# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
from enum import Enum
import msrest.serialization
from .._generated.models import (
LexicalAnalyzer,
LexicalTokenizer,
AnalyzeRequest,
CustomAnalyzer as _CustomAnalyzer,
EntityRecognitionSkill as _EntityRecognitionSkillV1,
EntityRecognitionSkillV3 as _EntityRecognitionSkillV3,
PatternAnalyzer as _PatternAnalyzer,
PatternTokenizer as _PatternTokenizer,
SearchResourceEncryptionKey as _SearchResourceEncryptionKey,
SearchIndexerDataSource as _SearchIndexerDataSource,
SearchIndexerSkill,
SearchIndexerSkillset as _SearchIndexerSkillset,
SentimentSkill as _SentimentSkillV1,
SentimentSkillV3 as _SentimentSkillV3,
SynonymMap as _SynonymMap,
DataSourceCredentials,
AzureActiveDirectoryApplicationCredentials,
)
DELIMITER = "|"
[docs]class SearchIndexerSkillset(_SearchIndexerSkillset):
"""A list of skills.
All required parameters must be populated in order to send to Azure.
:param name: Required. The name of the skillset.
:type name: str
:param description: The description of the skillset.
:type description: str
:param skills: Required. A list of skills in the skillset.
:type skills: list[~azure.search.documents.indexes.models.SearchIndexerSkill]
:param cognitive_services_account: Details about cognitive services to be used when running
skills.
:type cognitive_services_account:
~azure.search.documents.indexes.models.CognitiveServicesAccount
:param knowledge_store: Definition of additional projections to azure blob, table, or files, of
enriched data.
:type knowledge_store: ~azure.search.documents.indexes.models.SearchIndexerKnowledgeStore
:param e_tag: The ETag of the skillset.
:type e_tag: str
:param encryption_key: A description of an encryption key that you create in Azure Key Vault.
This key is used to provide an additional level of encryption-at-rest for your skillset
definition when you want full assurance that no one, not even Microsoft, can decrypt your
skillset definition in Azure Cognitive Search. Once you have encrypted your skillset
definition, it will always remain encrypted. Azure Cognitive Search will ignore attempts to set
this property to null. You can change this property as needed if you want to rotate your
encryption key; Your skillset definition will be unaffected. Encryption with customer-managed
keys is not available for free search services, and is only available for paid services created
on or after January 1, 2019.
:type encryption_key: ~azure.search.documents.indexes.models.SearchResourceEncryptionKey
"""
def __init__(
self,
**kwargs
):
super(SearchIndexerSkillset, self).__init__(**kwargs)
def _to_generated(self):
generated_skills = []
for skill in self.skills:
if hasattr(skill, '_to_generated'):
generated_skills.append(skill._to_generated()) # pylint:disable=protected-access
else:
generated_skills.append(skill)
assert len(generated_skills) == len(self.skills)
return _SearchIndexerSkillset(
name=getattr(self, 'name', None),
description=getattr(self, 'description', None),
skills=generated_skills,
cognitive_services_account=getattr(self, 'cognitive_services_account', None),
knowledge_store=getattr(self, 'knowledge_store', None),
e_tag=getattr(self, 'e_tag', None),
encryption_key=getattr(self, 'encryption_key', None)
)
@classmethod
def _from_generated(cls, skillset):
custom_skills = []
for skill in skillset.skills:
skill_cls = type(skill)
if skill_cls in [_EntityRecognitionSkillV1, _EntityRecognitionSkillV3]:
custom_skills.append(EntityRecognitionSkill._from_generated(skill)) # pylint:disable=protected-access
elif skill_cls in [_SentimentSkillV1, _SentimentSkillV3]:
custom_skills.append(SentimentSkill._from_generated(skill)) # pylint:disable=protected-access
else:
custom_skills.append(skill)
assert len(skillset.skills) == len(custom_skills)
kwargs = skillset.as_dict()
kwargs['skills'] = custom_skills
return cls(**kwargs)
[docs]class EntityRecognitionSkillVersion(str, Enum):
"""Specifies the Entity Recognition skill version to use."""
#: Use Entity Recognition skill V1.
V1 = "#Microsoft.Skills.Text.EntityRecognitionSkill"
#: Use Entity Recognition skill V3.
V3 = "#Microsoft.Skills.Text.V3.EntityRecognitionSkill"
#: Use latest version of Entity Recognition skill.
