[textanalytics] Moves textanalytics track2 and renames namespace/package (#9149)
* moves text analytics track 2 to sdk/textanalytics and renames to azure-ai-textanalytics * more renames * fix env var name for async testcase * bump azure-core version in setup.py * update shared reqs * readme.rst -> readme.md for nspkg * pylint fixes for nspkg * change nspkg version * update nspkg manifest.in * temp add azure-ai-nspkg to dev_requirements.txt until pkg released * update code from swagger fixes * review updates
This commit is contained in:
Krista Pratico
GitHub
Родитель
bae787e724
Коммит
647f54445c
|
|
@ -54,6 +54,7 @@ known_content_issues:
|
|||
- ['sdk/storage/azure-storage-nspkg/README.rst', '#4554']
|
||||
- ['sdk/storage/README.md', '#4554']
|
||||
- ['sdk/storage/azure-storage-file-datalake/swagger/README.md', '#4554']
|
||||
- ['sdk/textanalytics/azure-ai-textanalytics/samples/README.md', '#4554']
|
||||
|
||||
# nspckg and common.
|
||||
- ['sdk/cognitiveservices/azure-cognitiveservices-nspkg/README.rst', 'nspkg and common']
|
||||
|
|
@ -76,6 +77,7 @@ known_content_issues:
|
|||
- ['sdk/appconfiguration/azure-appconfiguration/README.md', 'nspkg and common']
|
||||
- ['sdk/appconfiguration/azure-appconfiguration/samples/README.md', 'nspkg and common']
|
||||
- ['sdk/keyvault/azure-keyvault-nspkg/README.md', 'nspkg and common']
|
||||
- ['sdk/textanalytics/azure-ai-nspkg/README.md', 'nspkg and common']
|
||||
|
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# root readme
|
||||
- ['README.md', 'root readme']
|
||||
|
|
|
|||
|
|
@ -10,4 +10,5 @@ MYPY_HARD_FAILURE_OPTED = [
|
|||
"azure-eventhub",
|
||||
"azure-eventhub-checkpointstoreblob",
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"azure-eventhub-checkpointstoreblob-aio",
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"azure-ai-textanalytics"
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]
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|
|
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|||
|
|
@ -0,0 +1,2 @@
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include *.md
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include azure/ai/__init__.py
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|
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@ -0,0 +1,16 @@
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|||
# Microsoft Azure SDK for Python
|
||||
|
||||
This is the Microsoft Azure ai Services namespace package.
|
||||
|
||||
This package is not intended to be installed directly by the end user.
|
||||
|
||||
Since version 3.0, this is Python 2 package only, Python 3.x SDKs will use `PEP420 <https://www.python.org/dev/peps/pep-0420/>` as namespace package strategy.
|
||||
To avoid issues with package servers that does not support `python_requires`, a Python 3 package is installed but is empty.
|
||||
|
||||
It provides the necessary files for other packages to extend the azure.ai namespace.
|
||||
|
||||
If you are looking to install the Azure client libraries, see the
|
||||
`azure <https://pypi.python.org/pypi/azure>`__ bundle package.
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1 @@
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|||
__path__ = __import__('pkgutil').extend_path(__path__, __name__)
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|
|
@ -0,0 +1 @@
|
|||
__path__ = __import__('pkgutil').extend_path(__path__, __name__)
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|
|
@ -0,0 +1,2 @@
|
|||
[packaging]
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auto_update = false
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|
|
@ -0,0 +1,59 @@
|
|||
#!/usr/bin/env python
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|
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#-------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
|
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# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#--------------------------------------------------------------------------
|
||||
import sys
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from setuptools import setup
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|
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# azure v0.x is not compatible with this package
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# azure v0.x used to have a __version__ attribute (newer versions don't)
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try:
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import azure
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try:
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ver = azure.__version__
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raise Exception(
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'This package is incompatible with azure=={}. '.format(ver) +
|
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'Uninstall it with "pip uninstall azure".'
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)
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except AttributeError:
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pass
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except ImportError:
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pass
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|
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PACKAGES = []
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# Do an empty package on Python 3 and not python_requires, since not everybody is ready
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# https://github.com/Azure/azure-sdk-for-python/issues/3447
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# https://github.com/Azure/azure-sdk-for-python/issues/3481
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if sys.version_info[0] < 3:
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PACKAGES = ['azure.ai']
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|
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setup(
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name='azure-ai-nspkg',
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version='1.0.0',
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description='Microsoft Azure ai Namespace Package [Internal]',
|
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long_description=open('README.md', 'r').read(),
|
||||
license='MIT License',
|
||||
author='Microsoft Corporation',
|
||||
author_email='azpysdkhelp@microsoft.com',
|
||||
url='https://github.com/Azure/azure-sdk-for-python',
|
||||
classifiers=[
|
||||
'Development Status :: 5 - Production/Stable',
|
||||
'Programming Language :: Python',
|
||||
'Programming Language :: Python :: 2',
|
||||
'Programming Language :: Python :: 2.7',
|
||||
'Programming Language :: Python :: 3',
|
||||
'Programming Language :: Python :: 3.5',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
'Programming Language :: Python :: 3.7',
|
||||
'Programming Language :: Python :: 3.8',
|
||||
'License :: OSI Approved :: MIT License',
|
||||
],
|
||||
zip_safe=False,
|
||||
packages=PACKAGES,
|
||||
install_requires=[
|
||||
'azure-nspkg>=3.0.0',
|
||||
]
|
||||
)
|
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|
|
@ -0,0 +1,116 @@
|
|||
# Change Log azure-ai-textanalytics
|
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|
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## 2020-01-06 1.0.0b1
|
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|
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Version (1.0.0b1) is the first preview of our efforts to create a user-friendly and Pythonic client library for Azure Text Analytics. For more information about this, and preview releases of other Azure SDK libraries, please visit
|
||||
https://azure.github.io/azure-sdk/releases/latest/python.html.
|
||||
|
||||
**Breaking changes: New API design**
|
||||
|
||||
- New namespace:
|
||||
- The namespace for Azure Text Analytics client library has changed from `azure.cognitiveservices.language.textanalytics` to `azure.ai.textanalytics`
|
||||
|
||||
- New operations and naming:
|
||||
- `detect_language` is renamed to `detect_languages`
|
||||
- `entities` is renamed to `recognize_entities`
|
||||
- `key_phrases` is renamed to `extract_key_phrases`
|
||||
- `sentiment` is renamed to `analyze_sentiment`
|
||||
- New operation `recognize_pii_entities` finds personally identifiable information entities in text
|
||||
- New operation `recognize_linked_entities` provides links from a well-known knowledge base for each recognized entity
|
||||
- New module-level operations `single_detect_language`, `single_recognize_entities`, `single_extract_key_phrases`, `single_analyze_sentiment`, `single_recognize_pii_entities`, and `single_recognize_linked_entities` perform
|
||||
function on a single string instead of a batch of text documents and can be imported from the `azure.ai.textanalytics` namespace.
|
||||
- New client and module-level async APIs added to subnamespace `azure.ai.textanalytics.aio`.
|
||||
- `MultiLanguageInput` has been renamed to `TextDocumentInput`
|
||||
- `LanguageInput` has been renamed to `DetectLanguageInput`
|
||||
- `DocumentLanguage` has been renamed to `DetectLanguageResult`
|
||||
- `DocumentEntities` has been renamed to `RecognizeEntitiesResult`
|
||||
- `DocumentLinkedEntities` has been renamed to `RecognizeLinkedEntitiesResult`
|
||||
- `DocumentKeyPhrases` has been renamed to `ExtractKeyPhrasesResult`
|
||||
- `DocumentSentiment` has been renamed to `AnalyzeSentimentResult`
|
||||
- `DocumentStatistics` has been renamed to `TextDocumentStatistics`
|
||||
- `RequestStatistics` has been renamed to `TextDocumentBatchStatistics`
|
||||
- `Entity` has been renamed to `NamedEntity`
|
||||
- `Match` has been renamed to `LinkedEntityMatch`
|
||||
- The batching methods' `documents` parameter has been renamed `inputs`
|
||||
|
||||
- New input types:
|
||||
- `detect_languages` can take as input a `list[DetectLanguageInput]` or a `list[str]`. A list of dict-like objects in the same shape as `DetectLanguageInput` is still accepted as input.
|
||||
- `recognize_entities`, `recognize_pii_entities`, `recognize_linked_entities`, `extract_key_phrases`, `analyze_sentiment` can take as input a `list[TextDocumentInput]` or `list[str]`.
|
||||
A list of dict-like objects in the same shape as `TextDocumentInput` is still accepted as input.
|
||||
|
||||
- New parameters/keyword arguments:
|
||||
- All operations now take a keyword argument `model_version` which allows the user to specify a string referencing the desired model version to be used for analysis. If no string specified, it will default to the latest, non-preview version.
|
||||
- `detect_languages` now takes a parameter `country_hint` which allows you to specify the country hint for the entire batch. Any per-item country hints will take precedence over a whole batch hint.
|
||||
- `recognize_entities`, `recognize_pii_entities`, `recognize_linked_entities`, `extract_key_phrases`, `analyze_sentiment` now take a parameter `language` which allows you to specify the language for the entire batch.
|
||||
Any per-item specified language will take precedence over a whole batch hint.
|
||||
- A `response_hook` keyword argument can be passed with a callback to use the raw response from the service. Additionally, values returned for `TextDocumentBatchStatistics` and `model_version` used must be retrieved using a response hook.
|
||||
- `show_stats` and `model_version` parameters move to keyword only arguments.
|
||||
|
||||
- New return types
|
||||
- The return types for the batching methods (`detect_languages`, `recognize_entities`, `recognize_pii_entities`, `recognize_linked_entities`, `extract_key_phrases`, `analyze_sentiment`) now return a heterogeneous list of
|
||||
result objects and document errors in the order passed in with the request. To iterate over the list and filter for result or error, a boolean on each object called `is_error` can be used to determine whether the returned response object at
|
||||
that index is a result or an error:
|
||||
- `detect_languages` now returns a List[Union[`DetectLanguageResult`, `DocumentError`]]
|
||||
- `recognize_entities` now returns a List[Union[`RecognizeEntitiesResult`, `DocumentError`]]
|
||||
- `recognize_pii_entities` now returns a List[Union[`RecognizePiiEntitiesResult`, `DocumentError`]]
|
||||
- `recognize_linked_entities` now returns a List[Union[`RecognizeLinkedEntitiesResult`, `DocumentError`]]
|
||||
- `extract_key_phrases` now returns a List[Union[`ExtractKeyPhrasesResult`, `DocumentError`]]
|
||||
- `analyze_sentiment` now returns a List[Union[`AnalyzeSentimentResult`, `DocumentError`]]
|
||||
- The module-level, single text operations will return a single result object or raise the error found on the document:
|
||||
- `single_detect_languages` returns a `DetectLanguageResult`
|
||||
- `single_recognize_entities` returns a `RecognizeEntitiesResult`
|
||||
- `single_recognize_pii_entities` returns a `RecognizePiiEntitiesResult`
|
||||
- `single_recognize_linked_entities` returns a `RecognizeLinkedEntitiesResult`
|
||||
- `single_extract_key_phrases` returns a `ExtractKeyPhrasesResult`
|
||||
- `single_analyze_sentiment` returns a `AnalyzeSentimentResult`
|
||||
|
||||
- New underlying REST pipeline implementation, based on the new `azure-core` library.
|
||||
- Client and pipeline configuration is now available via keyword arguments at both the client level, and per-operation. See reference documentation for a full list of optional configuration arguments.
|
||||
- Authentication using `azure-identity` credentials
|
||||
- see the
|
||||
[Azure Identity documentation](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/identity/azure-identity/README.md)
|
||||
for more information
|
||||
- New error hierarchy:
|
||||
- All service errors will now use the base type: `azure.core.exceptions.HttpResponseError`
|
||||
- There is one exception type derived from this base type for authentication errors:
|
||||
- `ClientAuthenticationError`: Authentication failed.
|
||||
|
||||
### 2019-03-12 0.2.0
|
||||
|
||||
**Features**
|
||||
|
||||
- Client class can be used as a context manager to keep the underlying HTTP session open for performance
|
||||
- New method "entities"
|
||||
- Model KeyPhraseBatchResultItem has a new parameter statistics
|
||||
- Model KeyPhraseBatchResult has a new parameter statistics
|
||||
- Model LanguageBatchResult has a new parameter statistics
|
||||
- Model LanguageBatchResultItem has a new parameter statistics
|
||||
- Model SentimentBatchResult has a new parameter statistics
|
||||
|
||||
**Breaking changes**
|
||||
|
||||
- TextAnalyticsAPI main client has been renamed TextAnalyticsClient
|
||||
- TextAnalyticsClient parameter is no longer a region but a complete endpoint
|
||||
|
||||
**General Breaking changes**
|
||||
|
||||
This version uses a next-generation code generator that *might* introduce breaking changes.
|
||||
|
||||
- Model signatures now use only keyword-argument syntax. All positional arguments must be re-written as keyword-arguments.
|
||||
To keep auto-completion in most cases, models are now generated for Python 2 and Python 3. Python 3 uses the "*" syntax for keyword-only arguments.
|
||||
- Enum types now use the "str" mixin (class AzureEnum(str, Enum)) to improve the behavior when unrecognized enum values are encountered.
|
||||
While this is not a breaking change, the distinctions are important, and are documented here:
|
||||
https://docs.python.org/3/library/enum.html#others
|
||||
At a glance:
|
||||
|
||||
- "is" should not be used at all.
|
||||
- "format" will return the string value, where "%s" string formatting will return `NameOfEnum.stringvalue`. Format syntax should be prefered.
|
||||
|
||||
**Bugfixes**
|
||||
|
||||
- Compatibility of the sdist with wheel 0.31.0
|
||||
|
||||
|
||||
### 2018-01-12 0.1.0
|
||||
|
||||
* Initial Release
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
recursive-include tests *.py
|
||||
recursive-include samples *.py *.md
|
||||
include *.md
|
||||
include azure/__init__.py
|
||||
include azure/ai/__init__.py
|
||||
|
|
@ -0,0 +1,370 @@
|
|||
# Azure Text Analytics client library for Python
|
||||
Text Analytics is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions:
|
||||
|
||||
* Sentiment Analysis
|
||||
* Named Entity Recognition
|
||||
* Language Detection
|
||||
* Key Phrase Extraction
|
||||
|
||||
[Source code](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics) | [Package (PyPI)](https://pypi.org/project/azure-ai-textanalytics/) | [API reference documentation](https://aka.ms/azsdk-python-textanalytics-ref-docs) | [Product documentation](https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview) | [Samples](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples)
|
||||
|
||||
## Getting started
|
||||
|
||||
### Prerequisites
|
||||
* Python 2.7, or 3.5 or later is required to use this package.
|
||||
* You must have an [Azure subscription](https://azure.microsoft.com/free/) and an
|
||||
[Cognitive Services or Text Analytics account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows) to use this package.
|
||||
|
||||
### Install the package
|
||||
Install the Azure Text Analytics client library for Python with [pip](https://pypi.org/project/pip/):
|
||||
|
||||
```bash
|
||||
pip install azure-ai-textanalytics --pre
|
||||
```
|
||||
|
||||
### Create a Cognitive Services or Text Analytics resource
|
||||
Text Analytics supports both [multi-service and single-service access](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows). Create a Cognitive Services resource if you plan
|
||||
to access multiple cognitive services under a single endpoint/key. For Text Analytics access only, create a Text Analytics resource.
|
||||
|
||||
You can create either resource using the
|
||||
[Azure Portal](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows#create-a-new-azure-cognitive-services-resource)
|
||||
or [Azure CLI](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account-cli?tabs=windows).
|
||||
Below is an example of how you can create a Text Analytics resource using the CLI:
|
||||
|
||||
```bash
|
||||
# Create a new resource group to hold the text analytics resource -
|
||||
# if using an existing resource group, skip this step
|
||||
az group create --name my-resource-group --location westus2
|
||||
```
|
||||
|
||||
```bash
|
||||
# Create text analytics
|
||||
az cognitiveservices account create \
|
||||
--name text-analytics-resource \
|
||||
--resource-group my-resource-group \
|
||||
--kind TextAnalytics \
|
||||
--sku F0 \
|
||||
--location westus2 \
|
||||
--yes
|
||||
```
|
||||
|
||||
### Create the client
|
||||
The Azure Text Analytics client library for Python allows you to engage with the Text Analytics service to
|
||||
analyze sentiment, recognize entities, detect language, and extract key phrases from text.
|
||||
Interaction with this service begins with an instance of a [client](#Client).
|
||||
To create a client object, you will need the cognitive services or text analytics endpoint to
|
||||
your resource and a credential that allows you access:
|
||||
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
|
||||
text_analytics = TextAnalyticsClient(endpoint="https://westus2.api.cognitive.microsoft.com/", credential=credential)
|
||||
```
|
||||
|
||||
Note that if you create a [custom subdomain](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-custom-subdomains)
|
||||
for your resource the endpoint may look different than in the above code snippet.
|
||||
For example, `https://<my-custom-subdomain>.cognitiveservices.azure.com/`.
|
||||
|
||||
#### Looking up the endpoint
|
||||
You can find the endpoint for your text analytics resource using the
|
||||
[Azure Portal](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows#get-the-keys-for-your-resource)
|
||||
or [Azure CLI](https://docs.microsoft.com/cli/azure/cognitiveservices/account?view=azure-cli-latest#az-cognitiveservices-account-show):
|
||||
|
||||
```bash
|
||||
# Get the endpoint for the text analytics resource
|
||||
az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "endpoint"
|
||||
```
|
||||
|
||||
#### Types of credentials
|
||||
The `credential` parameter may be provided as the subscription key to your resource or as a token from Azure Active Directory.
|
||||
See the full details regarding [authentication](https://docs.microsoft.com/azure/cognitive-services/authentication) of
|
||||
cognitive services.
|
||||
|
||||
1. To use a [subscription key](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows#get-the-keys-for-your-resource),
|
||||
provide the key as a string. This can be found in the Azure Portal under the "Quickstart"
|
||||
section or by running the following Azure CLI command:
|
||||
|
||||
```az cognitiveservices account keys list --name "resource-name" --resource-group "resource-group-name"```
|
||||
|
||||
Use the key as the credential parameter to authenticate the client:
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
service = TextAnalyticsClient(endpoint="https://westus2.api.cognitive.microsoft.com/", credential="<subscription_key>")
|
||||
```
|
||||
|
||||
2. To use an [Azure Active Directory (AAD) token credential](https://docs.microsoft.com/azure/cognitive-services/authentication#authenticate-with-azure-active-directory),
|
||||
provide an instance of the desired credential type obtained from the
|
||||
[azure-identity](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/identity/azure-identity#credentials) library.
|
||||
Note that regional endpoints do not support AAD authentication. Create a [custom subdomain](https://docs.microsoft.com/azure/cognitive-services/authentication#create-a-resource-with-a-custom-subdomain)
|
||||
name for your resource in order to use this type of authentication.
|
||||
|
||||
Authentication with AAD requires some initial setup:
|
||||
* [Install azure-identity](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/identity/azure-identity#install-the-package)
|
||||
* [Register a new AAD application](https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal)
|
||||
* [Grant access](https://docs.microsoft.com/azure/cognitive-services/authentication#assign-a-role-to-a-service-principal) to Text Analytics by assigning the `"Cognitive Services User"` role to your service principal.
|
||||
|
||||
After setup, you can choose which type of [credential](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/identity/azure-identity#credentials) from azure.identity to use.
|
||||
As an example, [DefaultAzureCredential](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/identity/azure-identity#defaultazurecredential)
|
||||
can be used to authenticate the client:
|
||||
|
||||
Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables:
|
||||
AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
|
||||
|
||||
Use the returned token credential to authenticate the client:
|
||||
```python
|
||||
from azure.identity import DefaultAzureCredential
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
token_credential = DefaultAzureCredential()
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(
|
||||
endpoint="https://<my-custom-subdomain>.cognitiveservices.azure.com/",
|
||||
credential=token_credential
|
||||
)
|
||||
```
|
||||
|
||||
## Key concepts
|
||||
|
||||
The following are types of text analysis that the service offers:
|
||||
|
||||
1. [Sentiment Analysis](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis)
|
||||
|
||||
Use sentiment analysis to find out what customers think of your brand or topic by analyzing raw text for clues about positive or negative sentiment.
|
||||
Scores closer to `1` indicate positive sentiment, while scores closer to `0` indicate negative sentiment.
|
||||
Sentiment analysis returns scores and labels at a document and sentence level.
|
||||
|
||||
2. [Named Entity Recognition](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking)
|
||||
|
||||
Use named entity recognition (NER) to identify different entities in text and categorize them into pre-defined classes, or types.
|
||||
Entity recognition in the client library provides three different methods depending on what you are interested in.
|
||||
* `recognize_entities()` can be used to identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more.
|
||||
* `recognize_pii_entities()` can be used to recognize personally identifiable information such as SSNs and bank account numbers.
|
||||
* `recognize_linked_entities()` can be used to identify and disambiguate the identity of an entity found in text (For example, determining whether
|
||||
"Mars" is being used as the planet or as the Roman god of war). This process uses Wikipedia as the knowledge base to which recognized entities are linked.
|
||||
|
||||
See a full list of [Named Entity Recognition Types](https://docs.microsoft.com/azure/cognitive-services/text-analytics/named-entity-types?tabs=personal).
|
||||
|
||||
3. [Language Detection](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-language-detection)
|
||||
|
||||
Detect the language of the input text and report a single language code for every document submitted on the request.
|
||||
The language code is paired with a score indicating the strength of the score.
|
||||
A wide range of languages, variants, dialects, and some regional/cultural languages are supported -
|
||||
see [supported languages](https://docs.microsoft.com/azure/cognitive-services/text-analytics/language-support#language-detection) for full details.
|
||||
|
||||
4. [Key Phrase Extraction](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-keyword-extraction)
|
||||
|
||||
Extract key phrases to quickly identify the main points in text.
|
||||
For example, for the input text "The food was delicious and there were wonderful staff", the main talking points returned: "food" and "wonderful staff".
|
||||
|
||||
See [Language and regional support](https://docs.microsoft.com/azure/cognitive-services/text-analytics/language-support) for what is currently available for each operation.
|
||||
|
||||
|
||||
### Client
|
||||
The Text Analytics client library provides a [TextAnalyticsClient](https://aka.ms/azsdk-python-textanalytics-textanalyticsclient) to do analysis on batches of documents.
|
||||
|
||||
|
||||
A batch contains the text documents you wish to process, and may contain a per-item ID and language/country hint
|
||||
if you choose. For example, a batch can simply be passed as a list of strings:
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
documents = ["I hated the movie. It was so slow!", "The movie made it into my top ten favorites.", "What a great movie!"]
|
||||
|
||||
result = text_analytics_client.analyze_sentiment(documents)
|
||||
```
|
||||
or as a list of [DetectLanguageInput](https://aka.ms/azsdk-python-textanalytics-detectlanguageinput) or [TextDocumentInput](https://aka.ms/azsdk-python-textanalytics-textdocumentinput):
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
documents = [
|
||||
{"id": "1", "language": "en", "text": "I hated the movie. It was so slow!"},
|
||||
{"id": "2", "language": "en", "text": "The movie made it into my top ten favorites."},
|
||||
{"id": "3", "language": "en", "text": "What a great movie!"}
|
||||
]
|
||||
result = text_analytics_client.analyze_sentiment(documents)
|
||||
```
|
||||
|
||||
The returned response will be index-matched with the order of the provided documents.
|
||||
|
||||
|
||||
### Single text operations
|
||||
The Text Analytics client library also provides module-level operations which can be performed on a single string
|
||||
of text.
|
||||
|
||||
These operations are an introduction to the client library and have simpler inputs and outputs than
|
||||
that of the batched client operations. The endpoint and credential are passed in with the desired text and
|
||||
other optional parameters.
|
||||
|
||||
```python
|
||||
from azure.ai.textanalytics import single_analyze_sentiment
|
||||
|
||||
text = "I did not like the restaurant. The food was too spicy."
|
||||
result = single_analyze_sentiment(endpoint=endpoint, credential=credential, text_input=text, language="en")
|
||||
|
||||
print(result.sentiment)
|
||||
```
|
||||
|
||||
|
||||
## Examples
|
||||
The following section provides several code snippets covering some of the most common Text Analytics tasks, including:
|
||||
|
||||
* [Analyze Sentiment](#analyze-sentient "Analyze sentiment")
|
||||
* [Recognize Entities](#recognize-entities "Recognize entities")
|
||||
* [Extract Key Phrases](#extract-key-phrases "Extract key phrases")
|
||||
* [Detect Languages](#detect-languages "Detect languages")
|
||||
|
||||
### Analyze sentiment
|
||||
Analyze sentiment in a batch of documents.
|
||||
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
|
||||
documents = [
|
||||
"I did not like the restaurant. The food was too spicy.",
|
||||
"The restaurant was decorated beautifully. The atmosphere was unlike any other restaurant I've been to.",
|
||||
"The food was yummy. :)"
|
||||
]
|
||||
|
||||
result = text_analytics_client.analyze_sentiment(documents, language="en")
|
||||
|
||||
for text in result:
|
||||
print(text.sentiment)
|
||||
```
|
||||
|
||||
### Recognize entities
|
||||
Recognize entities in a batch of documents.
|
||||
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
|
||||
documents = [
|
||||
"Microsoft was founded by Bill Gates and Paul Allen.",
|
||||
"Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle.",
|
||||
"Jeff bought three dozen eggs because there was a 50% discount."
|
||||
]
|
||||
|
||||
result = text_analytics_client.recognize_entities(documents, language="en")
|
||||
|
||||
for text in result:
|
||||
print(text.entities)
|
||||
```
|
||||
|
||||
### Extract key phrases
|
||||
Extract key phrases in a batch of documents.
|
||||
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
|
||||
documents = [
|
||||
"Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle.",
|
||||
"I need to take my cat to the veterinarian.",
|
||||
"I will travel to South America in the summer."
|
||||
]
|
||||
|
||||
result = text_analytics_client.extract_key_phrases(documents, language="en")
|
||||
|
||||
for text in result:
|
||||
print(text.key_phrases)
|
||||
```
|
||||
|
||||
### Detect languages
|
||||
Detect language in a batch of documents.
|
||||
|
||||
```python
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
|
||||
documents = [
|
||||
"This is written in English.",
|
||||
"Il documento scritto in italiano.",
|
||||
"Dies ist in englischer Sprache verfasst."
|
||||
]
|
||||
|
||||
result = text_analytics_client.detect_languages(documents)
|
||||
|
||||
for text in result:
|
||||
print(text.detected_languages[0].name)
|
||||
```
|
||||
|
||||
## Optional Configuration
|
||||
|
||||
Optional keyword arguments that can be passed in at the client and per-operation level.
|
||||
|
||||
### Retry Policy configuration
|
||||
|
||||
Use the following keyword arguments when instantiating a client to configure the retry policy:
|
||||
|
||||
* __retry_total__ (int): Total number of retries to allow. Takes precedence over other counts.
|
||||
Pass in `retry_total=0` if you do not want to retry on requests. Defaults to 10.
|
||||
* __retry_connect__ (int): How many connection-related errors to retry on. Defaults to 3.
|
||||
* __retry_read__ (int): How many times to retry on read errors. Defaults to 3.
|
||||
* __retry_status__ (int): How many times to retry on bad status codes. Defaults to 3.
|
||||
|
||||
### Other client / per-operation configuration
|
||||
|
||||
Other optional configuration keyword arguments that can be specified on the client or per-operation.
|
||||
|
||||
**Client keyword arguments:**
|
||||
|
||||
* __connection_timeout__ (int): A single float in seconds for the connection timeout. Defaults to 300 seconds.
|
||||
* __read_timeout__ (int): A single float in seconds for the read timeout. Defaults to 300 seconds.
|
||||
* __transport__ (Any): User-provided transport to send the HTTP request.
|
||||
|
||||
**Per-operation keyword arguments:**
|
||||
|
||||
* __response_hook__ (callable): The given callback uses the raw response returned from the service.
|
||||
Can also be passed in at the client.
|
||||
* __request_id__ (str): Optional user specified identification of the request.
|
||||
* __user_agent__ (str): Appends the custom value to the user-agent header to be sent with the request.
|
||||
* __logging_enable__ (bool): Enables logging at the DEBUG level. Defaults to False. Can also be passed in at
|
||||
the client level to enable it for all requests.
|
||||
* __headers__ (dict): Pass in custom headers as key, value pairs. E.g. `headers={'CustomValue': value}`
|
||||
|
||||
## Troubleshooting
|
||||
The Text Analytics client will raise exceptions defined in [Azure Core](https://github.com/Azure/azure-sdk-for-python/blob/master/sdk/core/azure-core/README.md).
|
||||
|
||||
## Next steps
|
||||
|
||||
### More sample code
|
||||
|
||||
These code samples show common scenario operations with the Azure Text Analytics client library.
|
||||
The async versions of the samples (the python sample files appended with `_async`) show asynchronous operations
|
||||
with Text Analytics and require Python 3.5 or later.
