Merge branch 'development' into kv-OIDC-UserProvisioning

Author: Karuna-Mendix

content/en/docs/appstore/use-content/platform-supported-content/modules/SAML/_index.md

@@ -457,7 +457,7 @@ You can set up custom user provisioning by selecting the **IdP Configuration** t
     * **Allow the module to create users** – This enables the module to create users based on user provisioning and attribute mapping configurations. When disabled, it will still update existing users. However, for new users, it will display an exception message stating that the login action was successful but no user has been configured.
         * By default, the value is set to *Yes*.
     * **Default Userrole** – the role assigned to newly created users and remains unchanged even when the user's details are updated. You can select one default user role. To assign additional roles, use the Access Token Parsing Microflow. If the Access Token Processing Microflow is selected, OIDC verifies the updated default role configuration and applies any changes to the user's role. Note that, bulk updates for existing users are not automated when the default role configuration is changed.
-    * **User Type** – this allows you to configure end-users of your application as internal or external. It is created upon the creation of the user and updated each time the user logs in.
+    * **User Type** – this allows you to configure end-users of your application as internal or external. It is created upon the creation of the user and updated each time the user logs in. For more information, see [Populate User Types](/howto/monitoring-troubleshooting/populate-user-type/).
         * By default, the value is set to *Internal*. 
 
 2. Under **Attribute Mapping**, for each piece of information you want to add to your custom user entity, select an **IdP Attribute** (claim) and specify the **Configured Entity Attribute** where you want to store the information.

content/en/docs/appstore/use-content/platform-supported-content/modules/aws/amazon-bedrock.md

@@ -107,7 +107,7 @@ You can follow a similar approach to implement any of the other operations in **
 
 ### Chatting with Large Language Models using the ChatCompletions Operation
 
-A common use case of the Amazon Bedrock Connector is the development of chatbots and chat solutions. The **ChatCompletions (without history / with history)** operations offer an easy way to connect to most of the text-generation models available on Amazon Bedrock. The ChatCompletions operations are built on top of Bedrock's Converse API, allowing you to talk to different models without the need of a model-specific implementation. For more information on the ChatCompletion operations, see [GenAI Commons: Chat Completions](/appstore/modules/genai/commons/#genai-generate).
+A common use case of the Amazon Bedrock Connector is the development of chatbots and chat solutions. The **ChatCompletions (without history / with history)** operations offer an easy way to connect to most of the text-generation models available on Amazon Bedrock. The ChatCompletions operations are built on top of Bedrock's Converse API, allowing you to talk to different models without the need of a model-specific implementation. For more information on the ChatCompletion operations, see [GenAI Commons: Chat Completions](/appstore/modules/genai/genai-for-mx/commons/#genai-generate).
 
 For an overview of supported models and model-specific capabilities and limitations, see [Amazon Bedrock Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features) in the AWS documentation.
 
@@ -187,14 +187,14 @@ To invoke a Bedrock agent for your Mendix app, do the following steps:
 
 ### Token Usage {#tokenusage}
 
-[Token usage](/appstore/modules/genai/commons/#token-usage) monitoring is now possible for the following operations: 
+[Token usage](/appstore/modules/genai/genai-for-mx/commons/#token-usage) monitoring is now possible for the following operations: 
 
 * Chat Completions with History
 * Chat Completion without History
 * Embeddings with Cohere Embed
 * Embeddings with Amazon Titan Embeddings
 
-For more information about using this feature, refer to the [GenAI commons documentation](/appstore/modules/genai/commons/#token-usage).
+For more information about using this feature, refer to the [GenAI commons documentation](/appstore/modules/genai/genai-for-mx/commons/#token-usage).
 
