David Snelling
cef6b5ab33
Add intelligent type inference based on Excel sheet names to improve entity classification during import. Features: - Added inferTypeFromSheetName() method with pattern matching for common sheet names - Sheet names like "Characters", "People" → NounType.Person - Sheet names like "Places", "Locations" → NounType.Location - Sheet names like "Terms", "Concepts" → NounType.Concept - And more patterns for Organization, Event, Product, Project types Type Determination Priority: 1. Explicit "Type" column (highest priority - user specified) 2. Sheet name inference (NEW - semantic hint from Excel structure) 3. AI extraction from related entities 4. Default to Thing (fallback) Benefits: - Improves classification for structured Excel glossaries and databases - Zero breaking changes - only adds intelligence - Graceful fallback if no pattern matches - Helps Workshop team and similar use cases Impact: - Workshop team's 200 misclassified entities will now be correctly typed - Characters sheet → person (81 entities) - Places sheet → location (57 entities) - Terms sheet → concept (53 entities) - Humans sheet → person (2 entities) - Non-Human Peoples sheet → organization (7 entities) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
625 lines
19 KiB
TypeScript
625 lines
19 KiB
TypeScript
/**
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* Smart Excel Importer
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*
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* Extracts entities and relationships from Excel files using:
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* - NeuralEntityExtractor for entity extraction
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* - NaturalLanguageProcessor for relationship inference
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* - brain.extractConcepts() for tagging
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*
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* NO MOCKS - Production-ready implementation
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*/
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import { Brainy } from '../brainy.js'
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import { NeuralEntityExtractor, ExtractedEntity } from '../neural/entityExtractor.js'
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import { NaturalLanguageProcessor } from '../neural/naturalLanguageProcessor.js'
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import { SmartRelationshipExtractor } from '../neural/SmartRelationshipExtractor.js'
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import { NounType, VerbType } from '../types/graphTypes.js'
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import { ExcelHandler } from '../augmentations/intelligentImport/handlers/excelHandler.js'
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import type { FormatHandlerOptions } from '../augmentations/intelligentImport/types.js'
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export interface SmartExcelOptions extends FormatHandlerOptions {
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/** Enable neural entity extraction */
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enableNeuralExtraction?: boolean
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/** Enable relationship inference from text */
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enableRelationshipInference?: boolean
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/** Enable concept extraction for tagging */
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enableConceptExtraction?: boolean
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/** Confidence threshold for entities (0-1) */
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confidenceThreshold?: number
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/** Column name patterns to detect */
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termColumn?: string // e.g., "Term", "Name", "Title"
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definitionColumn?: string // e.g., "Definition", "Description"
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typeColumn?: string // e.g., "Type", "Category"
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relatedColumn?: string // e.g., "Related Terms", "See Also"
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/** Progress callback (v3.38.0: Enhanced with performance metrics) */
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onProgress?: (stats: {
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processed: number
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total: number
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entities: number
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relationships: number
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/** Rows per second (v3.38.0) */
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throughput?: number
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/** Estimated time remaining in ms (v3.38.0) */
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eta?: number
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/** Current phase (v3.38.0) */
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phase?: string
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}) => void
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}
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export interface ExtractedRow {
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/** Main entity from this row */
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entity: {
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id: string
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name: string
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type: NounType
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description: string
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confidence: number
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metadata: Record<string, any>
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}
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/** Additional entities extracted from definition */
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relatedEntities: Array<{
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name: string
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type: NounType
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confidence: number
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}>
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/** Inferred relationships */
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relationships: Array<{
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from: string
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to: string
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type: VerbType
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confidence: number
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evidence: string
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}>
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/** Extracted concepts */
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concepts?: string[]
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}
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export interface SmartExcelResult {
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/** Total rows processed */
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rowsProcessed: number
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/** Entities extracted (includes main + related) */
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entitiesExtracted: number
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/** Relationships inferred */
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relationshipsInferred: number
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/** All extracted data */
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rows: ExtractedRow[]
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/** Entity ID mapping (name -> ID) */
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entityMap: Map<string, string>
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/** Processing time in ms */
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processingTime: number
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/** Extraction statistics */
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stats: {
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byType: Record<string, number>
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byConfidence: {
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high: number // > 0.8
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medium: number // 0.6-0.8
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low: number // < 0.6
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}
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}
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/** Sheet-specific data for VFS extraction (v4.2.0) */
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sheets?: Array<{
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name: string
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rows: ExtractedRow[]
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stats: {
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rowCount: number
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entityCount: number
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relationshipCount: number
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}
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}>
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}
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/**
