Potential AI suggested improvement items

[davidpcahill]

Modularization: The code could benefit from being split into multiple modules. For example:

config.py for configuration-related functions
database.py for database operations
docker_utils.py for Docker-related functions
ai_utils.py for AI interaction functions
experiment.py for experiment-related functions

Use of context managers: Implement context managers for resource management, especially for database connections and Docker clients. This ensures proper cleanup of resources.
Type hinting: Add type hints to function parameters and return values to improve code readability and catch potential type-related errors early.
Asynchronous operations: Consider using asynchronous programming (asyncio) for I/O-bound operations like API calls and database operations to improve performance.
Configuration management: Instead of reading the config file multiple times, read it once at startup and pass the configuration object to functions that need it.
Error handling: Implement more granular error handling and potentially a custom Exception hierarchy for better error management.
Logging improvements: Implement a rotating file handler for logs to prevent log files from growing too large.
Code duplication: There's some duplication in the AI response handling for Claude and OpenAI. This could be refactored into a common interface.
Constants: Move magic strings and numbers into named constants at the top of the file or in a separate constants.py file.
Docker resource management: Implement a more robust system for managing Docker resources, possibly using a context manager to ensure cleanup.
Unit tests: Add unit tests for individual functions to ensure reliability and ease future refactoring.
Documentation: Add more inline documentation and possibly generate API documentation using tools like Sphinx.
Environment variables: Consider using environment variables for sensitive information instead of reading from a file.
Dependency injection: Implement dependency injection for better testability and flexibility.
Rate limiting: Implement rate limiting for API calls to prevent hitting rate limits.
Caching: Implement caching for frequently accessed data to reduce database and API calls.
Code style: Ensure consistent code style throughout, possibly using a linter like flake8 or black for automatic formatting.
Configuration validation: Implement validation for the configuration file to catch misconfigurations early.
Improved experiment cycle: Consider implementing a more robust state machine for the experiment cycle to handle different states and transitions more clearly.
Parallel processing: If applicable, consider using multiprocessing for CPU-bound tasks to utilize multiple cores.