Create a generative AI app that uses tools

In this exercise, you’ll use the Microsoft Foundry portal and the Responses API to build an AI chat application. Then you’ll integrate knowledge into your application by using the web_search and file_search tools.

This exercise takes approximately 30 minutes.

Note: Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors.

Prerequisites

Before starting this exercise, ensure you have:

An active Azure subscription
Visual Studio Code installed
Python version 3.13.xx installed*
Git installed and configured
Azure CLI installed

* Python 3.14 is available, but some dependencies are not yet compiled for that release. The lab has been successfully tested with Python 3.13.12.

Create a Microsoft Foundry project

Microsoft Foundry uses projects to organize models, resources, data, and other assets used to develop an AI solution.

In a web browser, open the Microsoft 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 Foundry logo at the top left to navigate to the home page.
If it is not already enabled, in the tool bar the top of the page, enable the New Foundry option. Then, if prompted, create a new project with a unique name; expanding the Advanced options area to specify the following settings for your project:
- Foundry resource: Use the default name for your resource (usually {project_name}-resource)
- Subscription: Your Azure subscription
- Resource group: Create or select a resource group
- Region: Select any of the AI Foundry recommended regions
Wait for your project to be created. Then view its home page.

Deploy a model

Now deploy a model that you’ll use in your chat application.

In the Start building menu, select Find models (or on the Discover page, select the Models tab) to view the Microsoft Foundry model catalog.
In the model catalog, search for gpt-4.1.
Review the model card, and then deploy it using the default settings.
When the model has been deployed, it will open in the model playground.

Experiment with tools in the playground

Before developing a chat application, let’s explore how the model responds in the playground. This will help you understand why grounding data matters.

After deploying your model, you should be in the playground with that model selected. If not, select Build in the top menu bar, then select Models on the left, and then select the model you deployed.
In the model playground, in the pane on the left, ensure that your gpt-4.1 model is selected.

In the Instructions field, enter the following prompt:

You are a travel assistant that provides information on travel services available from Margie's Travel.

In the chat pane, enter the query What are some recommended tourist activities in New York next month? and review the response.

The response should be fairly generic — the model provides general knowledge based on its training data, but doesn’t have access to current information about what’s happening in New York next month.
In the pane on the left, under the instructions, in the Tools section, select Add and add the web_search tool.
In the chat pane, enter the same query What are some recommended tourist activities in New York tnext month? and review the response.

This time, the model uses the web_search tool to find current information about activities in New York.

Create an app that uses tools

Now that you’ve seen how tools can extend a model’s capabilities in the playground, let’s build a client application that uses tools to provide travel advice for Margie’s Travel customers.

Get the endpoint

You’ll need an endpoint to connect to the model from a client application. In this exercise, we’re going to use the OpenAI SDK to chat with the model; and we’ll use the Azure OpenAI endpoint with Entra ID authentication to connect to it.

Note: As an alternative to Entra ID authentication, you could use the API Key for the project. using Entra ID authentication is preferred whenever possible.

On the menu bar, select the Home page.
Note the Azure OpenAI Endpoint displayed there.

Tip: You’ll use the Azure OpenAI Endpoint in this exercise, not the project endpoint!

Get the application files from GitHub

The initial application files you’ll need to develop your chat application are provided in a GitHub repo.

Open Visual Studio Code.
Open the command palette (Ctrl+Shift+P) and use the Git:clone command to clone the https://github.com/microsoftlearning/mslearn-ai-studio repo to a local folder (it doesn’t matter which one). Then open it.

You may be prompted to confirm you trust the authors.

Prepare the application configuration

In Visual Studio Code, view the Extensions pane; and if it is not already installed, install the Python extension.
In the Command Palette, use the command python:select interpreter. Then select an existing environment if you have one, or create a new Venv environment based on your Python 3.1x installation.

Tip: If you are prompted to install dependencies, you can install the ones in the requirements.txt file in the /labfiles/tools/python/tools-app folder; but it’s OK if you don’t - we’ll install them later!
In the Explorer pane, navigate to the folder containing the application code files at /labfiles/tools/python/tools-app. The application files include:
- brochures (a folder containing Margie’s Travel brochures)
- .env (the application configuration file)
- requirements.txt (the Python package dependencies that need to be installed)
- tools-app.py (the code file for the application)
In the Explorer pane, right-click the tools-app folder containing the application files, and select Open in integrated terminal (or open a terminal in the Terminal menu and navigate to the /labfiles/tools/python/tools-app folder.)

