perf: pre-compute type embeddings at build time (zero runtime cost)
Major optimization - all type embeddings now built into package: Build-time generation: - Created scripts/buildTypeEmbeddings.ts to generate all type embeddings - Generates embeddings for 31 NounTypes + 40 VerbTypes at build time - Stores as base64-encoded binary data in embeddedTypeEmbeddings.ts - Added check script to rebuild only when needed Updated all consumers: - NeuralEntityExtractor: loads pre-computed embeddings (instant) - BrainyTypes: loads pre-computed embeddings (instant init) - NaturalLanguageProcessor: loads pre-computed embeddings (instant init) Build process: - Added npm run build:types to generate embeddings - Added npm run build:types:if-needed for conditional rebuild - Integrated into main build pipeline - Auto-rebuilds only when types or build script change Benefits: - Zero runtime cost - embeddings loaded instantly - Survives all container restarts - All 71 types always available (31 nouns + 40 verbs) - ~100KB memory overhead for permanent performance gain - Eliminates 5-10 second initialization delay This completes the type embedding optimization started in v3.32.5
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361
scripts/buildTypeEmbeddings.ts
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scripts/buildTypeEmbeddings.ts
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#!/usr/bin/env node
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/**
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* Build embedded type embeddings with pre-computed vectors
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* Generates embeddings for all 31 NounTypes + 40 VerbTypes
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* NO runtime computation, NO external files needed!
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*/
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import { TransformerEmbedding } from '../src/utils/embedding.js'
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import * as fs from 'fs/promises'
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import * as path from 'path'
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import { fileURLToPath } from 'url'
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import { NounType, VerbType } from '../src/types/graphTypes.js'
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const __dirname = path.dirname(fileURLToPath(import.meta.url))
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/**
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* Type descriptions for semantic matching
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* Copied from BrainyTypes for consistency
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*/
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const NOUN_TYPE_DESCRIPTIONS: Record<string, string> = {
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// Core Entity Types
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[NounType.Person]: 'person human individual user employee customer citizen member author creator agent actor participant',
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[NounType.Organization]: 'organization company business corporation institution agency department team group committee board',
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[NounType.Location]: 'location place address city country region area zone coordinate position site venue building',
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[NounType.Thing]: 'thing object item product device equipment tool instrument asset artifact material physical tangible',
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[NounType.Concept]: 'concept idea theory principle philosophy belief value abstract intangible notion thought',
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[NounType.Event]: 'event occurrence incident activity happening meeting conference celebration milestone timestamp date',
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// Digital/Content Types
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[NounType.Document]: 'document file report article paper text pdf word contract agreement record documentation',
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[NounType.Media]: 'media image photo video audio music podcast multimedia graphic visualization animation',
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[NounType.File]: 'file digital data binary code script program software archive package bundle',
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[NounType.Message]: 'message email chat communication notification alert announcement broadcast transmission',
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[NounType.Content]: 'content information data text material resource publication post blog webpage',
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// Collection Types
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[NounType.Collection]: 'collection group set list array category folder directory catalog inventory database',
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[NounType.Dataset]: 'dataset data table spreadsheet database records statistics metrics measurements analysis',
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// Business/Application Types
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[NounType.Product]: 'product item merchandise offering service feature application software solution package',
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[NounType.Service]: 'service offering subscription support maintenance utility function capability',
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[NounType.User]: 'user account profile member subscriber customer client participant identity credentials',
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[NounType.Task]: 'task action todo item job assignment duty responsibility activity step procedure',
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[NounType.Project]: 'project initiative program campaign effort endeavor plan scheme venture undertaking',
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// Descriptive Types
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[NounType.Process]: 'process workflow procedure method algorithm sequence pipeline operation routine protocol',
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[NounType.State]: 'state status condition phase stage mode situation circumstance configuration setting',
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[NounType.Role]: 'role position title function responsibility duty job capacity designation authority',
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[NounType.Topic]: 'subject topic theme category tag keyword area domain field discipline specialty',
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[NounType.Language]: 'language dialect locale tongue vernacular communication speech linguistics vocabulary',
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[NounType.Currency]: 'currency money dollar euro pound yen bitcoin payment financial monetary unit',
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[NounType.Measurement]: 'measurement metric quantity value amount size dimension weight height volume distance',
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// Scientific/Research Types
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[NounType.Hypothesis]: 'hypothesis theory proposition thesis assumption premise conjecture speculation prediction',
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[NounType.Experiment]: 'experiment test trial study research investigation analysis observation examination',
