Client library for NamSor API v2
npm install --save @datafire/namsor
let namsor = require('@datafire/namsor').create({
api_key: ""
});
.then(data => {
console.log(data);
});
NamSor API v2 : enpoints to process personal names (gender, cultural origin or ethnicity) in all alphabets or languages. Use GET methods for small tests, but prefer POST methods for higher throughput (batch processing of up to 100 names at a time). Need something you can't find here? We have many more features coming soon. Let us know, we'll do our best to add it!
Add usage credits to an API Key.
namsor.addCredits({
"apiKey": "",
"usageCredits": 0,
"userMessage": ""
}, context)
- input
object
- apiKey required
string
- usageCredits required
integer
- userMessage required
string
- apiKey required
- output SystemMetricsOut
Activate/deactivate anonymization for a source.
namsor.anonymize({
"source": "",
"anonymized": true
}, context)
- input
object
- source required
string
- anonymized required
boolean
- source required
Output schema unknown
List of API services and usage cost in Units (default is 1=ONE Unit).
namsor.availableServices(null, context)
This action has no parameters
- output APIPlansOut
Prints the current status of the classifiers.
namsor.apiStatus(null, context)
This action has no parameters
- output APIPlansOut
Print current API usage.
namsor.apiUsage(null, context)
This action has no parameters
- output APIPeriodUsageOut
Print historical API usage.
namsor.apiUsageHistory(null, context)
This action has no parameters
- output APIPeriodUsageOut
Print historical API usage (in an aggregated view, by service, by day/hour/min).
namsor.apiUsageHistoryAggregate(null, context)
This action has no parameters
- output APIPeriodUsageOut
List all available plans in the default currency (usd).
namsor.availablePlans(null, context)
This action has no parameters
- output APIPlansOut
List all available plans in the user's preferred currency.
namsor.availablePlans_1({
"token": ""
}, context)
- input
object
- token required
string
- token required
- output APIPlansOut
List possible currency options for billing (USD, EUR, GBP, ...)
namsor.billingCurrencies(null, context)
This action has no parameters
- output CurrenciesOut
Read the history billing information (invoices paid via Stripe or manually).
namsor.billingHistory({
"token": ""
}, context)
- input
object
- token required
string
- token required
- output BillingHistoryOut
Read the billing information (company name, address, phone, vat ID)
namsor.billingInfo({
"token": ""
}, context)
- input
object
- token required
string
- token required
- output BillingInfoInOut
Create a Stripe Customer, based on a payment card token (from secure StripeJS) and email.
namsor.charge({}, context)
- input
object
- body
object
- stripeEmail
string
- stripeToken
string
- stripeEmail
- body
- output APIKeyOut
Identify Chinese name candidates, based on the romanized name ex. Wang Xiaoming
namsor.chineseNameCandidates({
"chineseSurnameLatin": "",
"chineseGivenNameLatin": ""
}, context)
- input
object
- chineseSurnameLatin required
string
- chineseGivenNameLatin required
string
- chineseSurnameLatin required
- output RomanizedNameOut
Identify Chinese name candidates, based on the romanized name (firstName = chineseGivenName; lastName=chineseSurname), ex. Wang Xiaoming
namsor.chineseNameCandidatesBatch({}, context)
- input
object
- body BatchFirstLastNameIn
- output BatchNameMatchCandidatesOut
Identify Chinese name candidates, based on the romanized name (firstName = chineseGivenName; lastName=chineseSurname) ex. Wang Xiaoming.
namsor.chineseNameCandidatesGenderBatch({}, context)
- input
object
- body BatchFirstLastNameIn
- output BatchNameMatchCandidatesOut
Identify Chinese name candidates, based on the romanized name ex. Wang Xiaoming - having a known gender ('male' or 'female')
namsor.chineseNameGenderCandidates({
"chineseSurnameLatin": "",
"chineseGivenNameLatin": "",
"knownGender": ""
}, context)
- input
object
- chineseSurnameLatin required
string
- chineseGivenNameLatin required
string
- knownGender required
string
- chineseSurnameLatin required
- output RomanizedNameOut
Return a score for matching Chinese name ex. 王晓明 with a romanized name ex. Wang Xiaoming
namsor.chineseNameMatch({
"chineseSurnameLatin": "",
"chineseGivenNameLatin": "",
"chineseName": ""
}, context)
- input
object
- chineseSurnameLatin required
string
- chineseGivenNameLatin required
string
- chineseName required
string
- chineseSurnameLatin required
- output RomanizedNameOut
Identify Chinese name candidates, based on the romanized name (firstName = chineseGivenName; lastName=chineseSurname), ex. Wang Xiaoming
namsor.chineseNameMatchBatch({}, context)
- input
object
- body BatchFirstLastNameIn
- output BatchNameMatchCandidatesOut
Setting an API Key to a corporate status.
