# pylint: disable=too-many-lines
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
# pylint: disable=unused-argument
import re
from enum import Enum
from typing import Optional, List, Dict, Any, Union
from typing_extensions import Literal
from azure.core import CaseInsensitiveEnumMeta
from ._generated.models import (
LanguageInput,
MultiLanguageInput,
AgeResolution,
AreaResolution,
CurrencyResolution,
DateTimeResolution,
InformationResolution,
LengthResolution,
NumberResolution,
NumericRangeResolution,
OrdinalResolution,
SpeedResolution,
TemperatureResolution,
TemporalSpanResolution,
VolumeResolution,
WeightResolution,
HealthcareDocumentType,
)
from ._generated.v3_0 import models as _v3_0_models
from ._generated.v3_1 import models as _v3_1_models
from ._generated.v2022_10_01_preview import models as _v2022_10_01_preview_models
from ._check import is_language_api, string_index_type_compatibility
from ._dict_mixin import DictMixin
STRING_INDEX_TYPE_DEFAULT = "UnicodeCodePoint"
def _get_indices(relation):
return [int(s) for s in re.findall(r"\d+", relation)]
[docs]class TextAnalysisKind(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""Enumeration of supported Text Analysis kinds.
.. versionadded:: 2022-05-01
The *TextAnalysisKind* enum.
"""
SENTIMENT_ANALYSIS = "SentimentAnalysis"
ENTITY_RECOGNITION = "EntityRecognition"
PII_ENTITY_RECOGNITION = "PiiEntityRecognition"
KEY_PHRASE_EXTRACTION = "KeyPhraseExtraction"
ENTITY_LINKING = "EntityLinking"
HEALTHCARE = "Healthcare"
CUSTOM_ENTITY_RECOGNITION = "CustomEntityRecognition"
CUSTOM_DOCUMENT_CLASSIFICATION = "CustomDocumentClassification"
LANGUAGE_DETECTION = "LanguageDetection"
EXTRACTIVE_SUMMARIZATION = "ExtractiveSummarization"
ABSTRACTIVE_SUMMARIZATION = "AbstractiveSummarization"
DYNAMIC_CLASSIFICATION = "DynamicClassification"
[docs]class EntityAssociation(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""Describes if the entity is the subject of the text or if it describes someone else."""
SUBJECT = "subject"
OTHER = "other"
[docs]class EntityCertainty(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""Describes the entities certainty and polarity."""
POSITIVE = "positive"
POSITIVE_POSSIBLE = "positivePossible"
NEUTRAL_POSSIBLE = "neutralPossible"
NEGATIVE_POSSIBLE = "negativePossible"
NEGATIVE = "negative"
[docs]class EntityConditionality(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""Describes any conditionality on the entity."""
HYPOTHETICAL = "hypothetical"
CONDITIONAL = "conditional"
[docs]class HealthcareEntityRelation(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""Type of relation. Examples include: 'DosageOfMedication' or 'FrequencyOfMedication', etc."""
ABBREVIATION = "Abbreviation"
DIRECTION_OF_BODY_STRUCTURE = "DirectionOfBodyStructure"
DIRECTION_OF_CONDITION = "DirectionOfCondition"
DIRECTION_OF_EXAMINATION = "DirectionOfExamination"
DIRECTION_OF_TREATMENT = "DirectionOfTreatment"
DOSAGE_OF_MEDICATION = "DosageOfMedication"
FORM_OF_MEDICATION = "FormOfMedication"
FREQUENCY_OF_MEDICATION = "FrequencyOfMedication"
FREQUENCY_OF_TREATMENT = "FrequencyOfTreatment"
QUALIFIER_OF_CONDITION = "QualifierOfCondition"
RELATION_OF_EXAMINATION = "RelationOfExamination"
ROUTE_OF_MEDICATION = "RouteOfMedication"
TIME_OF_CONDITION = "TimeOfCondition"
TIME_OF_EVENT = "TimeOfEvent"
TIME_OF_EXAMINATION = "TimeOfExamination"
TIME_OF_MEDICATION = "TimeOfMedication"
TIME_OF_TREATMENT = "TimeOfTreatment"
UNIT_OF_CONDITION = "UnitOfCondition"
UNIT_OF_EXAMINATION = "UnitOfExamination"
VALUE_OF_CONDITION = "ValueOfCondition"
VALUE_OF_EXAMINATION = "ValueOfExamination"
[docs]class PiiEntityCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""Categories of Personally Identifiable Information (PII)."""
ABA_ROUTING_NUMBER = "ABARoutingNumber"
AR_NATIONAL_IDENTITY_NUMBER = "ARNationalIdentityNumber"
AU_BANK_ACCOUNT_NUMBER = "AUBankAccountNumber"
AU_DRIVERS_LICENSE_NUMBER = "AUDriversLicenseNumber"
AU_MEDICAL_ACCOUNT_NUMBER = "AUMedicalAccountNumber"
AU_PASSPORT_NUMBER = "AUPassportNumber"
AU_TAX_FILE_NUMBER = "AUTaxFileNumber"
AU_BUSINESS_NUMBER = "AUBusinessNumber"
AU_COMPANY_NUMBER = "AUCompanyNumber"
AT_IDENTITY_CARD = "ATIdentityCard"
AT_TAX_IDENTIFICATION_NUMBER = "ATTaxIdentificationNumber"
AT_VALUE_ADDED_TAX_NUMBER = "ATValueAddedTaxNumber"
AZURE_DOCUMENT_DB_AUTH_KEY = "AzureDocumentDBAuthKey"
AZURE_IAAS_DATABASE_CONNECTION_AND_SQL_STRING = (
"AzureIAASDatabaseConnectionAndSQLString"
)
AZURE_IO_T_CONNECTION_STRING = "AzureIoTConnectionString"
AZURE_PUBLISH_SETTING_PASSWORD = "AzurePublishSettingPassword"
AZURE_REDIS_CACHE_STRING = "AzureRedisCacheString"
AZURE_SAS = "AzureSAS"
AZURE_SERVICE_BUS_STRING = "AzureServiceBusString"
AZURE_STORAGE_ACCOUNT_KEY = "AzureStorageAccountKey"
AZURE_STORAGE_ACCOUNT_GENERIC = "AzureStorageAccountGeneric"
BE_NATIONAL_NUMBER = "BENationalNumber"
BE_NATIONAL_NUMBER_V2 = "BENationalNumberV2"
BE_VALUE_ADDED_TAX_NUMBER = "BEValueAddedTaxNumber"
BRCPF_NUMBER = "BRCPFNumber"
BR_LEGAL_ENTITY_NUMBER = "BRLegalEntityNumber"
BR_NATIONAL_IDRG = "BRNationalIDRG"
BG_UNIFORM_CIVIL_NUMBER = "BGUniformCivilNumber"
CA_BANK_ACCOUNT_NUMBER = "CABankAccountNumber"
CA_DRIVERS_LICENSE_NUMBER = "CADriversLicenseNumber"
CA_HEALTH_SERVICE_NUMBER = "CAHealthServiceNumber"
CA_PASSPORT_NUMBER = "CAPassportNumber"
CA_PERSONAL_HEALTH_IDENTIFICATION = "CAPersonalHealthIdentification"
CA_SOCIAL_INSURANCE_NUMBER = "CASocialInsuranceNumber"
CL_IDENTITY_CARD_NUMBER = "CLIdentityCardNumber"
CN_RESIDENT_IDENTITY_CARD_NUMBER = "CNResidentIdentityCardNumber"
CREDIT_CARD_NUMBER = "CreditCardNumber"
HR_IDENTITY_CARD_NUMBER = "HRIdentityCardNumber"
HR_NATIONAL_ID_NUMBER = "HRNationalIDNumber"
HR_PERSONAL_IDENTIFICATION_NUMBER = "HRPersonalIdentificationNumber"
HR_PERSONAL_IDENTIFICATION_OIB_NUMBER_V2 = "HRPersonalIdentificationOIBNumberV2"
CY_IDENTITY_CARD = "CYIdentityCard"
CY_TAX_IDENTIFICATION_NUMBER = "CYTaxIdentificationNumber"
CZ_PERSONAL_IDENTITY_NUMBER = "CZPersonalIdentityNumber"
CZ_PERSONAL_IDENTITY_V2 = "CZPersonalIdentityV2"
DK_PERSONAL_IDENTIFICATION_NUMBER = "DKPersonalIdentificationNumber"
DK_PERSONAL_IDENTIFICATION_V2 = "DKPersonalIdentificationV2"
DRUG_ENFORCEMENT_AGENCY_NUMBER = "DrugEnforcementAgencyNumber"
EE_PERSONAL_IDENTIFICATION_CODE = "EEPersonalIdentificationCode"
EU_DEBIT_CARD_NUMBER = "EUDebitCardNumber"
EU_DRIVERS_LICENSE_NUMBER = "EUDriversLicenseNumber"
EUGPS_COORDINATES = "EUGPSCoordinates"
EU_NATIONAL_IDENTIFICATION_NUMBER = "EUNationalIdentificationNumber"
EU_PASSPORT_NUMBER = "EUPassportNumber"
EU_SOCIAL_SECURITY_NUMBER = "EUSocialSecurityNumber"
EU_TAX_IDENTIFICATION_NUMBER = "EUTaxIdentificationNumber"
FI_EUROPEAN_HEALTH_NUMBER = "FIEuropeanHealthNumber"
FI_NATIONAL_ID = "FINationalID"
FI_NATIONAL_IDV2 = "FINationalIDV2"
FI_PASSPORT_NUMBER = "FIPassportNumber"
FR_DRIVERS_LICENSE_NUMBER = "FRDriversLicenseNumber"
FR_HEALTH_INSURANCE_NUMBER = "FRHealthInsuranceNumber"
FR_NATIONAL_ID = "FRNationalID"
FR_PASSPORT_NUMBER = "FRPassportNumber"
FR_SOCIAL_SECURITY_NUMBER = "FRSocialSecurityNumber"
FR_TAX_IDENTIFICATION_NUMBER = "FRTaxIdentificationNumber"
FR_VALUE_ADDED_TAX_NUMBER = "FRValueAddedTaxNumber"
DE_DRIVERS_LICENSE_NUMBER = "DEDriversLicenseNumber"
DE_PASSPORT_NUMBER = "DEPassportNumber"
DE_IDENTITY_CARD_NUMBER = "DEIdentityCardNumber"
DE_TAX_IDENTIFICATION_NUMBER = "DETaxIdentificationNumber"
DE_VALUE_ADDED_NUMBER = "DEValueAddedNumber"
GR_NATIONAL_ID_CARD = "GRNationalIDCard"
GR_NATIONAL_IDV2 = "GRNationalIDV2"
GR_TAX_IDENTIFICATION_NUMBER = "GRTaxIdentificationNumber"
HK_IDENTITY_CARD_NUMBER = "HKIdentityCardNumber"
HU_VALUE_ADDED_NUMBER = "HUValueAddedNumber"
HU_PERSONAL_IDENTIFICATION_NUMBER = "HUPersonalIdentificationNumber"
HU_TAX_IDENTIFICATION_NUMBER = "HUTaxIdentificationNumber"
IN_PERMANENT_ACCOUNT = "INPermanentAccount"
IN_UNIQUE_IDENTIFICATION_NUMBER = "INUniqueIdentificationNumber"
ID_IDENTITY_CARD_NUMBER = "IDIdentityCardNumber"
INTERNATIONAL_BANKING_ACCOUNT_NUMBER = "InternationalBankingAccountNumber"
IE_PERSONAL_PUBLIC_SERVICE_NUMBER = "IEPersonalPublicServiceNumber"
IE_PERSONAL_PUBLIC_SERVICE_NUMBER_V2 = "IEPersonalPublicServiceNumberV2"
IL_BANK_ACCOUNT_NUMBER = "ILBankAccountNumber"
IL_NATIONAL_ID = "ILNationalID"
IT_DRIVERS_LICENSE_NUMBER = "ITDriversLicenseNumber"
IT_FISCAL_CODE = "ITFiscalCode"
