litellm/tests/test_litellm/test_cost_calculation_log_level.py
2026-04-17 13:02:59 -07:00

141 lines
4.7 KiB
Python

"""Test that cost calculation uses appropriate log levels"""
import logging
import os
import sys
sys.path.insert(0, os.path.abspath("../../.."))
import litellm
from litellm import completion_cost
def test_cost_calculation_uses_debug_level():
"""
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.
Note: This test uses a custom log handler instead of caplog because
caplog doesn't work reliably with pytest-xdist parallel execution.
"""
from litellm._logging import verbose_logger
# Create a custom handler to capture log records
class LogRecordHandler(logging.Handler):
def __init__(self):
super().__init__()
self.records = []
def emit(self, record):
self.records.append(record)
# Set up custom handler
handler = LogRecordHandler()
handler.setLevel(logging.DEBUG)
original_level = verbose_logger.level
verbose_logger.setLevel(logging.DEBUG)
verbose_logger.addHandler(handler)
try:
# 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},
}
# Call completion_cost to trigger logs
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 handler.records
if "selected model name for cost calculation" in record.getMessage()
]
# 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}"
finally:
# Clean up: remove handler and restore original logger level
verbose_logger.removeHandler(handler)
verbose_logger.setLevel(original_level)
def test_batch_cost_calculation_uses_debug_level():
"""
Test that batch cost calculation logs also use DEBUG level.
Note: This test uses a custom log handler instead of caplog because
caplog doesn't work reliably with pytest-xdist parallel execution.
"""
from litellm.cost_calculator import batch_cost_calculator
from litellm.types.utils import Usage
from litellm._logging import verbose_logger
# Create a custom handler to capture log records
class LogRecordHandler(logging.Handler):
def __init__(self):
super().__init__()
self.records = []
def emit(self, record):
self.records.append(record)
# Set up custom handler
handler = LogRecordHandler()
handler.setLevel(logging.DEBUG)
original_level = verbose_logger.level
verbose_logger.setLevel(logging.DEBUG)
verbose_logger.addHandler(handler)
try:
# Create a mock usage object
usage = Usage(prompt_tokens=100, completion_tokens=200, total_tokens=300)
# Call batch_cost_calculator to trigger logs
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 handler.records
if "Calculating batch cost per token" in record.getMessage()
]
# 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}"
finally:
# Clean up: remove handler and restore original logger level
verbose_logger.removeHandler(handler)
verbose_logger.setLevel(original_level)