 ## Technical Reference {#technical-reference}
 
@@ -213,20 +213,20 @@ For additional information about available operations, refer to the sections bel
 
 #### ChatCompletions (With History) and ChatCompletions (Without History) {#chat-completions}
 
-The [ChatCompletions (with history)](/appstore/modules/genai/commons/#chat-completions-with-history) and [ChatCompletions (without history)](/appstore/modules/genai/commons/#chat-completions-without-history) activities can be used with a variety of supported LLMs.
+The [ChatCompletions (with history)](/appstore/modules/genai/genai-for-mx/commons/#chat-completions-with-history) and [ChatCompletions (without history)](/appstore/modules/genai/genai-for-mx/commons/#chat-completions-without-history) activities can be used with a variety of supported LLMs.
 
 Some capabilities of the chat completions operations are currently only available for specific models:
 
-* **Function Calling** - You can use function calling in all chat completions operations using a [supported model](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html) by adding a `ToolCollection` with a `Tool` via the [Tools: Add Function to Request](/appstore/modules/genai/commons/#add-function-to-request) operation. For more information about function calling, see the [Function Calling Documentation](/appstore/modules/genai/function-calling/).
+* **Function Calling** - You can use function calling in all chat completions operations using a [supported model](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html) by adding a `ToolCollection` with a `Tool` via the [Tools: Add Function to Request](/appstore/modules/genai/genai-for-mx/commons/#add-function-to-request) operation. For more information about function calling, see the [Function Calling Documentation](/appstore/modules/genai/function-calling/).
 
 **Function calling microflows**: A microflow used as a tool for function calling must satisfy the following conditions:
 
 1. One input parameter of type String or no input parameter.
 2. Return value of type String.
 
-* **Vision** - This operation supports the *vision* capability for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). With vision, you can send image prompts, in addition to the traditional text prompts. You can use vision by adding a `FileCollection` with a `File` to the `Message` using the [Files: Initialize Collection with File](/appstore/modules/genai/commons/#initialize-filecollection) or the [Files: Add to Collection](/appstore/modules/genai/commons/#add-file-to-collection) operation. Make sure to set the `FileType` attribute to **image**.
+* **Vision** - This operation supports the *vision* capability for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). With vision, you can send image prompts, in addition to the traditional text prompts. You can use vision by adding a `FileCollection` with a `File` to the `Message` using the [Files: Initialize Collection with File](/appstore/modules/genai/genai-for-mx/commons/#initialize-filecollection) or the [Files: Add to Collection](/appstore/modules/genai/genai-for-mx/commons/#add-file-to-collection) operation. Make sure to set the `FileType` attribute to **image**.
 
-* **Document Chat** - This operation supports the ability to chat with documents for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). To send a document to the model add a `FileCollection` with a `System.FileDocument` to the `Message` using the [Files: Initialize Collection with File](/appstore/modules/genai/commons/#initialize-filecollection) or the [Files: Add to Collection](/appstore/modules/genai/commons/#add-file-to-collection) operation. For Document Chat, it is not supported to create a `FileContent` from an URL using the above mentioned operations; Please use the `System.FileDocument` option. Make sure to set the `FileType` attribute to **document**.
+* **Document Chat** - This operation supports the ability to chat with documents for [supported models](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference-supported-models-features.html). To send a document to the model add a `FileCollection` with a `System.FileDocument` to the `Message` using the [Files: Initialize Collection with File](/appstore/modules/genai/genai-for-mx/commons/#initialize-filecollection) or the [Files: Add to Collection](/appstore/modules/genai/genai-for-mx/commons/#add-file-to-collection) operation. For Document Chat, it is not supported to create a `FileContent` from an URL using the above mentioned operations; Please use the `System.FileDocument` option. Make sure to set the `FileType` attribute to **document**.
 
