David Snelling
e420369850
A clean-room install smoke of the published GA pair caught the README telling scale-up users the native provider is auto-detected. It is not, by design: the loader (loadPlugins) does no auto-detection — undefined means no plugins, and only an explicit plugins: ['@soulcraft/cor'] loads the native engine (loud failure if it can't). The stale comment on the plugins config field in the public types claimed auto-detection and is corrected to match the loader; one phrase in the plugin-author guide likewise.
220 lines
11 KiB
Markdown
220 lines
11 KiB
Markdown
<p align="center">
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<img src="https://raw.githubusercontent.com/soulcraftlabs/brainy/main/brainy.png" alt="Brainy" width="180">
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</p>
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<h1 align="center">Brainy</h1>
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<p align="center">
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<b>Three database paradigms. One API. Zero configuration.</b><br>
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The in-process knowledge database for TypeScript — vector search, graph traversal,<br>
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and metadata filtering unified in a single query.
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</p>
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<p align="center">
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<a href="https://www.npmjs.com/package/@soulcraft/brainy"><img src="https://img.shields.io/npm/v/@soulcraft/brainy.svg" alt="npm version"></a>
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<a href="https://www.npmjs.com/package/@soulcraft/brainy"><img src="https://img.shields.io/npm/dm/@soulcraft/brainy.svg" alt="npm downloads"></a>
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<a href="https://github.com/soulcraftlabs/brainy/actions/workflows/ci.yml"><img src="https://github.com/soulcraftlabs/brainy/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
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<a href="https://soulcraft.com/docs"><img src="https://img.shields.io/badge/docs-soulcraft.com-blue.svg" alt="Documentation"></a>
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<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="MIT License"></a>
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<a href="https://www.typescriptlang.org/"><img src="https://img.shields.io/badge/%3C%2F%3E-TypeScript-%230074c1.svg" alt="TypeScript"></a>
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</p>
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<p align="center">
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<a href="#quick-start">Quick start</a> ·
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<a href="#one-query-three-engines">One query</a> ·
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<a href="#feature-tour">Features</a> ·
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<a href="#from-laptop-to-hundreds-of-millions">Scale with Cor</a> ·
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<a href="#documentation">Docs</a>
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</p>
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---
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Built because we were tired of stitching a vector store to a graph database to a document store — and spending weeks on plumbing before writing a line of business logic. Brainy indexes every fact **three ways at once** and lets one call query them together:
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| You write | Brainy indexes it as | You query it with |
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|---|---|---|
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| `data: 'Ada wrote the first program'` | a **384-dim vector** (local embedding — no API key) | `find({ query: 'computing pioneers' })` |
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| `metadata: { field: 'CS', year: 1843 }` | **structured fields** (O(1) exact, O(log n) range) | `find({ where: { year: { lessThan: 1900 } } })` |
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| `relate({ from: ada, to: babbage })` | a **typed, directed graph edge** | `find({ connected: { to: babbage, depth: 2 } })` |
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It runs **inside your process** — no server, no Docker, nothing to operate — and persists to plain files you can snapshot with a hard link.
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**New here?** → **[What is Brainy? — plain-language overview, no jargon](docs/eli5.md)**
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## Quick start
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```bash
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bun add @soulcraft/brainy # Bun ≥ 1.1 — recommended
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npm install @soulcraft/brainy # Node.js ≥ 22
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```
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```javascript
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import { Brainy, NounType, VerbType } from '@soulcraft/brainy'
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const brain = new Brainy() // in-memory; one line swaps to disk
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await brain.init()
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// Text auto-embeds locally; metadata auto-indexes
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const react = 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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subtype: 'library',
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metadata: { category: 'frontend', year: 2013 }
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})
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const next = await brain.add({
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data: 'Next.js is a React framework with server-side rendering',
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type: NounType.Concept,
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subtype: 'framework',
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metadata: { category: 'frontend', year: 2016 }
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})
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await brain.relate({ from: next, to: react, type: VerbType.DependsOn, subtype: 'runtime' })
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```
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## One query, three engines
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```javascript
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const results = await brain.find({
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query: 'modern frontend frameworks', // vector — what it means
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where: { year: { greaterThan: 2015 } }, // metadata — what it is
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connected: { to: react, depth: 2 } // graph — what it touches
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})
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```
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Every clause is optional; any combination composes. Under the hood Brainy plans the query across an HNSW vector index, a roaring-bitmap field index, and an adjacency graph index — and re-validates every result against your predicate before returning it, so a corrupt index can never hand you a wrong answer.
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## Feature tour
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### The database is a value
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Pin it, rewind it, fork it. Snapshot isolation without a server.
