Lab
Develop a multi-agent solution with Azure AI Foundry
Code:
import os
from dotenv import load_dotenv
# Add references
# Add references
from azure.ai.agents import AgentsClient
from azure.ai.agents.models import ConnectedAgentTool, MessageRole, ListSortOrder, ToolSet, FunctionTool
from azure.identity import DefaultAzureCredential
# Clear the console
os.system('cls' if os.name=='nt' else 'clear')
# Load environment variables from .env file
load_dotenv()
project_endpoint = os.getenv("PROJECT_ENDPOINT")
model_deployment = os.getenv("MODEL_DEPLOYMENT_NAME")
# Connect to the agents client
# Connect to the agents client
agents_client = AgentsClient(
endpoint=project_endpoint,
credential=DefaultAzureCredential(
exclude_environment_credential=True,
exclude_managed_identity_credential=True
),
)
with agents_client:
# Create an agent to prioritize support tickets
# Create an agent to prioritize support tickets
priority_agent_name = "priority_agent"
priority_agent_instructions = """
Assess how urgent a ticket is based on its description.
Respond with one of the following levels:
- High: User-facing or blocking issues
- Medium: Time-sensitive but not breaking anything
- Low: Cosmetic or non-urgent tasks
Only output the urgency level and a very brief explanation.
"""
priority_agent = agents_client.create_agent(
model=model_deployment,
name=priority_agent_name,
instructions=priority_agent_instructions
)
# Create an agent to assign tickets to the appropriate team
# Create an agent to assign tickets to the appropriate team
team_agent_name = "team_agent"
team_agent_instructions = """
Decide which team should own each ticket.
Choose from the following teams:
- Frontend
- Backend
- Infrastructure
- Marketing
Base your answer on the content of the ticket. Respond with the team name and a very brief explanation.
"""
team_agent = agents_client.create_agent(
model=model_deployment,
name=team_agent_name,
instructions=team_agent_instructions
)
# Create an agent to estimate effort for a support ticket
# Create an agent to estimate effort for a support ticket
effort_agent_name = "effort_agent"
effort_agent_instructions = """
Estimate how much work each ticket will require.
Use the following scale:
- Small: Can be completed in a day
- Medium: 2-3 days of work
- Large: Multi-day or cross-team effort
Base your estimate on the complexity implied by the ticket. Respond with the effort level and a brief justification.
"""
effort_agent = agents_client.create_agent(
model=model_deployment,
name=effort_agent_name,
instructions=effort_agent_instructions
)
# Create connected agent tools for the support agents
# Create connected agent tools for the support agents
priority_agent_tool = ConnectedAgentTool(
id=priority_agent.id,
name=priority_agent_name,
description="Assess the priority of a ticket"
)
team_agent_tool = ConnectedAgentTool(
id=team_agent.id,
name=team_agent_name,
description="Determines which team should take the ticket"
)
effort_agent_tool = ConnectedAgentTool(
id=effort_agent.id,
name=effort_agent_name,
description="Determines the effort required to complete the ticket"
)
# Create an agent to triage support ticket processing by using connected agents
# Create an agent to triage support ticket processing by using connected agents
triage_agent_name = "triage-agent"
triage_agent_instructions = """
Triage the given ticket. Use the connected tools to determine the ticket's priority,
which team it should be assigned to, and how much effort it may take.
"""
triage_agent = agents_client.create_agent(
model=model_deployment,
name=triage_agent_name,
instructions=triage_agent_instructions,
tools=[
priority_agent_tool.definitions[0],
team_agent_tool.definitions[0],
effort_agent_tool.definitions[0]
]
)
# Use the agents to triage a support issue
# Use the agents to triage a support issue
print("Creating agent thread.")
thread = agents_client.threads.create()
# Create the ticket prompt
prompt = input("\nWhat's the support problem you need to resolve?: ")
# Send a prompt to the agent
message = agents_client.messages.create(
thread_id=thread.id,
role=MessageRole.USER,
content=prompt,
)
# Run the thread usng the primary agent
print("\nProcessing agent thread. Please wait.")
