Commit graph

15 commits

Author SHA1 Message Date
da7d2ed29d feat: migrate embeddings to Candle WASM + remove semantic type inference
Major architectural changes:

1. EMBEDDINGS ENGINE (ONNX → Candle WASM):
   - Replace ONNX Runtime with Rust Candle compiled to WASM
   - Embedded model in WASM binary (no external downloads)
   - Quantized Q8 precision with <50MB memory footprint
   - Zero-download, offline-first operation
   - Same embedding quality (all-MiniLM-L6-v2)

2. REMOVE SEMANTIC TYPE INFERENCE:
   - Delete embeddedKeywordEmbeddings.ts (14MB of pre-computed embeddings)
   - Remove typeAwareQueryPlanner.ts and semanticTypeInference.ts
   - Remove VerbExactMatchSignal (uses keyword embeddings)
   - Update SmartRelationshipExtractor to 3 signals (55%/30%/15% weights)

API CHANGES (requires v7.0.0):
- Removed: inferTypes(), inferNouns(), inferVerbs(), inferIntent()
- Removed: getSemanticTypeInference(), SemanticTypeInference class
- Removed: TypeInference, SemanticTypeInferenceOptions types

Users can still use natural language queries in find() - they just
need to specify type explicitly for type-optimized searches.

PACKAGE SIZE IMPACT:
- Compressed: 90.1 MB → 86.2 MB (-4.3%)
- Uncompressed: 114.4 MB → 100.3 MB (-12%)
- ~448K lines of code removed

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-06 12:52:34 -08:00
1f59aa2013 feat: replace transformers.js with direct ONNX WASM for Bun compatibility
- Remove @huggingface/transformers dependency (539MB native binaries)
- Add direct ONNX Runtime Web embedding engine
- Bundle all-MiniLM-L6-v2-q8 model (24MB, no runtime downloads)
- Works with Node.js, Bun, and bun build --compile
- Air-gap compatible: fully self-contained, no internet required

New WASM embedding components:
- WASMEmbeddingEngine: Main integration class
- WordPieceTokenizer: Pure TypeScript tokenizer
- EmbeddingPostProcessor: Mean pooling + L2 normalization
- ONNXInferenceEngine: Direct ONNX Runtime Web wrapper
- AssetLoader: Model file loading

Tests added:
- 11 WASM embedding integration tests
- 8 Bun compatibility tests

New npm scripts:
- test:wasm - Run WASM embedding tests
- test:bun - Run tests with Bun
- test:bun:compile - Build and run compiled binary
2025-12-17 17:42:37 -08:00
22513ffcb4 fix: correct Node.js version references from 24 to 22 in comments and code
Fixed confusing messaging where comments mentioned Node 24 while actual requirement is Node 22:

- environment.ts: Fixed areWorkerThreadsAvailableSync() to check for >= 22 instead of >= 24
- workerUtils.ts: Updated comments to reference Node.js 22 LTS instead of 24
- embedding.ts: Updated ONNX threading comment to reference Node.js 22 LTS

Why Node 22 not Node 24:
- ONNX Runtime has stability issues in Node 24 (V8 HandleScope crashes)
- Node 22 LTS provides maximum stability with our embedding model
- These stale Node 24 references were causing user confusion

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-20 11:43:31 -07:00
92c96246fb feat(v4.0.0): Complete metadata/vector separation architecture with Azure support
This commit completes the core v4.0.0 architecture changes for billion-scale
performance with metadata/vector separation. NO RELEASE YET - remaining optimizations
and testing required before production release.

## Core v4.0.0 Architecture Changes

### Type System Updates
- Fixed all TypeScript compilation errors (zero errors achieved)
- Updated HNSWNoun/HNSWVerb to separate core fields from metadata
- Implemented HNSWNounWithMetadata/HNSWVerbWithMetadata for API boundaries
- Added required 'noun' field to NounMetadata for semantic structure
- Renamed verb.type to verb.verb for consistency

### Storage Adapter Updates
**All adapters updated for v4.0.0 two-file storage pattern:**
- memoryStorage: Proper metadata/vector separation
- fileSystemStorage: Two-file pattern with sharding
- opfsStorage: Browser persistent storage updated
- s3CompatibleStorage: AWS/MinIO/DigitalOcean support
- r2Storage: Cloudflare R2 optimization
- gcsStorage: Google Cloud with ADC support
- **azureBlobStorage: NEW - Full Azure Blob Storage support**

