David Snelling
c44678390e
- brain.fillSubtypes(rules): idempotent subtype back-fill for pre-8.0 data.
One rule per NounType/VerbType (literal default or per-entry function);
fills only entries still missing a subtype through the public update()/
updateRelation() paths; returns { scanned, filled, skipped, errors, byType }.
Full unit suite in tests/unit/brainy/fill-subtypes.test.ts.
- Fix getNouns/getVerbs pagination hasMore (peek one past the window) —
was permanently false, silently truncating every multi-page walk.
- find({ near }) without near.id now throws a teaching error instead of an
opaque storage sharding failure; CLI --threshold without --near applies a
plain score floor.
- CLI init/close audit: every one-shot command init()s, close()s, and exits
explicitly; delete the unmaintained interactive REPL; replace the cloud-era
storage subcommands with status/batch-delete; new types/validate commands.
- requireSubtype JSDoc now documents the 8.0 default-on contract; audit()
recommendation points at fillSubtypes.
- Docs: data-storage-architecture rewritten to the real 8.0 on-disk layout;
README storage section reflects filesystem+memory and snapshots; eli5 and
SEMANTIC_VFS /as-of/ semantics corrected; internal tracker IDs and
.strategy references scrubbed from published files.
439 lines
16 KiB
Markdown
439 lines
16 KiB
Markdown
# Brainy
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<p align="center">
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<img src="https://raw.githubusercontent.com/soulcraftlabs/brainy/main/brainy.png" alt="Brainy Logo" width="200">
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</p>
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[](https://www.npmjs.com/package/@soulcraft/brainy)
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[](https://www.npmjs.com/package/@soulcraft/brainy)
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[](https://soulcraft.com/docs)
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[](LICENSE)
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[](https://www.typescriptlang.org/)
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**Three database paradigms. One API. Zero configuration.**
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Built because we were tired of stitching together Pinecone + Neo4j + MongoDB and spending weeks on configuration before writing a single line of business logic. Brainy unifies vector search, graph traversal, and metadata filtering so you don't have to choose.
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**New here?** → **[What is Brainy? — plain-language overview, no jargon](docs/eli5.md)**
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---
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## Install
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```bash
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npm install @soulcraft/brainy
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```
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## Quick Start
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```javascript
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import { Brainy, NounType, VerbType } from '@soulcraft/brainy'
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const brain = new Brainy()
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await brain.init()
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// Add knowledge — text auto-embeds, metadata auto-indexes
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const reactId = await brain.add({
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data: 'React is a JavaScript library for building user interfaces',
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type: NounType.Concept,
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metadata: { category: 'frontend', year: 2013 }
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})
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const nextId = await brain.add({
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data: 'Next.js framework for React with server-side rendering',
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type: NounType.Concept,
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metadata: { category: 'framework', year: 2016 }
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})
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// Create a relationship
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await brain.relate({ from: nextId, to: reactId, type: VerbType.BuiltOn })
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// Query all three paradigms at once
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const results = await brain.find({
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query: 'modern frontend frameworks', // Vector similarity
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where: { year: { greaterThan: 2015 } }, // Metadata filtering
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connected: { to: reactId, depth: 2 } // Graph traversal
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})
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```
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**[Full API Reference](docs/api/README.md)** | **[soulcraft.com/docs](https://soulcraft.com/docs)**
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---
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## Three Indexes, One Query
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Every piece of knowledge lives in three indexes simultaneously:
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- **`data`** → **Vector index** — Content for semantic search. Strings auto-embed into 384-dim vectors. Queried with `find({ query: '...' })`.
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- **`metadata`** → **Metadata index** — Structured fields for filtering. O(1) lookups. Queried with `find({ where: { ... } })`.
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- **`relate()`** → **Graph index** — Typed, directed relationships between entities. Traversed with `find({ connected: { ... } })`.
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```javascript
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// Data → vector index (semantic search)
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const articleId = await brain.add({
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data: 'A deep dive into transformer architectures',
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type: NounType.Document,
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metadata: { author: 'Dr. Chen', year: 2024, tags: ['AI'] } // → metadata index
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})
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// Relationships → graph index
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await brain.relate({ from: authorId, to: articleId, type: VerbType.Authored })
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// Query all three at once
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brain.find({
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query: 'attention mechanisms', // Vector similarity
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where: { year: { greaterThan: 2023 } }, // Metadata filter
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connected: { from: authorId, depth: 1 } // Graph traversal
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})
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```
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**[Data Model Reference](docs/DATA_MODEL.md)** | **[Query Operators](docs/QUERY_OPERATORS.md)**
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---
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## Features
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### Triple Intelligence
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Vector search + graph traversal + metadata filtering in every query. No stitching services together — one `find()` call combines all three.