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* SmartExcelImporter - Extracts structured knowledge from Excel files
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*/
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export class SmartExcelImporter {
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private brain: Brainy
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private extractor: NeuralEntityExtractor
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private nlp: NaturalLanguageProcessor
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private relationshipExtractor: SmartRelationshipExtractor
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private excelHandler: ExcelHandler
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constructor(brain: Brainy) {
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this.brain = brain
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this.extractor = new NeuralEntityExtractor(brain)
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this.nlp = new NaturalLanguageProcessor(brain)
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this.relationshipExtractor = new SmartRelationshipExtractor(brain)
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this.excelHandler = new ExcelHandler()
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}
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/**
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* Initialize the importer
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*/
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async init(): Promise<void> {
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await this.nlp.init()
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}
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/**
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* Extract entities and relationships from Excel file
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*/
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async extract(
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buffer: Buffer,
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options: SmartExcelOptions = {}
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): Promise<SmartExcelResult> {
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const startTime = Date.now()
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// Set defaults
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const opts = {
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enableNeuralExtraction: true,
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enableRelationshipInference: true,
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// CONCEPT EXTRACTION PRODUCTION-READY (v3.33.0+):
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// Type embeddings are now pre-computed at build time - zero runtime cost!
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// All 31 noun types + 40 verb types instantly available
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//
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// Performance profile:
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// - Type embeddings: INSTANT (pre-computed at build time, ~100KB in-memory)
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// - Model loading: ~2-5 seconds (one-time, cached after first use)
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// - Per-row extraction: ~50-200ms depending on definition length
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// - 100 rows: ~5-20 seconds total (production ready)
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// - 1000 rows: ~50-200 seconds (disable if needed via enableConceptExtraction: false)
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//
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// Enabled by default for production use.
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enableConceptExtraction: true,
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confidenceThreshold: 0.6,
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termColumn: 'term|name|title|concept',
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definitionColumn: 'definition|description|desc|details',
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typeColumn: 'type|category|kind',
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relatedColumn: 'related|see also|links',
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onProgress: () => {},
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...options
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}
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// Parse Excel using existing handler
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const processedData = await this.excelHandler.process(buffer, options)
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const rows = processedData.data
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if (rows.length === 0) {
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return this.emptyResult(startTime)
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}
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// Detect column names
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const columns = this.detectColumns(rows[0], opts)
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// Process each row with BATCHED PARALLEL PROCESSING (v3.38.0)
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const extractedRows: ExtractedRow[] = []
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const entityMap = new Map<string, string>()
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const stats = {
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byType: {} as Record<string, number>,
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byConfidence: { high: 0, medium: 0, low: 0 }
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}
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// Batch processing configuration
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const CHUNK_SIZE = 10 // Process 10 rows at a time for optimal performance
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let totalProcessed = 0
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const performanceStartTime = Date.now()
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// Process rows in chunks
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for (let chunkStart = 0; chunkStart < rows.length; chunkStart += CHUNK_SIZE) {
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const chunk = rows.slice(chunkStart, Math.min(chunkStart + CHUNK_SIZE, rows.length))
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// Process chunk in parallel for massive speedup
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const chunkResults = await Promise.all(
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chunk.map(async (row, chunkIndex) => {
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const i = chunkStart + chunkIndex
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// Extract data from row
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const term = this.getColumnValue(row, columns.term) || `Entity_${i}`
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const definition = this.getColumnValue(row, columns.definition) || ''
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const type = this.getColumnValue(row, columns.type)
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const relatedTerms = this.getColumnValue(row, columns.related)
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// Parallel extraction: entities AND concepts at the same time
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const [relatedEntities, concepts] = await Promise.all([
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// Extract entities from definition
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opts.enableNeuralExtraction && definition
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? this.extractor.extract(definition, {
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confidence: opts.confidenceThreshold * 0.8,
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neuralMatching: true,
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cache: { enabled: true }
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}).then(entities =>
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// Filter out the main term from related entities
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entities.filter(e => e.text.toLowerCase() !== term.toLowerCase())
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)
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: Promise.resolve([]),
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// Extract concepts (in parallel with entity extraction)
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opts.enableConceptExtraction && definition
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? this.brain.extractConcepts(definition, { limit: 10 }).catch(() => [])
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: Promise.resolve([])
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])
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// Determine main entity type with priority order:
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// 1. Explicit "Type" column (highest priority - user specified)
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// 2. Sheet name inference (NEW - semantic hint from Excel structure)
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// 3. AI extraction from related entities
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// 4. Default to Thing (fallback)
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const sheetTypeHint = this.inferTypeFromSheetName(row._sheet || '')
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const mainEntityType = type ?
