Lavoisier Project Improvement Tasks

This document contains a prioritized checklist of tasks for improving the Lavoisier project. Each task is marked with a checkbox [ ] that can be checked off when completed.

Architecture and Structure

1. Create comprehensive architecture documentation with component diagrams
2. Implement a plugin system for extending pipeline functionality
3. Refactor the metacognition module to reduce complexity and improve maintainability
4. Implement a proper dependency injection system to reduce tight coupling
5. Standardize interfaces between components for better modularity
6. Implement a configuration validation system with schema definitions
7. Create a unified error handling strategy across all components
8. Implement a proper event system for inter-component communication

Testing and Quality Assurance

1. Increase unit test coverage to at least 80% for all modules
2. Implement integration tests for pipeline workflows
3. Add performance benchmarks and regression tests
4. Implement end-to-end tests for common user workflows
5. Fix the relative import issue in test_annotator.py
6. Set up continuous integration with GitHub Actions
7. Implement code quality checks (linting, type checking)
8. Add property-based testing for data processing functions

Documentation

1. Create comprehensive API documentation with examples
2. Improve inline code documentation and docstrings
3. Create user guides for common workflows
4. Document configuration options and their effects
5. Create developer onboarding documentation
6. Add tutorials for extending the system with custom components
7. Fix the GitHub repository URL in setup.py
8. Create changelog and versioning documentation

Performance and Scalability

1. Optimize memory usage in the numerical pipeline
2. Implement better caching strategies for intermediate results
3. Improve parallelization in data processing functions
4. Implement streaming processing for large datasets
5. Add support for distributed computing across multiple machines
6. Optimize LLM integration for better performance
7. Implement resource monitoring and adaptive resource allocation
8. Add support for GPU acceleration where applicable

Code Quality and Maintainability

1. Refactor long methods in metacognition.py to improve readability
2. Implement more specific exception types for better error handling
3. Improve thread safety in shared state access
4. Standardize naming conventions across the codebase
5. Remove duplicate code and implement shared utilities
6. Implement proper logging levels and structured logging
7. Add type hints to all functions and methods
8. Refactor the continuous learning implementation for better modularity

User Experience

1. Improve CLI interface with better help messages and examples
2. Add interactive visualization of analysis results
3. Implement progress reporting with estimated time remaining
4. Create a web-based dashboard for monitoring tasks
5. Improve error messages with actionable suggestions
6. Add support for configuration profiles for different use cases
7. Implement a wizard for common analysis workflows
8. Add export functionality for results in various formats

Security and Data Management

1. Implement proper authentication for API endpoints
2. Add data validation for all inputs
3. Implement secure storage for sensitive configuration
4. Add data provenance tracking for analysis results
5. Implement proper handling of temporary files
6. Add support for encrypted storage of results
7. Implement access control for shared deployments
8. Add audit logging for security-relevant operations

Dependencies and Environment

1. Update dependencies to latest stable versions
2. Add support for Python 3.10 and 3.11
3. Create Docker containers for easy deployment
4. Implement virtual environment management in the CLI
5. Add dependency pinning for reproducible builds
6. Create environment-specific configuration options
7. Implement graceful degradation when optional dependencies are missing
8. Add compatibility testing for different operating systems

Feature Enhancements

1. Implement additional annotation algorithms
2. Add support for more mass spectrometry file formats
3. Enhance LLM integration with domain-specific fine-tuning
4. Implement advanced visualization techniques for spectra
5. Add support for batch processing of multiple files
6. Implement a results comparison tool for different analysis methods
7. Add support for custom metadata in analysis results
8. Implement automated report generation