73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
import os
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import sys
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sys.path.insert(0, os.path.abspath("../.."))
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import asyncio
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import logging
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import pytest
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import litellm
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from litellm._logging import verbose_logger
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from litellm.integrations.literal_ai import LiteralAILogger
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verbose_logger.setLevel(logging.DEBUG)
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litellm.set_verbose = True
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@pytest.mark.asyncio
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async def test_literalai_queue_logging():
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try:
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# Initialize LiteralAILogger
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test_literalai_logger = LiteralAILogger()
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litellm.callbacks = [test_literalai_logger]
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test_literalai_logger.batch_size = 6
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litellm.set_verbose = True
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# Make multiple calls to ensure we don't hit the batch size
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for _ in range(5):
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response = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Test message"}],
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max_tokens=10,
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temperature=0.2,
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mock_response="This is a mock response",
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)
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await asyncio.sleep(3)
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# Check that logs are in the queue
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assert len(test_literalai_logger.log_queue) == 5
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# Now make calls to exceed the batch size
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for _ in range(3):
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await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Test message"}],
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max_tokens=10,
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temperature=0.2,
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mock_response="This is a mock response",
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)
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# Wait a short time for any asynchronous operations to complete
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await asyncio.sleep(1)
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print(
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"Length of literalai log queue: {}".format(
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len(test_literalai_logger.log_queue)
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)
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)
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# Check that the queue was flushed after exceeding batch size
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assert len(test_literalai_logger.log_queue) < 5
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# Clean up
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for cb in litellm.callbacks:
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if isinstance(cb, LiteralAILogger):
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await cb.async_httpx_client.client.aclose()
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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