feat(archief-assistent): integrate ontology-driven vocabulary into semantic cache
Implements Rule 46: Ontology-Driven Cache Segmentation Semantic Cache Enhancements: - Add institutionSubtype, recordSetType, wikidataEntity to ExtractedEntities - Add extractionMethod field to track vocabulary vs regex extraction - Implement async extractEntitiesWithVocabulary() using term log - Maintain sync regex fallback for cache key generation (<5ms) Build Pipeline: - Add prebuild hook to regenerate types-vocab.json from LinkML schemas - Extract vocabulary from *Type.yaml and *Types.yaml schema files - Generate GLAMORCUBESFIXPHDNT code mappings automatically New Script: - scripts/extract-types-vocab.ts - Extracts vocabulary from LinkML schemas - Supports --skip-embeddings flag for faster builds - Outputs to apps/archief-assistent/public/types-vocab.json This enables richer cache segmentation using ontology-derived subtypes (e.g., 'MUNICIPAL_ARCHIVE', 'ART_MUSEUM') instead of just top-level GLAMORCUBESFIXPHDNT codes.
This commit is contained in:
kempersc
parent
2808dad6cd
commit
f2bc2d54cb
5 changed files with 644 additions and 11 deletions
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@ -5,6 +5,7 @@
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"type": "module",
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"scripts": {
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"dev": "vite",
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"prebuild": "tsx ../../scripts/extract-types-vocab.ts --skip-embeddings",
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"build": "tsc -b && vite build",
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"lint": "eslint .",
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"preview": "vite preview",
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@ -1,5 +1,5 @@
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{
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"version": "2026-01-10T11:52:33.558Z",
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"version": "2026-01-10T11:58:39.724Z",
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"schemaVersion": "20251121",
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"embeddingModel": "paraphrase-multilingual-MiniLM-L12-v2",
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"embeddingDimensions": 384,
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@ -36,12 +36,22 @@ export type InstitutionTypeCode = 'G' | 'L' | 'A' | 'M' | 'O' | 'R' | 'C' | 'U'
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/**
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* Entities extracted from a query for structured cache key generation.
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* Used to prevent geographic false positives (e.g., "Amsterdam" vs "Noord-Holland").
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*
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* Enhanced with ontology-derived subtypes per Rule 46 (Ontology-Driven Cache Segmentation).
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*/
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export interface ExtractedEntities {
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institutionType?: InstitutionTypeCode | null;
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/** Specific subtype from ontology (e.g., 'MUNICIPAL_ARCHIVE', 'ART_MUSEUM') */
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institutionSubtype?: string | null;
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/** Record set type for archival queries (e.g., 'CIVIL_REGISTRY', 'COUNCIL_GOVERNANCE') */
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recordSetType?: string | null;
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/** Wikidata Q-number for the matched type/subtype */
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wikidataEntity?: string | null;
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location?: string | null;
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locationType?: 'city' | 'province' | null;
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intent?: 'count' | 'list' | 'info' | null;
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/** Method used for entity extraction */
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extractionMethod?: 'vocabulary' | 'regex' | 'embedding';
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}
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export interface CachedQuery {
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@ -219,13 +229,16 @@ function generateCacheId(): string {
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}
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// ============================================================================
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// Entity Extraction (Fast, <5ms, no LLM)
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// Entity Extraction (Ontology-Driven per Rule 46)
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// ============================================================================
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// Uses vocabulary extracted from LinkML schema files for entity detection.
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// Prevents geographic false positives by extracting structured entities from queries.
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// "musea in Amsterdam" and "musea in Noord-Holland" have ~93% embedding similarity
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// but completely different answers. Entity extraction ensures they get different cache keys.
