# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
import copy
from typing import ( # pylint: disable=unused-import
Union,
Optional,
Any,
List,
Dict,
TYPE_CHECKING
)
from functools import partial
from azure.core.polling import AsyncLROPoller
from azure.core.async_paging import AsyncItemPaged
from azure.core.tracing.decorator_async import distributed_trace_async
from azure.core.exceptions import HttpResponseError
from ._base_client_async import AsyncTextAnalyticsClientBase
from .._request_handlers import _validate_input, _determine_action_type, _check_string_index_type_arg
from .._response_handlers import (
process_http_response_error,
entities_result,
linked_entities_result,
key_phrases_result,
sentiment_result,
language_result,
pii_entities_result,
_get_deserialize
)
from .._response_handlers_async import healthcare_paged_result, analyze_paged_result
from .._models import (
DetectLanguageInput,
TextDocumentInput,
DetectLanguageResult,
RecognizeEntitiesResult,
RecognizeLinkedEntitiesResult,
ExtractKeyPhrasesResult,
AnalyzeSentimentResult,
DocumentError,
RecognizePiiEntitiesResult,
RecognizeEntitiesAction,
RecognizePiiEntitiesAction,
ExtractKeyPhrasesAction,
AnalyzeActionsResult,
AnalyzeActionsType,
RecognizeLinkedEntitiesAction,
AnalyzeSentimentAction
)
from .._lro import TextAnalyticsOperationResourcePolling
from .._async_lro import (
AnalyzeHealthcareEntitiesAsyncLROPollingMethod,
AsyncAnalyzeBatchActionsLROPollingMethod
)
if TYPE_CHECKING:
from azure.core.credentials_async import AsyncTokenCredential
from azure.core.credentials import AzureKeyCredential
from .._models import AnalyzeHealthcareEntitiesResultItem
[docs]class TextAnalyticsClient(AsyncTextAnalyticsClientBase):
"""The Text Analytics API is a suite of text analytics web services built with best-in-class
Microsoft machine learning algorithms. The API can be used to analyze unstructured text for
tasks such as sentiment analysis, key phrase extraction, and language detection. No training data
is needed to use this API - just bring your text data. This API uses advanced natural language
processing techniques to deliver best in class predictions.
Further documentation can be found in
https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview
:param str endpoint: Supported Cognitive Services or Text Analytics resource
endpoints (protocol and hostname, for example: https://westus2.api.cognitive.microsoft.com).
:param credential: Credentials needed for the client to connect to Azure.
This can be the an instance of AzureKeyCredential if using a
cognitive services/text analytics API key or a token credential
from :mod:`azure.identity`.
:type credential: :class:`~azure.core.credentials.AzureKeyCredential`
or :class:`~azure.core.credentials_async.AsyncTokenCredential`
:keyword str default_country_hint: Sets the default country_hint to use for all operations.
Defaults to "US". If you don't want to use a country hint, pass the string "none".
:keyword str default_language: Sets the default language to use for all operations.
Defaults to "en".
:keyword api_version: The API version of the service to use for requests. It defaults to the
latest service version. Setting to an older version may result in reduced feature compatibility.
:paramtype api_version: str or ~azure.ai.textanalytics.TextAnalyticsApiVersion
.. admonition:: Example:
.. literalinclude:: ../samples/async_samples/sample_authentication_async.py
:start-after: [START create_ta_client_with_key_async]
:end-before: [END create_ta_client_with_key_async]
:language: python
:dedent: 8
:caption: Creating the TextAnalyticsClient with endpoint and API key.
.. literalinclude:: ../samples/async_samples/sample_authentication_async.py
:start-after: [START create_ta_client_with_aad_async]
:end-before: [END create_ta_client_with_aad_async]
:language: python
:dedent: 8
:caption: Creating the TextAnalyticsClient with endpoint and token credential from Azure Active Directory.
