Mx Cloud GenAI Refactoring

content/en/docs/marketplace/genai/concepts/_index.md

@@ -23,13 +23,49 @@ One of the most important components for generative AI is the Large Language Mod
 For example, you can use an LLM to do:
 
 * **Text Generation:** Creating coherent and contextually relevant text from a prompt.
-* **Content Creation:** Assisting in writing articles, stories, and marketing copy.
-* **Translation:** Translating text from one language to another.
-* **Summarization:** Condensing long documents into concise summaries.
-* **Conversational Interaction:** Powering chatbots and virtual assistants to interact naturally with users.
 * **Data Generation:** Generating Mendix objects by combining JSON generation and [Import mappings](/refguide/import-mapping-action/).
 
+### Typical LLM Use Cases {#llm-use-cases}
+
+#### Text Generation
+
+* Develop interactive AI chatbots and virtual assistants that can carry out conversations naturally and engagingly.
+* By using text generation models, you can build applications with features such as:
+
+    * Draft documents (for example, content creation: Assist in writing articles, stories, and marketing copy)
+    * Write computer code
+    * Answer questions about a knowledge base or (large) texts
+    * Analyze texts
+    * Give software a natural language interface
+    * Tutor in a range of subjects
+    * Translate languages
+    * Simulate characters for games
+
 Some LLMs, such as [Anthropic Claude](/appstore/modules/aws/amazon-bedrock/) and [GPT-4o](/appstore/modules/genai/openai/), can also use one or more images as input, allowing you to ask questions about images for use cases such as object recognition, image to text (OCR), and validating whether an image is as intended.
+
+#### Embeddings Generation
+
+Convert strings into vector embeddings for various purposes based on the relatedness of texts.
+
+Embeddings are commonly used for the following:
+
+* Semantic Search (Meaning-based Similarity)
+* Clustering 
+* Recommendations 
+* Anomaly detection 
+* Diversity measurement 
+* Classification 
+
+You can combine embeddings with text generation capabilities and leverage specific sources of information to create a smart chat functionality tailored to your knowledge base.
+
+#### Knowledge Base
+
+Adding knowledge bases helps to tailor response generation to specific contexts by grounding them in data. Knowledge bases can store discrete data (commonly called chunks) and relevant information for end-user actions or application processes can be retrieved from them.
+
+Knowledge bases are often used for:
+
+1. [Retrieval Augmented Generation (RAG)](/appstore/modules/genai/rag/) retrieves relevant knowledge from the knowledge base, incorporates it into a prompt, and sends it to the model to generate a response.
+2. Semantic search enables advanced search capabilities by considering the semantic meaning of the text, going beyond exact and approximate matching. It allows the knowledge base to be searched for similar chunks effectively.
 
 ### What is an LLM Not?
 

content/en/docs/marketplace/genai/mendix-cloud-genai/_index.md

@@ -13,14 +13,18 @@ In order to help developers integrate GenAI capabilities into custom application
 
 ## Resources Overview
 
-The GenAI Resources portal provides an overview of all the resources you have access to. You can also request new GenAI Resources through the portal.  
-
 There are three different types of resources: 
 
 * Compute – Text Generation: generates human-like text based on given inputs, essential for applications requiring natural language generation. 
 
 * Knowledge Base: A knowledge base can be used to upload your data which then can be used by a text generation resource. 
 
-* Compute – Embeddings Generation: converts text into vector representations. An embeddings resource is required to uploading data to your Knowledge Base. 
+* Compute – Embeddings Generation: converts text into vector representations. An embeddings resource is required to uploading data to your Knowledge Base.
+
+## Getting started
+
+1. Learn about GenAI Resource Packs and how to acquire them in the [Mendix Cloud GenAI Resource Packs](/appstore/modules/genai/mx-cloud-genai/resource-packs/) document.
+2. Once you have access to GenAI resources, log in to the [Mendix Cloud GenAI portal](https://genai.home.mendix.com/) to generate access keys for your resources. This portal provides an overview of all the resources you have access to and you can also request new GenAI Resources there. For more information, see [Navigate through the Mendix Cloud GenAI Portal](https://docs.mendix.com/appstore/modules/genai/mx-cloud-genai/Navigate-MxGenAI/).
+3. Use a starter app containing the [Mendix Cloud GenAI Connector](https://marketplace.mendix.com/link/component/239449) (for example, the [BlankGenAI starter app](https://marketplace.mendix.com/link/component/227934)) or implement the connector in the Mendix application according to its documentation. Once you have imported access key in its configuration overview, you are connected to Mendix Cloud GenAI and can access available resources within your application. 
 