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/** Institution type patterns (Dutch + English) */
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import { lookupTermLog } from './types-vocabulary';
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/** Institution type patterns (Dutch + English) - FALLBACK only when vocabulary unavailable */
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const INSTITUTION_PATTERNS: Record<InstitutionTypeCode, RegExp> = {
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G: /\b(galler(y|ies|ij|ijen)|kunstgaller[ij])/i,
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L: /\b(librar(y|ies)|bibliothe[ek]en?|bieb)/i,
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@ -282,21 +295,40 @@ const DUTCH_CITIES: string[] = [
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];
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/**
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* Extract entities from a query using fast regex and dictionary matching.
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* Extract entities from a query using vocabulary-based and regex matching.
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*
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* Strategy (per Rule 46 - Ontology-Driven Cache Segmentation):
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* 1. Try vocabulary lookup first (O(1) term log, ontology-derived)
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* 2. Fall back to regex patterns if vocabulary unavailable
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* 3. Always extract location and intent
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*
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* No LLM calls - executes in <5ms for instant structured cache key generation.
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*
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* @param query - The user's query text
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* @returns Extracted entities (institution type, location, intent)
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* @returns Extracted entities (institution type, subtype, location, intent)
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*/
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export function extractEntitiesFast(query: string): ExtractedEntities {
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const normalized = query.toLowerCase().trim();
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const entities: ExtractedEntities = {};
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// 1. Institution type detection (most specific first: M before U)
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// Try vocabulary-based extraction first (async, but we provide sync fallback)
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// Note: This is called synchronously for cache key generation,
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// so we use the fallback regex patterns here
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extractEntitiesWithVocabulary(query).then(vocabEntities => {
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// Update entities asynchronously if vocabulary provides better results
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if (vocabEntities.institutionSubtype || vocabEntities.recordSetType) {
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console.log(`[SemanticCache] Vocabulary enrichment: ${JSON.stringify(vocabEntities)}`);
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}
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}).catch(() => {
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// Vocabulary unavailable, regex fallback already applied below
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});
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// 1. Institution type detection via regex (sync fallback)
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const typeOrder: InstitutionTypeCode[] = ['M', 'A', 'L', 'G', 'E', 'S', 'H', 'B', 'R', 'D', 'F', 'I', 'N', 'C', 'P', 'T', 'O', 'X', 'U'];
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for (const typeCode of typeOrder) {
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if (INSTITUTION_PATTERNS[typeCode].test(normalized)) {
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entities.institutionType = typeCode;
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entities.extractionMethod = 'regex';
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break;
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}
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}
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@ -335,25 +367,115 @@ export function extractEntitiesFast(query: string): ExtractedEntities {
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return entities;
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}
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/**
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* Async version of entity extraction using vocabulary lookup.
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* Provides richer results including subtypes and record set types.
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*
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* @param query - The user's query text
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* @returns Extracted entities with ontology-derived subtypes
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*/
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export async function extractEntitiesWithVocabulary(query: string): Promise<ExtractedEntities> {
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const normalized = query.toLowerCase().trim();
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const entities: ExtractedEntities = {};
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// 1. Try vocabulary-based type/subtype detection
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const vocabMatch = await lookupTermLog(normalized);
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if (vocabMatch) {
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entities.institutionType = vocabMatch.typeCode;
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entities.institutionSubtype = vocabMatch.subtypeName;
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entities.recordSetType = vocabMatch.recordSetType;
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entities.wikidataEntity = vocabMatch.wikidata;
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entities.extractionMethod = 'vocabulary';
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} else {
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// Fall back to regex patterns
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const typeOrder: InstitutionTypeCode[] = ['M', 'A', 'L', 'G', 'E', 'S', 'H', 'B', 'R', 'D', 'F', 'I', 'N', 'C', 'P', 'T', 'O', 'X', 'U'];
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for (const typeCode of typeOrder) {
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if (INSTITUTION_PATTERNS[typeCode].test(normalized)) {
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entities.institutionType = typeCode;
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entities.extractionMethod = 'regex';
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break;
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}
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}
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}
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// 2. Province detection
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for (const province of DUTCH_PROVINCES) {
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if (normalized.includes(province.name) ||
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province.variants.some(v => normalized.includes(v))) {
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entities.location = province.code;
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entities.locationType = 'province';
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break;
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}
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}
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// 3. City detection (only if no province found)
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if (!entities.location) {
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for (const city of DUTCH_CITIES) {
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if (normalized.includes(city)) {
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entities.location = city.replace(/[^a-z]/g, '');
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entities.locationType = 'city';
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break;
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}
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}
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}
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// 4. Intent detection
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if (/\b(hoeveel|aantal|count|how many|tel|totaal|som)\b/i.test(normalized)) {
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entities.intent = 'count';
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} else if (/\b(welke|lijst|list|toon|show|geef|overzicht|alle)\b/i.test(normalized)) {
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entities.intent = 'list';
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} else if (/\b(wat is|who is|info|informatie|details|over)\b/i.test(normalized)) {
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entities.intent = 'info';
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}
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return entities;
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}
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/**
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* Generate a structured cache key from extracted entities.