"""
def __init__( # type: ignore
self,
endpoint: str,
credential: Union["AzureKeyCredential", "AsyncTokenCredential"],
**kwargs: Any
) -> None:
super(TextAnalyticsClient, self).__init__(
endpoint=endpoint,
credential=credential,
**kwargs
)
self._api_version = kwargs.get("api_version")
self._default_language = kwargs.pop("default_language", "en")
self._default_country_hint = kwargs.pop("default_country_hint", "US")
self._string_code_unit = None if kwargs.get("api_version") == "v3.0" else "UnicodeCodePoint"
self._deserialize = _get_deserialize()
[docs] @distributed_trace_async
async def detect_language( # type: ignore
self,
documents: Union[List[str], List[DetectLanguageInput], List[Dict[str, str]]],
**kwargs: Any
) -> List[Union[DetectLanguageResult, DocumentError]]:
"""Detect language for a batch of documents.
Returns the detected language and a numeric score between zero and
one. Scores close to one indicate 100% certainty that the identified
language is true. See https://aka.ms/talangs for the list of enabled languages.
See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits
for document length limits, maximum batch size, and supported text encoding.
:param documents: The set of documents to process as part of this batch.
If you wish to specify the ID and country_hint on a per-item basis you must
use as input a list[:class:`~azure.ai.textanalytics.DetectLanguageInput`] or a list of
dict representations of :class:`~azure.ai.textanalytics.DetectLanguageInput`, like
`{"id": "1", "country_hint": "us", "text": "hello world"}`.
:type documents:
list[str] or list[~azure.ai.textanalytics.DetectLanguageInput] or
list[dict[str, str]]
:keyword str country_hint: Country of origin hint for the entire batch. Accepts two
letter country codes specified by ISO 3166-1 alpha-2. Per-document
country hints will take precedence over whole batch hints. Defaults to
"US". If you don't want to use a country hint, pass the string "none".
:keyword str model_version: This value indicates which model will
be used for scoring, e.g. "latest", "2019-10-01". If a model-version
is not specified, the API will default to the latest, non-preview version.
See here for more info: https://aka.ms/text-analytics-model-versioning
:keyword bool show_stats: If set to true, response will contain document
level statistics in the `statistics` field of the document-level response.
:keyword 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, Text Analytics logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the Text Analytics 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.
:return: The combined list of :class:`~azure.ai.textanalytics.DetectLanguageResult`
and :class:`~azure.ai.textanalytics.DocumentError` in the order the original documents
were passed in.
:rtype: list[~azure.ai.textanalytics.DetectLanguageResult,
~azure.ai.textanalytics.DocumentError]
:raises ~azure.core.exceptions.HttpResponseError or TypeError or ValueError:
.. admonition:: Example:
.. literalinclude:: ../samples/async_samples/sample_detect_language_async.py
:start-after: [START detect_language_async]
:end-before: [END detect_language_async]
:language: python
:dedent: 8
:caption: Detecting language in a batch of documents.
"""
country_hint_arg = kwargs.pop("country_hint", None)
country_hint = country_hint_arg if country_hint_arg is not None else self._default_country_hint
docs = _validate_input(documents, "country_hint", country_hint)
model_version = kwargs.pop("model_version", None)
show_stats = kwargs.pop("show_stats", False)
disable_service_logs = kwargs.pop("disable_service_logs", None)
if disable_service_logs is not None:
kwargs['logging_opt_out'] = disable_service_logs
try:
return await self._client.languages(
documents=docs,
model_version=model_version,
show_stats=show_stats,
cls=kwargs.pop("cls", language_result),
**kwargs
)
except HttpResponseError as error:
process_http_response_error(error)
[docs] @distributed_trace_async
async def recognize_entities( # type: ignore
self,
documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
**kwargs: Any
) -> List[Union[RecognizeEntitiesResult, DocumentError]]:
"""Recognize entities for a batch of documents.
Identifies and categorizes entities in your text as people, places,
organizations, date/time, quantities, percentages, currencies, and more.
For the list of supported entity types, check: https://aka.ms/taner
See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits
for document length limits, maximum batch size, and supported text encoding.
:param documents: The set of documents to process as part of this batch.
If you wish to specify the ID and language on a per-item basis you must
use as input a list[:class:`~azure.ai.textanalytics.TextDocumentInput`] or a list of
dict representations of :class:`~azure.ai.textanalytics.TextDocumentInput`, like
`{"id": "1", "language": "en", "text": "hello world"}`.
