David Snelling
6ef9fcb7a2
- The transact apply budget scales with the batch — max(30s, opCount x 2s) — or is exactly the caller's new TransactOptions.timeoutMs. A flat 30s cap silently limited honest bulk work to ~15 operations on network-attached storage (~2s/op measured in a production import) while looking generous for small batches. Internal batch paths (removeMany chunks) get the same scaling via the shared transactTimeoutBudget helper. - TransactionTimeoutError now reports the operation it stopped at as i/N with the operation's name, elapsed vs budgeted time, and states the batch rolled back atomically and is retryable; context carries the fields programmatically. - The transact operational envelope is documented (optimistic-concurrency guide): budget math, chunking with ifAbsent idempotency, when to reach for addMany vs transact, and the precompute pattern (embedBatch + per-op vector) that keeps inference out of the commit path. - Embedding claims made honest per an end-to-end probe of the built dist: batch and single embedding paths produce bit-identical vectors and a vector-supplied add is fully searchable, but batch throughput on the default WASM engine measures comparable to sequential (~160ms/text) — the 5-10x speedup claim in the addMany JSDoc is replaced with the measured reality and the actual win (inference outside the budgeted write path). |
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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.