 #### RetrieveAndGenerate {#retrieve-and-generate}
 
@@ -270,25 +270,25 @@ The history can be enabled using the `SessionId` parameter on the RetrieveAndGen
 This activity was introduced in Amazon Bedrock Connector version 3.1.0.
 {{% /alert %}}
 
-The [Generate Image](/appstore/modules/genai/commons/#generate-image) operation can be used to generate one or more images. Currently *Amazon Titan Image Generator G1* is the only supported model for image generation of the Amazon Bedrock Connector. 
+The [Generate Image](/appstore/modules/genai/genai-for-mx/commons/#generate-image) operation can be used to generate one or more images. Currently *Amazon Titan Image Generator G1* is the only supported model for image generation of the Amazon Bedrock Connector. 
 
-`GenAICommons.ImageOptions` can be an empty object. If provided, it allows you to set additional options for Image Generation and can be created by using the [Image: Create Options](/appstore/modules/genai/commons/#imageoptions-create) operation of GenAI Commons.
+`GenAICommons.ImageOptions` can be an empty object. If provided, it allows you to set additional options for Image Generation and can be created by using the [Image: Create Options](/appstore/modules/genai/genai-for-mx/commons/#imageoptions-create) operation of GenAI Commons.
 
-To retrieve actual image objects from the response, you can use the [Image: Get Generated Image (Single)](/appstore/modules/genai/commons/#image-get-single) or [Image: Get Generated Images (List)](/appstore/modules/genai/commons/#image-get-list) helper operations from GenAI Commons. 
+To retrieve actual image objects from the response, you can use the [Image: Get Generated Image (Single)](/appstore/modules/genai/genai-for-mx/commons/#image-get-single) or [Image: Get Generated Images (List)](/appstore/modules/genai/genai-for-mx/commons/#image-get-list) helper operations from GenAI Commons. 
 
 For Titan Image models, the `Image Generation: Add Titan Image Extension` operation can be used to configure Titan image-specific values (currently only *NegativeText*). 
 
 #### Generate Embeddings (String) {#embeddings-single-string}
 
-The [Generate Embeddings (String)](/appstore/modules/genai/commons/#embeddings-string) activity can be used to generate an embedding vector for a given input string with one of the Cohere Embed models or Titan Embeddings v2.
+The [Generate Embeddings (String)](/appstore/modules/genai/genai-for-mx/commons/#embeddings-string) activity can be used to generate an embedding vector for a given input string with one of the Cohere Embed models or Titan Embeddings v2.
 
 For Cohere Embed and Titan Embeddings, the request can be associated to their respective EmbeddingsOptions extension object which can be created with the [Embeddings Options: Add Cohere Embed Extension](#add-cohere-embed-extension) or [Embeddings Options: Add Titan Embeddings Extension](#add-titan-embeddings-extension) operation. Through this extension, it is possible to tailor the operation to more specific needs.
 
 Currently, embeddings are available for the Cohere Embed family and or Titan Embeddings v2.
 
 #### Generate Embeddings (Chunk Collection) {#embeddings-chunk-collection}
 
-The [Generate Embeddings (Chunk Collection)](/appstore/modules/genai/commons/#embeddings-chunk-collection) activity can be used to generate a collection of embedding vectors for a given collection of text chunks with one of the Cohere Embed models or Titan Embeddings v2.
+The [Generate Embeddings (Chunk Collection)](/appstore/modules/genai/genai-for-mx/commons/#embeddings-chunk-collection) activity can be used to generate a collection of embedding vectors for a given collection of text chunks with one of the Cohere Embed models or Titan Embeddings v2.
 
 For each model family, the request can be associated to an extension of the EmbeddingsOptions object which can be created with either the [Embeddings Options: Add Cohere Embed Extension](#add-cohere-embed-extension) or the [Embeddings Options: Add Titan Embeddings Extension](#add-titan-embeddings-extension) operation. Through this extension, it is possible to tailor the operation to more specific needs.
 

content/en/docs/appstore/use-content/platform-supported-content/modules/aws/aws-s3-connector.md

@@ -87,6 +87,20 @@ The name generated by the expression must be all lower-case. If the bucket name
 
