Standardize Logger Implementation with Loguru and Add Comprehensive Unit Tests

[markurtz]

Summary

Standardizes the logger implementation around the loguru library and latest standards for NM. It enhances logging flexibility and configurability and introduces comprehensive unit tests to ensure reliability.

Details

• Introduced a new logger.py module that utilizes the loguru library.
• The logger configuration supports console and file logging, with settings customizable via environment variables or direct function calls.
• Environment variables include options for disabling logging, clearing existing loggers, setting log levels for console and file logging, and specifying log file paths.
• Updated init.py to expose the logger configuration functions and logger instance.
• Added detailed docstrings and usage examples in logger.py.
• Implemented comprehensive unit tests in test_logger.py to cover various configurations and scenarios, ensuring robust functionality.

Test Plan

Automation Testing:

• Ran pytest to execute the newly added unit tests in test_logger.py.
• Verified that default, environment variable overrides, and explicit configurations are correctly applied and logged. Manual Testing:
• Manually tested logging outputs to console and file to ensure proper formatting and log levels are respected.

[parfeniukink]

@markurtz Side topic: "What about using the pydantic for the settings and environment variables management in thr project? It is still to be a dependency for the openai package btw"

[markurtz]

@markurtz Side topic: "What about using the pydantic for the settings and environment variables management in thr project? It is still to be a dependency for the openai package btw"

Good idea, I'm going to push this off, though, to another PR once the pydantic one lands and we can refactor around that