OpenHands/tests/unit/test_session.py

70 lines
1.7 KiB
Python

from unittest.mock import ANY, AsyncMock, patch
import pytest
from litellm.exceptions import (
RateLimitError,
)
from openhands.core.config.app_config import AppConfig
from openhands.core.config.llm_config import LLMConfig
from openhands.server.session.session import Session
from openhands.storage.memory import InMemoryFileStore
@pytest.fixture
def mock_status_callback():
return AsyncMock()
@pytest.fixture
def mock_sio():
return AsyncMock()
@pytest.fixture
def default_llm_config():
return LLMConfig(
model='gpt-4o',
api_key='test_key',
num_retries=2,
retry_min_wait=1,
retry_max_wait=2,
)
@pytest.mark.asyncio
@patch('openhands.llm.llm.litellm_completion')
async def test_notify_on_llm_retry(
mock_litellm_completion, mock_sio, default_llm_config
):
config = AppConfig()
config.set_llm_config(default_llm_config)
session = Session(
sid='..sid..',
file_store=InMemoryFileStore({}),
config=config,
sio=mock_sio,
user_id='..uid..',
)
session.queue_status_message = AsyncMock()
with patch('time.sleep') as _mock_sleep:
mock_litellm_completion.side_effect = [
RateLimitError(
'Rate limit exceeded', llm_provider='test_provider', model='test_model'
),
{'choices': [{'message': {'content': 'Retry successful'}}]},
]
llm = session._create_llm('..cls..')
llm.completion(
messages=[{'role': 'user', 'content': 'Hello!'}],
stream=False,
)
assert mock_litellm_completion.call_count == 2
session.queue_status_message.assert_called_once_with(
'info', 'STATUS$LLM_RETRY', ANY
)
await session.close()