diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index b55da01aff..cd37592d40 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -657,7 +657,7 @@ def completion_cost( # noqa: PLR0915 potential_model_names.append(model) for idx, model in enumerate(potential_model_names): try: - verbose_logger.info( + verbose_logger.debug( f"selected model name for cost calculation: {model}" ) @@ -1176,7 +1176,7 @@ def batch_cost_calculator( model=model, custom_llm_provider=custom_llm_provider ) - verbose_logger.info( + verbose_logger.debug( "Calculating batch cost per token. model=%s, custom_llm_provider=%s", model, custom_llm_provider, diff --git a/tests/test_litellm/test_cost_calculation_log_level.py b/tests/test_litellm/test_cost_calculation_log_level.py new file mode 100644 index 0000000000..4380ae8bf6 --- /dev/null +++ b/tests/test_litellm/test_cost_calculation_log_level.py @@ -0,0 +1,93 @@ +"""Test that cost calculation uses appropriate log levels""" +import logging +import os +import sys + +import pytest + +sys.path.insert(0, os.path.abspath("../../..")) + +import litellm +from litellm import completion_cost + + +def test_cost_calculation_uses_debug_level(caplog): + """ + Test that cost calculation logs use DEBUG level instead of INFO. + This ensures cost calculation details don't appear in production logs. + Part of fix for issue #9815. + """ + # Create a mock completion response + mock_response = { + "id": "test", + "object": "chat.completion", + "created": 1234567890, + "model": "gpt-3.5-turbo", + "choices": [{ + "index": 0, + "message": {"role": "assistant", "content": "Test response"}, + "finish_reason": "stop" + }], + "usage": { + "prompt_tokens": 10, + "completion_tokens": 20, + "total_tokens": 30 + } + } + + # Test that cost calculation logs are at DEBUG level + with caplog.at_level(logging.DEBUG): + try: + cost = completion_cost( + completion_response=mock_response, + model="gpt-3.5-turbo" + ) + except Exception: + pass # Cost calculation may fail, but we're checking log levels + + # Find the cost calculation log records + cost_calc_records = [ + record for record in caplog.records + if "selected model name for cost calculation" in record.message + ] + + # Verify that cost calculation logs are at DEBUG level + assert len(cost_calc_records) > 0, "No cost calculation logs found" + + for record in cost_calc_records: + assert record.levelno == logging.DEBUG, \ + f"Cost calculation log should be DEBUG level, but was {record.levelname}" + + +def test_batch_cost_calculation_uses_debug_level(caplog): + """ + Test that batch cost calculation logs also use DEBUG level. + """ + from litellm.cost_calculator import batch_cost_calculator + from litellm.types.utils import Usage + + # Create a mock usage object + usage = Usage(prompt_tokens=100, completion_tokens=200, total_tokens=300) + + # Test that batch cost calculation logs are at DEBUG level + with caplog.at_level(logging.DEBUG): + try: + batch_cost_calculator( + usage=usage, + model="gpt-3.5-turbo", + custom_llm_provider="openai" + ) + except Exception: + pass # May fail, but we're checking log levels + + # Find batch cost calculation log records + batch_cost_records = [ + record for record in caplog.records + if "Calculating batch cost per token" in record.message + ] + + # Verify logs exist and are at DEBUG level + if batch_cost_records: # May not always log depending on the code path + for record in batch_cost_records: + assert record.levelno == logging.DEBUG, \ + f"Batch cost calculation log should be DEBUG level, but was {record.levelname}" \ No newline at end of file