- Add importFile() method for single file imports
- Implement entity helper methods (linkEntities, findEntityOccurrences)
- Fix critical embedding tokenizer bug (char.charCodeAt error)
- Fix removeRelationship to actually remove using brain.unrelate()
- Add setMetadata/getMetadata methods
- Fix GitBridge to query real relationships and events
- Enable background Knowledge Layer processing
- Rewrite README to emphasize knowledge over files
- Add comprehensive VFS documentation (core, knowledge layer, examples)
- Add complete test suite covering all VFS methods
This completes the VFS implementation with full Knowledge Layer support,
enabling files as living knowledge that understand themselves, evolve
over time, and connect to everything related.
- Remove Node.js-specific imports from module level
- Use dynamic imports with isNode() environment checks
- Wrap all file system operations in conditional blocks
- Add fallback values for browser environments
- Ensure code works in both Node.js and browser contexts
This change enables Brainy to run in browser environments without
requiring Node.js polyfills, making it truly universal.
Co-Authored-By: dpsifr <noreply@dpsifr.com>
- Updated all fs, path, crypto, os, url, util, events, http, https, net, child_process, stream, and zlib imports
- Changed both static imports and dynamic imports to use node: protocol
- This makes Brainy more bundler-friendly by explicitly marking Node.js built-ins
- Prevents bundlers from attempting to polyfill or bundle these modules
- Reduces bundle size for web applications using Brainy
- Improves tree-shaking and dead code elimination
Benefits for external bundlers:
- Clear distinction between Node.js built-ins and external dependencies
- No ambiguity about what needs polyfilling
- Smaller bundles for browser builds
- Better compatibility with modern bundlers (Webpack 5, Vite, Rollup, esbuild)
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
- Unified embedding system with single EmbeddingManager
- Q8 model support with 75% smaller footprint (23MB vs 90MB)
- Intelligent precision auto-selection based on environment
- Clean cached embeddings with TTL and memory management
- Zero-config setup with smart defaults
- Complete storage structure documentation
- Removed legacy worker and hybrid managers
- Streamlined model configuration and precision management
- Changed confusing 'dtype' to 'precision' for model variant selection
- Fixed Q8 quantized model loading in transformers.js pipeline
- Added proper model file detection for q8 vs fp32 models
- Updated all references across codebase to use new parameter
- Maintains backward compatibility while providing clearer API
Changed default dtype from q8 to fp32 across all embedding implementations:
- embedding.ts: Default dtype changed to fp32
- worker-embedding.ts: Use fp32 for consistency
- universal-memory-manager.ts: Use fp32 for consistency
- lightweight-embedder.ts: Use fp32 for consistency
- hybridModelManager.ts: Use fp32 for all configurations
This ensures we use the exact same model (model.onnx) everywhere,
maintaining data compatibility and avoiding 404 errors for quantized models.
CRITICAL ARCHITECTURAL FIX:
- Change node-worker strategy to node-direct for ONNX compatibility
- Use single model instance on main thread instead of worker pool
- Prevents HandleScope V8 API locking errors in Node.js 22/24
- Reduces memory usage from 360MB+ to ~90MB (single model vs 4 workers)
- Maintains async operations using native transformers.js capabilities
Root Cause: ONNX runtime cannot properly handle V8 isolate context
switching between worker threads, causing fatal HandleScope errors.
Solution: Keep ONNX operations in main V8 isolate while preserving
all existing async functionality and performance.
Tested: Multiple concurrent addNoun operations work without errors.
- Implements automatic fallback chain: CDN → GitHub → Hugging Face
- Adds Soulcraft CDN as primary model source (models.soulcraft.com)
- GitHub release tar.gz extraction as reliable backup
- Zero configuration required - fully automatic
- Guarantees same model (all-MiniLM-L6-v2) across all sources
- 384-dimensional embeddings for data compatibility
- Local caching after first download
- Production-ready with multiple redundancy layers
BREAKING CHANGE: Removed tar-stream dependency, now uses native tar command
