Run markdownlint-cli2 on docs to find (and correct) linting errors.

Author: dbreseman

content/en/docs/apidocs-mxsdk/apidocs/authentication.md

@@ -42,4 +42,3 @@ To generate a PAT, follow the instructions in the [Personal Access Tokens](http:
 ### 3.2  Using the PAT
 
 Include an `Authorization` header with the value `MxToken {GENERATED_PAT}` to authenticate an API call. For an example, see the [Using the PAT](/apidocs-mxsdk/apidocs/webhooks-api/#use-pat) section of the *Webhooks API*.
-

content/en/docs/appstore/modules/_index.md

@@ -17,4 +17,3 @@ The modules available in the Mendix Marketplace can be added to your app to prov
 When you download these modules, they will go into the **App** > **Marketplace modules** folder.
 
 ## 2 Documents in This Category
-

content/en/docs/appstore/modules/data-importer-extension.md

@@ -189,4 +189,4 @@ To perform testing, you can do the following actions:
 
 ### 5.1 Unchecked Columns
 
-It is not possible to rename an attribute or change a data type if there are unchecked columns. To avoid this issue, format your Excel or CSV file in a way that does not require you to uncheck any columns after inputting to Studio Pro. 
+It is not possible to rename an attribute or change a data type if there are unchecked columns. To avoid this issue, format your Excel or CSV file in a way that does not require you to uncheck any columns after inputting to Studio Pro. 

content/en/docs/appstore/modules/deep-link.md

@@ -198,7 +198,7 @@ The functionality of the Deep Link module has been replaced by various built-in
 * **Page URLs** – For links to pages that have either no parameters or only parameters that are persistable entities, you should use [page URLs](/refguide/page-properties/#url). Using page URLs instead of microflow URLs increases performance speed because no microflow has to be executed. Furthermore, the URL will always be used for the page (even when opening it from a different source then the deep link). To keep the same URL as before, put the `name` of the deep link in the URL field, followed by the name of the attribute that was configured in the deep link. For example, use `product/{PageParameterName/AttributeName}` for a deep link with `name` `product` and `attribute` `AttributeName`.
     * Within the runtime setting of your application you can configure the page URL prefix. Change this from the default `p` to `link` to keep your existing URLs working. Note that after this you have to completely remove the Deep Link module from your app, or else your app will fail to start. 
 
-*  **Microflow URLs** – For cases not entirely covered by the page URL functionality, use [microflow URLs](/refguide/microflow/#url). Specifically, add a URL to the microflows you were using with the Deep Link module.
+* **Microflow URLs** – For cases not entirely covered by the page URL functionality, use [microflow URLs](/refguide/microflow/#url). Specifically, add a URL to the microflows you were using with the Deep Link module.
 
     {{% alert color="info" %}}If your Studio Pro version is below 10.9, then the microflow URLs do not support query parameters (for example `?param1=foo&param2=bar`), and therefore, you can end up with a different URL than before. To avoid this issue, make sure that you use Studio Pro version 10.9 or higher.{{% /alert %}}
 

content/en/docs/appstore/modules/openai/rag-example-implementation.md

@@ -63,7 +63,6 @@ You have access to a (remote) PostgreSQL database with the [pgvector](https://gi
 
 The OpenAI showcase application uses the Mendix [PgVector Knowledge Base](https://marketplace.mendix.com/link/component/225063) module from the Marketplace to run queries and statements on your remote database. If you want to know more about this or if you are looking for certain technical details, see [PgVector Knowledge Base](/appstore/modules/pgvector-knowledge-base/).
 
-
 The OpenAI showcase application uses this module to take care of creating the required tables in the remote vector database, including the open-source extension called pgvector. If you want to know more about the extension and how the tables look like in combination with this extension, see the [pgvector source code and documentation on GitHub](https://github.com/pgvector/pgvector).
 