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// Legal/Regulatory Types
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[NounType.Contract]: 'contract agreement deal treaty pact covenant license terms conditions policy',
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[NounType.Regulation]: 'regulation law rule policy standard compliance requirement guideline ordinance statute',
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// Technical Infrastructure Types
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[NounType.Interface]: 'interface API endpoint protocol specification contract schema definition connection',
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[NounType.Resource]: 'resource infrastructure server database storage compute memory bandwidth capacity asset'
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}
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const VERB_TYPE_DESCRIPTIONS: Record<string, string> = {
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// Core Relationship Types
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[VerbType.RelatedTo]: 'related connected associated linked correlated relevant pertinent applicable',
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[VerbType.Contains]: 'contains includes holds stores encompasses comprises consists incorporates',
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[VerbType.PartOf]: 'part component element member piece portion section segment constituent',
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[VerbType.LocatedAt]: 'located situated positioned placed found exists resides occupies',
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[VerbType.References]: 'references cites mentions points links refers quotes sources',
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// Temporal/Causal Types
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[VerbType.Precedes]: 'precedes before earlier prior previous antecedent preliminary foregoing',
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[VerbType.Succeeds]: 'succeeds follows after later subsequent next ensuing succeeding',
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[VerbType.Causes]: 'causes triggers induces produces generates results influences affects',
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[VerbType.DependsOn]: 'depends requires needs relies necessitates contingent prerequisite',
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[VerbType.Requires]: 'requires needs demands necessitates mandates obliges compels entails',
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// Creation/Transformation Types
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[VerbType.Creates]: 'creates makes produces generates builds constructs forms establishes',
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[VerbType.Transforms]: 'transforms converts changes modifies alters transitions morphs evolves',
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[VerbType.Becomes]: 'becomes turns evolves transforms changes transitions develops grows',
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[VerbType.Modifies]: 'modifies changes updates alters edits revises adjusts adapts',
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[VerbType.Consumes]: 'consumes uses utilizes depletes expends absorbs takes processes',
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// Ownership/Attribution Types
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[VerbType.Owns]: 'owns possesses holds controls manages administers governs maintains',
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[VerbType.AttributedTo]: 'attributed credited assigned ascribed authored written composed',
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[VerbType.CreatedBy]: 'created made produced generated built developed authored written',
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[VerbType.BelongsTo]: 'belongs property possession part member affiliate associated owned',
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// Social/Organizational Types
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[VerbType.MemberOf]: 'member participant affiliate associate belongs joined enrolled registered',
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[VerbType.WorksWith]: 'works collaborates cooperates partners teams assists helps supports',
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[VerbType.FriendOf]: 'friend companion buddy pal acquaintance associate connection relationship',
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[VerbType.Follows]: 'follows subscribes tracks monitors watches observes trails pursues',
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[VerbType.Likes]: 'likes enjoys appreciates favors prefers admires values endorses',
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[VerbType.ReportsTo]: 'reports answers subordinate accountable responsible supervised managed',
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[VerbType.Supervises]: 'supervises manages oversees directs leads controls guides administers',
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[VerbType.Mentors]: 'mentors teaches guides coaches instructs trains advises counsels',
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[VerbType.Communicates]: 'communicates talks speaks messages contacts interacts corresponds exchanges',
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// Descriptive/Functional Types
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[VerbType.Describes]: 'describes explains details documents specifies outlines depicts characterizes',
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[VerbType.Defines]: 'defines specifies establishes determines sets declares identifies designates',
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[VerbType.Categorizes]: 'categorizes classifies groups sorts organizes arranges labels tags',
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[VerbType.Measures]: 'measures quantifies gauges assesses evaluates calculates determines counts',
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[VerbType.Evaluates]: 'evaluates assesses analyzes reviews examines appraises judges rates',
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[VerbType.Uses]: 'uses utilizes employs applies operates handles manipulates exploits',
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[VerbType.Implements]: 'implements executes realizes performs accomplishes carries delivers completes',
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[VerbType.Extends]: 'extends expands enhances augments amplifies broadens enlarges develops',
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// Enhanced Relationships
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[VerbType.Inherits]: 'inherits derives extends receives obtains acquires succeeds legacy',
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[VerbType.Conflicts]: 'conflicts contradicts opposes clashes disputes disagrees incompatible inconsistent',
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[VerbType.Synchronizes]: 'synchronizes coordinates aligns harmonizes matches corresponds parallels coincides',
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[VerbType.Competes]: 'competes rivals contends contests challenges opposes vies struggles'
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}
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async function buildTypeEmbeddings() {
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console.log('🧠 Building embedded type embeddings for Brainy...')