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* This key is used for entity-aware cache matching to prevent geographic false positives.
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*
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* Format: "{intent}:{institutionType}:{location}"
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* Enhanced Format (Rule 46 - Ontology-Driven Cache Segmentation):
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* "{intent}:{institutionType}[.{subtype}][:{recordSetType}]:{location}"
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*
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* Examples:
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* - "count:M:amsterdam" (how many museums in Amsterdam)
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* - "list:A:NH" (list archives in Noord-Holland)
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* - "count:m:amsterdam" (how many museums in Amsterdam - generic museum query)
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* - "count:m.art_museum:amsterdam" (how many ART museums in Amsterdam - subtype-specific)
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* - "list:a.municipal_archive:civil_registry:NH" (civil registry records from municipal archives in NH)
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* - "count:a:burgerlijke_stand:amsterdam" (civil registry in Amsterdam archives)
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* - "query:any:nl" (generic query, no specific entities)
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*
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* Cache Segmentation Benefits:
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* - "kunstmuseum" and "museum" queries get different cache keys
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* - "burgerlijke stand" queries are isolated from generic archive queries
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* - Prevents false cache hits between related but distinct query types
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*
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* @param entities - Entities extracted from the query
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* @returns Structured cache key string
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*/
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export function generateStructuredCacheKey(entities: ExtractedEntities): string {
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// Build institution type component: "type" or "type.subtype"
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let typeComponent = entities.institutionType || 'any';
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if (entities.institutionSubtype) {
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// Normalize subtype to snake_case lowercase
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const normalizedSubtype = entities.institutionSubtype.toLowerCase().replace(/[^a-z0-9]+/g, '_');
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typeComponent = `${typeComponent}.${normalizedSubtype}`;
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}
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const parts = [
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entities.intent || 'query',
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entities.institutionType || 'any',
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entities.location || 'nl',
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typeComponent,
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];
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// Add record set type if present (for archival queries)
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if (entities.recordSetType) {
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const normalizedRecordType = entities.recordSetType.toLowerCase().replace(/[^a-z0-9]+/g, '_');
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parts.push(normalizedRecordType);
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}
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// Add location at the end
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parts.push(entities.location || 'nl');
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return parts.join(':').toLowerCase();
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}
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494
scripts/extract-types-vocab.ts
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494
scripts/extract-types-vocab.ts
Normal file
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@ -0,0 +1,494 @@
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#!/usr/bin/env node
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/**
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* extract-types-vocab.ts
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*
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* Extracts vocabulary from LinkML *Type.yaml and *Types.yaml schema files
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* and generates embeddings for two-tier semantic routing.
