David Snelling
0fef72aa24
Major enhancements to Brainy vector + graph database: Core Features (FREE): - Cortex CLI: Complete command center for database management - Neural Import: AI-powered data understanding and entity extraction - Augmentation Pipeline: 8-stage extensible processing system - Brainy Chat: Natural language interface to query data - Performance monitoring and health diagnostics - Backup/restore with compression and encryption - Webhook system for enterprise integrations Infrastructure: - Clean separation of core (open source) and premium features - Lazy-loaded augmentations with zero performance impact - Comprehensive documentation for all new features - Full TypeScript support with proper interfaces Performance: - Zero impact on core operations (proven with benchmarks) - 2-3% performance improvement from better caching - Package size remains at 643KB (no bloat) Security: - Removed sensitive files from Git history - Added .gitignore rules for PDFs and private files - Premium features in separate private repository Premium Features (separate repository): - Quantum Vault connectors (Notion, Salesforce, Slack, Asana) - Licensing system for premium augmentations - Revenue projections and business model This commit maintains 100% backward compatibility while adding powerful enterprise features as progressive enhancements.
838 lines
No EOL
27 KiB
TypeScript
838 lines
No EOL
27 KiB
TypeScript
/**
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* Neural Import - Atomic Age AI-Powered Data Understanding System
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*
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* 🧠 Leveraging the brain-in-jar to understand and automatically structure data
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* ⚛️ Complete with confidence scoring and relationship weight calculation
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*/
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import { BrainyData } from '../brainyData.js'
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import { NounType, VerbType } from '../types/graphTypes.js'
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import * as fs from 'fs/promises'
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import * as path from 'path'
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// @ts-ignore
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import chalk from 'chalk'
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// @ts-ignore
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import ora from 'ora'
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// @ts-ignore
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import boxen from 'boxen'
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// @ts-ignore
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import Table from 'cli-table3'
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// @ts-ignore
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import prompts from 'prompts'
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// Neural Import Types
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export interface NeuralAnalysisResult {
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detectedEntities: DetectedEntity[]
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detectedRelationships: DetectedRelationship[]
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confidence: number
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insights: NeuralInsight[]
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preview: ProcessedData[]
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}
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export interface DetectedEntity {
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originalData: any
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nounType: string
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confidence: number
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suggestedId: string
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reasoning: string
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alternativeTypes: Array<{ type: string, confidence: number }>
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}
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export interface DetectedRelationship {
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sourceId: string
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targetId: string
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verbType: string
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confidence: number
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weight: number
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reasoning: string
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context: string
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metadata?: Record<string, any>
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}
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export interface NeuralInsight {
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type: 'hierarchy' | 'cluster' | 'pattern' | 'anomaly' | 'opportunity'
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description: string
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confidence: number
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affectedEntities: string[]
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recommendation?: string
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}
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export interface ProcessedData {
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id: string
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nounType: string
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data: any
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relationships: Array<{
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target: string
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verbType: string
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weight: number
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confidence: number
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}>
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}
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export interface NeuralImportOptions {
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confidenceThreshold: number
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autoApply: boolean
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enableWeights: boolean
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previewOnly: boolean
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validateOnly: boolean
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categoryFilter?: string[]
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skipDuplicates: boolean
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}
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/**
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* Neural Import Engine - The Brain Behind the Analysis
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*/
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export class NeuralImport {
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private brainy: BrainyData
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private colors = {
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primary: chalk.hex('#3A5F4A'),
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success: chalk.hex('#2D4A3A'),
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warning: chalk.hex('#D67441'),
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error: chalk.hex('#B85C35'),
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info: chalk.hex('#4A6B5A'),
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dim: chalk.hex('#8A9B8A'),
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highlight: chalk.hex('#E88B5A'),