|
||||
|
||||
Authenticate the client with a Cognitive Services/Text Analytics subscription key or a token credential from [azure-identity](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/identity/azure-identity):
|
||||
* [sample_authentication.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_authentication.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_authentication_async.py))
|
||||
|
||||
In a batch of documents:
|
||||
* Detect languages: [sample_detect_languages.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_detect_languages.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_detect_languages_async.py))
|
||||
* Recognize entities: [sample_recognize_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_recognize_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_recognize_entities_async.py))
|
||||
* Recognize linked entities: [sample_recognize_linked_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_recognize_linked_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_recognize_linked_entities_async.py))
|
||||
* Recognize personally identifiable information: [sample_recognize_pii_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_recognize_pii_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_recognize_pii_entities_async.py))
|
||||
* Extract key phrases: [sample_extract_key_phrases.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_extract_key_phrases.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_extract_key_phrases_async.py))
|
||||
* Analyze sentiment: [sample_analyze_sentiment.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_analyze_sentiment.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_analyze_sentiment_async.py))
|
||||
|
||||
In a single string of text:
|
||||
* Detect language: [sample_single_detect_language.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_detect_language.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_detect_language_async.py))
|
||||
* Recognize entities: [sample_single_recognize_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_recognize_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_recognize_entities_async.py))
|
||||
* Recognize linked entities: [sample_single_recognize_linked_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_recognize_linked_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_recognize_linked_entities_async.py))
|
||||
* Recognize personally identifiable information: [sample_single_recognize_pii_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_recognize_pii_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_recognize_pii_entities_async.py))
|
||||
* Extract key phrases: [sample_single_extract_key_phrases.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_extract_key_phrases.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_extract_key_phrases_async.py))
|
||||
* Analyze sentiment: [sample_single_analyze_sentiment.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_analyze_sentiment.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_analyze_sentiment_async.py))
|
||||
|
||||
|
||||
### Additional documentation
|
||||
For more extensive documentation on Azure Cognitive Services, see the [Azure Cognitive Services documentation](https://docs.microsoft.com/azure/cognitive-services/) on docs.microsoft.com.
|
||||
|
||||
## Contributing
|
||||
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
|
||||
|
||||
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
|
||||
|
||||
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
|
||||
|
|
@ -0,0 +1 @@
|
|||
__path__ = __import__('pkgutil').extend_path(__path__, __name__) # type: ignore
|
||||
|
|
@ -0,0 +1 @@
|
|||
__path__ = __import__('pkgutil').extend_path(__path__, __name__) # type: ignore
|
||||
|
|
@ -0,0 +1,409 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
from typing import Any, Optional # pylint: disable=unused-import
|
||||
from ._text_analytics_client import TextAnalyticsClient
|
||||
from ._request_handlers import _validate_single_input
|
||||
from ._response_handlers import process_single_error
|
||||
from ._version import VERSION
|
||||
from ._models import (
|
||||
DetectLanguageInput,
|
||||
TextDocumentInput,
|
||||
DetectedLanguage,
|
||||
DocumentError,
|
||||
NamedEntity,
|
||||
LinkedEntity,
|
||||
AnalyzeSentimentResult,
|
||||
RecognizeEntitiesResult,
|
||||
DetectLanguageResult,
|
||||
TextAnalyticsError,
|
||||
InnerError,
|
||||
ExtractKeyPhrasesResult,
|
||||
RecognizeLinkedEntitiesResult,
|
||||
RecognizePiiEntitiesResult,
|
||||
TextDocumentStatistics,
|
||||
LinkedEntityMatch,
|
||||
TextDocumentBatchStatistics,
|
||||
SentenceSentiment,
|
||||
SentimentConfidenceScorePerLabel
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'TextAnalyticsClient',
|
||||
'DetectLanguageInput',
|
||||
'TextDocumentInput',
|
||||
'single_detect_language',
|
||||
'single_recognize_entities',
|
||||
'single_recognize_pii_entities',
|
||||
'single_recognize_linked_entities',
|
||||
'single_extract_key_phrases',
|
||||
'single_analyze_sentiment',
|
||||
'DetectedLanguage',
|
||||
'RecognizeEntitiesResult',
|
||||
'RecognizePiiEntitiesResult',
|
||||
'DetectLanguageResult',
|
||||
'NamedEntity',
|
||||
'TextAnalyticsError',
|
||||
'InnerError',
|
||||
'ExtractKeyPhrasesResult',
|
||||
'RecognizeLinkedEntitiesResult',
|
||||
'AnalyzeSentimentResult',
|
||||
'TextDocumentStatistics',
|
||||
'DocumentError',
|
||||
'LinkedEntity',
|
||||
'LinkedEntityMatch',
|
||||
'TextDocumentBatchStatistics',
|
||||
'SentenceSentiment',
|
||||
'SentimentConfidenceScorePerLabel'
|
||||
]
|
||||
|
||||
__version__ = VERSION
|
||||
|
||||
|
||||
def single_detect_language(
|
||||
endpoint, # type: str
|
||||
credential, # type: str
|
||||
input_text, # type: str
|
||||
country_hint="US", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> DetectLanguageResult
|
||||
"""Detect Language for a single document.
|
||||
|
||||
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.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to detect language from.
|
||||
:param str country_hint: The country hint for the text. Accepts two
|
||||
letter country codes specified by ISO 3166-1 alpha-2.
|
||||
Defaults to "US". If you don't want to use a country hint,
|
||||
pass the empty string "".
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of DetectLanguageResult.
|
||||
:rtype: ~azure.ai.textanalytics.DetectLanguageResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_single_detect_language.py
|
||||
:start-after: [START single_detect_language]
|
||||
:end-before: [END single_detect_language]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Detecting language in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "country_hint", country_hint)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = client.detect_languages(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # DetectLanguageResult
|
||||
|
||||
|
||||
def single_recognize_entities(
|
||||
endpoint, # type: str
|
||||
credential, # type: str
|
||||
input_text, # type: str
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> RecognizeEntitiesResult
|
||||
"""Named Entity Recognition for a single document.
|
||||
|
||||
Returns a list of general named entities in a given document.
|
||||
For a list of supported entity types, check: https://aka.ms/taner
|
||||
For a list of enabled languages, check: https://aka.ms/talangs
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to recognize entities from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of RecognizeEntitiesResult.
|
||||
:rtype: ~azure.ai.textanalytics.RecognizeEntitiesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_single_recognize_entities.py
|
||||
:start-after: [START single_recognize_entities]
|
||||
:end-before: [END single_recognize_entities]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize entities in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = client.recognize_entities(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # RecognizeEntitiesResult
|
||||
|
||||
|
||||
def single_recognize_pii_entities(
|
||||
endpoint, # type: str
|
||||
credential, # type: str
|
||||
input_text, # type: str
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> RecognizePiiEntitiesResult
|
||||
"""Recognize entities containing personal information for a single document.
|
||||
|
||||
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://aka.ms/talangs
|
||||
for the list of enabled languages.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to recognize entities from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of RecognizePiiEntitiesResult.
|
||||
:rtype: ~azure.ai.textanalytics.RecognizePiiEntitiesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_single_recognize_pii_entities.py
|
||||
:start-after: [START single_recognize_pii_entities]
|
||||
:end-before: [END single_recognize_pii_entities]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize personally identifiable information entities in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = client.recognize_pii_entities(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # RecognizePiiEntitiesResult
|
||||
|
||||
|
||||
def single_recognize_linked_entities(
|
||||
endpoint, # type: str
|
||||
credential, # type: str
|
||||
input_text, # type: str
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> RecognizeLinkedEntitiesResult
|
||||
"""Recognize linked entities from a well-known knowledge base
|
||||
for a single document.
|
||||
|
||||
Returns a list of recognized entities with links to a
|
||||
well-known knowledge base. See https://aka.ms/talangs for
|
||||
supported languages in Text Analytics API.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to recognize entities from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of RecognizeLinkedEntitiesResult
|
||||
:rtype: ~azure.ai.textanalytics.RecognizeLinkedEntitiesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_single_recognize_linked_entities.py
|
||||
:start-after: [START single_recognize_linked_entities]
|
||||
:end-before: [END single_recognize_linked_entities]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize linked entities in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = client.recognize_linked_entities(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # RecognizeLinkedEntitiesResult
|
||||
|
||||
|
||||
def single_extract_key_phrases(
|
||||
endpoint, # type: str
|
||||
credential, # type: str
|
||||
input_text, # type: str
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> ExtractKeyPhrasesResult
|
||||
"""Extract Key Phrases for a single document.
|
||||
|
||||
Returns a list of strings denoting the key phrases in the input
|
||||
text. See https://aka.ms/talangs for the list of enabled
|
||||
languages.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to extract key phrases from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of ExtractKeyPhrasesResult
|
||||
:rtype: ~azure.ai.textanalytics.ExtractKeyPhrasesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_single_extract_key_phrases.py
|
||||
:start-after: [START single_extract_key_phrases]
|
||||
:end-before: [END single_extract_key_phrases]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Extract key phrases in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = client.extract_key_phrases(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # ExtractKeyPhrasesResult
|
||||
|
||||
|
||||
def single_analyze_sentiment(
|
||||
endpoint, # type: str
|
||||
credential, # type: str
|
||||
input_text, # type: str
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> AnalyzeSentimentResult
|
||||
"""Analyze sentiment in a single document.
|
||||
|
||||
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://aka.ms/talangs for the list
|
||||
of enabled languages.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to analyze sentiment from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of AnalyzeSentimentResult
|
||||
:rtype: ~azure.ai.textanalytics.AnalyzeSentimentResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_single_analyze_sentiment.py
|
||||
:start-after: [START single_analyze_sentiment]
|
||||
:end-before: [END single_analyze_sentiment]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Analyze sentiment in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = client.analyze_sentiment(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # AnalyzeSentimentResult
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
import six
|
||||
from azure.core.configuration import Configuration
|
||||
from azure.core.pipeline import Pipeline
|
||||
from azure.core.pipeline.transport import RequestsTransport
|
||||
from azure.core.pipeline.policies import (
|
||||
UserAgentPolicy,
|
||||
HeadersPolicy,
|
||||
RequestIdPolicy,
|
||||
ProxyPolicy,
|
||||
NetworkTraceLoggingPolicy,
|
||||
RetryPolicy,
|
||||
RedirectPolicy,
|
||||
BearerTokenCredentialPolicy,
|
||||
DistributedTracingPolicy,
|
||||
HttpLoggingPolicy,
|
||||
)
|
||||
from ._policies import CognitiveServicesCredentialPolicy, TextAnalyticsResponseHook
|
||||
from ._version import VERSION
|
||||
|
||||
|
||||
class TextAnalyticsClientBase(object):
|
||||
def __init__(self, credential, **kwargs):
|
||||
self._pipeline = self._create_pipeline(credential, **kwargs)
|
||||
|
||||
def _create_pipeline(self, credential, **kwargs):
|
||||
credential_policy = None
|
||||
if credential is None:
|
||||
raise ValueError("Parameter 'credential' must not be None.")
|
||||
if hasattr(credential, "get_token"):
|
||||
credential_policy = BearerTokenCredentialPolicy(
|
||||
credential, "https://cognitiveservices.azure.com/.default"
|
||||
)
|
||||
elif isinstance(credential, six.string_types):
|
||||
credential_policy = CognitiveServicesCredentialPolicy(credential)
|
||||
elif credential is not None:
|
||||
raise TypeError("Unsupported credential: {}".format(credential))
|
||||
|
||||
config = self._create_configuration(**kwargs)
|
||||
config.transport = kwargs.get("transport") # type: ignore
|
||||
if not config.transport:
|
||||
config.transport = RequestsTransport(**kwargs)
|
||||
config.user_agent_policy.add_user_agent(
|
||||
"azsdk-python-azure-ai-textanalytics/{}".format(VERSION)
|
||||
)
|
||||
|
||||
policies = [
|
||||
config.headers_policy,
|
||||
config.user_agent_policy,
|
||||
RequestIdPolicy(**kwargs),
|
||||
config.proxy_policy,
|
||||
config.redirect_policy,
|
||||
config.retry_policy,
|
||||
credential_policy,
|
||||
config.logging_policy,
|
||||
TextAnalyticsResponseHook(**kwargs),
|
||||
DistributedTracingPolicy(**kwargs),
|
||||
HttpLoggingPolicy(**kwargs),
|
||||
]
|
||||
return Pipeline(config.transport, policies=policies)
|
||||
|
||||
def _create_configuration(self, **kwargs): # pylint: disable=no-self-use
|
||||
config = Configuration(**kwargs)
|
||||
config.user_agent_policy = kwargs.get("user_agent_policy") or UserAgentPolicy(**kwargs)
|
||||
config.headers_policy = kwargs.get("headers_policy") or HeadersPolicy(**kwargs)
|
||||
config.proxy_policy = kwargs.get("proxy_policy") or ProxyPolicy(**kwargs)
|
||||
config.logging_policy = kwargs.get("logging_policy") or NetworkTraceLoggingPolicy(**kwargs)
|
||||
config.retry_policy = kwargs.get("retry_policy") or RetryPolicy(**kwargs)
|
||||
config.redirect_policy = kwargs.get("redirect_policy") or RedirectPolicy(**kwargs)
|
||||
|
||||
return config
|
||||
|
||||
def __enter__(self):
|
||||
self._client.__enter__() # pylint:disable=no-member
|
||||
return self
|
||||
|
||||
def __exit__(self, *args):
|
||||
self._client.__exit__(*args) # pylint:disable=no-member
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from ._text_analytics_client import TextAnalyticsClient
|
||||
__all__ = ['TextAnalyticsClient']
|
||||
|
||||
from ._version import VERSION
|
||||
|
||||
__version__ = VERSION
|
||||
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
from azure.core.configuration import Configuration
|
||||
from azure.core.pipeline import policies
|
||||
|
||||
from ._version import VERSION
|
||||
|
||||
|
||||
class TextAnalyticsClientConfiguration(Configuration):
|
||||
"""Configuration for TextAnalyticsClient
|
||||
Note that all parameters used to create this instance are saved as instance
|
||||
attributes.
|
||||
|
||||
:param credentials: Credentials needed for the client to connect to Azure.
|
||||
:type credentials: :mod:`A msrestazure Credentials
|
||||
object<msrestazure.azure_active_directory>`
|
||||
:param endpoint: Supported Cognitive Services endpoints (protocol and
|
||||
hostname, for example: https://westus.api.cognitive.microsoft.com).
|
||||
:type endpoint: str
|
||||
"""
|
||||
|
||||
def __init__(self, credentials, endpoint, **kwargs):
|
||||
|
||||
if credentials is None:
|
||||
raise ValueError("Parameter 'credentials' must not be None.")
|
||||
if endpoint is None:
|
||||
raise ValueError("Parameter 'endpoint' must not be None.")
|
||||
|
||||
super(TextAnalyticsClientConfiguration, self).__init__(**kwargs)
|
||||
self._configure(**kwargs)
|
||||
|
||||
self.user_agent_policy.add_user_agent('azsdk-python-azure-ai-textanalytics/{}'.format(VERSION))
|
||||
self.generate_client_request_id = True
|
||||
|
||||
self.credentials = credentials
|
||||
self.endpoint = endpoint
|
||||
|
||||
def _configure(self, **kwargs):
|
||||
self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
|
||||
self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
|
||||
self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
|
||||
self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
|
||||
self.retry_policy = kwargs.get('retry_policy') or policies.RetryPolicy(**kwargs)
|
||||
self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
|
||||
self.redirect_policy = kwargs.get('redirect_policy') or policies.RedirectPolicy(**kwargs)
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from azure.core import PipelineClient
|
||||
from msrest import Serializer, Deserializer
|
||||
|
||||
from ._configuration import TextAnalyticsClientConfiguration
|
||||
from .operations import TextAnalyticsClientOperationsMixin
|
||||
from . import models
|
||||
|
||||
|
||||
class TextAnalyticsClient(TextAnalyticsClientOperationsMixin):
|
||||
"""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/en-us/azure/cognitive-services/text-analytics/overview
|
||||
|
||||
|
||||
:param credentials: Credentials needed for the client to connect to Azure.
|
||||
:type credentials: :mod:`A msrestazure Credentials
|
||||
object<msrestazure.azure_active_directory>`
|
||||
:param endpoint: Supported Cognitive Services endpoints (protocol and
|
||||
hostname, for example: https://westus.api.cognitive.microsoft.com).
|
||||
:type endpoint: str
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, credentials, endpoint, **kwargs):
|
||||
|
||||
base_url = '{Endpoint}/text/analytics/v3.0-preview.1'
|
||||
self._config = TextAnalyticsClientConfiguration(credentials, endpoint, **kwargs)
|
||||
self._client = PipelineClient(base_url=base_url, config=self._config, **kwargs)
|
||||
|
||||
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
|
||||
self.api_version = 'v3.0-preview.1'
|
||||
self._serialize = Serializer(client_models)
|
||||
self._deserialize = Deserializer(client_models)
|
||||
|
||||
|
||||
def close(self):
|
||||
self._client.close()
|
||||
def __enter__(self):
|
||||
self._client.__enter__()
|
||||
return self
|
||||
def __exit__(self, *exc_details):
|
||||
self._client.__exit__(*exc_details)
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
VERSION = "v3.0-preview.1"
|
||||
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from ._text_analytics_client_async import TextAnalyticsClient
|
||||
__all__ = ['TextAnalyticsClient']
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
from azure.core.configuration import Configuration
|
||||
from azure.core.pipeline import policies
|
||||
|
||||
from .._version import VERSION
|
||||
|
||||
|
||||
class TextAnalyticsClientConfiguration(Configuration):
|
||||
"""Configuration for TextAnalyticsClient
|
||||
Note that all parameters used to create this instance are saved as instance
|
||||
attributes.
|
||||
|
||||
:param credentials: Credentials needed for the client to connect to Azure.
|
||||
:type credentials: :mod:`A msrestazure Credentials
|
||||
object<msrestazure.azure_active_directory>`
|
||||
:param endpoint: Supported Cognitive Services endpoints (protocol and
|
||||
hostname, for example: https://westus.api.cognitive.microsoft.com).
|
||||
:type endpoint: str
|
||||
"""
|
||||
|
||||
def __init__(self, credentials, endpoint, **kwargs):
|
||||
|
||||
if credentials is None:
|
||||
raise ValueError("Parameter 'credentials' must not be None.")
|
||||
if endpoint is None:
|
||||
raise ValueError("Parameter 'endpoint' must not be None.")
|
||||
|
||||
super(TextAnalyticsClientConfiguration, self).__init__(**kwargs)
|
||||
self._configure(**kwargs)
|
||||
|
||||
self.user_agent_policy.add_user_agent('azsdk-python-azure-ai-textanalytics/{}'.format(VERSION))
|
||||
self.generate_client_request_id = True
|
||||
|
||||
self.credentials = credentials
|
||||
self.endpoint = endpoint
|
||||
|
||||
def _configure(self, **kwargs):
|
||||
self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
|
||||
self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
|
||||
self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
|
||||
self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
|
||||
self.retry_policy = kwargs.get('retry_policy') or policies.AsyncRetryPolicy(**kwargs)
|
||||
self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
|
||||
self.redirect_policy = kwargs.get('redirect_policy') or policies.AsyncRedirectPolicy(**kwargs)
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from azure.core import AsyncPipelineClient
|
||||
from msrest import Serializer, Deserializer
|
||||
|
||||
from ._configuration_async import TextAnalyticsClientConfiguration
|
||||
from .operations_async import TextAnalyticsClientOperationsMixin
|
||||
from .. import models
|
||||
|
||||
|
||||
class TextAnalyticsClient(TextAnalyticsClientOperationsMixin):
|
||||
"""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/en-us/azure/cognitive-services/text-analytics/overview
|
||||
|
||||
|
||||
:param credentials: Credentials needed for the client to connect to Azure.
|
||||
:type credentials: :mod:`A msrestazure Credentials
|
||||
object<msrestazure.azure_active_directory>`
|
||||
:param endpoint: Supported Cognitive Services endpoints (protocol and
|
||||
hostname, for example: https://westus.api.cognitive.microsoft.com).
|
||||
:type endpoint: str
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, credentials, endpoint, **kwargs):
|
||||
|
||||
base_url = '{Endpoint}/text/analytics/v3.0-preview.1'
|
||||
self._config = TextAnalyticsClientConfiguration(credentials, endpoint, **kwargs)
|
||||
self._client = AsyncPipelineClient(base_url=base_url, config=self._config, **kwargs)
|
||||
|
||||
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
|
||||
self.api_version = 'v3.0-preview.1'
|
||||
self._serialize = Serializer(client_models)
|
||||
self._deserialize = Deserializer(client_models)
|
||||
|
||||
|
||||
async def close(self):
|
||||
await self._client.close()
|
||||
async def __aenter__(self):
|
||||
await self._client.__aenter__()
|
||||
return self
|
||||
async def __aexit__(self, *exc_details):
|
||||
await self._client.__aexit__(*exc_details)
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from ._text_analytics_client_operations_async import TextAnalyticsClientOperationsMixin
|
||||
|
||||
__all__ = [
|
||||
'TextAnalyticsClientOperationsMixin',
|
||||
]
|
||||
|
|
@ -0,0 +1,451 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from azure.core.exceptions import map_error
|
||||
from ... import models
|
||||
import uuid
|
||||
|
||||
|
||||
class TextAnalyticsClientOperationsMixin:
|
||||
|
||||
async def entities_recognition_general(self, documents, model_version=None, show_stats=None, *, cls=None, **kwargs):
|
||||
"""Named Entity Recognition.
|
||||
|
||||
The API returns a list of general named entities in a given document.
|
||||
For the list of supported entity types, check <a
|
||||
href="https://aka.ms/taner">Supported Entity Types in Text Analytics
|
||||
API</a>. See the <a href="https://aka.ms/talangs">Supported languages
|
||||
in Text Analytics API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: EntitiesResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.EntitiesResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.entities_recognition_general.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('EntitiesResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
entities_recognition_general.metadata = {'url': '/entities/recognition/general'}
|
||||
|
||||
async def entities_recognition_pii(self, documents, model_version=None, show_stats=None, *, cls=None, **kwargs):
|
||||
"""Entities containing personal information.
|
||||
|
||||
The API returns a list of entities with personal information (\"SSN\",
|
||||
\"Bank Account\" etc) in the document. For the list of supported entity
|
||||
types, check <a href="https://aka.ms/tanerpii">Supported Entity Types
|
||||
in Text Analytics API</a>. See the <a
|
||||
href="https://aka.ms/talangs">Supported languages in Text Analytics
|
||||
API</a> for the list of enabled languages.
|
||||
.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: EntitiesResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.EntitiesResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.entities_recognition_pii.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('EntitiesResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
entities_recognition_pii.metadata = {'url': '/entities/recognition/pii'}
|
||||
|
||||
async def entities_linking(self, documents, model_version=None, show_stats=None, *, cls=None, **kwargs):
|
||||
"""Linked entities from a well-known knowledge base.
|
||||
|
||||
The API returns a list of recognized entities with links to a
|
||||
well-known knowledge base. See the <a
|
||||
href="https://aka.ms/talangs">Supported languages in Text Analytics
|
||||
API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: EntityLinkingResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.EntityLinkingResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.entities_linking.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('EntityLinkingResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
entities_linking.metadata = {'url': '/entities/linking'}
|
||||
|
||||
async def key_phrases(self, documents, model_version=None, show_stats=None, *, cls=None, **kwargs):
|
||||
"""Key Phrases.
|
||||
|
||||
The API returns a list of strings denoting the key phrases in the input
|
||||
text. See the <a href="https://aka.ms/talangs">Supported languages in
|
||||
Text Analytics API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: KeyPhraseResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.KeyPhraseResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.key_phrases.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('KeyPhraseResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
key_phrases.metadata = {'url': '/keyPhrases'}
|
||||
|
||||
async def languages(self, documents, model_version=None, show_stats=None, *, cls=None, **kwargs):
|
||||
"""Detect Language.
|
||||
|
||||
The API returns the detected language and a numeric score between 0 and
|
||||
1. Scores close to 1 indicate 100% certainty that the identified
|
||||
language is true. See the <a href="https://aka.ms/talangs">Supported
|
||||
languages in Text Analytics API</a> for the list of enabled languages.
|
||||
|
||||
:param documents:
|
||||
:type documents: list[~azure.ai.textanalytics.models.LanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: LanguageResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.LanguageResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.LanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.languages.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'LanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('LanguageResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
languages.metadata = {'url': '/languages'}
|
||||
|
||||
async def sentiment(self, documents, model_version=None, show_stats=None, *, cls=None, **kwargs):
|
||||
"""Sentiment.
|
||||
|
||||
The API 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 the <a
|
||||
href="https://aka.ms/talangs">Supported languages in Text Analytics
|
||||
API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: SentimentResponse or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.SentimentResponse
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.sentiment.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('SentimentResponse', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
sentiment.metadata = {'url': '/sentiment'}
|
||||
|
|
@ -0,0 +1,101 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
try:
|
||||
from ._models_py3 import DetectedLanguage
|
||||
from ._models_py3 import DocumentEntities
|
||||
from ._models_py3 import DocumentError
|
||||
from ._models_py3 import DocumentKeyPhrases
|
||||
from ._models_py3 import DocumentLanguage
|
||||
from ._models_py3 import DocumentLinkedEntities
|
||||
from ._models_py3 import DocumentSentiment
|
||||
from ._models_py3 import DocumentStatistics
|
||||
from ._models_py3 import EntitiesResult
|
||||
from ._models_py3 import Entity
|
||||
from ._models_py3 import EntityLinkingResult
|
||||
from ._models_py3 import InnerError
|
||||
from ._models_py3 import KeyPhraseResult
|
||||
from ._models_py3 import LanguageBatchInput
|
||||
from ._models_py3 import LanguageInput
|
||||
from ._models_py3 import LanguageResult
|
||||
from ._models_py3 import LinkedEntity
|
||||
from ._models_py3 import Match
|
||||
from ._models_py3 import MultiLanguageBatchInput
|
||||
from ._models_py3 import MultiLanguageInput
|
||||
from ._models_py3 import RequestStatistics
|
||||
from ._models_py3 import SentenceSentiment
|
||||
from ._models_py3 import SentimentConfidenceScorePerLabel
|
||||
from ._models_py3 import SentimentResponse
|
||||
from ._models_py3 import TextAnalyticsError, TextAnalyticsErrorException
|
||||
except (SyntaxError, ImportError):
|
||||
from ._models import DetectedLanguage
|
||||
from ._models import DocumentEntities
|
||||
from ._models import DocumentError
|
||||
from ._models import DocumentKeyPhrases
|
||||
from ._models import DocumentLanguage
|
||||
from ._models import DocumentLinkedEntities
|
||||
from ._models import DocumentSentiment
|
||||
from ._models import DocumentStatistics
|
||||
from ._models import EntitiesResult
|
||||
from ._models import Entity
|
||||
from ._models import EntityLinkingResult
|
||||
from ._models import InnerError
|
||||
from ._models import KeyPhraseResult
|
||||
from ._models import LanguageBatchInput
|
||||
from ._models import LanguageInput
|
||||
from ._models import LanguageResult
|
||||
from ._models import LinkedEntity
|
||||
from ._models import Match
|
||||
from ._models import MultiLanguageBatchInput
|
||||
from ._models import MultiLanguageInput
|
||||
from ._models import RequestStatistics
|
||||
from ._models import SentenceSentiment
|
||||
from ._models import SentimentConfidenceScorePerLabel
|
||||
from ._models import SentimentResponse
|
||||
from ._models import TextAnalyticsError, TextAnalyticsErrorException
|
||||
from ._text_analytics_client_enums import (
|
||||
ErrorCodeValue,
|
||||
InnerErrorCodeValue,
|
||||
DocumentSentimentValue,
|
||||
SentenceSentimentValue,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'DetectedLanguage',
|
||||
'DocumentEntities',
|
||||
'DocumentError',
|
||||
'DocumentKeyPhrases',
|
||||
'DocumentLanguage',
|
||||
'DocumentLinkedEntities',
|
||||
'DocumentSentiment',
|
||||
'DocumentStatistics',
|
||||
'EntitiesResult',
|
||||
'Entity',
|
||||
'EntityLinkingResult',
|
||||
'InnerError',
|
||||
'KeyPhraseResult',
|
||||
'LanguageBatchInput',
|
||||
'LanguageInput',
|
||||
'LanguageResult',
|
||||
'LinkedEntity',
|
||||
'Match',
|
||||
'MultiLanguageBatchInput',
|
||||
'MultiLanguageInput',
|
||||
'RequestStatistics',
|
||||
'SentenceSentiment',
|
||||
'SentimentConfidenceScorePerLabel',
|
||||
'SentimentResponse',
|
||||
'TextAnalyticsError', 'TextAnalyticsErrorException',
|
||||
'ErrorCodeValue',
|
||||
'InnerErrorCodeValue',
|
||||
'DocumentSentimentValue',
|
||||
'SentenceSentimentValue',
|
||||
]
|
||||
|
|
@ -0,0 +1,936 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from msrest.serialization import Model
|
||||
from azure.core.exceptions import HttpResponseError
|
||||
|
||||
|
||||
class DetectedLanguage(Model):
|
||||
"""DetectedLanguage.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param name: Required. Long name of a detected language (e.g. English,
|
||||
French).
|
||||
:type name: str
|
||||
:param iso6391_name: Required. A two letter representation of the detected
|
||||
language according to the ISO 639-1 standard (e.g. en, fr).
|
||||
:type iso6391_name: str
|
||||
:param score: Required. A confidence score between 0 and 1. Scores close
|
||||
to 1 indicate 100% certainty that the identified language is true.
|
||||
:type score: float
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'name': {'required': True},
|
||||
'iso6391_name': {'required': True},
|
||||
'score': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'name': {'key': 'name', 'type': 'str'},
|
||||
'iso6391_name': {'key': 'iso6391Name', 'type': 'str'},
|
||||
'score': {'key': 'score', 'type': 'float'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DetectedLanguage, self).__init__(**kwargs)
|
||||
self.name = kwargs.get('name', None)
|
||||
self.iso6391_name = kwargs.get('iso6391_name', None)
|
||||
self.score = kwargs.get('score', None)
|
||||
|
||||
|
||||
class DocumentEntities(Model):
|
||||
"""DocumentEntities.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param entities: Required. Recognized entities in the document.
|
||||
:type entities: list[~azure.ai.textanalytics.models.Entity]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'entities': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'entities': {'key': 'entities', 'type': '[Entity]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DocumentEntities, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.entities = kwargs.get('entities', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
|
||||
|
||||
class DocumentError(Model):
|
||||
"""DocumentError.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Document Id.
