Initial check-in of 3rd sample with pivot

docs/ai/quickstarts/quickstart-ai-chat-with-data.md

@@ -27,6 +27,76 @@ Get started with the .NET Azure OpenAI with a `gpt-35-turbo` model, from a simpl
 
     If you get an error message the Azure OpenAI resources may not have finished deploying. Wait a couple of minutes and try again.
 
+<!-- markdownlint-disable MD044 -->
+:::zone target="docs" pivot="semantic-kernel"
+<!-- markdownlint-enable MD044 -->
+
+## Explore the code
+
+Our application uses the `Microsoft.SemanticKernel` client SDK, which is available on [NuGet](https://www.nuget.org/packages/Microsoft.SemanticKernel), to send and receive requests to an Azure OpenAI service deployed in Azure.
+
+The `AzureOpenAIChatCompletionService` service facilitates the requests and responses.
+
+```csharp
+// == Create the Azure OpenAI Chat Completion Service  ==========
+AzureOpenAIChatCompletionService service = new(deployment, endpoint, key);
+```
+
+The entire application is contained within the **Program.cs** file. The first several lines of code loads up secrets and configuration values that were set in the `dotnet user-secrets` for you during the application provisioning.
+
+```csharp
+// == Retrieve the local secrets saved during the Azure deployment ==========
+var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
+string endpoint = config["AZURE_OPENAI_ENDPOINT"];
+string deployment = config["AZURE_OPENAI_GPT_NAME"];
+string key = config["AZURE_OPENAI_KEY"];
+```
+
+Once the `AzureOpenAIChatCompletionService` client is created, we read the content of the file `hikes.md` and use it to provide more context to the model by adding a system prompt. This instructs the model how you'd like it to act during the conversation.
+
+```csharp
+// Provide context for the AI model
+ChatHistory chatHistory = new($"""
+    You are upbeat and friendly. You introduce yourself when first saying hello. 
+    Provide a short answer only based on the user hiking records below:  
+
+    {File.ReadAllText("hikes.md")}
+    """);
+Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
+```
+
+Then you can add a user message to the model by using the `AddUserMessage` function.
+
+To have the model generate a response based off the system prompt and the user request, use the `GetChatMessageContentAsync` function.
+
+```csharp
+// Start the conversation
+chatHistory.AddUserMessage("Hi!");
+Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
+
+chatHistory.Add(await service.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 400 }));
+Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
+```
+
+To maintain the chat history or context, make sure you add the response from the model to the `chatHistory`. It's time to make our user request about our data again using the `AddUserMessage` and `GetChatMessageContentAsync` function.
+
+```csharp
+// Continue the conversation with a question.
+chatHistory.AddUserMessage("I would like to know the ratio of the hikes I've done in Canada compared to other countries.");
+Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
+
+chatHistory.Add(await service.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 400 }));
+Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");
+```
+
+Customize the system prompt and change the request, asking for different questions (ex: How many times did you hiked when it was raining? How many times did you hiked in 2021? etc.) to see how the model responds.
+
+:::zone-end
+
+<!-- markdownlint-disable MD044 -->
+:::zone target="docs" pivot="azure-openai-sdk"
+<!-- markdownlint-enable MD044 -->
+
 ## Explore the code
 
 Our application uses the `Azure.AI.OpenAI` client SDK, which is available on [NuGet](https://www.nuget.org/packages/Azure.AI.OpenAI), to send and receive requests to an Azure OpenAI service deployed in Azure.
@@ -110,6 +180,8 @@ response = await openAIClient.GetChatCompletionsAsync(completionOptions);
 
 Customize the system prompt and change the request, asking for different questions (ex: How many times did you hiked when it was raining? How many times did you hiked in 2021? etc.) to see how the model responds.
 
+:::zone-end
+
 ## Clean up resources
 
 When you no longer need the sample application or resources, remove the corresponding deployment and all resources.