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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
OpenAPI tool from the Azure Agents service using a synchronous client.
To learn more about OpenAPI specs, visit https://learn.microsoft.com/openapi
USAGE:
python sample_agents_openapi.py
Before running the sample:
pip install azure-ai-agents azure-identity jsonref
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
import jsonref
from azure.ai.agents import AgentsClient
from azure.identity import DefaultAzureCredential
from azure.ai.agents.models import OpenApiTool, OpenApiAnonymousAuthDetails
weather_asset_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/weather_openapi.json"))
countries_asset_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "../assets/countries.json"))
agents_client = AgentsClient(
endpoint=os.environ["PROJECT_ENDPOINT"],
credential=DefaultAzureCredential(),
)
# [START create_agent_with_openapi]
with open(weather_asset_file_path, "r") as f:
openapi_weather = jsonref.loads(f.read())
with open(countries_asset_file_path, "r") as f:
openapi_countries = jsonref.loads(f.read())
# Create Auth object for the OpenApiTool (note that connection or managed identity auth setup requires additional setup in Azure)
auth = OpenApiAnonymousAuthDetails()
# Initialize agent OpenApi tool using the read in OpenAPI spec
openapi_tool = OpenApiTool(
name="get_weather", spec=openapi_weather, description="Retrieve weather information for a location", auth=auth
)
openapi_tool.add_definition(
name="get_countries", spec=openapi_countries, description="Retrieve a list of countries", auth=auth
)
# Create agent with OpenApi tool and process agent run
with agents_client:
agent = agents_client.create_agent(
model=os.environ["MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="You are a helpful agent",
tools=openapi_tool.definitions,
)
# [END create_agent_with_openapi]
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="user",
content="What's the weather in Seattle and What is the name and population of the country that uses currency with abbreviation THB?",
)
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}")
run_steps = agents_client.run_steps.list(thread_id=thread.id, run_id=run.id)
# Loop through each step
for step in run_steps:
print(f"Step {step['id']} status: {step['status']}")
# Check if there are tool calls in the step details
step_details = step.get("step_details", {})
tool_calls = step_details.get("tool_calls", [])
if tool_calls:
print(" Tool calls:")
for call in tool_calls:
print(f" Tool Call ID: {call.get('id')}")
print(f" Type: {call.get('type')}")
function_details = call.get("function", {})
if function_details:
print(f" Function name: {function_details.get('name')}")
print() # add an extra newline between steps
# Delete the agent when done
agents_client.delete_agent(agent.id)
print("Deleted 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}")
|