:type documents:
list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
list[dict[str, str]]
:keyword str language: The 2 letter ISO 639-1 representation of language for the
entire batch. For example, use "en" for English; "es" for Spanish etc.
If not set, uses "en" for English as default. Per-document language will
take precedence over whole batch language. See https://aka.ms/talangs for
supported languages in Text Analytics API.
:keyword str model_version: This value indicates which model will
be used for scoring, e.g. "latest", "2019-10-01". If a model-version
is not specified, the API will default to the latest, non-preview version.
See here for more info: https://aka.ms/text-analytics-model-versioning
:keyword bool show_stats: If set to true, response will contain document
level statistics in the `statistics` field of the document-level response.
:keyword str string_index_type: Specifies the method used to interpret string offsets.
Can be one of 'UnicodeCodePoint' (default), 'Utf16CodePoint', or 'TextElement_v8'.
For additional information see https://aka.ms/text-analytics-offsets
:keyword 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, Text Analytics logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the Text Analytics 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.
:return: The combined list of :class:`~azure.ai.textanalytics.RecognizeEntitiesResult` and
:class:`~azure.ai.textanalytics.DocumentError` in the order the original documents were
passed in.
:rtype: list[~azure.ai.textanalytics.RecognizeEntitiesResult,
~azure.ai.textanalytics.DocumentError]
:raises ~azure.core.exceptions.HttpResponseError or TypeError or ValueError:
.. admonition:: Example:
.. literalinclude:: ../samples/async_samples/sample_recognize_entities_async.py
:start-after: [START recognize_entities_async]
:end-before: [END recognize_entities_async]
:language: python
:dedent: 8
:caption: Recognize entities in a batch of documents.
"""
language_arg = kwargs.pop("language", None)
language = language_arg if language_arg is not None else self._default_language
docs = _validate_input(documents, "language", language)
model_version = kwargs.pop("model_version", None)
show_stats = kwargs.pop("show_stats", False)
disable_service_logs = kwargs.pop("disable_service_logs", None)
if disable_service_logs is not None:
kwargs['logging_opt_out'] = disable_service_logs
string_index_type = _check_string_index_type_arg(
kwargs.pop("string_index_type", None),
self._api_version,
string_index_type_default=self._string_code_unit
)
if string_index_type:
kwargs.update({"string_index_type": string_index_type})
try:
return await self._client.entities_recognition_general(
documents=docs,
model_version=model_version,
show_stats=show_stats,
cls=kwargs.pop("cls", entities_result),
**kwargs
)
except HttpResponseError as error:
process_http_response_error(error)
[docs] @distributed_trace_async
async def recognize_pii_entities( # type: ignore
self,
documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
**kwargs: Any
) -> List[Union[RecognizePiiEntitiesResult, DocumentError]]:
"""Recognize entities containing personal information for a batch of documents.
Returns a list of personal information entities ("SSN",
"Bank Account", etc) in the document. For the list of supported entity types,
check https://aka.ms/tanerpii
See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits
for document length limits, maximum batch size, and supported text encoding.
:param documents: The set of documents to process as part of this batch.
If you wish to specify the ID and language on a per-item basis you must
use as input a list[:class:`~azure.ai.textanalytics.TextDocumentInput`] or a list of
dict representations of :class:`~azure.ai.textanalytics.TextDocumentInput`, like
`{"id": "1", "language": "en", "text": "hello world"}`.
:type documents:
list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
list[dict[str, str]]
:keyword str language: The 2 letter ISO 639-1 representation of language for the
entire batch. For example, use "en" for English; "es" for Spanish etc.
If not set, uses "en" for English as default. Per-document language will
take precedence over whole batch language. See https://aka.ms/talangs for
supported languages in Text Analytics API.
:keyword str model_version: This value indicates which model will
be used for scoring, e.g. "latest", "2019-10-01". If a model-version
is not specified, the API will default to the latest, non-preview version.
See here for more info: https://aka.ms/text-analytics-model-versioning
:keyword bool show_stats: If set to true, response will contain document
level statistics in the `statistics` field of the document-level response.
:keyword domain_filter: Filters the response entities to ones only included in the specified domain.
I.e., if set to 'phi', will only return entities in the Protected Healthcare Information domain.