namsor.corporateKey({
"apiKey": "",
"corporate": true
}, context)
- input
object
- apiKey required
string
- corporate required
boolean
- apiKey required
Output schema unknown
[USES 10 UNITS PER NAME] Infer the likely country of residence of a personal full name, or one surname. Assumes names as they are in the country of residence OR the country of origin.
namsor.country({
"personalNameFull": ""
}, context)
- input
object
- personalNameFull required
string
- personalNameFull required
- output PersonalNameGeoOut
[USES 10 UNITS PER NAME] Infer the likely country of residence of up to 100 personal full names, or surnames. Assumes names as they are in the country of residence OR the country of origin.
namsor.countryBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameGeoOut
Update debug level for a classifier
namsor.debugLevel({
"logger": "",
"level": ""
}, context)
- input
object
- logger required
string
- level required
string
- logger required
Output schema unknown
[USES 20 UNITS PER NAME] Infer the likely ethnicity/diaspora of a personal name, given a country of residence ISO2 code (ex. US, CA, AU, NZ etc.)
namsor.diaspora({
"countryIso2": "",
"firstName": "",
"lastName": ""
}, context)
- input
object
- countryIso2 required
string
- firstName required
string
- lastName required
string
- countryIso2 required
- output FirstLastNameDiasporaedOut
[USES 20 UNITS PER NAME] Infer the likely ethnicity/diaspora of up to 100 personal names, given a country of residence ISO2 code (ex. US, CA, AU, NZ etc.)
namsor.diasporaBatch({}, context)
- input
object
Flush counters.
namsor.flush(null, context)
This action has no parameters
Output schema unknown
Infer the likely gender of a name.
namsor.gender({
"firstName": "",
"lastName": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- firstName required
- output FirstLastNameGenderedOut
Infer the likely gender of up to 100 names, detecting automatically the cultural context.
namsor.genderBatch({}, context)
- input
object
- body BatchFirstLastNameIn
Infer the likely gender of a Chinese full name ex. 王晓明
namsor.genderChineseName({
"chineseName": ""
}, context)
- input
object
- chineseName required
string
- chineseName required
- output PersonalNameGenderedOut
Infer the likely gender of up to 100 full names ex. 王晓明
namsor.genderChineseNameBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameGenderedOut
Infer the likely gender of a Chinese name in LATIN (Pinyin).
namsor.genderChineseNamePinyin({
"chineseSurnameLatin": "",
"chineseGivenNameLatin": ""
}, context)
- input
object
- chineseSurnameLatin required
string
- chineseGivenNameLatin required
string
- chineseSurnameLatin required
- output FirstLastNameGenderedOut
Infer the likely gender of up to 100 Chinese names in LATIN (Pinyin).
namsor.genderChineseNamePinyinBatch({}, context)
- input
object
- body BatchFirstLastNameIn
Infer the likely gender of a full name, ex. John H. Smith
namsor.genderFull({
"fullName": ""
}, context)
- input
object
- fullName required
string
- fullName required
- output PersonalNameGenderedOut
Infer the likely gender of up to 100 full names, detecting automatically the cultural context.
namsor.genderFullBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameGenderedOut
Infer the likely gender of a full name, given a local context (ISO2 country code).
namsor.genderFullGeo({
"fullName": "",
"countryIso2": ""
}, context)
- input
object
- fullName required
string
- countryIso2 required
string
- fullName required
- output PersonalNameGenderedOut
Infer the likely gender of up to 100 full names, with a given cultural context (country ISO2 code).
namsor.genderFullGeoBatch({}, context)
- input
object
- output BatchPersonalNameGenderedOut
Infer the likely gender of a name, given a local context (ISO2 country code).
namsor.genderGeo({
"firstName": "",
"lastName": "",
"countryIso2": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- countryIso2 required
string
- firstName required
- output FirstLastNameGenderedOut
Infer the likely gender of up to 100 names, each given a local context (ISO2 country code).
namsor.genderGeoBatch({}, context)
- input
object
Infer the likely gender of a Japanese name in LATIN (Pinyin).
namsor.genderJapaneseNamePinyin({
"japaneseSurname": "",
"japaneseGivenName": ""
}, context)
- input
object
- japaneseSurname required
string
- japaneseGivenName required
string
- japaneseSurname required
- output FirstLastNameGenderedOut
Infer the likely gender of up to 100 Japanese names in LATIN (Pinyin).
namsor.genderJapaneseNamePinyinBatch({}, context)
- input
object
- body BatchFirstLastNameIn
Infer the likely gender of a Japanese full name ex. 王晓明
namsor.genderJapaneseNameFull({
"japaneseName": ""
}, context)
- input
object
- japaneseName required
string
- japaneseName required
- output PersonalNameGenderedOut
Infer the likely gender of up to 100 full names
namsor.genderJapaneseNameFullBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameGenderedOut
Invalidate system caches.
