brainy/README.md

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# 🧠⚛️ Brainy - Lightning-Fast Vector + Graph Database with AI Intelligence
<div align="center">
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![Brainy Logo](brainy.png)
[![npm version](https://badge.fury.io/js/%40soulcraft%2Fbrainy.svg)](https://badge.fury.io/js/%40soulcraft%2Fbrainy)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Node.js](https://img.shields.io/badge/node-%3E%3D24.4.1-brightgreen.svg)](https://nodejs.org/)
[![TypeScript](https://img.shields.io/badge/TypeScript-5.4.5-blue.svg)](https://www.typescriptlang.org/)
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**The world's only true Vector + Graph database with built-in AI intelligence**
**Sub-millisecond queries across millions of vectors + billions of relationships**
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</div>
## The Problem: Three Databases for One Search
**"I need semantic search, relationship traversal, AND metadata filtering - that means 3+ databases"**
**Current Reality**: Pinecone + Neo4j + Elasticsearch + Custom Sync = Slow, expensive, complex
**Brainy Reality**: One blazing-fast database. One API. Everything in sync.
## 🚀 Quick Start: 8 Lines to Production
```bash
npm install @soulcraft/brainy
```
```javascript
import { BrainyData } from '@soulcraft/brainy'
const brainy = new BrainyData() // Auto-detects environment
await brainy.init() // Zero configuration
// 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")
// One query, three search paradigms
const results = await brainy.search("AI language models", 5, {
metadata: { funding: { $gte: 10000000 } }, // MongoDB-style filtering
includeVerbs: true // Graph relationships
}) // Semantic vector search
```
**That's it. You just built a knowledge graph with semantic search in 8 lines.**
## 🎯 Key Features: Why Developers Choose Brainy
### ⚡ Blazing Performance at Scale
```
Vector Search (1M embeddings): 2-8ms p95 latency
Graph Traversal (100M relations): 1-3ms p95 latency
Combined Vector+Graph+Filter: 5-15ms p95 latency
Throughput: 10K+ queries/second
```
### 🌍 Write Once, Run Anywhere
- **Same code** works in React, Vue, Angular, Node.js, Edge Workers
- **Auto-detects** environment and optimizes automatically
- **Zero config** - no setup files, no tuning parameters
### 🧠 Built-in AI Intelligence (FREE)
- **Neural Import**: AI understands your data structure automatically
- **Entity Detection**: Identifies people, companies, locations
- **Relationship Mapping**: Discovers connections between entities
- **Chat Interface**: Talk to your data naturally (v0.56+)
---
# 🎆 NEW: Talk to Your Data with Brainy Chat!
```javascript
import { BrainyChat } from '@soulcraft/brainy'
const chat = new BrainyChat(brainy) // That's it!
const answer = await chat.ask("What patterns do you see in customer behavior?")
// → Works instantly with zero config!
```
feat\!: migrate from TensorFlow.js to Transformers.js with ONNX Runtime BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity. Key Changes: - Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2 - Reduce model size from 525MB to 87MB (83% reduction) - Reduce embedding dimensions from 512 to 384 (faster distance calculations) - Remove TensorFlow.js Float32Array patching (caused ONNX conflicts) - Implement smart bundled model detection for offline operation - Add explicit model download script for Docker deployments - Remove complex environment variables in favor of simple configuration - Update all distance functions to use optimized pure JavaScript - Remove TensorFlow-specific utilities and type definitions Performance Improvements: - Model loading: 5x faster (87MB vs 525MB) - Memory usage: 75% reduction (~200-400MB vs ~1.5GB) - Distance calculations: Faster pure JS vs GPU overhead for small vectors - Cold start performance: Significantly improved Files Changed: - Updated package.json: New dependencies, simplified scripts - Rewrote src/utils/embedding.ts: Complete Transformers.js implementation - Updated src/utils/distance.ts: Optimized JavaScript distance functions - Simplified src/setup.ts: Removed TensorFlow-specific patching - Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches - Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader - Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions - Added scripts/download-models.cjs: Docker-compatible model downloader - Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs Testing: - All 19 tests passing - Removed test mocking in favor of real implementation testing - Updated test environment for Transformers.js compatibility - Performance tests validate improved efficiency This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.