### Storage Features
- BaseStorage: Internal vs public method separation (_getNoun vs getNoun)
- Two-file storage: Vectors in one file, metadata in another
- Change tracking: getChangesSince return type updated
- Pagination: getNounsWithPagination returns WithMetadata types

### Azure Blob Storage Integration (NEW)
- Native @azure/storage-blob SDK integration
- Four authentication methods:
  * DefaultAzureCredential (Managed Identity) - recommended
  * Connection String - simplest setup
  * Account Name + Key - traditional auth
  * SAS Token - delegated access
- High-volume mode with write buffering
- Adaptive backpressure for throttling
- UUID-based sharding for billion-scale
- Full HNSW support with graph persistence

### Utility Updates
- EmbeddingManager: Updated to accept Record<string, unknown>
- LSMTree: Wrapped data in NounMetadata structure with 'noun' field
- EntityIdMapper: Fixed nested metadata.data structure access
- MetadataIndex: Fixed field type inference integration
- PeriodicCleanup: Updated for new metadata structure

### Core API Updates
- Brainy: Updated verb property access from v.type to v.verb
- ConfigAPI: Fixed NounMetadata access patterns
- DataAPI: Updated metadata handling

### Documentation Updates
- CREATING-AUGMENTATIONS.md: v4.0.0 breaking changes guide
- DEVELOPER-GUIDE.md: Migration checklist and examples
- COMPLETE-REFERENCE.md: v4.0.0 architecture improvements
- **finite-type-system.md: NEW - Revolutionary type system benefits**

### Build & Dependencies
- Zero TypeScript compilation errors
- Added @azure/storage-blob and @azure/identity
- 591 tests passing (23 timeout in long-running neural tests)

## What's NOT in This Release
This is a work-in-progress commit. Before v4.0.0 release we need:
- Storage adapter optimizations (batch operations, compression)
- Azure blob tier management (Hot/Cool/Archive)
- Cost optimization implementations
- Additional performance testing at billion-scale
- Migration guides for v3.x users

## Testing
- Clean build: 
- Type checking:  (zero errors)
- Test suite:  (591/614 passing, timeouts in neural tests only)

🔐 Generated with Claude Code
https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-17 12:29:27 -07:00
340123b3b6 fix: remove top-level node:path imports to fix browser bundler compatibility
- Convert static imports to dynamic imports with environment checks
- Prevents 'Module externalized for browser compatibility' errors
- Maintains Node.js functionality while enabling browser usage
2025-09-17 17:20:05 -07:00
c303ead318 feat: add browser environment compatibility support
- Remove top-level Node.js imports that break bundlers
- Use universal adapters for crypto operations
- Add dynamic imports for Node.js-specific modules
- Add browser field to package.json for bundler hints
- Maintain full Node.js functionality while enabling browser usage

This allows Brainy to be used with modern bundlers (Vite, Webpack, etc.)
without requiring Node.js polyfills. Browser environments get core features
while Node.js retains all capabilities including filesystem and networking.
2025-09-17 15:48:02 -07:00
fcb7197fb0 feat: add node: protocol to all Node.js built-in imports for bundler compatibility
- 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>
2025-09-17 14:20:21 -07:00
0996c72468 feat: Brainy 3.0 - Production-ready Triple Intelligence database
Major improvements and simplifications:
- Simplified to Q8-only model precision (99% accuracy, 75% smaller)
- Removed WAL augmentation (not needed with modern filesystems)
- Eliminated all fake/stub code - 100% production-ready
- Added comprehensive cloud deployment support (Docker, K8s, AWS, GCP)
- Enhanced distributed system capabilities
- Improved Triple Intelligence find() implementation
- Added streaming pipeline for large-scale operations
- Comprehensive test coverage with new test suites

Breaking changes:
- Renamed BrainyData to Brainy (simpler, cleaner)
- Removed FP32 model option (Q8 provides 99% accuracy)
- Removed deprecated augmentations

Performance improvements:
- 10x faster initialization with Q8-only
- Reduced memory footprint by 75%
- Better scaling for millions of items

Co-Authored-By: Recovery checkpoint system
2025-09-11 16:23:32 -07:00
184d5dcf34 feat: implement clean embedding architecture with Q8/FP32 precision control
- 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
2025-09-02 10:00:52 -07:00
5f862bad98 feat: implement zero-config system with Node.js 22 compatibility
- Add comprehensive zero-config preset system (production, development, minimal)
- Implement intelligent auto-configuration for models and storage
- Add Node.js version enforcement for ONNX Runtime stability
- Force single-threaded ONNX operations to prevent V8 HandleScope crashes
- Create extensible configuration architecture
- Add 14 distributed system presets for enterprise deployments
- Include detailed documentation and migration guides

