🧠 Brainy

A powerful, lightweight vector & graph database for browsers and Node.js

✨ Overview

Say hello to Brainy, your new favorite data sidekick! 🎉 Brainy combines the power of vector search with graph relationships in a lightweight, cross-platform database that's as smart as it is fun to use. Whether you're building AI applications, recommendation systems, or knowledge graphs, Brainy provides the tools you need to store, connect, and retrieve your data intelligently.

What makes Brainy special? It intelligently adapts to you and your environment! Like a chameleon with a PhD, Brainy automatically detects your platform, adjusts its storage strategy, and optimizes performance based on your usage patterns. The more you use it, the smarter it gets - learning from your data to provide increasingly relevant results and connections.

🚀 Key Features

• Vector Search - Find semantically similar content using embeddings (like having ESP for your data!)
• Graph Relationships - Connect data with meaningful relationships (your data's social network)
• Streaming Pipeline - Process data in real-time as it flows through the system (like a data waterslide!)
• Extensible Augmentations - Customize and extend functionality with pluggable components (LEGO blocks for your data!)
• Built-in Conduits - Sync and scale across instances with WebSocket and WebRTC (your data's teleportation system!)
• LLM Creation & Training - Build, train, and deploy language models from your graph data (your own personal AI factory!)
• Adaptive Intelligence - Automatically optimizes for your environment and usage patterns
• Cross-Platform - Works everywhere you do: browsers, Node.js, and server environments
• Persistent Storage - Data persists across sessions and scales to any size (no memory loss here, even for elephant-sized data!)
• TypeScript Support - Fully typed API with generics (for those who like their code tidy)
• CLI Tools - Powerful command-line interface for data management (command line wizardry)

📊 What Can You Build? (The Fun Stuff!)

• Semantic Search Engines - Find content based on meaning, not just keywords (mind-reading for your data!)
• Recommendation Systems - Suggest similar items based on vector similarity (like a friend who really gets your taste)
• Knowledge Graphs - Build connected data structures with relationships (your data's family tree)
• AI Applications - Store and retrieve embeddings for machine learning models (brain food for your AI)
• Custom Language Models - Create, train, and deploy LLMs from your graph data (your personal GPT factory!)
• Data Organization Tools - Automatically categorize and connect related information (like having a librarian in your code)
• Adaptive Experiences - Create applications that learn and evolve with your users (digital chameleons!)

🔧 Installation

Due to a dependency conflict between TensorFlow.js packages, use the --legacy-peer-deps flag when installing:

npm install @soulcraft/brainy --legacy-peer-deps

🏁 Quick Start

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

// Create and initialize the database
const db = new BrainyData()
await db.init()

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

const dogId = await db.add("Dogs are loyal companions", {
    noun: NounType.Thing,
    category: 'animal'
})

// Search for similar items
const results = await db.searchText("feline pets", 2)
console.log(results)
// Returns items similar to "feline pets" with similarity scores

// Add a relationship between items
await db.addVerb(catId, dogId, {
    verb: VerbType.RelatedTo,
    description: 'Both are common household pets'
})

🧩 How It Works (The Magic Behind the Curtain)

Brainy combines four key technologies to create its adaptive intelligence:

1. Vector Embeddings - Converts data (text, images, etc.) into numerical vectors that capture semantic meaning ( translating your data into brain-speak!)
2. HNSW Algorithm - Enables fast similarity search through a hierarchical graph structure (like a super-efficient treasure map for your data)
3. Adaptive Environment Detection - Automatically senses your platform and optimizes accordingly:
   • Adjusts performance parameters based on available resources
   • Learns from query patterns to optimize future searches
   • Tunes itself for your specific use cases the more you use it
4. Intelligent Storage Selection - Uses the best available storage option for your environment, scaling effortlessly to any data size (from bytes to petabytes!):
   • Browser: Origin Private File System (OPFS)
   • Node.js: File system
   • Server: S3-compatible storage (optional)
   • Fallback: In-memory storage
   • Automatically migrates between storage types as needed!

🚀 The Brainy Pipeline (Data's Wild Ride!)

