# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------

"""
DESCRIPTION:
    This sample demonstrates how to use an existing Hosted Agent and create a Session,
    configure an Agent endpoint for Responses protocol, and invoke the
    OpenAI Responses API against that agent endpoint using the
    asynchronous AIProjectClient.

    Sessions only work with Hosted Agents.

USAGE:
    python sample_agent_endpoint_async.py

    Before running the sample:

    pip install "azure-ai-projects>=2.3.0" python-dotenv aiohttp

    Set these environment variables with your own values:
    1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
       page of your Microsoft Foundry portal.
    2) FOUNDRY_HOSTED_AGENT_NAME - The name of an existing Hosted Agent.

    If you don't have a Hosted Agent, run `sample_create_hosted_agent_async.py` or
    `sample_create_hosted_agent_from_code_async.py` first to create one as a prerequisite.
"""

import asyncio
import os

from dotenv import load_dotenv

from azure.identity.aio import DefaultAzureCredential

from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import (
    AgentEndpointConfig,
    FixedRatioVersionSelectionRule,
    ProtocolConfiguration,
    ResponsesProtocolConfiguration,
    VersionSelector,
)
from azure.ai.projects.models import VersionRefIndicator
from hosted_agents_util import get_latest_active_agent_version_async

load_dotenv()


async def main():
    endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
    agent_name = os.environ["FOUNDRY_HOSTED_AGENT_NAME"]

    async with (
        DefaultAzureCredential() as credential,
        AIProjectClient(
            endpoint=endpoint,
            credential=credential,
        ) as project_client,
    ):

        agent = await get_latest_active_agent_version_async(project_client, agent_name)

        session = await project_client.agents.create_session(
            agent_name=agent_name,
            version_indicator=VersionRefIndicator(agent_version=agent.version),
        )
        print(f"Session created (id: {session.agent_session_id}, status: {session.status})")
        try:
            # Configure endpoint routing so this agent name serves the created version.
            # 100% of traffic is routed to the single created version.
            endpoint_config = AgentEndpointConfig(
                version_selector=VersionSelector(
                    version_selection_rules=[
                        FixedRatioVersionSelectionRule(agent_version=agent.version, traffic_percentage=100),
                    ]
                ),
                protocol_configuration=ProtocolConfiguration(responses=ResponsesProtocolConfiguration()),
            )

            patched_agent = await project_client.agents.update_details(
                agent_name=agent_name,
                agent_endpoint=endpoint_config,
            )
            print(f"Agent endpoint configured for agent: {patched_agent.name}")

            # Create an OpenAI client bound to the agent endpoint.
            openai_client = project_client.get_openai_client(agent_name=agent_name)

            # Call Responses API and bind the request to the created agent session.
            response = await openai_client.responses.create(
                input="What is the size of France in square miles?",
                extra_body={
                    "agent_session_id": session.agent_session_id,
                },
            )
            print(f"Response output: {response.output_text}")
        finally:
            await project_client.agents.delete_session(
                agent_name=agent_name,
                session_id=session.agent_session_id,
            )
            print(f"Session deleted (id: {session.agent_session_id})")


if __name__ == "__main__":
    asyncio.run(main())
