brainy/docs
David Snelling b7dfc52e94 feat: replace flat file indexing with adaptive chunked sparse indexing
Major refactor of metadata indexing system for production scalability:

Performance improvements:
- 630x file reduction: 560,000 flat files → 89 chunk files
- O(1) exact match queries with bloom filters (1% false positive rate)
- O(log n) range queries with zone maps (ClickHouse-inspired)
- Adaptive chunking: ~50 values per chunk optimizes I/O

Technical changes:
- NEW: src/utils/metadataIndexChunking.ts
  - BloomFilter: Probabilistic membership testing (FNV-1a + DJB2)
  - SparseIndex: Directory of chunks with metadata
  - ChunkManager: Handles chunk CRUD operations
  - AdaptiveChunkingStrategy: Field-specific optimization
  - ZoneMap: Min/max tracking for range query optimization

- REFACTORED: src/utils/metadataIndex.ts
  - Removed indexCache (flat file entry cache)
  - Removed dirtyEntries (flat file dirty tracking)
  - Removed sortedIndices (sorted index for range queries)
  - Removed 13 obsolete methods (sorted index operations, flat file I/O)
  - Simplified flush() to only flush field indexes
  - All fields now use chunked sparse indexing exclusively

- UPDATED: docs/architecture/index-architecture.md
  - Documented new chunked sparse index architecture
  - Added bloom filter and zone map explanations
  - Updated query algorithm examples
  - Added v3.42.0 version history

Benefits:
- Single code path (no more dual flat file + chunks)
- Immediate chunk flushing (no dirty tracking needed)
- Better I/O patterns (chunk-based instead of per-value files)
- Production-ready for billions of entities
- Zero breaking changes to public API

All tests passing. Ready for production.
2025-10-13 15:31:03 -07:00
..
api fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
architecture feat: replace flat file indexing with adaptive chunked sparse indexing 2025-10-13 15:31:03 -07:00
augmentations feat: remove legacy ImportManager, standardize getStats() API 2025-10-09 11:40:31 -07:00
deployment feat: Brainy 3.0 - Production-ready Triple Intelligence database 2025-09-11 16:23:32 -07:00
features feat: remove legacy ImportManager, standardize getStats() API 2025-10-09 11:40:31 -07:00
guides feat: implement always-adaptive caching with getCacheStats monitoring 2025-10-10 14:09:30 -07:00
operations feat: implement always-adaptive caching with getCacheStats monitoring 2025-10-10 14:09:30 -07:00
vfs feat: implement always-adaptive caching with getCacheStats monitoring 2025-10-10 14:09:30 -07:00
api-returns.md fix: metadata batch reading from correct directory 2025-10-06 15:43:45 -07:00
API_DECISION_TREE.md feat: add neural extraction APIs with NounType taxonomy 2025-09-29 13:51:47 -07:00
API_REFERENCE.md feat: implement always-adaptive caching with getCacheStats monitoring 2025-10-10 14:09:30 -07:00
CORE_API_PATTERNS.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
CREATING-AUGMENTATIONS.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
EXTENDING_STORAGE.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
FIND_SYSTEM.md docs: comprehensive documentation for type-aware find system 2025-09-12 13:41:29 -07:00
METADATA_CONTRACT_IMPLEMENTATION.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
MODEL_LOADING_QUICK_REFERENCE.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
NEURAL_API_PATTERNS.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
PERFORMANCE.md feat: add comprehensive zero-config validation system 2025-09-12 14:37:39 -07:00
QUICK-START.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
README.md fix: implement stub methods in Neural API clustering 2025-10-07 13:53:41 -07:00
RELEASE-GUIDE.md feat: Brainy 3.0 - Production-ready Triple Intelligence database 2025-09-11 16:23:32 -07:00
SCALING.md docs: add comprehensive scaling and storage architecture documentation 2025-09-08 14:49:25 -07:00
troubleshooting.md feat: implement always-adaptive caching with getCacheStats monitoring 2025-10-10 14:09:30 -07:00
universal-display-augmentation.md fix: update all imports and references from BrainyData to Brainy 2025-09-30 17:09:15 -07:00
VALIDATION.md feat: add comprehensive zero-config validation system 2025-09-12 14:37:39 -07:00
ZERO_CONFIG.md feat: implement always-adaptive caching with getCacheStats monitoring 2025-10-10 14:09:30 -07:00

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

Getting Started

Core Concepts

API Documentation

Advanced Topics

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

License

Brainy is MIT licensed. See LICENSE for details.