seguimiento-de-impuestos-glm-buscar-contactos-en-bamboohr/seguimiento-de-impuestos-glm-buscar-contactos-en-bamboohr.json

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'')\n  .normalize('NFD')\n  .replace(/[\\u0300-\\u036f]/g, '')\n  .toLowerCase()\n  .replace(/[^a-z0-9\\s]/g, ' ')\n  .replace(/\\s+/g, ' ')\n  .trim();\n\nconst tokens = (value) => normalize(value).split(' ').filter((token) => token.length > 1);\n\nconst levenshtein = (a, b) => {\n  if (!a) return b.length;\n  if (!b) return a.length;\n  const previous = Array.from({ length: b.length + 1 }, (_, index) => index);\n  for (let i = 1; i <= a.length; i += 1) {\n    let diagonal = previous[0];\n    previous[0] = i;\n    for (let j = 1; j <= b.length; j += 1) {\n      const old = previous[j];\n      const cost = a[i - 1] === b[j - 1] ? 0 : 1;\n      previous[j] = Math.min(previous[j] + 1, previous[j - 1] + 1, diagonal + cost);\n      diagonal = old;\n    }\n  }\n  return previous[b.length];\n};\n\nconst similarity = (a, b) => {\n  const longest = Math.max(a.length, b.length, 1);\n  return 1 - levenshtein(a, b) / longest;\n};\n\nconst tokenF1 = (aTokens, bTokens) => {\n  if (!aTokens.length || !bTokens.length) return 0;\n  const a = new Set(aTokens);\n  const b = new Set(bTokens);\n  const common = [...a].filter((token) => b.has(token)).length;\n  if (!common) return 0;\n  const precision = common / b.size;\n  const recall = common / a.size;\n  return (2 * precision * recall) / (precision + recall);\n};\n\nconst countryAliases = [\n  { code: 'DO', name: 'República Dominicana', aliases: ['dominican republic', 'republica dominicana', 'santo domingo', 'dominicana', 'rep dom'] },\n  { code: 'PR', name: 'Puerto Rico', aliases: ['puerto rico'] },\n  { code: 'JM', name: 'Jamaica', aliases: ['jamaica'] },\n  { code: 'TT', name: 'Trinidad y Tobago', aliases: ['trinidad and tobago', 'trinidad & tobago', 'trinidad y tobago', 'trinidad', 'tobago'] },\n  { code: 'SV', name: 'El Salvador', aliases: ['el salvador', 'salvador'] },\n  { code: 'GT', name: 'Guatemala', aliases: ['guatemala'] },\n  { code: 'HN', name: 'Honduras', aliases: ['honduras'] },\n  { code: 'CR', name: 'Costa Rica', aliases: ['costa rica'] },\n  { code: 'CO', name: 'Colombia', aliases: ['colombia'] },\n  { code: 'MX', name: 'México', aliases: ['mexico', 'méxico'] },\n  { code: 'NI', name: 'Nicaragua', aliases: ['nicaragua'] },\n  { code: 'PA', name: 'Panamá', aliases: ['panama', 'panamá'] },\n];\n\nconst resolveCountry = (employee) => {\n  const haystack = normalize([\n    employee.country,\n    employee.location,\n    employee.city,\n    employee.division,\n    employee.department,\n    employee.address1,\n  ].filter(Boolean).join(' '));\n\n  if (/\\bregional\\b|\\bregion\\b/.test(haystack)) {\n    return { code: 'REG', name: 'Regional' };\n  }\n\n  for (const country of countryAliases) {\n    if (country.aliases.some((alias) => haystack.includes(normalize(alias)))) {\n      return { code: country.code, name: country.name };\n    }\n  }\n\n  return { code: 'REG', name: 'Regional' };\n};\n\nconst resolveArea = (employee) => {\n  const text = normalize([\n    employee.department,\n    employee.division,\n    employee.jobTitle,\n    employee['customPosicion-Cliente'],\n  ].filter(Boolean).join(' '));\n\n  if (/nomina|payroll|compensacion|compensation|beneficios|benefits/.test(text)) return 'nomina';\n  if (/administracion|administrativo|administrative|finance|finanzas|contabilidad|accounting|impuesto|tax|tesoreria|treasury|conciliacion|compras|procurement/.test(text)) return 'admin';\n  return 'other';\n};\n\n\nconst queryNormalized = normalize(query);\nconst queryTokens = tokens(query);\nconst candidates = [];\n\nfor (const employee of employees) {\n  const constructedName = [employee.firstName, employee.middleName, employee.lastName]\n    .filter(Boolean)\n    .join(' ')\n    .replace(/\\s+/g, ' ')\n    .trim();\n  const displayName = String(employee.displayName ?? '').trim();\n  const fullName = displayName || constructedName;\n  if (!fullName) continue;\n\n  const nameVariants = [...new Set([fullName, constructedName, displayName].filter(Boolean))];\n  let score = 0;\n  for (const variant of nameVariants) {\n    const normalizedVariant = normalize(variant);\n    const variantTokens = tokens(variant);\n    let current = 0;\n    if (normalizedVariant === queryNormalized) current = 100;\n    else if (normalizedVariant.startsWith(queryNormalized)) current = 91;\n    else if (normalizedVariant.includes(queryNormalized)) current = 85;\n    else if (queryNormalized.includes(normalizedVariant)) current = 78;\n\n    const f1 = tokenF1(queryTokens, variantTokens);\n    const editSimilarity = similarity(queryNormalized, normalizedVariant);\n    current = Math.max(current, f1 * 82, editSimilarity * 76);\n    if (queryTokens.every((token) => variantTokens.includes(token))) current += 8;\n    score = Math.max(score, current);\n  }\n\n  const status = String(employee.status ?? employee.employmentStatus ?? '').trim();\n  if (/active|activo/i.test(status)) score += 3;\n  if (score < 45) continue;\n\n  const country = resolveCountry(employee);\n  const email = String(employee.workEmail || employee.email || '').trim().toLowerCase();\n\n  candidates.push({\n    id: String(employee.id ?? employee.employeeId ?? employee.employeeNumber ?? fullName),\n    employeeNumber: String(employee.employeeNumber ?? ''),\n    fullName,\n    displayName: displayName || fullName,\n    email,\n    jobTitle: String(employee.jobTitle ?? employee['customPosicion-Cliente'] ?? '').trim(),\n    department: String(employee.department ?? '').trim(),\n    division: String(employee.division ?? '').trim(),\n    location: String(employee.location ?? employee.city ?? '').trim(),\n    countryCode: country.code,\n    countryName: country.name,\n    area: resolveArea(employee),\n    status,\n    score: Math.min(100, Math.round(score)),\n    matchQuality: score >= 95 ? 'exacta' : score >= 72 ? 'alta' : 'media',\n  });\n}\n\nconst unique = new Map();\nfor (const candidate of candidates.sort((a, b) => {\n  const activeA = /active|activo/i.test(a.status) ? 1 : 0;\n  const activeB = /active|activo/i.test(b.status) ? 1 : 0;\n  return b.score - a.score || activeB - activeA || a.fullName.localeCompare(b.fullName);\n})) {\n  const key = candidate.id || candidate.email || normalize(candidate.fullName);\n  if (!unique.has(key)) unique.set(key, candidate);\n}\n\nreturn [{\n  json: {\n    ok: true,\n    query,\n    count: Math.min(unique.size, 8),\n    candidates: [...unique.values()].slice(0, 8),\n  },\n}];"
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          "content": "## BÚSQUEDA DE CONTACTOS EN BAMBOOHR\n\nRecibe desde la aplicación el nombre de una persona y busca coincidencias dentro del directorio actualizado de empleados de BambooHR.\n\n- Valida que la solicitud incluya un nombre válido antes de consultar BambooHR.\n- Devuelve una respuesta controlada cuando faltan datos o la solicitud es incorrecta.\n- Obtiene el reporte completo de empleados con su información laboral y de contacto.\n- Normaliza nombres, espacios, mayúsculas y caracteres especiales para realizar una búsqueda más confiable.\n- Identifica coincidencias exactas y aproximadas para reducir errores por nombres incompletos o diferencias de escritura.\n- Devuelve a la aplicación las personas encontradas para agregarlas automáticamente o permitir que el usuario seleccione la coincidencia correcta.",
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    "authors": "Isaac Aracena",
    "name": "Version 4cc439c7",
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