run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=triage_agent.id)
if run.status == "failed":
print(f"Run failed: {run.last_error}")
# Fetch and display messages
messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
for message in messages:
if message.text_messages:
last_msg = message.text_messages[-1]
print(f"{message.role}:\n{last_msg.text.value}\n")
# Clean up
# Clean up
print("Cleaning up agents:")
agents_client.delete_agent(triage_agent.id)
print("Deleted triage agent.")
agents_client.delete_agent(priority_agent.id)
print("Deleted priority agent.")
agents_client.delete_agent(team_agent.id)
print("Deleted team agent.")
agents_client.delete_agent(effort_agent.id)
print("Deleted effort agent.")audio-enabled chat app
code 1
import os
import requests
import base64
from dotenv import load_dotenv
# Add references
# Add references
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
def main():
# Clear the console
os.system('cls' if os.name=='nt' else 'clear')
try:
# Get configuration settings
load_dotenv()
project_endpoint = os.getenv("PROJECT_ENDPOINT")
model_deployment = os.getenv("MODEL_DEPLOYMENT")
# Initialize the project client
# Initialize the project client
project_client = AIProjectClient(
credential=DefaultAzureCredential(
exclude_environment_credential=True,
exclude_managed_identity_credential=True
),
endpoint=project_endpoint,
)
# Get a chat client
# Get a chat client
openai_client = project_client.get_openai_client(api_version="2024-10-21")
# Initialize prompts
system_message = "You are an AI assistant for a produce supplier company."
prompt = ""
# Loop until the user types 'quit'
while True:
prompt = input("\nAsk a question about the audio\n(or type 'quit' to exit)\n")
if prompt.lower() == "quit":
break
elif len(prompt) == 0:
print("Please enter a question.\n")
else:
print("Getting a response ...\n")
# Encode the audio file
# Encode the audio file
file_path = "https://github.com/MicrosoftLearning/mslearn-ai-language/raw/refs/heads/main/Labfiles/09-audio-chat/data/avocados.mp3"
response = requests.get(file_path)
response.raise_for_status()
audio_data = base64.b64encode(response.content).decode('utf-8')
# Get a response to audio input
# Get a response to audio input
response = openai_client.chat.completions.create(
model=model_deployment,
messages=[
{"role": "system", "content": system_message},
{ "role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "input_audio",
"input_audio": {
"data": audio_data,
"format": "mp3"
}
}
] }
]
)
print(response.choices[0].message.content)
except Exception as ex:
print(ex)
if __name__ == '__main__':
main()code 2
import os
import requests
import base64
from dotenv import load_dotenv
# Add references
# Add references
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
def main():
# Clear the console
os.system('cls' if os.name=='nt' else 'clear')
try:
# Get configuration settings
load_dotenv()
project_endpoint = os.getenv("PROJECT_ENDPOINT")
model_deployment = os.getenv("MODEL_DEPLOYMENT")
# Initialize the project client
# Initialize the project client
project_client = AIProjectClient(
credential=DefaultAzureCredential(
exclude_environment_credential=True,
exclude_managed_identity_credential=True
),
endpoint=project_endpoint,
)
# Get a chat client
# Get a chat client
openai_client = project_client.get_openai_client(api_version="2024-10-21")
# Initialize prompts
system_message = "You are an AI assistant for a produce supplier company."
prompt = ""
# Loop until the user types 'quit'
while True:
prompt = input("\nAsk a question about the audio\n(or type 'quit' to exit)\n")
if prompt.lower() == "quit":
break
elif len(prompt) == 0:
print("Please enter a question.\n")
else:
print("Getting a response ...\n")
# Encode the audio file
# Encode the audio file
file_path = "https://github.com/MicrosoftLearning/mslearn-ai-language/raw/refs/heads/main/Labfiles/09-audio-chat/data/avocados.mp3"
response = requests.get(file_path)
response.raise_for_status()
audio_data = base64.b64encode(response.content).decode('utf-8')
# Get a response to audio input
# Get a response to audio input
response = openai_client.chat.completions.create(
model=model_deployment,
messages=[
{"role": "system", "content": system_message},
{ "role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "input_audio",
"input_audio": {
"data": audio_data,
"format": "mp3"
}
}
] }
]
)
print(response.choices[0].message.content)
except Exception as ex:
print(ex)
if __name__ == '__main__':
main()