A powerful graph & vector data platform for AI applications across any environment

✨ What is Brainy?

Imagine a database that thinks like you do - connecting ideas, finding patterns, and getting smarter over time. Brainy is the AI-native database that brings vector search and knowledge graphs together in one powerful, ridiculously easy-to-use package.

🚀 Why Developers Love Brainy

• 🧠 It Just Works™ - No config files, no tuning parameters, no DevOps headaches. Brainy auto-detects your environment and optimizes itself
• 🌍 True Write-Once, Run-Anywhere - Same code runs in React, Angular, Vue, Node.js, Deno, Bun, serverless, edge workers, and even vanilla HTML
• ⚡ Scary Fast - Handles millions of vectors with sub-millisecond search. Built-in GPU acceleration when available
• 🎯 Self-Learning - Like having a database that goes to the gym. Gets faster and smarter the more you use it
• 🔮 AI-First Design - Built for the age of embeddings, RAG, and semantic search. Your LLMs will thank you
• 🎮 Actually Fun to Use - Clean API, great DX, and it does the heavy lifting so you can build cool stuff

🚀 Quick Start (30 seconds!)

Node.js TLDR

# Install
npm install brainy

# Use it

import { createAutoBrainy, NounType, VerbType } from 'brainy'

const brainy = createAutoBrainy()

// Add data with Nouns (entities)
const catId = await brainy.add("Siamese cats are elegant and vocal", { 
  noun: NounType.Thing,
  breed: "Siamese",
  category: "animal" 
})

const ownerId = await brainy.add("John loves his pets", { 
  noun: NounType.Person,
  name: "John Smith" 
})

// Connect with Verbs (relationships)
await brainy.addVerb(ownerId, catId, { 
  verb: VerbType.Owns,
  since: "2020-01-01" 
})

// Search by meaning
const results = await brainy.searchText("feline companions", 5)

// Search JSON documents by specific fields
const docs = await brainy.searchDocuments("Siamese", {
  fields: ['breed', 'category'],  // Search these fields
  weights: { breed: 2.0 },         // Prioritize breed matches
  limit: 10
})

// Find relationships
const johnsPets = await brainy.getVerbsBySource(ownerId, VerbType.Owns)

That's it! No config, no setup, it just works™

🎭 Key Features

Core Capabilities

• Vector Search - Find semantically similar content using embeddings
• Graph Relationships - Connect data with meaningful relationships
• JSON Document Search - Search within specific fields with prioritization
• Real-Time Syncing - WebSocket and WebRTC for distributed instances
• Streaming Pipeline - Process data in real-time as it flows through
• Model Control Protocol - Let AI models access your data

Smart Optimizations

• Auto-Configuration - Detects environment and optimizes automatically
• Adaptive Learning - Gets smarter with usage, optimizes itself over time
• Intelligent Partitioning - Semantic clustering with auto-tuning
• Multi-Level Caching - Hot/warm/cold caching with predictive prefetching
• Memory Optimization - 75% reduction with compression for large datasets

Developer Experience

• TypeScript Support - Fully typed API with generics
• Extensible Augmentations - Customize and extend functionality
• REST API - Web service wrapper for HTTP endpoints
• Auto-Complete - IntelliSense for all APIs and types

📦 Installation

Main Package

npm install brainy

Optional: Offline Models Package

npm install @soulcraft/brainy-models

The @soulcraft/brainy-models package provides offline access to the Universal Sentence Encoder model, eliminating network dependencies and ensuring consistent performance. Perfect for:

• Air-gapped environments - No internet? No problem
• Consistent performance - No network latency or throttling
• Privacy-focused apps - Keep everything local
• High-reliability systems - No external dependencies

import { createAutoBrainy } from 'brainy'
import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'

// Use the bundled model for offline operation
const brainy = createAutoBrainy({
  embeddingModel: BundledUniversalSentenceEncoder
})

🎨 Build Amazing Things

🤖 AI Chat Applications - Build ChatGPT-like apps with long-term memory and context awareness
🔍 Semantic Search Engines - Search by meaning, not keywords. Find "that thing that's like a cat but bigger" → returns "tiger"
🎯 Recommendation Engines - "Users who liked this also liked..." but actually good
🧬 Knowledge Graphs - Connect everything to everything. Wikipedia meets Neo4j meets magic
👁️ Computer Vision Apps - Store and search image embeddings. "Find all photos with dogs wearing hats"
🎵 Music Discovery - Find songs that "feel" similar. Spotify's Discover Weekly in your app
📚 Smart Documentation - Docs that answer questions. "How do I deploy to production?" → relevant guides
🛡️ Fraud Detection - Find patterns humans can't see. Anomaly detection on steroids
🌐 Real-Time Collaboration - Sync vector data across devices. Figma for AI data
🏥 Medical Diagnosis Tools - Match symptoms to conditions using embedding similarity

🧬 The Power of Nouns & Verbs

Brainy uses a graph-based data model that mirrors how humans think - with Nouns (entities) connected by Verbs (relationships). This isn't just vectors in a void; it's structured, meaningful data.

