🧠 Zero-Configuration AI Database with Triple Intelligence™ https://soulcraft.com
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David Snelling d4cb26c604 feat: mmap-vector backend wiring — HNSWIndex consumes vectorStore:mmap (2.4.0 #2)
Cortex already registers the vectorStore:mmap provider (its Rust
NativeMmapVectorStore), but brainy has never consumed it — preloadVectors
and getVectorSafe still go straight to storage.getNounVector for every id,
even when an mmap layer is available. This wires the consumer end.

Architecture:

- NEW MmapVectorBackend (src/hnsw/mmapVectorBackend.ts) — bridges brainy's
  UUID-keyed vector reads to a int-slot mmap file via the
  vectorStore:mmap provider. Slots are addressed by the stable int id
  from the post-2.4.0 #1 EntityIdMapper (the foundation this depends on).
  Auto-grows the file (doubling) when a write lands beyond capacity, so
  HNSWIndex never has to think about sizing. The class never touches
  per-entity storage — it owns only the mmap layer.

- HNSWIndex changes — adds a vectorBackend field + a setVectorBackend
  setter. The vector read paths (preloadVectors, getVectorSafe) try the
  mmap layer first; on a storage fallback hit, they LAZILY write back into
  the mmap slot. An upgraded install converges to the zero-copy fast path
  under live traffic — no big-bang migration step. The legacy per-entity
  path is preserved and still used when no backend is set.

- brainy.ts wiring — a new private wireMmapVectorBackend() runs once
  during init, after plugin activation + metadataIndex setup. It activates
  the backend only when (a) the vectorStore:mmap provider is registered,
  (b) the storage adapter resolves a real local path via
  getBinaryBlobPath(), and (c) the metadata index exposes its idMapper.
  Cloud adapters return null on (b) and the backend is silently skipped;
  HNSWIndex's behaviour is then identical to pre-2.4.0.

- Provider interfaces in plugin.ts — VectorStoreMmapProvider and
  VectorStoreMmapInstance document the contract cortex's class fulfils
  (the class IS the provider — static factory methods). Brainy depends on
  the interfaces, not on cortex; the structural match is verified when
  cortex 2.4.0 picks up this brainy release.

Tests (1428 total, +11 vs pre-2.4.0):

- tests/unit/hnsw/mmap-vector-backend.test.ts — 6 unit tests with an
  in-memory mock provider. Covers round-trip, batch reads with interleaved
  misses, slot stability (no re-slotting on overwrite), file growth without
  data loss, idempotent open, and null returns for unwritten slots. The
  real perf integration with cortex's NativeMmapVectorStore is exercised
  when cortex 2.4.0 wires this in.

- tests/unit/utils/entity-id-mapper-stability.test.ts — moved here from
  tests/regression/ (which is NOT in the unit-config include glob, so the
  five #23 tests were not actually being run by npm test). The unit
  config matches tests/unit/**/*.test.ts.

