feat: Implement Phase 1 - Plugin & Hook System
Core plugin architecture for LLM-driven optimization:
New Features:
- Hook system with 6 lifecycle points (pre_mesh, post_mesh, pre_solve, post_solve, post_extraction, custom_objectives)
- HookManager for centralized registration and execution
- Code validation with AST-based safety checks
- Feature registry (JSON) for LLM capability discovery
- Example plugin: log_trial_start
- 23 comprehensive tests (all passing)
Integration:
- OptimizationRunner now loads plugins automatically
- Hooks execute at 5 points in optimization loop
- Custom objectives can override total_objective via hooks
Safety:
- Module whitelist (numpy, scipy, pandas, optuna, pyNastran)
- Dangerous operation blocking (eval, exec, os.system, subprocess)
- Optional file operation permission flag
Files Added:
- optimization_engine/plugins/__init__.py
- optimization_engine/plugins/hooks.py
- optimization_engine/plugins/hook_manager.py
- optimization_engine/plugins/validators.py
- optimization_engine/feature_registry.json
- optimization_engine/plugins/pre_solve/log_trial_start.py
- tests/test_plugin_system.py (23 tests)
Files Modified:
- optimization_engine/runner.py (added hook integration)
Ready for Phase 2: LLM interface layer
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-15 14:46:49 -05:00
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# post_extraction hooks
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