LATEST = "#Microsoft.Skills.Text.V3.EntityRecognitionSkill"
[docs]class EntityRecognitionSkill(SearchIndexerSkill):
"""Using the Text Analytics API, extracts entities of different types from text.
All required parameters must be populated in order to send to Azure.
:param odata_type: Required. Identifies the concrete type of the skill.Constant filled by
server.
:type odata_type: str
:param name: The name of the skill which uniquely identifies it within the skillset. A skill
with no name defined will be given a default name of its 1-based index in the skills array,
prefixed with the character '#'.
:type name: str
:param description: The description of the skill which describes the inputs, outputs, and usage
of the skill.
:type description: str
:param context: Represents the level at which operations take place, such as the document root
or document content (for example, /document or /document/content). The default is /document.
:type context: str
:param inputs: Required. Inputs of the skills could be a column in the source data set, or the
output of an upstream skill.
:type inputs: list[~azure.search.documents.indexes.models.InputFieldMappingEntry]
:param outputs: Required. The output of a skill is either a field in a search index, or a value
that can be consumed as an input by another skill.
:type outputs: list[~azure.search.documents.indexes.models.OutputFieldMappingEntry]
:param categories: A list of entity categories that should be extracted.
:type categories: list[str or ~azure.search.documents.indexes.models.EntityCategory]
:param default_language_code: A value indicating which language code to use. Default is en.
Possible values include: "ar", "cs", "zh-Hans", "zh-Hant", "da", "nl", "en", "fi", "fr", "de",
"el", "hu", "it", "ja", "ko", "no", "pl", "pt-PT", "pt-BR", "ru", "es", "sv", "tr".
:type default_language_code: str or
~azure.search.documents.indexes.models.EntityRecognitionSkillLanguage
:param include_typeless_entities: Determines whether or not to include entities which are well
known but don't conform to a pre-defined type. If this configuration is not set (default), set
to null or set to false, entities which don't conform to one of the pre-defined types will not
be surfaced. Only valid for skill version 1.
:type include_typeless_entities: bool
:param minimum_precision: A value between 0 and 1 that be used to only include entities whose
confidence score is greater than the value specified. If not set (default), or if explicitly
set to null, all entities will be included.
:type minimum_precision: float
:param model_version: The version of the model to use when calling the Text Analytics service.
It will default to the latest available when not specified. We recommend you do not specify
this value unless absolutely necessary. Only valid from skill version 3.
:type model_version: str
:param skill_version: The version of the skill to use when calling the Text Analytics service.
It will default to V1 when not specified.
:type skill_version: ~azure.search.documents.indexes.models.EntityRecognitionSkillVersion
"""
_validation = {
'odata_type': {'required': True},
'inputs': {'required': True},
'outputs': {'required': True},
'minimum_precision': {'maximum': 1, 'minimum': 0},
}
_attribute_map = {
'odata_type': {'key': '@odata\\.type', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'context': {'key': 'context', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '[InputFieldMappingEntry]'},
'outputs': {'key': 'outputs', 'type': '[OutputFieldMappingEntry]'},
'categories': {'key': 'categories', 'type': '[str]'},
'default_language_code': {'key': 'defaultLanguageCode', 'type': 'str'},
'include_typeless_entities': {'key': 'includeTypelessEntities', 'type': 'bool'},
'minimum_precision': {'key': 'minimumPrecision', 'type': 'float'},
'model_version': {'key': 'modelVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
# pop skill_version from kwargs to avoid warning in msrest
skill_version = kwargs.pop('skill_version', EntityRecognitionSkillVersion.V1)
super(EntityRecognitionSkill, self).__init__(**kwargs)
self.skill_version = skill_version
self.odata_type = self.skill_version # type: str
self.categories = kwargs.get('categories', None)
self.default_language_code = kwargs.get('default_language_code', None)
self.minimum_precision = kwargs.get('minimum_precision', None)
self.include_typeless_entities = kwargs.get('include_typeless_entities', None)
self.model_version = kwargs.get('model_version', None)
def _to_generated(self):
if self.skill_version == EntityRecognitionSkillVersion.V1:
return _EntityRecognitionSkillV1(
inputs=self.inputs,
outputs=self.outputs,
name=self.name,
odata_type=self.odata_type,
categories=self.categories,
default_language_code=self.default_language_code,
include_typeless_entities=self.include_typeless_entities,
minimum_precision=self.minimum_precision,
model_version=self.model_version
)
if self.skill_version in [EntityRecognitionSkillVersion.V3, EntityRecognitionSkillVersion.LATEST]:
return _EntityRecognitionSkillV3(
inputs=self.inputs,
outputs=self.outputs,
name=self.name,
odata_type=self.odata_type,
categories=self.categories,
default_language_code=self.default_language_code,
include_typeless_entities=self.include_typeless_entities,
minimum_precision=self.minimum_precision,
model_version=self.model_version
)
return None
@classmethod
def _from_generated(cls, skill):
if not skill:
return None
kwargs = skill.as_dict()
if isinstance(skill, _EntityRecognitionSkillV1):
return EntityRecognitionSkill(
skill_version=EntityRecognitionSkillVersion.V1,
**kwargs
)
if isinstance(skill, _EntityRecognitionSkillV3):
return EntityRecognitionSkill(
skill_version=EntityRecognitionSkillVersion.V3,
**kwargs
)
return None
[docs]class SentimentSkillVersion(str, Enum):
""" Specifies the Sentiment Skill version to use."""