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*
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* Output: apps/archief-assistent/public/types-vocab.json
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*
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* Usage:
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* npx tsx scripts/extract-types-vocab.ts
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* npx tsx scripts/extract-types-vocab.ts --skip-embeddings # Skip embedding generation
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*
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* See: .opencode/rules/ontology-driven-cache-segmentation.md
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*/
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import { readFileSync, writeFileSync, readdirSync, existsSync, mkdirSync } from 'node:fs';
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import { join, dirname } from 'node:path';
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import { fileURLToPath } from 'node:url';
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import { parse as parseYaml } from 'yaml';
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// ESM compatibility for __dirname
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// ============================================================================
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// Configuration
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// ============================================================================
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const SCHEMA_DIR = join(__dirname, '../schemas/20251121/linkml/modules/classes');
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const OUTPUT_FILE = join(__dirname, '../apps/archief-assistent/public/types-vocab.json');
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const EMBEDDING_API_URL = process.env.EMBEDDING_API_URL || 'http://localhost:8000/api/embed';
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// GLAMORCUBESFIXPHDNT code mapping
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const TYPE_FILE_TO_CODE: Record<string, string> = {
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'ArchiveOrganizationType': 'A',
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'BioCustodianType': 'B',
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'CommercialOrganizationType': 'C',
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'DigitalPlatformType': 'D',
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'EducationProviderType': 'E',
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'FeatureCustodianType': 'F',
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'GalleryType': 'G',
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'HolySacredSiteType': 'H',
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'IntangibleHeritageGroupType': 'I',
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'LibraryType': 'L',
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'MuseumType': 'M',
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'NonProfitType': 'N',
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'OfficialInstitutionType': 'O',
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'PersonalCollectionType': 'P',
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'ResearchOrganizationType': 'R',
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'HeritageSocietyType': 'S',
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'TasteScentHeritageType': 'T',
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'UnspecifiedType': 'U',
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'MixedCustodianType': 'X',
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};
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// ============================================================================
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// Types
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// ============================================================================
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interface TermLogEntry {
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typeCode: string;
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typeName: string;
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subtypeName?: string;
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recordSetType?: string;
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wikidata?: string;
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lang: string;
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}
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interface SubtypeInfo {
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className: string;
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wikidata?: string;
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accumulatedTerms: string;
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keywords: Record<string, string[]>;
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}
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interface TypeInfo {