IT_VALUE_ADDED_TAX_NUMBER = "ITValueAddedTaxNumber"
JP_BANK_ACCOUNT_NUMBER = "JPBankAccountNumber"
JP_DRIVERS_LICENSE_NUMBER = "JPDriversLicenseNumber"
JP_PASSPORT_NUMBER = "JPPassportNumber"
JP_RESIDENT_REGISTRATION_NUMBER = "JPResidentRegistrationNumber"
JP_SOCIAL_INSURANCE_NUMBER = "JPSocialInsuranceNumber"
JP_MY_NUMBER_CORPORATE = "JPMyNumberCorporate"
JP_MY_NUMBER_PERSONAL = "JPMyNumberPersonal"
JP_RESIDENCE_CARD_NUMBER = "JPResidenceCardNumber"
LV_PERSONAL_CODE = "LVPersonalCode"
LT_PERSONAL_CODE = "LTPersonalCode"
LU_NATIONAL_IDENTIFICATION_NUMBER_NATURAL = "LUNationalIdentificationNumberNatural"
LU_NATIONAL_IDENTIFICATION_NUMBER_NON_NATURAL = (
"LUNationalIdentificationNumberNonNatural"
)
MY_IDENTITY_CARD_NUMBER = "MYIdentityCardNumber"
MT_IDENTITY_CARD_NUMBER = "MTIdentityCardNumber"
MT_TAX_ID_NUMBER = "MTTaxIDNumber"
NL_CITIZENS_SERVICE_NUMBER = "NLCitizensServiceNumber"
NL_CITIZENS_SERVICE_NUMBER_V2 = "NLCitizensServiceNumberV2"
NL_TAX_IDENTIFICATION_NUMBER = "NLTaxIdentificationNumber"
NL_VALUE_ADDED_TAX_NUMBER = "NLValueAddedTaxNumber"
NZ_BANK_ACCOUNT_NUMBER = "NZBankAccountNumber"
NZ_DRIVERS_LICENSE_NUMBER = "NZDriversLicenseNumber"
NZ_INLAND_REVENUE_NUMBER = "NZInlandRevenueNumber"
NZ_MINISTRY_OF_HEALTH_NUMBER = "NZMinistryOfHealthNumber"
NZ_SOCIAL_WELFARE_NUMBER = "NZSocialWelfareNumber"
NO_IDENTITY_NUMBER = "NOIdentityNumber"
PH_UNIFIED_MULTI_PURPOSE_ID_NUMBER = "PHUnifiedMultiPurposeIDNumber"
PL_IDENTITY_CARD = "PLIdentityCard"
PL_NATIONAL_ID = "PLNationalID"
PL_NATIONAL_IDV2 = "PLNationalIDV2"
PL_PASSPORT_NUMBER = "PLPassportNumber"
PL_TAX_IDENTIFICATION_NUMBER = "PLTaxIdentificationNumber"
PLREGON_NUMBER = "PLREGONNumber"
PT_CITIZEN_CARD_NUMBER = "PTCitizenCardNumber"
PT_CITIZEN_CARD_NUMBER_V2 = "PTCitizenCardNumberV2"
PT_TAX_IDENTIFICATION_NUMBER = "PTTaxIdentificationNumber"
RO_PERSONAL_NUMERICAL_CODE = "ROPersonalNumericalCode"
RU_PASSPORT_NUMBER_DOMESTIC = "RUPassportNumberDomestic"
RU_PASSPORT_NUMBER_INTERNATIONAL = "RUPassportNumberInternational"
SA_NATIONAL_ID = "SANationalID"
SG_NATIONAL_REGISTRATION_IDENTITY_CARD_NUMBER = (
"SGNationalRegistrationIdentityCardNumber"
)
SK_PERSONAL_NUMBER = "SKPersonalNumber"
SI_TAX_IDENTIFICATION_NUMBER = "SITaxIdentificationNumber"
SI_UNIQUE_MASTER_CITIZEN_NUMBER = "SIUniqueMasterCitizenNumber"
ZA_IDENTIFICATION_NUMBER = "ZAIdentificationNumber"
KR_RESIDENT_REGISTRATION_NUMBER = "KRResidentRegistrationNumber"
ESDNI = "ESDNI"
ES_SOCIAL_SECURITY_NUMBER = "ESSocialSecurityNumber"
ES_TAX_IDENTIFICATION_NUMBER = "ESTaxIdentificationNumber"
SQL_SERVER_CONNECTION_STRING = "SQLServerConnectionString"
SE_NATIONAL_ID = "SENationalID"
SE_NATIONAL_IDV2 = "SENationalIDV2"
SE_PASSPORT_NUMBER = "SEPassportNumber"
SE_TAX_IDENTIFICATION_NUMBER = "SETaxIdentificationNumber"
SWIFT_CODE = "SWIFTCode"
CH_SOCIAL_SECURITY_NUMBER = "CHSocialSecurityNumber"
TW_NATIONAL_ID = "TWNationalID"
TW_PASSPORT_NUMBER = "TWPassportNumber"
TW_RESIDENT_CERTIFICATE = "TWResidentCertificate"
TH_POPULATION_IDENTIFICATION_CODE = "THPopulationIdentificationCode"
TR_NATIONAL_IDENTIFICATION_NUMBER = "TRNationalIdentificationNumber"
UK_DRIVERS_LICENSE_NUMBER = "UKDriversLicenseNumber"
UK_ELECTORAL_ROLL_NUMBER = "UKElectoralRollNumber"
UK_NATIONAL_HEALTH_NUMBER = "UKNationalHealthNumber"
UK_NATIONAL_INSURANCE_NUMBER = "UKNationalInsuranceNumber"
UK_UNIQUE_TAXPAYER_NUMBER = "UKUniqueTaxpayerNumber"
USUK_PASSPORT_NUMBER = "USUKPassportNumber"
US_BANK_ACCOUNT_NUMBER = "USBankAccountNumber"
US_DRIVERS_LICENSE_NUMBER = "USDriversLicenseNumber"
US_INDIVIDUAL_TAXPAYER_IDENTIFICATION = "USIndividualTaxpayerIdentification"
US_SOCIAL_SECURITY_NUMBER = "USSocialSecurityNumber"
UA_PASSPORT_NUMBER_DOMESTIC = "UAPassportNumberDomestic"
UA_PASSPORT_NUMBER_INTERNATIONAL = "UAPassportNumberInternational"
ORGANIZATION = "Organization"
EMAIL = "Email"
URL = "URL"
AGE = "Age"
PHONE_NUMBER = "PhoneNumber"
IP_ADDRESS = "IPAddress"
DATE = "Date"
PERSON = "Person"
ADDRESS = "Address"
ALL = "All"
DEFAULT = "Default"
[docs]class HealthcareEntityCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""Healthcare Entity Category."""
BODY_STRUCTURE = "BodyStructure"
AGE = "Age"
GENDER = "Gender"
EXAMINATION_NAME = "ExaminationName"
DATE = "Date"
DIRECTION = "Direction"
FREQUENCY = "Frequency"
MEASUREMENT_VALUE = "MeasurementValue"
MEASUREMENT_UNIT = "MeasurementUnit"
RELATIONAL_OPERATOR = "RelationalOperator"
TIME = "Time"
GENE_OR_PROTEIN = "GeneOrProtein"
VARIANT = "Variant"
ADMINISTRATIVE_EVENT = "AdministrativeEvent"
CARE_ENVIRONMENT = "CareEnvironment"
HEALTHCARE_PROFESSION = "HealthcareProfession"
DIAGNOSIS = "Diagnosis"
SYMPTOM_OR_SIGN = "SymptomOrSign"
CONDITION_QUALIFIER = "ConditionQualifier"
MEDICATION_CLASS = "MedicationClass"
MEDICATION_NAME = "MedicationName"
DOSAGE = "Dosage"
MEDICATION_FORM = "MedicationForm"
MEDICATION_ROUTE = "MedicationRoute"
FAMILY_RELATION = "FamilyRelation"
TREATMENT_NAME = "TreatmentName"
[docs]class PiiEntityDomain(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""The different domains of PII entities that users can filter by"""
PROTECTED_HEALTH_INFORMATION = (
"phi" # See https://aka.ms/azsdk/language/pii for more information.
)
[docs]class DetectedLanguage(DictMixin):
"""DetectedLanguage contains the predicted language found in text,
its confidence score, and its ISO 639-1 representation.
.. versionadded:: 2022-10-01-preview
The *script* property.
"""
name: str
"""Long name of a detected language (e.g. English,
French)."""
iso6391_name: str
"""A two letter representation of the detected
language according to the ISO 639-1 standard (e.g. en, fr)."""
confidence_score: float
"""A confidence score between 0 and 1. Scores close
to 1 indicate 100% certainty that the identified language is true."""
script: Optional[str] = None
"""Identifies the script of the input document. Possible value is 'Latin'."""
def __init__(self, **kwargs: Any) -> None:
self.name = kwargs.get("name", None)
self.iso6391_name = kwargs.get("iso6391_name", None)
self.confidence_score = kwargs.get("confidence_score", None)
self.script = kwargs.get("script", None)
@classmethod
def _from_generated(cls, language):
script = language.script if hasattr(language, "script") else None
return cls(
name=language.name,
iso6391_name=language.iso6391_name,
confidence_score=language.confidence_score,
script=script
)
def __repr__(self) -> str:
return (
f"DetectedLanguage(name={self.name}, iso6391_name={self.iso6391_name}, "
f"confidence_score={self.confidence_score}, script={self.script})"[:1024]
)
[docs]class RecognizeEntitiesResult(DictMixin):
"""RecognizeEntitiesResult is a result object which contains
the recognized entities from a particular document.
.. versionadded:: 2022-10-01-preview
The *detected_language* property.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
entities: List["CategorizedEntity"]
"""Recognized entities in the document."""
warnings: List["TextAnalyticsWarning"]
"""Warnings encountered while processing document. Results will still be returned
if there are warnings, but they may not be fully accurate."""
statistics: Optional["TextDocumentStatistics"] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
detected_language: Optional[DetectedLanguage] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the DetectedLanguage for the document."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a RecognizeEntitiesResult."""
kind: Literal["EntityRecognition"] = "EntityRecognition"
"""The text analysis kind - "EntityRecognition"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.entities = kwargs.get("entities", None)
self.warnings = kwargs.get("warnings", [])
self.statistics = kwargs.get("statistics", None)
self.detected_language = kwargs.get("detected_language", None)
self.is_error: Literal[False] = False
self.kind: Literal["EntityRecognition"] = "EntityRecognition"
def __repr__(self) -> str:
return (
f"RecognizeEntitiesResult(id={self.id}, entities={repr(self.entities)}, "
f"warnings={repr(self.warnings)}, statistics={repr(self.statistics)}, "
f"detected_language={repr(self.detected_language)}, is_error={self.is_error}, "
f"kind={self.kind})"[:1024]
)
[docs]class RecognizePiiEntitiesResult(DictMixin):
"""RecognizePiiEntitiesResult is a result object which contains
the recognized Personally Identifiable Information (PII) entities
from a particular document.