📝 Nouns (What Things Are)

Nouns are your entities - the "things" in your data. Each noun has:

• A unique ID
• A vector representation (for similarity search)
• A type (Person, Document, Concept, etc.)
• Custom metadata

Available Noun Types:

Category	Types	Use For
Core Entities	Person, Organization, Location, Thing, Concept, Event	People, companies, places, objects, ideas, happenings
Digital Content	Document, Media, File, Message, Content	PDFs, images, videos, emails, posts, generic content
Collections	Collection, Dataset	Groups of items, structured data sets
Business	Product, Service, User, Task, Project	E-commerce, SaaS, project management
Descriptive	Process, State, Role	Workflows, conditions, responsibilities

🔗 Verbs (How Things Connect)

Verbs are your relationships - they give meaning to connections. Not just "these vectors are similar" but "this OWNS that" or "this CAUSES that".

Available Verb Types:

Category	Types	Examples
Core	RelatedTo, Contains, PartOf, LocatedAt, References	Generic relations, containment, location
Temporal	Precedes, Succeeds, Causes, DependsOn, Requires	Time sequences, causality, dependencies
Creation	Creates, Transforms, Becomes, Modifies, Consumes	Creation, change, consumption
Ownership	Owns, AttributedTo, CreatedBy, BelongsTo	Ownership, authorship, belonging
Social	MemberOf, WorksWith, FriendOf, Follows, Likes, ReportsTo	Social networks, organizations
Functional	Describes, Implements, Validates, Triggers, Serves	Functions, implementations, services

💡 Why This Matters

// Traditional vector DB: Just similarity
const similar = await vectorDB.search(embedding, 10)
// Result: [vector1, vector2, ...] - What do these mean? 🤷

// Brainy: Similarity + Meaning + Relationships
const catId = await brainy.add("Siamese cat", { 
  noun: NounType.Thing,
  breed: "Siamese" 
})
const ownerId = await brainy.add("John Smith", { 
  noun: NounType.Person 
})
await brainy.addVerb(ownerId, catId, { 
  verb: VerbType.Owns,
  since: "2020-01-01" 
})

// Now you can search with context!
const johnsPets = await brainy.getVerbsBySource(ownerId, VerbType.Owns)
const catOwners = await brainy.getVerbsByTarget(catId, VerbType.Owns)

🤔 Why Choose Brainy?

vs. Traditional Databases

❌ PostgreSQL with pgvector - Requires complex setup, tuning, and DevOps expertise
✅ Brainy - Zero config, auto-optimizes, works everywhere from browser to cloud

vs. Vector Databases

❌ Pinecone/Weaviate/Qdrant - Cloud-only, expensive, vendor lock-in
✅ Brainy - Run locally, in browser, or cloud. Your choice, your data

vs. Graph Databases

❌ Neo4j - Great for graphs, no vector support
✅ Brainy - Vectors + graphs in one. Best of both worlds

vs. DIY Solutions

❌ Building your own - Months of work, optimization nightmares
✅ Brainy - Production-ready in 30 seconds

🚀 Getting Started in 30 Seconds

React

import { createAutoBrainy } from 'brainy'
import { useEffect, useState } from 'react'

function SemanticSearch() {
  const [brainy] = useState(() => createAutoBrainy())
  const [results, setResults] = useState([])

  const search = async (query) => {
    const items = await brainy.searchText(query, 10)
    setResults(items)
  }

  return (
    <input onChange={(e) => search(e.target.value)} 
           placeholder="Search by meaning..." />
  )
}

Angular

import { Component, OnInit } from '@angular/core'
import { createAutoBrainy } from 'brainy'

@Component({
  selector: 'app-search',
  template: `
    <input (input)="search($event.target.value)" 
           placeholder="Semantic search...">
    <div *ngFor="let result of results">
      {{ result.text }}
    </div>
  `
})
export class SearchComponent implements OnInit {
  brainy = createAutoBrainy()
  results = []

  async search(query: string) {
    this.results = await this.brainy.searchText(query, 10)
  }
}

Vue 3

<script setup>
import { createAutoBrainy } from 'brainy'
import { ref } from 'vue'

const brainy = createAutoBrainy()
const results = ref([])

const search = async (query) => {
  results.value = await brainy.searchText(query, 10)
}
</script>

