brainy/docs/README.md
David Snelling 1d2da823ed fix: implement stub methods in Neural API clustering
Previously, clusterByDomain() and clusterByTime() methods contained
stub implementations that always returned empty arrays. This caused
empty results when attempting domain-based or temporal clustering.

Changes:
- Implement _getItemsByField() to query brain storage
- Implement _getItemsByTimeWindow() to filter by time windows
- Fix _groupByDomain() to check root, metadata, and data fields
- Implement _findCrossDomainMembers() for cross-domain analysis
- Implement _findCrossDomainClusters() to merge similar clusters
- Add comprehensive tests for domain and time clustering
- Update documentation structure to include VFS guides

The methods now properly query the brain's storage, filter results,
and return functional clustering data.
2025-10-07 13:53:41 -07:00

4.4 KiB

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.