.. versionadded:: 2022-10-01-preview
The *detected_language* property.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
entities: List["PiiEntity"]
"""Recognized PII entities in the document."""
redacted_text: str
"""Returns the text of the input document with all of the PII information
redacted out."""
warnings: List["TextAnalyticsWarning"]
"""Warnings encountered while processing document. Results will still be returned
if there are warnings, but they may not be fully accurate."""
statistics: Optional["TextDocumentStatistics"] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
detected_language: Optional[DetectedLanguage] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the DetectedLanguage for the document."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a RecognizePiiEntitiesResult."""
kind: Literal["PiiEntityRecognition"] = "PiiEntityRecognition"
"""The text analysis kind - "PiiEntityRecognition"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.entities = kwargs.get("entities", None)
self.redacted_text = kwargs.get("redacted_text", None)
self.warnings = kwargs.get("warnings", [])
self.statistics = kwargs.get("statistics", None)
self.detected_language = kwargs.get('detected_language', None)
self.is_error: Literal[False] = False
self.kind: Literal["PiiEntityRecognition"] = "PiiEntityRecognition"
def __repr__(self) -> str:
return (
f"RecognizePiiEntitiesResult(id={self.id}, entities={repr(self.entities)}, "
f"redacted_text={self.redacted_text}, warnings={repr(self.warnings)}, "
f"statistics={repr(self.statistics)}, detected_language={repr(self.detected_language)}, "
f"is_error={self.is_error}, kind={self.kind})"[:1024]
)
[docs]class AnalyzeHealthcareEntitiesResult(DictMixin):
"""
AnalyzeHealthcareEntitiesResult contains the Healthcare entities from a
particular document.
.. versionadded:: 2022-10-01-preview
The *fhir_bundle* and *detected_language* properties.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
entities: List["HealthcareEntity"]
"""Identified Healthcare entities in the document, i.e. in
the document "The subject took ibuprofen", "ibuprofen" is an identified entity
from the document."""
entity_relations: List["HealthcareRelation"]
"""Identified Healthcare relations between entities. For example, in the
document "The subject took 100mg of ibuprofen", we would identify the relationship
between the dosage of 100mg and the medication ibuprofen."""
warnings: List["TextAnalyticsWarning"]
"""Warnings encountered while processing document. Results will still be returned
if there are warnings, but they may not be fully accurate."""
statistics: Optional["TextDocumentStatistics"] = None
"""If show_stats=true was specified in the request this
field will contain information about the document payload."""
fhir_bundle: Optional[Dict[str, Any]] = None
"""If `fhir_version` is passed, this will contain a
FHIR compatible object for consumption in other Healthcare tools. For additional
information see https://www.hl7.org/fhir/overview.html."""
detected_language: Optional[str] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the detected language for the document."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a AnalyzeHealthcareEntitiesResult."""
kind: Literal["Healthcare"] = "Healthcare"
"""The text analysis kind - "Healthcare"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.entities = kwargs.get("entities", None)
self.entity_relations = kwargs.get("entity_relations", None)
self.warnings = kwargs.get("warnings", [])
self.statistics = kwargs.get("statistics", None)
self.fhir_bundle = kwargs.get("fhir_bundle", None)
self.detected_language = kwargs.get('detected_language', None)
self.is_error: Literal[False] = False
self.kind: Literal["Healthcare"] = "Healthcare"
@classmethod
def _from_generated(cls, healthcare_result):
entities = [
HealthcareEntity._from_generated(e) # pylint: disable=protected-access
for e in healthcare_result.entities
]
relations = [
HealthcareRelation._from_generated( # pylint: disable=protected-access
r, entities
)
for r in healthcare_result.relations
]
fhir_bundle = healthcare_result.fhir_bundle if hasattr(healthcare_result, "fhir_bundle") else None
detected_language = healthcare_result.detected_language \
if hasattr(healthcare_result, "detected_language") else None
return cls(
id=healthcare_result.id,
entities=entities,
entity_relations=relations,
warnings=[
TextAnalyticsWarning._from_generated( # pylint: disable=protected-access
w
)
for w in healthcare_result.warnings
],
statistics=TextDocumentStatistics._from_generated( # pylint: disable=protected-access
healthcare_result.statistics
),
fhir_bundle=fhir_bundle,
detected_language=detected_language # https://github.com/Azure/azure-sdk-for-python/issues/27171
# detected_language=DetectedLanguage._from_generated( # pylint: disable=protected-access
# healthcare_result.detected_language
# ) if hasattr(healthcare_result, "detected_language") and healthcare_result.detected_language else None
)
def __repr__(self) -> str:
return (
f"AnalyzeHealthcareEntitiesResult(id={self.id}, entities={repr(self.entities)}, "
f"entity_relations={repr(self.entity_relations)}, warnings={repr(self.warnings)}, "
f"statistics={repr(self.statistics)}, fhir_bundle={self.fhir_bundle}, "
f"detected_language={self.detected_language}, is_error={self.is_error}, kind={self.kind})"[:1024]
)
[docs]class HealthcareRelation(DictMixin):
"""HealthcareRelation is a result object which represents a relation detected in a document.
Every HealthcareRelation is an entity graph of a certain relation type,
where all entities are connected and have specific roles within the relation context.
.. versionadded:: 2022-10-01-preview
The *confidence_score* property.
"""
relation_type: str
"""The type of relation, i.e. the relationship between "100mg" and
"ibuprofen" in the document "The subject took 100 mg of ibuprofen" is "DosageOfMedication".
Possible values found in :class:`~azure.ai.textanalytics.HealthcareEntityRelation`"""
roles: List["HealthcareRelationRole"]
"""The roles present in this relation. I.e., in the document
"The subject took 100 mg of ibuprofen", the present roles are "Dosage" and "Medication"."""
confidence_score: Optional[float] = None
"""Confidence score between 0 and 1 of the extracted relation."""
def __init__(self, **kwargs: Any) -> None:
self.relation_type = kwargs.get("relation_type", None)
self.roles = kwargs.get("roles", None)
self.confidence_score = kwargs.get("confidence_score", None)
@classmethod
def _from_generated(cls, healthcare_relation_result, entities):
roles = [
HealthcareRelationRole._from_generated( # pylint: disable=protected-access
r, entities
)
for r in healthcare_relation_result.entities
]
confidence_score = healthcare_relation_result.confidence_score \
if hasattr(healthcare_relation_result, "confidence_score") else None
return cls(
relation_type=healthcare_relation_result.relation_type,
roles=roles,
confidence_score=confidence_score,
)
def __repr__(self) -> str:
return f"HealthcareRelation(relation_type={self.relation_type}, roles={repr(self.roles)}, " \
f"confidence_score={self.confidence_score})"[:1024]
[docs]class HealthcareRelationRole(DictMixin):
"""A model representing a role in a relation.
For example, in "The subject took 100 mg of ibuprofen",
"100 mg" is a dosage entity fulfilling the role "Dosage"
in the extracted relation "DosageOfMedication".
"""
name: str
"""The role of the entity in the relationship. I.e., in the relation
"The subject took 100 mg of ibuprofen", the dosage entity "100 mg" has role
"Dosage"."""
entity: "HealthcareEntity"
"""The entity that is present in the relationship. For example, in
"The subject took 100 mg of ibuprofen", this property holds the dosage entity
of "100 mg"."""
def __init__(self, **kwargs: Any) -> None:
self.name = kwargs.get("name", None)
self.entity = kwargs.get("entity", None)
@staticmethod
def _get_entity(healthcare_role_result, entities):
numbers = _get_indices(healthcare_role_result.ref)
entity_index = numbers[
1
] # first number parsed from index is document #, second is entity index
return entities[entity_index]
@classmethod
def _from_generated(cls, healthcare_role_result, entities):
return cls(
name=healthcare_role_result.role,
entity=HealthcareRelationRole._get_entity(healthcare_role_result, entities),
)
def __repr__(self) -> str:
return f"HealthcareRelationRole(name={self.name}, entity={repr(self.entity)})"[:1024]
[docs]class DetectLanguageResult(DictMixin):
"""DetectLanguageResult is a result object which contains
the detected language of a particular document.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
primary_language: DetectedLanguage
"""The primary language detected in the document."""
warnings: List["TextAnalyticsWarning"]
"""Warnings encountered while processing document. Results will still be returned
if there are warnings, but they may not be fully accurate."""
statistics: Optional["TextDocumentStatistics"] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a DetectLanguageResult."""
kind: Literal["LanguageDetection"] = "LanguageDetection"
"""The text analysis kind - "LanguageDetection"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.primary_language = kwargs.get("primary_language", None)
self.warnings = kwargs.get("warnings", [])
self.statistics = kwargs.get("statistics", None)
self.is_error: Literal[False] = False
self.kind: Literal["LanguageDetection"] = "LanguageDetection"
def __repr__(self) -> str:
return (
f"DetectLanguageResult(id={self.id}, primary_language={repr(self.primary_language)}, "
f"warnings={repr(self.warnings)}, statistics={repr(self.statistics)}, is_error={self.is_error}, "
f"kind={self.kind})"[:1024]
)
[docs]class CategorizedEntity(DictMixin):
"""CategorizedEntity contains information about a particular
entity found in text.
.. versionadded:: v3.1
The *offset* and *length* properties.
.. versionadded:: 2022-10-01-preview
The *resolutions* property.
"""
text: str
"""Entity text as appears in the request."""
category: str
"""Entity category, such as Person/Location/Org/SSN etc"""
length: int
"""The entity text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default."""
offset: int
"""The entity text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default."""
confidence_score: float
"""Confidence score between 0 and 1 of the extracted
entity."""
subcategory: Optional[str] = None
"""Entity subcategory, such as Age/Year/TimeRange etc"""
resolutions: List[
Union[
AgeResolution,
AreaResolution,
CurrencyResolution,
DateTimeResolution,
InformationResolution,
LengthResolution,
NumberResolution,
NumericRangeResolution,
OrdinalResolution,
SpeedResolution,
TemperatureResolution,
TemporalSpanResolution,
VolumeResolution,
WeightResolution,
]
]
"""The collection of entity resolution objects. More information can be found here:
https://aka.ms/azsdk/language/ner-resolutions"""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.category = kwargs.get("category", None)
self.subcategory = kwargs.get("subcategory", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.confidence_score = kwargs.get("confidence_score", None)
self.resolutions = kwargs.get("resolutions", None)
@classmethod
def _from_generated(cls, entity):
offset = entity.offset
length = entity.length
if isinstance(entity, _v3_0_models.Entity):
# we do not return offset for v3.0 since
# the correct encoding was not introduced for v3.0
offset = None
length = None
entity_resolutions = entity.resolutions if hasattr(entity, "resolutions") else None
return cls(
text=entity.text,
category=entity.category,
subcategory=entity.subcategory,
length=length,
offset=offset,
confidence_score=entity.confidence_score,
resolutions=entity_resolutions or []
)
def __repr__(self) -> str:
return (
f"CategorizedEntity(text={self.text}, category={self.category}, subcategory={self.subcategory}, "
f"length={self.length}, offset={self.offset}, confidence_score={self.confidence_score}, "
f"resolutions={repr(self.resolutions)})"[:1024]
)
[docs]class PiiEntity(DictMixin):
"""PiiEntity contains information about a Personally Identifiable
Information (PII) entity found in text.
"""
text: str
"""Entity text as appears in the request."""
category: str
"""Entity category, such as Financial Account
Identification/Social Security Number/Phone Number, etc."""
length: int
"""The PII entity text length. This value depends on the value
of the `string_index_type` parameter specified in the original request, which
is UnicodeCodePoints by default."""
offset: int
"""The PII entity text offset from the start of the document.
This value depends on the value of the `string_index_type` parameter specified
in the original request, which is UnicodeCodePoints by default."""
confidence_score: float
"""Confidence score between 0 and 1 of the extracted entity."""
subcategory: Optional[str] = None
"""Entity subcategory, such as Credit Card/EU
Phone number/ABA Routing Numbers, etc."""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.category = kwargs.get("category", None)
self.subcategory = kwargs.get("subcategory", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.confidence_score = kwargs.get("confidence_score", None)
@classmethod
def _from_generated(cls, entity):
return cls(
text=entity.text,
category=entity.category,
subcategory=entity.subcategory,
length=entity.length,
offset=entity.offset,
confidence_score=entity.confidence_score,
)
def __repr__(self) -> str:
return (
f"PiiEntity(text={self.text}, category={self.category}, subcategory={self.subcategory}, "
f"length={self.length}, offset={self.offset}, confidence_score={self.confidence_score})"[:1024]
)
[docs]class HealthcareEntity(DictMixin):
"""HealthcareEntity contains information about a Healthcare entity found in text.
