Merge pull request #273759 from aahill/oyd-fixes

updating db tab

Author: v-dirichards

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

@@ -62,59 +62,15 @@ The [Integrated Vector Database in vCore-based Azure Cosmos DB for MongoDB](/azu
 
 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.
 
->[!TIP]
->If you use Azure Cosmos DB (except for its vCore-based API for MongoDB), you may be eligible for the [Azure AI Advantage offer](/azure/cosmos-db/ai-advantage), which provides the equivalent of up to $6,000 in Azure Cosmos DB throughput credits.
-
-|Data source  | Description  |
+|Data ingested through Azure AI Search  | Description  |
 |---------|---------|
 | [Azure AI Search](/azure/search/search-what-is-azure-search)  | Use an existing Azure AI Search index with Azure OpenAI On Your Data.      |
-| [Azure Cosmos DB](/azure/cosmos-db/introduction)  | Azure Cosmos DB's API for Postgres and vCore-based API for MongoDB offer natively integrated vector indexing; therefore, they don't require Azure AI Search. However, its other APIs do require Azure AI Search for vector indexing. Azure Cosmos DB for NoSQL's natively integrated vector database debuts in mid-2024.     |
 |Upload files (preview)      | Upload files from your local machine to be stored in an Azure Blob Storage database, and ingested into Azure AI Search.         |
 |URL/Web address (preview)        | Web content from the URLs is stored in Azure Blob Storage.         |
 |Azure Blob Storage (preview) | Upload files from Azure Blob Storage to be ingested into an Azure AI Search index.         |
 
 :::image type="content" source="../media/use-your-data/azure-databases-and-ai-search.png" lightbox="../media/use-your-data/azure-databases-and-ai-search.png" alt-text="Diagram of vector indexing services.":::
 
-# [Vector Database in Azure Cosmos DB for MongoDB](#tab/mongo-db)
-
-### Prerequisites
-* [vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/introduction) account
-* A deployed [embedding model](../concepts/understand-embeddings.md)
-
-### Limitations
-* 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
-
-Use the script provided on [GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts#data-preparation) to prepare your data.
-
-<!--### Add your data source in Azure OpenAI Studio
-
-To add vCore-based Azure Cosmos DB for MongoDB as a data source, you'll 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 ** 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":::
-
-1. **Select Database**. In the dropdown menus, select the database name, database collection, and index name that you want to use as your data source. Select the embedding model deployment you would like to use for vector search on this data source, and acknowledge that you'll incur charges for using vector search. Then select **Next**.
-
-    :::image type="content" source="../media/use-your-data/select-mongo-database.png" alt-text="A screenshot showing the screen for adding Mongo DB settings in Azure OpenAI Studio." lightbox="../media/use-your-data/select-mongo-database.png":::
--->
-
-### Index field mapping
-
-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 to 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.
-* Vector fields (required): Select the field in your database that contains the vectors.
-
-:::image type="content" source="../media/use-your-data/mongo-index-mapping.png" alt-text="A screenshot showing the index field mapping options for Mongo DB." lightbox="../media/use-your-data/mongo-index-mapping.png":::
-
 # [Azure AI Search](#tab/ai-search)
 
 You might want to consider using an Azure AI Search index when you either want to:
@@ -179,6 +135,33 @@ 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](#tab/mongo-db)
+
+### Prerequisites
+* [vCore-based Azure Cosmos DB for MongoDB](/azure/cosmos-db/mongodb/vcore/introduction) account
+* A deployed [embedding model](../concepts/understand-embeddings.md)
+
+### Limitations
+* 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
+
+Use the script provided on [GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT/tree/main/scripts#data-preparation) to prepare your data.
+
+### Index field mapping
+
+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 to 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.
+* Vector fields (required): Select the field in your database that contains the vectors.
+
+:::image type="content" source="../media/use-your-data/mongo-index-mapping.png" alt-text="A screenshot showing the index field mapping options for Mongo DB." lightbox="../media/use-your-data/mongo-index-mapping.png":::
+
 # [Azure Blob Storage (preview)](#tab/blob-storage)
 
 You might want to use Azure Blob Storage as a data source if you want to connect to existing Azure Blob Storage and use files stored in your containers.