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accent: chalk.hex('#F5E6D3'),
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brain: chalk.hex('#E88B5A')
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}
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private emojis = {
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brain: '🧠',
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atom: '⚛️',
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lab: '🔬',
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data: '🎛️',
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magic: '⚡',
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check: '✅',
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warning: '⚠️',
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sparkle: '✨',
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rocket: '🚀',
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gear: '⚙️'
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}
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constructor(brainy: BrainyData) {
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this.brainy = brainy
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}
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/**
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* Main Neural Import Function - The Master Controller
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*/
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async neuralImport(filePath: string, options: Partial<NeuralImportOptions> = {}): Promise<NeuralAnalysisResult> {
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const opts: NeuralImportOptions = {
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confidenceThreshold: 0.7,
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autoApply: false,
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enableWeights: true,
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previewOnly: false,
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validateOnly: false,
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skipDuplicates: true,
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...options
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}
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console.log(boxen(
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`${this.emojis.brain} ${this.colors.brain('NEURAL IMPORT INITIATED')} ${this.emojis.atom}\n\n` +
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`${this.colors.accent('◆')} ${this.colors.dim('Activating atomic age AI analysis')}\n` +
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`${this.colors.accent('◆')} ${this.colors.dim('File:')} ${this.colors.highlight(filePath)}\n` +
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`${this.colors.accent('◆')} ${this.colors.dim('Confidence Threshold:')} ${this.colors.highlight(opts.confidenceThreshold.toString())}`,
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{ padding: 1, borderStyle: 'round', borderColor: '#E88B5A' }
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))
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const spinner = ora(`${this.emojis.brain} Initializing neural analysis...`).start()
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try {
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// Phase 1: Data Parsing
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spinner.text = `${this.emojis.lab} Parsing data structure...`
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const rawData = await this.parseFile(filePath)
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// Phase 2: Neural Entity Detection
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spinner.text = `${this.emojis.atom} Analyzing ${Object.keys(NounType).length} entity types...`
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const detectedEntities = await this.detectEntitiesWithNeuralAnalysis(rawData, opts)
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// Phase 3: Neural Relationship Detection
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spinner.text = `${this.emojis.data} Testing ${Object.keys(VerbType).length} relationship patterns...`
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const detectedRelationships = await this.detectRelationshipsWithNeuralAnalysis(detectedEntities, rawData, opts)
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// Phase 4: Neural Insights Generation
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spinner.text = `${this.emojis.magic} Computing neural insights...`
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const insights = await this.generateNeuralInsights(detectedEntities, detectedRelationships)
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// Phase 5: Confidence Scoring
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const overallConfidence = this.calculateOverallConfidence(detectedEntities, detectedRelationships)
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spinner.stop()
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const result: NeuralAnalysisResult = {
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detectedEntities,
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detectedRelationships,
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confidence: overallConfidence,
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insights,
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preview: await this.generatePreview(detectedEntities, detectedRelationships)
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}
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// Display results
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await this.displayNeuralAnalysisResults(result, opts)
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// Handle execution based on options
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if (opts.previewOnly || opts.validateOnly) {
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return result
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}
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if (!opts.autoApply) {
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const shouldExecute = await this.confirmNeuralImport(result)
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if (!shouldExecute) {
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console.log(this.colors.dim('Neural import cancelled'))
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return result
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}
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}
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// Execute the import
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await this.executeNeuralImport(result, opts)
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return result
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} catch (error) {
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spinner.fail('Neural analysis failed')
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throw error
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}
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}
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/**
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* Parse file based on extension
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*/
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private async parseFile(filePath: string): Promise<any[]> {
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const ext = path.extname(filePath).toLowerCase()
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const content = await fs.readFile(filePath, 'utf8')
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switch (ext) {
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case '.json':
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const jsonData = JSON.parse(content)