"""
text: str
"""Entity text as appears in the document."""
category: str
"""Entity category, see the :class:`~azure.ai.textanalytics.HealthcareEntityCategory`
type for possible healthcare entity categories."""
length: int
"""The entity text length. This value depends on the value
of the `string_index_type` parameter specified in the original request, which is
UnicodeCodePoints by default."""
offset: int
"""The entity text offset from the start of the document.
This value depends on the value of the `string_index_type` parameter specified
in the original request, which is UnicodeCodePoints by default."""
confidence_score: float
"""Confidence score between 0 and 1 of the extracted entity."""
subcategory: Optional[str] = None
"""Entity subcategory."""
assertion: Optional["HealthcareEntityAssertion"] = None
"""Contains various assertions about this entity. For example, if
an entity is a diagnosis, is this diagnosis 'conditional' on a symptom?
Are the doctors 'certain' about this diagnosis? Is this diagnosis 'associated'
with another diagnosis?"""
normalized_text: Optional[str] = None
"""Normalized version of the raw `text` we extract
from the document. Not all `text` will have a normalized version."""
data_sources: Optional[List["HealthcareEntityDataSource"]]
"""A collection of entity references in known data sources."""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.normalized_text = kwargs.get("normalized_text", None)
self.category = kwargs.get("category", None)
self.subcategory = kwargs.get("subcategory", None)
self.assertion = kwargs.get("assertion", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.confidence_score = kwargs.get("confidence_score", None)
self.data_sources = kwargs.get("data_sources", [])
@classmethod
def _from_generated(cls, healthcare_entity):
assertion = None
try:
if healthcare_entity.assertion:
assertion = HealthcareEntityAssertion._from_generated( # pylint: disable=protected-access
healthcare_entity.assertion
)
except AttributeError:
assertion = None
return cls(
text=healthcare_entity.text,
normalized_text=healthcare_entity.name,
category=healthcare_entity.category,
subcategory=healthcare_entity.subcategory,
assertion=assertion,
length=healthcare_entity.length,
offset=healthcare_entity.offset,
confidence_score=healthcare_entity.confidence_score,
data_sources=[
HealthcareEntityDataSource(entity_id=l.id, name=l.data_source)
for l in healthcare_entity.links
]
if healthcare_entity.links
else None,
)
def __hash__(self) -> int:
return hash(repr(self))
def __repr__(self) -> str:
return (
f"HealthcareEntity(text={self.text}, normalized_text={self.normalized_text}, "
f"category={self.category}, subcategory={self.subcategory}, assertion={repr(self.assertion)}, "
f"length={self.length}, offset={self.offset}, confidence_score={self.confidence_score}, "
f"data_sources={repr(self.data_sources)})"[:1024]
)
[docs]class HealthcareEntityAssertion(DictMixin):
"""Contains various assertions about a `HealthcareEntity`.
For example, if an entity is a diagnosis, is this diagnosis 'conditional' on a symptom?
Are the doctors 'certain' about this diagnosis? Is this diagnosis 'associated'
with another diagnosis?
"""
conditionality: Optional[str] = None
"""Describes whether the healthcare entity it's on is conditional
on another entity. For example, "If the patient has a fever, he has pneumonia", the diagnosis of pneumonia
is 'conditional' on whether the patient has a fever. Possible values are "hypothetical" and
"conditional"."""
certainty: Optional[str] = None
"""Describes how certain the healthcare entity it's on is. For example,
in "The patient may have a fever", the fever entity is not 100% certain, but is instead
"positivePossible". Possible values are "positive", "positivePossible", "neutralPossible",
"negativePossible", and "negative"."""
association: Optional[str] = None
"""Describes whether the healthcare entity it's on is the subject of the document, or
if this entity describes someone else in the document. For example, in "The subject's mother has
a fever", the "fever" entity is not associated with the subject themselves, but with the subject's
mother. Possible values are "subject" and "other"."""
def __init__(self, **kwargs: Any) -> None:
self.conditionality = kwargs.get("conditionality", None)
self.certainty = kwargs.get("certainty", None)
self.association = kwargs.get("association", None)
@classmethod
def _from_generated(cls, healthcare_assertion):
return cls(
conditionality=healthcare_assertion.conditionality,
certainty=healthcare_assertion.certainty,
association=healthcare_assertion.association,
)
def __repr__(self) -> str:
return f"HealthcareEntityAssertion(conditionality={self.conditionality}, certainty={self.certainty}, " \
f"association={self.association})"[:1024]
[docs]class HealthcareEntityDataSource(DictMixin):
"""
HealthcareEntityDataSource contains information representing an entity reference in a known data source.
"""
entity_id: str
"""ID of the entity in the given source catalog."""
name: str
"""The name of the entity catalog from where the entity was identified, such as UMLS, CHV, MSH, etc."""
def __init__(self, **kwargs: Any) -> None:
self.entity_id = kwargs.get("entity_id", None)
self.name = kwargs.get("name", None)
def __repr__(self) -> str:
return (
f"HealthcareEntityDataSource(entity_id={self.entity_id}, name={self.name})"[:1024]
)
[docs]class TextAnalyticsError(DictMixin):
"""TextAnalyticsError contains the error code, message, and
other details that explain why the batch or individual document
failed to be processed by the service.
"""
code: str
"""Error code. Possible values include
'invalidRequest', 'invalidArgument', 'internalServerError',
'serviceUnavailable', 'invalidParameterValue', 'invalidRequestBodyFormat',
'emptyRequest', 'missingInputRecords', 'invalidDocument', 'modelVersionIncorrect',
'invalidDocumentBatch', 'unsupportedLanguageCode', 'invalidCountryHint'"""
message: str
"""Error message."""
target: Optional[str] = None
"""Error target."""
def __init__(self, **kwargs: Any) -> None:
self.code = kwargs.get("code", None)
self.message = kwargs.get("message", None)
self.target = kwargs.get("target", None)
@classmethod
def _from_generated(cls, err):
if err.innererror:
return cls(
code=err.innererror.code,
message=err.innererror.message,
target=err.innererror.target,
)
return cls(code=err.code, message=err.message, target=err.target)
def __repr__(self) -> str:
return f"TextAnalyticsError(code={self.code}, message={self.message}, target={self.target})"[:1024]
[docs]class TextAnalyticsWarning(DictMixin):
"""TextAnalyticsWarning contains the warning code and message that explains why
the response has a warning.
"""
code: str
"""Warning code. Possible values include 'LongWordsInDocument',
'DocumentTruncated'."""
message: str
"""Warning message."""
def __init__(self, **kwargs: Any) -> None:
self.code = kwargs.get("code", None)
self.message = kwargs.get("message", None)
@classmethod
def _from_generated(cls, warning):
return cls(
code=warning.code,
message=warning.message,
)
def __repr__(self) -> str:
return f"TextAnalyticsWarning(code={self.code}, message={self.message})"[:1024]
[docs]class RecognizeLinkedEntitiesResult(DictMixin):
"""RecognizeLinkedEntitiesResult is a result object which contains
links to a well-known knowledge base, like for example, Wikipedia or Bing.
.. versionadded:: 2022-10-01-preview
The *detected_language* property.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
entities: List["LinkedEntity"]
"""Recognized well-known entities in the document."""
warnings: List[TextAnalyticsWarning]
"""Warnings encountered while processing document. Results will still be returned
if there are warnings, but they may not be fully accurate."""
statistics: Optional["TextDocumentStatistics"] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
detected_language: Optional[DetectedLanguage] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the DetectedLanguage for the document."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a RecognizeLinkedEntitiesResult."""
kind: Literal["EntityLinking"] = "EntityLinking"
"""The text analysis kind - "EntityLinking"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.entities = kwargs.get("entities", None)
self.warnings = kwargs.get("warnings", [])
self.statistics = kwargs.get("statistics", None)
self.detected_language = kwargs.get('detected_language', None)
self.is_error: Literal[False] = False
self.kind: Literal["EntityLinking"] = "EntityLinking"
def __repr__(self) -> str:
return (
f"RecognizeLinkedEntitiesResult(id={self.id}, entities={repr(self.entities)}, "
f"warnings={repr(self.warnings)}, statistics={repr(self.statistics)}, "
f"detected_language={repr(self.detected_language)}, is_error={self.is_error}, kind={self.kind})"[:1024]
)
[docs]class AnalyzeSentimentResult(DictMixin):
"""AnalyzeSentimentResult is a result object which contains
the overall predicted sentiment and confidence scores for your document
and a per-sentence sentiment prediction with scores.
.. versionadded:: 2022-10-01-preview
The *detected_language* property.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
sentiment: str
"""Predicted sentiment for document (Negative,
Neutral, Positive, or Mixed). Possible values include 'positive',
'neutral', 'negative', 'mixed'"""
confidence_scores: "SentimentConfidenceScores"
"""Document level sentiment confidence
scores between 0 and 1 for each sentiment label."""
sentences: List["SentenceSentiment"]
"""Sentence level sentiment analysis."""
warnings: List[TextAnalyticsWarning]
"""Warnings encountered while processing document. Results will still be returned
if there are warnings, but they may not be fully accurate."""
statistics: Optional["TextDocumentStatistics"] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
detected_language: Optional[DetectedLanguage] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the DetectedLanguage for the document."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a AnalyzeSentimentResult."""
kind: Literal["SentimentAnalysis"] = "SentimentAnalysis"
"""The text analysis kind - "SentimentAnalysis"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.sentiment = kwargs.get("sentiment", None)
self.warnings = kwargs.get("warnings", [])
self.statistics = kwargs.get("statistics", None)
self.confidence_scores = kwargs.get("confidence_scores", None)
self.sentences = kwargs.get("sentences", None)
self.detected_language = kwargs.get('detected_language', None)
self.is_error: Literal[False] = False
self.kind: Literal["SentimentAnalysis"] = "SentimentAnalysis"
def __repr__(self) -> str:
return (
f"AnalyzeSentimentResult(id={self.id}, sentiment={self.sentiment}, warnings={repr(self.warnings)}, "
f"statistics={repr(self.statistics)}, confidence_scores={repr(self.confidence_scores)}, "
f"sentences={repr(self.sentences)}, detected_language={repr(self.detected_language)}, "
f"is_error={self.is_error}, kind={self.kind})"[:1024]
)
[docs]class TextDocumentStatistics(DictMixin):
"""TextDocumentStatistics contains information about
the document payload.
"""
character_count: int
"""Number of text elements recognized in
the document."""
transaction_count: int
"""Number of transactions for the document."""
def __init__(self, **kwargs: Any) -> None:
self.character_count = kwargs.get("character_count", None)
self.transaction_count = kwargs.get("transaction_count", None)
@classmethod
def _from_generated(cls, stats):
if stats is None:
return None
return cls(
character_count=stats.characters_count,
transaction_count=stats.transactions_count,
)
def __repr__(self) -> str:
return f"TextDocumentStatistics(character_count={self.character_count}, " \
f"transaction_count={self.transaction_count})"[:1024]
[docs]class DocumentError(DictMixin):
"""DocumentError is an error object which represents an error on
the individual document.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
error: TextAnalyticsError
"""The document error."""
is_error: Literal[True] = True
"""Boolean check for error item when iterating over list of
results. Always True for an instance of a DocumentError."""
kind: Literal["DocumentError"] = "DocumentError"
"""Error kind - "DocumentError"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.error = kwargs.get("error", None)
self.is_error: Literal[True] = True
self.kind: Literal["DocumentError"] = "DocumentError"
def __getattr__(self, attr: str) -> Any:
result_set = set()
result_set.update(
RecognizeEntitiesResult().keys() # type: ignore[operator]
+ RecognizePiiEntitiesResult().keys()
+ DetectLanguageResult().keys()
+ RecognizeLinkedEntitiesResult().keys()
+ AnalyzeSentimentResult().keys()
+ ExtractKeyPhrasesResult().keys()
+ AnalyzeHealthcareEntitiesResult().keys()
+ RecognizeCustomEntitiesResult().keys()
+ ClassifyDocumentResult().keys()
+ ExtractSummaryResult().keys()
+ AbstractiveSummaryResult().keys()
+ DynamicClassificationResult().keys()
)
result_attrs = result_set.difference(DocumentError().keys())
if attr in result_attrs:
raise AttributeError(
"'DocumentError' object has no attribute '{}'. The service was unable to process this document:\n"
"Document Id: {}\nError: {} - {}\n".format(
attr, self.id, self.error.code, self.error.message
)
)
raise AttributeError(
f"'DocumentError' object has no attribute '{attr}'"
)
@classmethod
def _from_generated(cls, doc_err):
return cls(
id=doc_err.id,
error=TextAnalyticsError._from_generated( # pylint: disable=protected-access
doc_err.error
),
)
def __repr__(self) -> str:
return f"DocumentError(id={self.id}, error={repr(self.error)}, " \
f"is_error={self.is_error}, kind={self.kind})"[:1024]
[docs]class LinkedEntity(DictMixin):
"""LinkedEntity contains a link to the well-known recognized
entity in text. The link comes from a data source like Wikipedia
or Bing. It additionally includes all of the matches of this
entity found in the document.