Brainy's data processing pipeline transforms raw data into searchable, connected knowledge that gets smarter over time. Here's how the magic happens:

Raw Data → Embedding → Vector Storage → Graph Connections → Adaptive Learning → Query & Retrieval

Each time data flows through this pipeline, Brainy learns a little more about your usage patterns and environment, making future operations even faster and more relevant!

🔄 Pipeline Stages (The Journey of Your Data)

1. Data Ingestion 🍽️

   • Raw text or pre-computed vectors enter the pipeline (dinner time for data!)
   • Data is validated and prepared for processing (washing hands before eating)

2. Embedding Generation 🧠

   • Text is transformed into numerical vectors using embedding models (language → math magic)
   • Choose between TensorFlow Universal Sentence Encoder (high quality) or Simple Embedding (faster)
   • Custom embedding functions can be plugged in for specialized domains (bring your own secret sauce)

3. Vector Indexing 🔍

   • Vectors are indexed using the HNSW algorithm (filing your data in the brain cabinet)
   • Hierarchical structure enables lightning-fast similarity search (express lanes for your queries)
   • Configurable parameters for precision vs. performance tradeoffs (dial in your perfect balance)

4. Graph Construction 🕸️

   • Nouns (entities) become nodes in the knowledge graph (data gets its own social network)
   • Verbs (relationships) connect related entities (making friends and connections)
   • Typed relationships add semantic meaning to connections (not just friends, but BFFs)

5. Adaptive Learning 🌱

   • Analyzes usage patterns to optimize future operations (gets to know your habits)
   • Tunes performance parameters based on your environment (adapts to your digital home)
   • Adjusts search strategies based on query history (learns what you're really looking for)
   • Becomes more efficient and relevant the more you use it (like a good friendship)

6. Intelligent Storage 💾

   • Data is saved using the optimal storage for your environment (finds the coziest home for your data)
   • Automatic selection between OPFS, filesystem, S3, or memory (no manual configuration needed!)
   • Migrates between storage types as your application's needs evolve (moves houses without you noticing)
   • Scales effortlessly from tiny datasets to massive data collections (from ant-sized to elephant-sized data, no problem!)
   • Configurable storage adapters for custom persistence needs (design your own dream data home)

🧩 Augmentation Types

Brainy uses a powerful augmentation system to extend functionality. Augmentations are processed in the following order:

1. SENSE 👁️

   • Ingests and processes raw, unstructured data into nouns and verbs
   • Handles text, images, audio streams, and other input formats
   • Example: Converting raw text into structured entities

2. MEMORY 💾

   • Provides storage capabilities for data in different formats
   • Manages persistence across sessions
   • Example: Storing vectors in OPFS or filesystem

3. COGNITION 🧠

   • Enables advanced reasoning, inference, and logical operations
   • Analyzes relationships between entities
   • Creates and trains language models from graph data
   • Examples:
     • Inferring new connections between existing data
     • Building custom LLMs from your nouns and verbs

4. CONDUIT 🔌

   • Establishes high-bandwidth channels for structured data exchange
   • Connects with external systems and syncs between Brainy instances
   • Two built-in iConduit augmentations for scaling out and syncing:
     • WebSocket iConduit - Syncs data between browsers and servers (like a digital postal service with superpowers!)
     • WebRTC iConduit - Direct peer-to-peer syncing between browsers (like telepathy for your data, no middleman required!)
   • Examples:
     • Integrating with third-party APIs
     • Syncing Brainy instances between browsers using WebSockets
     • Peer-to-peer syncing between browsers using WebRTC

5. ACTIVATION ⚡

   • Initiates actions, responses, or data manipulations
   • Triggers events based on data changes
   • Example: Sending notifications when new data is processed

6. PERCEPTION 🔍

   • Interprets, contextualizes, and visualizes identified nouns and verbs
   • Creates meaningful representations of data
   • Example: Generating visualizations of graph relationships

7. DIALOG 💬

   • Facilitates natural language understanding and generation
   • Enables conversational interactions
   • Example: Processing user queries and generating responses

8. WEBSOCKET 🌐

   • Enables real-time communication via WebSockets
   • Can be combined with other augmentation types
   • Example: Streaming data processing in real-time

🌊 Streaming Data Support

Brainy's pipeline is designed to handle streaming data efficiently:

1. WebSocket Integration 🔄

   • Built-in support for WebSocket connections
   • Process data as it arrives without blocking
   • Example: setupWebSocketPipeline(url, dataType, options)