namsor.invalidateCache(null, context)
This action has no parameters
Output schema unknown
Identify japanese name candidates in KANJI, based on the romanized name ex. Yamamoto Sanae
namsor.japaneseNameKanjiCandidates({
"japaneseSurnameLatin": "",
"japaneseGivenNameLatin": ""
}, context)
- input
object
- japaneseSurnameLatin required
string
- japaneseGivenNameLatin required
string
- japaneseSurnameLatin required
- output RomanizedNameOut
Identify japanese name candidates in KANJI, based on the romanized name (firstName = japaneseGivenName; lastName=japaneseSurname), ex. Yamamoto Sanae
namsor.japaneseNameKanjiCandidatesBatch({}, context)
- input
object
- body BatchFirstLastNameIn
- output BatchNameMatchCandidatesOut
Romanize japanese name, based on the name in Kanji.
namsor.japaneseNameLatinCandidates({
"japaneseSurnameKanji": "",
"japaneseGivenNameKanji": ""
}, context)
- input
object
- japaneseSurnameKanji required
string
- japaneseGivenNameKanji required
string
- japaneseSurnameKanji required
- output RomanizedNameOut
Romanize japanese names, based on the name in KANJI
namsor.japaneseNameLatinCandidatesBatch({}, context)
- input
object
- body BatchFirstLastNameIn
- output BatchNameMatchCandidatesOut
Return a score for matching Japanese name in KANJI ex. 山本 早苗 with a romanized name ex. Yamamoto Sanae
namsor.japaneseNameMatch({
"japaneseSurnameLatin": "",
"japaneseGivenNameLatin": "",
"japaneseName": ""
}, context)
- input
object
- japaneseSurnameLatin required
string
- japaneseGivenNameLatin required
string
- japaneseName required
string
- japaneseSurnameLatin required
- output RomanizedNameOut
Return a score for matching a list of Japanese names in KANJI ex. 山本 早苗 with romanized names ex. Yamamoto Sanae
namsor.japaneseNameMatchBatch({}, context)
- input
object
- body BatchFirstLastNameIn
- output BatchNameMatchCandidatesOut
[CREDITS 1 UNIT] Feedback loop to better perform matching Japanese name in KANJI ex. 山本 早苗 with a romanized name ex. Yamamoto Sanae
namsor.japaneseNameMatchFeedbackLoop({
"japaneseSurnameLatin": "",
"japaneseGivenNameLatin": "",
"japaneseName": ""
}, context)
- input
object
- japaneseSurnameLatin required
string
- japaneseGivenNameLatin required
string
- japaneseName required
string
- japaneseSurnameLatin required
- output RomanizedNameOut
Activate/deactivate learning from a source.
namsor.learnable({
"source": "",
"learnable": true
}, context)
- input
object
- source required
string
- learnable required
boolean
- source required
Output schema unknown
Get the overall API counter
namsor.namsorCounter(null, context)
This action has no parameters
- output SoftwareVersionOut
[USES 10 UNITS PER NAME] Infer the likely country of origin of a personal name. Assumes names as they are in the country of origin. For US, CA, AU, NZ and other melting-pots : use 'diaspora' instead.
namsor.origin({
"firstName": "",
"lastName": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- firstName required
- output FirstLastNameOriginedOut
[USES 10 UNITS PER NAME] Infer the likely country of origin of up to 100 names, detecting automatically the cultural context.
namsor.originBatch({}, context)
- input
object
- body BatchFirstLastNameIn
Infer the likely first/last name structure of a name, ex. 王晓明 -> 王(surname) 晓明(given name)
namsor.parseChineseName({
"chineseName": ""
}, context)
- input
object
- chineseName required
string
- chineseName required
- output PersonalNameParsedOut
Infer the likely first/last name structure of a name, ex. 王晓明 -> 王(surname) 晓明(given name).
namsor.parseChineseNameBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameParsedOut
Infer the likely first/last name structure of a name, ex. 山本 早苗 or Yamamoto Sanae
namsor.parseJapaneseName({
"japaneseName": ""
}, context)
- input
object
- japaneseName required
string
- japaneseName required
- output PersonalNameParsedOut
Infer the likely first/last name structure of a name, ex. 山本 早苗 or Yamamoto Sanae
namsor.parseJapaneseNameBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameParsedOut
Infer the likely first/last name structure of a name, ex. John Smith or SMITH, John or SMITH; John.
namsor.parseName({
"nameFull": ""
}, context)
- input
object
- nameFull required
string
- nameFull required
- output PersonalNameParsedOut
Infer the likely first/last name structure of a name, ex. John Smith or SMITH, John or SMITH; John. For better accuracy, provide a geographic context.
namsor.parseNameGeo({
"nameFull": "",
"countryIso2": ""
}, context)
- input
object
- nameFull required
string
- countryIso2 required
string
- nameFull required
- output PersonalNameParsedOut
Infer the likely first/last name structure of a name, ex. John Smith or SMITH, John or SMITH; John.
namsor.parseNameBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameParsedOut
Infer the likely first/last name structure of a name, ex. John Smith or SMITH, John or SMITH; John. Giving a local context improves precision.