|
||||
:type id: str
|
||||
:param error: Required. Document Error.
|
||||
:type error: ~azure.ai.textanalytics.models.TextAnalyticsError
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'error': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'error': {'key': 'error', 'type': 'TextAnalyticsError'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DocumentError, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.error = kwargs.get('error', None)
|
||||
|
||||
|
||||
class DocumentKeyPhrases(Model):
|
||||
"""DocumentKeyPhrases.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param key_phrases: Required. A list of representative words or phrases.
|
||||
The number of key phrases returned is proportional to the number of words
|
||||
in the input document.
|
||||
:type key_phrases: list[str]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'key_phrases': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'key_phrases': {'key': 'keyPhrases', 'type': '[str]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DocumentKeyPhrases, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.key_phrases = kwargs.get('key_phrases', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
|
||||
|
||||
class DocumentLanguage(Model):
|
||||
"""DocumentLanguage.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param detected_languages: Required. A list of extracted languages.
|
||||
:type detected_languages:
|
||||
list[~azure.ai.textanalytics.models.DetectedLanguage]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'detected_languages': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'detected_languages': {'key': 'detectedLanguages', 'type': '[DetectedLanguage]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DocumentLanguage, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.detected_languages = kwargs.get('detected_languages', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
|
||||
|
||||
class DocumentLinkedEntities(Model):
|
||||
"""DocumentLinkedEntities.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param entities: Required. Recognized well-known entities in the document.
|
||||
:type entities: list[~azure.ai.textanalytics.models.LinkedEntity]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'entities': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'entities': {'key': 'entities', 'type': '[LinkedEntity]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DocumentLinkedEntities, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.entities = kwargs.get('entities', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
|
||||
|
||||
class DocumentSentiment(Model):
|
||||
"""DocumentSentiment.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param sentiment: Required. Predicted sentiment for document (Negative,
|
||||
Neutral, Positive, or Mixed). Possible values include: 'positive',
|
||||
'neutral', 'negative', 'mixed'
|
||||
:type sentiment: str or
|
||||
~azure.ai.textanalytics.models.DocumentSentimentValue
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
:param document_scores: Required. Document level sentiment confidence
|
||||
scores between 0 and 1 for each sentiment class.
|
||||
:type document_scores:
|
||||
~azure.ai.textanalytics.models.SentimentConfidenceScorePerLabel
|
||||
:param sentences: Required. Sentence level sentiment analysis.
|
||||
:type sentences: list[~azure.ai.textanalytics.models.SentenceSentiment]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'sentiment': {'required': True},
|
||||
'document_scores': {'required': True},
|
||||
'sentences': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'sentiment': {'key': 'sentiment', 'type': 'DocumentSentimentValue'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
'document_scores': {'key': 'documentScores', 'type': 'SentimentConfidenceScorePerLabel'},
|
||||
'sentences': {'key': 'sentences', 'type': '[SentenceSentiment]'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DocumentSentiment, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.sentiment = kwargs.get('sentiment', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
self.document_scores = kwargs.get('document_scores', None)
|
||||
self.sentences = kwargs.get('sentences', None)
|
||||
|
||||
|
||||
class DocumentStatistics(Model):
|
||||
"""if showStats=true was specified in the request this field will contain
|
||||
information about the document payload.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param characters_count: Required. Number of text elements recognized in
|
||||
the document.
|
||||
:type characters_count: int
|
||||
:param transactions_count: Required. Number of transactions for the
|
||||
document.
|
||||
:type transactions_count: int
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'characters_count': {'required': True},
|
||||
'transactions_count': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'characters_count': {'key': 'charactersCount', 'type': 'int'},
|
||||
'transactions_count': {'key': 'transactionsCount', 'type': 'int'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DocumentStatistics, self).__init__(**kwargs)
|
||||
self.characters_count = kwargs.get('characters_count', None)
|
||||
self.transactions_count = kwargs.get('transactions_count', None)
|
||||
|
||||
|
||||
class EntitiesResult(Model):
|
||||
"""EntitiesResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentEntities]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentEntities]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(EntitiesResult, self).__init__(**kwargs)
|
||||
self.documents = kwargs.get('documents', None)
|
||||
self.errors = kwargs.get('errors', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
self.model_version = kwargs.get('model_version', None)
|
||||
|
||||
|
||||
class Entity(Model):
|
||||
"""Entity.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param text: Required. Entity text as appears in the request.
|
||||
:type text: str
|
||||
:param type: Required. Entity type, such as Person/Location/Org/SSN etc
|
||||
:type type: str
|
||||
:param subtype: Entity sub type, such as Age/Year/TimeRange etc
|
||||
:type subtype: str
|
||||
:param offset: Required. Start position (in Unicode characters) for the
|
||||
entity text.
|
||||
:type offset: int
|
||||
:param length: Required. Length (in Unicode characters) for the entity
|
||||
text.
|
||||
:type length: int
|
||||
:param score: Required. Confidence score between 0 and 1 of the extracted
|
||||
entity.
|
||||
:type score: float
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'text': {'required': True},
|
||||
'type': {'required': True},
|
||||
'offset': {'required': True},
|
||||
'length': {'required': True},
|
||||
'score': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'type': {'key': 'type', 'type': 'str'},
|
||||
'subtype': {'key': 'subtype', 'type': 'str'},
|
||||
'offset': {'key': 'offset', 'type': 'int'},
|
||||
'length': {'key': 'length', 'type': 'int'},
|
||||
'score': {'key': 'score', 'type': 'float'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(Entity, self).__init__(**kwargs)
|
||||
self.text = kwargs.get('text', None)
|
||||
self.type = kwargs.get('type', None)
|
||||
self.subtype = kwargs.get('subtype', None)
|
||||
self.offset = kwargs.get('offset', None)
|
||||
self.length = kwargs.get('length', None)
|
||||
self.score = kwargs.get('score', None)
|
||||
|
||||
|
||||
class EntityLinkingResult(Model):
|
||||
"""EntityLinkingResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.DocumentLinkedEntities]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentLinkedEntities]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(EntityLinkingResult, self).__init__(**kwargs)
|
||||
self.documents = kwargs.get('documents', None)
|
||||
self.errors = kwargs.get('errors', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
self.model_version = kwargs.get('model_version', None)
|
||||
|
||||
|
||||
class InnerError(Model):
|
||||
"""InnerError.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param code: Required. Error code. Possible values include:
|
||||
'invalidParameterValue', 'invalidRequestBodyFormat', 'emptyRequest',
|
||||
'missingInputRecords', 'invalidDocument', 'modelVersionIncorrect',
|
||||
'invalidDocumentBatch', 'unsupportedLanguageCode', 'invalidCountryHint'
|
||||
:type code: str or ~azure.ai.textanalytics.models.InnerErrorCodeValue
|
||||
:param message: Required. Error message.
|
||||
:type message: str
|
||||
:param details: Error details.
|
||||
:type details: dict[str, str]
|
||||
:param target: Error target.
|
||||
:type target: str
|
||||
:param inner_error: Inner error contains more specific information.
|
||||
:type inner_error: ~azure.ai.textanalytics.models.InnerError
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'code': {'required': True},
|
||||
'message': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'code': {'key': 'code', 'type': 'InnerErrorCodeValue'},
|
||||
'message': {'key': 'message', 'type': 'str'},
|
||||
'details': {'key': 'details', 'type': '{str}'},
|
||||
'target': {'key': 'target', 'type': 'str'},
|
||||
'inner_error': {'key': 'innerError', 'type': 'InnerError'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(InnerError, self).__init__(**kwargs)
|
||||
self.code = kwargs.get('code', None)
|
||||
self.message = kwargs.get('message', None)
|
||||
self.details = kwargs.get('details', None)
|
||||
self.target = kwargs.get('target', None)
|
||||
self.inner_error = kwargs.get('inner_error', None)
|
||||
|
||||
|
||||
class KeyPhraseResult(Model):
|
||||
"""KeyPhraseResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentKeyPhrases]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentKeyPhrases]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(KeyPhraseResult, self).__init__(**kwargs)
|
||||
self.documents = kwargs.get('documents', None)
|
||||
self.errors = kwargs.get('errors', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
self.model_version = kwargs.get('model_version', None)
|
||||
|
||||
|
||||
class LanguageBatchInput(Model):
|
||||
"""LanguageBatchInput.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required.
|
||||
:type documents: list[~azure.ai.textanalytics.models.LanguageInput]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[LanguageInput]'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(LanguageBatchInput, self).__init__(**kwargs)
|
||||
self.documents = kwargs.get('documents', None)
|
||||
|
||||
|
||||
class LanguageInput(Model):
|
||||
"""LanguageInput.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param text: Required.
|
||||
:type text: str
|
||||
:param country_hint:
|
||||
:type country_hint: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'text': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'country_hint': {'key': 'countryHint', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(LanguageInput, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.text = kwargs.get('text', None)
|
||||
self.country_hint = kwargs.get('country_hint', None)
|
||||
|
||||
|
||||
class LanguageResult(Model):
|
||||
"""LanguageResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentLanguage]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentLanguage]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(LanguageResult, self).__init__(**kwargs)
|
||||
self.documents = kwargs.get('documents', None)
|
||||
self.errors = kwargs.get('errors', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
self.model_version = kwargs.get('model_version', None)
|
||||
|
||||
|
||||
class LinkedEntity(Model):
|
||||
"""LinkedEntity.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param name: Required. Entity Linking formal name.
|
||||
:type name: str
|
||||
:param matches: Required. List of instances this entity appears in the
|
||||
text.
|
||||
:type matches: list[~azure.ai.textanalytics.models.Match]
|
||||
:param language: Required. Language used in the data source.
|
||||
:type language: str
|
||||
:param id: Unique identifier of the recognized entity from the data
|
||||
source.
|
||||
:type id: str
|
||||
:param url: Required. URL for the entity's page from the data source.
|
||||
:type url: str
|
||||
:param data_source: Required. Data source used to extract entity linking,
|
||||
such as Wiki/Bing etc.
|
||||
:type data_source: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'name': {'required': True},
|
||||
'matches': {'required': True},
|
||||
'language': {'required': True},
|
||||
'url': {'required': True},
|
||||
'data_source': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'name': {'key': 'name', 'type': 'str'},
|
||||
'matches': {'key': 'matches', 'type': '[Match]'},
|
||||
'language': {'key': 'language', 'type': 'str'},
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'url': {'key': 'url', 'type': 'str'},
|
||||
'data_source': {'key': 'dataSource', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(LinkedEntity, self).__init__(**kwargs)
|
||||
self.name = kwargs.get('name', None)
|
||||
self.matches = kwargs.get('matches', None)
|
||||
self.language = kwargs.get('language', None)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.url = kwargs.get('url', None)
|
||||
self.data_source = kwargs.get('data_source', None)
|
||||
|
||||
|
||||
class Match(Model):
|
||||
"""Match.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param score: Required. If a well-known item is recognized, a decimal
|
||||
number denoting the confidence level between 0 and 1 will be returned.
|
||||
:type score: float
|
||||
:param text: Required. Entity text as appears in the request.
|
||||
:type text: str
|
||||
:param offset: Required. Start position (in Unicode characters) for the
|
||||
entity match text.
|
||||
:type offset: int
|
||||
:param length: Required. Length (in Unicode characters) for the entity
|
||||
match text.
|
||||
:type length: int
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'score': {'required': True},
|
||||
'text': {'required': True},
|
||||
'offset': {'required': True},
|
||||
'length': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'score': {'key': 'score', 'type': 'float'},
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'offset': {'key': 'offset', 'type': 'int'},
|
||||
'length': {'key': 'length', 'type': 'int'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(Match, self).__init__(**kwargs)
|
||||
self.score = kwargs.get('score', None)
|
||||
self.text = kwargs.get('text', None)
|
||||
self.offset = kwargs.get('offset', None)
|
||||
self.length = kwargs.get('length', None)
|
||||
|
||||
|
||||
class MultiLanguageBatchInput(Model):
|
||||
"""Contains a set of input documents to be analyzed by the service.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. The set of documents to process as part of
|
||||
this batch.
|
||||
:type documents: list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[MultiLanguageInput]'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(MultiLanguageBatchInput, self).__init__(**kwargs)
|
||||
self.documents = kwargs.get('documents', None)
|
||||
|
||||
|
||||
class MultiLanguageInput(Model):
|
||||
"""Contains an input document to be analyzed by the service.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. A unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param text: Required. The input text to process.
|
||||
:type text: str
|
||||
:param language: (Optional) This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, use "en" for English as default.
|
||||
:type language: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'text': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'language': {'key': 'language', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(MultiLanguageInput, self).__init__(**kwargs)
|
||||
self.id = kwargs.get('id', None)
|
||||
self.text = kwargs.get('text', None)
|
||||
self.language = kwargs.get('language', None)
|
||||
|
||||
|
||||
class RequestStatistics(Model):
|
||||
"""if showStats=true was specified in the request this field will contain
|
||||
information about the request payload.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents_count: Required. Number of documents submitted in the
|
||||
request.
|
||||
:type documents_count: int
|
||||
:param valid_documents_count: Required. Number of valid documents. This
|
||||
excludes empty, over-size limit or non-supported languages documents.
|
||||
:type valid_documents_count: int
|
||||
:param erroneous_documents_count: Required. Number of invalid documents.
|
||||
This includes empty, over-size limit or non-supported languages documents.
|
||||
:type erroneous_documents_count: int
|
||||
:param transactions_count: Required. Number of transactions for the
|
||||
request.
|
||||
:type transactions_count: long
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents_count': {'required': True},
|
||||
'valid_documents_count': {'required': True},
|
||||
'erroneous_documents_count': {'required': True},
|
||||
'transactions_count': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents_count': {'key': 'documentsCount', 'type': 'int'},
|
||||
'valid_documents_count': {'key': 'validDocumentsCount', 'type': 'int'},
|
||||
'erroneous_documents_count': {'key': 'erroneousDocumentsCount', 'type': 'int'},
|
||||
'transactions_count': {'key': 'transactionsCount', 'type': 'long'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(RequestStatistics, self).__init__(**kwargs)
|
||||
self.documents_count = kwargs.get('documents_count', None)
|
||||
self.valid_documents_count = kwargs.get('valid_documents_count', None)
|
||||
self.erroneous_documents_count = kwargs.get('erroneous_documents_count', None)
|
||||
self.transactions_count = kwargs.get('transactions_count', None)
|
||||
|
||||
|
||||
class SentenceSentiment(Model):
|
||||
"""SentenceSentiment.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param sentiment: Required. The predicted Sentiment for the sentence.
|
||||
Possible values include: 'positive', 'neutral', 'negative'
|
||||
:type sentiment: str or
|
||||
~azure.ai.textanalytics.models.SentenceSentimentValue
|
||||
:param sentence_scores: Required. The sentiment confidence score between 0
|
||||
and 1 for the sentence for all classes.
|
||||
:type sentence_scores:
|
||||
~azure.ai.textanalytics.models.SentimentConfidenceScorePerLabel
|
||||
:param offset: Required. The sentence offset from the start of the
|
||||
document.
|
||||
:type offset: int
|
||||
:param length: Required. The length of the sentence by Unicode standard.
|
||||
:type length: int
|
||||
:param warnings: The warnings generated for the sentence.
|
||||
:type warnings: list[str]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'sentiment': {'required': True},
|
||||
'sentence_scores': {'required': True},
|
||||
'offset': {'required': True},
|
||||
'length': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'sentiment': {'key': 'sentiment', 'type': 'SentenceSentimentValue'},
|
||||
'sentence_scores': {'key': 'sentenceScores', 'type': 'SentimentConfidenceScorePerLabel'},
|
||||
'offset': {'key': 'offset', 'type': 'int'},
|
||||
'length': {'key': 'length', 'type': 'int'},
|
||||
'warnings': {'key': 'warnings', 'type': '[str]'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(SentenceSentiment, self).__init__(**kwargs)
|
||||
self.sentiment = kwargs.get('sentiment', None)
|
||||
self.sentence_scores = kwargs.get('sentence_scores', None)
|
||||
self.offset = kwargs.get('offset', None)
|
||||
self.length = kwargs.get('length', None)
|
||||
self.warnings = kwargs.get('warnings', None)
|
||||
|
||||
|
||||
class SentimentConfidenceScorePerLabel(Model):
|
||||
"""Represents the confidence scores between 0 and 1 across all sentiment
|
||||
classes: positive, neutral, negative.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param positive: Required.
|
||||
:type positive: float
|
||||
:param neutral: Required.
|
||||
:type neutral: float
|
||||
:param negative: Required.
|
||||
:type negative: float
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'positive': {'required': True},
|
||||
'neutral': {'required': True},
|
||||
'negative': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'positive': {'key': 'positive', 'type': 'float'},
|
||||
'neutral': {'key': 'neutral', 'type': 'float'},
|
||||
'negative': {'key': 'negative', 'type': 'float'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(SentimentConfidenceScorePerLabel, self).__init__(**kwargs)
|
||||
self.positive = kwargs.get('positive', None)
|
||||
self.neutral = kwargs.get('neutral', None)
|
||||
self.negative = kwargs.get('negative', None)
|
||||
|
||||
|
||||
class SentimentResponse(Model):
|
||||
"""SentimentResponse.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Sentiment analysis per document.
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentSentiment]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentSentiment]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(SentimentResponse, self).__init__(**kwargs)
|
||||
self.documents = kwargs.get('documents', None)
|
||||
self.errors = kwargs.get('errors', None)
|
||||
self.statistics = kwargs.get('statistics', None)
|
||||
self.model_version = kwargs.get('model_version', None)
|
||||
|
||||
|
||||
class TextAnalyticsError(Model):
|
||||
"""TextAnalyticsError.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param code: Required. Error code. Possible values include:
|
||||
'invalidRequest', 'invalidArgument', 'internalServerError',
|
||||
'serviceUnavailable'
|
||||
:type code: str or ~azure.ai.textanalytics.models.ErrorCodeValue
|
||||
:param message: Required. Error message.
|
||||
:type message: str
|
||||
:param target: Error target.
|
||||
:type target: str
|
||||
:param inner_error: Inner error contains more specific information.
|
||||
:type inner_error: ~azure.ai.textanalytics.models.InnerError
|
||||
:param details: Details about specific errors that led to this reported
|
||||
error.
|
||||
:type details: list[~azure.ai.textanalytics.models.TextAnalyticsError]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'code': {'required': True},
|
||||
'message': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'code': {'key': 'code', 'type': 'ErrorCodeValue'},
|
||||
'message': {'key': 'message', 'type': 'str'},
|
||||
'target': {'key': 'target', 'type': 'str'},
|
||||
'inner_error': {'key': 'innerError', 'type': 'InnerError'},
|
||||
'details': {'key': 'details', 'type': '[TextAnalyticsError]'},
|
||||
}
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(TextAnalyticsError, self).__init__(**kwargs)
|
||||
self.code = kwargs.get('code', None)
|
||||
self.message = kwargs.get('message', None)
|
||||
self.target = kwargs.get('target', None)
|
||||
self.inner_error = kwargs.get('inner_error', None)
|
||||
self.details = kwargs.get('details', None)
|
||||
|
||||
|
||||
class TextAnalyticsErrorException(HttpResponseError):
|
||||
"""Server responsed with exception of type: 'TextAnalyticsError'.
|
||||
|
||||
:param deserialize: A deserializer
|
||||
:param response: Server response to be deserialized.
|
||||
"""
|
||||
|
||||
def __init__(self, response, deserialize, *args):
|
||||
|
||||
model_name = 'TextAnalyticsError'
|
||||
self.error = deserialize(model_name, response)
|
||||
if self.error is None:
|
||||
self.error = deserialize.dependencies[model_name]()
|
||||
super(TextAnalyticsErrorException, self).__init__(response=response)
|
||||
|
|
@ -0,0 +1,936 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from msrest.serialization import Model
|
||||
from azure.core.exceptions import HttpResponseError
|
||||
|
||||
|
||||
class DetectedLanguage(Model):
|
||||
"""DetectedLanguage.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param name: Required. Long name of a detected language (e.g. English,
|
||||
French).
|
||||
:type name: str
|
||||
:param iso6391_name: Required. A two letter representation of the detected
|
||||
language according to the ISO 639-1 standard (e.g. en, fr).
|
||||
:type iso6391_name: str
|
||||
:param score: Required. A confidence score between 0 and 1. Scores close
|
||||
to 1 indicate 100% certainty that the identified language is true.
|
||||
:type score: float
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'name': {'required': True},
|
||||
'iso6391_name': {'required': True},
|
||||
'score': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'name': {'key': 'name', 'type': 'str'},
|
||||
'iso6391_name': {'key': 'iso6391Name', 'type': 'str'},
|
||||
'score': {'key': 'score', 'type': 'float'},
|
||||
}
|
||||
|
||||
def __init__(self, *, name: str, iso6391_name: str, score: float, **kwargs) -> None:
|
||||
super(DetectedLanguage, self).__init__(**kwargs)
|
||||
self.name = name
|
||||
self.iso6391_name = iso6391_name
|
||||
self.score = score
|
||||
|
||||
|
||||
class DocumentEntities(Model):
|
||||
"""DocumentEntities.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param entities: Required. Recognized entities in the document.
|
||||
:type entities: list[~azure.ai.textanalytics.models.Entity]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'entities': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'entities': {'key': 'entities', 'type': '[Entity]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, entities, statistics=None, **kwargs) -> None:
|
||||
super(DocumentEntities, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.entities = entities
|
||||
self.statistics = statistics
|
||||
|
||||
|
||||
class DocumentError(Model):
|
||||
"""DocumentError.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Document Id.
|
||||
:type id: str
|
||||
:param error: Required. Document Error.
|
||||
:type error: ~azure.ai.textanalytics.models.TextAnalyticsError
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'error': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'error': {'key': 'error', 'type': 'TextAnalyticsError'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, error, **kwargs) -> None:
|
||||
super(DocumentError, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.error = error
|
||||
|
||||
|
||||
class DocumentKeyPhrases(Model):
|
||||
"""DocumentKeyPhrases.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param key_phrases: Required. A list of representative words or phrases.
|
||||
The number of key phrases returned is proportional to the number of words
|
||||
in the input document.
|
||||
:type key_phrases: list[str]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'key_phrases': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'key_phrases': {'key': 'keyPhrases', 'type': '[str]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, key_phrases, statistics=None, **kwargs) -> None:
|
||||
super(DocumentKeyPhrases, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.key_phrases = key_phrases
|
||||
self.statistics = statistics
|
||||
|
||||
|
||||
class DocumentLanguage(Model):
|
||||
"""DocumentLanguage.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param detected_languages: Required. A list of extracted languages.
|
||||
:type detected_languages:
|
||||
list[~azure.ai.textanalytics.models.DetectedLanguage]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'detected_languages': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'detected_languages': {'key': 'detectedLanguages', 'type': '[DetectedLanguage]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, detected_languages, statistics=None, **kwargs) -> None:
|
||||
super(DocumentLanguage, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.detected_languages = detected_languages
|
||||
self.statistics = statistics
|
||||
|
||||
|
||||
class DocumentLinkedEntities(Model):
|
||||
"""DocumentLinkedEntities.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param entities: Required. Recognized well-known entities in the document.
|
||||
:type entities: list[~azure.ai.textanalytics.models.LinkedEntity]
|
||||
:param statistics: if showStats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'entities': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'entities': {'key': 'entities', 'type': '[LinkedEntity]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, entities, statistics=None, **kwargs) -> None:
|
||||
super(DocumentLinkedEntities, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.entities = entities
|
||||
self.statistics = statistics
|
||||
|
||||
|
||||
class DocumentSentiment(Model):
|
||||
"""DocumentSentiment.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param sentiment: Required. Predicted sentiment for document (Negative,
|
||||
Neutral, Positive, or Mixed). Possible values include: 'positive',
|
||||
'neutral', 'negative', 'mixed'
|
||||
:type sentiment: str or
|
||||
~azure.ai.textanalytics.models.DocumentSentimentValue
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.DocumentStatistics
|
||||
:param document_scores: Required. Document level sentiment confidence
|
||||
scores between 0 and 1 for each sentiment class.
|
||||
:type document_scores:
|
||||
~azure.ai.textanalytics.models.SentimentConfidenceScorePerLabel
|
||||
:param sentences: Required. Sentence level sentiment analysis.
|
||||
:type sentences: list[~azure.ai.textanalytics.models.SentenceSentiment]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'sentiment': {'required': True},
|
||||
'document_scores': {'required': True},
|
||||
'sentences': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'sentiment': {'key': 'sentiment', 'type': 'DocumentSentimentValue'},
|
||||
'statistics': {'key': 'statistics', 'type': 'DocumentStatistics'},
|
||||
'document_scores': {'key': 'documentScores', 'type': 'SentimentConfidenceScorePerLabel'},
|
||||
'sentences': {'key': 'sentences', 'type': '[SentenceSentiment]'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, sentiment, document_scores, sentences, statistics=None, **kwargs) -> None:
|
||||
super(DocumentSentiment, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.sentiment = sentiment
|
||||
self.statistics = statistics
|
||||
self.document_scores = document_scores
|
||||
self.sentences = sentences
|
||||
|
||||
|
||||
class DocumentStatistics(Model):
|
||||
"""if showStats=true was specified in the request this field will contain
|
||||
information about the document payload.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param characters_count: Required. Number of text elements recognized in
|
||||
the document.
|
||||
:type characters_count: int
|
||||
:param transactions_count: Required. Number of transactions for the
|
||||
document.
|
||||
:type transactions_count: int
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'characters_count': {'required': True},
|
||||
'transactions_count': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'characters_count': {'key': 'charactersCount', 'type': 'int'},
|
||||
'transactions_count': {'key': 'transactionsCount', 'type': 'int'},
|
||||
}
|
||||
|
||||
def __init__(self, *, characters_count: int, transactions_count: int, **kwargs) -> None:
|
||||
super(DocumentStatistics, self).__init__(**kwargs)
|
||||
self.characters_count = characters_count
|
||||
self.transactions_count = transactions_count
|
||||
|
||||
|
||||
class EntitiesResult(Model):
|
||||
"""EntitiesResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentEntities]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentEntities]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents, errors, model_version: str, statistics=None, **kwargs) -> None:
|
||||
super(EntitiesResult, self).__init__(**kwargs)
|
||||
self.documents = documents
|
||||
self.errors = errors
|
||||
self.statistics = statistics
|
||||
self.model_version = model_version
|
||||
|
||||
|
||||
class Entity(Model):
|
||||
"""Entity.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param text: Required. Entity text as appears in the request.
|
||||
:type text: str
|
||||
:param type: Required. Entity type, such as Person/Location/Org/SSN etc
|
||||
:type type: str
|
||||
:param subtype: Entity sub type, such as Age/Year/TimeRange etc
|
||||
:type subtype: str
|
||||
:param offset: Required. Start position (in Unicode characters) for the
|
||||
entity text.
|
||||
:type offset: int
|
||||
:param length: Required. Length (in Unicode characters) for the entity
|
||||
text.
|
||||
:type length: int
|
||||
:param score: Required. Confidence score between 0 and 1 of the extracted
|
||||
entity.
|
||||
:type score: float
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'text': {'required': True},
|
||||
'type': {'required': True},
|
||||
'offset': {'required': True},
|
||||
'length': {'required': True},
|
||||
'score': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'type': {'key': 'type', 'type': 'str'},
|
||||
'subtype': {'key': 'subtype', 'type': 'str'},
|
||||
'offset': {'key': 'offset', 'type': 'int'},
|
||||
'length': {'key': 'length', 'type': 'int'},
|
||||
'score': {'key': 'score', 'type': 'float'},
|
||||
}
|
||||
|
||||
def __init__(self, *, text: str, type: str, offset: int, length: int, score: float, subtype: str=None, **kwargs) -> None:
|
||||
super(Entity, self).__init__(**kwargs)
|
||||
self.text = text
|
||||
self.type = type
|
||||
self.subtype = subtype
|
||||
self.offset = offset
|
||||
self.length = length
|
||||
self.score = score
|
||||
|
||||
|
||||
class EntityLinkingResult(Model):
|
||||
"""EntityLinkingResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.DocumentLinkedEntities]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentLinkedEntities]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents, errors, model_version: str, statistics=None, **kwargs) -> None:
|
||||
super(EntityLinkingResult, self).__init__(**kwargs)
|
||||
self.documents = documents
|
||||
self.errors = errors
|
||||
self.statistics = statistics
|
||||
self.model_version = model_version
|
||||
|
||||
|
||||
class InnerError(Model):
|
||||
"""InnerError.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param code: Required. Error code. Possible values include:
|
||||
'invalidParameterValue', 'invalidRequestBodyFormat', 'emptyRequest',
|
||||
'missingInputRecords', 'invalidDocument', 'modelVersionIncorrect',
|
||||
'invalidDocumentBatch', 'unsupportedLanguageCode', 'invalidCountryHint'
|
||||
:type code: str or ~azure.ai.textanalytics.models.InnerErrorCodeValue
|
||||
:param message: Required. Error message.
|
||||
:type message: str
|
||||
:param details: Error details.
|
||||
:type details: dict[str, str]
|
||||
:param target: Error target.
|
||||
:type target: str
|
||||
:param inner_error: Inner error contains more specific information.
|
||||
:type inner_error: ~azure.ai.textanalytics.models.InnerError
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'code': {'required': True},
|
||||
'message': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'code': {'key': 'code', 'type': 'InnerErrorCodeValue'},
|
||||
'message': {'key': 'message', 'type': 'str'},
|
||||
'details': {'key': 'details', 'type': '{str}'},
|
||||
'target': {'key': 'target', 'type': 'str'},
|
||||
'inner_error': {'key': 'innerError', 'type': 'InnerError'},
|
||||
}
|
||||
|
||||
def __init__(self, *, code, message: str, details=None, target: str=None, inner_error=None, **kwargs) -> None:
|
||||
super(InnerError, self).__init__(**kwargs)
|
||||
self.code = code
|
||||
self.message = message
|
||||
self.details = details
|
||||
self.target = target
|
||||
self.inner_error = inner_error
|
||||
|
||||
|
||||
class KeyPhraseResult(Model):
|
||||
"""KeyPhraseResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentKeyPhrases]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentKeyPhrases]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents, errors, model_version: str, statistics=None, **kwargs) -> None:
|
||||
super(KeyPhraseResult, self).__init__(**kwargs)
|
||||
self.documents = documents
|
||||
self.errors = errors
|
||||
self.statistics = statistics
|
||||
self.model_version = model_version
|
||||
|
||||
|
||||
class LanguageBatchInput(Model):
|
||||
"""LanguageBatchInput.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required.