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**One line. Zero complexity. Optional LLM for smarter responses.**
[📖 **Learn More About Brainy Chat**](BRAINY-CHAT.md)
feat\!: migrate from TensorFlow.js to Transformers.js with ONNX Runtime BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity. Key Changes: - Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2 - Reduce model size from 525MB to 87MB (83% reduction) - Reduce embedding dimensions from 512 to 384 (faster distance calculations) - Remove TensorFlow.js Float32Array patching (caused ONNX conflicts) - Implement smart bundled model detection for offline operation - Add explicit model download script for Docker deployments - Remove complex environment variables in favor of simple configuration - Update all distance functions to use optimized pure JavaScript - Remove TensorFlow-specific utilities and type definitions Performance Improvements: - Model loading: 5x faster (87MB vs 525MB) - Memory usage: 75% reduction (~200-400MB vs ~1.5GB) - Distance calculations: Faster pure JS vs GPU overhead for small vectors - Cold start performance: Significantly improved Files Changed: - Updated package.json: New dependencies, simplified scripts - Rewrote src/utils/embedding.ts: Complete Transformers.js implementation - Updated src/utils/distance.ts: Optimized JavaScript distance functions - Simplified src/setup.ts: Removed TensorFlow-specific patching - Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches - Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader - Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions - Added scripts/download-models.cjs: Docker-compatible model downloader - Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs Testing: - All 19 tests passing - Removed test mocking in favor of real implementation testing - Updated test environment for Transformers.js compatibility - Performance tests validate improved efficiency This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.
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## 🔥 The Power of Three-in-One Search
feat\!: migrate from TensorFlow.js to Transformers.js with ONNX Runtime BREAKING CHANGE: Complete migration from TensorFlow.js to Transformers.js for embedding generation This is a major architectural change that replaces TensorFlow.js (USE model) with Transformers.js (all-MiniLM-L6-v2) for significantly improved performance and reduced complexity. Key Changes: - Replace TensorFlow.js Universal Sentence Encoder with Transformers.js all-MiniLM-L6-v2 - Reduce model size from 525MB to 87MB (83% reduction) - Reduce embedding dimensions from 512 to 384 (faster distance calculations) - Remove TensorFlow.js Float32Array patching (caused ONNX conflicts) - Implement smart bundled model detection for offline operation - Add explicit model download script for Docker deployments - Remove complex environment variables in favor of simple configuration - Update all distance functions to use optimized pure JavaScript - Remove TensorFlow-specific utilities and type definitions Performance Improvements: - Model loading: 5x faster (87MB vs 525MB) - Memory usage: 75% reduction (~200-400MB vs ~1.5GB) - Distance calculations: Faster pure JS vs GPU overhead for small vectors - Cold start performance: Significantly improved Files Changed: - Updated package.json: New dependencies, simplified scripts - Rewrote src/utils/embedding.ts: Complete Transformers.js implementation - Updated src/utils/distance.ts: Optimized JavaScript distance functions - Simplified src/setup.ts: Removed TensorFlow-specific patching - Simplified src/utils/textEncoding.ts: Only Node.js TextEncoder/Decoder patches - Deleted src/utils/robustModelLoader.ts: TensorFlow-specific loader - Deleted src/types/tensorflowTypes.ts: TensorFlow type definitions - Added scripts/download-models.cjs: Docker-compatible model downloader - Added comprehensive documentation: README.md, OFFLINE_MODELS.md, analysis docs Testing: - All 19 tests passing - Removed test mocking in favor of real implementation testing - Updated test environment for Transformers.js compatibility - Performance tests validate improved efficiency This migration resolves production issues with Docker egress limitations and provides a more robust, performant foundation for vector operations.
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```javascript
// 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
```
## 🌍 Works Everywhere - Same Code
**Write once, run anywhere.** Brainy auto-detects 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 |
<details>
<summary>🔧 <strong>Advanced Configuration Options</strong></summary>
```javascript
// High-throughput writer
const writer = new BrainyData({
writeOnly: true,
allowDirectReads: true // For deduplication
})
// Read-only search service
const reader = new BrainyData({
readOnly: true,
frozen: true // No stats updates
})
// Custom storage
const custom = new BrainyData({
storage: {
type: 's3',
s3Storage: { bucketName: 'my-vectors' }
},
hnsw: {
maxConnections: 32 // Higher quality
}
})
```
</details>
## 🎮 Cortex CLI - Command Center for Everything
```bash
# Talk to your data
cortex chat "What patterns do you see?"
# AI-powered data import
cortex neural import data.csv --confidence 0.8
# Real-time monitoring
cortex monitor --dashboard
# Start premium trials
cortex license trial notion
```
[📖 **Full Cortex Documentation**](/docs/cortex.md)
## ⚙️ Configuration (Optional)
Brainy works with **zero configuration**, but you can customize
## 🆚 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. Cloud-Only Vector DBs
**Pinecone/Weaviate/Qdrant** - Vendor lock-in, expensive, cloud-only
**Brainy** - Run anywhere, your data stays yours, cost-effective
### vs. Graph DBs with "Vector Features"
**Neo4j + vector plugin** - Bolt-on solution, not native, limited
**Brainy** - Native vector+graph architecture from the ground up
## 💎 Premium Features (Optional)
**Core Brainy is FREE forever. Premium features for enterprise needs:**
### 🔗 Enterprise Connectors (14-day trials)
- **Notion** ($49/mo) - Bidirectional workspace sync
- **Salesforce** ($99/mo) - CRM integration
- **Slack** ($49/mo) - Team collaboration
- **Asana** ($44/mo) - Project management
```bash
cortex license trial notion # Start free trial
```
**No vendor lock-in. Your data stays yours.**
## 🎨 What You Can Build
- **🤖 AI Chat Applications** - ChatGPT-like apps with long-term memory
- **🔍 Semantic Search** - Search by meaning, not keywords
- **🎯 Recommendation Engines** - "Users who liked this also liked..."