<template>
  <input @input="search($event.target.value)" 
         placeholder="Find similar content...">
  <div v-for="result in results" :key="result.id">
    {{ result.text }}
  </div>
</template>

Svelte

<script>
  import { createAutoBrainy } from 'brainy'
  
  const brainy = createAutoBrainy()
  let results = []
  
  async function search(e) {
    results = await brainy.searchText(e.target.value, 10)
  }
</script>

<input on:input={search} placeholder="AI-powered search...">
{#each results as result}
  <div>{result.text}</div>
{/each}

Next.js (App Router)

// app/search/page.js
import { createAutoBrainy } from 'brainy'

export default function SearchPage() {
  async function search(formData) {
    'use server'
    const brainy = createAutoBrainy({ bucketName: 'vectors' })
    const query = formData.get('query')
    return await brainy.searchText(query, 10)
  }

  return (
    <form action={search}>
      <input name="query" placeholder="Search..." />
      <button type="submit">Search</button>
    </form>
  )
}

Node.js / Bun / Deno

import { createAutoBrainy } from 'brainy'

const brainy = createAutoBrainy()

// Add some data
await brainy.add("TypeScript is a typed superset of JavaScript", {
  category: 'programming'
})

// Search for similar content
const results = await brainy.searchText("JavaScript with types", 5)
console.log(results)

Vanilla JavaScript

<!DOCTYPE html>
<html>
<head>
  <script type="module">
    import { createAutoBrainy } from 'https://unpkg.com/brainy/dist/unified.min.js'
    
    window.brainy = createAutoBrainy()
    
    window.search = async function(query) {
      const results = await brainy.searchText(query, 10)
      document.getElementById('results').innerHTML = 
        results.map(r => `<div>${r.text}</div>`).join('')
    }
  </script>
</head>
<body>
  <input onkeyup="search(this.value)" placeholder="Search...">
  <div id="results"></div>
</body>
</html>

Cloudflare Workers

import { createAutoBrainy } from 'brainy'

export default {
  async fetch(request, env) {
    const brainy = createAutoBrainy({ 
      bucketName: env.R2_BUCKET 
    })
    
    const url = new URL(request.url)
    const query = url.searchParams.get('q')
    
    const results = await brainy.searchText(query, 10)
    return Response.json(results)
  }
}

AWS Lambda

import { createAutoBrainy } from 'brainy'

export const handler = async (event) => {
  const brainy = createAutoBrainy({ 
    bucketName: process.env.S3_BUCKET 
  })
  
  const results = await brainy.searchText(event.query, 10)
  
  return {
    statusCode: 200,
    body: JSON.stringify(results)
  }
}

Azure Functions

import { createAutoBrainy } from 'brainy'

module.exports = async function (context, req) {
  const brainy = createAutoBrainy({
    bucketName: process.env.AZURE_STORAGE_CONTAINER
  })
  
  const results = await brainy.searchText(req.query.q, 10)
  
  context.res = {
    body: results
  }
}

Google Cloud Functions

import { createAutoBrainy } from 'brainy'

export const searchHandler = async (req, res) => {
  const brainy = createAutoBrainy({
    bucketName: process.env.GCS_BUCKET
  })
  
  const results = await brainy.searchText(req.query.q, 10)
  res.json(results)
}

Google Cloud Run

# Dockerfile
FROM node:20-alpine
USER node
WORKDIR /app
COPY package*.json ./
RUN npm install brainy
COPY . .
CMD ["node", "server.js"]

// server.js
import { createAutoBrainy } from 'brainy'
import express from 'express'

const app = express()
const brainy = createAutoBrainy({
  bucketName: process.env.GCS_BUCKET
})

app.get('/search', async (req, res) => {
  const results = await brainy.searchText(req.query.q, 10)
  res.json(results)
})

const port = process.env.PORT || 8080
app.listen(port, () => console.log(`Brainy on Cloud Run: ${port}`))

# Deploy to Cloud Run
gcloud run deploy brainy-api \
  --source . \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

Vercel Edge Functions

import { createAutoBrainy } from 'brainy'

export const config = {
  runtime: 'edge'
}

export default async function handler(request) {
  const brainy = createAutoBrainy()
  const { searchParams } = new URL(request.url)
  const query = searchParams.get('q')
  
  const results = await brainy.searchText(query, 10)
  return Response.json(results)
}

Netlify Functions

import { createAutoBrainy } from 'brainy'

export async function handler(event, context) {
  const brainy = createAutoBrainy()
  const query = event.queryStringParameters.q
  
  const results = await brainy.searchText(query, 10)
  
  return {
    statusCode: 200,
    body: JSON.stringify(results)
  }
}

Supabase Edge Functions

import { createAutoBrainy } from 'brainy'
import { serve } from 'https://deno.land/std@0.168.0/http/server.ts'

serve(async (req) => {
  const brainy = createAutoBrainy()
  const url = new URL(req.url)
  const query = url.searchParams.get('q')
  
  const results = await brainy.searchText(query, 10)
  
  return new Response(JSON.stringify(results), {
    headers: { 'Content-Type': 'application/json' }
  })
})

Docker Container

FROM node:20-alpine
USER node
WORKDIR /app
COPY package*.json ./
RUN npm install brainy
COPY . .