2. Asynchronous Processing ⚡

   • Non-blocking architecture for real-time data handling
   • Parallel processing of incoming streams
   • Example: createWebSocketHandler(connection, dataType, options)

3. Event-Based Architecture 📡

   • Augmentations can listen to data feeds and streams
   • Real-time updates propagate through the pipeline
   • Example: listenToFeed(feedUrl, callback)

4. Threaded Execution 🧵

   • Optional multi-threading for high-performance streaming
   • Configurable execution modes (SEQUENTIAL, PARALLEL, THREADED)
   • Example: executeTypedPipeline(augmentations, method, args, { mode: ExecutionMode.THREADED })

🏃‍♀️ Running the Pipeline

The pipeline runs automatically when you:

// Add data (runs embedding → indexing → storage)
const id = await db.add("Your text data here", {metadata})

// Search (runs embedding → similarity search)
const results = await db.searchText("Your query here", 5)

// Connect entities (runs graph construction → storage)
await db.addVerb(sourceId, targetId, {verb: VerbType.RelatedTo})

Using the CLI:

# Add data through the CLI pipeline
brainy add "Your text data here" '{"noun":"Thing"}'

# Search through the CLI pipeline
brainy search "Your query here" --limit 5

# Connect entities through the CLI
brainy addVerb <sourceId> <targetId> RelatedTo

🔧 Extending the Pipeline

Brainy's pipeline is designed for extensibility at every stage:

1. Custom Embedding 🧩

   // Create your own embedding function
   const myEmbedder = async (text) => {
     // Your custom embedding logic here
     return [0.1, 0.2, 0.3, ...] // Return a vector
   }
   
   // Use it in Brainy
   const db = new BrainyData({
     embeddingFunction: myEmbedder
   })
   

2. Custom Distance Functions 📏

   // Define your own distance function
   const myDistance = (a, b) => {
     // Your custom distance calculation
     return Math.sqrt(a.reduce((sum, val, i) => sum + Math.pow(val - b[i], 2), 0))
   }
   
   // Use it in Brainy
   const db = new BrainyData({
     distanceFunction: myDistance
   })
   

3. Custom Storage Adapters 📦

   // Implement the StorageAdapter interface
   class MyStorage implements StorageAdapter {
     // Your storage implementation
   }
   
   // Use it in Brainy
   const db = new BrainyData({
     storageAdapter: new MyStorage()
   })
   

4. Augmentations System 🧠

   // Create custom augmentations to extend functionality
   const myAugmentation = {
     type: 'memory',
     name: 'my-custom-storage',
     // Implementation details
   }
   
   // Register with Brainy
   db.registerAugmentation(myAugmentation)
   

📝 Data Model

Brainy uses a graph-based data model with two primary concepts:

Nouns (Entities)

The main entities in your data (nodes in the graph):

• Each noun has a unique ID, vector representation, and metadata
• Nouns can be categorized by type (Person, Place, Thing, Event, Concept, etc.)
• Nouns are automatically vectorized for similarity search

Verbs (Relationships)

Connections between nouns (edges in the graph):

• Each verb connects a source noun to a target noun
• Verbs have types that define the relationship (RelatedTo, Controls, Contains, etc.)
• Verbs can have their own metadata to describe the relationship

🖥️ Command Line Interface

Brainy includes a powerful CLI for managing your data:

# Install globally
npm install -g @soulcraft/brainy --legacy-peer-deps

# Initialize a database
brainy init

# Add some data
brainy add "Cats are independent pets" '{"noun":"Thing","category":"animal"}'
brainy add "Dogs are loyal companions" '{"noun":"Thing","category":"animal"}'

# Search for similar items
brainy search "feline pets" 5

# Add relationships between items
brainy addVerb <sourceId> <targetId> RelatedTo '{"description":"Both are pets"}'

# Visualize the graph structure
brainy visualize
brainy visualize --root <id> --depth 3

🔄 Using During Development

# Run the CLI directly from the source
npm run cli help

# Generate a random graph for testing
npm run cli generate-random-graph --noun-count 20 --verb-count 40

🔍 Available Commands

Basic Database Operations:

• init - Initialize a new database
• add <text> [metadata] - Add a new noun with text and optional metadata
• search <query> [limit] - Search for nouns similar to the query
• get <id> - Get a noun by ID
• delete <id> - Delete a noun by ID
• addVerb <sourceId> <targetId> <verbType> [metadata] - Add a relationship
• getVerbs <id> - Get all relationships for a noun
• status - Show database status
• clear - Clear all data from the database
• generate-random-graph - Generate test data
• visualize - Visualize the graph structure
• completion-setup - Setup shell autocomplete

Pipeline and Augmentation Commands:

• list-augmentations - List all available augmentation types and registered augmentations
• augmentation-info <type> - Get detailed information about a specific augmentation type
• test-pipeline [text] - Test the sequential pipeline with sample data
  • -t, --data-type <type> - Type of data to process (default: 'text')
  • -m, --mode <mode> - Execution mode: sequential, parallel, threaded (default: 'sequential')
  • -s, --stop-on-error - Stop execution if an error occurs
  • -v, --verbose - Show detailed output
• stream-test - Test streaming data through the pipeline (simulated)
  • -c, --count <number> - Number of data items to stream (default: 5)
  • -i, --interval <ms> - Interval between data items in milliseconds (default: 1000)
  • -t, --data-type <type> - Type of data to process (default: 'text')
  • -v, --verbose - Show detailed output

LLM Commands:

• llm create - Create a new LLM model from Brainy data
  • -n, --name <name> - Name of the model
  • -d, --description <description> - Description of the model
  • -t, --type <type> - Type of model (simple, transformer, custom)
  • -v, --vocab-size <size> - Vocabulary size
  • -e, --embedding-dim <dim> - Embedding dimension
  • -h, --hidden-dim <dim> - Hidden dimension
  • -l, --layers <count> - Number of layers
  • --heads <count> - Number of attention heads (for transformer models)
• llm train <modelId> - Train an LLM model on Brainy data
  • -s, --max-samples <count> - Maximum number of training samples
  • -v, --validation-split <ratio> - Validation split ratio
  • -e, --epochs <count> - Number of training epochs
  • -b, --batch-size <size> - Batch size
  • -p, --patience <count> - Early stopping patience
• llm test <modelId> - Test an LLM model on Brainy data
  • -s, --test-size <count> - Number of test samples
  • -g, --generate-samples - Generate sample predictions
  • -c, --sample-count <count> - Number of samples to generate
• llm export <modelId> - Export an LLM model for deployment
  • -f, --format <format> - Export format (tfjs, json)
  • -o, --output <path> - Output path
  • -m, --include-metadata - Include metadata
  • -v, --include-vocab - Include vocabulary
• llm deploy <modelId> - Deploy an LLM model to the specified target
  • -t, --target <target> - Deployment target (browser, node, cloud)
  • -p, --provider <provider> - Cloud provider (aws, gcp, azure)
  • -e, --endpoint <url> - Endpoint URL for cloud deployment
  • -r, --region <region> - Region for cloud deployment
• llm generate <modelId> <prompt> - Generate text using an LLM model
  • -t, --temperature <temp> - Temperature for sampling
  • -k, --top-k <count> - Number of top tokens to consider
  • -l, --max-length <length> - Maximum length of generated text

🔌 API Reference

Database Management

// Initialize the database
await db.init()

// Clear all data
await db.clear()

// Get database status
const status = await db.status()

Working with Nouns (Entities)

// Add a noun (automatically vectorized)
const id = await db.add(textOrVector, {
    noun: NounType.Thing,
    // other metadata...
})

// Retrieve a noun
const noun = await db.get(id)

// Update noun metadata
await db.updateMetadata(id, {
    noun: NounType.Thing,
    // updated metadata...
})

// Delete a noun
await db.delete(id)

// Search for similar nouns
const results = await db.search(vectorOrText, numResults)
const textResults = await db.searchText("query text", numResults)

// Search by noun type
const thingNouns = await db.searchByNounTypes([NounType.Thing], numResults)

Working with Verbs (Relationships)

// Add a relationship between nouns
await db.addVerb(sourceId, targetId, {
    verb: VerbType.RelatedTo,
    // other metadata...
})

// Get all relationships
const verbs = await db.getAllVerbs()

// Get relationships by source noun
const outgoingVerbs = await db.getVerbsBySource(sourceId)

