David Snelling
fd5edb5a13
Subscribe once and receive one post-commit event per affected record for
EVERY canonical write, regardless of origin: direct calls, batch methods,
transact(), imports, and Virtual Filesystem writes all funnel through the
same commit points the feed is emitted from. This is the authoritative
in-process signal for live UIs, cache invalidation, and realtime sync layers
that forward it over their own transports.
Architecture — the post-commit dual of the ifRev precommit hook: mutation
methods hand lightweight event descriptors to the commit seam
(persistSingleOp / transact's plan), which stamps the committed
{generation, timestamp}, enriches entity deletes with the record's LAST
committed state from the commit's own before-images (free — Model B reads
them anyway; removeMany's id-only deletes gain full payloads this way), and
emits only after the commit succeeds — a losing CAS or rejected batch never
announces anything. Dispatch is a microtask FIFO after the mutex releases:
commit-ordered, a slow listener never delays a write, a throwing listener is
logged and isolated, and with no subscribers the write path constructs no
events at all. Single-point emission was chosen over per-method hooks
because the aggregation-hook pattern demonstrably drifted (relation ops and
removeMany were silently missing from it).
Coverage: add/update/remove (+ cascade unrelate per deleted relationship),
relate (both edges when bidirectional)/unrelate/updateRelation, per-item
events for addMany/updateMany/relateMany/removeMany, per-item events sharing
one generation for transact(), and transitively imports + VFS. clear() and
restore() — wholesale raw-state operations outside the per-record commit
path — emit a single store-level event meaning "refetch everything".
brain.close() drops all listeners.
New module src/events/changeFeed.ts (BrainyChangeEvent + ChangeFeed,
exported from the package root); guide docs/guides/reacting-to-changes.md.
Integration suite pins the contract: per-op payload fidelity, delete
last-state payloads, batch per-item emission, one-generation transact
batches, VFS-origin events, CAS-loser silence, ordering, listener isolation,
unsubscribe, and store-level events.
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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.