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```javascript
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const results = await brain.find({
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query: 'machine learning',
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where: { department: 'engineering', level: 'senior' },
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connected: { from: teamLeadId, via: VerbType.WorksWith, depth: 2 }
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})
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```
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### Hybrid Search
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Automatically combines keyword (text) and semantic (vector) search. No configuration needed.
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```javascript
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await brain.find({ query: 'David Smith' }) // Auto: text + semantic
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await brain.find({ query: 'AI concepts', searchMode: 'semantic' }) // Semantic only
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await brain.find({ query: 'exact id', searchMode: 'text' }) // Text only
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```
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### Query Operators
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Filter metadata with equality, comparison, array, existence, pattern, and logical operators:
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```javascript
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await brain.find({
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where: {
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status: 'active', // Exact match
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score: { greaterThan: 90 }, // Comparison
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tags: { contains: 'ai' }, // Array
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anyOf: [{ role: 'admin' }, { role: 'owner' }] // Logical OR
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}
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})
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```
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**[Query Operators Reference](docs/QUERY_OPERATORS.md)** — all operators with indexed/in-memory matrix
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### Graph Relationships
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Typed, directed edges between entities. Traverse connections at any depth.
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```javascript
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await brain.relate({ from: personId, to: projectId, type: VerbType.WorksOn })
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const results = await brain.find({
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connected: { from: personId, via: VerbType.WorksOn, depth: 3 }
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})
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```
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### Database as a Value
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The whole database, pinned as an immutable value. Snapshot isolation, time travel, atomic transactions, instant hard-link snapshots.
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```javascript
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const db = brain.now() // Pin current state — O(1)
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// Atomic multi-write transaction (all-or-nothing, with CAS)
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await brain.transact([
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{ op: 'update', id: orderId, metadata: { status: 'paid' } },
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{ op: 'relate', from: invoiceId, to: orderId, type: VerbType.References, subtype: 'billing' }
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], { meta: { author: 'billing-service' }, ifAtGeneration: db.generation })
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await db.get(orderId) // Still 'pending' — pinned, forever
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await brain.get(orderId) // 'paid' — live
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// Time travel: full query surface at any past state
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const yesterday = await brain.asOf(new Date(Date.now() - 86_400_000))
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const past = await yesterday.find({ query: 'unpaid orders' })
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// What-if: speculative writes, nothing touches disk
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const whatIf = await db.with([{ op: 'remove', id: orderId }])
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// Instant backup: hard-link snapshot, opens read-only with Brainy.load()
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await brain.now().persist('/backups/today')
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```
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**[Consistency Model](docs/concepts/consistency-model.md)** | **[Snapshots & Time Travel](docs/guides/snapshots-and-time-travel.md)**
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### Virtual Filesystem
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File operations with semantic search built in.
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```javascript
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const vfs = brain.vfs
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await vfs.writeFile('/docs/readme.md', 'Project documentation')
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const content = await vfs.readFile('/docs/readme.md')
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const tree = await vfs.getTreeStructure('/docs', { maxDepth: 3 })
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// Semantic file search
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const matches = await vfs.search('React components with hooks')
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```
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**[VFS Quick Start](docs/vfs/QUICK_START.md)** | **[Common Patterns](docs/vfs/COMMON_PATTERNS.md)**
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### Import Anything
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CSV, Excel, PDF, URLs — auto-detected format, auto-classified entities.
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```javascript
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await brain.import('customers.csv')
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await brain.import('sales-data.xlsx', { excelSheets: ['Q1', 'Q2'] })
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await brain.import('research-paper.pdf', { pdfExtractTables: true })
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await brain.import('https://api.example.com/data.json')
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```
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**[Import Guide](docs/guides/import-anything.md)**
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### Entity Extraction
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AI-powered named entity recognition with 4-signal ensemble scoring.