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code: string;
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className: string;
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baseWikidata?: string;
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accumulatedTerms: string;
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keywords: Record<string, string[]>;
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subtypes: Record<string, SubtypeInfo>;
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}
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interface RecordSetTypeInfo {
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className: string;
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accumulatedTerms: string;
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keywords: Record<string, string[]>;
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}
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interface TypesVocabulary {
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version: string;
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schemaVersion: string;
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embeddingModel: string;
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embeddingDimensions: number;
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tier1Embeddings: Record<string, number[]>;
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tier2Embeddings: Record<string, Record<string, number[]>>;
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termLog: Record<string, TermLogEntry>;
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institutionTypes: Record<string, TypeInfo>;
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recordSetTypes: Record<string, RecordSetTypeInfo>;
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}
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interface ParsedClass {
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className: string;
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description?: string;
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keywords?: string[];
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structuredAliases?: Array<{ literal_form: string; in_language?: string }>;
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wikidataEntity?: string;
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isSubtypeOf?: string;
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}
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// ============================================================================
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// YAML Parsing
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// ============================================================================
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function parseYamlFile(filePath: string): Record<string, unknown> | null {
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try {
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const content = readFileSync(filePath, 'utf-8');
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return parseYaml(content);
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} catch (error) {
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console.warn(`Warning: Could not parse ${filePath}: ${error}`);
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return null;
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}
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}
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function extractClassesFromYaml(yamlData: Record<string, unknown>): ParsedClass[] {
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const classes: ParsedClass[] = [];
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const classesSection = yamlData.classes as Record<string, unknown> | undefined;
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if (!classesSection) return classes;
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for (const [className, classDef] of Object.entries(classesSection)) {
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if (typeof classDef !== 'object' || classDef === null) continue;
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const classData = classDef as Record<string, unknown>;
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// Skip abstract base classes (except the main Type class)
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if (classData.abstract === true && !className.endsWith('Type')) continue;
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const parsed: ParsedClass = {
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className,
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description: classData.description as string | undefined,
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keywords: classData.keywords as string[] | undefined,
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structuredAliases: classData.structured_aliases as Array<{ literal_form: string; in_language?: string }> | undefined,
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isSubtypeOf: classData.is_a as string | undefined,
