David Snelling
0f4ab52ad9
Major enhancements for type safety and developer experience: - Add BrainyTypes static API for type management and AI-powered suggestions - Implement strict type validation for all 31 NounType categories - Remove dangerous generic add() method that bypassed type safety - Add intelligent type inference with confidence scoring - Provide helpful error messages with typo suggestions using Levenshtein distance - Update all internal code, examples, and documentation to use typed methods - Enhance CLI with new type management commands (types, suggest, validate) Breaking changes: - Remove deprecated add() method - use addNoun() with explicit type parameter - All addNoun() calls now require explicit type as second parameter This release significantly improves type safety across the entire system while maintaining backward compatibility for properly typed method calls. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
77 lines
No EOL
2.2 KiB
JavaScript
77 lines
No EOL
2.2 KiB
JavaScript
// Demo: Neural Type Inference vs Basic Pattern Matching
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import {
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inferNounTypeFromMetadata,
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inferNounTypeNeural
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} from './dist/utils/typeValidation.js'
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console.log('🧠 Brainy Type Inference: Pattern vs Neural\n')
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console.log('=' .repeat(50) + '\n')
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// Test cases showing the difference
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const testCases = [
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{
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name: 'Simple Person (both work)',
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data: {
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email: 'john@example.com',
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name: 'John Doe'
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}
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},
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{
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name: 'Complex Role (neural understands context)',
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data: {
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title: 'Engineering Manager',
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responsibilities: 'Leads team, reviews code, mentors developers',
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department: 'Technology'
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}
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},
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{
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name: 'Ambiguous Entity (neural uses semantic understanding)',
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data: {
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name: 'Tesla',
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founded: 2003,
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employees: 127855,
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products: ['Model S', 'Model 3', 'Model X']
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}
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},
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{
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name: 'Scientific Content (neural recognizes research)',
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data: {
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title: 'Effects of quantum entanglement on superconductivity',
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abstract: 'This study examines the relationship between quantum states...',
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methodology: 'Double-blind controlled experiment',
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results: 'Statistical significance p<0.05'
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}
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},
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{
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name: 'Legal Document (neural understands context)',
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data: {
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parties: ['Company A', 'Company B'],
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effectiveDate: '2024-01-01',
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terms: 'Non-disclosure agreement',
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jurisdiction: 'California'
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}
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}
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]
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async function runComparison() {
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for (const testCase of testCases) {
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console.log(`📝 Test: ${testCase.name}`)
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console.log(` Data: ${JSON.stringify(testCase.data, null, 2).split('\n').join('\n ')}`)
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// Basic pattern matching (synchronous)
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const basicType = inferNounTypeFromMetadata(testCase.data)
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console.log(` 🔍 Basic Pattern Match: ${basicType || 'content (default)'}`)
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// Neural inference (async, uses embeddings)
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const neuralType = await inferNounTypeNeural(testCase.data)
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console.log(` 🧠 Neural Inference: ${neuralType}`)
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if (basicType !== neuralType) {
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console.log(` ✨ Neural found better match!`)
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}
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console.log()
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}
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}
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runComparison().catch(console.error) |