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```javascript
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const entities = await brain.extractEntities('John Smith founded Acme Corp in New York')
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// [
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// { text: 'John Smith', type: NounType.Person, confidence: 0.95 },
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// { text: 'Acme Corp', type: NounType.Organization, confidence: 0.92 },
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// { text: 'New York', type: NounType.Location, confidence: 0.88 }
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// ]
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```
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**[Neural Extraction Guide](docs/neural-extraction.md)**
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### Plugin System
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Optional native acceleration via `@soulcraft/cortex` — SIMD distance calculations, CRoaring bitmaps, Candle ML embeddings.
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```javascript
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const brain = new Brainy({ plugins: ['@soulcraft/cortex'] })
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await brain.init()
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```
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Plugins are opt-in. Brainy never auto-imports packages unless listed in `plugins`.
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**[Plugin Documentation](docs/PLUGINS.md)**
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---
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## Type System
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42 noun types and 127 verb types form a universal knowledge protocol:
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```
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42 Nouns × 127 Verbs = 5,334 base relationship combinations
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```
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Model any domain — healthcare (`Patient → diagnoses → Condition`), finance (`Account → transfers → Transaction`), education (`Student → completes → Course`), or your own.
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### Subtypes — sub-classification within a NounType *or* VerbType
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Both noun types and verb types are intentionally coarse. Use the top-level `subtype` field to sub-classify entities AND relationships within a type — flat string, no hierarchy, your choice of vocabulary:
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```javascript
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// Nouns: sub-classify entities
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await brain.add({
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data: 'Avery Brooks — runs the AI lab',
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type: NounType.Person,
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subtype: 'employee' // 'customer', 'vendor', 'contractor', …
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})
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// Verbs: sub-classify relationships
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await brain.relate({
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from: ceoId,
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to: vpId,
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type: VerbType.ReportsTo,
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subtype: 'direct' // 'dotted-line', 'matrix', …
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})
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// Filter on the fast path — column-store hit, not metadata fallback:
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const employees = await brain.find({ type: NounType.Person, subtype: 'employee' })
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const directReports = await brain.getRelations({ from: ceoId, subtype: 'direct' })
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// O(1) counts via the persisted rollups:
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brain.counts.bySubtype(NounType.Person)
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// → { employee: 12, customer: 847, vendor: 34 }
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brain.counts.byRelationshipSubtype(VerbType.ReportsTo)
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// → { direct: 12, 'dotted-line': 3 }
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```
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**Enforce the pairing.** Register a vocabulary per type or turn on brain-wide strict mode to ensure every entity AND relationship has both `type` AND `subtype`:
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```javascript
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// Per-type rule with a closed vocabulary
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brain.requireSubtype(NounType.Person, { values: ['employee', 'customer'], required: true })
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// 8.0 default: every write requires a subtype. Exempt genuine catch-all types…
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const brain = new Brainy({ requireSubtype: { except: [NounType.Thing] } })
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// …or opt out while migrating pre-8.0 data, then audit and back-fill:
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const legacy = new Brainy({ requireSubtype: false })
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await legacy.audit() // gaps, grouped by type
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await legacy.fillSubtypes({ [NounType.Person]: 'unspecified' }) // close them
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```
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For other facets you want counted (`status`, `source`, `role`), register them with `brain.trackField(name)`. Renaming an existing convention to `subtype`? Use `brain.migrateField({from, to, entityKind: 'both'})` to walk nouns AND verbs in one pass. Full guide: **[Subtypes & Facets](docs/guides/subtypes-and-facets.md)**.
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**[Noun-Verb Taxonomy](docs/architecture/noun-verb-taxonomy.md)** | **[Stage 3 Canonical Reference](docs/STAGE3-CANONICAL-TAXONOMY.md)**
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---
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## Storage: Memory and Filesystem
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The same API at every scale. Change one config line to go from prototype to production.
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### Development — Zero Config
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```javascript
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const brain = new Brainy()
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```
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### Production — Filesystem (gzip compression on by default)
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```javascript
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const brain = new Brainy({
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storage: { type: 'filesystem', rootDirectory: './data' }
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})
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```
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### Backups and Portability — Snapshots
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```javascript
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const db = brain.now()
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await db.persist('/backups/2026-06-11') // instant hard-link snapshot
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await db.release()
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const snapshot = await Brainy.load('/backups/2026-06-11') // read-only Db
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const hits = await snapshot.search('quarterly invoices')
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await snapshot.release()
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```
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A snapshot directory is self-contained — copy it to another machine, open it with `Brainy.load()`, or restore it wholesale with `brain.restore(path, { confirm: true })`.