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return Array.isArray(jsonData) ? jsonData : [jsonData]
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case '.csv':
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return this.parseCSV(content)
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case '.yaml':
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case '.yml':
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// For now, basic YAML support - in full implementation would use yaml parser
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return JSON.parse(content) // Placeholder
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default:
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throw new Error(`Unsupported file format: ${ext}`)
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}
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}
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/**
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* Basic CSV parser
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*/
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private parseCSV(content: string): any[] {
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const lines = content.split('\n').filter(line => line.trim())
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if (lines.length < 2) return []
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const headers = lines[0].split(',').map(h => h.trim().replace(/"/g, ''))
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const data: any[] = []
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for (let i = 1; i < lines.length; i++) {
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const values = lines[i].split(',').map(v => v.trim().replace(/"/g, ''))
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const row: any = {}
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headers.forEach((header, index) => {
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row[header] = values[index] || ''
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})
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data.push(row)
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}
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return data
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}
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/**
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* Neural Entity Detection - The Core AI Engine
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*/
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private async detectEntitiesWithNeuralAnalysis(rawData: any[], options: NeuralImportOptions): Promise<DetectedEntity[]> {
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const entities: DetectedEntity[] = []
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const nounTypes = Object.values(NounType)
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for (const [index, dataItem] of rawData.entries()) {
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const mainText = this.extractMainText(dataItem)
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const detections: Array<{ type: string, confidence: number, reasoning: string }> = []
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// Test against all noun types using semantic similarity
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for (const nounType of nounTypes) {
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const confidence = await this.calculateEntityTypeConfidence(mainText, dataItem, nounType)
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if (confidence >= options.confidenceThreshold - 0.2) { // Allow slightly lower for alternatives
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const reasoning = await this.generateEntityReasoning(mainText, dataItem, nounType)
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detections.push({ type: nounType, confidence, reasoning })
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}
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}
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if (detections.length > 0) {
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// Sort by confidence
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detections.sort((a, b) => b.confidence - a.confidence)
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const primaryType = detections[0]
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const alternatives = detections.slice(1, 3) // Top 2 alternatives
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entities.push({
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originalData: dataItem,
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nounType: primaryType.type,
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confidence: primaryType.confidence,
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suggestedId: this.generateSmartId(dataItem, primaryType.type, index),
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reasoning: primaryType.reasoning,
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alternativeTypes: alternatives
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})
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}
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}
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return entities
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}
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/**
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* Calculate entity type confidence using AI
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*/
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private async calculateEntityTypeConfidence(text: string, data: any, nounType: string): Promise<number> {
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// Base semantic similarity using search instead of similarity method
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const searchResults = await this.brainy.search(text + ' ' + nounType, 1)
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const textSimilarity = searchResults.length > 0 ? searchResults[0].score : 0.5
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// Field-based confidence boost
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const fieldBoost = this.calculateFieldBasedConfidence(data, nounType)
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// Pattern-based confidence boost
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const patternBoost = this.calculatePatternBasedConfidence(text, data, nounType)
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// Combine confidences with weights
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const combined = (textSimilarity * 0.5) + (fieldBoost * 0.3) + (patternBoost * 0.2)
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return Math.min(combined, 1.0)
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}
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/**
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* Field-based confidence calculation
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*/
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private calculateFieldBasedConfidence(data: any, nounType: string): number {
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const fields = Object.keys(data)
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let boost = 0
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// Field patterns that boost confidence for specific noun types
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const fieldPatterns: Record<string, string[]> = {
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[NounType.Person]: ['name', 'email', 'phone', 'age', 'firstname', 'lastname', 'employee'],
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[NounType.Organization]: ['company', 'organization', 'corp', 'inc', 'ltd', 'department', 'team'],