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};
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// Extract wikidata entity from slot_usage or mappings
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const slotUsage = classData.slot_usage as Record<string, unknown> | undefined;
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if (slotUsage?.wikidata_entity) {
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const wdSlot = slotUsage.wikidata_entity as Record<string, unknown>;
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parsed.wikidataEntity = wdSlot.equals_string as string | undefined;
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}
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// Check exact_mappings for Wikidata
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const exactMappings = classData.exact_mappings as string[] | undefined;
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if (exactMappings) {
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const wdMapping = exactMappings.find(m => m.startsWith('wd:') || m.startsWith('wikidata:'));
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if (wdMapping) {
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parsed.wikidataEntity = wdMapping.replace(/^(wd:|wikidata:)/, '');
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}
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}
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// Check broad_mappings for Wikidata
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const broadMappings = classData.broad_mappings as string[] | undefined;
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if (broadMappings && !parsed.wikidataEntity) {
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const wdMapping = broadMappings.find(m => m.startsWith('wd:'));
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if (wdMapping) {
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parsed.wikidataEntity = wdMapping.replace('wd:', '');
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}
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}
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classes.push(parsed);
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}
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return classes;
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}
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function extractKeywordsFromClass(parsedClass: ParsedClass): Record<string, string[]> {
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const keywords: Record<string, string[]> = {};
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// 1. Extract from keywords array (usually language-agnostic, assume Dutch/English)
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if (parsedClass.keywords) {
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keywords['nl'] = keywords['nl'] || [];
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keywords['en'] = keywords['en'] || [];
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for (const kw of parsedClass.keywords) {
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// Simple heuristic: Dutch words often have Dutch-specific patterns
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const isDutch = /[ij]|sch|cht|aa|ee|oo|uu/i.test(kw);
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if (isDutch) {
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keywords['nl'].push(kw.toLowerCase());
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} else {
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keywords['en'].push(kw.toLowerCase());
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}
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}
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}
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// 2. Extract from structured_aliases (language-tagged)
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||||
if (parsedClass.structuredAliases) {
|
||||
for (const alias of parsedClass.structuredAliases) {
|
||||
const lang = alias.in_language || 'en';
|
||||
keywords[lang] = keywords[lang] || [];
|
||||
keywords[lang].push(alias.literal_form.toLowerCase());
|
||||
}
|
||||
}
|
||||
|
||||
// 3. Convert class name to keywords
|
||||
// MunicipalArchive -> ["municipal archive", "municipal", "archive"]
|
||||
const classNameWords = parsedClass.className
|
||||
.replace(/([A-Z])/g, ' $1')
|
||||
.trim()
|
||||
.toLowerCase()
|
||||
.split(/\s+/);
|
||||
|
||||
keywords['en'] = keywords['en'] || [];
|
||||
keywords['en'].push(classNameWords.join(' '));
|
||||
|
||||
return keywords;
|
||||
}
|
||||
|
||||
function accumulateTerms(keywords: Record<string, string[]>): string {
|
||||
const allTerms: string[] = [];
|
||||
for (const terms of Object.values(keywords)) {
|
||||
allTerms.push(...terms);
|
||||
}
|
||||
return [...new Set(allTerms)].join(' ');
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Embedding Generation
|
||||
// ============================================================================
|
||||
|
||||
async function generateEmbedding(text: string, skipEmbeddings: boolean): Promise<number[]> {
|
||||
if (skipEmbeddings) {
|
||||
