Develop an AI agent

In this exercise you’ll use Azure AI Agent Service to create a simple agent that analyzes data and creates charts. The agent uses the built-in Code Interpreter tool to dynamically generate the code required to create charts as images, and then saves the resulting chart images.

This exercise should take approximately 30 minutes to complete.

Create an Azure AI Foundry project

Let’s start by creating 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:
In the home page, select + Create project.
In the Create a project wizard, enter a suitable project name (for example, my-ai-project) and if an existing hub is suggested, choose the option to create a new one. Then review the Azure resources that will be automatically created to support your hub and project.
Select Customize and specify the following settings for your hub:
- Hub name: A unique name - for example my-ai-hub
- Subscription: Your Azure subscription
- Resource group: Create a new resource group with a unique name (for example, my-ai-resources), or select an existing one
- Location: Select a region from the following:*
  - australiaeast
  - eastus
  - eastus2
  - francecentral
  - swedencentral
- Connect Azure AI Services or Azure OpenAI: Create a new AI Services resource with an appropriate name (for example, my-ai-services) or use an existing one
- Connect Azure AI Search: Skip connecting
* At the time of writing, these regions support the gpt-4 model for use in agents. Model quotas are constrained at the tenant level by regional quotas. In the event of a quota limit being reached later in the exercise, there’s a possibility you may need to create another project in a different region.
Select Next and review your configuration. Then select Create and wait for the process to complete.
When your project is created, close any tips that are displayed and review the project page in Azure AI Foundry portal, which should look similar to the following image:
In the project overview page, in the Project details area, note the Project connection string. Later, you’ll use this connection string to connect to your project in a client application.

Deploy a generative AI model

Now you’re ready to deploy a generative AI language model to support your agent.

In the pane on the left for your project, in the My assets section, select the Models + endpoints page.
In the Models + endpoints page, in the Model deployments tab, in the + Deploy model menu, select Deploy base model.
Search for the gpt-4 model in the list, and then select and confirm it.
Deploy the model with the following settings by selecting Customize in the deployment details:
- Deployment name: A unique name for your model deployment - for example gpt-4 (remember the name you choose - you’ll need it later)
- Deployment type: Standard
- Model version: 0613
- Connected AI resource: Select your Azure OpenAI resource connection
- Tokens per Minute Rate Limit (thousands): 5K
- Content filter: DefaultV2
- Enable dynamic quota: Disabled
Note: Reducing the TPM helps avoid over-using the quota available in the subscription you are using. 5,000 TPM is sufficient for the data used in this exercise.
Wait for the deployment to complete.

Create an agent client app

Now you’re ready to create a client app that uses an agent. Some code has been provided for you in a GitHub repository.

Clone the repo containing the application code

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 PowerShell pane, enter the following commands to clone the GitHub repo containing the code files for this exercise:
```
rm -r ai-agents -f
git clone https://github.com/MicrosoftLearning/mslearn-ai-agents ai-agents
```
Tip: As you enter commands into the cloudshell, the ouput may take up a large amount of the screen buffer and the cursor on the current line may be obscured. You can clear the screen by entering the cls command to make it easier to focus on each task.
Enter the following command to change the working directory to the folder containing the code files and list them all.
```
cd ai-agents/Labfiles/02-build-ai-agent/Python
ls -a -l
```
The provided files include application code, configuration settings, and data.

Configure the application settings

In the cloud shell command line pane, enter the following command to install the libraries you’ll use:
```
pip install python-dotenv azure-identity azure-ai-projects
```
Enter the following command to edit the configuration file that has been provided:
```
code .env
```
The file is opened in a code editor.
In the code file, replace the your_project_connection_string placeholder with the connection string for your project (copied from the project Overview page in the Azure AI Foundry portal), and the your_model_deployment placeholder with the name you assigned to your gpt-4 model deployment.
After you’ve replaced the placeholders, use the CTRL+S command to save your changes and then use the CTRL+Q command to close the code editor while keeping the cloud shell command line open.

Write code for an agent app

Tip: As you add code, be sure to maintain the correct indentation. Use the comment indentation levels as a guide.

Enter the following command to edit the code file that has been provided:
```
code agent.py
```
Review the existing code, which retrieves the application configuration settings and loads data from data.txt to be analyzed. The rest of the file includes comments where you will add the necessary code to implement your data analysis agent.

Find the comment Add references and add the following code to import the classes you will need to build an Azure AI agent that uses the built-in code interpreter tool:

# Add references
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import FilePurpose, CodeInterpreterTool

Find the comment Connect to the Azure AI Foundry project and add the following code to connect to the Azure AI project.

Tip: Be careful to maintain the correct indentation level.
```
# Connect to the Azure AI Foundry project
project_client = AIProjectClient.from_connection_string(
     credential=DefaultAzureCredential
         (exclude_environment_credential=True,
          exclude_managed_identity_credential=True),
     conn_str=PROJECT_CONNECTION_STRING
)
with project_client:
```
The code connects to the Azure AI Foundry project using the current Azure credentials. The final with project_client statement starts a code block that defines the scope of the client, ensuring it is cleaned up when the code within the block is finished.