  {{< figure src="/attachments/appstore/platform-supported-content/modules/aws-s3-connector/microflow.png" alt="Configured microflow" class="no-border" >}}
 
+### Adding Metadata to an S3 Object
+
+You can add metadata to an S3 object using the **PutObject** or **CopyObject** operations. To retrieve this metadata, you can use the **GetObject** operation or view it directly in the Amazon S3 portal.
+
+To add metadata, perform the following steps:
+
+1. When adding metadata with **PutObject** or **CopyObject**, populate the **S3RequestMetaData** object with the necessary key-value pairs.
+
+    Every call utilizes **AbstractS3Request**, which works with **S3RequestMetaData**. This object uses **AbstractMetaData** as its generalization, enabling a one-to-many relationship for metadata management.
+
+2. Use the same **S3RequestMetaData** object to retrieve metadata with **GetObject**.
+
+{{< figure src="/attachments/appstore/platform-supported-content/modules/aws-s3-connector/s3metadata.png" alt="Metadata domain model" class="no-border" >}}
+ 
 ## Technical Reference {#technical-reference}
 
 The module includes technical reference documentation for the available entities, enumerations, activities, and other items that you can use in your application. You can view the information about each object in context by using the **Documentation** pane in Studio Pro.

content/en/docs/appstore/use-content/platform-supported-content/modules/database-connector-mx10.md

@@ -192,26 +192,26 @@ To test SSL-based connections from the Database Connection wizard, use the Certi
 1. Import the CA certificate file
     {{% /alert %}}
 
-1. If the PostgreSQL server requires Mendix to authenticate using a client certificate, add the client certificate details to the App Settings by clicking **Configuration** > **Edit** > **Custom**. See the [Running Locally](/howto/integration/use-a-client-certificate/) section of *Use a Client Certificate* for further instructions of how to add the certificate details.
+2. If the PostgreSQL server requires Mendix to authenticate using a client certificate, add the client certificate details to the App Settings by clicking **Configuration** > **Edit** > **Custom**. See the [Running Locally](/developerportal/deploy/use-a-client-certificate/) section of *Use a Client Certificate* for further instructions of how to add the certificate details.
 
     {{< figure src="/attachments/appstore/platform-supported-content/modules/external-database-connector/edit-configuration.png" class="no-border" >}}
 
-1. Add the connection details to the [Database Connection wizard](#connect-database). Fill in the following details:
+3. Add the connection details to the [Database Connection wizard](#connect-database). Fill in the following details:
     * Set SSL encryption to **Yes**
     * Set SSL mode as per your requirement
     * Add the Client certificate identifier; this must match the value provided in the custom settings dialog
 
     {{< figure src="/attachments/appstore/platform-supported-content/modules/external-database-connector/example-SSL-connection.png" class="no-border" >}}
 
-1. Click **Test Connection**.
+4. Click **Test Connection**.
 
-1. Run your application to test the connection for local runtime.
+5. Run your application to test the connection for local runtime.
 
 ### Running in the Cloud
 
 To connect to PostgreSQL when the application is running in Mendix Cloud, follow these steps:
 
-1. To configure SSL-based authentication in Mendix Cloud, add a CA certificate and client certificate for server configuration and the selected SSL mode. For more details, see the [Running in the Cloud](/howto/integration/use-a-client-certificate/#running-in-the-cloud) section of *Use a Client Certificate*.
+1. To configure SSL-based authentication in Mendix Cloud, add a CA certificate and client certificate for server configuration and the selected SSL mode. For more details, see the [Running in the Cloud](//developerportal/deploy/use-a-client-certificate/) section of *Use a Client Certificate*.
 2. After the client certificate has been added, double-click the client certificate and add the value `ClientCertificateIdentifier` to `Use Client Certificate for specific services`. This must match the value provided for the constant `ClientCertificateIdentifier`.
 3. Add the required values to the constants created for DBSource, DBUsername, DBPassword, and ClientCertificateIdentifier.