.. versionadded:: v3.1
The *bing_entity_search_api_id* property.
"""
name: str
"""Entity Linking formal name."""
matches: List["LinkedEntityMatch"]
"""List of instances this entity appears in the text."""
language: str
"""Language used in the data source."""
url: str
"""URL to the entity's page from the data source."""
data_source: str
"""Data source used to extract entity linking,
such as Wiki/Bing etc."""
data_source_entity_id: Optional[str] = None
"""Unique identifier of the recognized entity from the data
source."""
bing_entity_search_api_id: Optional[str] = None
"""Bing Entity Search unique identifier of the recognized entity.
Use in conjunction with the Bing Entity Search SDK to fetch additional relevant information."""
def __init__(self, **kwargs: Any) -> None:
self.name = kwargs.get("name", None)
self.matches = kwargs.get("matches", None)
self.language = kwargs.get("language", None)
self.data_source_entity_id = kwargs.get("data_source_entity_id", None)
self.url = kwargs.get("url", None)
self.data_source = kwargs.get("data_source", None)
self.bing_entity_search_api_id = kwargs.get("bing_entity_search_api_id", None)
@classmethod
def _from_generated(cls, entity):
bing_entity_search_api_id = (
entity.bing_id if hasattr(entity, "bing_id") else None
)
return cls(
name=entity.name,
matches=[
LinkedEntityMatch._from_generated(e) # pylint: disable=protected-access
for e in entity.matches
],
language=entity.language,
data_source_entity_id=entity.id,
url=entity.url,
data_source=entity.data_source,
bing_entity_search_api_id=bing_entity_search_api_id,
)
def __repr__(self) -> str:
return (
f"LinkedEntity(name={self.name}, matches={repr(self.matches)}, language={self.language}, "
f"data_source_entity_id={self.data_source_entity_id}, url={self.url}, "
f"data_source={self.data_source}, bing_entity_search_api_id={self.bing_entity_search_api_id})"[:1024]
)
[docs]class LinkedEntityMatch(DictMixin):
"""A match for the linked entity found in text. Provides
the confidence score of the prediction and where the entity
was found in the text.
.. versionadded:: v3.1
The *offset* and *length* properties.
"""
confidence_score: float
"""If a well-known item is recognized, a
decimal number denoting the confidence level between 0 and 1 will be
returned."""
text: str
"""Entity text as appears in the request."""
length: int
"""The linked entity match text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints by default."""
offset: int
"""The linked entity match text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default."""
def __init__(self, **kwargs: Any) -> None:
self.confidence_score = kwargs.get("confidence_score", None)
self.text = kwargs.get("text", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
@classmethod
def _from_generated(cls, match):
offset = match.offset
length = match.length
if isinstance(match, _v3_0_models.Match):
# we do not return offset for v3.0 since
# the correct encoding was not introduced for v3.0
offset = None
length = None
return cls(
confidence_score=match.confidence_score,
text=match.text,
length=length,
offset=offset,
)
def __repr__(self) -> str:
return f"LinkedEntityMatch(confidence_score={self.confidence_score}, text={self.text}, " \
f"length={self.length}, offset={self.offset})"[:1024]
[docs]class TextDocumentInput(DictMixin, MultiLanguageInput):
"""The input document to be analyzed by the service.
:keyword str id: Required. Unique, non-empty document identifier.
:keyword str text: Required. The input text to process.
:keyword str language: This is the 2 letter ISO 639-1 representation
of a language. For example, use "en" for English; "es" for Spanish etc.
For automatic language detection, use "auto" (Only supported by long-running
operation APIs with API version 2022-10-01-preview or newer). If
not set, uses "en" for English as default.
.. versionadded:: 2022-10-01-preview
The 'auto' option for language.
"""
id: str # pylint: disable=redefined-builtin
"""Required. Unique, non-empty document identifier."""
text: str
"""Required. The input text to process."""
language: Optional[str] = None
"""This is the 2 letter ISO 639-1 representation
of a language. For example, use "en" for English; "es" for Spanish etc.
For automatic language detection, use "auto" (Only supported by long-running
operation APIs with API version 2022-10-01-preview or newer). If
not set, uses "en" for English as default."""
def __init__(
self,
*,
id: str, # pylint: disable=redefined-builtin
text: str,
language: Optional[str] = None,
**kwargs: Any # pylint: disable=unused-argument
) -> None:
super().__init__(id=id, text=text, language=language)
self.id = id
self.text = text
self.language = language
def __repr__(self) -> str:
return f"TextDocumentInput(id={self.id}, text={self.text}, language={self.language})"[:1024]
[docs]class TextDocumentBatchStatistics(DictMixin):
"""TextDocumentBatchStatistics contains information about the
request payload. Note: This object is not returned
in the response and needs to be retrieved by a response hook.
"""
document_count: int
"""Number of documents submitted in the request"""
valid_document_count: int
"""Number of valid documents. This
excludes empty, over-size limit or non-supported languages documents."""
erroneous_document_count: int
"""Number of invalid documents.
This includes empty, over-size limit or non-supported languages documents."""
transaction_count: int
"""Number of transactions for the request."""
def __init__(self, **kwargs: Any) -> None:
self.document_count = kwargs.get("document_count", None)
self.valid_document_count = kwargs.get("valid_document_count", None)
self.erroneous_document_count = kwargs.get("erroneous_document_count", None)
self.transaction_count = kwargs.get("transaction_count", None)
@classmethod
def _from_generated(cls, statistics):
if statistics is None:
return None
return cls(
document_count=statistics["documentsCount"],
valid_document_count=statistics["validDocumentsCount"],
erroneous_document_count=statistics["erroneousDocumentsCount"],
transaction_count=statistics["transactionsCount"],
)
def __repr__(self) -> str:
return (
f"TextDocumentBatchStatistics(document_count={self.document_count}, "
f"valid_document_count={self.valid_document_count}, "
f"erroneous_document_count={self.erroneous_document_count}, "
f"transaction_count={self.transaction_count})"[:1024]
)
[docs]class SentenceSentiment(DictMixin):
"""SentenceSentiment contains the predicted sentiment and
confidence scores for each individual sentence in the document.
.. versionadded:: v3.1
The *offset*, *length*, and *mined_opinions* properties.
"""
text: str
"""The sentence text."""
sentiment: str
"""The predicted Sentiment for the sentence.
Possible values include 'positive', 'neutral', 'negative'"""
confidence_scores: "SentimentConfidenceScores"
"""The sentiment confidence score between 0
and 1 for the sentence for all labels."""
length: int
"""The sentence text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default."""
offset: int
"""The sentence text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default."""
mined_opinions: Optional[List["MinedOpinion"]] = None
"""The list of opinions mined from this sentence.
For example in the sentence "The food is good, but the service is bad", we would
mine the two opinions "food is good" and "service is bad". Only returned
if `show_opinion_mining` is set to True in the call to `analyze_sentiment` and
api version is v3.1 and up."""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.sentiment = kwargs.get("sentiment", None)
self.confidence_scores = kwargs.get("confidence_scores", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.mined_opinions = kwargs.get("mined_opinions", None)
@classmethod
def _from_generated(cls, sentence, results, sentiment):
offset = sentence.offset
length = sentence.length
if isinstance(sentence, _v3_0_models.SentenceSentiment):
# we do not return offset for v3.0 since
# the correct encoding was not introduced for v3.0
offset = None
length = None
if hasattr(sentence, "targets"):
mined_opinions = (
[
MinedOpinion._from_generated( # pylint: disable=protected-access
target, results, sentiment
)
for target in sentence.targets
]
if sentence.targets
else []
)
else:
mined_opinions = None
return cls(
text=sentence.text,
sentiment=sentence.sentiment,
confidence_scores=SentimentConfidenceScores._from_generated( # pylint: disable=protected-access
sentence.confidence_scores
),
length=length,
offset=offset,
mined_opinions=mined_opinions,
)
def __repr__(self) -> str:
return (
f"SentenceSentiment(text={self.text}, sentiment={self.sentiment}, "
f"confidence_scores={repr(self.confidence_scores)}, length={self.length}, "
f"offset={self.offset}, mined_opinions={repr(self.mined_opinions)})"[:1024]
)
[docs]class MinedOpinion(DictMixin):
"""A mined opinion object represents an opinion we've extracted from a sentence.
It consists of both a target that these opinions are about, and the assessments
representing the opinion.
"""
target: "TargetSentiment"
"""The target of an opinion about a product/service."""
assessments: List["AssessmentSentiment"]
"""The assessments representing the opinion of the target."""
def __init__(self, **kwargs: Any) -> None:
self.target = kwargs.get("target", None)
self.assessments = kwargs.get("assessments", None)
@staticmethod
def _get_assessments(
relations, results, sentiment
): # pylint: disable=unused-argument
if not relations:
return []
assessment_relations = [
r.ref for r in relations if r.relation_type == "assessment"
]
assessments = []
for assessment_relation in assessment_relations:
numbers = _get_indices(assessment_relation)
sentence_index = numbers[1]
assessment_index = numbers[2]
assessments.append(
sentiment.sentences[sentence_index].assessments[assessment_index]
)
return assessments
@classmethod
def _from_generated(cls, target, results, sentiment):
return cls(
target=TargetSentiment._from_generated( # pylint: disable=protected-access
target
),
assessments=[
AssessmentSentiment._from_generated( # pylint: disable=protected-access
assessment
)
for assessment in cls._get_assessments(
target.relations, results, sentiment
)
],
)
def __repr__(self) -> str:
return f"MinedOpinion(target={repr(self.target)}, assessments={repr(self.assessments)})"[:1024]
[docs]class TargetSentiment(DictMixin):
"""TargetSentiment contains the predicted sentiment,
confidence scores and other information about a key component of a product/service.
For example in "The food at Hotel Foo is good", "food" is an key component of
"Hotel Foo".
"""
text: str
"""The text value of the target."""
sentiment: str
"""The predicted Sentiment for the target. Possible values
include 'positive', 'mixed', and 'negative'."""
confidence_scores: "SentimentConfidenceScores"
"""The sentiment confidence score between 0
and 1 for the target for 'positive' and 'negative' labels. It's score
for 'neutral' will always be 0"""
length: int
"""The target text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default."""
offset: int
"""The target text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default."""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.sentiment = kwargs.get("sentiment", None)
self.confidence_scores = kwargs.get("confidence_scores", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
@classmethod
def _from_generated(cls, target):
return cls(
text=target.text,
sentiment=target.sentiment,
confidence_scores=SentimentConfidenceScores._from_generated( # pylint: disable=protected-access
target.confidence_scores
),
length=target.length,
offset=target.offset,
)
def __repr__(self) -> str:
return (
f"TargetSentiment(text={self.text}, sentiment={self.sentiment}, "
f"confidence_scores={repr(self.confidence_scores)}, "
f"length={self.length}, offset={self.offset})"[:1024]
)
[docs]class AssessmentSentiment(DictMixin):
"""AssessmentSentiment contains the predicted sentiment,
confidence scores and other information about an assessment given about
a particular target. For example, in the sentence "The food is good", the assessment
of the target 'food' is 'good'.
