David Snelling
61c247c923
A production deployment measured ~48 seconds on EVERY reopen of an 11k-entity brain. Root cause: brainy's rebuild gate decided from in-memory size()/count, which read 0 for a durable-but-not-resident index, so it re-read every entity file to rebuild from scratch. At GA we gave only the GRAPH provider a readiness contract (init() eager cold-load + isReady() honest signal) so it would never eat that spurious rebuild; the vector and metadata providers never got it, and brainy never even eager-inited the vector provider. Complete the contract symmetrically: - plugin.ts: VectorIndexProvider gains optional init()+isReady(); MetadataIndexProvider gains isReady() — mirroring GraphIndexProvider. Additive and optional; a provider that exposes nothing keeps today's behavior. - brainy.ts: eager-init every provider that exposes init() (after metadata init() so the id-mapper is hydrated first), then decide per leg in precedence order — migrating (skip) -> epoch drift (rebuild) -> isReady() -> a per-leg empty fallback. The old instant fast-path keyed off this.index.size()>0, a dishonest proxy that skipped the metadata/graph checks whenever the vector was warm and never fired on a real cold process anyway; removed. The per-leg fallbacks differ because "empty" means different things: the JS vector's rebuild() IS its load, so size()===0 correctly triggers it; the id-mapper backs metadata, so totalEntries===0 (past the empty-store return) is a real load failure; but entities do not imply edges, so a graph size()===0 is a valid empty state, not a load failure. - The JS graph now COLD-LOADS its durable LSM instead of re-deriving from a full canonical verb scan on every boot (baseStorage._initializeGraphIndex loads the persisted SSTables via a new GraphAdjacencyIndex.init(); it self-heals from canonical only when the durable state is genuinely missing). This removes an O(E)-per-open cost every filesystem consumer paid. - LSMTree.loadManifest loads its SSTables BEFORE publishing the relationship count, and resets to an honest-empty state on load failure — a tree can no longer claim persisted relationships while holding none (the silent-empty cold-load class the query-time guards exist to prevent). Verified end-to-end against a built brain: a warm reopen (with edges and edgeless) reloads only the JS vector; the graph and metadata cold-load with no rebuild, and queries return correct results. New tests in cold-open-rebuild-gate.test.ts pin the contract (isReady() defers, self-heal still fires); migration-deference updated to drive size-based deference through the vector, the leg where empty->rebuild remains correct. Pairs with the native provider's isReady()/init() implementation — brainy's gate defers only to a signal the provider exposes. |
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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 | ||
| 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.