See https://aka.ms/tanerpii for more information.
:paramtype domain_filter: str or ~azure.ai.textanalytics.PiiEntityDomainType
: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
`[PiiEntityCategoryType.US_SOCIAL_SECURITY_NUMBER]` for this kwarg.
:paramtype categories_filter: list[~azure.ai.textanalytics.PiiEntityCategoryType]
:keyword str string_index_type: Specifies the method used to interpret string offsets.
Can be one of 'UnicodeCodePoint' (default), 'Utf16CodePoint', or 'TextElement_v8'.
For additional information see https://aka.ms/text-analytics-offsets
:keyword 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, Text Analytics logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the Text Analytics 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.
:return: The combined list of :class:`~azure.ai.textanalytics.RecognizePiiEntitiesResult`
and :class:`~azure.ai.textanalytics.DocumentError` in the order the original documents
were passed in.
:rtype: list[~azure.ai.textanalytics.RecognizePiiEntitiesResult,
~azure.ai.textanalytics.DocumentError]
:raises ~azure.core.exceptions.HttpResponseError or TypeError or ValueError:
.. admonition:: Example:
.. literalinclude:: ../samples/sample_recognize_pii_entities.py
:start-after: [START recognize_pii_entities]
:end-before: [END recognize_pii_entities]
:language: python
:dedent: 8
:caption: Recognize personally identifiable information entities in a batch of documents.
"""
language_arg = kwargs.pop("language", None)
language = language_arg if language_arg is not None else self._default_language
docs = _validate_input(documents, "language", language)
model_version = kwargs.pop("model_version", None)
show_stats = kwargs.pop("show_stats", False)
domain_filter = kwargs.pop("domain_filter", None)
categories_filter = kwargs.pop("categories_filter", None)
string_index_type = _check_string_index_type_arg(
kwargs.pop("string_index_type", None),
self._api_version,
string_index_type_default=self._string_code_unit
)
if string_index_type:
kwargs.update({"string_index_type": string_index_type})
disable_service_logs = kwargs.pop("disable_service_logs", None)
if disable_service_logs is not None:
kwargs['logging_opt_out'] = disable_service_logs
try:
return await self._client.entities_recognition_pii(
documents=docs,
model_version=model_version,
show_stats=show_stats,
domain=domain_filter,
pii_categories=categories_filter,
cls=kwargs.pop("cls", pii_entities_result),
**kwargs
)
except ValueError as error:
if "API version v3.0 does not have operation 'entities_recognition_pii'" in str(error):
raise ValueError(
"'recognize_pii_entities' endpoint is only available for API version V3_1_PREVIEW and up"
)
raise error
except HttpResponseError as error:
process_http_response_error(error)
[docs] @distributed_trace_async
async def recognize_linked_entities( # type: ignore
self,
documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
**kwargs: Any
) -> List[Union[RecognizeLinkedEntitiesResult, DocumentError]]:
"""Recognize linked entities from a well-known knowledge base for a batch of documents.
Identifies and disambiguates the identity of each entity found in text (for example,
determining whether an occurrence of the word Mars refers to the planet, or to the
Roman god of war). Recognized entities are associated with URLs to a well-known
knowledge base, like Wikipedia.
See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits
for document length limits, maximum batch size, and supported text encoding.
:param documents: The set of documents to process as part of this batch.
If you wish to specify the ID and language on a per-item basis you must
use as input a list[:class:`~azure.ai.textanalytics.TextDocumentInput`] or a list of
dict representations of :class:`~azure.ai.textanalytics.TextDocumentInput`, like
`{"id": "1", "language": "en", "text": "hello world"}`.
:type documents:
list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
list[dict[str, str]]
:keyword str language: The 2 letter ISO 639-1 representation of language for the
entire batch. For example, use "en" for English; "es" for Spanish etc.
If not set, uses "en" for English as default. Per-document language will
take precedence over whole batch language. See https://aka.ms/talangs for
supported languages in Text Analytics API.
:keyword str model_version: This value indicates which model will
be used for scoring, e.g. "latest", "2019-10-01". If a model-version
is not specified, the API will default to the latest, non-preview version.
