Verify PersonalityLearner instantiation works correctly after AdaptationRate import fix. Tests confirm no NameError occurs.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
- Added ResourcePersonality import and initialization to ModelManager
- Created personality_aware_model_switch() method for graceful degradation notifications
- Only notifies users about capability downgrades, not upgrades (per requirements)
- Includes optional technical tips for resource optimization
- Updated proactive scaling callbacks to use personality-aware switching
- Enhanced failure handling with personality-driven resource requests
- Added _is_capability_downgrade() helper for capability comparison
- Added ProactiveScaler integration with HardwareTierDetector
- Implemented pre-flight resource checks before model inference
- Enhanced model selection with scaling recommendations
- Added graceful degradation handling for resource constraints
- Integrated performance metrics tracking for scaling decisions
- Added proactive upgrade execution with stabilization periods
- Enhanced status reporting with scaling information
- Maintained silent switching behavior per Phase 1 decisions
- Implement __main__.py with argparse command-line interface
- Add interactive chat loop for testing model switching
- Include status commands to show current model and resources
- Support models listing and manual model switching
- Add proper signal handling for graceful shutdown
- Include help text and usage examples
- Fix import issues for relative imports in package
- Initialize ModelManager, ContextManager, and subsystems
- Provide main conversation interface with process_message
- Support both synchronous and async operations
- Add system status monitoring and conversation history
- Include graceful shutdown with signal handlers
- Background resource monitoring and maintenance tasks
- Model switching commands and information methods
- Load model configuration from config/models.yaml
- Intelligent model selection based on system resources and context
- Dynamic model switching with silent behavior (no user notifications)
- Fallback chains for model failures
- Proper resource cleanup and error handling
- Background preloading capability
- Auto-retry on model failures with graceful degradation
