removing local links

Author: jeffwmartinez

articles/app-service/deploy-intelligent-apps-dotnet-to-azure-sql.md

@@ -11,19 +11,19 @@ ms.collection: ce-skilling-ai-copilot
 
 # Deploy a .NET Blazor app connected to Azure SQL and Azure OpenAI on Azure App Service
 
-When creating intelligent apps, you may want to ground the context of your app using your own SQL data. With the recent announcement of [Azure SQL vector support (preview)](https://devblogs.microsoft.com/azure-sql/announcing-eap-native-vector-support-in-azure-sql-database/), you can ground the context using the Azure SQL data you already have with new [vector functions](https://learn.microsoft.com/en-us/sql/t-sql/functions/vector-functions-transact-sql?view=azuresqldb-current) that help manage vector data. 
+When creating intelligent apps, you may want to ground the context of your app using your own SQL data. With the recent announcement of [Azure SQL vector support (preview)](https://devblogs.microsoft.com/azure-sql/announcing-eap-native-vector-support-in-azure-sql-database/), you can ground the context using the Azure SQL data you already have with new [vector functions](https://learn.microsoft.com/sql/t-sql/functions/vector-functions-transact-sql?view=azuresqldb-current) that help manage vector data. 
 
 In this tutorial, you'll create a RAG sample application by setting up a Hybrid vector search against your Azure SQL database using a .NET 8 Blazor app. This example builds from the previous documentation to deploy a [.NET Blazor app with OpenAI](/deploy-intelligent-apps?pivots=openai-dotnet).
 
 #### Prerequisites
 
-- An [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Ckeyless%2Ctypescript-keyless%2Cpython#set-up) resource with deployed models
+- An [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/quickstart?pivots=programming-language-csharp&tabs=command-line%2Ckeyless%2Ctypescript-keyless%2Cpython#set-up) resource with deployed models
 - A .NET 8 or 9 Blazor Web App deployed on App Service
 - An Azure SQL database resource with vector embeddings.
 
 ### Set up Blazor web app
 
-For this example, we're creating a simple chat box to interact with. If you're using the prerequisite .NET Blazor app from the [previous article](/deploy-intelligent-apps?pivots=openai-dotnet), you can skip the changes to the *OpenAI.razor* file as the content is the same. However, you need to make sure the following packages are installed:
+For this example, we're creating a simple chat box to interact with. If you're using the prerequisite .NET Blazor app from the [previous article](./deploy-intelligent-apps?pivots=openai-dotnet), you can skip the changes to the *OpenAI.razor* file as the content is the same. However, you need to make sure the following packages are installed:
 
 Install the following packages to interact with Azure OpenAI and Azure SQL.
 
@@ -59,7 +59,7 @@ Install the following packages to interact with Azure OpenAI and Azure SQL.
 
 ### API keys and endpoints
 
-Using the Azure OpenAI resource requires the use of API keys and endpoint values. See the documentation in the previous article for setting up [Azure Key Vault references](/deploy-intelligent-apps?pivots=openai-dotnet#api-keys-and-endpoints) to manage and handle your secrets with Azure OpenAI. Although not required, we do recommend using managed identity to secure your client without the need to manage API keys. See the previous [documentation](/deploy-intelligent-apps?pivots=openai-dotnet#secure-your-app-with-managed-identity) to set up your Azure OpenAI client in the next step to use managed identity with Azure OpenAI.
+Using the Azure OpenAI resource requires the use of API keys and endpoint values. See the documentation in the previous article for setting up [Azure Key Vault references](./deploy-intelligent-apps?pivots=openai-dotnet#api-keys-and-endpoints) to manage and handle your secrets with Azure OpenAI. Although not required, we do recommend using managed identity to secure your client without the need to manage API keys. See the previous [documentation](./deploy-intelligent-apps?pivots=openai-dotnet#secure-your-app-with-managed-identity) to set up your Azure OpenAI client in the next step to use managed identity with Azure OpenAI.
 
 ### Add Azure OpenAI client
 
@@ -138,15 +138,15 @@ In order to prepare your Azure SQL database for vector search, you need to make
 - `text-embedding-ada-002` is used to generate the embeddings
 - `gpt-3.5-turbo` is used for the language model
 
-These two models need to be deployed before continuing the next step. Visit the [documentation](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-openai) for deploying models with Azure OpenAI using Azure AI Foundry.
+These two models need to be deployed before continuing the next step. Visit the [documentation](https://learn.microsoft.com/azure/ai-studio/how-to/deploy-models-openai) for deploying models with Azure OpenAI using Azure AI Foundry.
 
 ### Vectorize your SQL database
 
 To perform a hybrid vector search on your Azure SQL database, you first need to have the appropriate embeddings in your database. There are many ways you can vectorize your database. One option is to use the following [Azure SQL database vectorizer](https://github.com/Azure-Samples/azure-sql-db-vectorizer) to generate embeddings for your SQL database. Vectorize your Azure SQL database before continuing.
 
 ### Create procedure to generate embeddings
 
-With [Azure SQL vector support (preview)](https://devblogs.microsoft.com/azure-sql/announcing-eap-native-vector-support-in-azure-sql-database/), you can create a stored procedure that will use a Vector data type to store generated embeddings for search queries. The stored procedure invokes an external REST API endpoint to get the embeddings. See the [documentation](https://learn.microsoft.com/en-us/azure-data-studio/quickstart-sql-database) to use Azure Data Studio to connect to your database before running the query. 
+With [Azure SQL vector support (preview)](https://devblogs.microsoft.com/azure-sql/announcing-eap-native-vector-support-in-azure-sql-database/), you can create a stored procedure that will use a Vector data type to store generated embeddings for search queries. The stored procedure invokes an external REST API endpoint to get the embeddings. See the [documentation](https://learn.microsoft.com/azure-data-studio/quickstart-sql-database) to use Azure Data Studio to connect to your database before running the query. 
 
 1. Use the following to create a stored procedure with your preferred SQL query editor. You need to populate the @url parameter with your Azure OpenAI resource name and populate the rest endpoint with the API key from your text embedding model. You'll notice the model name as part of the @url, which will be populated with your search query.