// Get relationships by target noun
const incomingVerbs = await db.getVerbsByTarget(targetId)

// Get relationships by type
const containsVerbs = await db.getVerbsByType(VerbType.Contains)

// Get a specific relationship
const verb = await db.getVerb(verbId)

// Delete a relationship
await db.deleteVerb(verbId)

Working with LLM Models

import { createLLMAugmentations } from '@soulcraft/brainy'

// Create LLM augmentations
const { cognition, activation } = await createLLMAugmentations()

// Create a new LLM model
const createResult = await cognition.createModel({
  name: 'my-model',
  description: 'A simple LLM model trained on Brainy data',
  modelType: 'simple',
  vocabSize: 5000,
  embeddingDim: 64,
  hiddenDim: 128,
  numLayers: 1
})

const modelId = createResult.data.modelId

// Train the model
const trainResult = await cognition.trainModel(modelId, {
  maxSamples: 100,
  validationSplit: 0.2,
  earlyStoppingPatience: 2
})

// Test the model
const testResult = await cognition.testModel(modelId, {
  testSize: 20,
  generateSamples: true,
  sampleCount: 3
})

// Generate text with the model
const generateResult = await cognition.generateText(modelId, 'What is a', {
  temperature: 0.7,
  topK: 5
})

// Export the model
const exportResult = await cognition.exportModel(modelId, {
  format: 'json',
  includeMetadata: true,
  includeVocab: true
})

// Deploy the model
const deployResult = await cognition.deployModel(modelId, {
  target: 'browser'
})

// Using the augmentation pipeline
const pipelineResult = await augmentationPipeline.executeCognitionPipeline(
  'createModel',
  [{
    name: 'pipeline-model',
    modelType: 'transformer',
    numHeads: 2,
    numLayers: 1
  }]
)

⚙️ Advanced Configuration

Custom Embedding

import {BrainyData, createSimpleEmbeddingFunction} from '@soulcraft/brainy'

// Use a custom embedding function (faster but less accurate)
const db = new BrainyData({
    embeddingFunction: createSimpleEmbeddingFunction()
})
await db.init()

// Directly embed text to vectors
const vector = await db.embed("Some text to convert to a vector")

Performance Tuning

import {BrainyData, euclideanDistance} from '@soulcraft/brainy'

// Configure with custom options
const db = new BrainyData({
    // Use Euclidean distance instead of default cosine distance
    distanceFunction: euclideanDistance,

    // HNSW index configuration for search performance
    hnsw: {
        M: 16,              // Max connections per noun
        efConstruction: 200, // Construction candidate list size
        efSearch: 50,       // Search candidate list size
    },

    // Noun and Verb type validation
    typeValidation: {
        enforceNounTypes: true,  // Validate noun types against NounType enum
        enforceVerbTypes: true,  // Validate verb types against VerbType enum
    },

    // Storage configuration
    storage: {
        requestPersistentStorage: true,
        // Uncomment to use cloud storage:
        // s3Storage: {
        //   bucketName: 'your-bucket',
        //   accessKeyId: 'your-key',
        //   secretAccessKey: 'your-secret',
        //   region: 'us-east-1'
        // }
    }
})

🧪 Distance Functions

• cosineDistance (default)
• euclideanDistance
• manhattanDistance
• dotProductDistance

🔋 Embedding Options

• Default: TensorFlow Universal Sentence Encoder (high quality)
• Alternative: Simple character-based embedding (faster)

🧰 Extensions

Brainy includes an augmentation system for extending functionality:

• Memory Augmentations: Different storage backends
• Sense Augmentations: Process raw data
• Cognition Augmentations: Reasoning and inference
• Dialog Augmentations: Natural language processing
• Perception Augmentations: Data interpretation and visualization
• Activation Augmentations: Trigger actions

🌐 Browser Compatibility

Works in all modern browsers:

• Chrome 86+
• Edge 86+
• Opera 72+
• Chrome for Android 86+

For browsers without OPFS support, falls back to in-memory storage.

