Build an Azure AI chat app with .NET

Get started with Semantic Kernel by creating a simple .NET 8 console chat application. The application will run locally and use the OpenAI gpt-35-turbo model deployed into an Azure OpenAI account. Follow these steps to provision Azure OpenAI and learn how to use Semantic Kernel.

Get started with the .NET Azure OpenAI SDK by creating a simple .NET 8 console chat application. The application will run locally and use the OpenAI gpt-35-turbo model deployed into an Azure OpenAI account. Follow these steps to provision Azure OpenAI and learn how to use the .NET Azure OpenAI SDK.

1. From a terminal or command prompt, navigate to the semantic-kernel\02-HikerAI directory.

1. From a terminal or command prompt, navigate to the azure-openai-sdk\02-HikerAI directory.

Understanding the code

Our application uses the Microsoft.SemanticKernel package, which is available on NuGet, to send and receive requests to an Azure OpenAI service deployed in Azure.

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.

// == 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"];

The AzureOpenAIChatCompletionService service facilitates the requests and responses.

// == Create the Azure OpenAI Chat Completion Service  ==========
AzureOpenAIChatCompletionService service = new(deployment, endpoint, key);

Once the AzureOpenAIChatCompletionService service is created, we 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.

// Start the conversation with context for the AI model
ChatHistory chatHistory = new("""
    You are a hiking enthusiast who helps people discover fun hikes in their area. You are upbeat and friendly. 
    You introduce yourself when first saying hello. When helping people out, you always ask them 
    for this information to inform the hiking recommendation you provide:

    1. Where they are located
    2. What hiking intensity they are looking for

    You will then provide three suggestions for nearby hikes that vary in length after you get that information. 
    You will also share an interesting fact about the local nature on the hikes when making a recommendation.
    """);

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.

// Add user message to chat history
chatHistory.AddUserMessage("Hi! Apparently you can help me find a hike that I will like?");

// Print User Message to console
Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");

// Get response
var response = await service.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 400 });

To maintain the chat history, make sure you add the response from the model.

// Add response to chat history
chatHistory.Add(response);

// Print Response to console
Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last().Content}");

Customize the system prompt and user message to see how the model responds to help you find a hike that you'll like.

Understanding the code

Our application uses the Azure.AI.OpenAI client SDK, which is available on NuGet, to send and receive requests to an Azure OpenAI service deployed in Azure.

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.

// == Retrieve the local secrets saved during the Azure deployment ==========
var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
string openAIEndpoint = config["AZURE_OPENAI_ENDPOINT"];
string openAIDeploymentName = config["AZURE_OPENAI_GPT_NAME"];
string openAiKey = config["AZURE_OPENAI_KEY"];

// == Creating the AIClient ==========
var endpoint = new Uri(openAIEndpoint);
var credentials = new AzureKeyCredential(openAiKey);

The OpenAIClient class facilitates the requests and responses. ChatCompletionOptions specifies parameters of how the model will respond.

var openAIClient = new OpenAIClient(endpoint, credentials);

var completionOptions = new ChatCompletionsOptions
{
    MaxTokens = 400,
    Temperature = 1f,
    FrequencyPenalty = 0.0f,
    PresencePenalty = 0.0f,
    NucleusSamplingFactor = 0.95f, // Top P
    DeploymentName = openAIDeploymentName
};

Once the OpenAIClient client is created, we 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.

var systemPrompt = 
"""
You are a hiking enthusiast who helps people discover fun hikes in their area. You are upbeat and friendly. 
You introduce yourself when first saying hello. When helping people out, you always ask them 
for this information to inform the hiking recommendation you provide:

1. Where they are located
2. What hiking intensity they are looking for

You will then provide three suggestions for nearby hikes that vary in length after you get that information. 
You will also share an interesting fact about the local nature on the hikes when making a recommendation.
""";

completionOptions.Messages.Add(new ChatRequestSystemMessage(systemPrompt));

Then you can add a user message to the model by using the ChatRequestUserMessage class.

To have the model generate a response based off the system prompt and the user request, use the GetChatCompletionsAsync function.

string userGreeting = """
Hi! 
Apparently you can help me find a hike that I will like?
""";

completionOptions.Messages.Add(new ChatRequestUserMessage(userGreeting));
Console.WriteLine($"\n\nUser >>> {userGreeting}");

ChatCompletions response = await openAIClient.GetChatCompletionsAsync(completionOptions);
ChatResponseMessage assistantResponse = response.Choices[0].Message;
Console.WriteLine($"\n\nAI >>> {assistantResponse.Content}");
completionOptions.Messages.Add(new ChatRequestAssisstantMessage(assistantResponse.Content)); 

To maintain the chat history or context, make sure you add the response from the model as a ChatRequestAssistantMessage.

Customize the system prompt and user message to see how the model responds to help you find a hike that you'll like.