Overview

The exercise walks you through building a generative AI chat app using the Azure AI Foundry SDK. You deploy the gpt-4o model in the Azure AI Foundry portal and then create a client application that interacts with that model. Both Python and C# implementations are provided.

Repository and Environment Setup

Clone the repository and navigate to the correct folder:

  • Python:

    rm -r mslearn-ai-foundry -f
git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry
cd mslearn-ai-foundry/labfiles/chat-app/python

  • C#:

    rm -r mslearn-ai-foundry -f
git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry
cd mslearn-ai-foundry/labfiles/chat-app/c-sharp

Install dependencies:

  • Python:

    python -m venv labenv
./labenv/bin/Activate.ps1
pip install python-dotenv azure-identity azure-ai-projects azure-ai-inference

  • C#:

    dotnet add package Azure.Identity
dotnet add package Azure.AI.Projects --version 1.0.0-beta.9
dotnet add package Azure.AI.Inference --version 1.0.0-beta.5

Configuration

Update the configuration file with your Azure AI Foundry project details:

  • Python: Edit the .env file.
  • C#: Edit the appsettings.json file.

Replace the placeholders:

  • your_project_endpoint → Your actual project endpoint.
  • your_model_deployment → The name of your gpt-4 deployment.

Code References

  1. Adding References

    • Python:

      # Add references
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.inference.models import SystemMessage, UserMessage, AssistantMessage

    • C#:

      // Add references
using Azure.Identity;
using Azure.AI.Projects;
using Azure.AI.Inference;

  2. Initializing the Project Client

    • Python:

      # Initialize the project client
projectClient = AIProjectClient(
    credential=DefaultAzureCredential(
        exclude_environment_credential=True,
        exclude_managed_identity_credential=True
    ),
    endpoint=project_connection,
)

    • C#:

      // Initialize the project client
DefaultAzureCredentialOptions options = new() {
    ExcludeEnvironmentCredential = true,
    ExcludeManagedIdentityCredential = true
};
var projectClient = new AIProjectClient(
    new Uri(project_connection),
    new DefaultAzureCredential(options)
);

  3. Getting a Chat Client

    • Python:

      # Get a chat client
chat = projectClient.inference.get_chat_completions_client()

    • C#:

      // Get a chat client
ChatCompletionsClient chat = projectClient.GetChatCompletionsClient();

  4. Initializing the Prompt with a System Message

    • Python:

      # Initialize prompt with system message
prompt = [SystemMessage("You are a helpful AI assistant that answers questions.")]

    • C#:

      // Initialize prompt with system message
var prompt = new List<ChatRequestMessage>(){
    new ChatRequestSystemMessage("You are a helpful AI assistant that answers questions.")
};

  5. Chat Interaction Loop

    • Python:

      # Get a chat completion
prompt.append(UserMessage(input_text))
response = chat.complete(model=model_deployment, messages=prompt)
completion = response.choices[0].message.content
print(completion)
prompt.append(AssistantMessage(completion))

    • C#:

      // Get a chat completion
prompt.Add(new ChatRequestUserMessage(input_text));
var requestOptions = new ChatCompletionsOptions() {
    Model = model_deployment,
    Messages = prompt
};
Response<ChatCompletions> response = chat.Complete(requestOptions);
var completion = response.Value.Content;
Console.WriteLine(completion);
prompt.Add(new ChatRequestAssistantMessage(completion));

  6. Running the Application

    • Python: Run using:
      python chat-app.py
    • C#: Run using:
      dotnet run

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