Get insight about your data from an .NET Azure AI chat app

Get started with Semantic Kernel and the gpt-35-turbo model, from a simple .NET 8.0 console application. Use the AI model to get analytics and information about your previous hikes. It consists of a simple console application, running locally, that will read the file hikes.md and send request to an Azure OpenAI service deployed in your Azure subscription and provide the result in the console. Follow these steps to provision Azure OpenAI and learn how to use Semantic Kernel.

Get started with the .NET Azure OpenAI with a gpt-35-turbo model, from a simple .NET 8.0 console application. Use the AI model to get analytics and information about your previous hikes. It consists of a simple console application, running locally, that will read the file hikes.md and send request to an Azure OpenAI service deployed in your Azure subscription and provide the result in the console. 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\03-ChattingAboutMyHikes directory.

1. From a terminal or command prompt, navigate to the azure-openai-sdk\03-ChattingAboutMyHikes directory.

Explore 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 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.

// 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.

// 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.

// 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.

Explore 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 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.

var systemPrompt = 
"""
You are upbeat and friendly. You introduce yourself when first saying hello. 
Provide a short answer only based on the user hiking records below:  

""" + markdown;

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! 
""";

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. It's time to make our user request about our data again using the ChatRequestUserMessage and GetChatCompletionsAsync function.

var hikeRequest = 
"""
I would like to know the ration of hike I did in Canada compare to hikes done in other countries.
""";

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