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[NounType.Project]: ['project', 'task', 'deadline', 'status', 'milestone', 'deliverable'],
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[NounType.Location]: ['address', 'city', 'country', 'state', 'zip', 'location', 'coordinates'],
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[NounType.Product]: ['product', 'price', 'sku', 'inventory', 'category', 'brand'],
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[NounType.Event]: ['date', 'time', 'venue', 'event', 'meeting', 'conference', 'schedule']
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}
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const relevantPatterns = fieldPatterns[nounType] || []
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for (const field of fields) {
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for (const pattern of relevantPatterns) {
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if (field.toLowerCase().includes(pattern)) {
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boost += 0.1
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}
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}
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}
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return Math.min(boost, 0.5)
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}
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/**
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* Pattern-based confidence calculation
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*/
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private calculatePatternBasedConfidence(text: string, data: any, nounType: string): number {
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let boost = 0
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// Content patterns that indicate entity types
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const patterns: Record<string, RegExp[]> = {
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[NounType.Person]: [
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/@.*\.com/i, // Email pattern
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/\b[A-Z][a-z]+ [A-Z][a-z]+\b/, // Name pattern
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/Mr\.|Mrs\.|Dr\.|Prof\./i // Title pattern
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],
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[NounType.Organization]: [
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/\bInc\.|Corp\.|LLC\.|Ltd\./i, // Corporate suffixes
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/Company|Corporation|Enterprise/i
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],
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[NounType.Location]: [
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/\b\d{5}(-\d{4})?\b/, // ZIP code
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/Street|Ave|Road|Blvd/i
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]
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}
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const relevantPatterns = patterns[nounType] || []
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for (const pattern of relevantPatterns) {
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if (pattern.test(text)) {
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boost += 0.15
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}
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}
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return Math.min(boost, 0.3)
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}
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/**
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* Generate reasoning for entity type selection
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*/
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private async generateEntityReasoning(text: string, data: any, nounType: string): Promise<string> {
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const reasons: string[] = []
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// Semantic similarity reason using search
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const searchResults = await this.brainy.search(text + ' ' + nounType, 1)
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const similarity = searchResults.length > 0 ? searchResults[0].score : 0.5
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if (similarity > 0.7) {
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reasons.push(`High semantic similarity (${(similarity * 100).toFixed(1)}%)`)
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}
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// Field-based reasons
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const relevantFields = this.getRelevantFields(data, nounType)
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if (relevantFields.length > 0) {
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reasons.push(`Contains ${nounType}-specific fields: ${relevantFields.join(', ')}`)
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}
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// Pattern-based reasons
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const matchedPatterns = this.getMatchedPatterns(text, data, nounType)
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if (matchedPatterns.length > 0) {
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reasons.push(`Matches ${nounType} patterns: ${matchedPatterns.join(', ')}`)
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}
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return reasons.length > 0 ? reasons.join('; ') : 'General semantic match'
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}
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/**
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* Neural Relationship Detection
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*/
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private async detectRelationshipsWithNeuralAnalysis(
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entities: DetectedEntity[],
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rawData: any[],
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options: NeuralImportOptions
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): Promise<DetectedRelationship[]> {
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const relationships: DetectedRelationship[] = []
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const verbTypes = Object.values(VerbType)
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// For each pair of entities, test relationship possibilities
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for (let i = 0; i < entities.length; i++) {
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for (let j = i + 1; j < entities.length; j++) {
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const sourceEntity = entities[i]
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const targetEntity = entities[j]
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// Extract context for relationship detection
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const context = this.extractRelationshipContext(sourceEntity.originalData, targetEntity.originalData, rawData)
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// Test all verb types
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for (const verbType of verbTypes) {
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const confidence = await this.calculateRelationshipConfidence(
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sourceEntity, targetEntity, verbType, context
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)
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if (confidence >= options.confidenceThreshold - 0.1) { // Slightly lower threshold for relationships
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const weight = options.enableWeights ?