"""
text: str
"""The assessment text."""
sentiment: str
"""The predicted Sentiment for the assessment. Possible values
include 'positive', 'mixed', and 'negative'."""
confidence_scores: "SentimentConfidenceScores"
"""The sentiment confidence score between 0
and 1 for the assessment for 'positive' and 'negative' labels. It's score
for 'neutral' will always be 0"""
length: int
"""The assessment text length. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoints
by default."""
offset: int
"""The assessment text offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoints by default."""
is_negated: bool
"""Whether the value of the assessment is negated. For example, in
"The food is not good", the assessment "good" is negated."""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.sentiment = kwargs.get("sentiment", None)
self.confidence_scores = kwargs.get("confidence_scores", None)
self.length = kwargs.get("length", None)
self.offset = kwargs.get("offset", None)
self.is_negated = kwargs.get("is_negated", None)
@classmethod
def _from_generated(cls, assessment):
return cls(
text=assessment.text,
sentiment=assessment.sentiment,
confidence_scores=SentimentConfidenceScores._from_generated( # pylint: disable=protected-access
assessment.confidence_scores
),
length=assessment.length,
offset=assessment.offset,
is_negated=assessment.is_negated,
)
def __repr__(self) -> str:
return (
f"AssessmentSentiment(text={self.text}, sentiment={self.sentiment}, "
f"confidence_scores={repr(self.confidence_scores)}, length={self.length}, "
f"offset={self.offset}, is_negated={self.is_negated})"[:1024]
)
[docs]class SentimentConfidenceScores(DictMixin):
"""The confidence scores (Softmax scores) between 0 and 1.
Higher values indicate higher confidence.
"""
positive: float
"""Positive score."""
neutral: float
"""Neutral score."""
negative: float
"""Negative score."""
def __init__(self, **kwargs: Any) -> None:
self.positive = kwargs.get("positive", 0.0)
self.neutral = kwargs.get("neutral", 0.0)
self.negative = kwargs.get("negative", 0.0)
@classmethod
def _from_generated(cls, score):
return cls(
positive=score.positive,
neutral=score.neutral if hasattr(score, "neutral") else 0.0,
negative=score.negative,
)
def __repr__(self) -> str:
return f"SentimentConfidenceScores(positive={self.positive}, " \
f"neutral={self.neutral}, negative={self.negative})"[:1024]
class _AnalyzeActionsType(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""The type of action that was applied to the documents"""
RECOGNIZE_ENTITIES = "recognize_entities" #: Entities Recognition action.
RECOGNIZE_PII_ENTITIES = (
"recognize_pii_entities" #: PII Entities Recognition action.
)
EXTRACT_KEY_PHRASES = "extract_key_phrases" #: Key Phrase Extraction action.
RECOGNIZE_LINKED_ENTITIES = (
"recognize_linked_entities" #: Linked Entities Recognition action.
)
ANALYZE_SENTIMENT = "analyze_sentiment" #: Sentiment Analysis action.
RECOGNIZE_CUSTOM_ENTITIES = "recognize_custom_entities"
SINGLE_LABEL_CLASSIFY = "single_label_classify"
MULTI_LABEL_CLASSIFY = "multi_label_classify"
ANALYZE_HEALTHCARE_ENTITIES = "analyze_healthcare_entities"
EXTRACT_SUMMARY = "extract_summary"
ABSTRACT_SUMMARY = "abstract_summary"
class ActionPointerKind(str, Enum, metaclass=CaseInsensitiveEnumMeta):
"""v3.1 only"""
RECOGNIZE_ENTITIES = "entityRecognitionTasks"
RECOGNIZE_PII_ENTITIES = "piiEntityRecognitionTasks"
EXTRACT_KEY_PHRASES = "keyPhraseExtractionTasks"
RECOGNIZE_LINKED_ENTITIES = "entityLinkingTasks"
ANALYZE_SENTIMENT = "sentimentAnalysisTasks"
[docs]class RecognizeEntitiesAction(DictMixin):
"""RecognizeEntitiesAction encapsulates the parameters for starting a long-running Entities Recognition operation.
If you just want to recognize entities in a list of documents, and not perform multiple
long running actions on the input of documents, call method `recognize_entities` instead
of interfacing with this model.
:keyword Optional[str] model_version: The model version to use for the analysis.
:keyword Optional[str] string_index_type: Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
"""
model_version: Optional[str] = None
"""The model version to use for the analysis."""
string_index_type: Optional[str] = None
"""Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets"""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
*,
model_version: Optional[str] = None,
string_index_type: Optional[str] = None,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.model_version = model_version
self.string_index_type: str = string_index_type if string_index_type is not None else STRING_INDEX_TYPE_DEFAULT
self.disable_service_logs = disable_service_logs
def __repr__(self) -> str:
return f"RecognizeEntitiesAction(model_version={self.model_version}, " \
f"string_index_type={self.string_index_type}, " \
f"disable_service_logs={self.disable_service_logs})"[:1024]
def _to_generated(self, api_version, task_id):
if is_language_api(api_version):
return _v2022_10_01_preview_models.EntitiesLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.EntitiesTaskParameters(
model_version=self.model_version,
string_index_type=string_index_type_compatibility(self.string_index_type),
logging_opt_out=self.disable_service_logs,
)
)
return _v3_1_models.EntitiesTask(
parameters=_v3_1_models.EntitiesTaskParameters(
model_version=self.model_version,
string_index_type=self.string_index_type,
logging_opt_out=self.disable_service_logs,
),
task_name=task_id
)
[docs]class AnalyzeSentimentAction(DictMixin):
"""AnalyzeSentimentAction encapsulates the parameters for starting a long-running
Sentiment Analysis operation.
If you just want to analyze sentiment in a list of documents, and not perform multiple
long running actions on the input of documents, call method `analyze_sentiment` instead
of interfacing with this model.
:keyword Optional[str] model_version: The model version to use for the analysis.
:keyword Optional[bool] show_opinion_mining: Whether to mine the opinions of a sentence and conduct more
granular analysis around the aspects of a product or service (also known as
aspect-based sentiment analysis). If set to true, the returned
:class:`~azure.ai.textanalytics.SentenceSentiment` objects
will have property `mined_opinions` containing the result of this analysis.
:keyword Optional[str] string_index_type: Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
"""
show_opinion_mining: Optional[bool] = None
"""Whether to mine the opinions of a sentence and conduct more
granular analysis around the aspects of a product or service (also known as
aspect-based sentiment analysis). If set to true, the returned
:class:`~azure.ai.textanalytics.SentenceSentiment` objects
will have property `mined_opinions` containing the result of this analysis."""
model_version: Optional[str] = None
"""The model version to use for the analysis."""
string_index_type: Optional[str] = None
"""Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets"""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
*,
show_opinion_mining: Optional[bool] = None,
model_version: Optional[str] = None,
string_index_type: Optional[str] = None,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.model_version = model_version
self.show_opinion_mining = show_opinion_mining
self.string_index_type: str = string_index_type if string_index_type is not None else STRING_INDEX_TYPE_DEFAULT
self.disable_service_logs = disable_service_logs
def __repr__(self) -> str:
return (
f"AnalyzeSentimentAction(model_version={self.model_version}, "
f"show_opinion_mining={self.show_opinion_mining}, "
f"string_index_type={self.string_index_type}, "
f"disable_service_logs={self.disable_service_logs}"[:1024]
)
def _to_generated(self, api_version, task_id):
if is_language_api(api_version):
return _v2022_10_01_preview_models.SentimentAnalysisLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.SentimentAnalysisTaskParameters(
model_version=self.model_version,
opinion_mining=self.show_opinion_mining,
string_index_type=string_index_type_compatibility(self.string_index_type),
logging_opt_out=self.disable_service_logs,
)
)
return _v3_1_models.SentimentAnalysisTask(
parameters=_v3_1_models.SentimentAnalysisTaskParameters(
model_version=self.model_version,
opinion_mining=self.show_opinion_mining,
string_index_type=self.string_index_type,
logging_opt_out=self.disable_service_logs,
),
task_name=task_id
)
[docs]class RecognizePiiEntitiesAction(DictMixin):
"""RecognizePiiEntitiesAction encapsulates the parameters for starting a long-running PII
Entities Recognition operation. See more information in the service docs: https://aka.ms/azsdk/language/pii
If you just want to recognize pii entities in a list of documents, and not perform multiple
long running actions on the input of documents, call method `recognize_pii_entities` instead
of interfacing with this model.
:keyword Optional[str] model_version: The model version to use for the analysis.
:keyword Optional[str] domain_filter: An optional string to set the PII domain to include only a
subset of the PII entity categories. Possible values include 'phi' or None.
:keyword categories_filter: Instead of filtering over all PII entity categories, you can pass in a list of
the specific PII entity categories you want to filter out. For example, if you only want to filter out
U.S. social security numbers in a document, you can pass in
`[PiiEntityCategory.US_SOCIAL_SECURITY_NUMBER]` for this kwarg.
:paramtype categories_filter: Optional[list[str or ~azure.ai.textanalytics.PiiEntityCategory]]
:keyword Optional[str] string_index_type: Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets
:keyword Optional[bool] disable_service_logs: Defaults to true, meaning that the Language service will not log your
input text on the service side for troubleshooting. If set to False, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
"""
categories_filter: Optional[List[Union[str, PiiEntityCategory]]] = None
"""Instead of filtering over all PII entity categories, you can pass in a list of
the specific PII entity categories you want to filter out. For example, if you only want to filter out
U.S. social security numbers in a document, you can pass in
`[PiiEntityCategory.US_SOCIAL_SECURITY_NUMBER]` for this kwarg."""
domain_filter: Optional[str] = None
"""An optional string to set the PII domain to include only a
subset of the PII entity categories. Possible values include 'phi' or None."""
model_version: Optional[str] = None
"""The model version to use for the analysis."""
string_index_type: Optional[str] = None
"""Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets"""
disable_service_logs: Optional[bool] = None
"""Defaults to true, meaning that the Language service will not log your
input text on the service side for troubleshooting. If set to False, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
*,
categories_filter: Optional[List[Union[str, PiiEntityCategory]]] = None,
domain_filter: Optional[str] = None,
model_version: Optional[str] = None,
string_index_type: Optional[str] = None,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.model_version = model_version
self.domain_filter = domain_filter
self.categories_filter = categories_filter
self.string_index_type: str = string_index_type if string_index_type is not None else STRING_INDEX_TYPE_DEFAULT
self.disable_service_logs = disable_service_logs
def __repr__(self) -> str:
return (
f"RecognizePiiEntitiesAction(model_version={self.model_version}, "
f"domain_filter={self.domain_filter}, categories_filter={self.categories_filter}, "
f"string_index_type={self.string_index_type}, "
f"disable_service_logs={self.disable_service_logs}"[:1024]
)
def _to_generated(self, api_version, task_id):
if is_language_api(api_version):
return _v2022_10_01_preview_models.PiiLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.PiiTaskParameters(
model_version=self.model_version,
domain=self.domain_filter,
pii_categories=self.categories_filter,
string_index_type=string_index_type_compatibility(self.string_index_type),
logging_opt_out=self.disable_service_logs,
)
)
return _v3_1_models.PiiTask(
parameters=_v3_1_models.PiiTaskParameters(
model_version=self.model_version,
domain=self.domain_filter,
pii_categories=self.categories_filter,
string_index_type=self.string_index_type,
logging_opt_out=self.disable_service_logs,
),
task_name=task_id
)
[docs]class RecognizeLinkedEntitiesAction(DictMixin):
"""RecognizeLinkedEntitiesAction encapsulates the parameters for starting a long-running Linked Entities
Recognition operation.