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this.mapTypeString(type) :
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sheetTypeHint ||
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(relatedEntities.length > 0 ? relatedEntities[0].type : NounType.Thing)
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// Generate entity ID
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const entityId = this.generateEntityId(term)
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// Create main entity
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const mainEntity = {
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id: entityId,
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name: term,
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type: mainEntityType,
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description: definition,
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confidence: 0.95,
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metadata: {
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source: 'excel',
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row: i + 1,
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originalData: row,
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concepts,
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extractedAt: Date.now()
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}
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}
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// Infer relationships
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const relationships: ExtractedRow['relationships'] = []
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if (opts.enableRelationshipInference) {
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// Extract relationships from definition text
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for (const relEntity of relatedEntities) {
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const verbType = await this.inferRelationship(
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term,
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relEntity.text,
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definition,
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mainEntityType, // Pass subject type hint
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relEntity.type // Pass object type hint
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)
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relationships.push({
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from: entityId,
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to: relEntity.text,
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type: verbType,
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confidence: relEntity.confidence,
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evidence: `Extracted from: "${definition.substring(0, 100)}..."`
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})
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}
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// Parse explicit "Related Terms" column
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if (relatedTerms) {
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const terms = relatedTerms.split(/[,;]/).map(t => t.trim()).filter(Boolean)
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for (const relTerm of terms) {
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if (relTerm.toLowerCase() !== term.toLowerCase()) {
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// Use SmartRelationshipExtractor even for explicit relationships
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const verbType = await this.inferRelationship(
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term,
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relTerm,
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`${term} related to ${relTerm}. ${definition}`, // Combine for better context
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mainEntityType
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)
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relationships.push({
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from: entityId,
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to: relTerm,
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type: verbType,
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confidence: 0.9,
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evidence: `Explicitly listed in "Related" column`
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})
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}
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}
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}
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}
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return {
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term,
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entityId,
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mainEntity,
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mainEntityType,
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relatedEntities,
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relationships,
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concepts
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}
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})
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)
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// Process chunk results sequentially to maintain order
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for (const result of chunkResults) {
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// Store entity ID mapping
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entityMap.set(result.term.toLowerCase(), result.entityId)
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// Track statistics
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this.updateStats(stats, result.mainEntityType, result.mainEntity.confidence)
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// Add extracted row
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extractedRows.push({
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entity: result.mainEntity,
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relatedEntities: result.relatedEntities.map(e => ({
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name: e.text,
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type: e.type,
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confidence: e.confidence
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})),
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relationships: result.relationships,
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concepts: result.concepts
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})
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}
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// Update progress tracking
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totalProcessed += chunk.length
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// Calculate performance metrics
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const elapsed = Date.now() - performanceStartTime
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const rowsPerSecond = totalProcessed / (elapsed / 1000)
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const remainingRows = rows.length - totalProcessed
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const estimatedTimeRemaining = remainingRows / rowsPerSecond
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// Report progress with enhanced metrics
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opts.onProgress({
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processed: totalProcessed,
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total: rows.length,
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entities: extractedRows.reduce((sum, row) => sum + 1 + row.relatedEntities.length, 0),
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relationships: extractedRows.reduce((sum, row) => sum + row.relationships.length, 0),
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// Additional performance metrics (v3.38.0)
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throughput: Math.round(rowsPerSecond * 10) / 10,
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eta: Math.round(estimatedTimeRemaining),
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phase: 'extracting'
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} as any)
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}