See here for more info: https://aka.ms/text-analytics-model-versioning
:keyword bool show_stats: If set to true, response will contain document
level statistics in the `statistics` field of the document-level response.
:keyword str string_index_type: Specifies the method used to interpret string offsets.
Can be one of 'UnicodeCodePoint' (default), 'Utf16CodePoint', or 'TextElement_v8'.
For additional information see https://aka.ms/text-analytics-offsets
:keyword 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, Text Analytics logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the Text Analytics 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.
:return: The combined list of :class:`~azure.ai.textanalytics.RecognizeLinkedEntitiesResult`
and :class:`~azure.ai.textanalytics.DocumentError` in the order the original documents
were passed in.
:rtype: list[~azure.ai.textanalytics.RecognizeLinkedEntitiesResult,
~azure.ai.textanalytics.DocumentError]
:raises ~azure.core.exceptions.HttpResponseError or TypeError or ValueError:
.. admonition:: Example:
.. literalinclude:: ../samples/async_samples/sample_recognize_linked_entities_async.py
:start-after: [START recognize_linked_entities_async]
:end-before: [END recognize_linked_entities_async]
:language: python
:dedent: 8
:caption: Recognize linked entities in a batch of documents.
"""
language_arg = kwargs.pop("language", None)
language = language_arg if language_arg is not None else self._default_language
docs = _validate_input(documents, "language", language)
model_version = kwargs.pop("model_version", None)
show_stats = kwargs.pop("show_stats", False)
disable_service_logs = kwargs.pop("disable_service_logs", None)
if disable_service_logs is not None:
kwargs['logging_opt_out'] = disable_service_logs
string_index_type = _check_string_index_type_arg(
kwargs.pop("string_index_type", None),
self._api_version,
string_index_type_default=self._string_code_unit
)
if string_index_type:
kwargs.update({"string_index_type": string_index_type})
try:
return await self._client.entities_linking(
documents=docs,
model_version=model_version,
show_stats=show_stats,
cls=kwargs.pop("cls", linked_entities_result),
**kwargs
)
except HttpResponseError as error:
process_http_response_error(error)
[docs] @distributed_trace_async
async def analyze_sentiment( # type: ignore
self,
documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
**kwargs: Any
) -> List[Union[AnalyzeSentimentResult, DocumentError]]:
"""Analyze sentiment for a batch of documents. Turn on opinion mining with `show_opinion_mining`.
Returns a sentiment prediction, as well as sentiment scores for
each sentiment class (Positive, Negative, and Neutral) for the document
and each sentence within it.
See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits
for document length limits, maximum batch size, and supported text encoding.
:param documents: The set of documents to process as part of this batch.
If you wish to specify the ID and language on a per-item basis you must
use as input a list[:class:`~azure.ai.textanalytics.TextDocumentInput`] or a list of
dict representations of :class:`~azure.ai.textanalytics.TextDocumentInput`, like
`{"id": "1", "language": "en", "text": "hello world"}`.
:type documents:
list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
list[dict[str, str]]
:keyword 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. Only available for
API version v3.1-preview and up.
:keyword str language: The 2 letter ISO 639-1 representation of language for the
entire batch. For example, use "en" for English; "es" for Spanish etc.
If not set, uses "en" for English as default. Per-document language will
take precedence over whole batch language. See https://aka.ms/talangs for
supported languages in Text Analytics API.
:keyword str model_version: This value indicates which model will
be used for scoring, e.g. "latest", "2019-10-01". If a model-version
is not specified, the API will default to the latest, non-preview version.
See here for more info: https://aka.ms/text-analytics-model-versioning
:keyword bool show_stats: If set to true, response will contain document
level statistics in the `statistics` field of the document-level response.
:keyword str string_index_type: Specifies the method used to interpret string offsets.
Can be one of 'UnicodeCodePoint' (default), 'Utf16CodePoint', or 'TextElement_v8'.
For additional information see https://aka.ms/text-analytics-offsets
:keyword 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, Text Analytics logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the Text Analytics 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:: v3.1-preview
The *show_opinion_mining* parameter.
:return: The combined list of :class:`~azure.ai.textanalytics.AnalyzeSentimentResult` and
:class:`~azure.ai.textanalytics.DocumentError` in the order the original documents were
passed in.
