Use ID instead of range (#45659)

Author: gewarren

docs/ai/quickstarts/build-vector-search-app.md

@@ -154,13 +154,13 @@ Complete the following steps to create a .NET console app that can:
 
 1. In the **Program.cs** file, add the following code to create a data set that describes a collection of cloud services:
 
-   :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" range="8-46":::
+   :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" id="DataSet":::
 
 1. Create and configure an `IEmbeddingGenerator` implementation to send requests to an embedding AI model:
 
     :::zone target="docs" pivot="azure-openai"
 
-    :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" range="48-58":::
+    :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" id="EmbeddingGen":::
 
     > [!NOTE]
     > <xref:Azure.Identity.DefaultAzureCredential> searches for authentication credentials from your local tooling. If you aren't using the `azd` template to provision the Azure OpenAI resource, you'll need to assign the `Azure AI Developer` role to the account you used to sign in to Visual Studio or the Azure CLI. For more information, see [Authenticate to Azure AI services with .NET](../azure-ai-services-authentication.md).
@@ -175,13 +175,13 @@ Complete the following steps to create a .NET console app that can:
 
 1. Create and populate a vector store with the cloud service data. Use the `IEmbeddingGenerator` implementation to create and assign an embedding vector for each record in the cloud service data:
 
-    :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" range="61-70":::
+    :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" id="VectorStore":::
 
    The embeddings are numerical representations of the semantic meaning for each data record, which makes them compatible with vector search features.
 
 1. Create an embedding for a search query and use it to perform a vector search on the vector store:
 
-    :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" range="72-88":::
+    :::code language="csharp" source="snippets/chat-with-data/azure-openai/program.cs" id="Search":::
 
 1. Use the `dotnet run` command to run the app:
 

docs/ai/quickstarts/snippets/chat-with-data/azure-openai/Program.cs

@@ -6,6 +6,7 @@
 using Microsoft.SemanticKernel.Connectors.InMemory;
 using VectorDataAI;
 
+// <SnippetDataSet>
 var cloudServices = new List<CloudService>()
 {
     new CloudService
@@ -45,7 +46,9 @@
             Description="Information retrieval at scale for traditional and conversational search applications, with security and options for AI enrichment and vectorization."
         }
 };
+// </SnippetDataSet>
 
+// <SnippetEmbeddingGen>
 // Load the configuration values
 var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
 string endpoint = config["AZURE_OPENAI_ENDPOINT"];
@@ -57,7 +60,9 @@
         new Uri(endpoint),
         new DefaultAzureCredential())
             .AsEmbeddingGenerator(modelId: model);
+// </SnippetEmbeddingGen>
 
+// <SnippetVectorStore>
 // Create and populate the vector store
 var vectorStore = new InMemoryVectorStore();
 Microsoft.Extensions.VectorData.IVectorStoreRecordCollection<int, CloudService> cloudServicesStore = vectorStore.GetCollection<int, CloudService>("cloudServices");
@@ -68,7 +73,9 @@
     service.Vector = await generator.GenerateEmbeddingVectorAsync(service.Description);
     await cloudServicesStore.UpsertAsync(service);
 }
+// </SnippetVectorStore>
 
+// <SnippetSearch>
 // Convert a search query to a vector and search the vector store
 string query = "Which Azure service should I use to store my Word documents?";
 ReadOnlyMemory<float> queryEmbedding = await generator.GenerateEmbeddingVectorAsync(query);
@@ -86,3 +93,4 @@
     Console.WriteLine($"Vector match score: {result.Score}");
     Console.WriteLine();
 }
+// </SnippetSearch>