|
||||
:type documents: list[~azure.ai.textanalytics.models.LanguageInput]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[LanguageInput]'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents, **kwargs) -> None:
|
||||
super(LanguageBatchInput, self).__init__(**kwargs)
|
||||
self.documents = documents
|
||||
|
||||
|
||||
class LanguageInput(Model):
|
||||
"""LanguageInput.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param text: Required.
|
||||
:type text: str
|
||||
:param country_hint:
|
||||
:type country_hint: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'text': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'country_hint': {'key': 'countryHint', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, text: str, country_hint: str=None, **kwargs) -> None:
|
||||
super(LanguageInput, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.text = text
|
||||
self.country_hint = country_hint
|
||||
|
||||
|
||||
class LanguageResult(Model):
|
||||
"""LanguageResult.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Response by document
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentLanguage]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentLanguage]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents, errors, model_version: str, statistics=None, **kwargs) -> None:
|
||||
super(LanguageResult, self).__init__(**kwargs)
|
||||
self.documents = documents
|
||||
self.errors = errors
|
||||
self.statistics = statistics
|
||||
self.model_version = model_version
|
||||
|
||||
|
||||
class LinkedEntity(Model):
|
||||
"""LinkedEntity.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param name: Required. Entity Linking formal name.
|
||||
:type name: str
|
||||
:param matches: Required. List of instances this entity appears in the
|
||||
text.
|
||||
:type matches: list[~azure.ai.textanalytics.models.Match]
|
||||
:param language: Required. Language used in the data source.
|
||||
:type language: str
|
||||
:param id: Unique identifier of the recognized entity from the data
|
||||
source.
|
||||
:type id: str
|
||||
:param url: Required. URL for the entity's page from the data source.
|
||||
:type url: str
|
||||
:param data_source: Required. Data source used to extract entity linking,
|
||||
such as Wiki/Bing etc.
|
||||
:type data_source: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'name': {'required': True},
|
||||
'matches': {'required': True},
|
||||
'language': {'required': True},
|
||||
'url': {'required': True},
|
||||
'data_source': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'name': {'key': 'name', 'type': 'str'},
|
||||
'matches': {'key': 'matches', 'type': '[Match]'},
|
||||
'language': {'key': 'language', 'type': 'str'},
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'url': {'key': 'url', 'type': 'str'},
|
||||
'data_source': {'key': 'dataSource', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, name: str, matches, language: str, url: str, data_source: str, id: str=None, **kwargs) -> None:
|
||||
super(LinkedEntity, self).__init__(**kwargs)
|
||||
self.name = name
|
||||
self.matches = matches
|
||||
self.language = language
|
||||
self.id = id
|
||||
self.url = url
|
||||
self.data_source = data_source
|
||||
|
||||
|
||||
class Match(Model):
|
||||
"""Match.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param score: Required. If a well-known item is recognized, a decimal
|
||||
number denoting the confidence level between 0 and 1 will be returned.
|
||||
:type score: float
|
||||
:param text: Required. Entity text as appears in the request.
|
||||
:type text: str
|
||||
:param offset: Required. Start position (in Unicode characters) for the
|
||||
entity match text.
|
||||
:type offset: int
|
||||
:param length: Required. Length (in Unicode characters) for the entity
|
||||
match text.
|
||||
:type length: int
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'score': {'required': True},
|
||||
'text': {'required': True},
|
||||
'offset': {'required': True},
|
||||
'length': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'score': {'key': 'score', 'type': 'float'},
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'offset': {'key': 'offset', 'type': 'int'},
|
||||
'length': {'key': 'length', 'type': 'int'},
|
||||
}
|
||||
|
||||
def __init__(self, *, score: float, text: str, offset: int, length: int, **kwargs) -> None:
|
||||
super(Match, self).__init__(**kwargs)
|
||||
self.score = score
|
||||
self.text = text
|
||||
self.offset = offset
|
||||
self.length = length
|
||||
|
||||
|
||||
class MultiLanguageBatchInput(Model):
|
||||
"""Contains a set of input documents to be analyzed by the service.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. The set of documents to process as part of
|
||||
this batch.
|
||||
:type documents: list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[MultiLanguageInput]'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents, **kwargs) -> None:
|
||||
super(MultiLanguageBatchInput, self).__init__(**kwargs)
|
||||
self.documents = documents
|
||||
|
||||
|
||||
class MultiLanguageInput(Model):
|
||||
"""Contains an input document to be analyzed by the service.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param id: Required. A unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param text: Required. The input text to process.
|
||||
:type text: str
|
||||
:param language: (Optional) This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, use "en" for English as default.
|
||||
:type language: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'id': {'required': True},
|
||||
'text': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'id': {'key': 'id', 'type': 'str'},
|
||||
'text': {'key': 'text', 'type': 'str'},
|
||||
'language': {'key': 'language', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, id: str, text: str, language: str=None, **kwargs) -> None:
|
||||
super(MultiLanguageInput, self).__init__(**kwargs)
|
||||
self.id = id
|
||||
self.text = text
|
||||
self.language = language
|
||||
|
||||
|
||||
class RequestStatistics(Model):
|
||||
"""if showStats=true was specified in the request this field will contain
|
||||
information about the request payload.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents_count: Required. Number of documents submitted in the
|
||||
request.
|
||||
:type documents_count: int
|
||||
:param valid_documents_count: Required. Number of valid documents. This
|
||||
excludes empty, over-size limit or non-supported languages documents.
|
||||
:type valid_documents_count: int
|
||||
:param erroneous_documents_count: Required. Number of invalid documents.
|
||||
This includes empty, over-size limit or non-supported languages documents.
|
||||
:type erroneous_documents_count: int
|
||||
:param transactions_count: Required. Number of transactions for the
|
||||
request.
|
||||
:type transactions_count: long
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents_count': {'required': True},
|
||||
'valid_documents_count': {'required': True},
|
||||
'erroneous_documents_count': {'required': True},
|
||||
'transactions_count': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents_count': {'key': 'documentsCount', 'type': 'int'},
|
||||
'valid_documents_count': {'key': 'validDocumentsCount', 'type': 'int'},
|
||||
'erroneous_documents_count': {'key': 'erroneousDocumentsCount', 'type': 'int'},
|
||||
'transactions_count': {'key': 'transactionsCount', 'type': 'long'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents_count: int, valid_documents_count: int, erroneous_documents_count: int, transactions_count: int, **kwargs) -> None:
|
||||
super(RequestStatistics, self).__init__(**kwargs)
|
||||
self.documents_count = documents_count
|
||||
self.valid_documents_count = valid_documents_count
|
||||
self.erroneous_documents_count = erroneous_documents_count
|
||||
self.transactions_count = transactions_count
|
||||
|
||||
|
||||
class SentenceSentiment(Model):
|
||||
"""SentenceSentiment.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param sentiment: Required. The predicted Sentiment for the sentence.
|
||||
Possible values include: 'positive', 'neutral', 'negative'
|
||||
:type sentiment: str or
|
||||
~azure.ai.textanalytics.models.SentenceSentimentValue
|
||||
:param sentence_scores: Required. The sentiment confidence score between 0
|
||||
and 1 for the sentence for all classes.
|
||||
:type sentence_scores:
|
||||
~azure.ai.textanalytics.models.SentimentConfidenceScorePerLabel
|
||||
:param offset: Required. The sentence offset from the start of the
|
||||
document.
|
||||
:type offset: int
|
||||
:param length: Required. The length of the sentence by Unicode standard.
|
||||
:type length: int
|
||||
:param warnings: The warnings generated for the sentence.
|
||||
:type warnings: list[str]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'sentiment': {'required': True},
|
||||
'sentence_scores': {'required': True},
|
||||
'offset': {'required': True},
|
||||
'length': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'sentiment': {'key': 'sentiment', 'type': 'SentenceSentimentValue'},
|
||||
'sentence_scores': {'key': 'sentenceScores', 'type': 'SentimentConfidenceScorePerLabel'},
|
||||
'offset': {'key': 'offset', 'type': 'int'},
|
||||
'length': {'key': 'length', 'type': 'int'},
|
||||
'warnings': {'key': 'warnings', 'type': '[str]'},
|
||||
}
|
||||
|
||||
def __init__(self, *, sentiment, sentence_scores, offset: int, length: int, warnings=None, **kwargs) -> None:
|
||||
super(SentenceSentiment, self).__init__(**kwargs)
|
||||
self.sentiment = sentiment
|
||||
self.sentence_scores = sentence_scores
|
||||
self.offset = offset
|
||||
self.length = length
|
||||
self.warnings = warnings
|
||||
|
||||
|
||||
class SentimentConfidenceScorePerLabel(Model):
|
||||
"""Represents the confidence scores between 0 and 1 across all sentiment
|
||||
classes: positive, neutral, negative.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param positive: Required.
|
||||
:type positive: float
|
||||
:param neutral: Required.
|
||||
:type neutral: float
|
||||
:param negative: Required.
|
||||
:type negative: float
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'positive': {'required': True},
|
||||
'neutral': {'required': True},
|
||||
'negative': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'positive': {'key': 'positive', 'type': 'float'},
|
||||
'neutral': {'key': 'neutral', 'type': 'float'},
|
||||
'negative': {'key': 'negative', 'type': 'float'},
|
||||
}
|
||||
|
||||
def __init__(self, *, positive: float, neutral: float, negative: float, **kwargs) -> None:
|
||||
super(SentimentConfidenceScorePerLabel, self).__init__(**kwargs)
|
||||
self.positive = positive
|
||||
self.neutral = neutral
|
||||
self.negative = negative
|
||||
|
||||
|
||||
class SentimentResponse(Model):
|
||||
"""SentimentResponse.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param documents: Required. Sentiment analysis per document.
|
||||
:type documents: list[~azure.ai.textanalytics.models.DocumentSentiment]
|
||||
:param errors: Required. Errors by document id.
|
||||
:type errors: list[~azure.ai.textanalytics.models.DocumentError]
|
||||
:param statistics:
|
||||
:type statistics: ~azure.ai.textanalytics.models.RequestStatistics
|
||||
:param model_version: Required. This field indicates which model is used
|
||||
for scoring.
|
||||
:type model_version: str
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'documents': {'required': True},
|
||||
'errors': {'required': True},
|
||||
'model_version': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'documents': {'key': 'documents', 'type': '[DocumentSentiment]'},
|
||||
'errors': {'key': 'errors', 'type': '[DocumentError]'},
|
||||
'statistics': {'key': 'statistics', 'type': 'RequestStatistics'},
|
||||
'model_version': {'key': 'modelVersion', 'type': 'str'},
|
||||
}
|
||||
|
||||
def __init__(self, *, documents, errors, model_version: str, statistics=None, **kwargs) -> None:
|
||||
super(SentimentResponse, self).__init__(**kwargs)
|
||||
self.documents = documents
|
||||
self.errors = errors
|
||||
self.statistics = statistics
|
||||
self.model_version = model_version
|
||||
|
||||
|
||||
class TextAnalyticsError(Model):
|
||||
"""TextAnalyticsError.
|
||||
|
||||
All required parameters must be populated in order to send to Azure.
|
||||
|
||||
:param code: Required. Error code. Possible values include:
|
||||
'invalidRequest', 'invalidArgument', 'internalServerError',
|
||||
'serviceUnavailable'
|
||||
:type code: str or ~azure.ai.textanalytics.models.ErrorCodeValue
|
||||
:param message: Required. Error message.
|
||||
:type message: str
|
||||
:param target: Error target.
|
||||
:type target: str
|
||||
:param inner_error: Inner error contains more specific information.
|
||||
:type inner_error: ~azure.ai.textanalytics.models.InnerError
|
||||
:param details: Details about specific errors that led to this reported
|
||||
error.
|
||||
:type details: list[~azure.ai.textanalytics.models.TextAnalyticsError]
|
||||
"""
|
||||
|
||||
_validation = {
|
||||
'code': {'required': True},
|
||||
'message': {'required': True},
|
||||
}
|
||||
|
||||
_attribute_map = {
|
||||
'code': {'key': 'code', 'type': 'ErrorCodeValue'},
|
||||
'message': {'key': 'message', 'type': 'str'},
|
||||
'target': {'key': 'target', 'type': 'str'},
|
||||
'inner_error': {'key': 'innerError', 'type': 'InnerError'},
|
||||
'details': {'key': 'details', 'type': '[TextAnalyticsError]'},
|
||||
}
|
||||
|
||||
def __init__(self, *, code, message: str, target: str=None, inner_error=None, details=None, **kwargs) -> None:
|
||||
super(TextAnalyticsError, self).__init__(**kwargs)
|
||||
self.code = code
|
||||
self.message = message
|
||||
self.target = target
|
||||
self.inner_error = inner_error
|
||||
self.details = details
|
||||
|
||||
|
||||
class TextAnalyticsErrorException(HttpResponseError):
|
||||
"""Server responsed with exception of type: 'TextAnalyticsError'.
|
||||
|
||||
:param deserialize: A deserializer
|
||||
:param response: Server response to be deserialized.
|
||||
"""
|
||||
|
||||
def __init__(self, response, deserialize, *args):
|
||||
|
||||
model_name = 'TextAnalyticsError'
|
||||
self.error = deserialize(model_name, response)
|
||||
if self.error is None:
|
||||
self.error = deserialize.dependencies[model_name]()
|
||||
super(TextAnalyticsErrorException, self).__init__(response=response)
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class ErrorCodeValue(str, Enum):
|
||||
|
||||
invalid_request = "invalidRequest"
|
||||
invalid_argument = "invalidArgument"
|
||||
internal_server_error = "internalServerError"
|
||||
service_unavailable = "serviceUnavailable"
|
||||
|
||||
|
||||
class InnerErrorCodeValue(str, Enum):
|
||||
|
||||
invalid_parameter_value = "invalidParameterValue"
|
||||
invalid_request_body_format = "invalidRequestBodyFormat"
|
||||
empty_request = "emptyRequest"
|
||||
missing_input_records = "missingInputRecords"
|
||||
invalid_document = "invalidDocument"
|
||||
model_version_incorrect = "modelVersionIncorrect"
|
||||
invalid_document_batch = "invalidDocumentBatch"
|
||||
unsupported_language_code = "unsupportedLanguageCode"
|
||||
invalid_country_hint = "invalidCountryHint"
|
||||
|
||||
|
||||
class DocumentSentimentValue(str, Enum):
|
||||
|
||||
positive = "positive"
|
||||
neutral = "neutral"
|
||||
negative = "negative"
|
||||
mixed = "mixed"
|
||||
|
||||
|
||||
class SentenceSentimentValue(str, Enum):
|
||||
|
||||
positive = "positive"
|
||||
neutral = "neutral"
|
||||
negative = "negative"
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from ._text_analytics_client_operations import TextAnalyticsClientOperationsMixin
|
||||
|
||||
__all__ = [
|
||||
'TextAnalyticsClientOperationsMixin',
|
||||
]
|
||||
|
|
@ -0,0 +1,451 @@
|
|||
# coding=utf-8
|
||||
# --------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#
|
||||
# Code generated by Microsoft (R) AutoRest Code Generator.
|
||||
# Changes may cause incorrect behavior and will be lost if the code is
|
||||
# regenerated.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
from azure.core.exceptions import map_error
|
||||
from .. import models
|
||||
import uuid
|
||||
|
||||
|
||||
class TextAnalyticsClientOperationsMixin(object):
|
||||
|
||||
def entities_recognition_general(self, documents, model_version=None, show_stats=None, cls=None, **kwargs):
|
||||
"""Named Entity Recognition.
|
||||
|
||||
The API returns a list of general named entities in a given document.
|
||||
For the list of supported entity types, check <a
|
||||
href="https://aka.ms/taner">Supported Entity Types in Text Analytics
|
||||
API</a>. See the <a href="https://aka.ms/talangs">Supported languages
|
||||
in Text Analytics API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: EntitiesResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.EntitiesResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.entities_recognition_general.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('EntitiesResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
entities_recognition_general.metadata = {'url': '/entities/recognition/general'}
|
||||
|
||||
def entities_recognition_pii(self, documents, model_version=None, show_stats=None, cls=None, **kwargs):
|
||||
"""Entities containing personal information.
|
||||
|
||||
The API returns a list of entities with personal information (\"SSN\",
|
||||
\"Bank Account\" etc) in the document. For the list of supported entity
|
||||
types, check <a href="https://aka.ms/tanerpii">Supported Entity Types
|
||||
in Text Analytics API</a>. See the <a
|
||||
href="https://aka.ms/talangs">Supported languages in Text Analytics
|
||||
API</a> for the list of enabled languages.
|
||||
.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: EntitiesResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.EntitiesResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.entities_recognition_pii.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('EntitiesResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
entities_recognition_pii.metadata = {'url': '/entities/recognition/pii'}
|
||||
|
||||
def entities_linking(self, documents, model_version=None, show_stats=None, cls=None, **kwargs):
|
||||
"""Linked entities from a well-known knowledge base.
|
||||
|
||||
The API returns a list of recognized entities with links to a
|
||||
well-known knowledge base. See the <a
|
||||
href="https://aka.ms/talangs">Supported languages in Text Analytics
|
||||
API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: EntityLinkingResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.EntityLinkingResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.entities_linking.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('EntityLinkingResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
entities_linking.metadata = {'url': '/entities/linking'}
|
||||
|
||||
def key_phrases(self, documents, model_version=None, show_stats=None, cls=None, **kwargs):
|
||||
"""Key Phrases.
|
||||
|
||||
The API returns a list of strings denoting the key phrases in the input
|
||||
text. See the <a href="https://aka.ms/talangs">Supported languages in
|
||||
Text Analytics API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: KeyPhraseResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.KeyPhraseResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.key_phrases.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('KeyPhraseResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
key_phrases.metadata = {'url': '/keyPhrases'}
|
||||
|
||||
def languages(self, documents, model_version=None, show_stats=None, cls=None, **kwargs):
|
||||
"""Detect Language.
|
||||
|
||||
The API returns the detected language and a numeric score between 0 and
|
||||
1. Scores close to 1 indicate 100% certainty that the identified
|
||||
language is true. See the <a href="https://aka.ms/talangs">Supported
|
||||
languages in Text Analytics API</a> for the list of enabled languages.
|
||||
|
||||
:param documents:
|
||||
:type documents: list[~azure.ai.textanalytics.models.LanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: LanguageResult or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.LanguageResult
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.LanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.languages.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'LanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('LanguageResult', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
languages.metadata = {'url': '/languages'}
|
||||
|
||||
def sentiment(self, documents, model_version=None, show_stats=None, cls=None, **kwargs):
|
||||
"""Sentiment.
|
||||
|
||||
The API 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 the <a
|
||||
href="https://aka.ms/talangs">Supported languages in Text Analytics
|
||||
API</a> for the list of enabled languages.
|
||||
|
||||
:param documents: The set of documents to process as part of this
|
||||
batch.
|
||||
:type documents:
|
||||
list[~azure.ai.textanalytics.models.MultiLanguageInput]
|
||||
:param model_version: (Optional) This value indicates which model will
|
||||
be used for scoring. If a model-version is not specified, the API
|
||||
should default to the latest, non-preview version.
|
||||
:type model_version: str
|
||||
:param show_stats: (Optional) if set to true, response will contain
|
||||
input and document level statistics.
|
||||
:type show_stats: bool
|
||||
:param callable cls: A custom type or function that will be passed the
|
||||
direct response
|
||||
:return: SentimentResponse or the result of cls(response)
|
||||
:rtype: ~azure.ai.textanalytics.models.SentimentResponse
|
||||
:raises:
|
||||
:class:`TextAnalyticsErrorException<azure.ai.textanalytics.models.TextAnalyticsErrorException>`
|
||||
"""
|
||||
error_map = kwargs.pop('error_map', None)
|
||||
input = models.MultiLanguageBatchInput(documents=documents)
|
||||
|
||||
# Construct URL
|
||||
url = self.sentiment.metadata['url']
|
||||
path_format_arguments = {
|
||||
'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True)
|
||||
}
|
||||
url = self._client.format_url(url, **path_format_arguments)
|
||||
|
||||
# Construct parameters
|
||||
query_parameters = {}
|
||||
if model_version is not None:
|
||||
query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
|
||||
if show_stats is not None:
|
||||
query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
|
||||
|
||||
# Construct headers
|
||||
header_parameters = {}
|
||||
header_parameters['Accept'] = 'application/json'
|
||||
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
|
||||
if self._config.generate_client_request_id:
|
||||
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
|
||||
|
||||
# Construct body
|
||||
body_content = self._serialize.body(input, 'MultiLanguageBatchInput')
|
||||
|
||||
# Construct and send request
|
||||
request = self._client.post(url, query_parameters, header_parameters, body_content)
|
||||
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
|
||||
response = pipeline_response.http_response
|
||||
|
||||
if response.status_code not in [200]:
|
||||
map_error(status_code=response.status_code, response=response, error_map=error_map)
|
||||
raise models.TextAnalyticsErrorException(response, self._deserialize)
|
||||
|
||||
deserialized = None
|
||||
if response.status_code == 200:
|
||||
deserialized = self._deserialize('SentimentResponse', response)
|
||||
|
||||
if cls:
|
||||
return cls(response, deserialized, None)
|
||||
|
||||
return deserialized
|
||||
sentiment.metadata = {'url': '/sentiment'}
|
||||
|
|
@ -0,0 +1,610 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
from ._generated.models._models import LanguageInput
|
||||
from ._generated.models._models import MultiLanguageInput
|
||||
|
||||
|
||||
class DictMixin(object):
|
||||
|
||||
def __setitem__(self, key, item):
|
||||
self.__dict__[key] = item
|
||||
|
||||
def __getitem__(self, key):
|
||||
return self.__dict__[key]
|
||||
|
||||
def __repr__(self):
|
||||
return str(self)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.keys())
|
||||
|
||||
def __delitem__(self, key):
|
||||
self.__dict__[key] = None
|
||||
|
||||
def __eq__(self, other):
|
||||
"""Compare objects by comparing all attributes."""
|
||||
if isinstance(other, self.__class__):
|
||||
return self.__dict__ == other.__dict__
|
||||
return False
|
||||
|
||||
def __ne__(self, other):
|
||||
"""Compare objects by comparing all attributes."""
|
||||
return not self.__eq__(other)
|
||||
|
||||
def __str__(self):
|
||||
return str({k: v for k, v in self.__dict__.items() if not k.startswith('_')})
|
||||
|
||||
def has_key(self, k):
|
||||
return k in self.__dict__
|
||||
|
||||
def update(self, *args, **kwargs):
|
||||
return self.__dict__.update(*args, **kwargs)
|
||||
|
||||
def keys(self):
|
||||
return [k for k in self.__dict__ if not k.startswith('_')]
|
||||
|
||||
def values(self):
|
||||
return [v for k, v in self.__dict__.items() if not k.startswith('_')]
|
||||
|
||||
def items(self):
|
||||
return [(k, v) for k, v in self.__dict__.items() if not k.startswith('_')]
|
||||
|
||||
def get(self, key, default=None):
|
||||
if key in self.__dict__:
|
||||
return self.__dict__[key]
|
||||
return default
|
||||
|
||||
|
||||
class DetectedLanguage(DictMixin):
|
||||
"""DetectedLanguage.
|
||||
|
||||
:param name: Long name of a detected language (e.g. English,
|
||||
French).
|
||||
:type name: str
|
||||
:param iso6391_name: A two letter representation of the detected
|
||||
language according to the ISO 639-1 standard (e.g. en, fr).
|
||||
:type iso6391_name: str
|
||||
:param score: A confidence score between 0 and 1. Scores close
|
||||
to 1 indicate 100% certainty that the identified language is true.
|
||||
:type score: float
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.name = kwargs.get("name", None)
|
||||
self.iso6391_name = kwargs.get("iso6391_name", None)
|
||||
self.score = kwargs.get("score", None)
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, language):
|
||||
return cls(
|
||||
name=language.name, iso6391_name=language.iso6391_name, score=language.score
|
||||
)
|
||||
|
||||
|
||||
class RecognizeEntitiesResult(DictMixin):
|
||||
"""RecognizeEntitiesResult.
|
||||
|
||||
:param id: Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param entities: Recognized entities in the document.
|
||||
:type entities:
|
||||
list[~azure.ai.textanalytics.NamedEntity]
|
||||
:param statistics: If show_stats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics:
|
||||
~azure.ai.textanalytics.TextDocumentStatistics
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always False for an instance of a RecognizeEntitiesResult.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.id = kwargs.get("id", None)
|
||||
self.entities = kwargs.get("entities", None)
|
||||
self.statistics = kwargs.get("statistics", None)
|
||||
self.is_error = False
|
||||
|
||||
|
||||
class RecognizePiiEntitiesResult(DictMixin):
|
||||
"""RecognizePiiEntitiesResult.
|
||||
|
||||
:param id: Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param entities: Recognized entities in the document.
|
||||
:type entities:
|
||||
list[~azure.ai.textanalytics.NamedEntity]
|
||||
:param statistics: If show_stats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics:
|
||||
~azure.ai.textanalytics.TextDocumentStatistics
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always False for an instance of a RecognizePiiEntitiesResult.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.id = kwargs.get("id", None)
|
||||
self.entities = kwargs.get("entities", None)
|
||||
self.statistics = kwargs.get("statistics", None)
|
||||
self.is_error = False
|
||||
|
||||
|
||||
class DetectLanguageResult(DictMixin):
|
||||
"""DetectLanguageResult.
|
||||
|
||||
:param id: Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param detected_languages: A list of extracted languages.
|
||||
:type detected_languages:
|
||||
list[~azure.ai.textanalytics.DetectedLanguage]
|
||||
:param statistics: If show_stats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics:
|
||||
~azure.ai.textanalytics.TextDocumentStatistics
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always False for an instance of a DetectLanguageResult.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.id = kwargs.get("id", None)
|
||||
self.detected_languages = kwargs.get("detected_languages", None)
|
||||
self.statistics = kwargs.get("statistics", None)
|
||||
self.is_error = False
|
||||
|
||||
|
||||
class NamedEntity(DictMixin):
|
||||
"""NamedEntity.
|
||||
|
||||
:param text: Entity text as appears in the request.
|
||||
:type text: str
|
||||
:param type: Entity type, such as Person/Location/Org/SSN etc
|
||||
:type type: str
|
||||
:param subtype: Entity sub type, such as Age/Year/TimeRange etc
|
||||
:type subtype: str
|
||||
:param offset: Start position (in Unicode characters) for the
|
||||
entity text.
|
||||
:type offset: int
|
||||
:param length: Length (in Unicode characters) for the entity
|
||||
text.
|
||||
:type length: int
|
||||
:param score: Confidence score between 0 and 1 of the extracted
|
||||
entity.
|
||||
:type score: float
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.text = kwargs.get('text', None)
|
||||
self.type = kwargs.get('type', None)
|
||||
self.subtype = kwargs.get('subtype', None)
|
||||
self.offset = kwargs.get('offset', None)
|
||||
self.length = kwargs.get('length', None)
|
||||
self.score = kwargs.get('score', None)
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, entity):
|
||||
return cls(
|
||||
text=entity.text,
|
||||
type=entity.type,
|
||||
subtype=entity.subtype,
|
||||
offset=entity.offset,
|
||||
length=entity.length,
|
||||
score=entity.score,
|
||||
)
|
||||
|
||||
|
||||
class TextAnalyticsError(DictMixin):
|
||||
"""TextAnalyticsError.
|
||||
|
||||
:param code: Error code. Possible values include:
|
||||
'invalidRequest', 'invalidArgument', 'internalServerError',
|
||||
'serviceUnavailable'
|
||||
:type code: str
|
||||
:param message: Error message.
|
||||
:type message: str
|
||||
:param target: Error target.
|
||||
:type target: str
|
||||
:param inner_error: Inner error contains more specific information.
|
||||
:type inner_error: ~azure.ai.textanalytics.InnerError
|
||||
:param details: Details about specific errors that led to this reported
|
||||
error.
|
||||
:type details: list[~azure.ai.textanalytics.TextAnalyticsError]
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.code = kwargs.get('code', None)
|
||||
self.message = kwargs.get('message', None)
|
||||
self.target = kwargs.get('target', None)
|
||||
self.inner_error = kwargs.get('inner_error', None)
|
||||
self.details = kwargs.get('details', None)
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, err):
|
||||
return cls(
|
||||
code=err.code.value,
|
||||
message=err.message,
|
||||
target=err.target,
|
||||
inner_error=InnerError._from_generated(err.inner_error), # pylint: disable=protected-access
|
||||
details=err.details,
|
||||
)
|
||||
|
||||
|
||||
class InnerError(DictMixin):
|
||||
"""InnerError.
|
||||
|
||||
:param code: Error code. Possible values include:
|
||||
'invalidParameterValue', 'invalidRequestBodyFormat', 'emptyRequest',
|
||||
'missingInputRecords', 'invalidDocument', 'modelVersionIncorrect',
|
||||
'invalidDocumentBatch', 'unsupportedLanguageCode', 'invalidCountryHint'
|
||||
:type code: str
|
||||
:param message: Error message.
|
||||
:type message: str
|
||||
:param details: Error details.
|
||||
:type details: dict[str, str]
|
||||
:param target: Error target.
|
||||
:type target: str
|
||||
:param inner_error: Inner error contains more specific information.
