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> |
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| architecture | ||
| augmentations | ||
| deployment | ||
| features | ||
| guides | ||
| operations | ||
| vfs | ||
| api-returns.md | ||
| API_DECISION_TREE.md | ||
| API_REFERENCE.md | ||
| CORE_API_PATTERNS.md | ||
| CREATING-AUGMENTATIONS.md | ||
| EXTENDING_STORAGE.md | ||
| FIND_SYSTEM.md | ||
| METADATA_CONTRACT_IMPLEMENTATION.md | ||
| MODEL_LOADING_QUICK_REFERENCE.md | ||
| NEURAL_API_PATTERNS.md | ||
| PERFORMANCE.md | ||
| QUICK-START.md | ||
| README.md | ||
| RELEASE-GUIDE.md | ||
| SCALING.md | ||
| troubleshooting.md | ||
| universal-display-augmentation.md | ||
| VALIDATION.md | ||
| ZERO_CONFIG.md | ||
Brainy Documentation
Welcome to the comprehensive documentation for Brainy, the multi-dimensional AI database with Triple Intelligence Engine.
📊 Implementation Status
- ✅ Production Ready: Core features working today
- 🚧 In Development: Features coming soon
- 📅 Roadmap: See ROADMAP.md
Quick Links
Getting Started
- Quick Start Guide - Get up and running in minutes
- Enterprise for Everyone - No limits, no tiers, everything free
- Natural Language Queries - Query with plain English
Core Concepts
- Zero Configuration - Auto-adapts to any environment
- Noun-Verb Taxonomy - Revolutionary data model
- Triple Intelligence - Unified query system
- Architecture Overview - System design
API Documentation
- API Reference - Complete API documentation
- TypeScript Types - Type definitions
Advanced Topics
- Augmentations System - Enterprise plugins & neural import
- Storage Architecture - Storage adapter system
- Performance Tuning - Optimization guide
- Migration Guide - Upgrading from 1.x
What is Brainy?
Brainy is a next-generation AI database that combines:
- Vector Search: Semantic similarity using HNSW indexing
- Graph Relationships: Complex relationship mapping and traversal
- Field Filtering: Precise metadata filtering with O(1) lookups
- Natural Language: Query in plain English
Key Features
🧠 Triple Intelligence Engine
All three intelligence types (vector, graph, field) work together in every query for optimal results.
📝 Noun-Verb Taxonomy
Model your data naturally as entities (nouns) and relationships (verbs) - no complex schemas needed.
🌍 Natural Language Queries
Ask questions in plain English and Brainy understands your intent:
await brain.find("recent articles about AI with high ratings")
⚡ Production Ready
- Universal storage (FileSystem, S3, OPFS, Memory)
- Zero configuration with intelligent defaults
- Full TypeScript support
- Cross-platform compatibility
Quick Example
import { Brainy } from 'brainy'
// Initialize
const brain = new Brainy()
await brain.init()
// Add entities (nouns)
const articleId = await brain.add("Revolutionary AI Breakthrough", {
type: "article",
category: "technology",
rating: 4.8
})
const authorId = await brain.add("Dr. Sarah Chen", {
type: "person",
role: "researcher"
})
// Create relationships (verbs)
await brain.relate(authorId, articleId, "authored", {
date: "2024-01-15",
contribution: "primary"
})
// Query naturally
const results = await brain.find("highly rated technology articles by researchers")
Documentation Structure
docs/
├── README.md # This file
├── guides/ # User guides
│ ├── getting-started.md # Quick start guide
│ ├── natural-language.md # NLP query guide
│ └── performance.md # Performance tuning
├── architecture/ # Technical architecture
│ ├── overview.md # System overview
│ ├── noun-verb-taxonomy.md # Data model
│ ├── triple-intelligence.md # Query system
│ └── storage.md # Storage layer
├── vfs/ # Virtual Filesystem
│ ├── README.md # VFS overview
│ ├── SEMANTIC_VFS.md # Semantic projections
│ ├── VFS_API_GUIDE.md # Complete API reference
│ └── QUICK_START.md # 5-minute setup
└── api/ # API documentation
├── README.md # API overview
├── brainy-data.md # Main class
└── types.md # TypeScript types
Community
- GitHub: github.com/brainy-org/brainy
- Issues: Report bugs or request features
- Discussions: Join the conversation
License
Brainy is MIT licensed. See LICENSE for details.