:rtype: list[~azure.ai.textanalytics.AnalyzeSentimentResult,
~azure.ai.textanalytics.DocumentError]
:raises ~azure.core.exceptions.HttpResponseError or TypeError or ValueError:
.. admonition:: Example:
.. literalinclude:: ../samples/async_samples/sample_analyze_sentiment_async.py
:start-after: [START analyze_sentiment_async]
:end-before: [END analyze_sentiment_async]
:language: python
:dedent: 8
:caption: Analyze sentiment in a batch of documents.
"""
language_arg = kwargs.pop("language", None)
language = language_arg if language_arg is not None else self._default_language
docs = _validate_input(documents, "language", language)
model_version = kwargs.pop("model_version", None)
show_stats = kwargs.pop("show_stats", False)
show_opinion_mining = kwargs.pop("show_opinion_mining", None)
disable_service_logs = kwargs.pop("disable_service_logs", None)
if disable_service_logs is not None:
kwargs['logging_opt_out'] = disable_service_logs
string_index_type = _check_string_index_type_arg(
kwargs.pop("string_index_type", None),
self._api_version,
string_index_type_default=self._string_code_unit
)
if string_index_type:
kwargs.update({"string_index_type": string_index_type})
if show_opinion_mining is not None:
kwargs.update({"opinion_mining": show_opinion_mining})
try:
return await self._client.sentiment(
documents=docs,
model_version=model_version,
show_stats=show_stats,
cls=kwargs.pop("cls", sentiment_result),
**kwargs
)
except TypeError as error:
if "opinion_mining" in str(error):
raise ValueError(
"'show_opinion_mining' is only available for API version v3.1-preview and up"
)
raise error
except HttpResponseError as error:
process_http_response_error(error)
def _healthcare_result_callback(self, doc_id_order, raw_response, _, headers, show_stats=False):
healthcare_result = self._deserialize(
self._client.models(api_version="v3.1-preview.5").HealthcareJobState,
raw_response
)
return healthcare_paged_result(
doc_id_order,
self._client.health_status,
raw_response,
healthcare_result,
headers,
show_stats=show_stats
)
[docs] @distributed_trace_async
async def begin_analyze_healthcare_entities( # type: ignore
self,
documents, # type: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]]
**kwargs # type: Any
): # type: (...) -> AsyncLROPoller[AsyncItemPaged[AnalyzeHealthcareEntitiesResultItem]]
"""Analyze healthcare entities and identify relationships between these entities in a batch of documents.
Entities are associated with references that can be found in existing knowledge bases,
such as UMLS, CHV, MSH, etc.
We also extract the relations found between entities, for example in "The subject took 100 mg of ibuprofen",
we would extract the relationship between the "100 mg" dosage and the "ibuprofen" medication.
NOTE: this endpoint is currently in gated preview, meaning your subscription needs to be allow-listed
for you to use this endpoint. More information about that here:
https://aka.ms/text-analytics-health-request-access
:param documents: The set of documents to process as part of this batch.
If you wish to specify the ID and language on a per-item basis you must
use as input a list[:class:`~azure.ai.textanalytics.TextDocumentInput`] or a list of
dict representations of :class:`~azure.ai.textanalytics.TextDocumentInput`, like
`{"id": "1", "language": "en", "text": "hello world"}`.
:type documents:
list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
list[dict[str, str]]
:keyword str model_version: This value indicates which model will
be used for scoring, e.g. "latest", "2019-10-01". If a model-version
is not specified, the API will default to the latest, non-preview version.
Currently not working on the service side at time of release, as service will
only use the latest model. Service is aware, and once it's been fixed on the service
side, the SDK should work automatically.
See here for more info: https://aka.ms/text-analytics-model-versioning
:keyword bool show_stats: If set to true, response will contain document level statistics.
:keyword str string_index_type: Specifies the method used to interpret string offsets.
Can be one of 'UnicodeCodePoint' (default), 'Utf16CodePoint', or 'TextElement_v8'.
For additional information see https://aka.ms/text-analytics-offsets
:keyword int polling_interval: Waiting time between two polls for LRO operations
if no Retry-After header is present. Defaults to 5 seconds.
