Create a generative AI chat app

In this exercise, you use the Azure AI Foundry SDK to create a simple chat app that connects to a project and chats with a language model.

This exercise takes approximately 40 minutes.

Note: This exercise is based on pre-release SDKs, which may be subject to change. Where necessary, we’ve used specific versions of packages; which may not reflect the latest available versions. You may experience some unexpected behavior, warnings, or errors.

Deploy a model in an Azure AI Foundry project

Let’s start by deploying a model in an Azure AI Foundry project.

In a web browser, open the Azure AI Foundry portal at https://ai.azure.com and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the Azure AI Foundry logo at the top left to navigate to the home page, which looks similar to the following image (close the Help pane if it’s open):
In the home page, in the Explore models and capabilities section, search for the gpt-4o model; which we’ll use in our project.
In the search results, select the gpt-4o model to see its details, and then at the top of the page for the model, select Use this model.
When prompted to create a project, enter a valid name for your project and expand Advanced options.
Select Customize and specify the following settings for your hub:
- Azure AI Foundry resource: A valid name for your Azure AI Foundry resource
- Subscription: Your Azure subscription
- Resource group: Create or select a resource group
- Region: Select any AI Services supported location*
* Some Azure AI resources are constrained by regional model quotas. In the event of a quota limit being exceeded later in the exercise, there’s a possibility you may need to create another resource in a different region.
Select Create and wait for your project, including the gpt-4 model deployment you selected, to be created.
When your project is created, the chat playground will be opened automatically.
In the Setup pane, note the name of your model deployment; which should be gpt-4o. You can confirm this by viewing the deployment in the Models and endpoints page (just open that page in the navigation pane on the left).
In the navigation pane on the left, select Overview to see the main page for your project; which looks like this:

Note: If an *Insufficient permissions** error is displayed, use the Fix me button to resolve it.

Create a client application to chat with the model

Now that you have deployed a model, you can use the Azure AI Foundry and Azure AI Model Inference SDKs to develop an application that chats with it.

Tip: You can choose to develop your solution using Python or Microsoft C#. Follow the instructions in the appropriate section for your chosen language.

Prepare the application configuration

In the Azure AI Foundry portal, view the Overview page for your project.
In the Project details area, note the Azure AI Foundry project endpoint. You’ll use this endpoint to connect to your project in a client application.
Open a new browser tab (keeping the Azure AI Foundry portal open in the existing tab). Then in the new tab, browse to the Azure portal at https://portal.azure.com; signing in with your Azure credentials if prompted.

Close any welcome notifications to see the Azure portal home page.
Use the [>_] button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a PowerShell environment with no storage in your subscription.

The cloud shell provides a command-line interface in a pane at the bottom of the Azure portal. You can resize or maximize this pane to make it easier to work in.

Note: If you have previously created a cloud shell that uses a Bash environment, switch it to PowerShell.
In the cloud shell toolbar, in the Settings menu, select Go to Classic version (this is required to use the code editor).

Ensure you've switched to the classic version of the cloud shell before continuing.
In the cloud shell pane, enter the following commands to clone the GitHub repo containing the code files for this exercise (type the command, or copy it to the clipboard and then right-click in the command line and paste as plain text):
```
rm -r mslearn-ai-foundry -f
git clone https://github.com/microsoftlearning/mslearn-ai-studio mslearn-ai-foundry
```
Tip: As you enter commands into the cloudshell, the output may take up a large amount of the screen buffer. You can clear the screen by entering the cls command to make it easier to focus on each task.
After the repo has been cloned, navigate to the folder containing the chat application code files:

Use the command below depending on your choice of programming language.