namsor.parseNameGeoBatch({}, context)
- input
object
- output BatchPersonalNameParsedOut
Infer the likely gender of up to 100 fully parsed names, detecting automatically the cultural context.
namsor.parsedGenderBatch({}, context)
- input
object
Infer the likely gender of up to 100 fully parsed names, detecting automatically the cultural context.
namsor.parsedGenderGeoBatch({}, context)
- input
object
Get the Stripe payment information associated with the current google auth session token.
namsor.paymentInfo({
"token": ""
}, context)
- input
object
- token required
string
- token required
- output APIKeyOut
[USES 11 UNITS PER NAME] Infer the likely country and phone prefix, given a personal name and formatted / unformatted phone number.
namsor.phoneCode({
"firstName": "",
"lastName": "",
"phoneNumber": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- phoneNumber required
string
- firstName required
- output FirstLastNamePhoneCodedOut
[USES 11 UNITS PER NAME] Infer the likely country and phone prefix, of up to 100 personal names, detecting automatically the local context given a name and formatted / unformatted phone number.
namsor.phoneCodeBatch({}, context)
- input
object
[USES 11 UNITS PER NAME] Infer the likely phone prefix, given a personal name and formatted / unformatted phone number, with a local context (ISO2 country of residence).
namsor.phoneCodeGeo({
"firstName": "",
"lastName": "",
"phoneNumber": "",
"countryIso2": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- phoneNumber required
string
- countryIso2 required
string
- firstName required
- output FirstLastNamePhoneCodedOut
[USES 11 UNITS PER NAME] Infer the likely country and phone prefix, of up to 100 personal names, with a local context (ISO2 country of residence).
namsor.phoneCodeGeoBatch({}, context)
- input
object
[CREDITS 1 UNIT] Feedback loop to better infer the likely phone prefix, given a personal name and formatted / unformatted phone number, with a local context (ISO2 country of residence).
namsor.phoneCodeGeoFeedbackLoop({
"firstName": "",
"lastName": "",
"phoneNumber": "",
"phoneNumberE164": "",
"countryIso2": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- phoneNumber required
string
- phoneNumberE164 required
string
- countryIso2 required
string
- firstName required
- output FirstLastNamePhoneCodedOut
Romanize the Chinese name to Pinyin, ex. 王晓明 -> Wang (surname) Xiaoming (given name)
namsor.pinyinChineseName({
"chineseName": ""
}, context)
- input
object
- chineseName required
string
- chineseName required
- output PersonalNameParsedOut
Romanize a list of Chinese name to Pinyin, ex. 王晓明 -> Wang (surname) Xiaoming (given name).
namsor.pinyinChineseNameBatch({}, context)
- input
object
- body BatchPersonalNameIn
- output BatchPersonalNameParsedOut
Procure an API Key (sent via Email), based on an auth token. Keep your API Key secret.
namsor.procureKey({
"token": ""
}, context)
- input
object
- token required
string
- token required
- output APIKeyOut
Redeploy UI from current dev branch.
namsor.redeployUI(null, context)
This action has no parameters
Output schema unknown
Redeploy UI from current dev branch.
namsor.redeployUI_1({
"live": true
}, context)
- input
object
- live required
boolean
- live required
Output schema unknown
Remove the user account.
namsor.removeUserAccount({
"token": ""
}, context)
- input
object
- token required
string
- token required
- output APIPlanSubscriptionOut
Remove (on behalf) a user account.
namsor.removeUserAccountOnBehalf({
"apiKey": ""
}, context)
- input
object
- apiKey required
string
- apiKey required
- output APIPlanSubscriptionOut
Stop learning and shutdown system.
namsor.shutdown(null, context)
This action has no parameters
Output schema unknown
Get the current software version
namsor.softwareVersion(null, context)
This action has no parameters
- output SoftwareVersionOut
Print basic source statistics.
namsor.sourceStats({
"source": ""
}, context)
- input
object
- source required
string
- source required
- output SystemMetricsOut
Print basic system statistics.
namsor.stats(null, context)
This action has no parameters
- output SystemMetricsOut
Connects a Stripe Account.
namsor.stripeConnect({}, context)
- input
object
- scope
string
- code
string
- error
string
- error_description
string
- scope
Output schema unknown
Subscribe to a give API plan, using the user's preferred or default currency.
namsor.subscribePlan({
"planName": "",
"token": ""
}, context)
- input
object
- planName required
string
- token required
string
- planName required
- output APIPlanSubscriptionOut
Subscribe to a give API plan, using the user's preferred or default currency (admin only).
namsor.subscribePlanOnBehalf({
"planName": "",
"apiKey": ""
}, context)
- input
object
- planName required
string
- apiKey required
string
- planName required
- output APIPlanSubscriptionOut
Print the taxonomy classes valid for the given classifier.