#: Use Sentiment skill V1.
V1 = "#Microsoft.Skills.Text.SentimentSkill"
#: Use Sentiment skill V3.
V3 = "#Microsoft.Skills.Text.V3.SentimentSkill"
#: Use latest version of Sentiment skill.
LATEST = "#Microsoft.Skills.Text.V3.SentimentSkill"
[docs]class SentimentSkill(SearchIndexerSkill):
"""V1: Text analytics positive-negative sentiment analysis, scored as a floating point value in a range of zero
to 1.
V3: Using the Text Analytics API, evaluates unstructured text and for each record, provides sentiment labels
(such as "negative", "neutral" and "positive") based on the highest confidence score found by the service at
a sentence and document-level.
All required parameters must be populated in order to send to Azure.
:param odata_type: Required. Identifies the concrete type of the skill.Constant filled by
server.
:type odata_type: str
:param name: The name of the skill which uniquely identifies it within the skillset. A skill
with no name defined will be given a default name of its 1-based index in the skills array,
prefixed with the character '#'.
:type name: str
:param description: The description of the skill which describes the inputs, outputs, and usage
of the skill.
:type description: str
:param context: Represents the level at which operations take place, such as the document root
or document content (for example, /document or /document/content). The default is /document.
:type context: str
:param inputs: Required. Inputs of the skills could be a column in the source data set, or the
output of an upstream skill.
:type inputs: list[~azure.search.documents.indexes.models.InputFieldMappingEntry]
:param outputs: Required. The output of a skill is either a field in a search index, or a value
that can be consumed as an input by another skill.
:type outputs: list[~azure.search.documents.indexes.models.OutputFieldMappingEntry]
:param default_language_code: A value indicating which language code to use. Default is en.
Possible values include: "da", "nl", "en", "fi", "fr", "de", "el", "it", "no", "pl", "pt-PT",
"ru", "es", "sv", "tr".
:type default_language_code: str or
~azure.search.documents.indexes.models.SentimentSkillLanguage
:param include_opinion_mining: If set to true, the skill output will include information from
Text Analytics for opinion mining, namely targets (nouns or verbs) and their associated
assessment (adjective) in the text. Default is false.
:type include_opinion_mining: bool
:param model_version: The version of the model to use when calling the Text Analytics service.
It will default to the latest available when not specified. We recommend you do not specify
this value unless absolutely necessary.
:type model_version: str
:param skill_version: The version of the skill to use when calling the Text Analytics service.
It will default to V1 when not specified.
