brainy/docs
David Snelling 4949b6a629 CHECKPOINT: Industry-standard 3-tier testing implemented
 MAJOR BREAKTHROUGH - Session 5 Success:
- Unit tests: 18/19 passing with mocked AI (<500MB RAM)
- Integration tests: Real AI models loading successfully
- Core features: Real embeddings, CRUD operations verified
- Architecture: All 11 augmentations, worker threads operational

📋 CRITICAL FINDINGS:
- Real AI models load and cache correctly
- 384D embeddings generate properly
- Core CRUD operations work with real transformers
- Memory management effective for production

⚠️ RELEASE BLOCKER IDENTIFIED:
- Search operations timeout in test environment
- Affects: search(), find(), clustering functionality
- Root cause: Likely worker communication during HNSW search
- Priority: MUST fix before 2.0.0 release

🎯 NEXT SESSION PRIORITIES:
1. Debug and fix search timeout issue
2. Verify search/find/clustering work in production
3. Final documentation cleanup
4. Release preparation

Confidence: 90% ready (pending search functionality verification)
2025-08-25 17:12:58 -07:00
..
api docs: consolidate and archive redundant documentation 2025-08-25 10:15:38 -07:00
api-design-archive docs: consolidate and archive redundant documentation 2025-08-25 10:15:38 -07:00
architecture feat: complete augmentation system architecture audit 2025-08-25 10:59:02 -07:00
augmentations feat: complete augmentation system architecture audit 2025-08-25 10:59:02 -07:00
augmentations-archive docs: consolidate and archive redundant documentation 2025-08-25 10:15:38 -07:00
features docs: consolidate and archive redundant documentation 2025-08-25 10:15:38 -07:00
guides docs: consolidate and archive redundant documentation 2025-08-25 10:15:38 -07:00
planning docs: consolidate and archive redundant documentation 2025-08-25 10:15:38 -07:00
CREATING-AUGMENTATIONS.md CHECKPOINT: Brainy 2.0 API refactor - pre-fixes state 2025-08-25 09:52:32 -07:00
MEMORY-REQUIREMENTS.md CHECKPOINT: Industry-standard 3-tier testing implemented 2025-08-25 17:12:58 -07:00
MODEL_LOADING_QUICK_REFERENCE.md CHECKPOINT: Brainy 2.0 API refactor - pre-fixes state 2025-08-25 09:52:32 -07:00
ONNX-OPTIMIZATIONS.md CHECKPOINT: Industry-standard 3-tier testing implemented 2025-08-25 17:12:58 -07:00
README.md CHECKPOINT: Brainy 2.0 API refactor - pre-fixes state 2025-08-25 09:52:32 -07:00
troubleshooting.md docs: consolidate and archive redundant documentation 2025-08-25 10:15:38 -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 { BrainyData } from 'brainy'

// Initialize
const brain = new BrainyData()
await brain.init()

// Add entities (nouns)
const articleId = await brain.addNoun("Revolutionary AI Breakthrough", {
  type: "article",
  category: "technology",
  rating: 4.8
})

const authorId = await brain.addNoun("Dr. Sarah Chen", {
  type: "person",
  role: "researcher"
})

// Create relationships (verbs)
await brain.addVerb(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

License

Brainy is MIT licensed. See LICENSE for details.