namsor.taxonomyClasses({
"classifierName": ""
}, context)
- input
object
- classifierName required
string
- classifierName required
- output APIPlansOut
Sets or update the billing information (company name, address, phone, vat ID)
namsor.updateBillingInfo({
"token": ""
}, context)
- input
object
- token required
string
- body BillingInfoInOut
- token required
- output BillingInfoInOut
Modifies the hard/soft limit on the API plan's overages (default is 0$ soft limit).
namsor.updateLimit({
"usageLimit": 0,
"hardOrSoft": true,
"token": ""
}, context)
- input
object
- usageLimit required
integer
- hardOrSoft required
boolean
- token required
string
- usageLimit required
- output APIPeriodUsageOut
Update the default Stripe card associated with the current google auth session token.
namsor.updatePaymentDefault({
"defautSourceId": "",
"token": ""
}, context)
- input
object
- defautSourceId required
string
- token required
string
- defautSourceId required
- output APIKeyOut
[USES 10 UNITS PER NAME] Infer a US resident's likely race/ethnicity according to US Census taxonomy W_NL (white, non latino), HL (hispano latino), A (asian, non latino), B_NL (black, non latino).
namsor.usRaceEthnicity({
"firstName": "",
"lastName": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- firstName required
[USES 10 UNITS PER NAME] Infer up-to 100 US resident's likely race/ethnicity according to US Census taxonomy.
namsor.usRaceEthnicityBatch({}, context)
- input
object
[USES 10 UNITS PER NAME] Infer a US resident's likely race/ethnicity according to US Census taxonomy, using (optional) ZIP5 code info. Output is W_NL (white, non latino), HL (hispano latino), A (asian, non latino), B_NL (black, non latino).
namsor.usRaceEthnicityZIP5({
"firstName": "",
"lastName": "",
"zip5Code": ""
}, context)
- input
object
- firstName required
string
- lastName required
string
- zip5Code required
string
- firstName required
[USES 10 UNITS PER NAME] Infer up-to 100 US resident's likely race/ethnicity according to US Census taxonomy, with (optional) ZIP code.
namsor.usZipRaceEthnicityBatch({}, context)
- input
object
Get the user profile associated with the current google auth session token.
namsor.userInfo({
"token": ""
}, context)
- input
object
- token required
string
- token required
- output APIKeyOut
Verifies an email, based on token sent to that email
namsor.verifyEmail({
"emailToken": ""
}, context)
- input
object
- emailToken required
string
- emailToken required
- output APIKeyOut
Verifies an email, based on token sent to that email
namsor.verifyRemoveEmail({
"emailToken": ""
}, context)
- input
object
- emailToken required
string
- emailToken required
- output APIKeyOut
Vetting of a source.
namsor.vet({
"source": "",
"vetted": true
}, context)
- input
object
- source required
string
- vetted required
boolean
- source required
Output schema unknown
- APIBillingPeriodUsageOut
object
- apiKey
string
- billingStatus
string
- hardLimit
integer
- periodEnded
integer
- periodStarted
integer
- softLimit
integer
- stripeCurrentPeriodEnd
integer
- stripeCurrentPeriodStart
integer
- subscriptionStarted
integer
- usage
integer
- apiKey
- APIClassifierOut
object
- classifierName
string
- learning
boolean
- probabilityCalibrated
boolean
- serving
boolean
- shuttingDown
boolean
- classifierName
- APIClassifierTaxonomyOut
object
- classifierName
string
- taxonomyClasses
array
- items
string
- items
- classifierName
- APIClassifiersStatusOut
object
- classifiers
array
- items APIClassifierOut
- softwareVersion SoftwareVersionOut
- classifiers
- APICounterV2Out
object
- apiKey APIKeyOut
- apiService
string
- createdDateTime
integer
- lastFlushedDateTime
integer
- lastUsedDateTime
integer
- serviceFeaturesUsage
object
- totalUsage
integer
- APIKeyOut
object
- admin
boolean
- anonymized
boolean
- apiKey
string
- corporate
boolean
- disabled
boolean
- learnable
boolean
- partner
boolean
- striped
boolean
- userId
string
- vetted
boolean
- admin
- APIPeriodUsageOut
object
- billingPeriod APIBillingPeriodUsageOut
- overageCurrency
string
- overageExclTax
number
- overageInclTax
number
- overageQuantity
integer
- subscription APIPlanSubscriptionOut
- APIPlanOut
object
- planName
string
- planQuota
integer
- price
number
- priceOverage
number
- planName
- APIPlanSubscriptionOut
object
- apiKey
string
- currency
string
- currencyFactor
number
- planBaseFeesKey
string
- planEnded
integer
- planName
string
- planQuota
integer
- planStarted
integer
- planStatus
string
- price
number