The 2.4.0 #2 follow-up will be the chunked-segment layout for remote
storage adapters (S3 / R2 / GCS) where a single growing file doesn't fit
immutable objects. For 2.4.0 release: local-FS only.
2026-05-28 10:37:47 -07:00
.claude/skills feat: add aggregation engine with incremental SUM/COUNT/AVG/MIN/MAX, GROUP BY, and time windows 2026-02-16 16:57:53 -08:00
assets/models/all-MiniLM-L6-v2 feat: migrate embeddings to Candle WASM + remove semantic type inference 2026-01-06 12:52:34 -08:00
bin feat: remove legacy ImportManager, standardize getStats() API 2025-10-09 11:40:31 -07:00
docs feat: array-unnest groupBy for aggregates + batch-embed entity extraction 2026-05-26 14:20:40 -07:00
examples refactor: remove augmentation system and semantic type matching 2026-02-01 10:48:56 -08:00
integrations feat: Integration Hub for external tool connectivity 2026-01-20 16:21:11 -08:00
models-cache/Xenova/all-MiniLM-L6-v2 🧠 Brainy 2.0.0 - Zero-Configuration AI Database with Triple Intelligence™ 2025-08-26 12:32:21 -07:00
scripts chore(release): create annotated tag so --follow-tags pushes it 2026-05-26 11:44:14 -07:00
src feat: mmap-vector backend wiring — HNSWIndex consumes vectorStore:mmap (2.4.0 #2) 2026-05-28 10:37:47 -07:00
tests feat: mmap-vector backend wiring — HNSWIndex consumes vectorStore:mmap (2.4.0 #2) 2026-05-28 10:37:47 -07:00
.aiignore feat: add distributed scaling and enterprise features for v3 2025-09-08 14:26:09 -07:00
.dockerignore feat: Brainy 3.0 - Production-ready Triple Intelligence database 2025-09-11 16:23:32 -07:00
.gitignore chore: gitignore Claude Code harness scheduled-tasks lockfile 2026-05-15 11:26:11 -07:00
.npmignore chore: Add .npmignore to exclude models from npm package 2025-08-26 13:37:44 -07:00
.nvmrc feat: update Node.js requirements to 22 LTS for ONNX compatibility 2025-08-28 16:05:14 -07:00
.versionrc.json feat: implement simpler, more reliable release workflow 2025-10-01 13:26:04 -07:00
brainy.png 🧠 Brainy 2.0.0 - Zero-Configuration AI Database with Triple Intelligence™ 2025-08-26 12:32:21 -07:00
bun.lock feat: migrate embeddings to Candle WASM + remove semantic type inference 2026-01-06 12:52:34 -08:00
CHANGELOG.md chore(release): 7.25.0 2026-05-27 15:45:33 -07:00
CLAUDE.md docs: add RELEASES.md + cross-project coordination section to CLAUDE.md 2026-02-28 10:07:34 -08:00
CONTRIBUTING.md feat: migrate embeddings to Candle WASM + remove semantic type inference 2026-01-06 12:52:34 -08:00
docker-compose.yml feat: Brainy 3.0 - Production-ready Triple Intelligence database 2025-09-11 16:23:32 -07:00
Dockerfile feat: Brainy 3.0 - Production-ready Triple Intelligence database 2025-09-11 16:23:32 -07:00
eslint.config.js chore: enforce consistent coding style and semicolon removal 2025-09-29 09:50:59 -07:00
LICENSE 🧠 Brainy 2.0.0 - Zero-Configuration AI Database with Triple Intelligence™ 2025-08-26 12:32:21 -07:00
package-lock.json chore(release): 7.25.0 2026-05-27 15:45:33 -07:00
package.json chore(release): 7.25.0 2026-05-27 15:45:33 -07:00
README.md feat: multi-process safety + read-only inspector mode 2026-05-15 11:25:05 -07:00
RELEASES.md feat: array-unnest groupBy for aggregates + batch-embed entity extraction 2026-05-26 14:20:40 -07:00
tsconfig.cli.json feat: complete CLI with VFS, data management, and Triple Intelligence search 2025-09-29 16:57:14 -07:00
tsconfig.json build: add CLI compilation config 2025-09-29 16:02:54 -07:00
vitest.config.memory.ts 🧠 Brainy 2.0.0 - Zero-Configuration AI Database with Triple Intelligence™ 2025-08-26 12:32:21 -07:00
vitest.config.ts 🧠 Brainy 2.0.0 - Zero-Configuration AI Database with Triple Intelligence™ 2025-08-26 12:32:21 -07:00

Brainy

Brainy Logo

npm version npm downloads Documentation MIT License TypeScript

Three database paradigms. One API. Zero configuration.

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.

New here?What is Brainy? — plain-language overview, no jargon


Install

npm install @soulcraft/brainy

Quick Start

import { Brainy, NounType, VerbType } from '@soulcraft/brainy'

const brain = new Brainy()
await brain.init()

// Add knowledge — text auto-embeds, metadata auto-indexes
const reactId = await brain.add({
  data: 'React is a JavaScript library for building user interfaces',
  type: NounType.Concept,
  metadata: { category: 'frontend', year: 2013 }
})

const nextId = await brain.add({
  data: 'Next.js framework for React with server-side rendering',
  type: NounType.Concept,
  metadata: { category: 'framework', year: 2016 }
})