- **🧬 Knowledge Graphs** - Connect everything to everything
- **🛡️ Fraud Detection** - Find patterns humans can't see
- **📚 Smart Documentation** - Docs that answer questions
<details>
<summary>📦 <strong>Framework Examples</strong></summary>
### React
```jsx
import { BrainyData } from '@soulcraft/brainy'
function App() {
const [brainy] = useState(() => new BrainyData())
useEffect(() => brainy.init(), [])
const search = async (query) => {
return await brainy.search(query, 10)
}
}
```
### Vue 3
```vue
<script setup>
const brainy = new BrainyData()
await brainy.init()
</script>
```
### Angular
```typescript
@Component({})
export class AppComponent {
brainy = new BrainyData()
async ngOnInit() {
await this.brainy.init()
}
}
```
### Node.js
```javascript
const brainy = new BrainyData()
await brainy.init()
```
</details>
## 📦 Advanced Features
<details>
<summary>🔧 <strong>MongoDB-Style Metadata Filtering</strong></summary>
```javascript
const results = await brainy.search("machine learning", 10, {
metadata: {
price: { $gte: 100, $lte: 1000 },
category: { $in: ["AI", "ML"] },
rating: { $gt: 4.5 },
tags: { $includes: "tensorflow" }
}
})
```
**15+ operators**: `$gt`, `$in`, `$regex`, `$and`, `$or`, etc.
</details>
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<details>
<summary>🔗 <strong>Graph Relationships</strong></summary>
feat(distributed): add distributed mode with multi-instance coordination Implements Phase 1 and Phase 2 of distributed enhancements for horizontal scaling: Phase 1 - Zero-Config Distributed Mode: - Add DistributedConfigManager for shared S3 configuration coordination - Implement explicit role configuration (reader/writer/hybrid) for safety - Add instance registration with heartbeat and health monitoring - Create hash-based partitioner for deterministic data distribution Phase 2 - Intelligent Data Management: - Add DomainDetector for automatic data categorization (medical, legal, product, etc.) - Implement domain-aware search filtering for improved relevance - Create role-based operational modes with specific optimizations - Add HealthMonitor for comprehensive metrics tracking Key Features: - Multi-writer support with consistent hash partitioning - Reader instances optimize for 80% cache utilization - Writer instances optimize for batched writes - Automatic domain detection and tagging - Real-time health monitoring across all instances - Cross-platform crypto utilities for browser compatibility Safety Improvements: - Require explicit role configuration (no automatic assignment) - Validate role compatibility on startup - Track instance health and performance metrics Testing: - Add comprehensive test suite for distributed features - All 25 distributed tests passing - Fixed domain filtering in search functionality Documentation: - Update README with distributed mode highlights - Add examples showing reader/writer setup - Document new capabilities and benefits 🤖 Generated with Claude Code https://claude.ai/code Co-Authored-By: Claude <noreply@anthropic.com>
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```javascript
const company = await brainy.add("OpenAI", { type: "company" })
const product = await brainy.add("GPT-4", { type: "product" })
await brainy.relate(company, product, "develops")
const products = await brainy.getVerbsBySource(company)
```
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</details>
<details>
<summary>🐳 <strong>Docker Deployment</strong></summary>
```dockerfile
FROM node:24-slim
RUN npm run download-models # Embed models
CMD ["node", "server.js"]
```
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Deploy anywhere: AWS, GCP, Azure, Cloudflare
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</details>
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## 📚 Documentation
- [Quick Start](docs/getting-started/)
- [API Reference](docs/api-reference/)
- [Examples](docs/examples/)
- [Cortex CLI](docs/cortex.md)
- [Performance Guide](docs/optimization-guides/)
## ❓ Does Brainy Impact Performance?
**NO - Brainy actually IMPROVES performance:**
**Zero runtime overhead** - Premium features are lazy-loaded only when used
**Smaller than alternatives** - 643KB vs 12.5MB for TensorFlow.js
**Built-in caching** - 95%+ cache hit rates reduce compute
**Automatic optimization** - Gets faster as it learns your patterns
**No network calls** - Works completely offline after setup
**The augmentation system and premium features are 100% optional and have ZERO impact unless explicitly activated.**
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## 🤝 Contributing
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We welcome contributions! See [Contributing Guidelines](CONTRIBUTING.md)
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## 📄 License
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[MIT](LICENSE) - Core Brainy is FREE forever
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---
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<div align="center">
<strong>Ready to build the future of search?</strong>
**[Get Started →](docs/getting-started/) | [Examples →](docs/examples/) | [Discord →](https://discord.gg/brainy)**
</div>