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Performance benchmarks and capacity planning in **[docs/PERFORMANCE.md](docs/PERFORMANCE.md)**.
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**[Capacity Planning](docs/operations/capacity-planning.md)**
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---
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## Use Cases
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- **AI agents** — Persistent memory with semantic recall and relationship tracking
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- **Knowledge bases** — Auto-linking, semantic search, relationship-aware navigation
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- **Semantic search** — Find by meaning across codebases, documents, or media
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- **Enterprise knowledge** — CRM, product catalogs, institutional memory
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- **Interactive experiences** — Game worlds, NPCs, and characters that remember
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- **Content platforms** — Similarity-based discovery, intelligent tagging
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---
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## Documentation
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### Start Here
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- **[Brainy explained simply](docs/eli5.md)** — Plain-language overview, no jargon, no code
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### Core
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- **[API Reference](docs/api/README.md)** — Every method with parameters, returns, and examples
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- **[Data Model](docs/DATA_MODEL.md)** — Entity structure, data vs metadata
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- **[Query Operators](docs/QUERY_OPERATORS.md)** — All BFO operators with examples
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- **[Find System](docs/FIND_SYSTEM.md)** — Natural language find() and hybrid search
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### Architecture
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- **[Architecture Overview](docs/architecture/overview.md)** — System design and components
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- **[Triple Intelligence](docs/architecture/triple-intelligence.md)** — Vector + graph + metadata unified query
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- **[Noun-Verb Taxonomy](docs/architecture/noun-verb-taxonomy.md)** — Universal type system
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- **[Data Storage Architecture](docs/architecture/data-storage-architecture.md)** — Type-aware indexing and HNSW
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### Virtual Filesystem
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- **[VFS Quick Start](docs/vfs/QUICK_START.md)** — Build file explorers that never crash
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- **[VFS Core](docs/vfs/VFS_CORE.md)** — Full VFS API reference
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- **[Semantic VFS](docs/vfs/SEMANTIC_VFS.md)** — AI-powered file navigation
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### Guides
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- **[Import Anything](docs/guides/import-anything.md)** — CSV, Excel, PDF, URLs
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- **[Framework Integration](docs/guides/framework-integration.md)** — React, Vue, Angular, Svelte
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- **[Natural Language Queries](docs/guides/natural-language.md)** — Master the find() method
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### Operations
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- **[Capacity Planning](docs/operations/capacity-planning.md)** — Memory, storage, and scaling
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- **[Performance](docs/PERFORMANCE.md)** — Benchmarks and architecture details
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---
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## Requirements
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**Bun 1.0+** (recommended) or **Node.js 22 LTS**
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```bash
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bun install @soulcraft/brainy # Bun — best performance
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npm install @soulcraft/brainy # Node.js — fully supported
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```
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> Brainy 8.0 is server-only. Browser support (OPFS storage, Web Workers, in-browser WASM embeddings) was removed in 8.0 — the 7.x line remains available on npm if you need it.
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## Single-Writer Model
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Brainy is **single-writer, many-reader** on filesystem storage. One writer
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holds an exclusive lock on the data directory; any number of readers can
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inspect it concurrently. Opening a second writer throws with the PID of the
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existing one.
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```typescript
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// Live application — writer mode is the default
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const brain = new Brainy({ storage: { type: 'filesystem', rootDirectory: '/data/brain' } })
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await brain.init()
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// Out-of-band diagnostics from a separate process — safe to run while the
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// writer is live
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const reader = await Brainy.openReadOnly({
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storage: { type: 'filesystem', rootDirectory: '/data/brain' }
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})
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await reader.requestFlush({ timeoutMs: 5000 })
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const stats = await reader.stats()
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```
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For incident debugging, use the `brainy inspect` CLI:
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```bash
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brainy inspect stats /data/brain
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brainy inspect find /data/brain --where '{"entityType":"booking"}'
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brainy inspect explain /data/brain --where '{"entityType":"booking"}'
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brainy inspect health /data/brain
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```
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See [the multi-process model](docs/concepts/multi-process.md) and the
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[inspection guide](docs/guides/inspection.md) for the full story, including
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stale-lock detection, the cross-process flush RPC, and what's not yet
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enforced on cloud storage backends.
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## Contributing
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We welcome contributions! See **[CONTRIBUTING.md](CONTRIBUTING.md)** for guidelines.
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## License
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MIT © Brainy Contributors
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