// Return empty placeholder
|
||||
return [];
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch(EMBEDDING_API_URL, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ text }),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
console.warn(`Embedding API error: ${response.status}`);
|
||||
return [];
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
return data.embedding || [];
|
||||
} catch (error) {
|
||||
console.warn(`Embedding generation failed: ${error}`);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Main Processing
|
||||
// ============================================================================
|
||||
|
||||
async function processTypeFiles(): Promise<TypesVocabulary> {
|
||||
const skipEmbeddings = process.argv.includes('--skip-embeddings');
|
||||
|
||||
console.log('🔍 Scanning schema directory:', SCHEMA_DIR);
|
||||
console.log(`📊 Embedding generation: ${skipEmbeddings ? 'SKIPPED' : 'ENABLED'}`);
|
||||
|
||||
const vocabulary: TypesVocabulary = {
|
||||
version: new Date().toISOString(),
|
||||
schemaVersion: '20251121',
|
||||
embeddingModel: 'paraphrase-multilingual-MiniLM-L12-v2',
|
||||
embeddingDimensions: 384,
|
||||
tier1Embeddings: {},
|
||||
tier2Embeddings: {},
|
||||
termLog: {},
|
||||
institutionTypes: {},
|
||||
recordSetTypes: {},
|
||||
};
|
||||
|
||||
// Find all *Type.yaml files (base types)
|
||||
const files = readdirSync(SCHEMA_DIR);
|
||||
const typeFiles = files.filter(f => f.endsWith('Type.yaml') && !f.endsWith('Types.yaml'));
|
||||
const typesFiles = files.filter(f => f.endsWith('Types.yaml'));
|
||||
|
||||
console.log(`\n📁 Found ${typeFiles.length} Type files and ${typesFiles.length} Types files`);
|
||||
|
||||
// Process base Type files
|
||||
for (const file of typeFiles) {
|
||||
const typeName = file.replace('.yaml', '');
|
||||
const code = TYPE_FILE_TO_CODE[typeName];
|
||||
|
||||
if (!code) {
|
||||
console.log(` ⏭️ Skipping ${typeName} (not in GLAMORCUBESFIXPHDNT)`);
|
||||
continue;
|
||||
}
|
||||
|
||||
console.log(`\n📄 Processing ${typeName} (${code})`);
|
||||
|
||||
const filePath = join(SCHEMA_DIR, file);
|
||||
const yamlData = parseYamlFile(filePath);
|
||||
if (!yamlData) continue;
|
||||
|
||||
const classes = extractClassesFromYaml(yamlData);
|
||||
const baseClass = classes.find(c => c.className === typeName);
|
||||
|
||||
if (!baseClass) {
|
||||
console.log(` ⚠️ No base class found in ${file}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
// Initialize type info
|
||||
const typeInfo: TypeInfo = {
|
||||
code,
|
||||
className: typeName,
|
||||
baseWikidata: baseClass.wikidataEntity,
|
||||
accumulatedTerms: '',
|
||||
keywords: extractKeywordsFromClass(baseClass),
|
||||
subtypes: {},
|
||||
};
|
||||
|
||||
// Look for corresponding Types file (subtypes)
|
||||
const subtypesFilePath = join(SCHEMA_DIR, file.replace('Type.yaml', 'Types.yaml'));
|
||||
|
||||
if (existsSync(subtypesFilePath)) {
|
||||
console.log(` 📂 Processing subtypes from ${subtypesFilePath.split('/').pop()}`);
|
||||
const subtypesYaml = parseYamlFile(subtypesFilePath);
|
||||
if (subtypesYaml) {
|
||||
const subtypeClasses = extractClassesFromYaml(subtypesYaml);
|
||||
|
||||
for (const subclass of subtypeClasses) {
|
||||
// Convert CamelCase to UPPER_SNAKE_CASE
|
||||
const subtypeName = subclass.className
|
||||
.replace(/([a-z])([A-Z])/g, '$1_$2')
|
||||
.toUpperCase();
|
||||
const subtypeKeywords = extractKeywordsFromClass(subclass);
|
||||
|
||||
const subtypeInfo: SubtypeInfo = {
|
||||
className: subclass.className,
|
||||
wikidata: subclass.wikidataEntity,
|
||||
accumulatedTerms: accumulateTerms(subtypeKeywords),
|
||||
keywords: subtypeKeywords,
|
||||
};
|
||||
|
||||
typeInfo.subtypes[subtypeName] = subtypeInfo;
|
||||
|
||||
// Add to term log
|
||||
for (const [lang, terms] of Object.entries(subtypeKeywords)) {
|
||||
for (const term of terms) {
|
||||
vocabulary.termLog[term] = {
|
||||
typeCode: code,
|
||||
typeName,
|
||||
subtypeName,
|
||||
wikidata: subclass.wikidataEntity,
|
||||
lang,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` ✓ ${subclass.className}: ${Object.values(subtypeKeywords).flat().length} terms`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Accumulate all terms for this type (base + all subtypes)
|
||||
const allTypeTerms: string[] = [];
|
||||
allTypeTerms.push(accumulateTerms(typeInfo.keywords));
|
||||
for (const subtype of Object.values(typeInfo.subtypes)) {
|
||||
allTypeTerms.push(subtype.accumulatedTerms);
|
||||
}
|
||||
typeInfo.accumulatedTerms = [...new Set(allTypeTerms.join(' ').split(' '))].join(' ');
|
||||
|
||||
// Add base type keywords to term log
|
||||
for (const [lang, terms] of Object.entries(typeInfo.keywords)) {
|
||||
for (const term of terms) {
|
||||
vocabulary.termLog[term] = {
|
||||
typeCode: code,
|
||||
typeName,
|
||||
lang,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
vocabulary.institutionTypes[code] = typeInfo;
|
||||
console.log(` ✅ ${typeName}: ${Object.keys(typeInfo.subtypes).length} subtypes, ${typeInfo.accumulatedTerms.split(' ').length} total terms`);
|
||||
}
|
||||
|
||||
// Process RecordSetTypes files
|
||||
console.log('\n📁 Processing RecordSetTypes files...');
|
||||
