David Snelling
b3c4f348ab
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. |
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| .. | ||
| api | ||
| architecture | ||
| augmentations | ||
| deployment | ||
| features | ||
| guides | ||
| vfs | ||
| API_REFERENCE.md | ||
| CREATING-AUGMENTATIONS.md | ||
| EXTENDING_STORAGE.md | ||
| FIND_SYSTEM.md | ||
| KNOWLEDGE_LAYER_OVERVIEW.md | ||
| METADATA_CONTRACT_IMPLEMENTATION.md | ||
| MODEL_LOADING_QUICK_REFERENCE.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 { BrainyData } from 'brainy'
// Initialize
const brain = new BrainyData()
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
└── 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.