☁️ Cloud Deployment

Brainy can be deployed as a standalone web service on various cloud platforms using the included cloud wrapper:

• AWS Lambda and API Gateway: Deploy as a serverless function with API Gateway
• Google Cloud Run: Deploy as a containerized service
• Cloudflare Workers: Deploy as a serverless function on the edge

The cloud wrapper provides both RESTful and WebSocket APIs for all Brainy operations, enabling both request-response and real-time communication patterns. It supports multiple storage backends and can be configured via environment variables.

Key features of the cloud wrapper:

• RESTful API for standard CRUD operations
• WebSocket API for real-time updates and subscriptions
• Support for multiple storage backends (Memory, FileSystem, S3)
• Configurable via environment variables
• Deployment scripts for AWS, Google Cloud, and Cloudflare

To get started with cloud deployment, see the Cloud Wrapper README.

🔗 Related Projects

• sodal-project cartographer - The defacto standard for interacting with Brainy

📚 Examples

The repository includes several examples:

• Web demo: examples/demo.html
• Basic usage: examples/basicUsage.js
• Custom storage: examples/customStorage.js
• Memory augmentations: examples/memoryAugmentationExample.js
• Conduit augmentations: examples/conduitAugmentationExample.js
• Browser-server search: examples/browser-server-search/ - Search a server-hosted Brainy instance from a browser

Syncing Brainy Instances

You can use the conduit augmentations to sync Brainy instances:

• WebSocket iConduit: For syncing between browsers and servers, or between servers (like a digital postal service with superpowers!). WebSockets cannot be used for direct browser-to-browser communication without a server in the middle.
• WebRTC iConduit: For direct peer-to-peer syncing between browsers (like telepathy for your data, no middleman required!). This is the recommended approach for browser-to-browser communication.

WebSocket Sync Example

import { 
  BrainyData, 
  augmentationPipeline, 
  createConduitAugmentation 
} from '@soulcraft/brainy'

// Create and initialize the database
const db = new BrainyData()
await db.init()

// Create a WebSocket conduit augmentation
const wsConduit = await createConduitAugmentation('websocket', 'my-websocket-sync')

// Register the augmentation with the pipeline
augmentationPipeline.register(wsConduit)

// Connect to another Brainy instance (server or browser)
const connectionResult = await augmentationPipeline.executeConduitPipeline(
  'establishConnection',
  ['wss://your-websocket-server.com/brainy-sync', { protocols: 'brainy-sync' }]
)

if (connectionResult[0] && (await connectionResult[0]).success) {
  const connection = (await connectionResult[0]).data

  // Read data from the remote instance
  const readResult = await augmentationPipeline.executeConduitPipeline(
    'readData',
    [{ connectionId: connection.connectionId, query: { type: 'getAllNouns' } }]
  )

  // Process and add the received data to the local instance
  if (readResult[0] && (await readResult[0]).success) {
    const remoteNouns = (await readResult[0]).data
    for (const noun of remoteNouns) {
      await db.add(noun.vector, noun.metadata)
    }
  }

  // Set up real-time sync by monitoring the stream
  await wsConduit.monitorStream(connection.connectionId, async (data) => {
    // Handle incoming data (e.g., new nouns, verbs, updates)
    if (data.type === 'newNoun') {
      await db.add(data.vector, data.metadata)
    } else if (data.type === 'newVerb') {
      await db.addVerb(data.sourceId, data.targetId, data.vector, data.options)
    }
  })
}

WebRTC Peer-to-Peer Sync Example

import { 
  BrainyData, 
  augmentationPipeline, 
  createConduitAugmentation 
} from '@soulcraft/brainy'

// Create and initialize the database
const db = new BrainyData()
await db.init()

// Create a WebRTC conduit augmentation
const webrtcConduit = await createConduitAugmentation('webrtc', 'my-webrtc-sync')

// Register the augmentation with the pipeline
augmentationPipeline.register(webrtcConduit)

// Connect to a peer using a signaling server
const connectionResult = await augmentationPipeline.executeConduitPipeline(
  'establishConnection',
  [
    'peer-id-to-connect-to', 
    { 
      signalServerUrl: 'wss://your-signal-server.com',
      localPeerId: 'my-peer-id',
      iceServers: [{ urls: 'stun:stun.l.google.com:19302' }]
    }
  ]
)

if (connectionResult[0] && (await connectionResult[0]).success) {
  const connection = (await connectionResult[0]).data

