Merge pull request #10055 from linaaaf/mx-genai-requirements

Mx genai requirements

Author: Karuna-Mendix

content/en/docs/marketplace/genai/mendix-cloud-genai/Mx GenAI Connector.md

@@ -10,11 +10,11 @@ aliases:
 
 ## Introduction
 
-The [Mendix Cloud GenAI connector](https://marketplace.mendix.com/link/component/239449) 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. Feel free to contact [[email protected]](mailto:[email protected]) to learn more.
+The [Mendix Cloud GenAI connector](https://marketplace.mendix.com/link/component/239449) 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. 
 
 ### Typical Use Cases
 
-The Mendix Cloud GenAI Connector is commonly used for text generation, embeddings, and knowledge bases. These use cases are described in more detail below:
+The Mendix Cloud GenAI Connector is commonly used for text generation, embeddings generation, and knowledge bases. These use cases are described in more detail below:
 
 #### Text Generation
 
@@ -31,19 +31,8 @@ The Mendix Cloud GenAI Connector is commonly used for text generation, embedding
     * Translate languages
     * Simulate characters for games
     * Image to text
-
-#### Knowledge Base
-
-The module enables tailoring generated responses to specific contexts by grounding them in data inside of a collection belonging to a Mendix Cloud GenAI knowledge base resource. This allows for the secure use of private company data or other non-public information when interacting with GenAI models within the Mendix app. It provides a low-code solution to store discrete data (commonly called chunks) in the knowledge base and retrieves relevant information for end-user actions or application processes.
-
-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.
-
-If you are looking for a step-by-step guide on how to get your application data into a Mendix Cloud Knowledge Base, refer [Grounding Your Large Language Model in Data – Mendix Cloud GenAI](/appstore/modules/genai/how-to/howto-groundllm/). Note that the Mendix Portal also provides options for importing data into your knowledge base, such as file uploads. For more information, see [Navigate through the Mendix Cloud GenAI Portal](/appstore/modules/genai/mx-cloud-genai/Navigate-MxGenAI/). This documentation focuses solely on adding data from an application using the connector.
-
-#### Embeddings
+ 
+#### Embeddings Generation
 
 Convert strings into vector embeddings for various purposes based on the relatedness of texts.
 
@@ -59,9 +48,24 @@ Embeddings are commonly used for the following:
 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.
 
 {{% alert color="info" %}}
-The Mendix Cloud GenAI Connector module generates embeddings internally when interacting with the knowledge base. Pure embedding operations are only required if additional processes, such as using the generated vectors instead of text, are needed. For example, a similar search algorithm could use vector distances to calculate relatedness.
+The Mendix Cloud GenAI Connector module generates embeddings internally when interacting with a knowledge base. Pure embedding operations are only required if additional processes, such as using the generated vectors instead of text, are needed. For example, a similar search algorithm could use vector distances to calculate relatedness.
 {{% /alert %}}
 
+#### Knowledge Base
+
+The module enables tailoring generated responses to specific contexts by grounding them in data inside of a collection belonging to a Mendix Cloud GenAI knowledge base resource. This allows for the secure use of private company data or other non-public information when interacting with GenAI models within the Mendix app. It provides a low-code solution to store discrete data (commonly called chunks) in the knowledge base and retrieves relevant information for end-user actions or application processes.
+
+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.
+
+If you are looking for a step-by-step guide on how to get your application data into a Mendix Cloud Knowledge Base, refer [Grounding Your Large Language Model in Data – Mendix Cloud GenAI](/appstore/modules/genai/how-to/howto-groundllm/). Note that the Mendix Portal also provides options for importing data into your knowledge base, such as file uploads. For more information, see [Navigate through the Mendix Cloud GenAI Portal](/appstore/modules/genai/mx-cloud-genai/Navigate-MxGenAI/). This documentation focuses solely on adding data from an application using the connector. 
+
+##### Architecture
+
+A Knowledge Base resource can comprise several collections. Each collection is specifically designed to hold numerous documents, serving as a logical grouping for related information based on its shared domain, purpose, or thematic focus. While collections provide a mechanism for data separation, with each corresponding to a [DeployedKnowledgebase](/appstore/modules/genai/genai-for-mx/commons/#deployed-knowledge-base), it is not best practice to create a large number of collections within a single Knowledge Base resource. A more performant and practical approach for achieving fine-grained data separation is through the strategic use of metadata. To learn more, see [Retrieve and Generate](/appstore/modules/genai/mx-cloud-genai/MxGenAI-connector/#retrieve-and-generate).
+
 ### Features
 
