Kontent.ai integration

The Kontent.ai integration brings your structured content from Kontent.ai into the composition layer, Uniform Canvas. Team members can now leverage your Kontent.ai content, along with data from any of your other sources, to create experiences without needing a developer.

Add the integration to your project#

1. In Uniform, open your project.

2. Navigate to the Integrations tab.

3. Scroll to the section Browse integrations.

4. Click Kontent.ai.

   

   The screen to add the Kontent integration to your project.

5. Click Add to project.

Configuration options#

Now that you've added the Kontent.ai integration there are two ways to bring it's information into Uniform:

• Use Uniform Mesh and its edgehancing capabilities.
• Use manual enhancers.

Learn more about approaches for connecting to external data.

Uniform recommends you connect your front-end application to any content from your Kontent.ai project, you'll need to configure the data source. Then you can add data types to help connect your Kontent.ai entities to your Uniform components with automatic content fetching and edge caching.

You can also extend the authoring interface using Kontent.ai content with personalization capability by adding a custom element.

Configure the data source#

Once you've installed the integration, the next step is to connect your Kontent.ai environment to your Uniform project by creating a new data source.

1. Navigate to Experience > Data Types. A data type is an element that can be retrieved from a data source, so the first step is to identify a data source.

2. Create a new data source by clicking the Add data type button and select the "Create new" option under the Kontent.ai label.

   

   Add a data type.
   

   Where to create a new data type from the available data sources.

3. Enter the credentials.

   

   There is a drop down list of sources available to your project.

4. Name your data source. Choose a name that helps to inform your editorial users where the content is coming from. The name of the Kontent.ai environment is a good option here.

Kontent.ai data types#

Before you start connecting Kontent.ai data to your Uniform compositions, it's important to first create some data types. Data types define which types of Kontent.ai data editors can use. This could be simple, like allowing an editor to select a single "hero" entity, or more perhaps a more complex query such as "the five latest news article entries."

1. Go to Experience > Data Types and click the Add data type button from below the Kontent.ai data source that you set-up earlier.

   

   Add a data type after selecting the data source.

2. Next, you will want to select and configure an archetype from the options below.

Archetype: Query#

The query archetype will return a list of content entries containing the queried terms, up to the depth set by the editor in Uniform. This can be useful for dynamic queries where the most up-to-date information is needed, such as a "latest content" section.

Once you configure the archetype, you can test to see what JSON data would be returned when it's used in an experience.

The data returned searching 'Title.'

Archetype: Single Item#

The single item archetype will always return a single entry from Kontent.ai, based on a selection by the editor in Uniform. This is useful for non-list elements that render as an independent item, such as a single "hero" entry or "article" entry. It can accept a variable from a dynamic token (such as a slug) and retrieve the corresponding entry.

Select a single item from available data.

Archetype: Multiple Items#

The multiple items archetype lets an editor hand pick multiple entries to render as a list. This is useful when a small selection of specific entries is required, such as in a "featured articles" carousel.

Showing the data returned after selecting mutliple items from the source.

Archetype: Items by element#

The Items by element archetype will return a list of content entries containing the exact matches to queried terms. This can be useful for dynamic queries where the most up-to-date information is needed, such as a "latest content" section.

Showing the data returned after searching elements.