Merge pull request #271811 from wmwxwa/patch-17

Mitigate trademark issues in use-your-data.md

Author: prmerger-automator[bot]

articles/ai-services/openai/concepts/use-your-data.md

@@ -60,7 +60,7 @@ There's an [upload limit](../quotas-limits.md), and there are some caveats about
 
 You need to connect to a data source to upload your data. When you want to use your data to chat with an Azure OpenAI model, your data is chunked in a search index so that relevant data can be found based on user queries.
 
-The [Integrated Vector Database in Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) natively supports integration with Azure OpenAI On Your Data.
+The [Integrated Vector Database in vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) natively supports integration with Azure OpenAI On Your Data.
 
 For some data sources such as uploading files from your local machine (preview) or data contained in a blob storage account (preview), Azure AI Search is used. When you choose the following data sources, your data is ingested into an Azure AI Search index.
 
@@ -139,15 +139,15 @@ If you want to implement additional value-based criteria for query execution, yo
 
 [!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
 
-# [Vector Database in Azure Cosmos DB for MongoDB vCore](#tab/mongo-db)
+# [Vector Database in Azure Cosmos DB for MongoDB](#tab/mongo-db)
 
 ### Prerequisites
-* [Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/introduction) account
+* [vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/introduction) account
 * A deployed [embedding model](../concepts/understand-embeddings.md)
 
 ### Limitations
-* Only Azure Cosmos DB for MongoDB vCore is supported.
-* The search type is limited to [Integrated Vector Database in Azure Cosmos DB for MongoDB vCore](/azure/cosmos-db/mongodb/vcore/vector-search) with an Azure OpenAI embedding model.
+* Only vCore-based Azure Cosmos DB for MongoDB is supported.
+* The search type is limited to [Integrated Vector Database in Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/vector-search) with an Azure OpenAI embedding model.
 * This implementation works best on unstructured and spatial data.
 
 ### Data preparation
@@ -156,9 +156,9 @@ Use the script provided on [GitHub](https://github.com/microsoft/sample-app-aoai
 
 <!--### Add your data source in Azure OpenAI Studio
 
-To add Azure Cosmos DB for MongoDB vCore as a data source, you will need an existing Azure Cosmos DB for MongoDB vCore index containing your data, and a deployed Azure OpenAI Ada embeddings model that will be used for vector search.
+To add vCore-based Azure Cosmos DB for MongoDB as a data source, you will need an existing Azure Cosmos DB for MongoDB index containing your data, and a deployed Azure OpenAI Ada embeddings model that will be used for vector search.
 
-1. In the [Azure OpenAI portal](https://oai.azure.com/portal) chat playground, select **Add your data**. In the panel that appears, select **Azure Cosmos DB for MongoDB vCore** as the data source. 
+1. In the [Azure OpenAI portal](https://oai.azure.com/portal) chat playground, select **Add your data**. In the panel that appears, select ** vCore-based Azure Cosmos DB for MongoDB** as the data source. 
 1. Select your Azure subscription and database account, then connect to your Azure Cosmos DB account by providing your Azure Cosmos DB account username and password.
     
     :::image type="content" source="../media/use-your-data/add-mongo-data-source.png" alt-text="A screenshot showing the screen for adding Mongo DB as a data source in Azure OpenAI Studio." lightbox="../media/use-your-data/add-mongo-data-source.png":::
@@ -170,7 +170,7 @@ To add Azure Cosmos DB for MongoDB vCore as a data source, you will need an exis
 
 ### Index field mapping
 
-When you add your Azure Cosmos DB for MongoDB vCore data source, you can specify  data fields to properly map your data for retrieval.
+When you add your vCore-based Azure Cosmos DB for MongoDB data source, you can specify data fields to properly map your data for retrieval.
 
 * Content data (required): One or more provided fields that will be used to ground the model on your data. For multiple fields, separate the values with commas, with no spaces.
 * File name/title/URL: Used to display more information when a document is referenced in the chat.