If you just want to recognize linked entities in a list of documents, and not perform multiple
long running actions on the input of documents, call method `recognize_linked_entities` instead
of interfacing with this model.
:keyword Optional[str] model_version: The model version to use for the analysis.
:keyword Optional[str] string_index_type: Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
"""
model_version: Optional[str] = None
"""The model version to use for the analysis."""
string_index_type: Optional[str] = None
"""Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets"""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
*,
model_version: Optional[str] = None,
string_index_type: Optional[str] = None,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.model_version = model_version
self.string_index_type: str = string_index_type if string_index_type is not None else STRING_INDEX_TYPE_DEFAULT
self.disable_service_logs = disable_service_logs
def __repr__(self) -> str:
return (
f"RecognizeLinkedEntitiesAction(model_version={self.model_version}, "
f"string_index_type={self.string_index_type}), "
f"disable_service_logs={self.disable_service_logs}"[:1024]
)
def _to_generated(self, api_version, task_id):
if is_language_api(api_version):
return _v2022_10_01_preview_models.EntityLinkingLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.EntityLinkingTaskParameters(
model_version=self.model_version,
string_index_type=string_index_type_compatibility(self.string_index_type),
logging_opt_out=self.disable_service_logs,
)
)
return _v3_1_models.EntityLinkingTask(
parameters=_v3_1_models.EntityLinkingTaskParameters(
model_version=self.model_version,
string_index_type=self.string_index_type,
logging_opt_out=self.disable_service_logs,
),
task_name=task_id
)
[docs]class RecognizeCustomEntitiesAction(DictMixin):
"""RecognizeCustomEntitiesAction encapsulates the parameters for starting a long-running custom entity
recognition operation. For information on regional support of custom features and how to train a model to
recognize custom entities, see https://aka.ms/azsdk/textanalytics/customentityrecognition
:param str project_name: Required. This field indicates the project name for the model.
:param str deployment_name: This field indicates the deployment name for the model.
:keyword Optional[str] string_index_type: Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
.. versionadded:: 2022-05-01
The *RecognizeCustomEntitiesAction* model.
"""
project_name: str
"""This field indicates the project name for the model."""
deployment_name: str
"""This field indicates the deployment name for the model."""
string_index_type: Optional[str] = None
"""Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets"""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
project_name: str,
deployment_name: str,
*,
string_index_type: Optional[str] = None,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.project_name = project_name
self.deployment_name = deployment_name
self.disable_service_logs = disable_service_logs
self.string_index_type: str = string_index_type if string_index_type is not None else STRING_INDEX_TYPE_DEFAULT
def __repr__(self) -> str:
return (
f"RecognizeCustomEntitiesAction(project_name={self.project_name}, "
f"deployment_name={self.deployment_name}, disable_service_logs={self.disable_service_logs}, "
f"string_index_type={self.string_index_type})"[:1024]
)
def _to_generated(self, api_version, task_id): # pylint: disable=unused-argument
return _v2022_10_01_preview_models.CustomEntitiesLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.CustomEntitiesTaskParameters(
project_name=self.project_name,
deployment_name=self.deployment_name,
string_index_type=string_index_type_compatibility(self.string_index_type),
logging_opt_out=self.disable_service_logs,
)
)
[docs]class RecognizeCustomEntitiesResult(DictMixin):
"""RecognizeCustomEntitiesResult is a result object which contains
the custom recognized entities from a particular document.
.. versionadded:: 2022-10-01-preview
The *detected_language* property.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier that matches the
document id that was passed in with the request. If not specified
in the request, an id is assigned for the document."""
entities: List[CategorizedEntity]
"""Recognized custom entities in the document."""
warnings: List[TextAnalyticsWarning]
"""Warnings encountered while processing document."""
statistics: Optional[TextDocumentStatistics] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
detected_language: Optional[DetectedLanguage] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the DetectedLanguage for the document."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a RecognizeCustomEntitiesResult."""
kind: Literal["CustomEntityRecognition"] = "CustomEntityRecognition"
"""The text analysis kind - "CustomEntityRecognition"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.entities = kwargs.get("entities", None)
self.warnings = kwargs.get("warnings", [])
self.statistics = kwargs.get("statistics", None)
self.detected_language = kwargs.get("detected_language", None)
self.is_error: Literal[False] = False
self.kind: Literal["CustomEntityRecognition"] = "CustomEntityRecognition"
def __repr__(self) -> str:
return (
f"RecognizeCustomEntitiesResult(id={self.id}, entities={repr(self.entities)}, "
f"warnings={repr(self.warnings)}, statistics={repr(self.statistics)}, "
f"detected_language={repr(self.detected_language)}, is_error={self.is_error},"
f" kind={self.kind})"[:1024]
)
@classmethod
def _from_generated(cls, result):
return cls(
id=result.id,
entities=[
CategorizedEntity._from_generated(e) # pylint: disable=protected-access
for e in result.entities
],
warnings=[
TextAnalyticsWarning._from_generated( # pylint: disable=protected-access
w
)
for w in result.warnings
],
statistics=TextDocumentStatistics._from_generated( # pylint: disable=protected-access
result.statistics
),
detected_language=DetectedLanguage._from_generated( # pylint: disable=protected-access
result.detected_language
) if hasattr(result, "detected_language") and result.detected_language else None
)
[docs]class MultiLabelClassifyAction(DictMixin):
"""MultiLabelClassifyAction encapsulates the parameters for starting a long-running custom multi label
classification operation. For information on regional support of custom features and how to train a model to
classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities
:param str project_name: Required. This field indicates the project name for the model.
:param str deployment_name: Required. This field indicates the deployment name for the model.
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
.. versionadded:: 2022-05-01
The *MultiLabelClassifyAction* model.
"""
project_name: str
"""This field indicates the project name for the model."""
deployment_name: str
"""This field indicates the deployment name for the model."""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
project_name: str,
deployment_name: str,
*,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.project_name = project_name
self.deployment_name = deployment_name
self.disable_service_logs = disable_service_logs
def __repr__(self) -> str:
return (
f"MultiLabelClassifyAction(project_name={self.project_name}, deployment_name={self.deployment_name}, "
f"disable_service_logs={self.disable_service_logs})"[:1024]
)
def _to_generated(self, api_version, task_id): # pylint: disable=unused-argument
return _v2022_10_01_preview_models.CustomMultiLabelClassificationLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.CustomMultiLabelClassificationTaskParameters(
project_name=self.project_name,
deployment_name=self.deployment_name,
logging_opt_out=self.disable_service_logs,
)
)
[docs]class ClassifyDocumentResult(DictMixin):
"""ClassifyDocumentResult is a result object which contains
the classifications for a particular document.
.. versionadded:: 2022-10-01-preview
The *detected_language* property.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier."""
classifications: List["ClassificationCategory"]
"""Recognized classification results in the document."""
warnings: List[TextAnalyticsWarning]
"""Warnings encountered while processing document."""
statistics: Optional[TextDocumentStatistics] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
detected_language: Optional[DetectedLanguage] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the DetectedLanguage for the document."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a ClassifyDocumentResult."""
kind: Literal["CustomDocumentClassification"] = "CustomDocumentClassification"
"""The text analysis kind - "CustomDocumentClassification"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get('id', None)
self.classifications = kwargs.get('classifications', None)
self.warnings = kwargs.get('warnings', [])
self.statistics = kwargs.get('statistics', None)
self.detected_language = kwargs.get('detected_language', None)
self.is_error: Literal[False] = False
self.kind: Literal["CustomDocumentClassification"] = "CustomDocumentClassification"
def __repr__(self) -> str:
return (
f"ClassifyDocumentResult(id={self.id}, classifications={repr(self.classifications)}, "
f"warnings={repr(self.warnings)}, statistics={repr(self.statistics)}, "
f"detected_language={repr(self.detected_language)} "
f"is_error={self.is_error}, kind={self.kind})"[:1024]
)
@classmethod
def _from_generated(cls, result):
return cls(
id=result.id,
classifications=[
ClassificationCategory._from_generated(e) # pylint: disable=protected-access
for e in result.class_property
],
warnings=[
TextAnalyticsWarning._from_generated( # pylint: disable=protected-access
w
)
for w in result.warnings
],
statistics=TextDocumentStatistics._from_generated( # pylint: disable=protected-access
result.statistics
),
detected_language=DetectedLanguage._from_generated( # pylint: disable=protected-access
result.detected_language
) if hasattr(result, "detected_language") and result.detected_language else None
)
[docs]class SingleLabelClassifyAction(DictMixin):
"""SingleLabelClassifyAction encapsulates the parameters for starting a long-running custom single label
classification operation. For information on regional support of custom features and how to train a model to
classify your documents, see https://aka.ms/azsdk/textanalytics/customfunctionalities
:param str project_name: Required. This field indicates the project name for the model.
:param str deployment_name: Required. This field indicates the deployment name for the model.
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
.. versionadded:: 2022-05-01
The *SingleLabelClassifyAction* model.
"""
project_name: str
"""This field indicates the project name for the model."""
deployment_name: str
"""This field indicates the deployment name for the model."""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
project_name: str,
deployment_name: str,
*,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.project_name = project_name
self.deployment_name = deployment_name
self.disable_service_logs = disable_service_logs
def __repr__(self) -> str:
return (
f"SingleLabelClassifyAction(project_name={self.project_name}, deployment_name={self.deployment_name}, "
f"disable_service_logs={self.disable_service_logs})"[:1024]
)
def _to_generated(self, api_version, task_id): # pylint: disable=unused-argument
return _v2022_10_01_preview_models.CustomSingleLabelClassificationLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.CustomSingleLabelClassificationTaskParameters(
project_name=self.project_name,
deployment_name=self.deployment_name,
logging_opt_out=self.disable_service_logs,
)
)
[docs]class ClassificationCategory(DictMixin):
"""ClassificationCategory represents a classification of the input document.
"""
category: str
"""Classification category for the document."""
confidence_score: float
"""Confidence score between 0 and 1 of the recognized classification."""
def __init__(self, **kwargs: Any) -> None:
self.category = kwargs.get('category', None)
self.confidence_score = kwargs.get('confidence_score', None)
def __repr__(self) -> str:
return f"ClassificationCategory(category={self.category}, " \
f"confidence_score={self.confidence_score})"[:1024]
@classmethod
def _from_generated(cls, result):
return cls(
category=result.category,
confidence_score=result.confidence_score
)
[docs]class AnalyzeHealthcareEntitiesAction(DictMixin):
"""AnalyzeHealthcareEntitiesAction encapsulates the parameters for starting a long-running
healthcare entities analysis operation.
If you just want to analyze healthcare entities in a list of documents, and not perform multiple
long running actions on the input of documents, call method `begin_analyze_healthcare_entities` instead
of interfacing with this model.
:keyword Optional[str] model_version: The model version to use for the analysis.
:keyword Optional[str] string_index_type: Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
:keyword Optional[str] fhir_version: The FHIR Spec version that the result will use to format the fhir_bundle
on the result object. For additional information see https://www.hl7.org/fhir/overview.html.
The only acceptable values to pass in are None and "4.0.1". The default value is None.
:keyword document_type: Document type that can be provided as input for Fhir Documents. Expect to
have fhir_version provided when used. Behavior of using None enum is the same as not using the
document_type parameter. Known values are: "None", "ClinicalTrial", "DischargeSummary",
"ProgressNote", "HistoryAndPhysical", "Consult", "Imaging", "Pathology", and "ProcedureNote".
:paramtype document_type: Optional[str or ~azure.ai.textanalytics.HealthcareDocumentType]
.. versionadded:: 2022-05-01
The *AnalyzeHealthcareEntitiesAction* model.
