Add brain.extract() and brain.extractConcepts() methods that use
NeuralEntityExtractor with embeddings and sophisticated NounType
taxonomy (30+ entity types) for semantic entity and concept extraction.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add production-ready Virtual File System with intelligent Knowledge Layer:
Core VFS Features:
- Complete file system operations (read, write, mkdir, etc.)
- Intelligent PathResolver with 4-layer caching system
- Chunked storage for large files with real compression
- Embedding generation for semantic operations
- File relationships and metadata tracking
- Import functionality from local filesystem
Knowledge Layer Integration:
- EventRecorder for complete file history and temporal coupling
- SemanticVersioning with content-based change detection
- PersistentEntitySystem for character/entity tracking across files
- ConceptSystem for universal concept mapping and graphs
- GitBridge for import/export between VFS and Git repositories
Architecture:
- KnowledgeAugmentation properly integrated into Brainy augmentation system
- KnowledgeLayer wrapper provides real-time VFS operation interception
- Background processing ensures VFS operations remain fast
- All components use real Brainy embed() method for embeddings
- Support for creative writing, coding projects, and project management
Technical Implementation:
- Fixed all stub/mock implementations with real working code
- TypeScript compilation passes without errors
- Comprehensive test suite demonstrating all features
- Documentation covering architecture and usage patterns
- Backwards compatible with existing Brainy functionality
This enables scenarios like writing books with persistent characters,
managing coding projects with concept tracking, and complete project
coordination with intelligent file relationships.
- Add incremental sorted index updates during CRUD operations for consistent <5ms range queries
- Implement parallel search optimization with vector, metadata, and graph intelligence fusion
- Fix metadata-only query handling to properly return results without vector search
- Fix NLP recursive call issue by using embed() instead of add()
- Add cardinality tracking for smart index optimization
- Store entity data in metadata for proper retrieval
- Add comprehensive performance documentation
This improves query performance from O(n) to O(log n) for range queries
and ensures consistent fast performance without lazy loading delays.
- Implements intelligent display fields with AI-generated titles and descriptions
- Leverages existing IntelligentTypeMatcher for semantic type detection
- Adds lazy computation with LRU caching for zero performance impact
- Enhances CLI with clean, minimal formatting (no visual clutter)
- Provides method-based API (getDisplay()) to avoid namespace conflicts
- Maintains 100% backward compatibility with existing code
- Enables by default with complete isolation architecture
- Includes comprehensive tests and documentation
The augmentation transforms search results and data display with smart,
contextual information while maintaining Soulcraft's clean aesthetic.