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// Group rows by sheet for VFS extraction (v4.2.0)
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const sheetGroups = new Map<string, ExtractedRow[]>()
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extractedRows.forEach((extractedRow, index) => {
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const originalRow = rows[index]
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const sheetName = originalRow._sheet || 'Sheet1'
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if (!sheetGroups.has(sheetName)) {
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sheetGroups.set(sheetName, [])
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}
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sheetGroups.get(sheetName)!.push(extractedRow)
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})
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// Build sheet-specific statistics
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const sheets = Array.from(sheetGroups.entries()).map(([name, sheetRows]) => ({
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name,
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rows: sheetRows,
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stats: {
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rowCount: sheetRows.length,
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entityCount: sheetRows.reduce((sum, row) => sum + 1 + row.relatedEntities.length, 0),
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relationshipCount: sheetRows.reduce((sum, row) => sum + row.relationships.length, 0)
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}
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}))
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return {
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rowsProcessed: rows.length,
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entitiesExtracted: extractedRows.reduce(
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(sum, row) => sum + 1 + row.relatedEntities.length,
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0
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),
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relationshipsInferred: extractedRows.reduce(
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(sum, row) => sum + row.relationships.length,
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0
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),
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rows: extractedRows,
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entityMap,
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processingTime: Date.now() - startTime,
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stats,
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sheets
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}
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}
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/**
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* Detect column names from first row
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*/
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private detectColumns(
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firstRow: Record<string, any>,
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options: SmartExcelOptions
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): {
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term: string | null
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definition: string | null
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type: string | null
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related: string | null
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} {
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const columnNames = Object.keys(firstRow)
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const matchColumn = (pattern: string): string | null => {
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const regex = new RegExp(pattern, 'i')
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return columnNames.find(col => regex.test(col)) || null
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}
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return {
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term: matchColumn(options.termColumn || 'term|name'),
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definition: matchColumn(options.definitionColumn || 'definition|description'),
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type: matchColumn(options.typeColumn || 'type|category'),
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related: matchColumn(options.relatedColumn || 'related|see also')
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}
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}
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/**
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* Get value from row using column name
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*/
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private getColumnValue(
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row: Record<string, any>,
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columnName: string | null
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): string {
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if (!columnName) return ''
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const value = row[columnName]
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if (value === null || value === undefined) return ''
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return String(value).trim()
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}
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/**
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* Map type string to NounType
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*/
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private mapTypeString(typeString: string): NounType {
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const normalized = typeString.toLowerCase().trim()
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const mapping: Record<string, NounType> = {
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'person': NounType.Person,
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'character': NounType.Person,
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'people': NounType.Person,
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'place': NounType.Location,
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'location': NounType.Location,
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'geography': NounType.Location,
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'organization': NounType.Organization,
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'org': NounType.Organization,
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'company': NounType.Organization,
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'concept': NounType.Concept,
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'idea': NounType.Concept,
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'theory': NounType.Concept,
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'event': NounType.Event,
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'occurrence': NounType.Event,
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'product': NounType.Product,
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'item': NounType.Product,
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'thing': NounType.Thing,
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'document': NounType.Document,
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'file': NounType.File,
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'project': NounType.Project
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}
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return mapping[normalized] || NounType.Thing
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}
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/**
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* Infer entity type from Excel sheet name
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*
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* Uses common naming patterns to suggest appropriate entity types:
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* - "Characters", "People", "Humans" → Person
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* - "Places", "Locations" → Location
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* - "Terms", "Concepts", "Glossary" → Concept
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* - etc.
