The world's only true Vector + Graph database - unified semantic search and knowledge graphs

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The Search Problem Every Developer Faces

"I need to find similar content, explore relationships, AND filter by metadata - but that means juggling 3+ databases"

❌ Current Reality: Pinecone + Neo4j + Elasticsearch + Custom Sync Logic
✅ Brainy Reality: One database. One API. All three search types.

🔥 The Power of Three-in-One Search

// This ONE query does what used to require 3 databases:
const results = await brainy.search("AI startups in healthcare", 10, {
  // 🔍 Vector: Semantic similarity 
  includeVerbs: true,
  
  // 🔗 Graph: Relationship traversal
  verbTypes: ["invests_in", "partners_with"],
  
  // 📊 Faceted: MongoDB-style filtering  
  metadata: {
    industry: "healthcare",
    funding: { $gte: 1000000 },
    stage: { $in: ["Series A", "Series B"] }
  }
})
// Returns: Companies similar to your query + their relationships + matching your criteria

Three search paradigms. One lightning-fast query. Zero complexity.

🚀 Install & Go

npm install @soulcraft/brainy

import { BrainyData } from '@soulcraft/brainy'

const brainy = new BrainyData()  // Auto-detects your environment
await brainy.init()              // Auto-configures everything

// Add data with relationships
const openai = await brainy.add("OpenAI", { type: "company", funding: 11000000 })
const gpt4 = await brainy.add("GPT-4", { type: "product", users: 100000000 })
await brainy.relate(openai, gpt4, "develops")

// Search across all dimensions
const results = await brainy.search("AI language models", 5, {
  metadata: { funding: { $gte: 10000000 } },
  includeVerbs: true
})

That's it. You just built a knowledge graph with semantic search and faceted filtering in 8 lines.

🔥 MAJOR UPDATES: What's New in v0.51, v0.49 & v0.48

🎯 v0.51: Revolutionary Developer Experience

Problem-focused approach that gets you productive in seconds!

• ✅ Problem-Solution Narrative - Immediately understand why Brainy exists
• ✅ 8-Line Quickstart - Three search types in one simple demo
• ✅ Streamlined Documentation - Focus on what matters most
• ✅ Clear Positioning - The only true Vector + Graph database

🎯 v0.49: Filter Discovery & Performance Improvements

Discover available filters and scale to millions of items!

// Discover what filters are available - O(1) field lookup
const categories = await brainy.getFilterValues('category')
// Returns: ['electronics', 'books', 'clothing', ...]

const fields = await brainy.getFilterFields()  // O(1) operation
// Returns: ['category', 'price', 'brand', 'rating', ...]

• ✅ Filter Discovery API: O(1) field discovery for instant filter UI generation
• ✅ Improved Performance: Removed deprecated methods, now uses pagination everywhere
• ✅ Better Scalability: Hybrid indexing with O(1) field access scales to millions
• ✅ Smart Caching: LRU cache for frequently accessed filters
• ✅ Zero Configuration: Everything auto-optimizes based on usage patterns

🚀 v0.48: MongoDB-Style Metadata Filtering

Powerful querying with familiar syntax - filter DURING search for maximum performance!

const results = await brainy.search("wireless headphones", 10, {
  metadata: {
    category: { $in: ["electronics", "audio"] },
    price: { $lte: 200 },
    rating: { $gte: 4.0 },
    brand: { $ne: "Generic" }
  }
})

• ✅ 15+ MongoDB Operators: $gt, $in, $regex, $and, $or, $includes, etc.
• ✅ Automatic Indexing: Zero configuration, maximum performance
• ✅ Nested Fields: Use dot notation for complex objects
• ✅ 100% Backward Compatible: Your existing code works unchanged

⚡ v0.46: Transformers.js Migration

Replaced TensorFlow.js for better performance and true offline operation!

• ✅ 95% Smaller Package: 643 kB vs 12.5 MB
• ✅ 84% Smaller Models: 87 MB vs 525 MB models
• ✅ True Offline: Zero network calls after initial download
• ✅ 5x Fewer Dependencies: Clean tree, no peer dependency issues
• ✅ Same API: Drop-in replacement, existing code works unchanged

🏆 Why Brainy Wins

• 🧠 Triple Search Power - Vector + Graph + Faceted filtering in one query
• 🌍 Runs Everywhere - Same code: React, Node.js, serverless, edge
• ⚡ Zero Config - Auto-detects environment, optimizes itself
• 🔄 Always Synced - No data consistency nightmares between systems
• 📦 Truly Offline - Works without internet after initial setup
• 🔒 Your Data - Run locally, in browser, or your own cloud

