In this quickstart, you create a .NET console AI chat app to connect to an AI model with local function calling enabled. The app uses the Microsoft.Extensions.AI library so you can write code using AI abstractions rather than a specific SDK. AI abstractions enable you to change the underlying AI model with minimal code changes.
On Windows, PowerShell v7+ is required. To validate your version, run pwsh in a terminal. It should return the current version. If it returns an error, execute the following command: dotnet tool update --global PowerShell.
You can also use Semantic Kernel to accomplish the tasks in this article. Semantic Kernel is a lightweight, open-source SDK that lets you build AI agents and integrate the latest AI models into your .NET apps.
Clone the sample repository
You can create your own app using the steps in the sections ahead, or you can clone the GitHub repository that contains the completed sample apps for all of the quickstarts. If you plan to use Azure OpenAI, the sample repo is also structured as an Azure Developer CLI template that can provision an Azure OpenAI resource for you.
The sample GitHub repository is structured as an Azure Developer CLI (azd) template, which azd can use to provision the Azure OpenAI service and model for you.
From a terminal or command prompt, navigate to the src\quickstarts\azure-openai directory of the sample repo.
Run the azd up command to provision the Azure OpenAI resources. It might take several minutes to create the Azure OpenAI service and deploy the model.
azd up
azd also configures the required user secrets for the sample app, such as the Azure OpenAI endpoint and model name.
From a terminal or command prompt, navigate to the root of your project directory.
Run the following commands to configure your Azure OpenAI endpoint and model name for the sample app:
dotnet user-secrets init
dotnet user-secrets set AZURE_OPENAI_ENDPOINT <your-openai-key>
dotnet user-secrets set AZURE_OPENAI_GPT_NAME <your-azure-openai-model-name>
Configure the app
Navigate to the root of your .NET projet from a terminal or command prompt.
Run the following commands to configure your OpenAI API key as a secret for the sample app:
dotnet user-secrets init
dotnet user-secrets set OpenAIKey <your-openai-key>
dotnet user-secrets set ModelName <your-openai-model-name>
Add the app code
The app uses the Microsoft.Extensions.AI package to send and receive requests to the AI model.
In the Program.cs file, add the following code to connect and authenticate to the AI model. The ChatClient is also configured to use function invocation, which allows .NET functions in your code to be called by the AI model.
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.AI;
using Azure.AI.OpenAI;
using Azure.Identity;
var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
string endpoint = config["AZURE_OPENAI_ENDPOINT"];
string deployment = config["AZURE_OPENAI_GPT_NAME"];
IChatClient client =
new ChatClientBuilder()
.UseFunctionInvocation()
.Use(
new AzureOpenAIClient(new Uri(endpoint),
new DefaultAzureCredential())
.AsChatClient(deployment));
Note
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.
using Microsoft.Extensions.AI;
using Microsoft.Extensions.Configuration;
using OpenAI;
var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
string model = config["ModelName"];
string key = config["OpenAIKey"];
IChatClient client =
new ChatClientBuilder()
.UseFunctionInvocation()
.Use(
new OpenAIClient(key)
.AsChatClient(model));
Create a new ChatOptions object that contains an inline function the AI model can call to get the current weather. The function declaration includes a delegate to run logic and name and description parameters to describe the purpose of the function to the AI model.
// Add a new plugin with a local .NET function that should be available to the AI model
var chatOptions = new ChatOptions
{
Tools = [AIFunctionFactory.Create((string location, string unit) =>
{
// Here you would call a weather API to get the weather for the location
return "Periods of rain or drizzle, 15 C";
},
"get_current_weather",
"Get the current weather in a given location")]
};
Add a system prompt to the chatHistory to provide context and instructions to the model. Send a user prompt with a question that requires the AI model to call the registered function to properly answer the question.
// System prompt to provide context
List<ChatMessage> chatHistory = [new(ChatRole.System, """
You are a hiking enthusiast who helps people discover fun hikes in their area. You are upbeat and friendly.
""")];
// Weather conversation relevant to the registered function
chatHistory.Add(new ChatMessage(ChatRole.User,
"I live in Montreal and I'm looking for a moderate intensity hike. What's the current weather like? "));
Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last()}");
var response = await client.CompleteAsync(chatHistory, chatOptions);
chatHistory.Add(new ChatMessage(ChatRole.Assistant, response.Message.Contents));
Console.WriteLine($"{chatHistory.Last().Role} >>> {chatHistory.Last()}");
Use the dotnet run command to run the app:
dotnet run
The app prints a the completion response from the AI model that includes data provided by the .NET function. The AI model understood the registered function was available and called it automatically to generate a proper response.
Clean up resources
When you no longer need the sample application or resources, remove the corresponding deployment and all resources.
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