Zero Configuration & Auto-Adaptation

Current Status: Basic zero-config is fully functional. Advanced auto-adaptation features are in development.

Overview

Brainy is designed with "Zero Config by Default, Infinite Tunability" philosophy. It automatically detects your environment, adapts to available resources, learns from usage patterns, and optimizes itself for your specific workload—all without any configuration.

Zero Configuration Magic

Instant Start

import { Brainy } from 'brainy'

// That's it. No config needed.
const brain = new Brainy()
await brain.init()

// Brainy automatically:
// ✓ Detects environment (Node.js, Browser, Edge, Deno)
// ✓ Chooses optimal storage (FileSystem, OPFS, Memory)
// ✓ Downloads required models (if needed)
// ✓ Configures vector dimensions (384 optimal)
// ✓ Sets up indexing strategies
// ✓ Enables appropriate augmentations
// ✓ Configures caching layers
// ✓ Optimizes for your hardware

Environment Detection ✅ Available

Brainy automatically detects and adapts to your runtime:

// Brainy's environment detection
const environment = {
  // Runtime detection
  isNode: typeof process !== 'undefined',
  isBrowser: typeof window !== 'undefined',
  isDeno: typeof Deno !== 'undefined',
  isEdge: typeof EdgeRuntime !== 'undefined',
  isWebWorker: typeof WorkerGlobalScope !== 'undefined',
  
  // Capability detection
  hasFileSystem: /* auto-detected */,
  hasIndexedDB: /* auto-detected */,
  hasOPFS: /* auto-detected */,
  hasWebGPU: /* auto-detected */,
  hasWASM: /* auto-detected */,
  
  // Resource detection
  cpuCores: /* auto-detected */,
  memory: /* auto-detected */,
  storage: /* auto-detected */
}

Auto-Adaptive Storage ✅ Available

Current: Brainy automatically selects the best storage adapter for your environment.

Storage Selection Logic

// Brainy's intelligent storage selection
async function autoSelectStorage() {
  // Server environments
  if (environment.isNode) {
    if (await hasWritePermission('./data')) {
      return 'filesystem'  // Best for servers
    } else if (process.env.S3_BUCKET) {
      return 's3'  // Cloud deployment
    } else {
      return 'memory'  // Fallback for restricted environments
    }
  }
  
  // Browser environments
  if (environment.isBrowser) {
    if (await navigator.storage.estimate() > 1GB) {
      return 'opfs'  // Best for modern browsers
    } else if (indexedDB) {
      return 'indexeddb'  // Fallback for older browsers
    } else {
      return 'memory'  // In-memory for restricted contexts
    }
  }
  
  // Edge environments
  if (environment.isEdge) {
    return 'kv'  // Use edge KV stores (Cloudflare, Vercel)
  }
}

Storage Migration

Brainy seamlessly migrates between storage types:

// Start with memory storage (development)
const brain = new Brainy()  // Auto-selects memory

// Later, migrate to production storage
await brain.migrate({
  to: 'filesystem',
  path: './production-data'
})
// All data seamlessly transferred

Learning & Optimization 🚧 Coming Soon

Note: These features are planned for Q2 2025. Currently, Brainy uses static optimizations.

Query Pattern Learning 🚧 Planned

Brainy learns from your query patterns and optimizes accordingly:

// Brainy observes query patterns
class QueryPatternLearner {
  analyze(queries: Query[]) {
    return {
      // Frequency analysis
      mostCommonFields: this.getTopFields(queries),
      avgResultSize: this.getAvgSize(queries),
      temporalPatterns: this.getTimePatterns(queries),
      
      // Relationship analysis  
      commonTraversals: this.getGraphPatterns(queries),
      typicalDepth: this.getAvgDepth(queries),
      
      // Performance analysis
      slowQueries: this.getSlowQueries(queries),
      cacheability: this.getCacheability(queries)
    }
  }
}

// Automatic optimizations based on learning:
// - Creates indexes for frequently queried fields
// - Pre-computes common graph traversals
// - Adjusts cache sizes based on working set
// - Optimizes vector search parameters

Auto-Indexing 🚧 Planned

Brainy automatically creates indexes based on usage:

// No manual index configuration needed
await brain.find({ where: { category: "tech" } })  // First query
// Brainy notices 'category' field usage

await brain.find({ where: { category: "science" } })  // Second query
// Pattern detected - auto-creates category index

await brain.find({ where: { category: "tech" } })  // Third query
// Now using index - 100x faster!