🔮 Coming Soon

• 🤖 MCP Integration - Let Claude, GPT, and other AI models query your data directly
• ⚡ LLM Generation - Built-in content generation powered by your knowledge graph
• 🌊 Real-time Sync - Live updates across distributed instances

🎨 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

🌍 Works Everywhere - Same Code

Write once, run anywhere. Brainy auto-detects your environment and optimizes automatically:

🌐 Browser Frameworks (React, Angular, Vue)

import { BrainyData } from '@soulcraft/brainy'

// SAME CODE in React, Angular, Vue, Svelte, etc.
const brainy = new BrainyData()
await brainy.init()  // Auto-uses OPFS in browsers

// Add entities and relationships
const john = await brainy.add("John is a software engineer", { type: "person" })
const jane = await brainy.add("Jane is a data scientist", { type: "person" })
const ai = await brainy.add("AI Project", { type: "project" })

await brainy.relate(john, ai, "works_on")
await brainy.relate(jane, ai, "leads")

// Search by meaning
const engineers = await brainy.search("software developers", 5)

// Traverse relationships
const team = await brainy.getVerbsByTarget(ai)  // Who works on AI Project?

📦 Full React Component Example

import { BrainyData } from '@soulcraft/brainy'
import { useEffect, useState } from 'react'

function Search() {
  const [brainy, setBrainy] = useState(null)
  const [results, setResults] = useState([])

  useEffect(() => {
    const init = async () => {
      const db = new BrainyData()
      await db.init()
      // Add your data...
      setBrainy(db)
    }
    init()
  }, [])

  const search = async (query) => {
    const results = await brainy?.search(query, 5) || []
    setResults(results)
  }

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

📦 Full Angular Component Example

import { Component, signal, OnInit } from '@angular/core'
import { BrainyData } from '@soulcraft/brainy'

@Component({
  selector: 'app-search',
  template: `<input (input)="search($event.target.value)" placeholder="Search...">`
})
export class SearchComponent implements OnInit {
  brainy = new BrainyData()

  async ngOnInit() {
    await this.brainy.init()
    // Add your data...
  }

  async search(query: string) {
    const results = await this.brainy.search(query, 5)
    // Display results...
  }
}

📦 Full Vue Example

<script setup>
  import { BrainyData } from '@soulcraft/brainy'
  import { ref, onMounted } from 'vue'

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

  onMounted(async () => {
    const db = new BrainyData()
    await db.init()
    // Add your data...
    brainy.value = db
  })

  const search = async (query) => {
    const results = await brainy.value?.search(query, 5) || []
    setResults(results)
  }
</script>

<template>
  <input @input="search($event.target.value)" placeholder="Search..." />
</template>

🟢 Node.js / Serverless / Edge

import { BrainyData } from '@soulcraft/brainy'

// SAME CODE works in Node.js, Vercel, Netlify, Cloudflare Workers, Deno, Bun
const brainy = new BrainyData()
await brainy.init()  // Auto-detects environment and optimizes

// Add entities and relationships
await brainy.add("Python is great for data science", { type: "fact" })
await brainy.add("JavaScript rules the web", { type: "fact" })

// Search by meaning
const results = await brainy.search("programming languages", 5)

// Optional: Production with S3/R2 storage (auto-detected in cloud environments)
const productionBrainy = new BrainyData({
  storage: {
    s3Storage: { bucketName: process.env.BUCKET_NAME }
  }
})

That's it! Same code, everywhere. Zero-to-Smart™

Brainy automatically detects and optimizes for your environment:

Environment	Storage	Optimization
🌐 Browser	OPFS	Web Workers, Memory Cache
🟢 Node.js	FileSystem / S3	Worker Threads, Clustering
⚡ Serverless	S3 / Memory	Cold Start Optimization
🔥 Edge	Memory / KV	Minimal Footprint

🌐 Distributed Mode (NEW!)

Scale horizontally with zero configuration! Brainy now supports distributed deployments with automatic coordination:

• 🌐 Multi-Instance Coordination - Multiple readers and writers working in harmony
• 🏷️ Smart Domain Detection - Automatically categorizes data (medical, legal, product, etc.)
• 📊 Real-Time Health Monitoring - Track performance across all instances
• 🔄 Automatic Role Optimization - Readers optimize for cache, writers for throughput
• 🗂️ Intelligent Partitioning - Hash-based partitioning for perfect load distribution

// Writer Instance - Ingests data from multiple sources
const writer = new BrainyData({
  storage: { s3Storage: { bucketName: 'my-bucket' } },
  distributed: { role: 'writer' }  // Explicit role for safety
})