 If you want to know more about RAG, embeddings, or [vector databases](/appstore/modules/pgvector-knowledge-base/vector-database-setup/), make sure to review the links on the **Resources** page of the OpenAI showcase application or in the [Read More](#read-more) section.

content/en/docs/appstore/modules/pgvector-knowledge-base/_index.md

@@ -68,6 +68,7 @@ After following the general setup above, you are all set to use the microflows a
 #### 3.2.1 `Create label` {#create-label}
 
 Labels can optionally be used to attach additional information to chunks. That will be used for custom filtering during the retrieval step. Each Label stands for a single key-value combination. In the operations to create a knowledge base Chunk, a list of Labels can be passed as optional input. During the retrieval, if a list of Labels is provided as search input, all key-value pairs passed in the form of Label objects to the operation must match any previously-attached labels to the chunk during population. Examples for typical key-value pairs are:
+
 * Category: Bug, Feature
 * Status: Open, Closed, In Progress
 * Machine Type: MachineX, MachineY
@@ -88,13 +89,13 @@ A typical pattern for populating a knowledge base is as follows:
 #### 3.3.1 `(Re)populate Knowledge Base` {#repopulate-knowledge-base}
 
 This operation handles the following:
+
 * clearing the knowledge base if it does exist 
 * creating the empty knowledge base if it does not exist
 * inserting all provided chunks with their labels into the knowledge base
 
 The population handles a whole list of Chunks at once which should be created by using the `Create Chunk` operation. It is possible to have multiple knowledge bases in the same database in parallel by providing different knowledge base names in combination with the same [DatabaseConfiguration](#databaseconfiguration-entity).
 
-
 ### 3.4 Retrieve Operations {#retrieve-operations}
 
 Currently, two operations are available for on-demand retrieval of data chunks from a knowlege base. Both operations work on a single knowledge base (specified by the name) on a single database server (specified by the [DatabaseConfiguration](#databaseconfiguration-entity)). Apart from a regular [Retrieve](#retrieve), an additional operation was exposed to [Retrieve Nearest Neighbors](#retrieve-nearest-neighbors), where the cosine distance between the input vector and the vectors of the records in the knowledge base is calculated. In both cases it is possible to filter on [Labels](#create-label).
@@ -136,7 +137,6 @@ This is an entity to store the connection details to a PostgreSQL database.
 | `Password`           | This is the encrypted password that is used during the authentication to the database server. |
 | `Username`           | This is the username that is used during the authentication to the database server.           |
 
-
 ##### 4.1.1.2 `DatabasePassword` {#databasepassword}
 
 This non-persistent entity is only used for editing the `DatabasePassword`. The database password is stored in the Mendix database as an encrypted string in the [DatabaseConfiguration](#databaseconfiguration-entity) entity. 
@@ -167,7 +167,6 @@ This entity represents a discrete piece of knowledge that needs to go into or co
 
 The **PgVectorKnowledgeBase.User** module role has read access to all attributes of `Chunk` which facilitates easy implementation on pages where retrieved data is shown.
 
-
 ##### 4.1.2.2 `Label` {#label} 
 
 This represents additional information that is to be stored with the chunks in the knowledge base. It can be used for custom filtering during retrieval. A chunk can be associated to multiple labels; labels in turn can be shared across multiple chunks.
@@ -179,7 +178,6 @@ This represents additional information that is to be stored with the chunks in t
 
 The **PgVectorKnowledgeBase.User** module role has read access to all attributes of `Label` which facilitates easy implementation on pages where retrieved data is shown.
 
-
 ### 4.2 Enumerations {#enumerations} 
 
 An enumeration is a predefined list of values that can be used as an attribute type. For more information about enumerations in general, see [Enumerations](/refguide/enumerations/). 
@@ -204,6 +202,7 @@ Activities define the actions that are executed from a microflow or nanoflow. In
 Operations that can be used in multiple knowledge base processes and do not fall into a specific category.
 
 ##### 4.3.1.1 Create Label {#create-label-technical} 
+
 The `Create Label` activity is intended for creating [Labels](#label). The given input parameters are assigned to a newly created label. The label is added to the provided `LabelList` which is intended to be used afterwards for passing into [Create Chunk](#create-chunk-technical).
 
 **Input parameters**
@@ -214,7 +213,6 @@ The `Create Label` activity is intended for creating [Labels](#label). The given
 | `Value`  | String                     | mandatory                     | This is the input to assign the value of the new label.             |
 | `LabelList`          | List of [Labels](#label)                                                    | mandatory | This is for adding the label to a list that can be used outside of this Activity. |
 
-
 #### 4.3.2 (Re)populate Operations {#repopulate-operations-technical} 
 
 Operations that support the (re)creation and population of a knowledge base.
@@ -297,19 +295,17 @@ The `DatabaseConfiguration` that is passed must contain the connection details t
 | `MaxNumberOfResults`          | Integer/Long                                                      | optional                    | This can be used to limit the number of results that should be returned. 
 | `LabelList`          | List of [Labels](#label)                                                    | optional | This list is for additional filtering in the retrieve. Only chunks that comply with the labels will be returned. |
 