 In the current version, Mendix supports text generation (including function/tool calling, chat with images, and chat with documents), vector embedding generation, knowledge base storage, and retrieval of knowledge base chunks.

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

@@ -24,12 +24,13 @@ Mendix Cloud GenAI Resource Packs is a premium Mendix product that requires an a
 
 Mendix Cloud Model Resource Packs provide customers with a monthly quota of input and output tokens for Anthropic's Claude and Cohere's Embed models. This allows customers to implement typical Generative AI use cases using text generation, embeddings, and knowledge bases.
 
-### Supported models
+### Supported Models
 
 The Mendix Cloud GenAI Resource Packs provide access to the following models:
 
-* Anthropic Claude v3.5 Sonnet v1
-* Cohere Embed v3 (English & multilingual options)
+* Anthropic Claude 3.5 Sonnet v1
+* Anthropic Claude 3.7 Sonnet (Cross-region inference profile)
+* Cohere Embed v3 (English and multilingual options)
 
 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.
 
@@ -39,8 +40,8 @@ Customers looking to leverage other models in addition to the above can also tak
 
 | GenAI Model Resource Pack Plan             | S                 | M                 | L                 |
 | ------------------------------------------ | ----------------- | ----------------- | ----------------- |
-| Anthropic Claude V3.5 (Tokens in/month)    | 2.5 million in    | 5 million         | 10 million        |
-| Anthropic Claude V3.5 (Tokens out/month)   | 1.25 million out  | 2.5 million       | 5 million         |
+| Anthropic Claude (any version) (Tokens in/month)    | 2.5 million in    | 5 million         | 10 million        |
+| Anthropic Claude (any version) (Tokens out/month)   | 1.25 million out  | 2.5 million       | 5 million         |
 | Cohere Embed V3 (Tokens in/month)          | 5 million in      | 10 million        | 20 million        |
 
 ## Knowledge Bases
@@ -75,7 +76,11 @@ The Mendix Portal allows easy access to manage the resources, through the GenAI
 
 ## 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.
+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
 

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

@@ -77,14 +77,16 @@ On the **Content** page, you can find information on adding knowledge to your Kn
 Currently, you have the following options for adding data to a Knowledge Base:
 
 * Add files (for example, TXT or PDF)
-* Add data from a Mendix application
+* Add data from a Mendix application.
 
 #### Add Files 
 
 When you select the **Add Files Like .TXT or .PDF** option, you can upload documents directly to the GenAI portal. Before uploading, you also have the option to add metadata. For more information, see the [metadata](#metadata) section below.
 
 {{% alert color="info" %}} Only TXT and PDF files are supported. {{% /alert %}} 
 
+Before uploading, you can choose to upload the data to a new collection, the default collection, or another existing collection within the resource. A Knowledge Base resource can comprise several collections. Each collection is specifically designed to hold numerous documents, serving as a logical grouping for related information based on its shared domain, purpose, or thematic focus. While collections provide a mechanism for data separation, with each corresponding to a [DeployedKnowledgebase](/appstore/modules/genai/genai-for-mx/commons/#deployed-knowledge-base), it is not best practice to create a large number of collections within a single Knowledge Base resource. A more performant and practical approach for achieving fine-grained data separation is through the strategic use of metadata.   
+
 ##### Metadata {#metadata}
 
 Metadata is additional information that can be attached to data in a GenAI knowledge base. Unlike the actual content, metadata provides structured details that help in organizing, searching, and filtering information more efficiently. It helps manage large datasets by allowing to retrieve of relevant data based on specific attributes rather than relying solely on similarity-based searches.