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// Count types
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const nounTypes = Object.keys(NOUN_TYPE_DESCRIPTIONS)
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const verbTypes = Object.keys(VERB_TYPE_DESCRIPTIONS)
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console.log(`📊 Processing ${nounTypes.length} noun types and ${verbTypes.length} verb types...`)
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// Initialize TransformerEmbedding for embedding (one-time only!)
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const embedder = new TransformerEmbedding({
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verbose: true,
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localFilesOnly: false // Allow downloading models during build
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})
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await embedder.init()
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console.log('✅ TransformerEmbedding initialized')
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// Generate noun type embeddings
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const nounEmbeddings = new Map<string, number[]>()
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console.log('📝 Generating noun type embeddings...')
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for (const [type, description] of Object.entries(NOUN_TYPE_DESCRIPTIONS)) {
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try {
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const embedding = await embedder.embed(description)
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if (embedding && Array.isArray(embedding)) {
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nounEmbeddings.set(type, embedding)
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console.log(` ✓ ${type}`)
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}
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} catch (error) {
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console.warn(` ⚠️ Failed to embed noun type: ${type}`)
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}
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}
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// Generate verb type embeddings
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const verbEmbeddings = new Map<string, number[]>()
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console.log('📝 Generating verb type embeddings...')
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for (const [type, description] of Object.entries(VERB_TYPE_DESCRIPTIONS)) {
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try {
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const embedding = await embedder.embed(description)
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if (embedding && Array.isArray(embedding)) {
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verbEmbeddings.set(type, embedding)
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console.log(` ✓ ${type}`)
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}
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} catch (error) {
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console.warn(` ⚠️ Failed to embed verb type: ${type}`)
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}
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}
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console.log(`✅ Generated ${nounEmbeddings.size} noun embeddings and ${verbEmbeddings.size} verb embeddings`)
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// Get embedding dimension
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const embeddingDim = nounEmbeddings.size > 0 ?
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Array.from(nounEmbeddings.values())[0]?.length ?? 384 :
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384
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// Convert to compact binary format
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const totalTypes = nounTypes.length + verbTypes.length
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const totalFloats = totalTypes * embeddingDim
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const buffer = new ArrayBuffer(totalFloats * 4)
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const view = new DataView(buffer)
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let offset = 0
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// Pack noun embeddings
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for (const type of nounTypes) {
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const embedding = nounEmbeddings.get(type) || new Array(embeddingDim).fill(0)
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for (let i = 0; i < embeddingDim; i++) {
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view.setFloat32(offset, embedding[i], true) // little-endian
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offset += 4
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}
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}
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// Pack verb embeddings
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for (const type of verbTypes) {
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const embedding = verbEmbeddings.get(type) || new Array(embeddingDim).fill(0)
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for (let i = 0; i < embeddingDim; i++) {
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view.setFloat32(offset, embedding[i], true) // little-endian
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offset += 4
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}
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}
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// Convert to base64
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const uint8 = new Uint8Array(buffer)
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const base64 = Buffer.from(uint8).toString('base64')
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// Generate TypeScript file
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const tsContent = `/**
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* 🧠 BRAINY EMBEDDED TYPE EMBEDDINGS
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*
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* AUTO-GENERATED - DO NOT EDIT
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* Generated: ${new Date().toISOString()}
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* Noun Types: ${nounTypes.length}
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* Verb Types: ${verbTypes.length}
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*
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* This file contains pre-computed embeddings for all NounTypes and VerbTypes.
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* No runtime computation needed, instant availability!