|
||||
:type inner_error: ~azure.ai.textanalytics.InnerError
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.code = kwargs.get('code', None)
|
||||
self.message = kwargs.get('message', None)
|
||||
self.details = kwargs.get('details', None)
|
||||
self.target = kwargs.get('target', None)
|
||||
self.inner_error = kwargs.get('inner_error', None)
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, inner_err):
|
||||
return cls(
|
||||
code=inner_err.code.value,
|
||||
message=inner_err.message,
|
||||
details=inner_err.details,
|
||||
target=inner_err.target,
|
||||
inner_error=inner_err.inner_error
|
||||
)
|
||||
|
||||
|
||||
class ExtractKeyPhrasesResult(DictMixin):
|
||||
"""ExtractKeyPhrasesResult.
|
||||
|
||||
:param id: Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param key_phrases: A list of representative words or phrases.
|
||||
The number of key phrases returned is proportional to the number of words
|
||||
in the input document.
|
||||
:type key_phrases: list[str]
|
||||
:param statistics: If show_stats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics:
|
||||
~azure.ai.textanalytics.TextDocumentStatistics
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always False for an instance of a ExtractKeyPhrasesResult.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.id = kwargs.get("id", None)
|
||||
self.key_phrases = kwargs.get("key_phrases", None)
|
||||
self.statistics = kwargs.get("statistics", None)
|
||||
self.is_error = False
|
||||
|
||||
|
||||
class RecognizeLinkedEntitiesResult(DictMixin):
|
||||
"""RecognizeLinkedEntitiesResult.
|
||||
|
||||
:param id: Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param entities: Recognized well-known entities in the document.
|
||||
:type entities:
|
||||
list[~azure.ai.textanalytics.LinkedEntity]
|
||||
:param statistics: If show_stats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics:
|
||||
~azure.ai.textanalytics.TextDocumentStatistics
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always False for an instance of a RecognizeLinkedEntitiesResult.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.id = kwargs.get("id", None)
|
||||
self.entities = kwargs.get("entities", None)
|
||||
self.statistics = kwargs.get("statistics", None)
|
||||
self.is_error = False
|
||||
|
||||
|
||||
class AnalyzeSentimentResult(DictMixin):
|
||||
"""AnalyzeSentimentResult.
|
||||
|
||||
:param id: Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param sentiment: Predicted sentiment for document (Negative,
|
||||
Neutral, Positive, or Mixed). Possible values include: 'positive',
|
||||
'neutral', 'negative', 'mixed'
|
||||
:type sentiment: str
|
||||
:param statistics: If show_stats=true was specified in the request this
|
||||
field will contain information about the document payload.
|
||||
:type statistics:
|
||||
~azure.ai.textanalytics.TextDocumentStatistics
|
||||
:param document_scores: Document level sentiment confidence
|
||||
scores between 0 and 1 for each sentiment class.
|
||||
:type document_scores:
|
||||
~azure.ai.textanalytics.SentimentConfidenceScorePerLabel
|
||||
:param sentences: Sentence level sentiment analysis.
|
||||
:type sentences:
|
||||
list[~azure.ai.textanalytics.SentenceSentiment]
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always False for an instance of a AnalyzeSentimentResult.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.id = kwargs.get("id", None)
|
||||
self.sentiment = kwargs.get("sentiment", None)
|
||||
self.statistics = kwargs.get("statistics", None)
|
||||
self.document_scores = kwargs.get("document_scores", None)
|
||||
self.sentences = kwargs.get("sentences", None)
|
||||
self.is_error = False
|
||||
|
||||
|
||||
class TextDocumentStatistics(DictMixin):
|
||||
"""If showStats=true was specified in the request this field will contain
|
||||
information about the document payload.
|
||||
|
||||
:param character_count: Number of text elements recognized in
|
||||
the document.
|
||||
:type character_count: int
|
||||
:param transaction_count: Number of transactions for the
|
||||
document.
|
||||
:type transaction_count: int
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
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,
|
||||
)
|
||||
|
||||
|
||||
class DocumentError(DictMixin):
|
||||
"""DocumentError.
|
||||
|
||||
:param id: Document Id.
|
||||
:type id: str
|
||||
:param error: Document Error.
|
||||
:type error: ~azure.ai.textanalytics.TextAnalyticsError
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always True for an instance of a DocumentError.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.id = kwargs.get("id", None)
|
||||
self.error = kwargs.get("error", None)
|
||||
self.is_error = True
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, doc_err):
|
||||
return cls(
|
||||
id=doc_err.id,
|
||||
error=TextAnalyticsError._from_generated(doc_err.error), # pylint: disable=protected-access
|
||||
is_error=True
|
||||
)
|
||||
|
||||
|
||||
class DetectLanguageInput(LanguageInput):
|
||||
"""Contains an input document to be analyzed for type of language.
|
||||
|
||||
:param id: Required. Unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param text: Required. The input text to process.
|
||||
:type text: str
|
||||
:param country_hint: A country hint to help better detect
|
||||
the language of the text. Accepts two letter country codes
|
||||
specified by ISO 3166-1 alpha-2. Defaults to "US". Pass
|
||||
in the empty string "" to not use a country_hint.
|
||||
:type country_hint: str
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(DetectLanguageInput, self).__init__(**kwargs)
|
||||
self.id = kwargs.get("id", None)
|
||||
self.text = kwargs.get("text", None)
|
||||
self.country_hint = kwargs.get("country_hint", None)
|
||||
|
||||
|
||||
class LinkedEntity(DictMixin):
|
||||
"""LinkedEntity.
|
||||
|
||||
:param name: Entity Linking formal name.
|
||||
:type name: str
|
||||
:param matches: List of instances this entity appears in the
|
||||
text.
|
||||
:type matches:
|
||||
list[~azure.ai.textanalytics.LinkedEntityMatch]
|
||||
:param language: Language used in the data source.
|
||||
:type language: str
|
||||
:param id: Unique identifier of the recognized entity from the data
|
||||
source.
|
||||
:type id: str
|
||||
:param url: URL for the entity's page from the data source.
|
||||
:type url: str
|
||||
:param data_source: Data source used to extract entity linking,
|
||||
such as Wiki/Bing etc.
|
||||
:type data_source: str
|
||||
:param bool is_error: Boolean check for error item when iterating over list of
|
||||
results. Always False for an instance of a LinkedEntity.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.name = kwargs.get("name", None)
|
||||
self.matches = kwargs.get("matches", None)
|
||||
self.language = kwargs.get("language", None)
|
||||
self.id = kwargs.get("id", None)
|
||||
self.url = kwargs.get("url", None)
|
||||
self.data_source = kwargs.get("data_source", None)
|
||||
self.is_error = False
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, entity):
|
||||
return cls(
|
||||
name=entity.name,
|
||||
matches=[LinkedEntityMatch._from_generated(e) for e in entity.matches], # pylint: disable=protected-access
|
||||
language=entity.language,
|
||||
id=entity.id,
|
||||
url=entity.url,
|
||||
data_source=entity.data_source,
|
||||
)
|
||||
|
||||
|
||||
class LinkedEntityMatch(DictMixin):
|
||||
"""LinkedEntityMatch.
|
||||
|
||||
:param score: If a well-known item is recognized, a
|
||||
decimal number denoting the confidence level between 0 and 1 will be
|
||||
returned.
|
||||
:type score: float
|
||||
:param text: Entity text as appears in the request.
|
||||
:type text: str
|
||||
:param offset: Start position (in Unicode characters) for the
|
||||
entity match text.
|
||||
:type offset: int
|
||||
:param length: Length (in Unicode characters) for the entity
|
||||
match text.
|
||||
:type length: int
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.score = kwargs.get("score", None)
|
||||
self.text = kwargs.get("text", None)
|
||||
self.offset = kwargs.get("offset", None)
|
||||
self.length = kwargs.get("length", None)
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, match):
|
||||
return cls(
|
||||
score=match.score, text=match.text, offset=match.offset, length=match.length
|
||||
)
|
||||
|
||||
|
||||
class TextDocumentInput(MultiLanguageInput):
|
||||
"""Contains an input document to be analyzed by the service.
|
||||
|
||||
:param id: Required. A unique, non-empty document identifier.
|
||||
:type id: str
|
||||
:param text: Required. The input text to process.
|
||||
:type text: str
|
||||
:param language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:type language: str
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(TextDocumentInput, self).__init__(**kwargs)
|
||||
self.id = kwargs.get("id", None)
|
||||
self.text = kwargs.get("text", None)
|
||||
self.language = kwargs.get("language", None)
|
||||
|
||||
|
||||
class TextDocumentBatchStatistics(DictMixin):
|
||||
"""If show_stats=true was specified in the request this field will contain
|
||||
information about the request payload. Note: This object is not returned
|
||||
in the response and needs to be retrieved by a response hook.
|
||||
|
||||
:param document_count: Number of documents submitted in the request.
|
||||
:type document_count: int
|
||||
:param valid_document_count: Number of valid documents. This
|
||||
excludes empty, over-size limit or non-supported languages documents.
|
||||
:type valid_document_count: int
|
||||
:param erroneous_document_count: Number of invalid documents.
|
||||
This includes empty, over-size limit or non-supported languages documents.
|
||||
:type erroneous_document_count: int
|
||||
:param transaction_count: Number of transactions for the request.
|
||||
:type transaction_count: long
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
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"],
|
||||
)
|
||||
|
||||
|
||||
class SentenceSentiment(DictMixin):
|
||||
"""SentenceSentiment.
|
||||
|
||||
:param sentiment: The predicted Sentiment for the sentence.
|
||||
Possible values include: 'positive', 'neutral', 'negative'
|
||||
:type sentiment: str
|
||||
:param sentence_scores: The sentiment confidence score between 0
|
||||
and 1 for the sentence for all classes.
|
||||
:type sentence_scores:
|
||||
~azure.ai.textanalytics.SentimentConfidenceScorePerLabel
|
||||
:param offset: The sentence offset from the start of the
|
||||
document.
|
||||
:type offset: int
|
||||
:param length: The length of the sentence by Unicode standard.
|
||||
:type length: int
|
||||
:param warnings: The warnings generated for the sentence.
|
||||
:type warnings: list[str]
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.sentiment = kwargs.get("sentiment", None)
|
||||
self.sentence_scores = kwargs.get("sentence_scores", None)
|
||||
self.offset = kwargs.get("offset", None)
|
||||
self.length = kwargs.get("length", None)
|
||||
self.warnings = kwargs.get("warnings", None)
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, sentence):
|
||||
return cls(
|
||||
sentiment=sentence.sentiment.value,
|
||||
sentence_scores=SentimentConfidenceScorePerLabel._from_generated(sentence.sentence_scores), # pylint: disable=protected-access
|
||||
offset=sentence.offset,
|
||||
length=sentence.length,
|
||||
warnings=sentence.warnings,
|
||||
)
|
||||
|
||||
|
||||
class SentimentConfidenceScorePerLabel(DictMixin):
|
||||
"""Represents the confidence scores between 0 and 1 across all sentiment
|
||||
classes: positive, neutral, negative.
|
||||
|
||||
:param positive: Positive score.
|
||||
:type positive: float
|
||||
:param neutral: Neutral score.
|
||||
:type neutral: float
|
||||
:param negative: Negative score.
|
||||
:type negative: float
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.positive = kwargs.get('positive', None)
|
||||
self.neutral = kwargs.get('neutral', None)
|
||||
self.negative = kwargs.get('negative', None)
|
||||
|
||||
@classmethod
|
||||
def _from_generated(cls, score):
|
||||
return cls(
|
||||
positive=score.positive,
|
||||
neutral=score.neutral,
|
||||
negative=score.negative
|
||||
)
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
from azure.core.pipeline.policies import ContentDecodePolicy
|
||||
from azure.core.pipeline.policies import SansIOHTTPPolicy, HTTPPolicy
|
||||
from ._models import TextDocumentBatchStatistics
|
||||
|
||||
|
||||
class CognitiveServicesCredentialPolicy(SansIOHTTPPolicy):
|
||||
def __init__(self, cognitiveservices_key):
|
||||
self.cognitiveservices_key = cognitiveservices_key
|
||||
super(CognitiveServicesCredentialPolicy, self).__init__()
|
||||
|
||||
def on_request(self, request):
|
||||
request.http_request.headers[
|
||||
"Ocp-Apim-Subscription-Key"
|
||||
] = self.cognitiveservices_key
|
||||
request.http_request.headers["X-BingApis-SDK-Client"] = "Python-SDK"
|
||||
|
||||
|
||||
class TextAnalyticsResponseHook(HTTPPolicy):
|
||||
def __init__(self, **kwargs):
|
||||
self._response_callback = kwargs.get("response_hook")
|
||||
super(TextAnalyticsResponseHook, self).__init__()
|
||||
|
||||
def send(self, request):
|
||||
response_callback = request.context.options.pop("response_hook", self._response_callback)
|
||||
if response_callback:
|
||||
response = self.next.send(request)
|
||||
data = ContentDecodePolicy.deserialize_from_http_generics(response.http_response)
|
||||
statistics = data.get("statistics", None)
|
||||
model_version = data.get("modelVersion", None)
|
||||
|
||||
batch_statistics = TextDocumentBatchStatistics._from_generated(statistics) # pylint: disable=protected-access
|
||||
response.statistics = batch_statistics
|
||||
response.model_version = model_version
|
||||
response.raw_response = data
|
||||
response_callback(response)
|
||||
return response
|
||||
return self.next.send(request)
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
|
||||
import six
|
||||
|
||||
from ._models import (
|
||||
DetectLanguageInput,
|
||||
TextDocumentInput,
|
||||
)
|
||||
|
||||
|
||||
def _validate_single_input(text, hint, hint_value):
|
||||
"""Validate text input is string. Let service handle
|
||||
validity of hint and hint value.
|
||||
|
||||
:param str text: A single text document.
|
||||
:param str hint: Could be country_hint or language
|
||||
:param str hint_value: The user passed country_hint or language
|
||||
:return: A DetectLanguageInput or TextDocumentInput
|
||||
"""
|
||||
if isinstance(text, six.string_types):
|
||||
return [{"id": "0", hint: hint_value, "text": text}]
|
||||
raise TypeError("Text parameter must be string.")
|
||||
|
||||
|
||||
def _validate_batch_input(documents, hint, whole_batch_hint):
|
||||
"""Validate that batch input has either all string docs
|
||||
or dict/DetectLanguageInput/TextDocumentInput, not a mix of both.
|
||||
Assign country and language hints on a whole batch or per-item
|
||||
basis.
|
||||
|
||||
:param list documents: The input documents.
|
||||
:return: A list of DetectLanguageInput or TextDocumentInput
|
||||
"""
|
||||
if not isinstance(documents, list):
|
||||
raise TypeError("Documents parameter must be a list.")
|
||||
|
||||
if not all(isinstance(x, six.string_types) for x in documents):
|
||||
if not all(isinstance(x, (dict, TextDocumentInput, DetectLanguageInput)) for x in documents):
|
||||
raise TypeError("Mixing string and dictionary/object input unsupported.")
|
||||
|
||||
string_batch = []
|
||||
for idx, doc in enumerate(documents):
|
||||
if isinstance(doc, six.string_types):
|
||||
document = {"id": str(idx), hint: whole_batch_hint, "text": doc}
|
||||
string_batch.append(document)
|
||||
if isinstance(doc, dict):
|
||||
item_hint = doc.get(hint, None)
|
||||
if item_hint is None:
|
||||
doc[hint] = whole_batch_hint
|
||||
if isinstance(doc, TextDocumentInput):
|
||||
item_hint = doc.language
|
||||
if item_hint is None:
|
||||
doc.language = whole_batch_hint
|
||||
if isinstance(doc, DetectLanguageInput):
|
||||
item_hint = doc.country_hint
|
||||
if item_hint is None:
|
||||
doc.country_hint = whole_batch_hint
|
||||
|
||||
return string_batch if string_batch else documents
|
||||
|
|
@ -0,0 +1,162 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
import json
|
||||
from azure.core.pipeline.policies import ContentDecodePolicy
|
||||
from azure.core.exceptions import (
|
||||
HttpResponseError,
|
||||
ClientAuthenticationError,
|
||||
DecodeError,
|
||||
)
|
||||
from ._models import (
|
||||
RecognizeEntitiesResult,
|
||||
NamedEntity,
|
||||
TextDocumentStatistics,
|
||||
RecognizeLinkedEntitiesResult,
|
||||
RecognizePiiEntitiesResult,
|
||||
LinkedEntity,
|
||||
ExtractKeyPhrasesResult,
|
||||
AnalyzeSentimentResult,
|
||||
SentenceSentiment,
|
||||
DetectLanguageResult,
|
||||
DetectedLanguage,
|
||||
DocumentError,
|
||||
SentimentConfidenceScorePerLabel,
|
||||
TextAnalyticsError
|
||||
)
|
||||
|
||||
|
||||
def process_single_error(error):
|
||||
"""Configure and raise a DocumentError for single text operation errors.
|
||||
"""
|
||||
try:
|
||||
error_message = error.error["inner_error"]["message"]
|
||||
error_code = error.error["code"]
|
||||
error_message += "\nErrorCode:{}".format(error_code)
|
||||
except KeyError:
|
||||
raise HttpResponseError(message="There was an unknown error with the request.")
|
||||
error = HttpResponseError(message=error_message)
|
||||
error.error_code = error_code
|
||||
raise error
|
||||
|
||||
|
||||
def process_batch_error(error):
|
||||
"""Raise detailed error message for HttpResponseErrors
|
||||
"""
|
||||
raise_error = HttpResponseError
|
||||
if error.status_code == 401:
|
||||
raise_error = ClientAuthenticationError
|
||||
error_message = error.message
|
||||
error_code = error.status_code
|
||||
error_body = None
|
||||
|
||||
try:
|
||||
error_body = ContentDecodePolicy.deserialize_from_http_generics(error.response)
|
||||
except DecodeError:
|
||||
pass
|
||||
|
||||
try:
|
||||
if error_body is not None:
|
||||
error_resp = error_body["error"]
|
||||
if "innerError" in error_resp:
|
||||
error_resp = error_resp["innerError"]
|
||||
|
||||
error_message = error_resp["message"]
|
||||
error_code = error_resp["code"]
|
||||
error_message += "\nErrorCode:{}".format(error_code)
|
||||
except KeyError:
|
||||
raise HttpResponseError(message="There was an unknown error with the request.")
|
||||
|
||||
error = raise_error(message=error_message, response=error.response)
|
||||
error.error_code = error_code
|
||||
raise error
|
||||
|
||||
|
||||
def order_results(response, combined):
|
||||
"""Order results in the order the user passed them in.
|
||||
|
||||
:param response: Used to get the original documents in the request
|
||||
:param combined: A combined list of the results | errors
|
||||
:return: In order list of results | errors (if any)
|
||||
"""
|
||||
request = json.loads(response.request.body)["documents"]
|
||||
mapping = {item.id: item for item in combined}
|
||||
ordered_response = [mapping[item["id"]] for item in request]
|
||||
return ordered_response
|
||||
|
||||
|
||||
def prepare_result(func):
|
||||
def wrapper(response, obj, response_headers): # pylint: disable=unused-argument
|
||||
if obj.errors:
|
||||
combined = obj.documents + obj.errors
|
||||
results = order_results(response, combined)
|
||||
else:
|
||||
results = obj.documents
|
||||
|
||||
for idx, item in enumerate(results):
|
||||
if hasattr(item, "error"):
|
||||
results[idx] = DocumentError(id=item.id, error=TextAnalyticsError._from_generated(item.error)) # pylint: disable=protected-access
|
||||
else:
|
||||
results[idx] = func(item)
|
||||
return results
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
@prepare_result
|
||||
def language_result(language):
|
||||
return DetectLanguageResult(
|
||||
id=language.id,
|
||||
detected_languages=[DetectedLanguage._from_generated(l) for l in language.detected_languages], # pylint: disable=protected-access
|
||||
statistics=TextDocumentStatistics._from_generated(language.statistics), # pylint: disable=protected-access
|
||||
)
|
||||
|
||||
|
||||
@prepare_result
|
||||
def entities_result(entity):
|
||||
return RecognizeEntitiesResult(
|
||||
id=entity.id,
|
||||
entities=[NamedEntity._from_generated(e) for e in entity.entities], # pylint: disable=protected-access
|
||||
statistics=TextDocumentStatistics._from_generated(entity.statistics), # pylint: disable=protected-access
|
||||
)
|
||||
|
||||
|
||||
@prepare_result
|
||||
def pii_entities_result(entity):
|
||||
return RecognizePiiEntitiesResult(
|
||||
id=entity.id,
|
||||
entities=[NamedEntity._from_generated(e) for e in entity.entities], # pylint: disable=protected-access
|
||||
statistics=TextDocumentStatistics._from_generated(entity.statistics), # pylint: disable=protected-access
|
||||
)
|
||||
|
||||
|
||||
@prepare_result
|
||||
def linked_entities_result(entity):
|
||||
return RecognizeLinkedEntitiesResult(
|
||||
id=entity.id,
|
||||
entities=[LinkedEntity._from_generated(e) for e in entity.entities], # pylint: disable=protected-access
|
||||
statistics=TextDocumentStatistics._from_generated(entity.statistics), # pylint: disable=protected-access
|
||||
)
|
||||
|
||||
|
||||
@prepare_result
|
||||
def key_phrases_result(phrases):
|
||||
return ExtractKeyPhrasesResult(
|
||||
id=phrases.id,
|
||||
key_phrases=phrases.key_phrases,
|
||||
statistics=TextDocumentStatistics._from_generated(phrases.statistics), # pylint: disable=protected-access
|
||||
)
|
||||
|
||||
|
||||
@prepare_result
|
||||
def sentiment_result(sentiment):
|
||||
return AnalyzeSentimentResult(
|
||||
id=sentiment.id,
|
||||
sentiment=sentiment.sentiment.value,
|
||||
statistics=TextDocumentStatistics._from_generated(sentiment.statistics), # pylint: disable=protected-access
|
||||
document_scores=SentimentConfidenceScorePerLabel._from_generated(sentiment.document_scores), # pylint: disable=protected-access
|
||||
sentences=[SentenceSentiment._from_generated(s) for s in sentiment.sentences], # pylint: disable=protected-access
|
||||
)
|
||||
|
|
@ -0,0 +1,429 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
from typing import ( # pylint: disable=unused-import
|
||||
Union,
|
||||
Optional,
|
||||
Any,
|
||||
List,
|
||||
Dict,
|
||||
TYPE_CHECKING,
|
||||
)
|
||||
from azure.core.tracing.decorator import distributed_trace
|
||||
from ._generated.models import TextAnalyticsErrorException
|
||||
from ._generated._text_analytics_client import TextAnalyticsClient as TextAnalytics
|
||||
from ._base_client import TextAnalyticsClientBase
|
||||
from ._request_handlers import _validate_batch_input
|
||||
from ._response_handlers import (
|
||||
process_batch_error,
|
||||
entities_result,
|
||||
linked_entities_result,
|
||||
key_phrases_result,
|
||||
sentiment_result,
|
||||
language_result,
|
||||
pii_entities_result
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ._models import (
|
||||
DetectLanguageInput,
|
||||
TextDocumentInput,
|
||||
DetectLanguageResult,
|
||||
RecognizeEntitiesResult,
|
||||
RecognizeLinkedEntitiesResult,
|
||||
ExtractKeyPhrasesResult,
|
||||
AnalyzeSentimentResult,
|
||||
RecognizePiiEntitiesResult,
|
||||
DocumentError,
|
||||
)
|
||||
|
||||
|
||||
class TextAnalyticsClient(TextAnalyticsClientBase):
|
||||
"""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 cognitive services/text analytics subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_authentication.py
|
||||
:start-after: [START create_ta_client_with_key]
|
||||
:end-before: [END create_ta_client_with_key]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Creating the TextAnalyticsClient with endpoint and subscription key.
|
||||
|
||||
.. literalinclude:: ../samples/sample_authentication.py
|
||||
:start-after: [START create_ta_client_with_aad]
|
||||
:end-before: [END create_ta_client_with_aad]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Creating the TextAnalyticsClient with endpoint and token credential from Azure Active Directory.
|
||||
"""
|
||||
|
||||
def __init__(self, endpoint, credential, **kwargs):
|
||||
# type: (str, Any, Any) -> None
|
||||
super(TextAnalyticsClient, self).__init__(credential=credential, **kwargs)
|
||||
self._client = TextAnalytics(
|
||||
endpoint=endpoint, credentials=credential, pipeline=self._pipeline
|
||||
)
|
||||
|
||||
@distributed_trace
|
||||
def detect_languages( # type: ignore
|
||||
self,
|
||||
inputs, # type: Union[List[str], List[DetectLanguageInput], List[Dict[str, str]]]
|
||||
country_hint="US", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> List[Union[DetectLanguageResult, DocumentError]]
|
||||
"""Detects 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.
|
||||
|
||||
:param inputs: 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[DetectLanguageInput] or a list of dict representations of
|
||||
DetectLanguageInput, like `{"id": "1", "country_hint": "us", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.DetectLanguageInput]
|
||||
:param str country_hint: A country 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 empty string "".
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document
|
||||
level statistics.
|
||||
:return: The combined list of DetectLanguageResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.DetectLanguageResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_detect_languages.py
|
||||
:start-after: [START batch_detect_languages]
|
||||
:end-before: [END batch_detect_languages]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Detecting language in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "country_hint", country_hint)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return self._client.languages(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=language_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace
|
||||
def recognize_entities( # type: ignore
|
||||
self,
|
||||
inputs, # type: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]]
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> List[Union[RecognizeEntitiesResult, DocumentError]]
|
||||
"""Named Entity Recognition for a batch of documents.
|
||||
|
||||
Returns a list of general named entities in a given document.
|
||||
For a list of supported entity types, check: https://aka.ms/taner
|
||||
For a list of enabled languages, check: https://aka.ms/talangs
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document
|
||||
level statistics.
|
||||
:return: The combined list of RecognizeEntitiesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.RecognizeEntitiesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_recognize_entities.py
|
||||
:start-after: [START batch_recognize_entities]
|
||||
:end-before: [END batch_recognize_entities]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize entities in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return self._client.entities_recognition_general(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=entities_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace
|
||||
def recognize_pii_entities( # type: ignore
|
||||
self,
|
||||
inputs, # type: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]]
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> 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://aka.ms/talangs
|
||||
for the list of enabled languages.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of RecognizePiiEntitiesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.RecognizePiiEntitiesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_recognize_pii_entities.py
|
||||
:start-after: [START batch_recognize_pii_entities]
|
||||
:end-before: [END batch_recognize_pii_entities]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize personally identifiable information entities in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return self._client.entities_recognition_pii(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=pii_entities_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace
|
||||
def recognize_linked_entities( # type: ignore
|
||||
self,
|
||||
inputs, # type: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]]
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> List[Union[RecognizeLinkedEntitiesResult, DocumentError]]
|
||||
"""Recognize linked entities from a well-known knowledge base for a batch of documents.
|
||||
|
||||
Returns a list of recognized entities with links to a
|
||||
well-known knowledge base. See https://aka.ms/talangs for
|
||||
supported languages in Text Analytics API.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of RecognizeLinkedEntitiesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.RecognizeLinkedEntitiesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_recognize_linked_entities.py
|
||||
:start-after: [START batch_recognize_linked_entities]
|
||||
:end-before: [END batch_recognize_linked_entities]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize linked entities in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return self._client.entities_linking(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=linked_entities_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace
|
||||
def extract_key_phrases( # type: ignore
|
||||
self,
|
||||
inputs, # type: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]]
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> List[Union[ExtractKeyPhrasesResult, DocumentError]]
|
||||
"""Extract Key Phrases from a batch of documents.
|
||||
|
||||
Returns a list of strings denoting the key phrases in the input
|
||||
text. See https://aka.ms/talangs for the list of enabled
|
||||
languages.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of ExtractKeyPhrasesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.ExtractKeyPhrasesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_extract_key_phrases.py
|
||||
:start-after: [START batch_extract_key_phrases]
|
||||
:end-before: [END batch_extract_key_phrases]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Extract the key phrases in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return self._client.key_phrases(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=key_phrases_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace
|
||||
def analyze_sentiment( # type: ignore
|
||||
self,
|
||||
inputs, # type: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]]
|
||||
language="en", # type: Optional[str]
|
||||
**kwargs # type: Any
|
||||
):
|
||||
# type: (...) -> List[Union[AnalyzeSentimentResult, DocumentError]]
|
||||
"""Analyze sentiment for a batch of documents.
|
||||
|
||||
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://aka.ms/talangs for the list
|
||||
of enabled languages.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of AnalyzeSentimentResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.AnalyzeSentimentResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/sample_analyze_sentiment.py
|
||||
:start-after: [START batch_analyze_sentiment]
|
||||
:end-before: [END batch_analyze_sentiment]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Analyze sentiment in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return self._client.sentiment(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=sentiment_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
VERSION = "1.0.0b1"
|
||||
|
|
@ -0,0 +1,368 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
from typing import Any, Optional # pylint: disable=unused-import
|
||||
from .._request_handlers import _validate_single_input
|
||||
from .._response_handlers import process_single_error
|
||||
from ._text_analytics_client_async import TextAnalyticsClient
|
||||
from .._models import (
|
||||
DetectLanguageResult,
|
||||
RecognizeEntitiesResult,
|
||||
RecognizePiiEntitiesResult,
|
||||
RecognizeLinkedEntitiesResult,
|
||||
ExtractKeyPhrasesResult,
|
||||
AnalyzeSentimentResult,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'TextAnalyticsClient',
|
||||
'single_detect_language',
|
||||
'single_recognize_entities',
|
||||
'single_recognize_pii_entities',
|
||||
'single_recognize_linked_entities',
|
||||
'single_extract_key_phrases',
|
||||
'single_analyze_sentiment',
|
||||
]
|
||||
|
||||
|
||||
async def single_detect_language(
|
||||
endpoint: str,
|
||||
credential: str,
|
||||
input_text: str,
|
||||
country_hint: Optional[str] = "US",
|
||||
**kwargs: Any
|
||||
) -> DetectLanguageResult:
|
||||
"""Detect Language for a single document.
|
||||
|
||||
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.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to detect language from.
|
||||
:param str country_hint: The country hint for the text. Accepts two
|
||||
letter country codes specified by ISO 3166-1 alpha-2.
|
||||
Defaults to "US". If you don't want to use a country hint,
|
||||
pass the empty string "".