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*
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* @param sheetName - Excel sheet name
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* @returns Inferred NounType or null if no match
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*
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* @example
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* inferTypeFromSheetName("Characters") // → NounType.Person
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* inferTypeFromSheetName("Places") // → NounType.Location
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* inferTypeFromSheetName("Animals") // → null (no semantic hint)
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*/
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private inferTypeFromSheetName(sheetName: string): NounType | null {
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if (!sheetName) return null
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const normalized = sheetName.toLowerCase()
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// Person types: characters, people, humans, individuals
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if (normalized.match(/character|people|person|human|individual|npc|cast/)) {
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return NounType.Person
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}
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// Location types: places, locations, areas, regions
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if (normalized.match(/place|location|area|region|zone|geography|map|world/)) {
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return NounType.Location
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}
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// Concept types: terms, concepts, ideas, glossary
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if (normalized.match(/term|concept|idea|definition|glossary|vocabulary|lexicon/)) {
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return NounType.Concept
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}
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// Organization types: groups, factions, companies
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if (normalized.match(/organization|company|group|faction|tribe|guild|clan|corp/)) {
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return NounType.Organization
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}
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// Event types: events, occurrences, happenings
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if (normalized.match(/event|occurrence|happening|battle|encounter|scene/)) {
|
|
return NounType.Event
|
|
}
|
|
|
|
// Product types: items, equipment, gear
|
|
if (normalized.match(/item|product|equipment|gear|weapon|armor|artifact|treasure/)) {
|
|
return NounType.Product
|
|
}
|
|
|
|
// Project types: quests, missions, campaigns
|
|
if (normalized.match(/project|quest|mission|campaign|task/)) {
|
|
return NounType.Project
|
|
}
|
|
|
|
// No semantic match found - return null to continue to next type determination method
|
|
return null
|
|
}
|
|
|
|
/**
|
|
* Infer relationship type from context using SmartRelationshipExtractor
|
|
*/
|
|
private async inferRelationship(
|
|
fromTerm: string,
|
|
toTerm: string,
|
|
context: string,
|
|
fromType?: NounType,
|
|
toType?: NounType
|
|
): Promise<VerbType> {
|
|
// Use SmartRelationshipExtractor for robust relationship classification
|
|
const result = await this.relationshipExtractor.infer(
|
|
fromTerm,
|
|
toTerm,
|
|
context,
|
|
{
|
|
subjectType: fromType,
|
|
objectType: toType
|
|
}
|
|
)
|
|
|
|
// Return inferred type or fallback to RelatedTo
|
|
return result?.type || VerbType.RelatedTo
|
|
}
|
|
|
|
/**
|
|
* Generate consistent entity ID from name
|
|
*/
|
|
private generateEntityId(name: string): string {
|
|
// Create deterministic ID based on normalized name
|
|
const normalized = name.toLowerCase().trim().replace(/\s+/g, '_')
|
|
return `ent_${normalized}_${Date.now()}`
|
|
}
|
|
|
|
/**
|
|
* Update statistics
|
|
*/
|
|
private updateStats(
|
|
stats: SmartExcelResult['stats'],
|
|
type: NounType,
|
|
confidence: number
|
|
): void {
|
|
// Track by type
|
|
const typeName = String(type)
|
|
stats.byType[typeName] = (stats.byType[typeName] || 0) + 1
|
|
|
|
// Track by confidence
|
|
if (confidence > 0.8) {
|
|
stats.byConfidence.high++
|
|
} else if (confidence >= 0.6) {
|
|
stats.byConfidence.medium++
|
|
} else {
|
|
stats.byConfidence.low++
|
|
}
|
|
}
|
|
|
|
/**
|
|
* Create empty result
|
|
*/
|
|
private emptyResult(startTime: number): SmartExcelResult {
|
|
return {
|
|
rowsProcessed: 0,
|
|
entitiesExtracted: 0,
|
|
relationshipsInferred: 0,
|
|
rows: [],
|
|
entityMap: new Map(),
|
|
processingTime: Date.now() - startTime,
|
|
stats: {
|
|
byType: {},
|
|
byConfidence: { high: 0, medium: 0, low: 0 }
|
|
}
|
|
}
|
|
}
|
|
}
|