Adaptive Caching 🚧 Planned

Cache strategies adapt to your access patterns:

class AdaptiveCache {
  async adapt(metrics: AccessMetrics) {
    if (metrics.hitRate < 0.3) {
      // Low hit rate - switch strategy
      this.strategy = 'lfu'  // Least Frequently Used
    } else if (metrics.workingSet > this.size) {
      // Working set too large - increase size
      this.size = Math.min(metrics.workingSet * 1.5, maxMemory)
    } else if (metrics.temporalLocality > 0.8) {
      // High temporal locality - use time-based eviction
      this.strategy = 'ttl'
      this.ttl = metrics.avgAccessInterval * 2
    }
  }
}

Performance Auto-Scaling 🚧 Coming Soon

Dynamic Batch Sizing

Brainy adjusts batch sizes based on system load:

class DynamicBatcher {
  calculateOptimalBatch() {
    const cpuUsage = process.cpuUsage()
    const memoryUsage = process.memoryUsage()
    
    if (cpuUsage < 30 && memoryUsage < 50) {
      return 1000  // System idle - large batches
    } else if (cpuUsage < 60 && memoryUsage < 70) {
      return 100   // Moderate load - medium batches
    } else {
      return 10    // High load - small batches
    }
  }
}

// Automatically applied during bulk operations
for (const item of millionItems) {
  await brain.add(item)  // Internally batched optimally
}

Memory Management

Automatic memory pressure handling:

class MemoryManager {
  async handlePressure() {
    const usage = process.memoryUsage()
    const available = os.freemem()
    
    if (available < 100 * 1024 * 1024) {  // Less than 100MB free
      // Emergency mode
      await this.flushCaches()
      await this.compactIndexes()
      await this.offloadToDisk()
    } else if (usage.heapUsed / usage.heapTotal > 0.9) {
      // Preventive mode
      await this.reduceCacheSizes()
      await this.pauseBackgroundTasks()
    }
  }
}

Connection Pooling

Automatic connection management for storage backends:

class ConnectionPool {
  async getOptimalPoolSize() {
    // Adapts based on workload
    const metrics = await this.getMetrics()
    
    if (metrics.waitTime > 100) {
      // Queries waiting - increase pool
      this.size = Math.min(this.size * 1.5, this.maxSize)
    } else if (metrics.idleConnections > this.size * 0.5) {
      // Too many idle - decrease pool
      this.size = Math.max(this.size * 0.7, this.minSize)
    }
    
    return this.size
  }
}

Model Auto-Selection

Embedding Model Selection

Brainy chooses the best embedding model for your use case:

async function autoSelectModel(data: Sample[]) {
  const analysis = {
    languages: detectLanguages(data),
    domainSpecific: detectDomain(data),
    averageLength: getAvgLength(data),
    requiresMultilingual: languages.length > 1
  }
  
  if (analysis.requiresMultilingual) {
    return 'multilingual-e5-base'  // Handles 100+ languages
  } else if (analysis.domainSpecific === 'code') {
    return 'codebert-base'  // Optimized for code
  } else if (analysis.averageLength > 512) {
    return 'all-mpnet-base-v2'  // Better for long text
  } else {
    return 'all-MiniLM-L6-v2'  // Fast and efficient default
  }
}

Model Downloading

Models are automatically downloaded when needed:

// First use - model auto-downloads
const brain = new Brainy()
await brain.init()  // Downloads model if not cached

// Intelligent model caching
const modelCache = {
  location: process.env.MODEL_CACHE || '~/.brainy/models',
  maxSize: 5 * 1024 * 1024 * 1024,  // 5GB max
  strategy: 'lru',  // Least recently used eviction
  
  // CDN selection based on location
  cdn: await selectFastestCDN([
    'https://cdn.brainy.io',
    'https://brainy.b-cdn.net',
    'https://models.huggingface.co'
  ])
}

Workload Detection

Pattern Recognition

Brainy identifies your workload type and optimizes:

enum WorkloadType {
  OLTP = 'oltp',  // Many small transactions
  OLAP = 'olap',  // Analytical queries
  STREAMING = 'streaming',  // Real-time ingestion
  BATCH = 'batch',  // Bulk processing
  HYBRID = 'hybrid'  // Mixed workload
}

class WorkloadDetector {
  detect(metrics: OperationMetrics): WorkloadType {
    if (metrics.writesPerSecond > 1000 && metrics.avgWriteSize < 1024) {
      return WorkloadType.STREAMING
    } else if (metrics.avgQueryComplexity > 0.8 && metrics.avgResultSize > 10000) {
      return WorkloadType.OLAP
    } else if (metrics.batchOperations > metrics.singleOperations) {
      return WorkloadType.BATCH
    } else if (metrics.writeReadRatio > 0.3 && metrics.writeReadRatio < 0.7) {
      return WorkloadType.HYBRID
    } else {
      return WorkloadType.OLTP
    }
  }
}