Note: Opening the terminal in Visual Studio Code will automatically activate the Python environment. You may need to enable running scripts on your system.
Ensure that the terminal is open in the //labfiles/tools/python/tools-app folder with the prefix (.venv) to indicate that the Python environment you created is active.
Install the OpenAI SDK, Azure identity, and other required packages by running the following command:
```
 pip install -r requirements.txt
```
In the Explorer pane, in the /labfiles/tools/python/tools-app folder, select the .env file to open it. Then update the configuration values to include the Azure OpenAI Endpoint and your gpt-4.1 model deployment.

Tip: Copy the Azure OpenAI Endpoint (not the project endpoint!) from the project home page in the Foundry portal, and rename the model deployment if your deployment isn’t named gpt-4.1.

Save the modified configuration file.

Write code to implement chat with tools

In the Explorer pane, in the /labfiles/tools/python/tools-app folder, select the tools-app.py file to open it.
Review the existing code. You will add code to use the OpenAI SDK to access your model.

Tip: As you add code to the code file, be sure to maintain the correct indentation.
At the top of the code file, under the existing namespace references, find the comment Import namespaces and add the following code to import the namespace you will need to use the OpenAI SDK:
```
# import namespaces
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
```

In the main function, note that code to load the endpoint and key from the configuration file has already been provided. Then find the comment Initialize the OpenAI client, and add the following code to create a client for the OpenAI API:

# Initialize the OpenAI client
token_provider = get_bearer_token_provider(
     DefaultAzureCredential(), "https://ai.azure.com/.default"
)
    
openai_client = OpenAI(
     base_url=azure_openai_endpoint,
     api_key=token_provider
)

In the mainfunction, find the comment Create vector store and upload files, and add the following code:

# Create vector store and upload files
print("Creating vector store and uploading files...")
vector_store = openai_client.vector_stores.create(
     name="travel-brochures"
)
file_streams = [open(f, "rb") for f in glob.glob("brochures/*.pdf")]
if not file_streams:
     print("No PDF files found in the brochures folder!")
     return
file_batch = openai_client.vector_stores.file_batches.upload_and_poll(
     vector_store_id=vector_store.id,
     files=file_streams
)
for f in file_streams:
     f.close()
print(f"Vector store created with {file_batch.file_counts.completed} files.")

This code creates a vector store for your model, and uploads the brochures to it. We’ll use this vector store with the file_search tool.

In the main function, note that code to request a user prompt until the user quits the app has been provided. Within this loop, find the Get a response using tools comment, and add the following code:

# Get a response using tools
response = openai_client.responses.create(
     model=model_deployment,
     instructions="""
     You are a travel assistant that provides information on travel services available from Margie's Travel.
     Answer questions about services offered by Margie's Travel using the provided travel brochures.
     Search the web for general information about destinations or current travel advice.
     """,
     input=input_text,
     previous_response_id=last_response_id,
     tools=[
         {
             "type": "file_search",
             "vector_store_ids": [vector_store.id]
         },
         {
             "type": "web_search_preview"
         }
     ]
)
print(response.output_text)
last_response_id = response.id

This code submits a prompt and specifies that the file_search tool can be used to search the vector store and the web_search tool can be used or general web searches.

Save the changes to the code file. Then, in the terminal pane, use the following command to sign into Azure.
```
 az login
```
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 sign into Azure. Then complete the sign in process in the command line, viewing (and confirming if necessary) the details of the subscription containing your Foundry resource.
After you have signed in, enter the following command to run the application:
```
python tools-app.py
```
The program should run in the terminal (if not, resolve any errors and try again).
When prompted, enter What's happening in San Francisco next month? and review the response from your generative AI model.

The response should include information retrieved using the web_search tool.
Try this follow-up question: What hotels does Margie's Travel offer there?

The response should include information retrieved using the file_search tool.
When you’re finished, enter quit to exit the program.

Clean up

If you’ve finished exploring Microsoft Foundry, 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.