proofreading

Author: Karuna-Mendix

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

@@ -24,7 +24,7 @@ To use this connector, you need configuration keys to authenticate to the Mendix
 
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-The Mendix Cloud GenAI Connector module generates embeddings internally when interacting with a knowledge base. This means that you do not need to create embedding keys yourself when interacting with a Mendix Cloud knowledge base. Direct 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. This means that you do not need to create embedding keys yourself when interacting with a Mendix Cloud knowledge base. Direct 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.
 
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@@ -47,7 +47,7 @@ Follow the steps below to get started:
 * Make sure to configure the [Encryption module](/appstore/modules/encryption/#configuration) before you connect your app to Mendix Cloud GenAI.
 * Add the module role `MxGenAIConnector.Administrator` to your Administrator **User roles** in the **Security** settings of your app. 
 * Add the `Configuration_Overview` page (**USE_ME** > **Configuration**) to your navigation, or add the `Snippet_Configuration` to a page that is already part of your navigation. Alternatively, you can register your key by using the `Configuration_RegisterByString` microflow.
-* Complete the runtime setup of Mendix Cloud GenAI configuration by navigating to the page mentioned above. Import a key generated in the [Mendix Cloud GenAI Portal](https://genai.home.mendix.com) or provided to you and click **Test Key** to validate its functionality. Note that this key establishes a connection between the Mendix Cloud resources and your application. It contains all the information required to set up the connection.
+* Complete the runtime setup of the Mendix Cloud GenAI configuration by navigating to the page mentioned above. Import a key generated in the [Mendix Cloud GenAI Portal](https://genai.home.mendix.com) or provided to you and click **Test Key** to validate its functionality. Note that this key establishes a connection between the Mendix Cloud resources and your application. It contains all the information required to set up the connection.
 
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 When using an Embeddings Model Resource together with a Knowledge Base Resource, you do not need to import both keys. Importing the Knowledge Base Resource key automatically generates the connection details for the embeddings generation model.
@@ -148,19 +148,19 @@ The model uses the file name when analyzing documents, which may introduce a pot
 
 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.
 
-Below is a diagram showing how resources are organized into separate collections. This approach allows multiple use cases to share common resource while the option to only add the required collections to the conversation context is preserved. For example, both employee onboarding and IT ticket support require information about IT setup and equipment. However, only, onboarding needs knowledge about the company culture and values, while only IT support requires access to historical support ticket data.
+Below is a diagram showing how resources are organized into separate collections. This approach allows multiple use cases to share a common resource while the option to only add the required collections to the conversation context is preserved. For example, both employee onboarding and IT ticket support require information about IT setup and equipment. However, only onboarding needs knowledge about the company culture and values, while only IT support requires access to historical support ticket data.
 
 {{< figure src="/attachments/appstore/platform-supported-content/modules/genai/navigate_mxgenai/GenAIKnowledgeBaseResource.png" >}}
 
 While collections provide a mechanism for data separation, 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 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 relevant data based on specific attributes rather than relying solely on similarity-based searches.
+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 the retrieval of relevant data based on specific attributes rather than relying solely on similarity-based searches.
 
 Metadata consists of key-value pairs and serves as additional information connected to the data, though it is not part of the vectorization itself.
 
-In the employee onboarding and IT ticket support example, instead of having two different collections, such as IT setup and equipment and historical support tickets, there could be one named 'Company IT'. To retrieve tickets only and no other information from this collection, add the below metadata during insertion.
+In the employee onboarding and IT ticket support example, instead of having two different collections, such as IT setup, and equipment and historical support tickets, there could be one named 'Company IT'. To retrieve tickets only and no other information from this collection, add the metadata below during insertion.
 
 ```text
 key: `Category`, value: `Ticket`
@@ -170,7 +170,7 @@ The model then generates its response using the specified metadata instead of so
 
 {{< figure src="/attachments/appstore/platform-supported-content/modules/genai/navigate_mxgenai/GenAIKBMetadataSeparation.png" >}}
 
-Using metadata, even more fine-grained filtering becomes feasible. Each ticket may have associated metadata such as
+Using metadata, even more fine-grained filtering becomes feasible. Each ticket may have associated metadata, such as
 
 * key: `Ticket Type`, value: `Bug`
 * key: `Status`, value: `Solved`
@@ -180,7 +180,7 @@ Instead of relying solely on similarity-based searches of ticket descriptions, u
 
 #### How to get data into a knowledge base 
 
-If you are looking for a step-by-step guide on how to get your application data into a collection inside of a Mendix Cloud Knowledge Base Resource, refer to [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 inside of a Mendix application and using the connector. 
+If you are looking for a step-by-step guide on how to get your application data into a collection inside of a Mendix Cloud Knowledge Base Resource, refer to [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 inside a Mendix application and using the connector. 
 
 ### Knowledge Base Operations
 
@@ -191,7 +191,7 @@ Dealing with knowledge bases involves two main stages:
 1. [Insertion of knowledge](#knowledge-base-insertion)
 2. [Retrieval of knowledge (Nearest neighbor)](#knowledge-base-retrieval)
 
-You do not need to manually add embeddings to a chunk, as the connector handles this internally. To see all existing collections for a knowledge base configuration, go to the **Knowledge Base** tab on the [Mendix Cloud GenAI Configuration](#configuration) page and refresh the view on the right. Alternatively, use the `Get Collections` action to retrieve a synchronized list of collections inside of your knowledge base resource to include in your module. Lastly, you can delete a collection using the `Delete Collection` action.
+You do not need to manually add embeddings to a chunk, as the connector handles this internally. To see all existing collections for a knowledge base configuration, go to the **Knowledge Base** tab on the [Mendix Cloud GenAI Configuration](#configuration) page and refresh the view on the right. Alternatively, use the `Get Collections` action to retrieve a synchronized list of collections inside your knowledge base resource to include in your module. Lastly, you can delete a collection using the `Delete Collection` action.
 
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 The knowledge chunks are stored in an AWS OpenSearch Serverless database to ensure scalable and high-performance vector calculations—for example, retrieving the nearest neighbors of a given input. Inserted or modified chunks are only available for read operations (retrieval) in the knowledge base within 60-120 seconds. For more information, see [AWS documentation](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless-vector-search.html#serverless-vector-limitations).