Optimization Strategies

Different optimizations for different workloads:

class WorkloadOptimizer {
  optimize(workload: WorkloadType) {
    switch (workload) {
      case WorkloadType.STREAMING:
        return {
          entityRegistry: true,  // Deduplication
          batchSize: 1000,
          walEnabled: true,
          cacheSize: 'small',
          indexStrategy: 'lazy'
        }
        
      case WorkloadType.OLAP:
        return {
          entityRegistry: false,
          batchSize: 10000,
          walEnabled: false,
          cacheSize: 'large',
          indexStrategy: 'eager',
          parallelQueries: true
        }
        
      case WorkloadType.BATCH:
        return {
          entityRegistry: false,
          batchSize: 50000,
          walEnabled: false,
          cacheSize: 'minimal',
          indexStrategy: 'deferred'
        }
        
      default:
        return this.defaultConfig
    }
  }
}

Hardware Adaptation 🚧 Coming Soon

Note: GPU acceleration and hardware optimization planned for Q3 2025.

CPU Optimization

Adapts to available CPU resources:

class CPUAdapter {
  async optimize() {
    const cores = os.cpus().length
    const type = os.cpus()[0].model
    
    // Parallel processing based on cores
    this.parallelism = Math.max(1, cores - 1)  // Leave one core free
    
    // SIMD detection for vector operations
    if (type.includes('Intel') || type.includes('AMD')) {
      this.enableSIMD = await checkSIMDSupport()
    }
    
    // Thread pool sizing
    this.threadPoolSize = cores * 2  // Optimal for I/O bound
    
    // Vector search optimization
    if (cores >= 8) {
      this.hnswConstruction = 200  // Higher quality index
      this.hnswSearch = 100  // More accurate search
    } else {
      this.hnswConstruction = 100  // Balanced
      this.hnswSearch = 50  // Faster search
    }
  }
}

Memory Adaptation

Intelligent memory allocation:

class MemoryAdapter {
  async configure() {
    const totalMemory = os.totalmem()
    const availableMemory = os.freemem()
    
    // Allocate based on available memory
    const allocation = {
      cache: Math.min(availableMemory * 0.25, 2 * GB),
      vectors: Math.min(availableMemory * 0.30, 4 * GB),
      indexes: Math.min(availableMemory * 0.20, 2 * GB),
      working: Math.min(availableMemory * 0.25, 2 * GB)
    }
    
    // Adjust for low memory systems
    if (totalMemory < 4 * GB) {
      allocation.cache *= 0.5
      allocation.vectors *= 0.7
      this.enableSwapping = true
    }
    
    return allocation
  }
}

GPU Acceleration

Automatic GPU detection and utilization:

class GPUAdapter {
  async detect() {
    // WebGPU in browsers
    if (navigator?.gpu) {
      const adapter = await navigator.gpu.requestAdapter()
      return {
        available: true,
        type: 'webgpu',
        memory: adapter.limits.maxBufferSize,
        compute: adapter.limits.maxComputeWorkgroupsPerDimension
      }
    }
    
    // CUDA in Node.js
    if (process.platform === 'linux' || process.platform === 'win32') {
      const hasCuda = await checkCudaSupport()
      if (hasCuda) {
        return {
          available: true,
          type: 'cuda',
          memory: await getCudaMemory(),
          compute: await getCudaCores()
        }
      }
    }
    
    return { available: false }
  }
  
  async optimize(gpu: GPUInfo) {
    if (gpu.available) {
      // Offload vector operations to GPU
      this.vectorOps = 'gpu'
      this.embeddingGeneration = 'gpu'
      this.matrixMultiplication = 'gpu'
      
      // Larger batch sizes for GPU
      this.batchSize = gpu.memory > 8 * GB ? 10000 : 1000
    }
  }
}

Network Adaptation

Bandwidth Detection

Optimizes for available network bandwidth:

class NetworkAdapter {
  async measureBandwidth() {
    const testSize = 1 * MB
    const start = Date.now()
    await this.transfer(testSize)
    const duration = Date.now() - start
    
    const bandwidth = (testSize / duration) * 1000  // bytes/sec
    
    if (bandwidth < 1 * MB) {
      // Low bandwidth - optimize
      this.compression = 'aggressive'
      this.batchTransfers = true
      this.cacheRemote = true
    } else if (bandwidth > 100 * MB) {
      // High bandwidth
      this.compression = 'minimal'
      this.parallelTransfers = true
    }
  }
}

Latency Optimization

Adapts to network latency:

class LatencyOptimizer {
  async optimize() {
    const latency = await this.measureLatency()
    
    if (latency > 100) {  // High latency
      // Batch operations
      this.minBatchSize = 100
      
      // Aggressive prefetching
      this.prefetchDepth = 3
      
      // Local caching
      this.cacheStrategy = 'aggressive'
      
      // Connection pooling
      this.connectionPool = Math.min(latency / 10, 50)
    }
  }
}

Cloud Provider Detection 🚧 Coming Soon

Note: Cloud provider auto-detection planned for Q3 2025.