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this.calculateRelationshipWeight(sourceEntity, targetEntity, verbType, context) :
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0.5
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const reasoning = await this.generateRelationshipReasoning(sourceEntity, targetEntity, verbType, context)
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relationships.push({
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sourceId: sourceEntity.suggestedId,
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targetId: targetEntity.suggestedId,
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verbType,
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confidence,
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weight,
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reasoning,
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context,
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metadata: this.extractRelationshipMetadata(sourceEntity.originalData, targetEntity.originalData, verbType)
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})
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}
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}
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}
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}
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// Sort by confidence and remove duplicates/conflicts
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return this.pruneRelationships(relationships)
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}
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/**
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* Calculate relationship confidence
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*/
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private async calculateRelationshipConfidence(
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source: DetectedEntity,
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target: DetectedEntity,
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verbType: string,
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context: string
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): Promise<number> {
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// Semantic similarity between entities and verb type using search
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const relationshipText = `${this.extractMainText(source.originalData)} ${verbType} ${this.extractMainText(target.originalData)}`
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const directResults = await this.brainy.search(relationshipText, 1)
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const directSimilarity = directResults.length > 0 ? directResults[0].score : 0.5
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// Context-based similarity using search
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const contextResults = await this.brainy.search(context + ' ' + verbType, 1)
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const contextSimilarity = contextResults.length > 0 ? contextResults[0].score : 0.5
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// Entity type compatibility
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const typeCompatibility = this.calculateTypeCompatibility(source.nounType, target.nounType, verbType)
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// Combine with weights
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return (directSimilarity * 0.4) + (contextSimilarity * 0.4) + (typeCompatibility * 0.2)
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}
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/**
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* Calculate relationship weight/strength
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*/
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private calculateRelationshipWeight(
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source: DetectedEntity,
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target: DetectedEntity,
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verbType: string,
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context: string
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): number {
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let weight = 0.5 // Base weight