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of DetectLanguageResult.
|
||||
:rtype: ~azure.ai.textanalytics.DetectLanguageResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_single_detect_language_async.py
|
||||
:start-after: [START single_detect_language_async]
|
||||
:end-before: [END single_detect_language_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Detecting language in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "country_hint", country_hint)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
async with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = await client.detect_languages(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # DetectLanguageResult
|
||||
|
||||
|
||||
async def single_recognize_entities(
|
||||
endpoint: str,
|
||||
credential: str,
|
||||
input_text: str,
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> RecognizeEntitiesResult:
|
||||
"""Named Entity Recognition for a single document.
|
||||
|
||||
Returns a list of general named entities in a given document.
|
||||
For a list of supported entity types, check: https://aka.ms/taner
|
||||
For a list of enabled languages, check: https://aka.ms/talangs
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to recognize entities from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of RecognizeEntitiesResult.
|
||||
:rtype: ~azure.ai.textanalytics.RecognizeEntitiesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_single_recognize_entities_async.py
|
||||
:start-after: [START single_recognize_entities_async]
|
||||
:end-before: [END single_recognize_entities_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize entities in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
async with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = await client.recognize_entities(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # RecognizeEntitiesResult
|
||||
|
||||
|
||||
async def single_recognize_pii_entities(
|
||||
endpoint: str,
|
||||
credential: str,
|
||||
input_text: str,
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> RecognizePiiEntitiesResult:
|
||||
"""Recognize entities containing personal information for a single document.
|
||||
|
||||
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://aka.ms/talangs
|
||||
for the list of enabled languages.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to recognize entities from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of RecognizePiiEntitiesResult.
|
||||
:rtype: ~azure.ai.textanalytics.RecognizePiiEntitiesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_single_recognize_pii_entities_async.py
|
||||
:start-after: [START single_recognize_pii_entities_async]
|
||||
:end-before: [END single_recognize_pii_entities_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize personally identifiable information entities in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
async with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = await client.recognize_pii_entities(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # RecognizePiiEntitiesResult
|
||||
|
||||
|
||||
async def single_recognize_linked_entities(
|
||||
endpoint: str,
|
||||
credential: str,
|
||||
input_text: str,
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> RecognizeLinkedEntitiesResult:
|
||||
"""Recognize linked entities from a well-known knowledge base
|
||||
for a single document.
|
||||
|
||||
Returns a list of recognized entities with links to a
|
||||
well-known knowledge base. See https://aka.ms/talangs for
|
||||
supported languages in Text Analytics API.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to recognize entities from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of RecognizeLinkedEntitiesResult
|
||||
:rtype: ~azure.ai.textanalytics.RecognizeLinkedEntitiesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_single_recognize_linked_entities_async.py
|
||||
:start-after: [START single_recognize_linked_entities_async]
|
||||
:end-before: [END single_recognize_linked_entities_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize linked entities in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
async with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = await client.recognize_linked_entities(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # RecognizeLinkedEntitiesResult
|
||||
|
||||
|
||||
async def single_extract_key_phrases(
|
||||
endpoint: str,
|
||||
credential: str,
|
||||
input_text: str,
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> ExtractKeyPhrasesResult:
|
||||
"""Extract Key Phrases for a single document.
|
||||
|
||||
Returns a list of strings denoting the key phrases in the input
|
||||
text. See https://aka.ms/talangs for the list of enabled
|
||||
languages.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to extract key phrases from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of ExtractKeyPhrasesResult
|
||||
:rtype: ~azure.ai.textanalytics.ExtractKeyPhrasesResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_single_extract_key_phrases_async.py
|
||||
:start-after: [START single_extract_key_phrases_async]
|
||||
:end-before: [END single_extract_key_phrases_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Extract key phrases in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
async with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = await client.extract_key_phrases(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # ExtractKeyPhrasesResult
|
||||
|
||||
|
||||
async def single_analyze_sentiment(
|
||||
endpoint: str,
|
||||
credential: str,
|
||||
input_text: str,
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> AnalyzeSentimentResult:
|
||||
"""Analyze sentiment in a single document.
|
||||
|
||||
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://aka.ms/talangs for the list
|
||||
of enabled languages.
|
||||
|
||||
:param str endpoint: Supported Cognitive Services 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 cognitive services subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
:param str input_text: The single string to analyze sentiment from.
|
||||
:param str language: This is the 2 letter ISO 639-1 representation
|
||||
of a language. For example, use "en" for English; "es" for Spanish etc. If
|
||||
not set, uses "en" for English as default.
|
||||
:keyword bool show_stats: If set to true, response will contain
|
||||
document level statistics.
|
||||
: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.
|
||||
:return: An instance of AnalyzeSentimentResult
|
||||
:rtype: ~azure.ai.textanalytics.AnalyzeSentimentResult
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_single_analyze_sentiment_async.py
|
||||
:start-after: [START single_analyze_sentiment_async]
|
||||
:end-before: [END single_analyze_sentiment_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Analyze sentiment in a single string.
|
||||
"""
|
||||
doc = _validate_single_input(input_text, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
async with TextAnalyticsClient(endpoint, credential=credential, **kwargs) as client:
|
||||
response = await client.analyze_sentiment(
|
||||
inputs=doc,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
**kwargs
|
||||
)
|
||||
if response[0].is_error:
|
||||
return process_single_error(response[0]) # DocumentError
|
||||
return response[0] # AnalyzeSentimentResult
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
import six
|
||||
from azure.core.pipeline import AsyncPipeline
|
||||
from azure.core.configuration import Configuration
|
||||
from azure.core.pipeline.policies import (
|
||||
UserAgentPolicy,
|
||||
HeadersPolicy,
|
||||
ProxyPolicy,
|
||||
RequestIdPolicy,
|
||||
NetworkTraceLoggingPolicy,
|
||||
AsyncRetryPolicy,
|
||||
AsyncRedirectPolicy,
|
||||
AsyncBearerTokenCredentialPolicy,
|
||||
HttpLoggingPolicy,
|
||||
DistributedTracingPolicy
|
||||
)
|
||||
from .._policies import CognitiveServicesCredentialPolicy
|
||||
from ._policies_async import AsyncTextAnalyticsResponseHook
|
||||
from .._version import VERSION
|
||||
|
||||
|
||||
class AsyncTextAnalyticsClientBase(object):
|
||||
|
||||
def __init__(self, credential, **kwargs):
|
||||
self._pipeline = self._create_pipeline(credential, **kwargs)
|
||||
|
||||
def _create_pipeline(self, credential, **kwargs):
|
||||
credential_policy = None
|
||||
if credential is None:
|
||||
raise ValueError("Parameter 'credential' must not be None.")
|
||||
if hasattr(credential, "get_token"):
|
||||
credential_policy = AsyncBearerTokenCredentialPolicy(
|
||||
credential, "https://cognitiveservices.azure.com/.default"
|
||||
)
|
||||
elif isinstance(credential, six.string_types):
|
||||
credential_policy = CognitiveServicesCredentialPolicy(credential)
|
||||
elif credential is not None:
|
||||
raise TypeError("Unsupported credential: {}".format(credential))
|
||||
|
||||
config = self._create_configuration(**kwargs)
|
||||
config.transport = kwargs.get("transport") # type: ignore
|
||||
if not config.transport:
|
||||
try:
|
||||
from azure.core.pipeline.transport import AioHttpTransport
|
||||
except ImportError:
|
||||
raise ImportError("Unable to create async transport. Please check aiohttp is installed.")
|
||||
config.transport = AioHttpTransport(**kwargs)
|
||||
config.user_agent_policy.add_user_agent(
|
||||
'azsdk-python-azure-ai-textanalytics/{}'.format(VERSION)
|
||||
)
|
||||
|
||||
policies = [
|
||||
config.headers_policy,
|
||||
config.user_agent_policy,
|
||||
RequestIdPolicy(**kwargs),
|
||||
config.proxy_policy,
|
||||
AsyncRedirectPolicy(**kwargs),
|
||||
AsyncRetryPolicy(**kwargs),
|
||||
credential_policy,
|
||||
config.logging_policy,
|
||||
AsyncTextAnalyticsResponseHook(**kwargs),
|
||||
DistributedTracingPolicy(**kwargs),
|
||||
HttpLoggingPolicy(**kwargs)
|
||||
]
|
||||
return AsyncPipeline(config.transport, policies=policies)
|
||||
|
||||
def _create_configuration(self, **kwargs): # pylint: disable=no-self-use
|
||||
config = Configuration(**kwargs)
|
||||
config.user_agent_policy = kwargs.get('user_agent_policy') or UserAgentPolicy(**kwargs)
|
||||
config.headers_policy = kwargs.get('headers_policy') or HeadersPolicy(**kwargs)
|
||||
config.proxy_policy = kwargs.get('proxy_policy') or ProxyPolicy(**kwargs)
|
||||
config.logging_policy = kwargs.get('logging_policy') or NetworkTraceLoggingPolicy(**kwargs)
|
||||
return config
|
||||
|
||||
async def __aenter__(self):
|
||||
await self._client.__aenter__() # pylint: disable=no-member
|
||||
return self
|
||||
|
||||
async def __aexit__(self, *args):
|
||||
await self._client.__aexit__(*args) # pylint: disable=no-member
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
import asyncio
|
||||
from azure.core.pipeline.policies import ContentDecodePolicy
|
||||
from azure.core.pipeline.policies import AsyncHTTPPolicy
|
||||
from .._models import TextDocumentBatchStatistics
|
||||
|
||||
|
||||
class AsyncTextAnalyticsResponseHook(AsyncHTTPPolicy):
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self._response_callback = kwargs.get('response_hook')
|
||||
super(AsyncTextAnalyticsResponseHook, self).__init__()
|
||||
|
||||
async def send(self, request):
|
||||
response_callback = request.context.options.pop("response_hook", self._response_callback)
|
||||
if response_callback:
|
||||
response = await self.next.send(request)
|
||||
data = ContentDecodePolicy.deserialize_from_http_generics(response.http_response)
|
||||
statistics = data.get("statistics", None)
|
||||
model_version = data.get("modelVersion", None)
|
||||
|
||||
batch_statistics = TextDocumentBatchStatistics._from_generated(statistics) # pylint: disable=protected-access
|
||||
response.statistics = batch_statistics
|
||||
response.model_version = model_version
|
||||
response.raw_response = data
|
||||
if asyncio.iscoroutine(response_callback):
|
||||
await response_callback(response)
|
||||
else:
|
||||
response_callback(response)
|
||||
return response
|
||||
return await self.next.send(request)
|
||||
|
|
@ -0,0 +1,419 @@
|
|||
# coding=utf-8
|
||||
# ------------------------------------
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
|
||||
from typing import ( # pylint: disable=unused-import
|
||||
Union,
|
||||
Optional,
|
||||
Any,
|
||||
List,
|
||||
Dict,
|
||||
)
|
||||
from azure.core.tracing.decorator_async import distributed_trace_async
|
||||
from .._generated.models import TextAnalyticsErrorException
|
||||
from .._generated.aio._text_analytics_client_async import TextAnalyticsClient as TextAnalytics
|
||||
from ._base_client_async import AsyncTextAnalyticsClientBase
|
||||
from .._request_handlers import _validate_batch_input
|
||||
from .._response_handlers import (
|
||||
process_batch_error,
|
||||
entities_result,
|
||||
linked_entities_result,
|
||||
key_phrases_result,
|
||||
sentiment_result,
|
||||
language_result,
|
||||
pii_entities_result
|
||||
)
|
||||
from .._models import (
|
||||
DetectLanguageInput,
|
||||
TextDocumentInput,
|
||||
DetectLanguageResult,
|
||||
RecognizeEntitiesResult,
|
||||
RecognizeLinkedEntitiesResult,
|
||||
ExtractKeyPhrasesResult,
|
||||
AnalyzeSentimentResult,
|
||||
RecognizePiiEntitiesResult,
|
||||
DocumentError,
|
||||
)
|
||||
|
||||
|
||||
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 cognitive services/text analytics subscription key or a token credential
|
||||
from azure.identity.
|
||||
:type credential: str or ~azure.core.credentials.TokenCredential
|
||||
|
||||
.. 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 subscription 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__(self, endpoint, credential, **kwargs):
|
||||
# type: (str, Any, Any) -> None
|
||||
super(TextAnalyticsClient, self).__init__(credential=credential, **kwargs)
|
||||
self._client = TextAnalytics(
|
||||
endpoint=endpoint, credentials=credential, pipeline=self._pipeline
|
||||
)
|
||||
|
||||
@distributed_trace_async
|
||||
async def detect_languages( # type: ignore
|
||||
self,
|
||||
inputs: Union[List[str], List[DetectLanguageInput], List[Dict[str, str]]],
|
||||
country_hint: Optional[str] = "US",
|
||||
**kwargs: Any
|
||||
) -> List[Union[DetectLanguageResult, DocumentError]]:
|
||||
"""Detects 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.
|
||||
|
||||
:param inputs: 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[DetectLanguageInput] or a list of dict representations of
|
||||
DetectLanguageInput, like `{"id": "1", "country_hint": "us", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.DetectLanguageInput]
|
||||
:param str country_hint: A country 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 empty string "".
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document
|
||||
level statistics.
|
||||
:return: The combined list of DetectLanguageResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.DetectLanguageResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_detect_languages_async.py
|
||||
:start-after: [START batch_detect_languages_async]
|
||||
:end-before: [END batch_detect_languages_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Detecting language in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "country_hint", country_hint)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return await self._client.languages(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=language_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace_async
|
||||
async def recognize_entities( # type: ignore
|
||||
self,
|
||||
inputs: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> List[Union[RecognizeEntitiesResult, DocumentError]]:
|
||||
"""Named Entity Recognition for a batch of documents.
|
||||
|
||||
Returns a list of general named entities in a given document.
|
||||
For the list of supported entity types, check: https://aka.ms/taner
|
||||
For the list of enabled languages, check: https://aka.ms/talangs
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of RecognizeEntitiesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.RecognizeEntitiesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_recognize_entities_async.py
|
||||
:start-after: [START batch_recognize_entities_async]
|
||||
:end-before: [END batch_recognize_entities_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize entities in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return await self._client.entities_recognition_general(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=entities_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace_async
|
||||
async def recognize_pii_entities( # type: ignore
|
||||
self,
|
||||
inputs: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
|
||||
language: Optional[str] = "en",
|
||||
**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://aka.ms/talangs
|
||||
for the list of enabled languages.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of RecognizePiiEntitiesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.RecognizePiiEntitiesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_recognize_pii_entities_async.py
|
||||
:start-after: [START batch_recognize_pii_entities_async]
|
||||
:end-before: [END batch_recognize_pii_entities_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize personally identifiable information entities in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return await self._client.entities_recognition_pii(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=pii_entities_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace_async
|
||||
async def recognize_linked_entities( # type: ignore
|
||||
self,
|
||||
inputs: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> List[Union[RecognizeLinkedEntitiesResult, DocumentError]]:
|
||||
"""Recognize linked entities from a well-known knowledge base for a batch of documents.
|
||||
|
||||
Returns a list of recognized entities with links to a
|
||||
well-known knowledge base. See https://aka.ms/talangs for
|
||||
supported languages in Text Analytics API.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of RecognizeLinkedEntitiesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.RecognizeLinkedEntitiesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_recognize_linked_entities_async.py
|
||||
:start-after: [START batch_recognize_linked_entities_async]
|
||||
:end-before: [END batch_recognize_linked_entities_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Recognize linked entities in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return await self._client.entities_linking(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=linked_entities_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace_async
|
||||
async def extract_key_phrases( # type: ignore
|
||||
self,
|
||||
inputs: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> List[Union[ExtractKeyPhrasesResult, DocumentError]]:
|
||||
"""Extract Key Phrases from a batch of documents.
|
||||
|
||||
Returns a list of strings denoting the key phrases in the input
|
||||
text. See https://aka.ms/talangs for the list of enabled
|
||||
languages.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of ExtractKeyPhrasesResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.ExtractKeyPhrasesResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_extract_key_phrases_async.py
|
||||
:start-after: [START batch_extract_key_phrases_async]
|
||||
:end-before: [END batch_extract_key_phrases_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Extract the key phrases in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return await self._client.key_phrases(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=key_phrases_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
||||
@distributed_trace_async
|
||||
async def analyze_sentiment( # type: ignore
|
||||
self,
|
||||
inputs: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]],
|
||||
language: Optional[str] = "en",
|
||||
**kwargs: Any
|
||||
) -> List[Union[AnalyzeSentimentResult, DocumentError]]:
|
||||
"""Analyze sentiment for a batch of documents.
|
||||
|
||||
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://aka.ms/talangs for the list
|
||||
of enabled languages.
|
||||
|
||||
:param inputs: 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[TextDocumentInput] or a list of dict representations of
|
||||
TextDocumentInput, like `{"id": "1", "language": "en", "text": "hello world"}`.
|
||||
:type inputs:
|
||||
list[str] or list[~azure.ai.textanalytics.TextDocumentInput]
|
||||
:param 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.
|
||||
: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.
|
||||
:keyword bool show_stats: If set to true, response will contain document level statistics.
|
||||
:return: The combined list of AnalyzeSentimentResults and DocumentErrors in the order
|
||||
the original documents were passed in.
|
||||
:rtype: list[~azure.ai.textanalytics.AnalyzeSentimentResult,
|
||||
~azure.ai.textanalytics.DocumentError]
|
||||
:raises ~azure.core.exceptions.HttpResponseError:
|
||||
|
||||
.. admonition:: Example:
|
||||
|
||||
.. literalinclude:: ../samples/async_samples/sample_analyze_sentiment_async.py
|
||||
:start-after: [START batch_analyze_sentiment_async]
|
||||
:end-before: [END batch_analyze_sentiment_async]
|
||||
:language: python
|
||||
:dedent: 8
|
||||
:caption: Analyze sentiment in a batch of documents.
|
||||
"""
|
||||
docs = _validate_batch_input(inputs, "language", language)
|
||||
model_version = kwargs.pop("model_version", None)
|
||||
show_stats = kwargs.pop("show_stats", False)
|
||||
try:
|
||||
return await self._client.sentiment(
|
||||
documents=docs,
|
||||
model_version=model_version,
|
||||
show_stats=show_stats,
|
||||
cls=sentiment_result,
|
||||
**kwargs
|
||||
)
|
||||
except TextAnalyticsErrorException as error:
|
||||
process_batch_error(error)
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See LICENSE.txt in the project root for
|
||||
# license information.
|
||||
# -------------------------------------------------------------------------
|
||||
import sys
|
||||
|
||||
# Ignore async tests for Python < 3.5
|
||||
collect_ignore_glob = []
|
||||
if sys.version_info < (3, 5):
|
||||
collect_ignore_glob.append("tests/*_async.py")
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
-e ../../../tools/azure-sdk-tools
|
||||
-e ../../cognitiveservices/azure-mgmt-cognitiveservices
|
||||
../../core/azure-core
|
||||
aiohttp>=3.0; python_version >= '3.5'
|
||||
../azure-ai-nspkg
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
[mypy]
|
||||
python_version = 3.6
|
||||
warn_unused_configs = True
|
||||
ignore_missing_imports = True
|
||||
|
||||
# Per-module options:
|
||||
|
||||
[mypy-azure.ai.textanalytics._generated.*]
|
||||
ignore_errors = True
|
||||
|
||||
[mypy-azure.core.*]
|
||||
ignore_errors = True
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
---
|
||||
topic: sample
|
||||
languages:
|
||||
- python
|
||||
products:
|
||||
- azure
|
||||
- azure-ai-textanalytics
|
||||
---
|
||||
|
||||
# Samples for Azure Text Analytics client library for Python
|
||||
|
||||
These code samples show common scenario operations with the Azure Text Analytics client library.
|
||||
The async versions of the samples (the python sample files appended with `_async`) show asynchronous operations
|
||||
with Text Analytics and require Python 3.5 or later.
|
||||
|
||||
Authenticate the client with a Cognitive Services/Text Analytics subscription key or a token credential from [azure-identity](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/identity/azure-identity):
|
||||
* [sample_authentication.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_authentication.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_authentication_async.py))
|
||||
|
||||
In a batch of documents:
|
||||
* Detect languages: [sample_detect_languages.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_detect_languages.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_detect_languages_async.py))
|
||||
* Recognize entities: [sample_recognize_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_recognize_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_recognize_entities_async.py))
|
||||
* Recognize linked entities: [sample_recognize_linked_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_recognize_linked_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_recognize_linked_entities_async.py))
|
||||
* Recognize personally identifiable information: [sample_recognize_pii_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_recognize_pii_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_recognize_pii_entities_async.py))
|
||||
* Extract key phrases: [sample_extract_key_phrases.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_extract_key_phrases.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_extract_key_phrases_async.py))
|
||||
* Analyze sentiment: [sample_analyze_sentiment.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_analyze_sentiment.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_analyze_sentiment_async.py))
|
||||
|
||||
In a single string of text:
|
||||
* Detect language: [sample_single_detect_language.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_detect_language.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_detect_language_async.py))
|
||||
* Recognize entities: [sample_single_recognize_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_recognize_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_recognize_entities_async.py))
|
||||
* Recognize linked entities: [sample_single_recognize_linked_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_recognize_linked_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_recognize_linked_entities_async.py))
|
||||
* Recognize personally identifiable information: [sample_single_recognize_pii_entities.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_recognize_pii_entities.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_recognize_pii_entities_async.py))
|
||||
* Extract key phrases: [sample_single_extract_key_phrases.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_extract_key_phrases.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_extract_key_phrases_async.py))
|
||||
* Analyze sentiment: [sample_single_analyze_sentiment.py](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/sample_single_analyze_sentiment.py) ([async version](https://github.com/Azure/azure-sdk-for-python/tree/master/sdk/textanalytics/azure-ai-textanalytics/samples/async_samples/sample_single_analyze_sentiment_async.py))
|
||||
|
||||
## Prerequisites
|
||||
* Python 2.7, or 3.5 or later is required to use this package (3.5 or later if using asyncio)
|
||||
* You must have an [Azure subscription](https://azure.microsoft.com/free/) and an
|
||||
[Azure Text Analytics account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account?tabs=singleservice%2Cwindows) to run these samples.
|
||||
|
||||
## Setup
|
||||
|
||||
1. Install the Azure Text Analytics client library for Python with [pip](https://pypi.org/project/pip/):
|
||||
|
||||
```bash
|
||||
pip install azure-ai-textanalytics --pre
|
||||
```
|
||||
|
||||
2. Clone or download this sample repository
|
||||
3. Open the sample folder in Visual Studio Code or your IDE of choice.
|
||||
|
||||
## Running the samples
|
||||
|
||||
1. Open a terminal window and `cd` to the directory that the samples are saved in.
|
||||
2. Set the environment variables specified in the sample file you wish to run.
|
||||
3. Follow the usage described in the file, e.g. `python sample_detect_language.py`
|
||||
|
||||
## Next steps
|
||||
|
||||
Check out the [API reference documentation](https://westus.dev.cognitive.microsoft.com/docs/services/TextAnalytics-v3-0-Preview-1/operations/Languages) to learn more about
|
||||
what you can do with the Azure Text Analytics client library.
|
||||
|
|
@ -0,0 +1,166 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_analyze_sentiment_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to analyze sentiment in a batch of documents.
|
||||
An overall and per-sentence sentiment is returned.
|
||||
|
||||
USAGE:
|
||||
python sample_analyze_sentiment_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: I had the best day of my life.
|
||||
Overall sentiment: positive
|
||||
Overall scores: positive=0.999; neutral=0.999; negative=0.999
|
||||
|
||||
Sentence 1 sentiment: positive
|
||||
Sentence score: positive=0.999; neutral=0.999; negative=0.999
|
||||
Offset: 0
|
||||
Length: 30
|
||||
|
||||
------------------------------------
|
||||
Document text: This was a waste of my time. The speaker put me to sleep.
|
||||
Overall sentiment: negative
|
||||
Overall scores: positive=0.061; neutral=0.061; negative=0.061
|
||||
|
||||
Sentence 1 sentiment: negative
|
||||
Sentence score: positive=0.000; neutral=0.000; negative=0.000
|
||||
Offset: 0
|
||||
Length: 28
|
||||
|
||||
Sentence 2 sentiment: neutral
|
||||
Sentence score: positive=0.122; neutral=0.122; negative=0.122
|
||||
Offset: 29
|
||||
Length: 28
|
||||
|
||||
------------------------------------
|
||||
Document text: No tengo dinero ni nada que dar...
|
||||
Overall sentiment: negative
|
||||
Overall scores: positive=0.026; neutral=0.026; negative=0.026
|
||||
|
||||
Sentence 1 sentiment: negative
|
||||
Sentence score: positive=0.026; neutral=0.026; negative=0.026
|
||||
Offset: 0
|
||||
Length: 34
|
||||
|
||||
------------------------------------
|
||||
Document text: L'hôtel n'était pas très confortable. L'éclairage était trop sombre.
|
||||
Overall sentiment: negative
|
||||
Overall scores: positive=0.165; neutral=0.165; negative=0.165
|
||||
|
||||
Sentence 1 sentiment: negative
|
||||
Sentence score: positive=0.186; neutral=0.186; negative=0.186
|
||||
Offset: 0
|
||||
Length: 37
|
||||
|
||||
Sentence 2 sentiment: negative
|
||||
Sentence score: positive=0.143; neutral=0.143; negative=0.143
|
||||
Offset: 38
|
||||
Length: 30
|
||||
|
||||
------------------------------------
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class AnalyzeSentimentSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def analyze_sentiment_async(self):
|
||||
# [START batch_analyze_sentiment_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"I had the best day of my life.",
|
||||
"This was a waste of my time. The speaker put me to sleep.",
|
||||
"No tengo dinero ni nada que dar...",
|
||||
"L'hôtel n'était pas très confortable. L'éclairage était trop sombre."
|
||||
]
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.analyze_sentiment(documents)
|
||||
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("Document text: {}".format(documents[idx]))
|
||||
print("Overall sentiment: {}".format(doc.sentiment))
|
||||
# [END batch_analyze_sentiment_async]
|
||||
print("Overall scores: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f} \n".format(
|
||||
doc.document_scores.positive,
|
||||
doc.document_scores.neutral,
|
||||
doc.document_scores.negative,
|
||||
))
|
||||
for idx, sentence in enumerate(doc.sentences):
|
||||
print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
|
||||
print("Sentence score: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f}".format(
|
||||
sentence.sentence_scores.positive,
|
||||
sentence.sentence_scores.neutral,
|
||||
sentence.sentence_scores.negative,
|
||||
))
|
||||
print("Offset: {}".format(sentence.offset))
|
||||
print("Length: {}\n".format(sentence.length))
|
||||
print("------------------------------------")
|
||||
|
||||
async def alternative_scenario_analyze_sentiment_async(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "I had the best day of my life."},
|
||||
{"id": "1", "language": "en",
|
||||
"text": "This was a waste of my time. The speaker put me to sleep."},
|
||||
{"id": "2", "language": "es", "text": "No tengo dinero ni nada que dar..."},
|
||||
{"id": "3", "language": "fr",
|
||||
"text": "L'hôtel n'était pas très confortable. L'éclairage était trop sombre."}
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.analyze_sentiment(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
sample = AnalyzeSentimentSampleAsync()
|
||||
await sample.analyze_sentiment_async()
|
||||
await sample.alternative_scenario_analyze_sentiment_async()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_authentication_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to authenticate with the text analytics service.
|
||||
|
||||
There are two supported methods of authentication:
|
||||
1) Use a cognitive services subscription key
|
||||
2) Use a token credential to authenticate with Azure Active Directory
|
||||
|
||||
See more details about authentication here:
|
||||
https://docs.microsoft.com/azure/cognitive-services/authentication
|
||||
|
||||
USAGE:
|
||||
python sample_authentication_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
3) AZURE_CLIENT_ID - the client ID of your active directory application.
|
||||
4) AZURE_TENANT_ID - the tenant ID of your active directory application.
|
||||
5) AZURE_CLIENT_SECRET - the secret of your active directory application.
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class AuthenticationSampleAsync(object):
|
||||
|
||||
async def authentication_with_subscription_key_async(self):
|
||||
# [START create_ta_client_with_key_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
# [END create_ta_client_with_key_async]
|
||||
|
||||
doc = ["I need to take my cat to the veterinarian."]
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.detect_languages(doc)
|
||||
|
||||
print("Language detected: {}".format(result[0].detected_languages[0].name))
|
||||
print("Confidence score: {}".format(result[0].detected_languages[0].score))
|
||||
|
||||
async def authentication_with_azure_active_directory_async(self):
|
||||
"""DefaultAzureCredential will use the values from the environment
|
||||
variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
|
||||
"""
|
||||
# [START create_ta_client_with_aad_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
from azure.identity.aio import DefaultAzureCredential
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
credential = DefaultAzureCredential()
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, credential=credential)
|
||||
# [END create_ta_client_with_aad_async]
|
||||
|
||||
doc = ["I need to take my cat to the veterinarian."]