:type skill_version: ~azure.search.documents.indexes.models.SentimentSkillVersion
"""
_validation = {
'odata_type': {'required': True},
'inputs': {'required': True},
'outputs': {'required': True},
}
_attribute_map = {
'odata_type': {'key': '@odata\\.type', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'context': {'key': 'context', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '[InputFieldMappingEntry]'},
'outputs': {'key': 'outputs', 'type': '[OutputFieldMappingEntry]'},
'default_language_code': {'key': 'defaultLanguageCode', 'type': 'str'},
'include_opinion_mining': {'key': 'includeOpinionMining', 'type': 'bool'},
'model_version': {'key': 'modelVersion', 'type': 'str'},
}
def __init__(
self,
**kwargs
):
# pop skill_version from kwargs to avoid warning in msrest
skill_version = kwargs.pop('skill_version', SentimentSkillVersion.V1)
super(SentimentSkill, self).__init__(**kwargs)
self.skill_version = skill_version
self.odata_type = self.skill_version # type: str
self.default_language_code = kwargs.get('default_language_code', None)
self.include_opinion_mining = kwargs.get('include_opinion_mining', False)
self.model_version = kwargs.get('model_version', None)
def _to_generated(self):
if self.skill_version == SentimentSkillVersion.V1:
return _SentimentSkillV1(
inputs=self.inputs,
outputs=self.outputs,
name=self.name,
odata_type=self.odata_type,
default_language_code=self.default_language_code,
include_opinion_mining=self.include_opinion_mining,
model_version=self.model_version
)
if self.skill_version in [SentimentSkillVersion.V3, SentimentSkillVersion.LATEST]:
return _SentimentSkillV3(
inputs=self.inputs,
outputs=self.outputs,
name=self.name,
odata_type=self.odata_type,
default_language_code=self.default_language_code,
include_opinion_mining=self.include_opinion_mining,
model_version=self.model_version
)
return None
@classmethod
def _from_generated(cls, skill):
if not skill:
return None
kwargs = skill.as_dict()
if isinstance(skill, _SentimentSkillV1):
return SentimentSkill(
skill_version=SentimentSkillVersion.V1,
**kwargs
)
if isinstance(skill, _SentimentSkillV3):
return SentimentSkill(
skill_version=SentimentSkillVersion.V3,
**kwargs
)
return None
[docs]class AnalyzeTextOptions(msrest.serialization.Model):
"""Specifies some text and analysis components used to break that text into tokens.
All required parameters must be populated in order to send to Azure.
:param text: Required. The text to break into tokens.
:type text: str
:param analyzer_name: The name of the analyzer to use to break the given text. If this parameter is
not specified, you must specify a tokenizer instead. The tokenizer and analyzer parameters are
mutually exclusive. Possible values include: "ar.microsoft", "ar.lucene", "hy.lucene",
"bn.microsoft", "eu.lucene", "bg.microsoft", "bg.lucene", "ca.microsoft", "ca.lucene", "zh-
Hans.microsoft", "zh-Hans.lucene", "zh-Hant.microsoft", "zh-Hant.lucene", "hr.microsoft",
"cs.microsoft", "cs.lucene", "da.microsoft", "da.lucene", "nl.microsoft", "nl.lucene",
"en.microsoft", "en.lucene", "et.microsoft", "fi.microsoft", "fi.lucene", "fr.microsoft",
"fr.lucene", "gl.lucene", "de.microsoft", "de.lucene", "el.microsoft", "el.lucene",
"gu.microsoft", "he.microsoft", "hi.microsoft", "hi.lucene", "hu.microsoft", "hu.lucene",
"is.microsoft", "id.microsoft", "id.lucene", "ga.lucene", "it.microsoft", "it.lucene",
"ja.microsoft", "ja.lucene", "kn.microsoft", "ko.microsoft", "ko.lucene", "lv.microsoft",
"lv.lucene", "lt.microsoft", "ml.microsoft", "ms.microsoft", "mr.microsoft", "nb.microsoft",
"no.lucene", "fa.lucene", "pl.microsoft", "pl.lucene", "pt-BR.microsoft", "pt-BR.lucene", "pt-
PT.microsoft", "pt-PT.lucene", "pa.microsoft", "ro.microsoft", "ro.lucene", "ru.microsoft",
"ru.lucene", "sr-cyrillic.microsoft", "sr-latin.microsoft", "sk.microsoft", "sl.microsoft",
"es.microsoft", "es.lucene", "sv.microsoft", "sv.lucene", "ta.microsoft", "te.microsoft",
"th.microsoft", "th.lucene", "tr.microsoft", "tr.lucene", "uk.microsoft", "ur.microsoft",
"vi.microsoft", "standard.lucene", "standardasciifolding.lucene", "keyword", "pattern",
"simple", "stop", "whitespace".
:type analyzer_name: str or ~azure.search.documents.indexes.models.LexicalAnalyzerName
:param tokenizer_name: The name of the tokenizer to use to break the given text. If this parameter
is not specified, you must specify an analyzer instead. The tokenizer and analyzer parameters
are mutually exclusive. Possible values include: "classic", "edgeNGram", "keyword_v2",
"letter", "lowercase", "microsoft_language_tokenizer", "microsoft_language_stemming_tokenizer",
"nGram", "path_hierarchy_v2", "pattern", "standard_v2", "uax_url_email", "whitespace".