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```javascript
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const db = brain.now() // pin current state — O(1)
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await brain.transact([ // atomic all-or-nothing, CAS-guarded
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{ op: 'update', id: order, metadata: { status: 'paid' } },
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{ op: 'relate', from: invoice, to: order, type: VerbType.References, subtype: 'billing' }
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], { ifAtGeneration: db.generation })
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await db.get(order) // still 'pending' — pinned forever
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await brain.get(order) // 'paid' — live
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const lastWeek = await brain.asOf(Date.now() - 7 * 86_400_000) // full query surface, past state
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const whatIf = await db.with([{ op: 'remove', id: order }]) // speculative — never touches disk
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await brain.now().persist('/backups/today') // instant hard-link snapshot
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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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### Local embeddings — no API keys
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Strings embed on-device with a bundled MiniLM model (WASM). Semantic search works offline, in CI, and on air-gapped machines, at zero cost per call. Hybrid keyword + semantic ranking is the default:
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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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```
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### A typed graph, not a bag of edges
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42 entity types × 127 relationship types form a shared vocabulary for any domain — healthcare (`Patient → diagnoses → Condition`), finance (`Account → transfers → Transaction`), yours. Your own taxonomy layers on with `subtype`, enforced at write time:
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```javascript
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await brain.add({ data: 'Avery Brooks', type: NounType.Person, subtype: 'employee' })
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brain.counts.bySubtype(NounType.Person) // O(1) — { employee: 12, customer: 847 }
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brain.requireSubtype(NounType.Person, { values: ['employee', 'customer'], required: true })
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```
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**[Type system](docs/architecture/noun-verb-taxonomy.md)** · **[Subtypes & facets](docs/guides/subtypes-and-facets.md)**
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### Graph analytics built in
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```javascript
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await brain.graph.rank() // which entities matter most (centrality)
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await brain.graph.communities() // natural clusters
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await brain.graph.path(a, b) // how two things connect
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await brain.graph.subgraph([seed], { depth: 2 }) // bounded neighborhood → { nodes, edges }
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await brain.graph.export() // whole graph, one O(N+E) streaming pass
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```
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### Write-time aggregations
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`SUM` / `COUNT` / `AVG` / `MIN` / `MAX` with `GROUP BY` and time windows, maintained incrementally on every write — reads are O(1) lookups, not scans. **[Aggregation guide](docs/guides/aggregation.md)**
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### Import anything
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```javascript
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await brain.import('customers.csv')
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await brain.import('sales.xlsx') // every sheet
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await brain.import('research-paper.pdf') // tables extracted
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await brain.import('https://api.example.com/data.json')
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```
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Entities auto-classify on the way in; `brain.extractEntities(text)` exposes the same NER ensemble directly. **[Import guide](docs/guides/import-anything.md)**
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### A filesystem that understands content
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```javascript
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await brain.vfs.writeFile('/docs/readme.md', 'Project documentation')
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await brain.vfs.search('React components with hooks') // semantic file search
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```
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**[VFS quick start](docs/vfs/QUICK_START.md)**
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### Operations-grade by default
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- **Single-writer, many-reader** — an exclusive lock protects the data directory; `Brainy.openReadOnly()` and the `brainy inspect` CLI examine a live brain from another process, safely.
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- **Self-upgrading data files** — a 7.x brain opens under 8.x and migrates itself behind an observable lock (`getIndexStatus().migration`), with an automatic pre-upgrade backup. No migration scripts.
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- **No silent wrong answers** — cold-open guards self-heal or throw typed errors (`MetadataIndexNotReadyError`, `GraphIndexNotReadyError`); they never return `[]` for data that exists.
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**[Multi-process model](docs/concepts/multi-process.md)** · **[Inspection guide](docs/guides/inspection.md)**
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## From laptop to hundreds of millions
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Brainy's TypeScript engines take you a long way. When you outgrow them, add the native engine — **the API doesn't change**:
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```bash
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npm install @soulcraft/cor
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```
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```javascript
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const brain = new Brainy({
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plugins: ['@soulcraft/cor'], // explicit opt-in — brainy never auto-loads a package
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storage: { type: 'filesystem', path: './data' }
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})
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await brain.init() // same code — native engines underneath
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```
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Plugins are explicit: an engine swap is always visible in your config, and a listed plugin that fails to load fails **loud** — never a silent fallback to the JS engines. [`@soulcraft/cor`](https://www.npmjs.com/package/@soulcraft/cor) (Brainy 8.x ↔ Cor 3.x, version-matched) registers Rust implementations behind every provider seam: SIMD distance kernels, memory-mapped storage, a disk-native vector index that doesn't need your dataset in RAM, durable LSM field/graph indexes that serve cold opens instantly, and native aggregation. Recall@10 measured **0.99 / 0.96 / 0.96 at 1M / 10M / 100M vectors** in Cor's release gate.
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Open core, commercial accelerator: Brainy is MIT and complete on its own; Cor is licensed and funds both.
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## Performance
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- JS distance kernels: **~6× faster cosine, ~1.4× euclidean** than 7.x (measured: [`tests/benchmarks/distance-microbench.mjs`](tests/benchmarks/distance-microbench.mjs), 384-dim, median of 41).
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- Whole-graph reads are single **O(N + E)** cursor walks — a consumer-measured 19k-edge export dropped from ~27 s of per-node calls to one scan.
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- Full numbers and capacity planning: **[docs/PERFORMANCE.md](docs/PERFORMANCE.md)** · **[docs/SCALING.md](docs/SCALING.md)**
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## Use cases
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**AI agent memory** — persistent semantic recall with relationship tracking · **Knowledge bases** — auto-linking and meaning-aware navigation · **Semantic search** over codebases, documents, media · **Enterprise data** — CRM, catalogs, institutional memory · **Games & simulations** — worlds and characters that remember.
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## Documentation
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| Start | Core | Going deeper |
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| [Brainy explained simply](docs/eli5.md) | [API reference](docs/api/README.md) | [Architecture overview](docs/architecture/overview.md) |
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| [Installation](docs/guides/installation.md) | [Data model](docs/DATA_MODEL.md) | [Consistency model](docs/concepts/consistency-model.md) |
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| [Natural-language queries](docs/guides/natural-language.md) | [Query operators](docs/QUERY_OPERATORS.md) | [Multi-process model](docs/concepts/multi-process.md) |
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| | [Find system](docs/FIND_SYSTEM.md) | [Scaling](docs/SCALING.md) |
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## Requirements
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**Bun ≥ 1.1** (recommended) or **Node.js ≥ 22**. Brainy 8.x is server-only; the 7.x line remains on npm for browser use.
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## Contributing & license
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Contributions welcome — see **[CONTRIBUTING.md](CONTRIBUTING.md)**. MIT © Brainy Contributors.
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