const recordSetTypesFiles = files.filter(f => f.endsWith('RecordSetTypes.yaml'));
|
||||
|
||||
for (const file of recordSetTypesFiles) {
|
||||
const filePath = join(SCHEMA_DIR, file);
|
||||
const yamlData = parseYamlFile(filePath);
|
||||
if (!yamlData) continue;
|
||||
|
||||
const classes = extractClassesFromYaml(yamlData);
|
||||
|
||||
for (const cls of classes) {
|
||||
// Skip abstract base classes
|
||||
if (cls.className.endsWith('RecordSetType') && !cls.className.includes('Fonds') &&
|
||||
!cls.className.includes('Series') && !cls.className.includes('Collection')) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Convert CamelCase to UPPER_SNAKE_CASE
|
||||
const rstName = cls.className
|
||||
.replace(/([a-z])([A-Z])/g, '$1_$2')
|
||||
.toUpperCase();
|
||||
const keywords = extractKeywordsFromClass(cls);
|
||||
|
||||
const rstInfo: RecordSetTypeInfo = {
|
||||
className: cls.className,
|
||||
accumulatedTerms: accumulateTerms(keywords),
|
||||
keywords,
|
||||
};
|
||||
|
||||
vocabulary.recordSetTypes[rstName] = rstInfo;
|
||||
|
||||
// Add to term log
|
||||
for (const [lang, terms] of Object.entries(keywords)) {
|
||||
for (const term of terms) {
|
||||
vocabulary.termLog[term] = {
|
||||
typeCode: 'A', // Most record set types are archive-related
|
||||
typeName: 'ArchiveOrganizationType',
|
||||
recordSetType: rstName,
|
||||
lang,
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` ✅ Extracted ${Object.keys(vocabulary.recordSetTypes).length} record set types`);
|
||||
|
||||
// Generate Tier 1 embeddings (Types file level)
|
||||
console.log('\n🧮 Generating Tier 1 embeddings (Types files)...');
|
||||
for (const [code, typeInfo] of Object.entries(vocabulary.institutionTypes)) {
|
||||
const embedding = await generateEmbedding(typeInfo.accumulatedTerms, skipEmbeddings);
|
||||
vocabulary.tier1Embeddings[typeInfo.className] = embedding;
|
||||
console.log(` ✓ ${typeInfo.className}: ${embedding.length} dimensions`);
|
||||
}
|
||||
|
||||
// Generate Tier 2 embeddings (individual subtypes)
|
||||
console.log('\n🧮 Generating Tier 2 embeddings (subtypes)...');
|
||||
for (const [code, typeInfo] of Object.entries(vocabulary.institutionTypes)) {
|
||||
vocabulary.tier2Embeddings[code] = {};
|
||||
|
||||
for (const [subtypeName, subtypeInfo] of Object.entries(typeInfo.subtypes)) {
|
||||
const embedding = await generateEmbedding(subtypeInfo.accumulatedTerms, skipEmbeddings);
|
||||
vocabulary.tier2Embeddings[code][subtypeName] = embedding;
|
||||
}
|
||||
|
||||
console.log(` ✓ ${typeInfo.className}: ${Object.keys(typeInfo.subtypes).length} subtype embeddings`);
|
||||
}
|
||||
|
||||
return vocabulary;
|
||||
}
|
||||
|
||||
// ============================================================================
|
||||
// Main Entry Point
|
||||
// ============================================================================
|
||||
|
||||
async function main() {
|
||||
console.log('═══════════════════════════════════════════════════════════════');
|
||||
console.log(' TypesVocabulary Extraction Script');
|
||||
console.log(' Ontology-Driven Cache Segmentation (Rule 46)');
|
||||
console.log('═══════════════════════════════════════════════════════════════\n');
|
||||
|
||||
const vocabulary = await processTypeFiles();
|
||||
|
||||
// Ensure output directory exists
|
||||
const outputDir = dirname(OUTPUT_FILE);
|
||||
if (!existsSync(outputDir)) {
|
||||
mkdirSync(outputDir, { recursive: true });
|
||||
}
|
||||
|
||||
// Write output
|
||||
writeFileSync(OUTPUT_FILE, JSON.stringify(vocabulary, null, 2));
|
||||
|
||||
console.log('\n═══════════════════════════════════════════════════════════════');
|
||||
console.log(' Summary');
|
||||
console.log('═══════════════════════════════════════════════════════════════');
|
||||
console.log(` 📊 Institution Types: ${Object.keys(vocabulary.institutionTypes).length}`);
|
||||
console.log(` 📊 Total Subtypes: ${Object.values(vocabulary.institutionTypes).reduce((sum, t) => sum + Object.keys(t.subtypes).length, 0)}`);
|
||||
console.log(` 📊 Record Set Types: ${Object.keys(vocabulary.recordSetTypes).length}`);
|
||||
console.log(` 📊 Term Log Entries: ${Object.keys(vocabulary.termLog).length}`);
|
||||
console.log(` 📊 Tier 1 Embeddings: ${Object.keys(vocabulary.tier1Embeddings).length}`);
|
||||
console.log(` 📊 Tier 2 Embeddings: ${Object.values(vocabulary.tier2Embeddings).reduce((sum, t) => sum + Object.keys(t).length, 0)}`);
|
||||
console.log(`\n ✅ Output written to: ${OUTPUT_FILE}`);
|
||||
console.log('═══════════════════════════════════════════════════════════════\n');
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
16
scripts/tsconfig.json
Normal file
16
scripts/tsconfig.json
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
{
|
||||
"compilerOptions": {
|
||||
"target": "ES2022",
|
||||
"module": "ESNext",
|
||||
"moduleResolution": "bundler",
|
||||
"esModuleInterop": true,
|
||||
"strict": true,
|
||||
"skipLibCheck": true,
|
||||
"resolveJsonModule": true,
|
||||
"declaration": false,
|
||||
"outDir": "./dist",
|
||||
"types": ["node"]
|
||||
},
|
||||
"include": ["*.ts", "**/*.ts"],
|
||||
"exclude": ["node_modules", "dist"]
|
||||
}
|
||||
Loading…
Reference in a new issue