- priceOverage
number
- priceOverageUSD
number
- priceUSD
number
- priorPlanStarted
integer
- stripeCustomerId
string
- stripeStatus
string
- stripeSubscription
string
- taxRate
number
- userId
string
- apiKey
- APIPlansOut
object
- currencyIso3
string
- currencySymbol
string
- plans
array
- items APIPlanOut
- usageRatioForDupplicates
integer
- currencyIso3
- APIServiceOut
object
- costInUnits
integer
- serviceGroup
string
- serviceName
string
- costInUnits
- APIServicesOut
object
- apiServices
array
- items APIServiceOut
- apiServices
- APIUsageAggregatedOut
object
- colHeaders
array
- items
string
- items
- data
array
- items
array
- items
integer
- items
- items
- historyTruncated
boolean
- periodEnd
integer
- periodStart
integer
- rowHeaders
array
- items
string
- items
- timeUnit
string
- totalUsage
integer
- colHeaders
- BatchFirstLastNameDiasporaedOut
object
: Represents the output of inferring the LIKELY ethnicity from a personal name, given an country of residence.- personalNames
array
- personalNames
- BatchFirstLastNameGenderIn
object
- personalNames
array
- items FirstLastNameGenderIn
- personalNames
- BatchFirstLastNameGenderedOut
object
: Represents the output of inferring the LIKELY gender from a list of personal names.- personalNames
array
- items FirstLastNameGenderedOut
- personalNames
- BatchFirstLastNameGeoIn
object
- personalNames
array
- items FirstLastNameGeoIn
- personalNames
- BatchFirstLastNameGeoZippedIn
object
- personalNames
array
- items FirstLastNameGeoZippedIn
- personalNames
- BatchFirstLastNameIn
object
- personalNames
array
- items FirstLastNameIn
- personalNames
- BatchFirstLastNameOriginedOut
object
: Represents the output of inferring the LIKELY origin from a list of personal names.- personalNames
array
- items FirstLastNameOriginedOut
- personalNames
- BatchFirstLastNamePhoneCodedOut
object
: Represents the output of inferring the LIKELY country and phone code of personal names+phones.- personalNamesWithPhoneNumbers
array
- personalNamesWithPhoneNumbers
- BatchFirstLastNamePhoneNumberGeoIn
object
- personalNamesWithPhoneNumbers
array
- personalNamesWithPhoneNumbers
- BatchFirstLastNamePhoneNumberIn
object
- personalNamesWithPhoneNumbers
array
- personalNamesWithPhoneNumbers
- BatchFirstLastNameUSRaceEthnicityOut
object
: Represents the output of inferring the LIKELY US 'race/ethnicity' from a personal name, given US country of residence and (optionally) a ZIP5 code.- personalNames
array
- personalNames
- BatchMatchPersonalFirstLastNameIn
object
- personalNames
array
- personalNames
- BatchNameMatchCandidatesOut
object
- namesAndMatchCandidates
array
- items NameMatchCandidatesOut
- namesAndMatchCandidates
- BatchNameMatchedOut
object
- matchedNames
array
- items NameMatchedOut
- matchedNames
- BatchParsedFullNameGeoIn
object
- personalNames
array
- items ParsedFullNameGeoIn
- personalNames
- BatchParsedFullNameIn
object
- personalNames
array
- items ParsedFullNameIn
- personalNames
- BatchPersonalNameGenderedOut
object
- personalNames
array
- items PersonalNameGenderedOut
- personalNames
- BatchPersonalNameGeoIn
object
- personalNames
array
- items PersonalNameGeoIn
- personalNames
- BatchPersonalNameGeoOut
object
- personalNames
array
- items PersonalNameGeoOut
- personalNames
- BatchPersonalNameIn
object
- personalNames
array
- items PersonalNameIn
- personalNames
- BatchPersonalNameParsedOut
object
- personalNames
array
- items PersonalNameParsedOut
- personalNames
- BillingHistoryOut
object
- corporateInvoices
array
- items InvoiceOut
- stripeInvoices
array
- items InvoiceOut
- corporateInvoices
- BillingInfoInOut
object
- addressCity
string
- addressCountry
string
- addressLine1
string
- addressLine2
string
- addressPostalCode
string
- addressState
string
- billingEmail
string
- customerName
string
- customerPhone
string
- preferredCurrency
string
- vatID
string
- addressCity
- CacheMetricsOut
object
: Simple metrics system caches- cacheName
string
- cacheStats
string
- cacheName
- ClassifierMetricsOut
object
: Simple metrics on a classifier- aiNonVettedEstimatePrecision
number
- aiNonVettedEstimateRecall
number
- aiNonVettedEstimateTotal
integer
- aiNonVettedExpectedClassMetrics
array
- items ExpectedClassMetricsOut
- aiNonVettedLearnTotal
integer
- aiStartTime
integer
- aiVettedEstimatePrecision
number
- aiVettedEstimateRecall
number
- aiVettedEstimateTotal
integer
- aiVettedExpectedClassMetrics
array
- items ExpectedClassMetricsOut
- aiVettedLearnTotal
integer
- bufferSize