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.detect_languages(doc)
|
||||
|
||||
print("Language detected: {}".format(result[0].detected_languages[0].name))
|
||||
print("Confidence score: {}".format(result[0].detected_languages[0].score))
|
||||
|
||||
|
||||
async def main():
|
||||
sample = AuthenticationSampleAsync()
|
||||
await sample.authentication_with_subscription_key_async()
|
||||
await sample.authentication_with_azure_active_directory_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,126 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_detect_languages_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect language in a batch of different
|
||||
documents.
|
||||
|
||||
USAGE:
|
||||
python sample_detect_languages_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: This is a document written in English.
|
||||
Language detected: English
|
||||
ISO6391 name: en
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: Este es un document escrito en Español.
|
||||
Language detected: Spanish
|
||||
ISO6391 name: es
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: 这是一个用中文写的文件
|
||||
Language detected: Chinese_Simplified
|
||||
ISO6391 name: zh_chs
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: Dies ist ein Dokument in englischer Sprache.
|
||||
Language detected: German
|
||||
ISO6391 name: de
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: Detta är ett dokument skrivet på engelska.
|
||||
Language detected: Swedish
|
||||
ISO6391 name: sv
|
||||
Confidence score: 1.0
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class DetectLanguagesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def detect_languages_async(self):
|
||||
# [START batch_detect_languages_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"This document is written in English.",
|
||||
"Este es un document escrito en Español.",
|
||||
"这是一个用中文写的文件",
|
||||
"Dies ist ein Dokument in englischer Sprache.",
|
||||
"Detta är ett dokument skrivet på engelska."
|
||||
]
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.detect_languages(documents)
|
||||
|
||||
for idx, doc in enumerate(result):
|
||||
if not doc.is_error:
|
||||
print("Document text: {}".format(documents[idx]))
|
||||
print("Language detected: {}".format(doc.detected_languages[0].name))
|
||||
print("ISO6391 name: {}".format(doc.detected_languages[0].iso6391_name))
|
||||
print("Confidence score: {}\n".format(doc.detected_languages[0].score))
|
||||
if doc.is_error:
|
||||
print(doc.id, doc.error)
|
||||
# [END batch_detect_languages_async]
|
||||
|
||||
async def alternative_scenario_detect_languages_async(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[DetectLanguageInput] and supplying your own IDs and country hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "country_hint": "US", "text": "This is a document written in English."},
|
||||
{"id": "1", "country_hint": "MX", "text": "Este es un document escrito en Español."},
|
||||
{"id": "2", "country_hint": "CN", "text": "这是一个用中文写的文件"},
|
||||
{"id": "3", "country_hint": "DE", "text": "Dies ist ein Dokument in englischer Sprache."},
|
||||
{"id": "4", "country_hint": "SE", "text": "Detta är ett dokument skrivet på engelska."}
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.detect_languages(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
sample = DetectLanguagesSampleAsync()
|
||||
await sample.detect_languages_async()
|
||||
await sample.alternative_scenario_detect_languages_async()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,99 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_extract_key_phrases_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract key talking points from a batch of documents.
|
||||
|
||||
USAGE:
|
||||
python sample_extract_key_phrases_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
['King County', 'United States', 'Washington', 'city', 'miles', 'Redmond', 'Seattle']
|
||||
['cat', 'veterinarian']
|
||||
['South America', 'summer']
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class ExtractKeyPhrasesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def extract_key_phrases_async(self):
|
||||
# [START batch_extract_key_phrases_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle.",
|
||||
"I need to take my cat to the veterinarian.",
|
||||
"I will travel to South America in the summer.",
|
||||
]
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.extract_key_phrases(documents)
|
||||
|
||||
for doc in result:
|
||||
if not doc.is_error:
|
||||
print(doc.key_phrases)
|
||||
if doc.is_error:
|
||||
print(doc.id, doc.error)
|
||||
# [END batch_extract_key_phrases_async]
|
||||
|
||||
async def alternative_scenario_extract_key_phrases_async(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en",
|
||||
"text": "Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle."},
|
||||
{"id": "1", "language": "en",
|
||||
"text": "I need to take my cat to the veterinarian."},
|
||||
{"id": "2", "language": "en", "text": "I will travel to South America in the summer."}
|
||||
]
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.extract_key_phrases(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
sample = ExtractKeyPhrasesSampleAsync()
|
||||
await sample.extract_key_phrases_async()
|
||||
await sample.alternative_scenario_extract_key_phrases_async()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,110 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_recognize_entities_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize named entities in a batch of documents.
|
||||
|
||||
USAGE:
|
||||
python sample_recognize_entities_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: Microsoft was founded by Bill Gates and Paul Allen.
|
||||
Entity: Microsoft Type: Organization Confidence Score: 1.0
|
||||
Entity: Bill Gates Type: Person Confidence Score: 1.0
|
||||
Entity: Paul Allen Type: Person Confidence Score: 1.0
|
||||
|
||||
Document text: I had a wonderful trip to Seattle last week.
|
||||
Entity: Seattle Type: Location Confidence Score: 0.806
|
||||
Entity: last week Type: DateTime Confidence Score: 0.8
|
||||
|
||||
Document text: I visited the Space Needle 2 times.
|
||||
Entity: Space Needle Type: Organization Confidence Score: 0.922
|
||||
Entity: 2 Type: Quantity Confidence Score: 0.8
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class RecognizeEntitiesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def recognize_entities_async(self):
|
||||
# [START batch_recognize_entities_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"Microsoft was founded by Bill Gates and Paul Allen.",
|
||||
"I had a wonderful trip to Seattle last week.",
|
||||
"I visited the Space Needle 2 times.",
|
||||
]
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.recognize_entities(documents)
|
||||
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("\nDocument text: {}".format(documents[idx]))
|
||||
for entity in doc.entities:
|
||||
print("Entity: \t", entity.text, "\tType: \t", entity.type,
|
||||
"\tConfidence Score: \t", round(entity.score, 3))
|
||||
# [END batch_recognize_entities_async]
|
||||
|
||||
async def alternative_scenario_recognize_entities_async(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "Microsoft was founded by Bill Gates and Paul Allen."},
|
||||
{"id": "1", "language": "de", "text": "I had a wonderful trip to Seattle last week."},
|
||||
{"id": "2", "language": "es", "text": "I visited the Space Needle 2 times."},
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.recognize_entities(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
sample = RecognizeEntitiesSampleAsync()
|
||||
await sample.recognize_entities_async()
|
||||
await sample.alternative_scenario_recognize_entities_async()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,177 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_recognize_linked_entities_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect linked entities in a batch of documents.
|
||||
Each entity found in the document will have a link associated with it from a
|
||||
data source.
|
||||
|
||||
USAGE:
|
||||
python sample_recognize_linked_entities_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: Microsoft moved its headquarters to Bellevue, Washington in January 1979.
|
||||
|
||||
Entity: Bellevue, Washington
|
||||
Url: https://en.wikipedia.org/wiki/Bellevue,_Washington
|
||||
Data Source: Wikipedia
|
||||
Score: 0.698
|
||||
Offset: 36
|
||||
Length: 20
|
||||
|
||||
Entity: Microsoft
|
||||
Url: https://en.wikipedia.org/wiki/Microsoft
|
||||
Data Source: Wikipedia
|
||||
Score: 0.159
|
||||
Offset: 0
|
||||
Length: 9
|
||||
|
||||
Entity: January
|
||||
Url: https://en.wikipedia.org/wiki/January
|
||||
Data Source: Wikipedia
|
||||
Score: 0.007
|
||||
Offset: 60
|
||||
Length: 7
|
||||
|
||||
------------------------------------------
|
||||
Document text: Steve Ballmer stepped down as CEO of Microsoft and was succeeded by Satya Nadella.
|
||||
|
||||
Entity: Steve Ballmer
|
||||
Url: https://en.wikipedia.org/wiki/Steve_Ballmer
|
||||
Data Source: Wikipedia
|
||||
Score: 0.672
|
||||
Offset: 0
|
||||
Length: 13
|
||||
|
||||
Entity: Satya Nadella
|
||||
Url: https://en.wikipedia.org/wiki/Satya_Nadella
|
||||
Data Source: Wikipedia
|
||||
Score: 0.681
|
||||
Offset: 68
|
||||
Length: 13
|
||||
|
||||
Entity: Microsoft
|
||||
Url: https://en.wikipedia.org/wiki/Microsoft
|
||||
Data Source: Wikipedia
|
||||
Score: 0.164
|
||||
Offset: 37
|
||||
Length: 9
|
||||
|
||||
Entity: Chief executive officer
|
||||
Url: https://en.wikipedia.org/wiki/Chief_executive_officer
|
||||
Data Source: Wikipedia
|
||||
Score: 0.074
|
||||
Offset: 30
|
||||
Length: 3
|
||||
|
||||
------------------------------------------
|
||||
Document text: Microsoft superó a Apple Inc. como la compañía más valiosa que cotiza en bolsa en el mundo.
|
||||
|
||||
Entity: Apple Inc.
|
||||
Url: https://en.wikipedia.org/wiki/Apple_Inc.
|
||||
Data Source: Wikipedia
|
||||
Score: 0.677
|
||||
Offset: 19
|
||||
Length: 10
|
||||
|
||||
Entity: Microsoft
|
||||
Url: https://en.wikipedia.org/wiki/Microsoft
|
||||
Data Source: Wikipedia
|
||||
Score: 0.132
|
||||
Offset: 0
|
||||
Length: 9
|
||||
|
||||
------------------------------------------
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class RecognizeLinkedEntitiesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def recognize_linked_entities_async(self):
|
||||
# [START batch_recognize_linked_entities_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"Microsoft moved its headquarters to Bellevue, Washington in January 1979.",
|
||||
"Steve Ballmer stepped down as CEO of Microsoft and was succeeded by Satya Nadella.",
|
||||
"Microsoft superó a Apple Inc. como la compañía más valiosa que cotiza en bolsa en el mundo.",
|
||||
]
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.recognize_linked_entities(documents)
|
||||
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("Document text: {}\n".format(documents[idx]))
|
||||
for entity in doc.entities:
|
||||
print("Entity: {}".format(entity.name))
|
||||
print("Url: {}".format(entity.url))
|
||||
print("Data Source: {}".format(entity.data_source))
|
||||
for match in entity.matches:
|
||||
print("Score: {0:.3f}".format(match.score))
|
||||
print("Offset: {}".format(match.offset))
|
||||
print("Length: {}\n".format(match.length))
|
||||
print("------------------------------------------")
|
||||
# [END batch_recognize_linked_entities_async]
|
||||
|
||||
async def alternative_scenario_recognize_linked_entities_async(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "Microsoft moved its headquarters to Bellevue, Washington in January 1979."},
|
||||
{"id": "1", "language": "en", "text": "Steve Ballmer stepped down as CEO of Microsoft and was succeeded by Satya Nadella."},
|
||||
{"id": "2", "language": "es", "text": "Microsoft superó a Apple Inc. como la compañía más valiosa que cotiza en bolsa en el mundo."},
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.recognize_linked_entities(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
sample = RecognizeLinkedEntitiesSampleAsync()
|
||||
await sample.recognize_linked_entities_async()
|
||||
await sample.alternative_scenario_recognize_linked_entities_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_recognize_pii_entities_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize personally identifiable information in a batch of documents.
|
||||
|
||||
USAGE:
|
||||
python sample_recognize_pii_entities_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: The employee's SSN is 555-55-5555.
|
||||
Entity: 555-55-5555
|
||||
Type: U.S. Social Security Number (SSN)
|
||||
Confidence Score: 0.85
|
||||
|
||||
Document text: Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check.
|
||||
Entity: 111000025
|
||||
Type: ABA Routing Number
|
||||
Confidence Score: 0.75
|
||||
|
||||
Document text: Is 998.214.865-68 your Brazilian CPF number?
|
||||
Entity: 998.214.865-68
|
||||
Type: Brazil CPF Number
|
||||
Confidence Score: 0.85
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class RecognizePiiEntitiesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def recognize_pii_entities_async(self):
|
||||
# [START batch_recognize_pii_entities_async]
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"The employee's SSN is 555-55-5555.",
|
||||
"Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check.",
|
||||
"Is 998.214.865-68 your Brazilian CPF number?"
|
||||
]
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.recognize_pii_entities(documents)
|
||||
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("Document text: {}".format(documents[idx]))
|
||||
for entity in doc.entities:
|
||||
print("Entity: {}".format(entity.text))
|
||||
print("Type: {}".format(entity.type))
|
||||
print("Confidence Score: {}\n".format(entity.score))
|
||||
# [END batch_recognize_pii_entities_async]
|
||||
|
||||
async def alternative_scenario_recognize_pii_entities_async(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics.aio import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "The employee's SSN is 555-55-5555."},
|
||||
{"id": "1", "language": "en", "text": "Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check."},
|
||||
{"id": "2", "language": "en", "text": "Is 998.214.865-68 your Brazilian CPF number?"}
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
async with text_analytics_client:
|
||||
result = await text_analytics_client.recognize_pii_entities(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
async def main():
|
||||
sample = RecognizePiiEntitiesSampleAsync()
|
||||
await sample.recognize_pii_entities_async()
|
||||
await sample.alternative_scenario_recognize_pii_entities_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,94 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_analyze_sentiment_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to analyze sentiment from a single string.
|
||||
An overall sentiment and a per-sentence sentiment is returned.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_analyze_sentiment_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Overall sentiment: mixed
|
||||
Overall scores: positive=0.338; neutral=0.338; negative=0.338
|
||||
|
||||
Sentence 1 sentiment: neutral
|
||||
Offset: 0
|
||||
Length: 35
|
||||
Sentence score: positive=0.006; neutral=0.006; negative=0.006
|
||||
|
||||
Sentence 2 sentiment: positive
|
||||
Offset: 36
|
||||
Length: 32
|
||||
Sentence score: positive=0.999; neutral=0.999; negative=0.999
|
||||
|
||||
Sentence 3 sentiment: negative
|
||||
Offset: 69
|
||||
Length: 39
|
||||
Sentence score: positive=0.010; neutral=0.010; negative=0.010
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class SingleAnalyzeSentimentSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def analyze_sentiment_async(self):
|
||||
# [START single_analyze_sentiment_async]
|
||||
from azure.ai.textanalytics.aio import single_analyze_sentiment
|
||||
|
||||
text = "I visited the restaurant last week. The portions were very generous. However, I did not like what " \
|
||||
"I ordered."
|
||||
|
||||
result = await single_analyze_sentiment(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
print("Overall sentiment: {}".format(result.sentiment))
|
||||
print("Overall scores: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f} \n".format(
|
||||
result.document_scores.positive,
|
||||
result.document_scores.neutral,
|
||||
result.document_scores.negative,
|
||||
))
|
||||
|
||||
for idx, sentence in enumerate(result.sentences):
|
||||
print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
|
||||
print("Offset: {}".format(sentence.offset))
|
||||
print("Length: {}".format(sentence.length))
|
||||
print("Sentence score: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f} \n".format(
|
||||
sentence.sentence_scores.positive,
|
||||
sentence.sentence_scores.neutral,
|
||||
sentence.sentence_scores.negative,
|
||||
))
|
||||
# [END single_analyze_sentiment_async]
|
||||
|
||||
async def main():
|
||||
sample = SingleAnalyzeSentimentSampleAsync()
|
||||
await sample.analyze_sentiment_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,72 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_detect_language_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect the language of a single string.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_detect_language_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Language detected: English
|
||||
Confidence score: 1.0
|
||||
|
||||
Document Statistics:
|
||||
Text character count: 42
|
||||
Transactions count: 1
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class SingleDetectLanguageSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def detect_language_async(self):
|
||||
# [START single_detect_language_async]
|
||||
from azure.ai.textanalytics.aio import single_detect_language
|
||||
|
||||
text = "I need to take my cat to the veterinarian."
|
||||
|
||||
result = await single_detect_language(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
country_hint="US",
|
||||
show_stats=True
|
||||
)
|
||||
|
||||
print("Language detected: {}".format(result.detected_languages[0].name))
|
||||
print("Confidence score: {}\n".format(result.detected_languages[0].score))
|
||||
print("Document Statistics:")
|
||||
print("Text character count: {}".format(result.statistics.character_count))
|
||||
print("Transactions count: {}".format(result.statistics.transaction_count))
|
||||
# [END single_detect_language_async]
|
||||
|
||||
|
||||
async def main():
|
||||
sample = SingleDetectLanguageSampleAsync()
|
||||
await sample.detect_language_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,72 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_extract_key_phrases_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract key phrases from a single string.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_extract_key_phrases_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Key phrases found:
|
||||
|
||||
King County
|
||||
United States
|
||||
Washington
|
||||
city
|
||||
miles
|
||||
Redmond
|
||||
Seattle
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class SingleExtractKeyPhrasesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def extract_key_phrases_async(self):
|
||||
# [START single_extract_key_phrases_async]
|
||||
from azure.ai.textanalytics.aio import single_extract_key_phrases
|
||||
|
||||
text = "Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle."
|
||||
|
||||
result = await single_extract_key_phrases(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
print("Key phrases found:\n")
|
||||
for phrase in result.key_phrases:
|
||||
print(phrase)
|
||||
# [END single_extract_key_phrases_async]
|
||||
|
||||
|
||||
async def main():
|
||||
sample = SingleExtractKeyPhrasesSampleAsync()
|
||||
await sample.extract_key_phrases_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_recognize_entities_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize entities in a single string.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_recognize_entities_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Entity: Microsoft
|
||||
Type: Organization
|
||||
Confidence Score: 1.000
|
||||
|
||||
Entity: Bill Gates
|
||||
Type: Person
|
||||
Confidence Score: 1.000
|
||||
|
||||
Entity: Paul Allen
|
||||
Type: Person
|
||||
Confidence Score: 0.999
|
||||
|
||||
Entity: April 4, 1975
|
||||
Type: DateTime
|
||||
Confidence Score: 0.800
|
||||
|
||||
Entity: Altair
|
||||
Type: Organization
|
||||
Confidence Score: 0.525
|
||||
|
||||
Entity: 8800
|
||||
Type: Quantity
|
||||
Confidence Score: 0.80
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class SingleRecognizeEntitiesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def recognize_entities_async(self):
|
||||
# [START single_recognize_entities_async]
|
||||
from azure.ai.textanalytics.aio import single_recognize_entities
|
||||
|
||||
text = "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975," \
|
||||
" to develop and sell BASIC interpreters for the Altair 8800."
|
||||
|
||||
result = await single_recognize_entities(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
for entity in result.entities:
|
||||
print("Entity: {}".format(entity.text))
|
||||
print("Type: {}".format(entity.type))
|
||||
print("Confidence Score: {0:.3f}\n".format(entity.score))
|
||||
# [END single_recognize_entities_async]
|
||||
|
||||
|
||||
async def main():
|
||||
sample = SingleRecognizeEntitiesSampleAsync()
|
||||
await sample.recognize_entities_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,107 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_recognize_linked_entities_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize entities in a single string
|
||||
and returns links to the entities from a well-known knowledge base.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_recognize_linked_entities_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Entity: Easter Island
|
||||
Url: https://en.wikipedia.org/wiki/Easter_Island
|
||||
Data Source: Wikipedia
|
||||
|
||||
Where this entity appears in the text:
|
||||
Match 1: Easter Island
|
||||
Score: 0.272
|
||||
Offset: 0
|
||||
Length: 13
|
||||
|
||||
Match 2: Rapa Nui
|
||||
Score: 0.054
|
||||
Offset: 97
|
||||
Length: 8
|
||||
|
||||
Entity: Polynesia
|
||||
Url: https://en.wikipedia.org/wiki/Polynesia
|
||||
Data Source: Wikipedia
|
||||
|
||||
Where this entity appears in the text:
|
||||
Match 1: Polynesia
|
||||
Score: 0.163
|
||||
Offset: 67
|
||||
Length: 9
|
||||
|
||||
Entity: Chile
|
||||
Url: https://en.wikipedia.org/wiki/Chile
|
||||
Data Source: Wikipedia
|
||||
|
||||
Where this entity appears in the text:
|
||||
Match 1: Chilean
|
||||
Score: 0.045
|
||||
Offset: 17
|
||||
Length: 7
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class SingleRecognizeLinkedEntitiesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def recognize_linked_entities_async(self):
|
||||
# [START single_recognize_linked_entities_async]
|
||||
from azure.ai.textanalytics.aio import single_recognize_linked_entities
|
||||
|
||||
text = "Easter Island, a Chilean territory, is a remote volcanic island in Polynesia. " \
|
||||
"Its native name is Rapa Nui."
|
||||
|
||||
result = await single_recognize_linked_entities(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
for entity in result.entities:
|
||||
print("Entity: {}".format(entity.name))
|
||||
print("Url: {}".format(entity.url))
|
||||
print("Data Source: {}\n".format(entity.data_source))
|
||||
print("Where this entity appears in the text:")
|
||||
for idx, match in enumerate(entity.matches):
|
||||
print("Match {}: {}".format(idx+1, match.text))
|
||||
print("Score: {0:.3f}".format(match.score))
|
||||
print("Offset: {}".format(match.offset))
|
||||
print("Length: {}\n".format(match.length))
|
||||
# [END single_recognize_linked_entities_async]
|
||||
|
||||
|
||||
async def main():
|
||||
sample = SingleRecognizeLinkedEntitiesSampleAsync()
|
||||
await sample.recognize_linked_entities_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_recognize_pii_entities_async.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize personally identifiable
|
||||
information in a single string. For the list of supported entity types,
|
||||
see https://aka.ms/tanerpii
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_recognize_pii_entities_async.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Entity: 111000025
|
||||
Type: ABA Routing Number
|
||||
Confidence Score: 0.75
|
||||
|
||||
Entity: 555-55-5555
|
||||
Type: U.S. Social Security Number (SSN)
|
||||
Confidence Score: 0.85
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
|
||||
|
||||
class SingleRecognizePiiEntitiesSampleAsync(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
async def recognize_pii_entities_async(self):
|
||||
# [START single_recognize_pii_entities_async]
|
||||
from azure.ai.textanalytics.aio import single_recognize_pii_entities
|
||||
|
||||
text = "The employee's ABA number is 111000025 and his SSN is 555-55-5555."
|
||||
|
||||
result = await single_recognize_pii_entities(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
for entity in result.entities:
|
||||
print("Entity: {}".format(entity.text))
|
||||
print("Type: {}".format(entity.type))
|
||||
print("Confidence Score: {}\n".format(entity.score))
|
||||
# [END single_recognize_pii_entities_async]
|
||||
|
||||
|
||||
async def main():
|
||||
sample = SingleRecognizePiiEntitiesSampleAsync()
|
||||
await sample.recognize_pii_entities_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
loop = asyncio.get_event_loop()
|
||||
loop.run_until_complete(main())
|
||||
|
|
@ -0,0 +1,157 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_analyze_sentiment.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to analyze sentiment in a batch of documents.
|
||||
An overall and per-sentence sentiment is returned.
|
||||
|
||||
USAGE:
|
||||
python sample_analyze_sentiment.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: I had the best day of my life.
|
||||
Overall sentiment: positive
|
||||
Overall scores: positive=0.999; neutral=0.999; negative=0.999
|
||||
|
||||
Sentence 1 sentiment: positive
|
||||
Sentence score: positive=0.999; neutral=0.999; negative=0.999
|
||||
Offset: 0
|
||||
Length: 30
|
||||
|
||||
------------------------------------
|
||||
Document text: This was a waste of my time. The speaker put me to sleep.
|
||||
Overall sentiment: negative
|
||||
Overall scores: positive=0.061; neutral=0.061; negative=0.061
|
||||
|
||||
Sentence 1 sentiment: negative
|
||||
Sentence score: positive=0.000; neutral=0.000; negative=0.000
|
||||
Offset: 0
|
||||
Length: 28
|
||||
|
||||
Sentence 2 sentiment: neutral
|
||||
Sentence score: positive=0.122; neutral=0.122; negative=0.122
|
||||
Offset: 29
|
||||
Length: 28
|
||||
|
||||
------------------------------------
|
||||
Document text: No tengo dinero ni nada que dar...
|
||||
Overall sentiment: negative
|
||||
Overall scores: positive=0.026; neutral=0.026; negative=0.026
|
||||
|
||||
Sentence 1 sentiment: negative
|
||||
Sentence score: positive=0.026; neutral=0.026; negative=0.026
|
||||
Offset: 0
|
||||
Length: 34
|
||||
|
||||
------------------------------------
|
||||
Document text: L'hôtel n'était pas très confortable. L'éclairage était trop sombre.
|
||||
Overall sentiment: negative
|
||||
Overall scores: positive=0.165; neutral=0.165; negative=0.165
|
||||
|
||||
Sentence 1 sentiment: negative
|
||||
Sentence score: positive=0.186; neutral=0.186; negative=0.186
|
||||
Offset: 0
|
||||
Length: 37
|
||||
|
||||
Sentence 2 sentiment: negative
|
||||
Sentence score: positive=0.143; neutral=0.143; negative=0.143
|
||||
Offset: 38
|
||||
Length: 30
|
||||
|
||||
------------------------------------
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class AnalyzeSentimentSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def analyze_sentiment(self):
|
||||
# [START batch_analyze_sentiment]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"I had the best day of my life.",
|
||||
"This was a waste of my time. The speaker put me to sleep.",
|
||||
"No tengo dinero ni nada que dar...",
|
||||
"L'hôtel n'était pas très confortable. L'éclairage était trop sombre."
|
||||
]
|
||||
|
||||
result = text_analytics_client.analyze_sentiment(documents)
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("Document text: {}".format(documents[idx]))
|
||||
print("Overall sentiment: {}".format(doc.sentiment))
|
||||
# [END batch_analyze_sentiment]
|
||||
print("Overall scores: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f} \n".format(
|
||||
doc.document_scores.positive,
|
||||
doc.document_scores.neutral,
|
||||
doc.document_scores.negative,
|
||||
))
|
||||
for idx, sentence in enumerate(doc.sentences):
|
||||
print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
|
||||
print("Sentence score: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f}".format(
|
||||
sentence.sentence_scores.positive,
|
||||
sentence.sentence_scores.neutral,
|
||||
sentence.sentence_scores.negative,
|
||||
))
|
||||
print("Offset: {}".format(sentence.offset))
|
||||
print("Length: {}\n".format(sentence.length))
|
||||
print("------------------------------------")
|
||||
|
||||
def alternative_scenario_analyze_sentiment(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "I had the best day of my life."},
|
||||
{"id": "1", "language": "en",
|
||||
"text": "This was a waste of my time. The speaker put me to sleep."},
|
||||
{"id": "2", "language": "es", "text": "No tengo dinero ni nada que dar..."},
|
||||
{"id": "3", "language": "fr",
|
||||
"text": "L'hôtel n'était pas très confortable. L'éclairage était trop sombre."}
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
result = text_analytics_client.analyze_sentiment(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = AnalyzeSentimentSample()
|
||||
sample.analyze_sentiment()
|
||||
sample.alternative_scenario_analyze_sentiment()
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_authentication.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to authenticate with the text analytics service.
|
||||
|
||||
There are two supported methods of authentication:
|
||||
1) Use a cognitive services subscription key
|
||||
2) Use a token credential to authenticate with Azure Active Directory
|
||||
|
||||
See more details about authentication here:
|
||||
https://docs.microsoft.com/azure/cognitive-services/authentication
|
||||
|
||||
USAGE:
|
||||
python sample_authentication.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
3) AZURE_CLIENT_ID - the client ID of your active directory application.
|
||||
4) AZURE_TENANT_ID - the tenant ID of your active directory application.
|
||||
5) AZURE_CLIENT_SECRET - the secret of your active directory application.
