618 lines
27 KiB
JavaScript
618 lines
27 KiB
JavaScript
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/**
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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 { NounType, VerbType } from '../types/graphTypes.js';
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import * as fs from '../universal/fs.js';
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import * as path from '../universal/path.js';
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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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/**
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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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constructor(brainy) {
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this.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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this.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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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, options = {}) {
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const opts = {
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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(`${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:')} ${this.colors.highlight(opts.confidenceThreshold.toString())}`, { padding: 1, borderStyle: 'round', borderColor: '#E88B5A' }));
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const spinner = ora(`${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 = {
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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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}
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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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async parseFile(filePath) {
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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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parseCSV(content) {
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const lines = content.split('\n').filter(line => line.trim());
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if (lines.length < 2)
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return [];
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const headers = lines[0].split(',').map(h => h.trim().replace(/"/g, ''));
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const data = [];
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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 = {};
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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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async detectEntitiesWithNeuralAnalysis(rawData, options) {
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const entities = [];
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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 = [];
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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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async calculateEntityTypeConfidence(text, data, nounType) {
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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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calculateFieldBasedConfidence(data, nounType) {
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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 = {
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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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calculatePatternBasedConfidence(text, data, nounType) {
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let boost = 0;
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// Content patterns that indicate entity types
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const patterns = {
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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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async generateEntityReasoning(text, data, nounType) {
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const reasons = [];
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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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async detectRelationshipsWithNeuralAnalysis(entities, rawData, options) {
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const relationships = [];
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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(sourceEntity, targetEntity, verbType, context);
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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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async calculateRelationshipConfidence(source, target, verbType, context) {
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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);
|
|||
|
|
}
|
|||
|
|
/**
|
|||
|
|
* Calculate relationship weight/strength
|
|||
|
|
*/
|
|||
|
|
calculateRelationshipWeight(source, target, verbType, context) {
|
|||
|
|
let weight = 0.5; // Base weight
|
|||
|
|
// Context richness (more descriptive = stronger)
|
|||
|
|
const contextWords = context.split(' ').length;
|
|||
|
|
weight += Math.min(contextWords / 20, 0.2);
|
|||
|
|
// Entity importance (higher confidence entities = stronger relationships)
|
|||
|
|
const avgEntityConfidence = (source.confidence + target.confidence) / 2;
|
|||
|
|
weight += avgEntityConfidence * 0.2;
|
|||
|
|
// Verb type specificity (more specific verbs = stronger)
|
|||
|
|
const verbSpecificity = this.getVerbSpecificity(verbType);
|
|||
|
|
weight += verbSpecificity * 0.1;
|
|||
|
|
return Math.min(weight, 1.0);
|
|||
|
|
}
|