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword 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, Text Analytics logs your
input text for 48 hours, solely to allow for troubleshooting issues in providing you with
the Text Analytics 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.
:return: An instance of an AnalyzeHealthcareEntitiesAsyncLROPoller. Call `result()` on the poller
object to return a pageable of :class:`~azure.ai.textanalytics.AnalyzeHealthcareResultItem`.
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.core.paging.AsyncItemPaged[
~azure.ai.textanalytics.AnalyzeHealthcareEntitiesResultItem]]
:raises ~azure.core.exceptions.HttpResponseError or TypeError or ValueError or NotImplementedError:
.. admonition:: Example:
.. literalinclude:: ../samples/async_samples/sample_analyze_healthcare_entities_async.py
:start-after: [START analyze_healthcare_entities_async]
:end-before: [END analyze_healthcare_entities_async]
:language: python
:dedent: 8
:caption: Analyze healthcare entities in a batch of documents.
"""
language_arg = kwargs.pop("language", None)
language = language_arg if language_arg is not None else self._default_language
docs = _validate_input(documents, "language", language)
model_version = kwargs.pop("model_version", None)
show_stats = kwargs.pop("show_stats", False)
polling_interval = kwargs.pop("polling_interval", 5)
continuation_token = kwargs.pop("continuation_token", None)
string_index_type = kwargs.pop("string_index_type", self._string_code_unit)
disable_service_logs = kwargs.pop("disable_service_logs", None)
doc_id_order = [doc.get("id") for doc in docs]
my_cls = kwargs.pop(
"cls", partial(self._healthcare_result_callback, doc_id_order, show_stats=show_stats)
)
polling_kwargs = kwargs
operation_kwargs = copy.copy(kwargs)
if disable_service_logs is not None:
operation_kwargs['logging_opt_out'] = disable_service_logs
try:
return await self._client.begin_health(
docs,
model_version=model_version,
string_index_type=string_index_type,
cls=my_cls,
polling=AnalyzeHealthcareEntitiesAsyncLROPollingMethod(
text_analytics_client=self._client,
timeout=polling_interval,
lro_algorithms=[
TextAnalyticsOperationResourcePolling(show_stats=show_stats)
],
**polling_kwargs),
continuation_token=continuation_token,
**operation_kwargs
)
except ValueError as error:
if "API version v3.0 does not have operation 'begin_health'" in str(error):
raise ValueError(
"'begin_analyze_healthcare_entities' endpoint is only available for API version V3_1_PREVIEW and up"
)
raise error
except HttpResponseError as error:
process_http_response_error(error)
def _analyze_result_callback(self, doc_id_order, task_order, raw_response, _, headers, show_stats=False):
analyze_result = self._deserialize(
self._client.models(api_version="v3.1-preview.5").AnalyzeJobState,
raw_response
)
return analyze_paged_result(
doc_id_order,
task_order,
self._client.analyze_status,
raw_response,
analyze_result,
headers,
show_stats=show_stats
)
[docs] @distributed_trace_async
async def begin_analyze_actions( # type: ignore
self,
documents, # type: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]]
actions, # type: List[Union[RecognizeEntitiesAction, RecognizeLinkedEntitiesAction, RecognizePiiEntitiesAction, ExtractKeyPhrasesAction, AnalyzeSentimentAction]] # pylint: disable=line-too-long
**kwargs # type: Any
): # type: (...) -> AsyncLROPoller[AsyncItemPaged[AnalyzeActionsResult]]
"""Start a long-running operation to perform a variety of text analysis actions over a batch of documents.
:param documents: The set of documents to process as part of this batch.
If you wish to specify the ID and language on a per-item basis you must
use as input a list[:class:`~azure.ai.textanalytics.TextDocumentInput`] or a list of
dict representations of :class:`~azure.ai.textanalytics.TextDocumentInput`, like
`{"id": "1", "language": "en", "text": "hello world"}`.
:type documents:
list[str] or list[~azure.ai.textanalytics.TextDocumentInput] or
list[dict[str, str]]
:param actions: A heterogeneous list of actions to perform on the inputted documents.
Each action object encapsulates the parameters used for the particular action type.
