David Snelling
f8f64780b1
The read gate stops consulting the unnamed isReady() boolean: every provider
may expose healthReport() (sync, O(1), composed from exact ledgers —
HealthReport with a monotonic generation, per-invariant source
ledger|deep|unledgered, missing {count, sample}), and one readiness
authority (assessProviderHealth) derives the verdict. Unledgered families
are UNKNOWN — never healthy, never broken; a report that throws is a loud
not-ready, never a shrug. Reads at the four index choke points refuse with
the typed NotReady errors, narrated once per (provider, generation) — a
read NEVER starts a store walk:
- the first-read lazy build retires (open builds instead, regardless of
size — the ≥10k deferral and the "lazy loading on first query" branch go;
disableAutoRebuild is re-meant honestly in its docs);
- the verify*Live read-path rebuild triggers retire (refuse-or-serve);
- the read-time consistency probe that could launch a dark rebuild from an
ordinary find() retires;
- repairIndex({ rebuild: ['metadata'|'graph'|'vector'] | 'all' }) is the
one explicit door: rebuilds the named leg unconditionally and reports
rebuilt per family; bare repairIndex() stays report-driven.
test(lifecycle): the biography lane — a store's whole life, refereed
tests/lifecycle/: an independent shadow model referees every read after
every chapter (founding, a working day, clean restart, crash, repair,
second life). Chapters 1-3 green. Chapters 4-6 assert the true contract and
are marked .fails as a release-blocking finding (the kill-matrix
convention): after a crash + adopt reopen the metadata index computes its
'catchup' watermark verdict and nothing consumes it — find() serves the
pre-crash index while canonical and counts recover. The catchup wiring is
the cure; a passing .fails will force the marker's removal. The lane runs
in the integration gate (config + coverage guard).
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|---|---|---|
| .. | ||
| api | ||
| architecture | ||
| concepts | ||
| guides | ||
| operations | ||
| vfs | ||
| ADR-001-generational-mvcc.md | ||
| BATCHING.md | ||
| DATA_MODEL.md | ||
| DEVELOPER_LEARNING_PATH.md | ||
| eli5.md | ||
| FIND_SYSTEM.md | ||
| MIGRATION-V3-TO-V4.md | ||
| neural-extraction.md | ||
| path-registry.md | ||
| performance-envelopes.md | ||
| PERFORMANCE.md | ||
| PLUGINS.md | ||
| PRODUCTION_SERVICE_ARCHITECTURE.md | ||
| QUERY_OPERATORS.md | ||
| README.md | ||
| RELEASE-GUIDE.md | ||
| SCALING.md | ||
| STAGE3-CANONICAL-TAXONOMY.md | ||
| transactions.md | ||
| troubleshooting.md | ||
| universal-display-augmentation.md | ||
Brainy Documentation
The multi-dimensional AI database with Triple Intelligence — vector search, graph traversal, and metadata filtering in one unified API.
Quick Start
import { Brainy, NounType, VerbType } from '@soulcraft/brainy'
const brain = new Brainy()
await brain.init()
// Add entities — data is embedded for semantic search, metadata is indexed for filtering
const id = await brain.add({
data: 'Revolutionary AI Breakthrough',
type: NounType.Document,
metadata: { category: 'technology', rating: 4.8 }
})
// Search with Triple Intelligence
const results = await brain.find({
query: 'artificial intelligence', // Semantic search (on data)
where: { rating: { greaterThan: 4.0 } }, // Metadata filter
connected: { from: authorId, depth: 2 } // Graph traversal
})
Core Documentation
| Document | Description |
|---|---|
| API Reference | Complete API documentation — start here |
| Data Model | Entity structure, data vs metadata, storage fields |
| Query Operators | All BFO operators with examples and indexed/in-memory matrix |
| Find System | Natural language find() and hybrid search details |
| Consistency Model | The Db API guarantees — snapshot isolation, atomic transactions, time travel |
Architecture
| Document | Description |
|---|---|
| Architecture Overview | High-level system design |
| Triple Intelligence | Vector + Graph + Metadata unified query |
| Noun-Verb Taxonomy | 42 nouns + 127 verbs type system |
| Stage 3 Canonical Taxonomy | Complete type reference |
| Storage Architecture | Storage adapters and optimization |
| Index Architecture | Vector, Graph, and Metadata indexing |
| Zero Configuration | Auto-adapts to any environment |
Virtual Filesystem (VFS)
| Document | Description |
|---|---|
| VFS Quick Start | Get started in 30 seconds |
| VFS Core | Core concepts and architecture |
| VFS API Guide | Complete VFS API reference |
| Common Patterns | VFS usage patterns |
See vfs/ for the complete VFS documentation set.
Guides
| Document | Description |
|---|---|
| Import Anything | CSV, Excel, PDF, URL imports |
| Snapshots & Time Travel | Backups, restore, what-if analysis, audit trails |
| Natural Language | Query in plain English |
| Neural API | AI-powered features |
| Enterprise for Everyone | No limits, no tiers |
| Framework Integration | React, Vue, Angular, Svelte |
Storage & Deployment
| Document | Description |
|---|---|
| Storage Architecture | Filesystem and memory adapters, on-disk artifact layout, operator-layer backup |
| Capacity Planning | Scale to millions of entities |
Plugins
| Document | Description |
|---|---|
| Plugins | Plugin system overview — providers, plugins config, brain.use() |
Performance & Scaling
| Document | Description |
|---|---|
| Performance | Optimization techniques |
| Scaling | Scale to billions of entities |
| Batching | Batch operations guide |
Migration & Reference
| Document | Description |
|---|---|
| v3 to v4 Migration | Upgrade guide |
| Release Guide | How to release new versions |
| Production Architecture | Ops reference |
Internal
| Document | Description |
|---|---|
| Audit Report | Feature audit |
| Honest Status | Actual implementation status |
License
Brainy is MIT licensed. See LICENSE for details.