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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 client. Instead of creating a new Agent
and Conversation, it retrieves existing ones.
For OpenAI operations in this sample, see:
https://platform.openai.com/docs/api-reference/conversations/retrieve?lang=python
https://platform.openai.com/docs/api-reference/conversations/create-items?lang=python
USAGE:
python sample_agent_retrieve_basic.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" azure-identity openai 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) AGENT_NAME - The name of an existing Agent in your Microsoft Foundry project.
3) CONVERSATION_ID - The ID of an existing Conversation associated with the Agent
"""
import os
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
load_dotenv()
agent_name = os.environ["AGENT_NAME"]
conversation_id = os.environ["CONVERSATION_ID"]
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
project_client.get_openai_client() as openai_client,
):
# Retrieves latest version of an existing Agent
agent = project_client.agents.get(agent_name=agent_name)
print(f"Agent retrieved (id: {agent.id}, name: {agent.name}, version: {agent.versions.latest.version})")
# Retrieved a stored conversation
conversation = openai_client.conversations.retrieve(conversation_id=conversation_id)
print(f"Retrieved conversation (id: {conversation.id})")
# Add a new user text message to the conversation
openai_client.conversations.items.create(
conversation_id=conversation.id,
items=[{"type": "message", "role": "user", "content": "How many feet are in a mile?"}],
)
print(f"Added a 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}")
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