open-brainy/src/neural/embeddedTypeEmbeddings.ts

/**
 * 🧠 BRAINY EMBEDDED TYPE EMBEDDINGS
 *
 * AUTO-GENERATED - DO NOT EDIT
 * Generated: 2026-06-29T10:04:19-07:00
 * Noun Types: 42
 * Verb Types: 127
 *
 * This file contains pre-computed embeddings for all NounTypes and VerbTypes.
 * No runtime computation needed, instant availability!
 */

import { NounType, VerbType } from '../types/graphTypes.js'
import { Vector } from '../coreTypes.js'

// Type metadata
export const TYPE_METADATA = {
  nounTypes: 42,
  verbTypes: 127,
  totalTypes: 169,
  embeddingDimensions: 384,
  generatedAt: "2026-06-29T10:04:19-07:00",
  sizeBytes: {
    embeddings: 259584,
    base64: 346112
  }
}

// All noun types in order
const NOUN_TYPE_ORDER: NounType[] = ["person","organization","location","thing","concept","event","agent","organism","substance","quality","timeInterval","function","proposition","document","media","file","message","collection","dataset","product","service","task","project","process","state","role","language","currency","measurement","hypothesis","experiment","contract","regulation","interface","resource","custom","socialGroup","institution","norm","informationContent","informationBearer","relationship"]

// All verb types in order
const VERB_TYPE_ORDER: VerbType[] = ["instanceOf","subclassOf","participatesIn","relatedTo","contains","partOf","references","locatedAt","adjacentTo","precedes","during","occursAt","causes","enables","prevents","dependsOn","requires","creates","transforms","becomes","modifies","consumes","destroys","owns","attributedTo","hasQuality","realizes","affects","composedOf","inherits","memberOf","worksWith","friendOf","follows","likes","reportsTo","mentors","communicates","describes","defines","categorizes","measures","evaluates","uses","implements","extends","equivalentTo","believes","conflicts","synchronizes","competes","canCause","mustCause","wouldCauseIf","couldBe","mustBe","counterfactual","knows","doubts","desires","intends","fears","loves","hates","hopes","perceives","learns","probablyCauses","uncertainRelation","correlatesWith","approximatelyEquals","greaterThan","similarityDegree","moreXThan","hasDegree","partiallyHas","carries","encodes","obligatedTo","permittedTo","prohibitedFrom","shouldDo","mustNotDo","trueInContext","perceivedAs","interpretedAs","validInFrame","trueFrom","overlaps","immediatelyAfter","eventuallyLeadsTo","simultaneousWith","hasDuration","recurringWith","containsSpatially","overlapsSpatially","surrounds","connectedTo","above","below","inside","outside","facing","represents","embodies","opposes","alliesWith","conformsTo","measuredIn","convertsTo","hasMagnitude","dimensionallyEquals","persistsThrough","gainsProperty","losesProperty","remainsSame","functionalPartOf","topologicalPartOf","temporalPartOf","conceptualPartOf","rigidlyDependsOn","functionallyDependsOn","historicallyDependsOn","endorses","contradicts","supports","supersedes"]

// Pre-computed embeddings (338.0KB base64)
const EMBEDDINGS_BASE64 = "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

// Decode embeddings at startup (happens once, <10ms)
function decodeEmbeddings(): Uint8Array {
  if (typeof Buffer !== 'undefined') {
    // Node.js environment
    return Buffer.from(EMBEDDINGS_BASE64, 'base64')
  } else if (typeof atob !== 'undefined') {
    // Browser environment
    const binaryString = atob(EMBEDDINGS_BASE64)
    const bytes = new Uint8Array(binaryString.length)
    for (let i = 0; i < binaryString.length; i++) {
      bytes[i] = binaryString.charCodeAt(i)
    }
    return bytes
  }
  return new Uint8Array(0)
}

// Cached decoded embeddings
let decodedEmbeddings: Uint8Array | null = null

/**
 * Get noun type embeddings as a Map for fast lookup
 * This is called once and cached
 */
export function getNounTypeEmbeddings(): Map<NounType, Vector> {
  if (!decodedEmbeddings) {
    decodedEmbeddings = decodeEmbeddings()
  }

  const embeddings = new Map<NounType, Vector>()
  const view = new DataView(decodedEmbeddings.buffer)
  const embeddingSize = 384

  NOUN_TYPE_ORDER.forEach((type, index) => {
    const offset = index * embeddingSize * 4
    const embedding = new Float32Array(embeddingSize)

    for (let i = 0; i < embeddingSize; i++) {
      embedding[i] = view.getFloat32(offset + i * 4, true)
    }

    embeddings.set(type, Array.from(embedding))
  })

  return embeddings
}

/**
 * Get verb type embeddings as a Map for fast lookup
 * This is called once and cached
 */
export function getVerbTypeEmbeddings(): Map<VerbType, Vector> {
  if (!decodedEmbeddings) {
    decodedEmbeddings = decodeEmbeddings()
  }

  const embeddings = new Map<VerbType, Vector>()
  const view = new DataView(decodedEmbeddings.buffer)
  const embeddingSize = 384

  // Verb embeddings start after noun embeddings
  const verbStartOffset = 42 * embeddingSize * 4

  VERB_TYPE_ORDER.forEach((type, index) => {
    const offset = verbStartOffset + index * embeddingSize * 4
    const embedding = new Float32Array(embeddingSize)

    for (let i = 0; i < embeddingSize; i++) {
      embedding[i] = view.getFloat32(offset + i * 4, true)
    }

    embeddings.set(type, Array.from(embedding))
  })

  return embeddings
}

// Import logging
import { prodLog } from '../utils/logger.js'
prodLog.info(`🧠 Brainy Type Embeddings loaded: ${TYPE_METADATA.nounTypes} nouns, ${TYPE_METADATA.verbTypes} verbs, ${(TYPE_METADATA.sizeBytes.embeddings / 1024).toFixed(1)}KB`)