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*/
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import { NounType, VerbType } from '../types/graphTypes.js'
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import { Vector } from '../coreTypes.js'
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// Type metadata
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export const TYPE_METADATA = {
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nounTypes: ${nounTypes.length},
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verbTypes: ${verbTypes.length},
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totalTypes: ${totalTypes},
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embeddingDimensions: ${embeddingDim},
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generatedAt: "${new Date().toISOString()}",
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sizeBytes: {
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embeddings: ${buffer.byteLength},
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base64: ${base64.length}
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}
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}
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// All noun types in order
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const NOUN_TYPE_ORDER: NounType[] = ${JSON.stringify(nounTypes)}
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// All verb types in order
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const VERB_TYPE_ORDER: VerbType[] = ${JSON.stringify(verbTypes)}
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// Pre-computed embeddings (${(base64.length / 1024).toFixed(1)}KB base64)
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const EMBEDDINGS_BASE64 = "${base64}"
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// Decode embeddings at startup (happens once, <10ms)
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function decodeEmbeddings(): Uint8Array {
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if (typeof Buffer !== 'undefined') {
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// Node.js environment
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return Buffer.from(EMBEDDINGS_BASE64, 'base64')
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} else if (typeof atob !== 'undefined') {
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// Browser environment
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const binaryString = atob(EMBEDDINGS_BASE64)
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const bytes = new Uint8Array(binaryString.length)
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for (let i = 0; i < binaryString.length; i++) {
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bytes[i] = binaryString.charCodeAt(i)
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}
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return bytes
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}
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return new Uint8Array(0)
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}
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// Cached decoded embeddings
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let decodedEmbeddings: Uint8Array | null = null
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/**
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* Get noun type embeddings as a Map for fast lookup
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* This is called once and cached
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*/
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export function getNounTypeEmbeddings(): Map<NounType, Vector> {
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if (!decodedEmbeddings) {
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decodedEmbeddings = decodeEmbeddings()
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}
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const embeddings = new Map<NounType, Vector>()
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const view = new DataView(decodedEmbeddings.buffer)
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const embeddingSize = ${embeddingDim}
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NOUN_TYPE_ORDER.forEach((type, index) => {
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const offset = index * embeddingSize * 4
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const embedding = new Float32Array(embeddingSize)
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for (let i = 0; i < embeddingSize; i++) {
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embedding[i] = view.getFloat32(offset + i * 4, true)
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}
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embeddings.set(type, Array.from(embedding))
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})
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return embeddings
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}
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/**
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* Get verb type embeddings as a Map for fast lookup
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* This is called once and cached
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*/
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export function getVerbTypeEmbeddings(): Map<VerbType, Vector> {
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if (!decodedEmbeddings) {
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decodedEmbeddings = decodeEmbeddings()
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}
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const embeddings = new Map<VerbType, Vector>()
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const view = new DataView(decodedEmbeddings.buffer)
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const embeddingSize = ${embeddingDim}
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// Verb embeddings start after noun embeddings
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const verbStartOffset = ${nounTypes.length} * embeddingSize * 4
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VERB_TYPE_ORDER.forEach((type, index) => {
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const offset = verbStartOffset + index * embeddingSize * 4
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const embedding = new Float32Array(embeddingSize)
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for (let i = 0; i < embeddingSize; i++) {
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embedding[i] = view.getFloat32(offset + i * 4, true)
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}
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embeddings.set(type, Array.from(embedding))
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})
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return embeddings
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}
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// Import logging
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import { prodLog } from '../utils/logger.js'
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prodLog.info(\`🧠 Brainy Type Embeddings loaded: \${TYPE_METADATA.nounTypes} nouns, \${TYPE_METADATA.verbTypes} verbs, \${(TYPE_METADATA.sizeBytes.embeddings / 1024).toFixed(1)}KB\`)
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`
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// Write the TypeScript file
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const outputPath = path.join(__dirname, '..', 'src', 'neural', 'embeddedTypeEmbeddings.ts')
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await fs.writeFile(outputPath, tsContent)
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// Report statistics
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console.log(`
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✅ EMBEDDED TYPE EMBEDDINGS BUILT SUCCESSFULLY!
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================================================
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Noun Types: ${nounTypes.length}
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Verb Types: ${verbTypes.length}
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Total Types: ${totalTypes}
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Embedding Dimensions: ${embeddingDim}
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File sizes:
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Embeddings binary: ${(buffer.byteLength / 1024).toFixed(1)} KB
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Base64 encoded: ${(base64.length / 1024).toFixed(1)} KB
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Output: ${outputPath}
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Type embeddings are now embedded directly in Brainy!
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No runtime computation needed, instant availability.
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`)
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}
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// Run if called directly
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if (import.meta.url === `file://${process.argv[1]}`) {
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buildTypeEmbeddings().catch(console.error)
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}
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export { buildTypeEmbeddings }
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