 ## Relevant Sources

content/en/docs/marketplace/genai/mendix-cloud-genai/mendix-cloud-grp.md

@@ -35,7 +35,7 @@ The Mendix Cloud GenAI Resource Packs provide access to the following models:
 
 The models are available through the Mendix Cloud, leveraging AWS's highly secure Amazon Bedrock multi-tenant architecture. This architecture employs advanced logical isolation techniques to effectively segregate customer data, requests, and responses, ensuring a level of data protection that aligns with global security compliance requirements. Customer prompts, requests, and responses are neither stored nor used for model training. Your data remains your data.
 
-Customers looking to leverage other models in addition to the above can also take advantage of Mendix's [(Azure) OpenAI Connector](/appstore/modules/genai/reference-guide/external-connectors/openai/) and Amazon [Bedrock Connector](/appstore/modules/genai/reference-guide/external-connectors/bedrock/) to integrate numerous other models into their apps.
+Customers looking to leverage other models in addition to the above can also take advantage of Mendix's [(Azure) OpenAI Connector](/appstore/modules/genai/reference-guide/external-connectors/openai/), Amazon [Bedrock Connector](/appstore/modules/genai/reference-guide/external-connectors/bedrock/), and [Mistral Connector](/appstore/modules/genai/reference-guide/external-connectors/mistral/) to integrate numerous other models into their apps.
 
 ### Technical Details for Model Resource Packs
 
@@ -65,31 +65,15 @@ Knowledge Bases are based on elastically scaling, serverless OpenSearch vector d
 | Memory                                | Elastic       |
 | Disk Space                            | 10 GB         |
 
-## Mendix Portal
-
-The Mendix Portal allows easy access to manage the resources, through the GenAI Resources section in the portal.
-
-* Get insight into the consumption of input/output tokens against entitlements for Models
-* Manage content for Knowledge Bases
-* Manage team access to all resources
-* Create and manage connection keys to connect your apps with all resources
-* Track activity logs for team access and connection key management
-
-## Mendix Cloud GenAI Connector
-
-The [Mendix Cloud GenAI connector](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/) lets you utilize Mendix Cloud GenAI resource packs directly within your Mendix application. It allows you to integrate generative AI by dragging and dropping common operations from its toolbox. Note that any versions older than the ones listed below are no longer functional:  
-
-* GenAI for Mendix bundle v2.4.1 (Mendix 9) (contains Mendix Cloud GenAI connector) or
-* Mendix Cloud GenAI connector v3.1.1 (no `DeployedKnowledgeBase` support) or
-* Mendix Cloud GenAI connector v4.4.0 (`DeployedKnowledgeBase` support).
-
 ## Regional Availability
 
 Mendix Cloud GenAI Resource Packs are available in the following regions of Mendix Cloud:
 
 * Europe (Frankfurt) - eu-central-1
 * Canada (Montreal) - ca-central-1
 
+The availability of model versions depends on the selected region.
+
 ## Understanding Third-Party Requirements
 
 Mendix AI services are powered by third-party technologies, including AWS Bedrock, Anthropic, and Cohere. To help you succeed with your implementation, here is what to do next:
@@ -109,6 +93,28 @@ Save these links for future reference. Always review the terms before starting d
 Compliance with these terms is mandatory to maintain access to the services.
 {{% /alert %}}
 
+## More resources
+
+### Mendix Cloud GenAI Portal
+
+The [Mendix Cloud GenAI Portal](https://genai.home.mendix.com/) allows easy access to manage the resources through the GenAI Resources section of the portal.
+
+* Get insight into the consumption of input/output tokens for Text and Embeddings Generation Resources.
+* Manage content for Knowledge Bases.
+* Manage team access to all resources.
+* Create and manage connection keys to connect your apps with all resources.
+* Track activity logs for team access and connection key management.
+
+For more information, see [Navigate through the Mendix Cloud GenAI Portal](/appstore/modules/genai/mx-cloud-genai/Navigate-MxGenAI/).
+
+### Mendix Cloud GenAI Connector
+
+The [Mendix Cloud GenAI connector](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/) lets you utilize Mendix Cloud GenAI resource packs directly within your Mendix application. It allows you to integrate generative AI by dragging and dropping common operations from its toolbox. Note that any versions older than the ones listed below are no longer functional:  
+
+* GenAI for Mendix bundle v2.4.1 (Mendix 9) (contains Mendix Cloud GenAI connector) or
+* Mendix Cloud GenAI connector v3.1.1 (no `DeployedKnowledgeBase` support) or
+* Mendix Cloud GenAI connector v4.4.0 (`DeployedKnowledgeBase` support).
+
 ## FAQ
 
 ### What Happens to Data Processed by Mendix Cloud GenAI Services?