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class AuthenticationSample(object):
|
||||
|
||||
def authentication_with_subscription_key(self):
|
||||
# [START create_ta_client_with_key]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, key)
|
||||
# [END create_ta_client_with_key]
|
||||
|
||||
doc = ["I need to take my cat to the veterinarian."]
|
||||
result = text_analytics_client.detect_languages(doc)
|
||||
|
||||
print("Language detected: {}".format(result[0].detected_languages[0].name))
|
||||
print("Confidence score: {}".format(result[0].detected_languages[0].score))
|
||||
|
||||
def authentication_with_azure_active_directory(self):
|
||||
"""DefaultAzureCredential will use the values from the environment
|
||||
variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET
|
||||
"""
|
||||
# [START create_ta_client_with_aad]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
from azure.identity import DefaultAzureCredential
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
credential = DefaultAzureCredential()
|
||||
|
||||
text_analytics_client = TextAnalyticsClient(endpoint, credential=credential)
|
||||
# [END create_ta_client_with_aad]
|
||||
|
||||
doc = ["I need to take my cat to the veterinarian."]
|
||||
result = text_analytics_client.detect_languages(doc)
|
||||
|
||||
print("Language detected: {}".format(result[0].detected_languages[0].name))
|
||||
print("Confidence score: {}".format(result[0].detected_languages[0].score))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = AuthenticationSample()
|
||||
sample.authentication_with_subscription_key()
|
||||
sample.authentication_with_azure_active_directory()
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_detect_languages.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect language in a batch of different
|
||||
documents.
|
||||
|
||||
USAGE:
|
||||
python sample_detect_languages.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: This is a document written in English.
|
||||
Language detected: English
|
||||
ISO6391 name: en
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: Este es un document escrito en Español.
|
||||
Language detected: Spanish
|
||||
ISO6391 name: es
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: 这是一个用中文写的文件
|
||||
Language detected: Chinese_Simplified
|
||||
ISO6391 name: zh_chs
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: Dies ist ein Dokument in englischer Sprache.
|
||||
Language detected: German
|
||||
ISO6391 name: de
|
||||
Confidence score: 1.0
|
||||
|
||||
Document text: Detta är ett dokument skrivet på engelska.
|
||||
Language detected: Swedish
|
||||
ISO6391 name: sv
|
||||
Confidence score: 1.0
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class DetectLanguagesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def detect_languages(self):
|
||||
# [START batch_detect_languages]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"This document is written in English.",
|
||||
"Este es un document escrito en Español.",
|
||||
"这是一个用中文写的文件",
|
||||
"Dies ist ein Dokument in englischer Sprache.",
|
||||
"Detta är ett dokument skrivet på engelska."
|
||||
]
|
||||
|
||||
result = text_analytics_client.detect_languages(documents)
|
||||
|
||||
for idx, doc in enumerate(result):
|
||||
if not doc.is_error:
|
||||
print("Document text: {}".format(documents[idx]))
|
||||
print("Language detected: {}".format(doc.detected_languages[0].name))
|
||||
print("ISO6391 name: {}".format(doc.detected_languages[0].iso6391_name))
|
||||
print("Confidence score: {}\n".format(doc.detected_languages[0].score))
|
||||
if doc.is_error:
|
||||
print(doc.id, doc.error)
|
||||
# [END batch_detect_languages]
|
||||
|
||||
def alternative_scenario_detect_languages(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[DetectLanguageInput] and supplying your own IDs and country hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "country_hint": "US", "text": "This is a document written in English."},
|
||||
{"id": "1", "country_hint": "MX", "text": "Este es un document escrito en Español."},
|
||||
{"id": "2", "country_hint": "CN", "text": "这是一个用中文写的文件"},
|
||||
{"id": "3", "country_hint": "DE", "text": "Dies ist ein Dokument in englischer Sprache."},
|
||||
{"id": "4", "country_hint": "SE", "text": "Detta är ett dokument skrivet på engelska."}
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
result = text_analytics_client.detect_languages(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = DetectLanguagesSample()
|
||||
sample.detect_languages()
|
||||
sample.alternative_scenario_detect_languages()
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_extract_key_phrases.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract key talking points from a batch of documents.
|
||||
|
||||
USAGE:
|
||||
python sample_extract_key_phrases.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
['King County', 'United States', 'Washington', 'city', 'miles', 'Redmond', 'Seattle']
|
||||
['cat', 'veterinarian']
|
||||
['South America', 'summer']
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class ExtractKeyPhrasesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def extract_key_phrases(self):
|
||||
# [START batch_extract_key_phrases]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle.",
|
||||
"I need to take my cat to the veterinarian.",
|
||||
"I will travel to South America in the summer.",
|
||||
]
|
||||
|
||||
result = text_analytics_client.extract_key_phrases(documents)
|
||||
for doc in result:
|
||||
if not doc.is_error:
|
||||
print(doc.key_phrases)
|
||||
if doc.is_error:
|
||||
print(doc.id, doc.error)
|
||||
# [END batch_extract_key_phrases]
|
||||
|
||||
def alternative_scenario_extract_key_phrases(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en",
|
||||
"text": "Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle."},
|
||||
{"id": "1", "language": "en",
|
||||
"text": "I need to take my cat to the veterinarian."},
|
||||
{"id": "2", "language": "en", "text": "I will travel to South America in the summer."}
|
||||
]
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
result = text_analytics_client.extract_key_phrases(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = ExtractKeyPhrasesSample()
|
||||
sample.extract_key_phrases()
|
||||
sample.alternative_scenario_extract_key_phrases()
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_recognize_entities.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize named entities in a batch of documents.
|
||||
|
||||
USAGE:
|
||||
python sample_recognize_entities.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: Microsoft was founded by Bill Gates and Paul Allen.
|
||||
Entity: Microsoft Type: Organization Confidence Score: 1.0
|
||||
Entity: Bill Gates Type: Person Confidence Score: 1.0
|
||||
Entity: Paul Allen Type: Person Confidence Score: 1.0
|
||||
|
||||
Document text: I had a wonderful trip to Seattle last week.
|
||||
Entity: Seattle Type: Location Confidence Score: 0.806
|
||||
Entity: last week Type: DateTime Confidence Score: 0.8
|
||||
|
||||
Document text: I visited the Space Needle 2 times.
|
||||
Entity: Space Needle Type: Organization Confidence Score: 0.922
|
||||
Entity: 2 Type: Quantity Confidence Score: 0.8
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class RecognizeEntitiesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def recognize_entities(self):
|
||||
# [START batch_recognize_entities]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"Microsoft was founded by Bill Gates and Paul Allen.",
|
||||
"I had a wonderful trip to Seattle last week.",
|
||||
"I visited the Space Needle 2 times.",
|
||||
]
|
||||
|
||||
result = text_analytics_client.recognize_entities(documents)
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("\nDocument text: {}".format(documents[idx]))
|
||||
for entity in doc.entities:
|
||||
print("Entity: \t", entity.text, "\tType: \t", entity.type,
|
||||
"\tConfidence Score: \t", round(entity.score, 3))
|
||||
# [END batch_recognize_entities]
|
||||
|
||||
def alternative_scenario_recognize_entities(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "Microsoft was founded by Bill Gates and Paul Allen."},
|
||||
{"id": "1", "language": "de", "text": "I had a wonderful trip to Seattle last week."},
|
||||
{"id": "2", "language": "es", "text": "I visited the Space Needle 2 times."},
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
result = text_analytics_client.recognize_entities(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = RecognizeEntitiesSample()
|
||||
sample.recognize_entities()
|
||||
sample.alternative_scenario_recognize_entities()
|
||||
|
|
@ -0,0 +1,169 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_recognize_linked_entities.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect linked entities in a batch of documents.
|
||||
Each entity found in the document will have a link associated with it from a
|
||||
data source.
|
||||
|
||||
USAGE:
|
||||
python sample_recognize_linked_entities.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: Microsoft moved its headquarters to Bellevue, Washington in January 1979.
|
||||
|
||||
Entity: Bellevue, Washington
|
||||
Url: https://en.wikipedia.org/wiki/Bellevue,_Washington
|
||||
Data Source: Wikipedia
|
||||
Score: 0.698
|
||||
Offset: 36
|
||||
Length: 20
|
||||
|
||||
Entity: Microsoft
|
||||
Url: https://en.wikipedia.org/wiki/Microsoft
|
||||
Data Source: Wikipedia
|
||||
Score: 0.159
|
||||
Offset: 0
|
||||
Length: 9
|
||||
|
||||
Entity: January
|
||||
Url: https://en.wikipedia.org/wiki/January
|
||||
Data Source: Wikipedia
|
||||
Score: 0.007
|
||||
Offset: 60
|
||||
Length: 7
|
||||
|
||||
------------------------------------------
|
||||
Document text: Steve Ballmer stepped down as CEO of Microsoft and was succeeded by Satya Nadella.
|
||||
|
||||
Entity: Steve Ballmer
|
||||
Url: https://en.wikipedia.org/wiki/Steve_Ballmer
|
||||
Data Source: Wikipedia
|
||||
Score: 0.672
|
||||
Offset: 0
|
||||
Length: 13
|
||||
|
||||
Entity: Satya Nadella
|
||||
Url: https://en.wikipedia.org/wiki/Satya_Nadella
|
||||
Data Source: Wikipedia
|
||||
Score: 0.681
|
||||
Offset: 68
|
||||
Length: 13
|
||||
|
||||
Entity: Microsoft
|
||||
Url: https://en.wikipedia.org/wiki/Microsoft
|
||||
Data Source: Wikipedia
|
||||
Score: 0.164
|
||||
Offset: 37
|
||||
Length: 9
|
||||
|
||||
Entity: Chief executive officer
|
||||
Url: https://en.wikipedia.org/wiki/Chief_executive_officer
|
||||
Data Source: Wikipedia
|
||||
Score: 0.074
|
||||
Offset: 30
|
||||
Length: 3
|
||||
|
||||
------------------------------------------
|
||||
Document text: Microsoft superó a Apple Inc. como la compañía más valiosa que cotiza en bolsa en el mundo.
|
||||
|
||||
Entity: Apple Inc.
|
||||
Url: https://en.wikipedia.org/wiki/Apple_Inc.
|
||||
Data Source: Wikipedia
|
||||
Score: 0.677
|
||||
Offset: 19
|
||||
Length: 10
|
||||
|
||||
Entity: Microsoft
|
||||
Url: https://en.wikipedia.org/wiki/Microsoft
|
||||
Data Source: Wikipedia
|
||||
Score: 0.132
|
||||
Offset: 0
|
||||
Length: 9
|
||||
|
||||
------------------------------------------
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class RecognizeLinkedEntitiesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def recognize_linked_entities(self):
|
||||
# [START batch_recognize_linked_entities]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"Microsoft moved its headquarters to Bellevue, Washington in January 1979.",
|
||||
"Steve Ballmer stepped down as CEO of Microsoft and was succeeded by Satya Nadella.",
|
||||
"Microsoft superó a Apple Inc. como la compañía más valiosa que cotiza en bolsa en el mundo.",
|
||||
]
|
||||
|
||||
result = text_analytics_client.recognize_linked_entities(documents)
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("Document text: {}\n".format(documents[idx]))
|
||||
for entity in doc.entities:
|
||||
print("Entity: {}".format(entity.name))
|
||||
print("Url: {}".format(entity.url))
|
||||
print("Data Source: {}".format(entity.data_source))
|
||||
for match in entity.matches:
|
||||
print("Score: {0:.3f}".format(match.score))
|
||||
print("Offset: {}".format(match.offset))
|
||||
print("Length: {}\n".format(match.length))
|
||||
print("------------------------------------------")
|
||||
# [END batch_recognize_linked_entities]
|
||||
|
||||
def alternative_scenario_recognize_linked_entities(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "Microsoft moved its headquarters to Bellevue, Washington in January 1979."},
|
||||
{"id": "1", "language": "en", "text": "Steve Ballmer stepped down as CEO of Microsoft and was succeeded by Satya Nadella."},
|
||||
{"id": "2", "language": "es", "text": "Microsoft superó a Apple Inc. como la compañía más valiosa que cotiza en bolsa en el mundo."},
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
result = text_analytics_client.recognize_linked_entities(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = RecognizeLinkedEntitiesSample()
|
||||
sample.recognize_linked_entities()
|
||||
sample.alternative_scenario_recognize_linked_entities()
|
||||
|
|
@ -0,0 +1,103 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_recognize_pii_entities.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize personally identifiable information in a batch of documents.
|
||||
|
||||
USAGE:
|
||||
python sample_recognize_pii_entities.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Document text: The employee's SSN is 555-55-5555.
|
||||
Entity: 555-55-5555
|
||||
Type: U.S. Social Security Number (SSN)
|
||||
Confidence Score: 0.85
|
||||
|
||||
Document text: Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check.
|
||||
Entity: 111000025
|
||||
Type: ABA Routing Number
|
||||
Confidence Score: 0.75
|
||||
|
||||
Document text: Is 998.214.865-68 your Brazilian CPF number?
|
||||
Entity: 998.214.865-68
|
||||
Type: Brazil CPF Number
|
||||
Confidence Score: 0.85
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class RecognizePiiEntitiesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def recognize_pii_entities(self):
|
||||
# [START batch_recognize_pii_entities]
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
documents = [
|
||||
"The employee's SSN is 555-55-5555.",
|
||||
"Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check.",
|
||||
"Is 998.214.865-68 your Brazilian CPF number?"
|
||||
]
|
||||
|
||||
result = text_analytics_client.recognize_pii_entities(documents)
|
||||
docs = [doc for doc in result if not doc.is_error]
|
||||
|
||||
for idx, doc in enumerate(docs):
|
||||
print("Document text: {}".format(documents[idx]))
|
||||
for entity in doc.entities:
|
||||
print("Entity: {}".format(entity.text))
|
||||
print("Type: {}".format(entity.type))
|
||||
print("Confidence Score: {}\n".format(entity.score))
|
||||
# [END batch_recognize_pii_entities]
|
||||
|
||||
def alternative_scenario_recognize_pii_entities(self):
|
||||
"""This sample demonstrates how to retrieve batch statistics, the
|
||||
model version used, and the raw response returned from the service.
|
||||
|
||||
It additionally shows an alternative way to pass in the input documents
|
||||
using a list[TextDocumentInput] and supplying your own IDs and language hints along
|
||||
with the text.
|
||||
"""
|
||||
from azure.ai.textanalytics import TextAnalyticsClient
|
||||
text_analytics_client = TextAnalyticsClient(endpoint=self.endpoint, credential=self.key)
|
||||
|
||||
documents = [
|
||||
{"id": "0", "language": "en", "text": "The employee's SSN is 555-55-5555."},
|
||||
{"id": "1", "language": "en", "text": "Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check."},
|
||||
{"id": "2", "language": "en", "text": "Is 998.214.865-68 your Brazilian CPF number?"}
|
||||
]
|
||||
|
||||
extras = []
|
||||
|
||||
def callback(resp):
|
||||
extras.append(resp.statistics)
|
||||
extras.append(resp.model_version)
|
||||
extras.append(resp.raw_response)
|
||||
|
||||
result = text_analytics_client.recognize_pii_entities(
|
||||
documents,
|
||||
show_stats=True,
|
||||
model_version="latest",
|
||||
response_hook=callback
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = RecognizePiiEntitiesSample()
|
||||
sample.recognize_pii_entities()
|
||||
sample.alternative_scenario_recognize_pii_entities()
|
||||
|
|
@ -0,0 +1,91 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_analyze_sentiment.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to analyze sentiment from a single string.
|
||||
An overall sentiment and a per-sentence sentiment is returned.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_analyze_sentiment.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Overall sentiment: mixed
|
||||
Overall scores: positive=0.338; neutral=0.338; negative=0.338
|
||||
|
||||
Sentence 1 sentiment: neutral
|
||||
Offset: 0
|
||||
Length: 35
|
||||
Sentence score: positive=0.006; neutral=0.006; negative=0.006
|
||||
|
||||
Sentence 2 sentiment: positive
|
||||
Offset: 36
|
||||
Length: 32
|
||||
Sentence score: positive=0.999; neutral=0.999; negative=0.999
|
||||
|
||||
Sentence 3 sentiment: negative
|
||||
Offset: 69
|
||||
Length: 39
|
||||
Sentence score: positive=0.010; neutral=0.010; negative=0.010
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class SingleAnalyzeSentimentSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def analyze_sentiment(self):
|
||||
# [START single_analyze_sentiment]
|
||||
from azure.ai.textanalytics import single_analyze_sentiment
|
||||
|
||||
text = "I visited the restaurant last week. The portions were very generous. However, I did not like what " \
|
||||
"I ordered."
|
||||
|
||||
result = single_analyze_sentiment(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
print("Overall sentiment: {}".format(result.sentiment))
|
||||
print("Overall scores: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f} \n".format(
|
||||
result.document_scores.positive,
|
||||
result.document_scores.neutral,
|
||||
result.document_scores.negative,
|
||||
))
|
||||
|
||||
for idx, sentence in enumerate(result.sentences):
|
||||
print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
|
||||
print("Offset: {}".format(sentence.offset))
|
||||
print("Length: {}".format(sentence.length))
|
||||
print("Sentence score: positive={0:.3f}; neutral={0:.3f}; negative={0:.3f} \n".format(
|
||||
sentence.sentence_scores.positive,
|
||||
sentence.sentence_scores.neutral,
|
||||
sentence.sentence_scores.negative,
|
||||
))
|
||||
# [END single_analyze_sentiment]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = SingleAnalyzeSentimentSample()
|
||||
sample.analyze_sentiment()
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_detect_language.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect the language of a single string.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_detect_language.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Language detected: English
|
||||
Confidence score: 1.0
|
||||
|
||||
Document Statistics:
|
||||
Text character count: 42
|
||||
Transactions count: 1
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class SingleDetectLanguageSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def detect_language(self):
|
||||
# [START single_detect_language]
|
||||
from azure.ai.textanalytics import single_detect_language
|
||||
|
||||
text = "I need to take my cat to the veterinarian."
|
||||
|
||||
result = single_detect_language(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
country_hint="US",
|
||||
show_stats=True
|
||||
)
|
||||
|
||||
print("Language detected: {}".format(result.detected_languages[0].name))
|
||||
print("Confidence score: {}\n".format(result.detected_languages[0].score))
|
||||
print("Document Statistics:")
|
||||
print("Text character count: {}".format(result.statistics.character_count))
|
||||
print("Transactions count: {}".format(result.statistics.transaction_count))
|
||||
# [END single_detect_language]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = SingleDetectLanguageSample()
|
||||
sample.detect_language()
|
||||
|
|
@ -0,0 +1,67 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_extract_key_phrases.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract key phrases from a single string.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_extract_key_phrases.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Key phrases found:
|
||||
|
||||
King County
|
||||
United States
|
||||
Washington
|
||||
city
|
||||
miles
|
||||
Redmond
|
||||
Seattle
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class SingleExtractKeyPhrasesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def extract_key_phrases(self):
|
||||
# [START single_extract_key_phrases]
|
||||
from azure.ai.textanalytics import single_extract_key_phrases
|
||||
|
||||
text = "Redmond is a city in King County, Washington, United States, located 15 miles east of Seattle."
|
||||
|
||||
result = single_extract_key_phrases(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
print("Key phrases found:\n")
|
||||
for phrase in result.key_phrases:
|
||||
print(phrase)
|
||||
# [END single_extract_key_phrases]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = SingleExtractKeyPhrasesSample()
|
||||
sample.extract_key_phrases()
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_recognize_entities.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize entities in a single string.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_recognize_entities.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Entity: Microsoft
|
||||
Type: Organization
|
||||
Confidence Score: 1.000
|
||||
|
||||
Entity: Bill Gates
|
||||
Type: Person
|
||||
Confidence Score: 1.000
|
||||
|
||||
Entity: Paul Allen
|
||||
Type: Person
|
||||
Confidence Score: 0.999
|
||||
|
||||
Entity: April 4, 1975
|
||||
Type: DateTime
|
||||
Confidence Score: 0.800
|
||||
|
||||
Entity: Altair
|
||||
Type: Organization
|
||||
Confidence Score: 0.525
|
||||
|
||||
Entity: 8800
|
||||
Type: Quantity
|
||||
Confidence Score: 0.80
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class SingleRecognizeEntitiesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def recognize_entities(self):
|
||||
# [START single_recognize_entities]
|
||||
from azure.ai.textanalytics import single_recognize_entities
|
||||
|
||||
text = "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975," \
|
||||
" to develop and sell BASIC interpreters for the Altair 8800."
|
||||
|
||||
result = single_recognize_entities(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
for entity in result.entities:
|
||||
print("Entity: {}".format(entity.text))
|
||||
print("Type: {}".format(entity.type))
|
||||
print("Confidence Score: {0:.3f}\n".format(entity.score))
|
||||
# [END single_recognize_entities]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = SingleRecognizeEntitiesSample()
|
||||
sample.recognize_entities()
|
||||
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_recognize_linked_entities.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize entities in a single string
|
||||
and returns links to the entities from a well-known knowledge base.
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_recognize_linked_entities.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Entity: Easter Island
|
||||
Url: https://en.wikipedia.org/wiki/Easter_Island
|
||||
Data Source: Wikipedia
|
||||
|
||||
Where this entity appears in the text:
|
||||
Match 1: Easter Island
|
||||
Score: 0.272
|
||||
Offset: 0
|
||||
Length: 13
|
||||
|
||||
Match 2: Rapa Nui
|
||||
Score: 0.054
|
||||
Offset: 97
|
||||
Length: 8
|
||||
|
||||
Entity: Polynesia
|
||||
Url: https://en.wikipedia.org/wiki/Polynesia
|
||||
Data Source: Wikipedia
|
||||
|
||||
Where this entity appears in the text:
|
||||
Match 1: Polynesia
|
||||
Score: 0.163
|
||||
Offset: 67
|
||||
Length: 9
|
||||
|
||||
Entity: Chile
|
||||
Url: https://en.wikipedia.org/wiki/Chile
|
||||
Data Source: Wikipedia
|
||||
|
||||
Where this entity appears in the text:
|
||||
Match 1: Chilean
|
||||
Score: 0.045
|
||||
Offset: 17
|
||||
Length: 7
|
||||
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class SingleRecognizeLinkedEntitiesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def recognize_linked_entities(self):
|
||||
# [START single_recognize_linked_entities]
|
||||
from azure.ai.textanalytics import single_recognize_linked_entities
|
||||
|
||||
text = "Easter Island, a Chilean territory, is a remote volcanic island in Polynesia. " \
|
||||
"Its native name is Rapa Nui."
|
||||
|
||||
result = single_recognize_linked_entities(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
for entity in result.entities:
|
||||
print("Entity: {}".format(entity.name))
|
||||
print("Url: {}".format(entity.url))
|
||||
print("Data Source: {}\n".format(entity.data_source))
|
||||
print("Where this entity appears in the text:")
|
||||
for idx, match in enumerate(entity.matches):
|
||||
print("Match {}: {}".format(idx+1, match.text))
|
||||
print("Score: {0:.3f}".format(match.score))
|
||||
print("Offset: {}".format(match.offset))
|
||||
print("Length: {}\n".format(match.length))
|
||||
# [END single_recognize_linked_entities]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = SingleRecognizeLinkedEntitiesSample()
|
||||
sample.recognize_linked_entities()
|
||||
|
|
@ -0,0 +1,68 @@
|
|||
# coding: utf-8
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
|
||||
"""
|
||||
FILE: sample_single_recognize_pii_entities.py
|
||||
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to recognize personally identifiable
|
||||
information in a single string. For the list of supported entity types,
|
||||
see https://aka.ms/tanerpii
|
||||
|
||||
This module-level, single method is meant to be used as an introduction
|
||||
for new users of the text analytics service. For optimum use of the service,
|
||||
use the methods that support batching documents.
|
||||
|
||||
USAGE:
|
||||
python sample_single_recognize_pii_entities.py
|
||||
|
||||
Set the environment variables with your own values before running the sample:
|
||||
1) AZURE_TEXT_ANALYTICS_ENDPOINT - the endpoint to your cognitive services resource.
|
||||
2) AZURE_TEXT_ANALYTICS_KEY - your text analytics subscription key
|
||||
|
||||
OUTPUT:
|
||||
Entity: 111000025
|
||||
Type: ABA Routing Number
|
||||
Confidence Score: 0.75
|
||||
|
||||
Entity: 555-55-5555
|
||||
Type: U.S. Social Security Number (SSN)
|
||||
Confidence Score: 0.85
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
|
||||
class SingleRecognizePiiEntitiesSample(object):
|
||||
|
||||
endpoint = os.getenv("AZURE_TEXT_ANALYTICS_ENDPOINT")
|
||||
key = os.getenv("AZURE_TEXT_ANALYTICS_KEY")
|
||||
|
||||
def recognize_pii_entities(self):
|
||||
# [START single_recognize_pii_entities]
|
||||
from azure.ai.textanalytics import single_recognize_pii_entities
|
||||
|
||||
text = "The employee's ABA number is 111000025 and his SSN is 555-55-5555."
|
||||
|
||||
result = single_recognize_pii_entities(
|
||||
endpoint=self.endpoint,
|
||||
credential=self.key,
|
||||
input_text=text,
|
||||
language="en"
|
||||
)
|
||||
|
||||
for entity in result.entities:
|
||||
print("Entity: {}".format(entity.text))
|
||||
print("Type: {}".format(entity.type))
|
||||
print("Confidence Score: {}\n".format(entity.score))
|
||||
# [END single_recognize_pii_entities]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample = SingleRecognizePiiEntitiesSample()
|
||||
sample.recognize_pii_entities()
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
[packaging]
|
||||
package_name = "azure-ai-textanalytics"
|
||||
package_nspkg = "azure-ai-nspkg"
|
||||
package_pprint_name = "Azure Text Analytics"
|
||||
package_doc_id = ""
|
||||
is_stable = false
|
||||
is_arm = false
|
||||
need_msrestazure = false
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
[bdist_wheel]
|
||||
universal=1
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
#-------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
#--------------------------------------------------------------------------
|
||||
|
||||
import re
|
||||
import os.path
|
||||
from io import open
|
||||
from setuptools import find_packages, setup
|
||||
|
||||
# Change the PACKAGE_NAME only to change folder and different name
|
||||
PACKAGE_NAME = "azure-ai-textanalytics"
|
||||
PACKAGE_PPRINT_NAME = "Azure Text Analytics"
|
||||
|
||||
# a-b-c => a/b/c
|
||||
package_folder_path = PACKAGE_NAME.replace('-', '/')
|
||||
# a-b-c => a.b.c
|
||||
namespace_name = PACKAGE_NAME.replace('-', '.')
|
||||
|
||||
# azure v0.x is not compatible with this package
|
||||
# azure v0.x used to have a __version__ attribute (newer versions don't)
|
||||
try:
|
||||
import azure
|
||||
try:
|
||||
ver = azure.__version__
|
||||
raise Exception(
|
||||
'This package is incompatible with azure=={}. '.format(ver) +
|
||||
'Uninstall it with "pip uninstall azure".'
|
||||
)
|
||||
except AttributeError:
|
||||
pass
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
# Version extraction inspired from 'requests'
|
||||
with open(os.path.join(package_folder_path, '_version.py'), 'r') as fd:
|
||||
version = re.search(r'^VERSION\s*=\s*[\'"]([^\'"]*)[\'"]',
|
||||
fd.read(), re.MULTILINE).group(1)
|
||||
|
||||
if not version:
|
||||
raise RuntimeError('Cannot find version information')
|
||||
|
||||
with open('README.md', encoding='utf-8') as f:
|
||||
readme = f.read()
|
||||
with open('HISTORY.md', encoding='utf-8') as f:
|
||||
history = f.read()
|
||||
|
||||
setup(
|
||||
name=PACKAGE_NAME,
|
||||
version=version,
|
||||
description='Microsoft {} Client Library for Python'.format(PACKAGE_PPRINT_NAME),
|
||||
long_description=readme + "\n\n" + history,
|
||||
long_description_content_type='text/markdown',
|
||||
license='MIT License',
|
||||
author='Microsoft Corporation',
|
||||
author_email='azpysdkhelp@microsoft.com',
|
||||
url='https://github.com/Azure/azure-sdk-for-python',
|
||||
classifiers=[
|
||||
'Development Status :: 4 - Beta',
|
||||
'Programming Language :: Python',
|
||||
'Programming Language :: Python :: 2',
|
||||
'Programming Language :: Python :: 2.7',
|
||||
'Programming Language :: Python :: 3',
|
||||
'Programming Language :: Python :: 3.5',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
'Programming Language :: Python :: 3.7',
|
||||
'Programming Language :: Python :: 3.8',
|
||||
'License :: OSI Approved :: MIT License',
|
||||
],
|
||||
zip_safe=False,
|
||||
packages=find_packages(exclude=[
|
||||
'tests',
|
||||
# Exclude packages that will be covered by PEP420 or nspkg
|
||||
'azure',
|
||||
'azure.ai',
|
||||
]),
|
||||
install_requires=[
|
||||
"azure-core<2.0.0,>=1.1.0",
|
||||
"msrest>=0.6.0",
|
||||
'azure-common~=1.1',
|
||||
'six>=1.6',
|
||||
],
|
||||
extras_require={
|
||||
":python_version<'3.0'": ['azure-ai-nspkg'],
|
||||
":python_version<'3.5'": ['typing'],
|
||||
}
|
||||
)
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
|
||||
# coding: utf-8
|
||||
# -------------------------------------------------------------------------
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License. See License.txt in the project root for
|
||||
# license information.
|
||||
# --------------------------------------------------------------------------
|
||||
import asyncio
|
||||
import functools
|
||||
from devtools_testutils.cognitiveservices_testcase import CognitiveServiceTest
|
||||
from azure.core.credentials import AccessToken
|
||||
|
||||
|
||||
LOGGING_FORMAT = '%(asctime)s %(name)-20s %(levelname)-5s %(message)s'
|
||||
|
||||
|
||||
class AsyncFakeTokenCredential(object):
|
||||
"""Protocol for classes able to provide OAuth tokens.
|
||||
:param str scopes: Lets you specify the type of access needed.
|
||||
"""
|
||||
def __init__(self):
|
||||
self.token = AccessToken("YOU SHALL NOT PASS", 0)
|
||||
|
||||
async def get_token(self, *args):
|
||||
return self.token
|
||||
|
||||
|
||||
class AsyncCognitiveServiceTestCase(CognitiveServiceTest):
|
||||
@staticmethod
|
||||
def await_prepared_test(test_fn):
|
||||
"""Synchronous wrapper for async test methods. Used to avoid making changes
|
||||
upstream to AbstractPreparer (which doesn't await the functions it wraps)
|
||||
"""
|
||||
|
||||
@functools.wraps(test_fn)
|
||||
def run(test_class_instance, *args, **kwargs):
|
||||
loop = asyncio.get_event_loop()
|
||||
return loop.run_until_complete(test_fn(test_class_instance, **kwargs))
|
||||
|
||||
return run
|
||||
|
||||
def get_oauth_endpoint(self):
|
||||
return self.get_settings_value("TEXT_ANALYTICS_ACCOUNT_NAME")
|
||||
|
||||
def generate_oauth_token(self):
|
||||
if self.is_live:
|
||||
from azure.identity.aio import ClientSecretCredential
|
||||
return ClientSecretCredential(
|
||||
self.get_settings_value("AZURE_TENANT_ID"),
|
||||
self.get_settings_value("AZURE_CLIENT_ID"),
|
||||
self.get_settings_value("AZURE_CLIENT_SECRET"),
|
||||
)
|
||||
return self.generate_fake_token()
|
||||
|
||||
def generate_fake_token(self):
|
||||
return AsyncFakeTokenCredential()
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
interactions:
|
||||
- request:
|
||||
body: '{"documents": [{"id": "1", "text": "", "language": "en"}, {"id": "2", "text":
|
||||
"I did not like the hotel we stayed it. It was too expensive.", "language":
|
||||
"english"}, {"id": "3", "text": "", "language": "en"}]}'
|
||||
headers:
|
||||
Accept:
|
||||
- application/json
|
||||
Accept-Encoding:
|
||||
- gzip, deflate
|
||||
Connection:
|
||||
- keep-alive
|
||||
Content-Length:
|
||||
- '209'
|
||||
Content-Type:
|
||||
- application/json; charset=utf-8
|
||||
User-Agent:
|
||||
- azsdk-python-core/1.1.0b1 Python/3.7.3 (Windows-10-10.0.18362-SP0) azsdk-python-textanalyticsclient/3.0.0b1
|
||||
X-BingApis-SDK-Client:
|
||||
- Python-SDK
|
||||
method: POST
|
||||
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