Add a new COGNITION augmentation that automatically generates intelligent weight and confidence scores for verb relationships using semantic analysis, frequency patterns, and temporal factors.
Key features:
- Semantic proximity scoring using entity embeddings
- Frequency amplification for repeated relationships
- Temporal decay for time-based relationship strength
- Learning and adaptation from user feedback
- Zero-configuration setup (just enable: true)
- Off by default to maintain backward compatibility
Integration points:
- New intelligentVerbScoring config in BrainyDataConfig
- Automatic scoring in addVerb() when weight not provided
- Feedback methods: provideFeedbackForVerbScoring(), getVerbScoringStats()
- Export/import learning data for persistence
- Full augmentation pipeline integration
Documentation:
- Comprehensive usage guide at /docs/guides/intelligent-verb-scoring.md
- Examples for simple and advanced configurations
- Learning workflows and troubleshooting
Tests:
- Complete test coverage for all features
- Configuration, semantic scoring, learning, and error handling
- Performance and integration testing
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Remove Rollup bundling in favor of direct TypeScript compilation
- Move from bundled models to dynamic model loading with configurable paths
- Add Docker deployment examples and documentation
- Implement robust model loader with fallback mechanisms
- Update storage adapters for better cross-environment compatibility
- Add comprehensive tests for model loading and package installation
- Simplify package.json scripts and remove complex build configurations
- Clean up deprecated demo files and old bundling scripts
BREAKING CHANGE: Models are no longer bundled with the package. They are now loaded dynamically from CDN or custom paths.
- Added `getFile` method to `FileSystemHandle` interface for enhanced TypeScript compatibility with File System Access API.
- Improved maintainability by reformatting and aligning imports in `brainyData.ts`.
- Standardized spacing and indentation across structured comments and interface fields.
Purpose: Improve TypeScript support for file system operations and enhance code readability with consistent formatting and import management.
- Removed outdated Node.js v24 compatibility patches for `TextEncoder` and `TextDecoder` in `cli-wrapper.js` and other modules since TensorFlow.js now supports Node.js v24+ natively.
- Introduced a utility module `src/utils/tensorflowUtils.ts` for TensorFlow.js compatibility, defining global `PlatformNode` and related utilities.
- Enhanced `fileSystemStorage.ts` with improved Node.js module loading mechanisms for better compatibility across environments.
- Simplified dependency requirements by reducing the minimum Node.js version to `>=23.0.0`.
- Updated `package.json`, `cli-package/package.json`, and `README.md` versions to `0.9.25`.
- Harmonized `cli-package` and main package dependencies to ensure version alignment.
- Improved global compatibility with TensorFlow.js in mixed Node.js environments.
This update modernizes TensorFlow.js integration, reduces maintenance efforts, and aligns compatibility with current Node.js and TensorFlow.js capabilities.
Applied consistent formatting to align with the project's style guidelines, including adjustments to line breaks, parentheses, and object destructuring. These changes enhance code readability and maintainability without altering functionality.