.. versionadded:: 2022-10-01-preview
The *fhir_version* and *document_type* keyword arguments.
"""
model_version: Optional[str] = None
"""The model version to use for the analysis."""
string_index_type: Optional[str] = None
"""Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets"""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
fhir_version: Optional[str] = None
"""The FHIR Spec version that the result will use to format the fhir_bundle
on the result object. For additional information see https://www.hl7.org/fhir/overview.html.
The only acceptable values to pass in are None and "4.0.1". The default value is None."""
document_type: Optional[Union[str, HealthcareDocumentType]] = None
"""Document type that can be provided as input for Fhir Documents. Expect to
have fhir_version provided when used. Behavior of using None enum is the same as not using the
document_type parameter. Known values are "None", "ClinicalTrial", "DischargeSummary",
"ProgressNote", "HistoryAndPhysical", "Consult", "Imaging", "Pathology", and "ProcedureNote"."""
def __init__(
self,
*,
model_version: Optional[str] = None,
string_index_type: Optional[str] = None,
disable_service_logs: Optional[bool] = None,
fhir_version: Optional[str] = None,
document_type: Optional[Union[str, HealthcareDocumentType]] = None,
**kwargs: Any
) -> None:
self.model_version = model_version
self.string_index_type: str = string_index_type if string_index_type is not None else STRING_INDEX_TYPE_DEFAULT
self.disable_service_logs = disable_service_logs
self.fhir_version = fhir_version
self.document_type = document_type
def __repr__(self) -> str:
return (
f"AnalyzeHealthcareEntitiesAction(model_version={self.model_version}, "
f"string_index_type={self.string_index_type}, disable_service_logs={self.disable_service_logs}, "
f"fhir_version={self.fhir_version}, document_type={self.document_type})"[:1024]
)
def _to_generated(self, api_version, task_id): # pylint: disable=unused-argument
return _v2022_10_01_preview_models.HealthcareLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.HealthcareTaskParameters(
model_version=self.model_version,
string_index_type=string_index_type_compatibility(self.string_index_type),
logging_opt_out=self.disable_service_logs,
fhir_version=self.fhir_version,
document_type=self.document_type,
)
)
[docs]class SummarySentence(DictMixin):
"""Represents a single sentence from the extractive text summarization.
.. versionadded:: 2022-10-01-preview
The *SummarySentence* model.
"""
text: str
"""The extracted sentence text."""
rank_score: float
"""A float value representing the relevance of the sentence within
the summary. Higher values indicate higher importance."""
offset: int
"""The sentence offset from the start of the document.
The value depends on the value of the `string_index_type` parameter
set in the original request, which is UnicodeCodePoint by default."""
length: int
"""The length of the sentence. This value depends on the value of the
`string_index_type` parameter set in the original request, which is UnicodeCodePoint
by default."""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.rank_score = kwargs.get("rank_score", None)
self.offset = kwargs.get("offset", None)
self.length = kwargs.get("length", None)
def __repr__(self) -> str:
return f"SummarySentence(text={self.text}, rank_score={self.rank_score}, " \
f"offset={self.offset}, length={self.length})"[:1024]
@classmethod
def _from_generated(cls, sentence):
return cls(
text=sentence.text,
rank_score=sentence.rank_score,
offset=sentence.offset,
length=sentence.length,
)
[docs]class AbstractiveSummaryResult(DictMixin):
"""AbstractiveSummaryResult is a result object which contains
the summary generated for a particular document.
.. versionadded:: 2022-10-01-preview
The *AbstractiveSummaryResult* model.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier. Required."""
summaries: List["AbstractiveSummary"]
"""A list of abstractive summaries. Required."""
warnings: List[TextAnalyticsWarning]
"""Warnings encountered while processing document. Results will still be returned
if there are warnings, but they may not be fully accurate."""
detected_language: Optional[DetectedLanguage] = None
"""If 'language' is set to 'auto' for the document in the request this
field will contain the DetectedLanguage for the document."""
statistics: Optional[TextDocumentStatistics] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a AbstractiveSummaryResult."""
kind: Literal["AbstractiveSummarization"] = "AbstractiveSummarization"
"""The text analysis kind - "AbstractiveSummarization"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get("id", None)
self.detected_language = kwargs.get("detected_language", None)
self.warnings = kwargs.get("warnings", None)
self.statistics = kwargs.get("statistics", None)
self.summaries = kwargs.get("summaries", None)
self.is_error: Literal[False] = False
self.kind: Literal["AbstractiveSummarization"] = "AbstractiveSummarization"
def __repr__(self) -> str:
return (
f"AbstractiveSummaryResult(id={self.id}, detected_language={repr(self.detected_language)}, "
f"warnings={repr(self.warnings)}, statistics={repr(self.statistics)}, "
f"summaries={repr(self.summaries)}, is_error={self.is_error}, kind={self.kind})"[:1024]
)
@classmethod
def _from_generated(cls, result):
return cls(
id=result.id,
detected_language=DetectedLanguage._from_generated( # pylint: disable=protected-access
result.detected_language
) if hasattr(result, "detected_language") and result.detected_language else None,
warnings=[
TextAnalyticsWarning._from_generated( # pylint: disable=protected-access
w
)
for w in result.warnings
],
statistics=TextDocumentStatistics._from_generated( # pylint: disable=protected-access
result.statistics
),
summaries=[
AbstractiveSummary._from_generated(summary) # pylint: disable=protected-access
for summary in result.summaries
],
)
[docs]class AbstractiveSummary(DictMixin):
"""An object representing a single summary with context for given document.
.. versionadded:: 2022-10-01-preview
The *AbstractiveSummary* model.
"""
text: str
"""The text of the summary. Required."""
contexts: List["SummaryContext"]
"""The context list of the summary."""
def __init__(self, **kwargs: Any) -> None:
self.text = kwargs.get("text", None)
self.contexts = kwargs.get("contexts", None)
def __repr__(self) -> str:
return f"AbstractiveSummary(text={self.text}, contexts={repr(self.contexts)})"[:1024]
@classmethod
def _from_generated(cls, result):
return cls(
text=result.text,
contexts=[
SummaryContext._from_generated(context) # pylint: disable=protected-access
for context in result.contexts
] if result.contexts else []
)
[docs]class SummaryContext(DictMixin):
"""The context of the summary.
.. versionadded:: 2022-10-01-preview
The *SummaryContext* model.
"""
offset: int
"""Start position for the context. Use of different 'string_index_type' values can
affect the offset returned. Required."""
length: int
"""The length of the context. Use of different 'string_index_type' values can affect
the length returned. Required."""
def __init__(self, **kwargs: Any) -> None:
self.offset = kwargs.get("offset", None)
self.length = kwargs.get("length", None)
def __repr__(self) -> str:
return f"SummaryContext(offset={self.offset}, length={self.length})"[:1024]
@classmethod
def _from_generated(cls, summary):
return cls(
offset=summary.offset,
length=summary.length
)
[docs]class AbstractiveSummaryAction(DictMixin):
"""AbstractiveSummaryAction encapsulates the parameters for starting a long-running
abstractive summarization operation. For a conceptual discussion of extractive summarization,
see the service documentation:
https://learn.microsoft.com/azure/cognitive-services/language-service/summarization/overview
Abstractive summarization generates a summary for the input documents. Abstractive summarization
is different from extractive summarization in that extractive summarization is the strategy of
concatenating extracted sentences from the input document into a summary, while abstractive
summarization involves paraphrasing the document using novel sentences.
.. note:: The abstractive summarization feature is part of a gated preview. Request access here:
https://aka.ms/applyforgatedsummarizationfeatures
:keyword Optional[int] sentence_count: It controls the approximate number of sentences in the output summaries.
:keyword Optional[str] model_version: The model version to use for the analysis.
:keyword Optional[str] string_index_type: Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets
:keyword Optional[bool] disable_service_logs: If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai.
.. versionadded:: 2022-10-01-preview
The *AbstractiveSummaryAction* model.
"""
sentence_count: Optional[int] = None
"""It controls the approximate number of sentences in the output summaries."""
model_version: Optional[str] = None
"""The model version to use for the analysis."""
string_index_type: Optional[str] = None
"""Specifies the method used to interpret string offsets.
`UnicodeCodePoint`, the Python encoding, is the default. To override the Python default,
you can also pass in `Utf16CodeUnit` or `TextElement_v8`. For additional information
see https://aka.ms/text-analytics-offsets"""
disable_service_logs: Optional[bool] = None
"""If set to true, you opt-out of having your text input
logged on the service side for troubleshooting. By default, the Language service logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the service's natural language processing functions. Setting this parameter to true,
disables input logging and may limit our ability to remediate issues that occur. Please see
Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for
additional details, and Microsoft Responsible AI principles at
https://www.microsoft.com/ai/responsible-ai."""
def __init__(
self,
*,
sentence_count: Optional[int] = None,
model_version: Optional[str] = None,
string_index_type: Optional[str] = None,
disable_service_logs: Optional[bool] = None,
**kwargs: Any
) -> None:
self.sentence_count = sentence_count
self.model_version = model_version
self.string_index_type: str = string_index_type if string_index_type is not None else STRING_INDEX_TYPE_DEFAULT
self.disable_service_logs = disable_service_logs
def __repr__(self) -> str:
return (
f"AbstractiveSummaryAction(model_version={self.model_version}, "
f"string_index_type={self.string_index_type}, disable_service_logs={self.disable_service_logs}, "
f"sentence_count={self.sentence_count})"[:1024]
)
def _to_generated(self, api_version, task_id): # pylint: disable=unused-argument
return _v2022_10_01_preview_models.AbstractiveSummarizationLROTask(
task_name=task_id,
parameters=_v2022_10_01_preview_models.AbstractiveSummarizationTaskParameters(
model_version=self.model_version,
string_index_type=string_index_type_compatibility(self.string_index_type),
logging_opt_out=self.disable_service_logs,
sentence_count=self.sentence_count,
)
)
[docs]class DynamicClassificationResult(DictMixin):
"""DynamicClassificationResult is a result object which contains
the classifications for a particular document.
.. versionadded:: 2022-10-01-preview
The *DynamicClassificationResult* model.
"""
id: str # pylint: disable=redefined-builtin
"""Unique, non-empty document identifier."""
classifications: List[ClassificationCategory]
"""Recognized classification results in the document."""
warnings: List[TextAnalyticsWarning]
"""Warnings encountered while processing document."""
statistics: Optional[TextDocumentStatistics] = None
"""If `show_stats=True` was specified in the request this
field will contain information about the document payload."""
is_error: Literal[False] = False
"""Boolean check for error item when iterating over list of
results. Always False for an instance of a DynamicClassificationResult."""
kind: Literal["DynamicClassification"] = "DynamicClassification"
"""The text analysis kind - "DynamicClassification"."""
def __init__(self, **kwargs: Any) -> None:
self.id = kwargs.get('id', None)
self.classifications = kwargs.get('classifications', None)
self.warnings = kwargs.get('warnings', [])
self.statistics = kwargs.get('statistics', None)
self.is_error: Literal[False] = False
self.kind: Literal["DynamicClassification"] = "DynamicClassification"
def __repr__(self) -> str:
return (
f"DynamicClassificationResult(id={self.id}, classifications={repr(self.classifications)}, "
f"warnings={repr(self.warnings)}, statistics={repr(self.statistics)}, "
f"is_error={self.is_error}, kind={self.kind})"[:1024]
)
@classmethod
def _from_generated(cls, result):
return cls(
id=result.id,
classifications=[
ClassificationCategory._from_generated(c) # pylint: disable=protected-access
for c in result.classifications
],
warnings=[
TextAnalyticsWarning._from_generated( # pylint: disable=protected-access
w
)
for w in result.warnings
],
statistics=TextDocumentStatistics._from_generated( # pylint: disable=protected-access
result.statistics
),
)