CMD ["node", "server.js"]

// server.js
import { createAutoBrainy } from 'brainy'
import express from 'express'

const app = express()
const brainy = createAutoBrainy()

app.get('/search', async (req, res) => {
  const results = await brainy.searchText(req.query.q, 10)
  res.json(results)
})

app.listen(3000, () => console.log('Brainy running on port 3000'))

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: brainy-api
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: brainy
        image: your-registry/brainy-api:latest
        env:
        - name: S3_BUCKET
          value: "your-vector-bucket"

Railway.app

// server.js
import { createAutoBrainy } from 'brainy'

const brainy = createAutoBrainy({
  bucketName: process.env.RAILWAY_VOLUME_NAME
})

// Railway automatically handles the rest!

Render.com

# render.yaml
services:
  - type: web
    name: brainy-api
    env: node
    buildCommand: npm install brainy
    startCommand: node server.js
    envVars:
      - key: BRAINY_STORAGE
        value: persistent-disk

🚀 Quick Examples

Basic Usage

import { BrainyData, NounType, VerbType } from 'brainy'

// Initialize
const db = new BrainyData()
await db.init()

// Add data (automatically vectorized)
const catId = await db.add("Cats are independent pets", {
  noun: NounType.Thing,
  category: 'animal'
})

// Search for similar items
const results = await db.searchText("feline pets", 5)

// Add relationships
await db.addVerb(catId, dogId, {
  verb: VerbType.RelatedTo,
  description: 'Both are pets'
})

AutoBrainy (Recommended)

import { createAutoBrainy } from 'brainy'

// Everything auto-configured!
const brainy = createAutoBrainy()

// Just start using it
await brainy.addVector({ id: '1', vector: [0.1, 0.2, 0.3], text: 'Hello' })
const results = await brainy.search([0.1, 0.2, 0.3], 10)

Scenario-Based Setup

import { createQuickBrainy } from 'brainy'

// Choose your scale: 'small', 'medium', 'large', 'enterprise'
const brainy = await createQuickBrainy('large', {
  bucketName: 'my-vector-db'
})

With Offline Models

import { createAutoBrainy } from 'brainy'
import { BundledUniversalSentenceEncoder } from '@soulcraft/brainy-models'

// Use bundled model for offline operation
const brainy = createAutoBrainy({
  embeddingModel: BundledUniversalSentenceEncoder,
  // Model loads from local files, no network needed!
})

// Works exactly the same, but 100% offline
await brainy.add("This works without internet!", { 
  noun: NounType.Content 
})

🌐 Live Demo

Try the interactive demo - See Brainy in action with animations and examples.

🔧 Environment Support

Environment	Storage	Threading	Auto-Configured
Browser	OPFS	Web Workers	✅
Node.js	FileSystem/S3	Worker Threads	✅
Serverless	Memory/S3	Limited	✅
Edge Functions	Memory/KV	Limited	✅

📚 Documentation

Getting Started

• Quick Start Guide - Get up and running in minutes
• Installation - Detailed setup instructions
• Environment Setup - Platform-specific configuration

User Guides

• Search and Metadata - Advanced search techniques
• JSON Document Search - Field-based searching
• Production Migration - Deployment best practices

API Reference

• Core API - Complete method reference
• Configuration Options - All configuration parameters
• Auto-Configuration API - Intelligent setup

Optimization & Scaling

• Large-Scale Optimizations - Handle millions of vectors
• Memory Management - Efficient resource usage
• S3 Migration Guide - Cloud storage setup

Examples & Patterns

• Code Examples - Real-world usage patterns
• Integrations - Third-party services
• Performance Patterns - Optimization techniques

Technical Documentation

• Architecture Overview - System design and internals
• Testing Guide - Testing strategies
• Statistics & Monitoring - Performance tracking

🤝 Contributing

We welcome contributions! Please see:

• Contributing Guidelines
• Developer Documentation
• Code of Conduct

📄 License

MIT

🔗 Related Projects

• Cartographer - Standardized interfaces for Brainy

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Ready to build something amazing? Get started with Brainy today!