Find the comment Upload the data file and create a CodeInterpreterTool, within the with project_client block, and add the following code to upload the data file to the project and create a CodeInterpreterTool that can access the data in it:

# Upload the data file and create a CodeInterpreterTool
file = project_client.agents.upload_file_and_poll(
     file_path=file_path, purpose=FilePurpose.AGENTS
)
print(f"Uploaded {file.filename}")

code_interpreter = CodeInterpreterTool(file_ids=[file.id])

Find the comment Define an agent that uses the CodeInterpreterTool and add the following code to define an AI agent that analyzes data and can use the code interpreter tool you defined previously:

# Define an agent that uses the CodeInterpreterTool
agent = project_client.agents.create_agent(
     model=MODEL_DEPLOYMENT,
     name="data-agent",
     instructions="You are an AI agent that analyzes the data in the file that has been uploaded. If the user requests a chart, create it and save it as a .png file.",
     tools=code_interpreter.definitions,
     tool_resources=code_interpreter.resources,
)
print(f"Using agent: {agent.name}")

Find the comment Create a thread for the conversation and add the following code to start a thread on which the chat session with the agent will run:
```
# Create a thread for the conversation
thread = project_client.agents.create_thread()
```
Note that the next section of code sets up a loop for a user to enter a prompt, ending when the user enters “quit”.

Find the comment Send a prompt to the agent and add the following code to add a user message to the prompt (along with the data from the file that was loaded previously), and then run thread with the agent.

# Send a prompt to the agent
message = project_client.agents.create_message(
     thread_id=thread.id,
     role="user",
     content=user_prompt,
 )

 run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)

Find the comment Check the run status for failures and add the following code to show any errors that occur.

# Check the run status for failures
if run.status == "failed":
     print(f"Run failed: {run.last_error}")

Find the comment Show the latest response from the agent and add the following code to retrieve the messages from the completed thread and display the last one that was sent by the agent.

# Show the latest response from the agent
messages = project_client.agents.list_messages(thread_id=thread.id)
last_msg = messages.get_last_text_message_by_role("assistant")
if last_msg:
     print(f"Last Message: {last_msg.text.value}")

Find the comment Get the conversation history, which is after the loop ends, and add the following code to print out the messages from the conversation thread; reversing the order to show them in chronological sequence

# Get the conversation history
print("\nConversation Log:\n")
messages = project_client.agents.list_messages(thread_id=thread.id)
for message_data in reversed(messages.data):
     last_message_content = message_data.content[-1]
     print(f"{message_data.role}: {last_message_content.text.value}\n")

Find the comment Get any generated files and add the following code to get any file path annotations from the messages (which indicate that the agent saved a file in its internal storage) and copy the files to the app folder.

# Get any generated files
for file_path_annotation in messages.file_path_annotations:
     project_client.agents.save_file(file_id=file_path_annotation.file_path.file_id, file_name=Path(file_path_annotation.text).name)
     print(f"File saved as {Path(file_path_annotation.text).name}")

Find the comment Clean up and add the following code to delete the agent and thread when no longer needed.

# Clean up
project_client.agents.delete_agent(agent.id)
project_client.agents.delete_thread(thread.id)

Review the code, using the comments to understand how it:
- Connects to the AI Foundry project.
- Uploads the data file and creates a code interpreter tool that can access it.
- Creates a new agent that uses the code interpreter tool and has explicit instructions to analyze the data and create charts as .png files.
- Runs a thread with a prompt message from the user along with the data to be analyzed.
- Checks the status of the run in case there’s a failure
- Retrieves the messages from the completed thread and displays the last one sent by the agent.
- Displays the conversation history
- Saves each file that was generated.
- Deletes the agent and thread when they are no longer required.
Save the code file (CTRL+S) when you have finished. You can also close the code editor (CTRL+Q); though you may want to keep it open in case you need to make any edits to the code you added. In either case, keep the cloud shell command line pane open.

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.
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 agent.py
```
The application runs using the credentials for your authenticated Azure session to connect to your project and create and run the agent.
When prompted, view the data that the app has loaded from the data.txt text file. Then enter a prompt such as:
```
What's the category with the highest cost?
```
Tip: If the app fails because the rate limit is exceeded. Wait a few seconds and try the prompt again.
View the response. Then enter another prompt, this time requesting a chart:
```
Create a pie chart showing cost by category.
```
The agent should selectively use the code interpreter tool as required, in this case to create a chart based on your request.
You can continue the conversation if you like. The thread is stateful, so it retains the conversation history - meaning that the agent has the full context for each response. Enter quit when you’re done.
Review the conversation messages that were retrieved from the thread, and the files that were generated.
When the application has finished, use the download command to download each .png file that was saved in the app folder. For example:
```
download ./<file_name>.png
```
The download command creates a popup link at the bottom right of your browser, which you can click to download and open the file.

Summary

In this exercise, you used the Azure AI Agent Service SDK to create a client application that uses an AI agent. The agent uses the built-in Code Interpreter tool to run dynamic code that creates images.

Clean up

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

Return to the browser tab containing the Azure portal (or re-open the Azure portal at https://portal.azure.com in a new browser tab) 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.