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# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to use Agent operations with the Connected Agent tool from
the Azure Agents service using a synchronous client.
USAGE:
python sample_agents_connected_agent.py
Before running the sample:
pip install azure-ai-projects azure-ai-agents azure-identity
Set these environment variables with your own values:
1) PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Azure AI Foundry portal.
2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
the "Models + endpoints" tab in your Azure AI Foundry project.
"""
import os
from azure.ai.projects import AIProjectClient
from azure.ai.agents.models import ConnectedAgentTool, MessageRole
from azure.identity import DefaultAzureCredential
project_client = AIProjectClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
connected_agent_name = "stock_price_bot"
with project_client:
agents_client = project_client.agents
stock_price_agent = agents_client.create_agent(
model=os.environ["MODEL_DEPLOYMENT_NAME"],
name=connected_agent_name,
instructions=(
"Your job is to get the stock price of a company. If asked for the Microsoft stock price, always return $350."
),
)
# [START create_agent_with_connected_agent_tool]
# Initialize Connected Agent tool with the agent id, name, and description
connected_agent = ConnectedAgentTool(
id=stock_price_agent.id, name=connected_agent_name, description="Gets the stock price of a company"
)
# Create agent with the Connected Agent tool and process assistant run
agent = agents_client.create_agent(
model=os.environ["MODEL_DEPLOYMENT_NAME"],
name="my-assistant",
instructions="You are a helpful assistant, and use the connected agents to get stock prices.",
tools=connected_agent.definitions,
)
# [END create_agent_with_connected_agent_tool]
print(f"Created agent, ID: {agent.id}")
# Create thread for communication
thread = agents_client.threads.create()
print(f"Created thread, ID: {thread.id}")
# Create message to thread
message = agents_client.messages.create(
thread_id=thread.id,
role=MessageRole.USER,
content="What is the stock price of Microsoft?",
)
print(f"Created message, ID: {message.id}")
# Create and process Agent run in thread with tools
run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
print(f"Run finished with status: {run.status}")
if run.status == "failed":
print(f"Run failed: {run.last_error}")
# Delete the Agent when done
agents_client.delete_agent(agent.id)
print("Deleted agent")
# Delete the connected Agent when done
agents_client.delete_agent(stock_price_agent.id)
print("Deleted stock price agent")
# Fetch and log all messages
messages = agents_client.messages.list(thread_id=thread.id)
for msg in messages:
if msg.text_messages:
last_text = msg.text_messages[-1]
print(f"{msg.role}: {last_text.text.value}")
|
