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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to run basic Prompt Agent operations
using the synchronous AIProjectClient.
The OpenAI compatible Responses and Conversation calls in this sample are made using
the OpenAI client from the `openai` package. See https://platform.openai.com/docs/api-reference
for more information.
USAGE:
python sample_agent_basic.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" azure-identity python-dotenv
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Microsoft Foundry portal.
2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
the "Models + endpoints" tab in your Microsoft Foundry project.
"""
import os
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition
load_dotenv()
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
):
# [START prompt_agent_basic]
with project_client.get_openai_client() as openai_client:
agent = project_client.agents.create_version(
agent_name="MyAgent",
definition=PromptAgentDefinition(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
instructions="You are a helpful assistant that answers general questions",
),
)
print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
conversation = openai_client.conversations.create(
items=[{"type": "message", "role": "user", "content": "What is the size of France in square miles?"}],
)
print(f"Created conversation with initial user message (id: {conversation.id})")
response = openai_client.responses.create(
conversation=conversation.id,
extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
input="",
)
print(f"Response output: {response.output_text}")
openai_client.conversations.items.create(
conversation_id=conversation.id,
items=[{"type": "message", "role": "user", "content": "And what is the capital city?"}],
)
print(f"Added a second user message to the conversation")
response = openai_client.responses.create(
conversation=conversation.id,
extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
input="",
)
print(f"Response output: {response.output_text}")
openai_client.conversations.delete(conversation_id=conversation.id)
print("Conversation deleted")
project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
print("Agent deleted")
# [END prompt_agent_basic]
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