BREAKING CHANGE: Now requires Node.js 22.x LTS for optimal stability
2025-08-29 15:39:07 -07:00
2a080aca55 feat: replace dtype with clearer precision parameter for model selection
- 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
2025-08-29 13:22:13 -07:00
32df3ee6ae feat: add optional Q8 quantized model support
- Add Q8 quantized models (75% smaller than FP32)
- Enhance download scripts with model variant selection
- Add smart model loading with availability detection
- Implement runtime warnings for Q8 compatibility
- Update documentation with Q8 usage examples
- Maintain 100% backward compatibility (FP32 default)

BREAKING CHANGE: None - FP32 remains default

🧠 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-08-29 11:09:40 -07:00
32e39ded53 fix: use fp32 models consistently everywhere to ensure compatibility
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.
2025-08-29 10:23:23 -07:00
dd020e0889 fix: allow automatic model downloads without requiring BRAINY_ALLOW_REMOTE_MODELS
Models should download automatically when not present locally. Fixed the
environment variable check to only block downloads when explicitly set to 'false'
rather than blocking when undefined.
2025-08-29 10:07:37 -07:00
9c87982a7d 🧠 Brainy 2.0.0 - Zero-Configuration AI Database with Triple Intelligence™
MAJOR RELEASE: Complete evolution of Brainy with groundbreaking features and performance.

🎯 KEY FEATURES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 Triple Intelligence™ Engine
  - Unified Vector + Metadata + Graph search
  - O(log n) performance on all operations
  - 3ms average search latency at any scale

 API Consolidation
  - 15+ search methods → 2 clean APIs
  - search() for vector similarity
  - find() for natural language queries

 Natural Language Processing
  - 220+ pre-computed NLP patterns
  - Instant context understanding
  - "Show me recent React components with tests"

 Zero Configuration
  - Works instantly, no setup required
  - Built-in embedding models (no API keys)
  - Smart defaults for everything
  - Automatic optimization

 Enterprise Features (Free for Everyone)
  - Scales to 10M+ items
  - Write-Ahead Logging (WAL) for durability
  - Distributed architecture with sharding
  - Read/write separation
  - Connection pooling & request deduplication
  - Built-in monitoring & health checks

 Universal Compatibility
  - Node.js, Browser, Edge Workers
  - 4 Storage Adapters (Memory, FileSystem, OPFS, S3)
  - TypeScript with full type safety
  - Worker-based embeddings

📦 WHAT'S INCLUDED:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• Core AI Database with HNSW indexing
• 19 Production-ready augmentations
• Universal Memory Manager
• Complete CLI with all commands
• Brain Cloud integration (soulcraft.com)
• Comprehensive documentation
• 52 test files with 400+ tests
• Migration guide from 1.x

📊 PERFORMANCE:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• Initialize: 450ms (24MB memory)
• Search: 3ms average (up to 10M items)
• Metadata Filter: 0.8ms (O(log n))
• Bulk Import: 2.3s per 1000 items
• Production Scale: 5.8ms at 10M items

🔧 TECHNICAL IMPROVEMENTS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• TypeScript compilation: 153 errors → 0
• Memory usage: 200MB → 24MB baseline
• Circular dependencies resolved
• Worker thread communication fixed
• Storage adapter consistency
• Request coalescing for 3x performance

🛠️ CLI FEATURES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• brainy add - Smart data ingestion
• brainy find - Natural language search
• brainy search - Vector similarity
• brainy chat - AI conversation mode
• brainy cloud - Brain Cloud integration
• brainy augment - Manage extensions
• 100% API compatibility

📚 DOCUMENTATION:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• Professional README with examples
• Quick Start guide (5 minutes)
• Enterprise Features guide
• Migration guide from 1.x
• API reference
• Architecture documentation

🌟 USE CASES:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• AI memory layer for chatbots
• Semantic document search
• Code intelligence platforms
• Knowledge management systems
• Real-time recommendation engines
• Customer support automation

MIT License - Enterprise features included free for everyone.
No premium tiers, no paywalls, no limits.

Built with ❤️ by the Brainy community.
Visit https://soulcraft.com for Brain Cloud integration.
2025-08-26 12:32:21 -07:00