// Create a relationship
await brain.relate({ from: nextId, to: reactId, type: VerbType.BuiltOn })

// Query all three paradigms at once
const results = await brain.find({
  query: 'modern frontend frameworks',            // Vector similarity
  where: { year: { greaterThan: 2015 } },         // Metadata filtering
  connected: { to: reactId, depth: 2 }            // Graph traversal
})

Full API Reference | soulcraft.com/docs


Three Indexes, One Query

Every piece of knowledge lives in three indexes simultaneously:

  • dataVector index — Content for semantic search. Strings auto-embed into 384-dim vectors. Queried with find({ query: '...' }).
  • metadataMetadata index — Structured fields for filtering. O(1) lookups. Queried with find({ where: { ... } }).
  • relate()Graph index — Typed, directed relationships between entities. Traversed with find({ connected: { ... } }).
// Data → vector index (semantic search)
const articleId = await brain.add({
  data: 'A deep dive into transformer architectures',
  type: NounType.Document,
  metadata: { author: 'Dr. Chen', year: 2024, tags: ['AI'] }  // → metadata index
})

// Relationships → graph index
await brain.relate({ from: authorId, to: articleId, type: VerbType.Authored })

// Query all three at once
brain.find({
  query: 'attention mechanisms',                  // Vector similarity
  where: { year: { greaterThan: 2023 } },         // Metadata filter
  connected: { from: authorId, depth: 1 }         // Graph traversal
})

Data Model Reference | Query Operators


Features

Triple Intelligence

Vector search + graph traversal + metadata filtering in every query. No stitching services together — one find() call combines all three.

const results = await brain.find({
  query: 'machine learning',
  where: { department: 'engineering', level: 'senior' },
  connected: { from: teamLeadId, via: VerbType.WorksWith, depth: 2 }
})

Automatically combines keyword (text) and semantic (vector) search. No configuration needed.

await brain.find({ query: 'David Smith' })             // Auto: text + semantic
await brain.find({ query: 'AI concepts', searchMode: 'semantic' })  // Semantic only
await brain.find({ query: 'exact id', searchMode: 'text' })         // Text only

Query Operators

Filter metadata with equality, comparison, array, existence, pattern, and logical operators:

await brain.find({
  where: {
    status: 'active',                          // Exact match
    score: { greaterThan: 90 },                // Comparison
    tags: { contains: 'ai' },                  // Array
    anyOf: [{ role: 'admin' }, { role: 'owner' }]  // Logical OR
  }
})

Query Operators Reference — all operators with indexed/in-memory matrix

Graph Relationships

Typed, directed edges between entities. Traverse connections at any depth.

await brain.relate({ from: personId, to: projectId, type: VerbType.WorksOn })

const results = await brain.find({
  connected: { from: personId, via: VerbType.WorksOn, depth: 3 }
})

Git-Style Branching

Fork your entire database in <100ms. Snowflake-style copy-on-write.

const experiment = await brain.fork('test-migration')
await experiment.add({ data: 'test data', type: NounType.Concept })
await experiment.commit({ message: 'Add test data', author: 'dev@co.com' })
await brain.checkout('test-migration')

// Time-travel: query at any past commit
const snapshot = await brain.asOf(commitId)
const pastResults = await snapshot.find({ query: 'historical data' })
await snapshot.close()

Branching Documentation

Entity Versioning

Save, restore, and compare entity snapshots.

const userId = await brain.add({ data: 'Alice', type: NounType.Person })
await brain.versions.save(userId, { tag: 'v1.0' })

await brain.update(userId, { data: 'Alice Smith' })
await brain.versions.save(userId, { tag: 'v2.0' })

const diff = await brain.versions.compare(userId, 1, 2)
await brain.versions.restore(userId, 1)

Virtual Filesystem

File operations with semantic search built in.

const vfs = brain.vfs

await vfs.writeFile('/docs/readme.md', 'Project documentation')
const content = await vfs.readFile('/docs/readme.md')
const tree = await vfs.getTreeStructure('/docs', { maxDepth: 3 })

// Semantic file search
const matches = await vfs.search('React components with hooks')