-
 **Return value**
 
 | Name                 | Type                                      | Description                                                  |
 | -------------------- | ----------------------------------------- | ------------------------------------------------------------ |
 | `ChunkList` | List of [Chunks](#chunk) | This list is the result of the retrieval. |
 
-
 ## 5 Showcase Application {#showcase-application}
 
 For more inspiration or guidance on how to use those operations in your logic and how to combine it with use cases in the context of generative AI, Mendix highly recommends downloading the [OpenAI showcase app](https://marketplace.mendix.com/link/component/220475) from the Marketplace. This application contains various examples in the context of generative AI, some of which use the PgVector Knowledge Base module for storing embedding vectors.
 {{% alert color="info" %}}For more information on how to set up a vector database for retrieval augmented generation (RAG),  see [RAG Example Implementation in the OpenAI Showcase Application](/appstore/modules/openai-connector/rag-example-implementation/).{{% /alert %}}
 
 ## 6 Read More {#read-more}
 
-* [pgvector: Open-Source Extension For Vector Similarity Search For PostgreSQL](https://github.com/pgvector/pgvector?tab=readme-ov-file#pgvector)
+* [pgvector: Open-Source Extension For Vector Similarity Search For PostgreSQL](https://github.com/pgvector/pgvector?tab=readme-ov-file#pgvector)

content/en/docs/appstore/modules/snowflake/_index.md

@@ -35,4 +35,3 @@ The [REST SQL connector](https://marketplace.mendix.com/link/component/225717),
 The REST SQL connector requires an additional step to transform data rows received from the REST SQL API into Mendix objects. For more information, see [Snowflake REST SQL Connector](/appstore/connectors/snowflake/snowflake-rest-sql/).
 
 ## 3 Documents in This Category
-

content/en/docs/appstore/modules/snowflake/snowflake-rest-sql.md

@@ -21,6 +21,7 @@ The Snowflake REST SQL connector provides a way to first setup key-pair authenti
 * Use [Snowflake Cortex LLM functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions)
 
 The current version of the connector supports the following actions:
+
 * Authentication with an RSA key pair according to PKCS #8 standard
 * Execution of single SQL statements
 * Synchronous execution of calls
@@ -31,8 +32,8 @@ The Snowflake REST SQL connector requires Mendix Studio Pro version 9.18.0 or ab
 
 To use the Snowflake REST SQL connector, you must also install and configure the following modules from the Mendix marketplace:
 
-*  [Community Commons](https://marketplace.mendix.com/link/component/170) - This module is a required dependency for the Snowflake REST SQL connector.
-*  [Encryption](https://marketplace.mendix.com/link/component/1011) - This module is a required dependency for the Snowflake REST SQL connector. The EncryptionKey constant must be set up in your application settings.
+* [Community Commons](https://marketplace.mendix.com/link/component/170) - This module is a required dependency for the Snowflake REST SQL connector.
+* [Encryption](https://marketplace.mendix.com/link/component/1011) - This module is a required dependency for the Snowflake REST SQL connector. The EncryptionKey constant must be set up in your application settings.
 
 ### 1.3 Licensing and Cost
 

content/en/docs/appstore/modules/teamcenter-extension.md

@@ -15,6 +15,7 @@ The [Teamcenter Extension](https://marketplace.mendix.com/link/component/225544)
 The Teamcenter Extension offers a list of use cases for which domain models and microflows can be created. After you select a use case, it uses an import mapping approach similar to Mendix [import mapping](/refguide/import-mappings/). Here, the Teamcenter Extension allows you to select data from the business model of your Teamcenter instance. Based on the selected use case, the import mapping and, for some use cases, additional configurations, the Teamcenter Extension generates and updates the domain model for your integration and generates one or more ready-to-use microflows that you can drag and drop into your application logic.
 
 The extension offers the following integration options:
+
 * Get item revisions
 * Get workspace objects
 * Create item and item revision
@@ -55,9 +56,11 @@ Follow the instructions in [Using Marketplace Content](/appstore/overview/use-co
 The `Get ItemRevision` action allows you to generate a microflow to search for and retrieve `ItemRevisions` or its specialization from Teamcenter and the corresponding domain model. The resulting microflow implements the saved query `Item Revision...` from Teamcenter.
 
