snippet model with your main query pipeline.

Note

Assuming the ART and QS models are created successfully, and the main query pipeline is properly configured, the ART and QS features should now be enabled in your IPX interface. For Smart Snippets, you must enable the feature in your IPX search interface.

Step 2C: Enable Smart Snippets in your IPX interface

Once you’ve created your Smart Snippet model and associated it with your main query pipeline, you must enable the feature in your IPX search interface configuration.

Note

Smart Snippets aren’t supported in classic IPX search interfaces. Classic IPX interface configurations appear with a Classic badge on the In-Product Experiences (platform-ca | platform-eu | platform-au) page.

Classic badge

Step 3: Enable Coveo ML CR

Enabling the Coveo ML Content Recommendation (CR) feature will allow your IPX interface to suggest content related to the page the end user is currently viewing.

Step 3A: Create the recommendation query pipeline

  1. On the Query Pipelines (platform-ca | platform-eu | platform-au) page, click Add pipeline.

  2. In the Add a Query Pipeline panel that opens, select the Configuration tab.

  3. Enter a Pipeline name (for example, IPX_Workplace_Recommendation_Pipeline).

  4. (Optional) Enter a Description for the query pipeline to help Administration Console users understand its purpose.

  5. (Optional) Select a Use case to categorize your query pipeline.

  6. (Recommended) Under Condition, create a Search Hub is <SEARCH_HUB> AND Recommendation is Recommendation query pipeline condition, where you replace <SEARCH_HUB> with the search hub value of your IPX interface. Adding Recommendation is Recommendation to the condition ensures that this query pipeline is used for content recommendations.

    Note

    The In-Product Experiences (platform-ca | platform-eu | platform-au) page shows the search hub value for each of your IPX configurations.

    In-Product Experiences page
    Example

    If your IPX search hub value is IPX_Workplace, the condition for your recommendation query pipeline should be:

    Condition for recommendation
  7. Click Add Pipeline.

Note

The above configuration ensures that this query pipeline is used only for content recommendations, and that manual search requests from your IPX interface won’t be routed to this query pipeline. When using content recommendations, the main query pipeline that you created for your IPX interface must have a Search Hub is <SEARCH_HUB> and Recommendation is empty condition.

To complete Coveo ML CR configuration, create and associate a CR model to this pipeline.

Step 3B: Create the CR model

Note

Assuming that enough usage analytics data is available in your organization, Coveo will generate the model. Generating a model typically takes about 30 minutes, depending on the amount of data to process.

Step 3C: Associate the CR model

Associate the CR model with the recommendation query pipeline that you created for your IPX.

Important

CR models can’t be associated with pipelines that contain other model types.

Note

Assuming the platform was able to generate the CR model, and the recommendation query pipeline is properly configured, the Coveo ML CR feature should now be enabled in your IPX interface.

Step 4: Define custom relevance tuning rules (advanced)

If needed, you can define custom relevance tuning rules in the main query pipeline that you created for your IPX interface.

Use the following table as a reference:

Rule type Use case

Thesaurus

Defines synonyms to expand in user queries.

Featured results

Provides a high-ranking score boost to certain items.

Stop words

Defines terms to ignore in the basic query expression (q).

Ranking expressions

Increases or decreases the ranking scores of certain items by a certain amount.

Ranking weights

Fine-tunes the default weights of the standard index ranking factors.

Triggers

Defines actions to execute in the search panel under certain circumstances.

Filters

Appends expressions to the basic (q), advanced (aq), constant (cq), disjunction (dq), or large (lq) query expression.

Query parameters

Sets or overrides the values of certain search request parameters.

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