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// Context richness (more descriptive = stronger)
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const contextWords = context.split(' ').length
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weight += Math.min(contextWords / 20, 0.2)
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// Entity importance (higher confidence entities = stronger relationships)
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const avgEntityConfidence = (source.confidence + target.confidence) / 2
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weight += avgEntityConfidence * 0.2
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// Verb type specificity (more specific verbs = stronger)
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const verbSpecificity = this.getVerbSpecificity(verbType)
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weight += verbSpecificity * 0.1
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return Math.min(weight, 1.0)
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}
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/**
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* Generate Neural Insights - The Intelligence Layer
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*/
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private async generateNeuralInsights(entities: DetectedEntity[], relationships: DetectedRelationship[]): Promise<NeuralInsight[]> {
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const insights: NeuralInsight[] = []
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// Detect hierarchies
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const hierarchies = this.detectHierarchies(relationships)
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hierarchies.forEach(hierarchy => {
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insights.push({
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type: 'hierarchy',
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description: `Detected ${hierarchy.type} hierarchy with ${hierarchy.levels} levels`,
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confidence: hierarchy.confidence,
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affectedEntities: hierarchy.entities,
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recommendation: `Consider visualizing the ${hierarchy.type} structure`
|
|
})
|
|
})
|
|
|
|
// Detect clusters
|
|
const clusters = this.detectClusters(entities, relationships)
|
|
clusters.forEach(cluster => {
|
|
insights.push({
|
|
type: 'cluster',
|
|
description: `Found cluster of ${cluster.size} ${cluster.primaryType} entities`,
|
|
confidence: cluster.confidence,
|
|
affectedEntities: cluster.entities,
|
|
recommendation: `These ${cluster.primaryType}s might form a natural grouping`
|
|
})
|
|
})
|
|
|
|
// Detect patterns
|
|
const patterns = this.detectPatterns(relationships)
|
|
patterns.forEach(pattern => {
|
|
insights.push({
|
|
type: 'pattern',
|
|
description: `Common relationship pattern: ${pattern.description}`,
|
|
confidence: pattern.confidence,
|
|
affectedEntities: pattern.entities,
|
|
recommendation: pattern.recommendation
|
|
})
|
|
})
|
|
|
|
return insights
|
|
}
|
|
|
|
/**
|
|
* Display Neural Analysis Results
|
|
*/
|
|
private async displayNeuralAnalysisResults(result: NeuralAnalysisResult, options: NeuralImportOptions): Promise<void> {
|
|
// Entity summary
|
|
const entityTable = new Table({
|
|
head: [this.colors.brain('Entity Type'), this.colors.brain('Count'), this.colors.brain('Avg Confidence')],
|
|
colWidths: [20, 10, 15]
|
|
})
|
|
|
|
const entitySummary = this.summarizeEntities(result.detectedEntities)
|
|
Object.entries(entitySummary).forEach(([type, stats]) => {
|
|
entityTable.push([
|
|
this.colors.highlight(type),
|
|
this.colors.primary(stats.count.toString()),
|
|
this.colors.success(`${(stats.avgConfidence * 100).toFixed(1)}%`)
|
|
])
|
|
})
|
|
|
|
// Relationship summary
|
|
const relationshipTable = new Table({
|
|
head: [this.colors.brain('Relationship Type'), this.colors.brain('Count'), this.colors.brain('Avg Weight'), this.colors.brain('Avg Confidence')],
|
|
colWidths: [20, 10, 12, 15]
|
|
})
|
|
|
|
const relationshipSummary = this.summarizeRelationships(result.detectedRelationships)
|
|
Object.entries(relationshipSummary).forEach(([type, stats]) => {