integer
- classifierName
string
- classifyDurationsCurrent
number
- classifyDurationsSummary
number
- factKeysSize
integer
- factsLearned
integer
- featuresSize
integer
- hostAddress
string
- learnDurationsCurrent
number
- learnDurationsSummary
number
- learnQueueSize
integer
- metricTimeStamp
integer
- preClassifyQueueSize
integer
- softwareVersion
string
- aiNonVettedEstimatePrecision
- CurrenciesOut
object
- currenciesIso3
array
- items
string
- items
- currenciesIso3
- DeployUIOut
object
- errorMessage
string
- succeeded
boolean
- errorMessage
- ExpectedClassMetricsOut
object
: Simple metrics on a classifier, for a given expected class- aiEstimatePrecision
number
- aiEstimateRecall
number
- aiEstimateTotal
integer
- aiLearnTotal
integer
- classifierName
string
- expectedClass
string
- aiEstimatePrecision
- FeedbackLoopOut
object
- feedbackCredits
integer
- feedbackCredits
- FirstLastNameDiasporaedOut
object
: Represents the output of inferring the LIKELY ethnicity from a personal name, given an country of residence.- countryIso2
string
- ethnicitiesTop
array
: List ethnicities (top 10)- items
string
: List ethnicities (top 10)
- items
- ethnicity
string
- ethnicityAlt
string
- firstName
string
- id
string
- lastName
string
- lifted
boolean
- score
number
: Compatibility to NamSor_v1 Origin score value
- countryIso2
- FirstLastNameGenderIn
object
- firstName
string
- gender
string
- id
string
- lastName
string
- firstName
- FirstLastNameGenderedOut
object
: Represents the output of inferring the LIKELY gender from a personal name.- firstName
string
- genderScale
number
: Compatibility to NamSor_v1 Gender Scale M[-1..U..+1]F value - id
string
- lastName
string
- likelyGender
string
(values: male, female, unknown): Most likely gender - probabilityCalibrated
number
- score
number
- firstName
- FirstLastNameGeoIn
object
- countryIso2
string
- firstName
string
- id
string
- lastName
string
- countryIso2
- FirstLastNameGeoZippedIn
object
- countryIso2
string
- firstName
string
- id
string
- lastName
string
- zipCode
string
- countryIso2
- FirstLastNameIn
object
- firstName
string
- id
string
- lastName
string
- firstName
- FirstLastNameOriginedOut
object
: Represents the output of inferring the LIKELY country of Origin from a personal name.- countriesOriginTop
array
: List countries of Origin (top 10)- items
string
: List countries of Origin (top 10)
- items
- countryOrigin
string
: Most likely country of Origin - countryOriginAlt
string
: Second best alternative : country of Origin - firstName
string
- id
string
- lastName
string
- probabilityAltCalibrated
number
- probabilityCalibrated
number
- regionOrigin
string
: Most likely region of Origin (based on countryOrigin ISO2 code) - score
number
: Compatibility to NamSor_v1 Origin score value - subRegionOrigin
string
: Most likely region of Origin (based on countryOrigin ISO2 code) - topRegionOrigin
string
: Most likely region of Origin (based on countryOrigin ISO2 code)
- countriesOriginTop
- FirstLastNameOut
object
- firstName
string
- id
string
- lastName
string
- firstName
- FirstLastNamePhoneCodedOut
object
: Represents the output of inferring the LIKELY country and phone code from a personal name and phone number.- countryIso2
string
- firstName
string
- id
string
- internationalPhoneNumberVerified
string
- lastName
string
- originCountryIso2
string
- originCountryIso2Alt
string
- phoneCountryCode
integer
- phoneCountryCodeAlt
integer
- phoneCountryIso2
string
- phoneCountryIso2Alt
string
- phoneCountryIso2Verified
string
- phoneNumber
string
- score
number
- verified
boolean
- countryIso2
- FirstLastNamePhoneNumberGeoIn
object
- FirstLastNameOriginedOut FirstLastNameOriginedOut
- countryIso2
string
- countryIso2Alt
string
- firstName
string
- id
string
- lastName
string
- phoneNumber
string
- FirstLastNamePhoneNumberIn
object
- FirstLastNameOriginedOut FirstLastNameOriginedOut
- firstName
string
- id
string
- lastName
string
- phoneNumber
string
- FirstLastNameUSRaceEthnicityOut
object
: Represents the output of inferring the LIKELY US 'race/ethnicity' from a personal name, given US country of residence and (optionally) a ZIP5 code.- firstName
string
- id
string
- lastName
string
- probabilityAltCalibrated
number
- probabilityCalibrated
number
- raceEthnicitiesTop
array
: List 'race'/ethnicities- items
string
: List 'race'/ethnicities
- items
- raceEthnicity
string
(values: W_NL, HL, A, B_NL): Most likely US 'race'/ethnicity - raceEthnicityAlt
string