Automatic Cloud Optimization

Detects and optimizes for cloud providers:

class CloudDetector {
  async detect() {
    // AWS Detection
    if (process.env.AWS_REGION || await canReachMetadata('169.254.169.254')) {
      return {
        provider: 'aws',
        instance: await getEC2InstanceType(),
        region: process.env.AWS_REGION,
        services: {
          storage: 's3',
          cache: 'elasticache',
          compute: 'lambda'
        }
      }
    }
    
    // Google Cloud Detection
    if (process.env.GOOGLE_CLOUD_PROJECT || await canReachMetadata('metadata.google.internal')) {
      return {
        provider: 'gcp',
        instance: await getGCEInstanceType(),
        region: process.env.GOOGLE_CLOUD_REGION,
        services: {
          storage: 'gcs',
          cache: 'memorystore',
          compute: 'cloud-run'
        }
      }
    }
    
    // Vercel Edge Detection
    if (process.env.VERCEL) {
      return {
        provider: 'vercel',
        region: process.env.VERCEL_REGION,
        services: {
          storage: 'vercel-kv',
          cache: 'edge-config',
          compute: 'edge-runtime'
        }
      }
    }
  }
}

Development vs Production

Automatic Environment Detection

class EnvironmentDetector {
  detect() {
    const indicators = {
      // Development indicators
      isDevelopment: 
        process.env.NODE_ENV === 'development' ||
        process.env.DEBUG ||
        process.argv.includes('--dev') ||
        isLocalhost() ||
        hasDevTools(),
        
      // Test indicators
      isTest:
        process.env.NODE_ENV === 'test' ||
        process.env.CI ||
        isTestRunner(),
        
      // Production indicators
      isProduction:
        process.env.NODE_ENV === 'production' ||
        process.env.VERCEL ||
        process.env.NETLIFY ||
        !isLocalhost()
    }
    
    return indicators
  }
}

// Different defaults for different environments
const config = environment.isProduction ? {
  storage: 'filesystem',
  wal: true,
  monitoring: true,
  compression: true,
  caching: 'aggressive'
} : {
  storage: 'memory',
  wal: false,
  monitoring: false,
  compression: false,
  caching: 'minimal'
}

Error Recovery

Automatic Fallbacks

Brainy automatically recovers from errors:

class AutoRecovery {
  async handleStorageFailure() {
    try {
      await this.primaryStorage.write(data)
    } catch (error) {
      console.warn('Primary storage failed, trying fallback')
      
      // Try secondary storage
      if (this.secondaryStorage) {
        await this.secondaryStorage.write(data)
      } else {
        // Fall back to memory
        await this.memoryStorage.write(data)
        
        // Schedule retry
        this.scheduleRetry(data)
      }
    }
  }
  
  async handleModelFailure() {
    try {
      return await this.primaryModel.embed(text)
    } catch (error) {
      // Fall back to simpler model
      return await this.fallbackModel.embed(text)
    }
  }
}

Configuration Override

While zero-config is default, you can override when needed:

// Explicit configuration when needed
const brain = new Brainy({
  // Override auto-detection
  storage: {
    type: 'filesystem',
    path: '/custom/path'
  },
  
  // Override auto-optimization
  optimization: {
    autoIndex: false,
    autoCache: false,
    autoBatch: false
  },
  
  // Override auto-scaling
  scaling: {
    maxMemory: 2 * GB,
    maxConnections: 100,
    maxBatchSize: 1000
  }
})

Monitoring Auto-Adaptation

Brainy provides visibility into its auto-adaptation:

brain.on('adaptation', (event) => {
  console.log(`Brainy adapted: ${event.type}`)
  console.log(`Reason: ${event.reason}`)
  console.log(`Before: ${JSON.stringify(event.before)}`)
  console.log(`After: ${JSON.stringify(event.after)}`)
})

// Example events:
// - Index created for frequently queried field
// - Cache strategy changed due to low hit rate
// - Batch size increased due to high throughput
// - Storage migrated due to space constraints
// - Model switched due to multilingual content

Conclusion

Brainy's zero-configuration and auto-adaptation capabilities mean you can focus on your application logic while Brainy handles:

Environment detection and optimization
Storage selection and migration
Performance tuning and scaling
Resource management
Error recovery
Workload optimization

Just create a Brainy instance and start using it. Brainy will learn, adapt, and optimize itself for your specific use case—no configuration required.