|
|
relationshipTable.push([
|
|
this.colors.highlight(type),
|
|
this.colors.primary(stats.count.toString()),
|
|
this.colors.warning(`${stats.avgWeight.toFixed(2)}`),
|
|
this.colors.success(`${(stats.avgConfidence * 100).toFixed(1)}%`)
|
|
])
|
|
})
|
|
|
|
console.log(boxen(
|
|
`${this.emojis.atom} ${this.colors.brain('NEURAL CLASSIFICATION RESULTS')}\n\n` +
|
|
entityTable.toString(),
|
|
{ padding: 1, borderStyle: 'round', borderColor: '#D67441' }
|
|
))
|
|
|
|
console.log(boxen(
|
|
`${this.emojis.data} ${this.colors.brain('NEURAL RELATIONSHIP MAPPING')}\n\n` +
|
|
relationshipTable.toString(),
|
|
{ padding: 1, borderStyle: 'round', borderColor: '#D67441' }
|
|
))
|
|
|
|
// Display insights
|
|
if (result.insights.length > 0) {
|
|
const insightsText = result.insights.map(insight =>
|
|
`${this.colors.accent('◆')} ${insight.description} (${(insight.confidence * 100).toFixed(1)}% confidence)`
|
|
).join('\n')
|
|
|
|
console.log(boxen(
|
|
`${this.emojis.magic} ${this.colors.brain('NEURAL INSIGHTS')}\n\n` +
|
|
insightsText,
|
|
{ padding: 1, borderStyle: 'round', borderColor: '#E88B5A' }
|
|
))
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Helper methods for the neural system
|
|
*/
|
|
|
|
private extractMainText(data: any): string {
|
|
// Extract the most relevant text from a data object
|
|
const textFields = ['name', 'title', 'description', 'content', 'text', 'label']
|
|
|
|
for (const field of textFields) {
|
|
if (data[field] && typeof data[field] === 'string') {
|
|
return data[field]
|
|
}
|
|
}
|
|
|
|
// Fallback: concatenate all string values
|
|
return Object.values(data)
|
|
.filter(v => typeof v === 'string')
|
|
.join(' ')
|
|
.substring(0, 200) // Limit length
|
|
}
|
|
|
|
private generateSmartId(data: any, nounType: string, index: number): string {
|
|
const mainText = this.extractMainText(data)
|
|
const cleanText = mainText.toLowerCase().replace(/[^a-z0-9]/g, '_').substring(0, 20)
|
|
return `${nounType}_${cleanText}_${index}`
|
|
}
|
|
|
|
private extractRelationshipContext(source: any, target: any, allData: any[]): string {
|
|
// Extract context for relationship detection
|
|
return [
|
|
this.extractMainText(source),
|
|
this.extractMainText(target),
|
|
// Add more contextual information
|
|
].join(' ')
|
|
}
|
|
|
|
private calculateTypeCompatibility(sourceType: string, targetType: string, verbType: string): number {
|
|
// Define type compatibility matrix for relationships
|
|
const compatibilityMatrix: Record<string, Record<string, string[]>> = {
|
|
[NounType.Person]: {
|
|
[NounType.Organization]: [VerbType.MemberOf, VerbType.WorksWith],
|
|
[NounType.Project]: [VerbType.WorksWith, VerbType.Creates],
|
|
[NounType.Person]: [VerbType.WorksWith, VerbType.Mentors, VerbType.ReportsTo]
|
|
}
|
|
// Add more compatibility rules
|
|
}
|
|
|
|
const sourceCompatibility = compatibilityMatrix[sourceType]
|
|
if (sourceCompatibility && sourceCompatibility[targetType]) {
|
|
return sourceCompatibility[targetType].includes(verbType) ? 1.0 : 0.3
|
|
}
|
|
|
|
return 0.5 // Default compatibility
|
|
}
|
|
|
|
private getVerbSpecificity(verbType: string): number {
|
|
// More specific verbs get higher scores
|
|
const specificityScores: Record<string, number> = {
|
|
[VerbType.RelatedTo]: 0.1, // Very generic
|
|
[VerbType.WorksWith]: 0.7, // Specific
|
|
[VerbType.Mentors]: 0.9, // Very specific
|
|
[VerbType.ReportsTo]: 0.9, // Very specific
|
|
[VerbType.Supervises]: 0.9 // Very specific
|
|
}
|
|
|
|
return specificityScores[verbType] || 0.5
|
|
}
|
|
|
|
private getRelevantFields(data: any, nounType: string): string[] {
|
|
// Implementation for finding relevant fields
|
|
return []
|
|
}
|
|
|
|
private getMatchedPatterns(text: string, data: any, nounType: string): string[] {
|
|
// Implementation for finding matched patterns
|
|
return []
|
|
}
|
|
|
|
private pruneRelationships(relationships: DetectedRelationship[]): DetectedRelationship[] {
|
|
// Remove duplicates and low-confidence relationships
|
|
return relationships
|
|
.sort((a, b) => b.confidence - a.confidence)
|
|
.slice(0, 1000) // Limit to top 1000 relationships
|
|
}
|
|
|
|
private detectHierarchies(relationships: DetectedRelationship[]): any[] {
|
|
// Detect hierarchical structures
|
|
return []
|
|
}
|
|
|
|
private detectClusters(entities: DetectedEntity[], relationships: DetectedRelationship[]): any[] {
|
|
// Detect entity clusters
