Initial commit: Brainy - Multi-Dimensional AI Database

Open source vector database with HNSW indexing, graph relationships,
and metadata facets. Features CLI with professional augmentation registry
integration for discovering extensions and capabilities.

scripts/extract-models.js

@@ -0,0 +1,201 @@
+#!/usr/bin/env node
+
+/**
+ * Extract Brainy Models Script
+ * 
+ * Automatically extracts models from @soulcraft/brainy-models during Docker builds
+ * Works across all cloud providers (Google Cloud Run, AWS Lambda/ECS, Azure Container Instances, Cloudflare Workers)
+ */
+
+import { existsSync, mkdirSync, cpSync, readFileSync, writeFileSync } from 'fs'
+import { join, dirname } from 'path'
+import { fileURLToPath } from 'url'
+
+const __filename = fileURLToPath(import.meta.url)
+const __dirname = dirname(__filename)
+
+function log(message) {
+  console.log(`[Brainy Model Extractor] ${message}`)
+}
+
+async function extractModels() {
+  try {
+    log('🔍 Checking for @soulcraft/brainy-models...')
+    
+    // Get the project root (one level up from scripts/)
+    const projectRoot = join(__dirname, '..')
+    const modelsPackagePath = join(projectRoot, 'node_modules', '@soulcraft', 'brainy-models')
+    
+    if (!existsSync(modelsPackagePath)) {
+      log('⚠️ @soulcraft/brainy-models not found - skipping model extraction')
+      log('   Models will be downloaded at runtime (slower startup)')
+      return false
+    }
+    
+    log('✅ Found @soulcraft/brainy-models package')
+    
+    // Create the models directory in the project root
+    const targetModelsDir = join(projectRoot, 'models')
+    
+    if (existsSync(targetModelsDir)) {
+      log('📁 Models directory already exists - removing old version')
+      // Remove existing models directory to ensure clean extraction
+      try {
+        import('fs').then(fs => {
+          fs.rmSync(targetModelsDir, { recursive: true, force: true })
+        })
+      } catch (error) {
+        log(`⚠️ Could not remove existing models directory: ${error.message}`)
+      }
+    }
+    
+    log('📦 Creating models directory...')
+    mkdirSync(targetModelsDir, { recursive: true })
+    
+    // Look for models in the package
+    const possibleModelsPaths = [
+      join(modelsPackagePath, 'models'),
+      join(modelsPackagePath, 'dist', 'models'),
+      modelsPackagePath // Root of the package
+    ]
+    
+    let modelsSourcePath = null
+    for (const path of possibleModelsPaths) {
+      if (existsSync(path)) {
+        // Check if this directory contains model files
+        try {
+          const fs = await import('fs')
+          const files = fs.readdirSync(path)
+          if (files.length > 0) {
+            modelsSourcePath = path
+            break
+          }
+        } catch (error) {
+          continue
+        }
+      }
+    }
+    
+    if (!modelsSourcePath) {
+      log('❌ Could not find models in @soulcraft/brainy-models package')
+      return false
+    }
+    
+    log(`📋 Copying models from: ${modelsSourcePath}`)
+    log(`📋 Copying models to: ${targetModelsDir}`)
+    
+    // Copy all models
+    try {
+      cpSync(modelsSourcePath, targetModelsDir, { 
+        recursive: true,
+        force: true,
+        filter: (src, dest) => {
+          // Skip node_modules and other unnecessary files
+          const filename = src.split('/').pop() || ''
+          return !filename.startsWith('.') && filename !== 'node_modules'
+        }
+      })
+      
+      log('✅ Models extracted successfully!')
+      
+      // Create a marker file to indicate successful extraction
+      const markerFile = join(targetModelsDir, '.brainy-models-extracted')
+      writeFileSync(markerFile, JSON.stringify({
+        extractedAt: new Date().toISOString(),
+        sourcePackage: '@soulcraft/brainy-models',
+        extractorVersion: '1.0.0'
+      }, null, 2))
+      
+      // List extracted models
+      try {
+        const fs = await import('fs')
+        const extractedItems = fs.readdirSync(targetModelsDir)
+        log(`📊 Extracted items: ${extractedItems.join(', ')}`)
+      } catch (error) {
+        log('📊 Model extraction completed (could not list contents)')
+      }
+      
+      return true
+      
+    } catch (error) {
+      log(`❌ Failed to copy models: ${error.message}`)
+      return false
+    }
+    
+  } catch (error) {
+    log(`❌ Model extraction failed: ${error.message}`)
+    return false
+  }
+}
+
+// Auto-detect environment and provide helpful information
+function detectEnvironment() {
+  const envs = []
+  
+  // Docker detection
+  if (existsSync('/.dockerenv') || process.env.DOCKER_CONTAINER) {
+    envs.push('Docker')
+  }
+  
+  // Cloud provider detection
+  if (process.env.GOOGLE_CLOUD_PROJECT || process.env.GAE_SERVICE) {
+    envs.push('Google Cloud')
+  }
+  
+  if (process.env.AWS_EXECUTION_ENV || process.env.AWS_LAMBDA_FUNCTION_NAME) {
+    envs.push('AWS')
+  }
+  
+  if (process.env.AZURE_CLIENT_ID || process.env.WEBSITE_SITE_NAME) {
+    envs.push('Azure')
+  }
+  
+  if (process.env.CF_PAGES || process.env.CLOUDFLARE_ACCOUNT_ID) {
+    envs.push('Cloudflare')
+  }
+  
+  if (process.env.VERCEL || process.env.VERCEL_ENV) {
+    envs.push('Vercel')
+  }
+  
+  if (process.env.NETLIFY || process.env.NETLIFY_BUILD_BASE) {
+    envs.push('Netlify')
+  }
+  
+  return envs
+}
+
+// Main execution
+async function main() {
+  log('🚀 Starting Brainy model extraction...')
+  
+  const detectedEnvs = detectEnvironment()
+  if (detectedEnvs.length > 0) {
+    log(`🌐 Detected environment(s): ${detectedEnvs.join(', ')}`)
+  }
+  
+  const success = await extractModels()
+  
+  if (success) {
+    log('🎉 Model extraction completed successfully!')
+    log('💡 Models are now embedded in your container/deployment')
+    log('💡 No runtime model downloads required!')
+    
+    // Set environment variable hint for runtime
+    log('💡 Runtime will automatically detect extracted models')
+  } else {
+    log('⚠️ Model extraction failed or skipped')
+    log('💡 Application will fall back to runtime model downloads')
+    log('💡 Consider installing @soulcraft/brainy-models for better performance')
+  }
+}
+
+// Run if called directly
+if (import.meta.url === `file://${process.argv[1]}`) {
+  main().catch(error => {
+    console.error('Fatal error:', error)
+    process.exit(1)
+  })
+}
+
+export { extractModels, detectEnvironment }