|||
|
|
/**
|
|||
|
|
* Generate Neural Insights - The Intelligence Layer
|
|||
|
|
*/
|
|||
|
|
async generateNeuralInsights(entities, relationships) {
|
|||
|
|
const insights = [];
|
|||
|
|
// Detect hierarchies
|
|||
|
|
const hierarchies = this.detectHierarchies(relationships);
|
|||
|
|
hierarchies.forEach(hierarchy => {
|
|||
|
|
insights.push({
|
|||
|
|
type: 'hierarchy',
|
|||
|
|
description: `Detected ${hierarchy.type} hierarchy with ${hierarchy.levels} levels`,
|
|||
|
|
confidence: hierarchy.confidence,
|
|||
|
|
affectedEntities: hierarchy.entities,
|
|||
|
|
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: ${pattern.description}`,
|
|||
|
|
confidence: pattern.confidence,
|
|||
|
|
affectedEntities: pattern.entities,
|
|||
|
|
recommendation: pattern.recommendation
|
|||
|
|
});
|
|||
|
|
});
|
|||
|
|
return insights;
|
|||
|
|
}
|
|||
|
|
/**
|
|||
|
|
* Display Neural Analysis Results
|
|||
|
|
*/
|
|||
|
|
async displayNeuralAnalysisResults(result, options) {
|
|||
|
|
// Entity summary
|
|||
|
|
const entityTable = new Table({
|
|||
|
|
head: [this.colors.brain('Entity Type'), this.colors.brain('Count'), this.colors.brain('Avg Confidence')],
|
|||
|
|
colWidths: [20, 10, 15]
|
|||
|
|
});
|
|||
|
|
const entitySummary = this.summarizeEntities(result.detectedEntities);
|
|||
|
|
Object.entries(entitySummary).forEach(([type, stats]) => {
|
|||
|
|
entityTable.push([
|
|||
|
|
this.colors.highlight(type),
|
|||
|
|
this.colors.primary(stats.count.toString()),
|
|||
|
|
this.colors.success(`${(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} ${this.colors.brain('NEURAL INSIGHTS')}\n\n` +
|
|||
|
|
insightsText, { padding: 1, borderStyle: 'round', borderColor: '#E88B5A' }));
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
/**
|
|||
|
|
* Helper methods for the neural system
|
|||
|
|
*/
|
|||
|
|
extractMainText(data) {
|
|||
|
|
// Extract the most relevant text from a data object
|
|||
|
|
const textFields = ['name', 'title', 'description', 'content', 'text', 'label'];
|
|||
|
|
for (const field of textFields) {
|
|||
|
|
if (data[field] && typeof data[field] === 'string') {
|
|||
|
|
return data[field];
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
// Fallback: concatenate all string values
|
|||
|
|
return Object.values(data)
|
|||
|
|
.filter(v => typeof v === 'string')
|
|||
|
|
.join(' ')
|
|||
|
|
.substring(0, 200); // Limit length
|
|||
|
|
}
|
|||
|
|
generateSmartId(data, nounType, index) {
|
|||
|
|
const mainText = this.extractMainText(data);
|
|||
|
|
const cleanText = mainText.toLowerCase().replace(/[^a-z0-9]/g, '_').substring(0, 20);
|
|||
|
|
return `${nounType}_${cleanText}_${index}`;
|
|||
|
|
}
|
|||
|
|
extractRelationshipContext(source, target, allData) {
|
|||
|
|
// Extract context for relationship detection
|
|||
|
|
return [
|
|||
|
|
this.extractMainText(source),
|
|||
|
|
this.extractMainText(target),
|
|||
|
|
// Add more contextual information
|
|||
|
|
].join(' ');
|
|||
|
|
}
|
|||
|
|
calculateTypeCompatibility(sourceType, targetType, verbType) {
|
|||
|
|
// Define type compatibility matrix for relationships
|
|||
|
|
const compatibilityMatrix = {
|
|||
|
|
[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
|
|||
|
|
}
|
|||
|
|
getVerbSpecificity(verbType) {
|
|||
|
|
// More specific verbs get higher scores
|
|||
|
|
const specificityScores = {
|
|||
|
|
[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;
|
|||
|
|
}
|
|||
|
|
getRelevantFields(data, nounType) {
|
|||
|
|
// Implementation for finding relevant fields
|
|||
|
|
return [];
|
|||
|
|
}
|
|||
|
|
getMatchedPatterns(text, data, nounType) {
|
|||
|
|
// Implementation for finding matched patterns
|
|||
|
|
return [];
|
|||
|
|
}
|
|||
|
|
pruneRelationships(relationships) {
|
|||
|
|
// Remove duplicates and low-confidence relationships
|
|||
|
|
return relationships
|
|||
|
|
.sort((a, b) => b.confidence - a.confidence)
|
|||
|
|
.slice(0, 1000); // Limit to top 1000 relationships
|
|||
|
|
}
|
|||
|
|
detectHierarchies(relationships) {
|
|||
|
|
// Detect hierarchical structures
|
|||
|
|
return [];
|
|||
|
|
}
|
|||
|
|
detectClusters(entities, relationships) {
|
|||
|
|
// Detect entity clusters
|
|||
|
|
return [];
|
|||
|
|
}
|
|||
|
|
detectPatterns(relationships) {
|
|||
|
|
// Detect relationship patterns
|
|||
|
|
return [];
|
|||
|
|
}
|
|||
|
|
summarizeEntities(entities) {
|
|||
|
|
const summary = {};
|
|||
|
|
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;
|
|||
|
|
}
|
|||
|
|
summarizeRelationships(relationships) {
|
|||
|
|
const summary = {};
|
|||
|
|
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;
|
|||
|
|
}
|
|||
|
|
calculateOverallConfidence(entities, relationships) {
|
|||
|
|
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;
|
|||
|
|
}
|
|||
|
|
async generatePreview(entities, relationships) {
|
|||
|
|
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
|
|||
|
|
}))
|
|||
|
|
}));
|
|||
|
|
}
|
|||
|
|
async confirmNeuralImport(result) {
|
|||
|
|
const { confirm } = await prompts({
|
|||
|
|
type: 'confirm',
|
|||
|
|
name: 'confirm',
|
|||
|
|
message: `${this.emojis.rocket} Execute neural import?`,
|
|||
|
|
initial: true
|
|||
|
|
});
|
|||
|
|
return confirm;
|
|||
|
|
}
|
|||
|
|
async executeNeuralImport(result, options) {
|
|||
|
|
const spinner = ora(`${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, 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;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
async generateRelationshipReasoning(source, target, verbType, context) {
|
|||
|
|
return `Neural analysis detected ${verbType} relationship based on semantic context`;
|
|||
|
|
}
|
|||
|
|
extractRelationshipMetadata(sourceData, targetData, verbType) {
|
|||
|
|
return {
|
|||
|
|
sourceType: typeof sourceData,
|
|||
|
|
targetType: typeof targetData,
|
|||
|
|
detectedBy: 'neural-import',
|
|||
|
|
timestamp: new Date().toISOString()
|
|||
|
|
};
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
//# sourceMappingURL=neuralImport.js.map
|