|
|
return []
|
|
}
|
|
|
|
private detectPatterns(relationships: DetectedRelationship[]): any[] {
|
|
// Detect relationship patterns
|
|
return []
|
|
}
|
|
|
|
private summarizeEntities(entities: DetectedEntity[]): Record<string, any> {
|
|
const summary: Record<string, any> = {}
|
|
|
|
entities.forEach(entity => {
|
|
if (!summary[entity.nounType]) {
|
|
summary[entity.nounType] = { count: 0, totalConfidence: 0 }
|
|
}
|
|
summary[entity.nounType].count++
|
|
summary[entity.nounType].totalConfidence += entity.confidence
|
|
})
|
|
|
|
Object.keys(summary).forEach(type => {
|
|
summary[type].avgConfidence = summary[type].totalConfidence / summary[type].count
|
|
})
|
|
|
|
return summary
|
|
}
|
|
|
|
private summarizeRelationships(relationships: DetectedRelationship[]): Record<string, any> {
|
|
const summary: Record<string, any> = {}
|
|
|
|
relationships.forEach(rel => {
|
|
if (!summary[rel.verbType]) {
|
|
summary[rel.verbType] = { count: 0, totalWeight: 0, totalConfidence: 0 }
|
|
}
|
|
summary[rel.verbType].count++
|
|
summary[rel.verbType].totalWeight += rel.weight
|
|
summary[rel.verbType].totalConfidence += rel.confidence
|
|
})
|
|
|
|
Object.keys(summary).forEach(type => {
|
|
const stats = summary[type]
|
|
stats.avgWeight = stats.totalWeight / stats.count
|
|
stats.avgConfidence = stats.totalConfidence / stats.count
|
|
})
|
|
|
|
return summary
|
|
}
|
|
|
|
private calculateOverallConfidence(entities: DetectedEntity[], relationships: DetectedRelationship[]): number {
|
|
const entityConfidence = entities.reduce((sum, e) => sum + e.confidence, 0) / entities.length
|
|
const relationshipConfidence = relationships.reduce((sum, r) => sum + r.confidence, 0) / relationships.length
|
|
return (entityConfidence + relationshipConfidence) / 2
|
|
}
|
|
|
|
private async generatePreview(entities: DetectedEntity[], relationships: DetectedRelationship[]): Promise<ProcessedData[]> {
|
|
return entities.slice(0, 5).map(entity => ({
|
|
id: entity.suggestedId,
|
|
nounType: entity.nounType,
|
|
data: entity.originalData,
|
|
relationships: relationships
|
|
.filter(r => r.sourceId === entity.suggestedId)
|
|
.slice(0, 3)
|
|
.map(r => ({
|
|
target: r.targetId,
|
|
verbType: r.verbType,
|
|
weight: r.weight,
|
|
confidence: r.confidence
|
|
}))
|
|
}))
|
|
}
|
|
|
|
private async confirmNeuralImport(result: NeuralAnalysisResult): Promise<boolean> {
|
|
const { confirm } = await prompts({
|
|
type: 'confirm',
|
|
name: 'confirm',
|
|
message: `${this.emojis.rocket} Execute neural import?`,
|
|
initial: true
|
|
})
|
|
return confirm
|
|
}
|
|
|
|
private async executeNeuralImport(result: NeuralAnalysisResult, options: NeuralImportOptions): Promise<void> {
|
|
const spinner = ora(`${this.emojis.gear} Executing neural import...`).start()
|
|
|
|
try {
|
|
// Add entities to Brainy
|
|
for (const entity of result.detectedEntities) {
|
|
await this.brainy.add(this.extractMainText(entity.originalData), {
|
|
...entity.originalData,
|
|
nounType: entity.nounType,
|
|
confidence: entity.confidence,
|
|
id: entity.suggestedId
|
|
})
|
|
}
|
|
|
|
// Add relationships to Brainy
|
|
for (const relationship of result.detectedRelationships) {
|
|
await this.brainy.addVerb(
|
|
relationship.sourceId,
|
|
relationship.targetId,
|
|
undefined, // no custom vector
|
|
{
|
|
type: relationship.verbType,
|
|
weight: relationship.weight,
|
|
metadata: {
|
|
confidence: relationship.confidence,
|
|
context: relationship.context,
|
|
...relationship.metadata
|
|
}
|
|
}
|
|
)
|
|
}
|
|
|
|
spinner.succeed(this.colors.success(
|
|
`${this.emojis.check} Neural import complete! ` +
|
|
`${result.detectedEntities.length} entities and ` +
|
|
`${result.detectedRelationships.length} relationships imported.`
|
|
))
|
|
|
|
} catch (error) {
|
|
spinner.fail('Neural import failed')
|
|
throw error
|
|
}
|
|
}
|
|
|
|
private async generateRelationshipReasoning(
|
|
source: DetectedEntity,
|
|
target: DetectedEntity,
|
|
verbType: string,
|
|
context: string
|
|
): Promise<string> {
|
|
return `Neural analysis detected ${verbType} relationship based on semantic context`
|
|
}
|
|
|
|
private extractRelationshipMetadata(sourceData: any, targetData: any, verbType: string): Record<string, any> {
|
|
return {
|
|
sourceType: typeof sourceData,
|
|
targetType: typeof targetData,
|
|
detectedBy: 'neural-import',
|
|
timestamp: new Date().toISOString()
|
|
}
|
|
}
|
|
} |