VFS Quick Start | Common Patterns

Import Anything

CSV, Excel, PDF, URLs — auto-detected format, auto-classified entities.

await brain.import('customers.csv')
await brain.import('sales-data.xlsx', { excelSheets: ['Q1', 'Q2'] })
await brain.import('research-paper.pdf', { pdfExtractTables: true })
await brain.import('https://api.example.com/data.json')

Import Guide

Entity Extraction

AI-powered named entity recognition with 4-signal ensemble scoring.

const entities = await brain.extractEntities('John Smith founded Acme Corp in New York')
// [
//   { text: 'John Smith', type: NounType.Person, confidence: 0.95 },
//   { text: 'Acme Corp', type: NounType.Organization, confidence: 0.92 },
//   { text: 'New York', type: NounType.Location, confidence: 0.88 }
// ]

Neural Extraction Guide

Plugin System

Optional native acceleration via @soulcraft/cortex — SIMD distance calculations, CRoaring bitmaps, Candle ML embeddings.

const brain = new Brainy({ plugins: ['@soulcraft/cortex'] })
await brain.init()

Plugins are opt-in. Brainy never auto-imports packages unless listed in plugins.

Plugin Documentation


Type System

42 noun types and 127 verb types form a universal knowledge protocol:

42 Nouns × 127 Verbs = 5,334 base relationship combinations

Model any domain — healthcare (Patient → diagnoses → Condition), finance (Account → transfers → Transaction), education (Student → completes → Course), or your own.

Noun-Verb Taxonomy | Stage 3 Canonical Reference


Storage: Memory to Cloud

The same API at every scale. Change one config line to go from prototype to production.

Development — Zero Config

const brain = new Brainy()

Production — Filesystem with Compression

const brain = new Brainy({
  storage: { type: 'filesystem', path: './data', compression: true }
})

Cloud — S3, GCS, Azure, Cloudflare R2

const brain = new Brainy({
  storage: {
    type: 's3',
    s3Storage: { bucketName: 'my-knowledge-base', region: 'us-east-1' }
  }
})

Performance benchmarks and capacity planning in docs/PERFORMANCE.md.

Cloud Deployment Guide | Capacity Planning


Use Cases

  • AI agents — Persistent memory with semantic recall and relationship tracking
  • Knowledge bases — Auto-linking, semantic search, relationship-aware navigation
  • Semantic search — Find by meaning across codebases, documents, or media
  • Enterprise knowledge — CRM, product catalogs, institutional memory
  • Interactive experiences — Game worlds, NPCs, and characters that remember
  • Content platforms — Similarity-based discovery, intelligent tagging

Documentation

Start Here

Core

Architecture

Virtual Filesystem

Guides

Operations


Requirements

Bun 1.0+ (recommended) or Node.js 22 LTS

bun install @soulcraft/brainy    # Bun — best performance
npm install @soulcraft/brainy    # Node.js — fully supported

Deprecation Notice: Browser support (OPFS, Web Workers, WASM embeddings) is deprecated in v7.10.0 and will be removed in v8.0.0. Brainy v8+ will be server-only.

Single-Writer Model

Brainy is single-writer, many-reader on filesystem storage. One writer holds an exclusive lock on the data directory; any number of readers can inspect it concurrently. Opening a second writer throws with the PID of the existing one.

// Live application — writer mode is the default
const brain = new Brainy({ storage: { type: 'filesystem', rootDirectory: '/data/brain' } })
await brain.init()

// Out-of-band diagnostics from a separate process — safe to run while the
// writer is live
const reader = await Brainy.openReadOnly({
  storage: { type: 'filesystem', rootDirectory: '/data/brain' }
})
await reader.requestFlush({ timeoutMs: 5000 })
const stats = await reader.stats()

For incident debugging, use the brainy inspect CLI:

brainy inspect stats   /data/brain
brainy inspect find    /data/brain --where '{"entityType":"booking"}'
brainy inspect explain /data/brain --where '{"entityType":"booking"}'
brainy inspect health  /data/brain

See the multi-process model and the inspection guide for the full story, including stale-lock detection, the cross-process flush RPC, and what's not yet enforced on cloud storage backends.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT © Brainy Contributors