Python
```
cd mslearn-ai-foundry/labfiles/chat-app/python
```
C#
```
cd mslearn-ai-foundry/labfiles/chat-app/c-sharp
```

In the cloud shell command-line pane, enter the following command to install the libraries you’ll use:

Python

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

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

Enter the following command to edit the configuration file that has been provided:

Python
```
code .env
```
C#
```
code appsettings.json
```
The file is opened in a code editor.
In the code file, replace the your_project_endpoint placeholder with the enpoint for your project (copied from the project Overview page in the Azure AI Foundry portal), and the your_model_deployment placeholder with the name of your gpt-4 model deployment.
After you’ve replaced the placeholders, within the code editor, use the CTRL+S command or Right-click > Save to save your changes and then use the CTRL+Q command or Right-click > Quit to close the code editor while keeping the cloud shell command line open.

Write code to connect to your project and chat with your model

Tip: As you add code, be sure to maintain the correct indentation.

Enter the following command to edit the code file that has been provided:

Python
```
code chat-app.py
```
C#
```
code Program.cs
```

In the code file, note the existing statements that have been added at the top of the file to import the necessary SDK namespaces. Then, find the comment Add references, and add the following code to reference the namespaces in the libraries you installed previously:

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

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

In the main function, under the comment Get configuration settings, note that the code loads the project connection string and model deployment name values you defined in the configuration file.

Find the comment Initialize the project client, and add the following code to connect to your Azure AI Foundry project using the Azure credentials you’re currently signed in with:

Tip: Be careful to maintain the correct indentation level for your code.

Python

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

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

Find the comment Get a chat client, and add the following code to create a client object for chatting with a model:

Python
```
# Get a chat client
chat = projectClient.inference.get_chat_completions_client()
```
C#
```
// Get a chat client
ChatCompletionsClient chat = projectClient.GetChatCompletionsClient();
```
Note: This code uses the Azure AI Foundry project client to create a secure connection to the default Azure AI Model Inference service endpoint associated with your project. You can also connect directly to the endpoint by using the Azure AI Model Inference SDK, specifying the endpoint URI displayed for the service connection in the Azure AI Foundry portal or in the corresponding Azure AI Services resource page in the Azure portal, and using an authentication key or Entra credential token. For more information about connecting to the Azure AI Model Inferencing service, see Azure AI Model Inference API.

Find the comment Initialize prompt with system message, and add the following code to initialize a collection of messages with a system prompt.

Python

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

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

Note that the code includes a loop to allow a user to input a prompt until they enter “quit”. Then in the loop section, find the comment Get a chat completion and add the following code to add the user input to the prompt, retrieve the completion from your model, and add the completion to the prompt (so that you retain chat history for future iterations):

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))

// 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));

Use the CTRL+S command to save your changes to the code file.

Sign into Azure and run the app

In the cloud shell command-line pane, enter the following command to sign into Azure.
```
az login
```
You must sign into Azure - even though the cloud shell session is already authenticated.

Note: In most scenarios, just using az login will be sufficient. However, if you have subscriptions in multiple tenants, you may need to specify the tenant by using the –tenant parameter. See Sign into Azure interactively using the Azure CLI for details.
When prompted, follow the instructions to open the sign-in page in a new tab and enter the authentication code provided and your Azure credentials. Then complete the sign in process in the command line, selecting the subscription containing your Azure AI Foundry hub if prompted.
After you have signed in, enter the following command to run the application:

Python
```
python chat-app.py
```
C#
```
dotnet run
```
When prompted, enter a question, such as What is the fastest animal on Earth? and review the response from your generative AI model.
Try some follow-up questions, like Where can I see one? or Are they endangered?. The conversation should continue, using the chat history as context for each iteration.
When you’re finished, enter quit to exit the program.

Tip: If the app fails because the rate limit is exceeded. Wait a few seconds and try again. If there is insufficient quota available in your subscription, the model may not be able to respond.

Summary

In this exercise, you used the Azure AI Foundry SDK to create a client application for a generative AI model that you deployed in an Azure AI Foundry project.

Clean up

If you’ve finished exploring Azure AI Foundry portal, you should delete the resources you have created in this exercise to avoid incurring unnecessary Azure costs.

Open the Azure portal and view the contents of the resource group where you deployed the resources used in this exercise.
On the toolbar, select Delete resource group.
Enter the resource group name and confirm that you want to delete it.