// Reader Instance - Optimized for search queries
const reader = new BrainyData({
  storage: { s3Storage: { bucketName: 'my-bucket' } },
  distributed: { role: 'reader' }  // 80% memory for cache
})

// Data automatically gets domain tags
await writer.add("Patient shows symptoms of...", {
  diagnosis: "flu"  // Auto-tagged as 'medical' domain
})

// Domain-aware search across all partitions
const results = await reader.search("medical symptoms", 10, {
  filter: { domain: 'medical' }  // Only search medical data
})

// Monitor health across all instances
const health = reader.getHealthStatus()
console.log(`Instance ${health.instanceId}: ${health.status}`)

🆚 Why Not Just Use...?

vs. Multiple Databases

❌ Pinecone + Neo4j + Elasticsearch - 3 databases, sync nightmares, 3x the cost
✅ Brainy - One database, always synced, built-in intelligence

vs. Traditional Solutions

❌ PostgreSQL + pgvector + extensions - Complex setup, performance issues
✅ Brainy - Zero config, purpose-built for AI, works everywhere

vs. Cloud-Only Vector DBs

❌ Pinecone/Weaviate/Qdrant - Vendor lock-in, expensive, cloud-only
✅ Brainy - Run anywhere, your data stays yours, cost-effective

vs. Graph Databases with "Vector Features"

❌ Neo4j + vector plugin - Bolt-on solution, not native, limited
✅ Brainy - Native vector+graph architecture from the ground up

📦 Advanced Features

🔧 MongoDB-Style Metadata Filtering

const results = await brainy.search("machine learning", 10, {
  metadata: {
    // Comparison operators
    price: { $gte: 100, $lte: 1000 },
    category: { $in: ["AI", "ML", "Data"] },
    rating: { $gt: 4.5 },
    
    // Logical operators  
    $and: [
      { status: "active" },
      { verified: true }
    ],
    
    // Text operators
    description: { $regex: "neural.*network", $options: "i" },
    
    // Array operators
    tags: { $includes: "tensorflow" }
  }
})

15+ operators supported: $gt, $gte, $lt, $lte, $eq, $ne, $in, $nin, $and, $or, $not, $regex, $includes, $exists, $size

🔗 Graph Relationships & Traversal

// Create entities and relationships
const company = await brainy.add("OpenAI", { type: "company" })
const product = await brainy.add("GPT-4", { type: "product" })
const person = await brainy.add("Sam Altman", { type: "person" })

// Create meaningful relationships
await brainy.relate(company, product, "develops")
await brainy.relate(person, company, "leads")
await brainy.relate(product, person, "created_by")

// Traverse relationships
const products = await brainy.getVerbsBySource(company) // What OpenAI develops
const leaders = await brainy.getVerbsByTarget(company)  // Who leads OpenAI
const connections = await brainy.findSimilar(product, { 
  relationType: "develops" 
})

// Search with relationship context
const results = await brainy.search("AI models", 10, {
  includeVerbs: true,
  verbTypes: ["develops", "created_by"],
  searchConnectedNouns: true
})

🌐 Universal Storage & Deployment

// Development: File system
const dev = new BrainyData({ 
  storage: { fileSystem: { path: './data' } } 
})

// Production: S3/R2  
const prod = new BrainyData({
  storage: { s3Storage: { bucketName: 'my-vectors' } }
})

// Browser: OPFS
const browser = new BrainyData() // Auto-detects OPFS

// Edge: Memory
const edge = new BrainyData({
  storage: { memory: {} }
})

// Redis: High performance  
const redis = new BrainyData({
  storage: { redis: { connectionString: 'redis://...' } }
})

Extend with any storage: MongoDB, PostgreSQL, DynamoDB - see storage adapters guide

🐳 Docker & Cloud Deployment

# Production-ready Dockerfile
FROM node:24-slim AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run download-models  # Embed models for offline operation
RUN npm run build

FROM node:24-slim AS production  
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/models ./models  # Offline models included
CMD ["node", "dist/server.js"]

Deploy to: Google Cloud Run, AWS Lambda/ECS, Azure Container Instances, Cloudflare Workers, Railway, Render, Vercel, anywhere Docker runs.

📚 Documentation & Resources

• 🚀 Quick Start Guide - Get up and running in minutes
• 📖 API Reference - Complete method documentation
• 💡 Examples - Real-world usage patterns
• ⚡ Performance Guide - Scale to millions of vectors
• 🔧 Storage Adapters - Universal storage compatibility

🤝 Contributing

We welcome contributions! Please see:

• Contributing Guidelines
• Developer Documentation
• Code of Conduct

📄 License

MIT

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