  // Set up real-time sync by monitoring the stream
  await webrtcConduit.monitorStream(connection.connectionId, async (data) => {
    // Handle incoming data (e.g., new nouns, verbs, updates)
    if (data.type === 'newNoun') {
      await db.add(data.vector, data.metadata)
    } else if (data.type === 'newVerb') {
      await db.addVerb(data.sourceId, data.targetId, data.vector, data.options)
    }
  })

  // When adding new data locally, also send to the peer
  const nounId = await db.add("New data to sync", { noun: "Thing" })

  // Send the new noun to the peer
  await augmentationPipeline.executeConduitPipeline(
    'writeData',
    [
      { 
        connectionId: connection.connectionId, 
        data: { 
          type: 'newNoun',
          id: nounId,
          vector: (await db.get(nounId)).vector,
          metadata: (await db.get(nounId)).metadata
        } 
      }
    ]
  )
}

Browser-Server Search Example

Brainy supports searching a server-hosted instance from a browser, storing results locally, and performing further searches against the local instance:

import { BrainyData } from '@soulcraft/brainy'

// Create and initialize the database with remote server configuration
const db = new BrainyData({
  remoteServer: {
    url: 'wss://your-brainy-server.com/ws',
    protocols: 'brainy-sync',
    autoConnect: true // Connect automatically during initialization
  }
})
await db.init()

// Or connect manually after initialization
if (!db.isConnectedToRemoteServer()) {
  await db.connectToRemoteServer('wss://your-brainy-server.com/ws', 'brainy-sync')
}

// Search the remote server (results are stored locally)
const remoteResults = await db.searchText('machine learning', 5, { searchMode: 'remote' })

// Search the local database (includes previously stored results)
const localResults = await db.searchText('machine learning', 5, { searchMode: 'local' })

// Perform a combined search (local first, then remote if needed)
const combinedResults = await db.searchText('neural networks', 5, { searchMode: 'combined' })

// Add data to both local and remote instances
const id = await db.addToBoth('Deep learning is a subset of machine learning', {
  noun: 'Concept',
  category: 'AI',
  tags: ['deep learning', 'neural networks']
})

// Clean up when done
await db.shutDown()

For a complete example with HTML interface, see the browser-server-search example.

📋 Requirements

• Node.js >= 23.11.0

🤝 Contributing

Code Style Guidelines

Brainy follows a specific code style to maintain consistency throughout the codebase:

1. No Semicolons: All code in the project should avoid using semicolons wherever possible. This applies to:

   • Source code files (.ts, .js)
   • Generated code
   • Code examples in documentation
   • Templates and snippets

2. Formatting: The project uses Prettier for code formatting with the following settings:

   • No semicolons ("semi": false)
   • Single quotes for strings ("singleQuote": true)
   • 2-space indentation ("tabWidth": 2)
   • Always use parentheses around arrow function parameters ("arrowParens": "always")
   • Place the closing bracket of JSX elements on the same line ("bracketSameLine": true)
   • Add spaces between brackets and object literals ("bracketSpacing": true)
   • Line width of 80 characters ("printWidth": 80)
   • No trailing commas ("trailingComma": "none")
   • Use spaces instead of tabs ("useTabs": false)

3. Linting: ESLint is configured with the following rules:

   • No semicolons:

     "semi": ["error", "never"],
     "@typescript-eslint/semi": ["error", "never"]
     

   • Unused variables handling:

     "no-unused-vars": "off",
     "@typescript-eslint/no-unused-vars": ["warn", {
       "args": "after-used",
       "argsIgnorePattern": "^_"
     }]
     

   • Extends recommended ESLint and TypeScript ESLint configurations

4. TypeScript Configuration:

   • Strict type checking enabled ("strict": true)
   • Consistent casing in file names enforced ("forceConsistentCasingInFileNames": true)
   • ES2020 target with Node.js module system
   • Source maps generated for debugging

5. Commit Messages:

   • Use the imperative mood ("Add feature" not "Added feature")
   • Keep the first line concise (under 50 characters)
   • Reference issues and pull requests where appropriate
   • Provide detailed explanations in the commit body when necessary

When contributing to the project, please ensure your code follows these guidelines. The project's CI/CD pipeline will automatically check for style violations.

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests with npm test
5. Submit a pull request

📄 License

MIT