(values: W_NL, HL, A, B_NL): Second most likely US 'race'/ethnicity - score
number
: Compatibility to NamSor_v1 Origin score value
- firstName
- InvoiceItemOut
object
- amount
integer
- currency
string
- description
string
- invoiceItemType
string
- itemId
string
- planDesc
string
- planName
string
- planNickname
string
- quantity
integer
- subscription
string
- subscriptionItem
string
- amount
- InvoiceOut
object
- amountDue
integer
- amountPaid
integer
- amountRemaining
integer
- attempted
boolean
- currency
string
- description
string
- dueDate
string
- invoiceDate
string
- invoiceId
string
- invoicePdf
string
- invoiceStatus
string
- isStriped
boolean
- items
array
- items InvoiceItemOut
- periodEnd
string
- periodStart
string
- receiptNumber
string
- stripeCustomerId
string
- subTotal
integer
- tax
integer
- taxPercent
integer
- total
integer
- userId
string
- amountDue
- MatchPersonalFirstLastNameIn
object
- id
string
- name
string
- name1 FirstLastNameIn
- name2 PersonalNameIn
- id
- NamSorCounterOut
object
- counter
integer
- counter
- NameMatchCandidateOut
object
- candidateName
string
- probability
number
- candidateName
- NameMatchCandidatesOut
object
- firstName
string
- id
string
- lastName
string
- matchCandidates
array
- items NameMatchCandidateOut
- firstName
- NameMatchedOut
object
- id
string
- matchStatus
string
- score
number
- id
- ParsedFullNameGeoIn
object
- countryIso2
string
- firstName
string
- id
string
- lastName
string
- middleName
string
- prefixOrTitle
string
- suffix
string
- countryIso2
- ParsedFullNameIn
object
- firstName
string
- id
string
- lastName
string
- middleName
string
- prefixOrTitle
string
- suffix
string
- firstName
- PersonalNameGenderedOut
object
- genderScale
number
: Compatibility to NamSor_v1 Gender Scale M[-1..U..+1]F value - id
string
- likelyGender
string
(values: male, female, unknown): Most likely gender - name
string
- probabilityCalibrated
number
- score
number
- genderScale
- PersonalNameGeoIn
object
- countryIso2
string
- id
string
- name
string
- countryIso2
- PersonalNameGeoOut
object
- countriesTop
array
: List countries (top 10)- items
string
: List countries (top 10)
- items
- country
string
- countryAlt
string
- id
string
- name
string
- probabilityAltCalibrated
number
- probabilityCalibrated
number
- region
string
- score
number
- subRegion
string
- topRegion
string
- countriesTop
- PersonalNameIn
object
- id
string
- name
string
- id
- PersonalNameParsedOut
object
- firstLastName FirstLastNameOut
- id
string
- name
string
- nameParserType
string
- nameParserTypeAlt
string
- score
number
- RomanizedNameOut
object
- id
string
- latinName
string
- originalName
string
- score
number
- sourceLanguage
string
- sourceScript
string
- targetLanguage
string
- targetScript
string
- id
- SoftwareVersionOut
object
- softwareNameAndVersion
string
- softwareVersion
array
- items
integer
- items
- softwareNameAndVersion
- SourceDetailedMetricsOut
object
: Simple metrics on source, with details by classifier.- aiEstimatePrecision
number
- aiEstimateRecall
number
- aiEstimateTotal
integer
- aiLearnTotal
integer
- aiStartTime
integer
- classifierName
string
- expectedClassMetrics
array
- items ExpectedClassMetricsOut
- metricTimeStamp
integer
- snapshotDate
integer
- source APIKeyOut
- aiEstimatePrecision
- SourceMetricsOut
object
: Simple metrics on a classifier, for a given source- aiEstimatePrecision
number
- aiEstimateRecall
number
- aiEstimateTotal
integer
- aiLearnTotal
integer
- aiStartTime
integer
- classifierName
string
- metricTimeStamp
integer
- snapshotDate
integer
- source APIKeyOut
- aiEstimatePrecision
- StripeCardOut
object
- brand
string
- defaultCard
boolean
- expMonth
integer
- expYear
integer
- last4
string
- sourceId
string
- brand
- StripeCustomerOut
object
- sourceCountry
string
- sourceCurrency
string
- stripeCustomerId
string
- stripedCards
array
- items StripeCardOut
- sourceCountry
- SystemMetricsOut
object
- cacheMetrics
array
- items CacheMetricsOut
- classifierMetrics
array
- items ClassifierMetricsOut
- freeMem
integer
- maxMem
integer
- sourceMetrics
array
- items SourceMetricsOut
- totalMem
integer
- cacheMetrics
- UserInfoOut
object
- apiKey
string
- disabled
boolean
- displayName
string
- email
string
- emailVerified
boolean
- firstKnownIpAddress
string
- otherInfos
array
- items UserInfoOut
- phoneNumber
string
- photoUrl
string
- providerId
string
- stripeCustomerId
string
- stripePerishableKey
string
- timeStamp
integer
- uid
string
- verifyToken
string
- apiKey