 #### 3.1.2 Get Workspace Objects {#getworkspaceobjects}
+
 The `Get Workspace Objects` action allows you to configure and generate a microflow to search for and retrieve workspace objects or their specialization from Teamcenter and the corresponding domain model. This action implements the saved query `General..` from Teamenter
 
 #### 3.1.3 Create Item and Item Revision {#createitem-and-itemrevision}
+
 The `Create Item and Item Revision` action allows you to configure and generate a microflow to create an Item with `ItemRevision` or its specializations in Teamcenter and the corresponding domain model. The resulting microflow implements the `Create Object and Update Properties` actions from the Teamcenter Connector. With the `Create Object` action, the `Item` and `ItemRevision` get created in Teamcenter, setting the Teamcenter properties that need to be set upon creation. With the `Update Properties` action, the remaining properties are updated in Teamcenter.
 
 #### 3.1.4 Update Item and Item Revision {#updateitem-and-itemrevision}
@@ -69,12 +72,15 @@ The `Update Item and Item Revision` action allows you to generate a microflow to
 The `Revise Item and Item Revision` action allows you to generate a microflow to revise an `ItemRevision` or its specializations in Teamcenter and the corresponding domain model. The resulting microflow implements the `Revise Object and Update Properties` actions from the Teamcenter Connector. With the `Revise Object` action, a new `ItemRevision` is created, setting the Teamcenter properties that need to be set upon revising. With the `Update Properties` action, the remaining properties are updated in Teamcenter.
 
 #### 3.1.6 Get Structures
+
 The `Get Structures` action allows you to generate microflows and corresponding domain model to configure a BOM window and retrieve structure data from Teamcenter. This feature supports the retrieval of structures with the following:
+
 * `RevisionRule` (or default `RevisionRule`)
 * `VariantRule`
 * `BOMWindow` property flags
 
 Depending on the configuration, microflows are generated to do the following:
+
 * Create `BOMWindow` (implementing the `Create BOM Windows2` action from the Teamcenter Connector)
 * Retrieve `RevisionRules` (implementing the `Get Revision Rules` action from the Teamcenter Connector)
 * Retrieve `VariantRules` (implementing the `Get Variant Rule` action from the Teamcenter Connector)
@@ -97,6 +103,7 @@ This feature is designed specifically for generating microflows and domain model
 | Create BOM configurations                                | Use Active Workspace           |
 
 ### 3.2 Landing Page {#homepage}
+
 To open the Teamcenter Extension in Studio Pro, go to **View** > **Teamcenter**. The landing page opens with two tabs: **Menu** and **Settings**.
 
 The **Menu** tab displays use cases or actions you can create artifacts for using the extension.
@@ -116,6 +123,7 @@ Clicking any one of the actions opens an empty [import mapping](/refguide/import
 During configuration, the import mapping page will build up a preview of the Mendix domain model involved in the integration. In addition, the import mapping page shows the corresponding business objects on the Teamcenter side. For this, the Teamcenter Extension displays both the display names of the objects and their properties, references, relations, and the corresponding technical names, as they will end up in the Mendix domain model.
 
 In the Teamcenter Extension, the import mapping consists of the following steps:
+
 1. Object mapping: As Teamcenter works with many layers of specializations of its business objects, in the import mapping page, you need to configure which object type you want to retrieve from Teamcenter and what Mendix objects need to be created, when retrieving these business objects. This is called object mapping.
 2. Selection of properties, references, and relations: Configure  which properties, references, and relations you want to retrieve from Teamcenter and include in your Mendix model .
 
@@ -146,8 +154,9 @@ In the import mapping sidebar, you can configure which Teamcenter properties, re
 The sidebar shows all properties, references, and relations for the configured Teamcenter object. Depending on the use case, each one of them is accompanied with check boxes for reading ({{% icon name="view" %}}) and writing ({{% icon name="pencil" %}}) for you to configure what to include when retrieving data from or creating data in Teamcenter.
 
 You often see that check boxes are selected by default or grayed out. In general, the following rules apply:
-1.	Properties that are already available on the Mendix entity or one of its generalizations are selected by default and cannot be unchecked.
-2.	Properties, references, and relations for Marketplace entities are disabled by default, as it is not good practice to change Mendix marketplace content.
+
+1.    Properties that are already available on the Mendix entity or one of its generalizations are selected by default and cannot be unchecked.
+2.    Properties, references, and relations for Marketplace entities are disabled by default, as it is not good practice to change Mendix marketplace content.
 
 As an example, if a check box for reading ({{% icon name="view" %}}) is selected and grayed out, it means that property already exists as an attribute on the selected object or one of its generalizations. Similarly, if a check box for writing ({{% icon name="pencil" %}}) is selected and grayed out, it means the property is required during creation or revision of the selected object.