The outputted action results will be in the same order you inputted your actions.
Duplicate actions in list not supported.
:type actions:
list[RecognizeEntitiesAction or RecognizePiiEntitiesAction or ExtractKeyPhrasesAction or
RecognizeLinkedEntitiesAction or AnalyzeSentimentAction]
:keyword str display_name: An optional display name to set for the requested analysis.
:keyword str language: The 2 letter ISO 639-1 representation of language for the
entire batch. For example, use "en" for English; "es" for Spanish etc.
If not set, uses "en" for English as default. Per-document language will
take precedence over whole batch language. See https://aka.ms/talangs for
supported languages in Text Analytics API.
:keyword bool show_stats: If set to true, response will contain document level statistics.
:keyword int polling_interval: Waiting time between two polls for LRO operations
if no Retry-After header is present. Defaults to 30 seconds.
:return: An instance of an LROPoller. Call `result()` on the poller
object to return a pageable heterogeneous list of the action results in the order
the actions were sent in this method.
:rtype:
~azure.core.polling.AsyncLROPoller[~azure.core.async_paging.AsyncItemPaged[
~azure.ai.textanalytics.AnalyzeActionsResult]]
:raises ~azure.core.exceptions.HttpResponseError or TypeError or ValueError or NotImplementedError:
.. admonition:: Example:
.. literalinclude:: ../samples/async_samples/sample_analyze_actions_async.py
:start-after: [START analyze_async]
:end-before: [END analyze_async]
:language: python
:dedent: 8
:caption: Start a long-running operation to perform a variety of text analysis actions over
a batch of documents.
"""
display_name = kwargs.pop("display_name", None)
language_arg = kwargs.pop("language", None)
language = language_arg if language_arg is not None else self._default_language
docs = self._client.models(api_version="v3.1-preview.5").MultiLanguageBatchInput(
documents=_validate_input(documents, "language", language)
)
show_stats = kwargs.pop("show_stats", False)
polling_interval = kwargs.pop("polling_interval", 5)
continuation_token = kwargs.pop("continuation_token", None)
doc_id_order = [doc.get("id") for doc in docs.documents]
task_order = [_determine_action_type(action) for action in actions]
try:
analyze_tasks = self._client.models(api_version='v3.1-preview.5').JobManifestTasks(
entity_recognition_tasks=[
t.to_generated() for t in
[a for a in actions if _determine_action_type(a) == AnalyzeActionsType.RECOGNIZE_ENTITIES]
],
entity_recognition_pii_tasks=[
t.to_generated() for t in
[a for a in actions if _determine_action_type(a) == AnalyzeActionsType.RECOGNIZE_PII_ENTITIES]
],
key_phrase_extraction_tasks=[
t.to_generated() for t in
[a for a in actions if _determine_action_type(a) == AnalyzeActionsType.EXTRACT_KEY_PHRASES]
],
entity_linking_tasks=[
t.to_generated() for t in
[
a for a in actions if \
_determine_action_type(a) == AnalyzeActionsType.RECOGNIZE_LINKED_ENTITIES
]
],
sentiment_analysis_tasks=[
t.to_generated() for t in
[a for a in actions if _determine_action_type(a) == AnalyzeActionsType.ANALYZE_SENTIMENT]
]
)
analyze_body = self._client.models(api_version='v3.1-preview.5').AnalyzeBatchInput(
display_name=display_name,
tasks=analyze_tasks,
analysis_input=docs
)
return await self._client.begin_analyze(
body=analyze_body,
cls=kwargs.pop("cls", partial(
self._analyze_result_callback, doc_id_order, task_order, show_stats=show_stats
)),
polling=AsyncAnalyzeBatchActionsLROPollingMethod(
timeout=polling_interval,
lro_algorithms=[
TextAnalyticsOperationResourcePolling(show_stats=show_stats)
],
**kwargs),
continuation_token=continuation_token,
**kwargs
)
except ValueError as error:
if "API version v3.0 does not have operation 'begin_analyze'" in str(error):
raise ValueError(
"'begin_analyze_actions' endpoint is only available for API version V3_1_PREVIEW and up"
)
raise error
except HttpResponseError as error:
process_http_response_error(error)