Merge branch 'development' of https://github.com/mendix/docs into development

Author: MariaShaposhnikova

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/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.
 

content/en/docs/appstore/use-content/platform-supported-content/modules/document-generation.md

@@ -365,7 +365,7 @@ If you encounter any issues while [registering your app environment](#register-a
 | Error | Error message | Description | Suggestion |
 |-------|------------------|-------------|------------|
 | **Invalid Developer Credentials** | "Invalid developer credentials" | The developer information as provided in the **Email** and **API key** fields is incorrect. | Verify that the provided email address in the **Email** field matches the username in your Mendix developer profile, and also that the API key that is being used is correct and still active. |
-| **Invalid App** | <ul><li>"Invalid app"</li></ul><ul><li>"App not found for the given user"</li></ul> | The provided apple ID is either incorrect or the developer (based on the **Email** and **API key** fields) does not have access to this app. | Verify that the **App ID** field is correct, and also that the developer account corresponding to the details entered in the **Email** and **API key** fields has access to the given app. |
+| **Invalid App** | <ul><li>"Invalid app"</li></ul><ul><li>"App not found for the given user"</li></ul> | The provided App ID is either incorrect or the developer (based on the **Email** and **API key** fields) does not have access to this app. | Verify that the **App ID** field is correct, and also that the developer account corresponding to the details entered in the **Email** and **API key** fields has access to the given app. |
 | **Invalid Application URL** | "Application URL does not match any of the environment URLs" | The app corresponding to the **App ID** field does not contain any environment that matches the URL given in the **Application URL** field. | Verify that the **App ID** and **Application URL** fields are correct. |
 | **Invalid Deployment Type** | <ul><li>"Application should be deployed on Mendix Cloud"</li></ul><ul><li>"Deployment type should be Mendix Cloud"</li></ul> | The provided **Application URL** is either incorrect or the chosen **Deployment type** is incorrect for this app. | Verify that the entered **Application URL** is correct and that you have chosen the correct **Deployment type**. |
 | **Unable to Reach App** | <ul><li>"Domain verification failed, unable to reach app"</li></ul><ul><li>"Domain verification failed, unable to reach verification endpoint"</li></ul><ul><li>"Domain verification failed, verification endpoint inactive" </li></ul>| The cloud service was unable to reach your app. | Verify that you enabled the `ASu_DocumentGeneration_Initialize` after startup microflow and also allowed access to the DocGen request handler. For more information, see [Enabling the DocGen Request Handler](#enable-docgen). |

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

@@ -2,7 +2,7 @@
 title: "Get Started with Generative AI"
 url: /appstore/modules/genai/get-started/
 linktitle: "Get Started with GenAI"
-weight: 10
+weight: 20
 description: "Describes the concepts behind generative AI and what you might implement with it."
 aliases:
     - /appstore/modules/genai/using-gen-ai/ 

content/en/docs/appstore/use-content/platform-supported-content/modules/genai/concepts/prompt-engineering.md

@@ -37,9 +37,9 @@ A user prompt is another fundamental type. It is the user’s input, question, o
 
 ### Context Prompt
 
-Depending on the project or use case, adding contextual information to the model may be necessary. Normally, this information, called context prompt or conversation history, is sent in the same interaction as the system and user prompt. It captures the historical information of the conversation to maintain coherence with the end user and be context aware.  In the Mendix app chatbot setup, developers configure this within their application, and it is included in the request sent to the LLM using the [Chat Completions (with history)](/appstore/modules/genai/commons/#chat-completions-with-history) operation.
+Depending on the project or use case, adding contextual information to the model may be necessary. Normally, this information, called context prompt or conversation history, is sent in the same interaction as the system and user prompt. It captures the historical information of the conversation to maintain coherence with the end user and be context aware.  In the Mendix app chatbot setup, developers configure this within their application, and it is included in the request sent to the LLM using the [Chat Completions (with history)](/appstore/modules/genai/genai-for-mx/commons/#chat-completions-with-history) operation.
 
-To understand this concept, imagine a user interacting with a chatbot while asking, *How should I start?*. If in previous interactions, the user asked about Mendix, the LLM will understand that the question refers to the Mendix apps. In cases where the context is not needed, such as in command-based interactions where the inquiry could be: *Turn on the lights* and the LLM does not need any historical conversation, developers can use operations like [Chat Completions (without history)](/appstore/modules/genai/commons/#chat-completions-without-history).
+To understand this concept, imagine a user interacting with a chatbot while asking, *How should I start?*. If in previous interactions, the user asked about Mendix, the LLM will understand that the question refers to the Mendix apps. In cases where the context is not needed, such as in command-based interactions where the inquiry could be: *Turn on the lights* and the LLM does not need any historical conversation, developers can use operations like [Chat Completions (without history)](/appstore/modules/genai/genai-for-mx/commons/#chat-completions-without-history).
 
 ## Typical Components of a Prompt