:type tokenizer_name: str or ~azure.search.documents.indexes.models.LexicalTokenizerName
:param normalizer_name: The name of the normalizer to use to normalize the given text. Possible
values include: "asciifolding", "elision", "lowercase", "standard", "uppercase".
:type normalizer_name: str or ~azure.search.documents.indexes.models.LexicalNormalizerName
:param token_filters: An optional list of token filters to use when breaking the given text.
This parameter can only be set when using the tokenizer parameter.
:type token_filters: list[str or ~azure.search.documents.indexes.models.TokenFilterName]
:param char_filters: An optional list of character filters to use when breaking the given text.
This parameter can only be set when using the tokenizer parameter.
:type char_filters: list[str]
"""
_validation = {
"text": {"required": True},
}
_attribute_map = {
"text": {"key": "text", "type": "str"},
"analyzer_name": {"key": "analyzerName", "type": "str"},
"tokenizer_name": {"key": "tokenizerName", "type": "str"},
"normalizer_name": {"key": "normalizerName", "type": "str"},
"token_filters": {"key": "tokenFilters", "type": "[str]"},
"char_filters": {"key": "charFilters", "type": "[str]"},
}
def __init__(self, **kwargs):
super(AnalyzeTextOptions, self).__init__(**kwargs)
self.text = kwargs["text"]
self.analyzer_name = kwargs.get("analyzer_name", None)
self.tokenizer_name = kwargs.get("tokenizer_name", None)
self.normalizer_name = kwargs.get("normalizer_name", None)
self.token_filters = kwargs.get("token_filters", None)
self.char_filters = kwargs.get("char_filters", None)
def _to_analyze_request(self):
return AnalyzeRequest(
text=self.text,
analyzer=self.analyzer_name,
tokenizer=self.tokenizer_name,
normalizer=self.normalizer_name,
token_filters=self.token_filters,
char_filters=self.char_filters,
)
[docs]class CustomAnalyzer(LexicalAnalyzer):
"""Allows you to take control over the process of converting text into indexable/searchable tokens.
It's a user-defined configuration consisting of a single predefined tokenizer and one or more filters.
The tokenizer is responsible for breaking text into tokens, and the filters for modifying tokens
emitted by the tokenizer.
All required parameters must be populated in order to send to Azure.
:param odata_type: Required. Identifies the concrete type of the analyzer.Constant filled by
server.
:type odata_type: str
:param name: Required. The name of the analyzer. It must only contain letters, digits, spaces,
dashes or underscores, can only start and end with alphanumeric characters, and is limited to
128 characters.
:type name: str
:param tokenizer_name: Required. The name of the tokenizer to use to divide continuous text into a
sequence of tokens, such as breaking a sentence into words. Possible values include: "classic",
"edgeNGram", "keyword_v2", "letter", "lowercase", "microsoft_language_tokenizer",
"microsoft_language_stemming_tokenizer", "nGram", "path_hierarchy_v2", "pattern",
"standard_v2", "uax_url_email", "whitespace".
:type tokenizer_name: str or ~azure.search.documents.indexes.models.LexicalTokenizerName
:param token_filters: A list of token filters used to filter out or modify the tokens generated
by a tokenizer. For example, you can specify a lowercase filter that converts all characters to
lowercase. The filters are run in the order in which they are listed.
:type token_filters: list[str or ~azure.search.documents.indexes.models.TokenFilterName]
:param char_filters: A list of character filters used to prepare input text before it is
processed by the tokenizer. For instance, they can replace certain characters or symbols. The
filters are run in the order in which they are listed.
:type char_filters: list[str]
"""
_validation = {
"odata_type": {"required": True},
"name": {"required": True},
"tokenizer_name": {"required": True},
}
_attribute_map = {
"odata_type": {"key": "@odata\\.type", "type": "str"},
"name": {"key": "name", "type": "str"},
"tokenizer_name": {"key": "tokenizerName", "type": "str"},
"token_filters": {"key": "tokenFilters", "type": "[str]"},
"char_filters": {"key": "charFilters", "type": "[str]"},
}
def __init__(self, **kwargs):
super(CustomAnalyzer, self).__init__(**kwargs)
self.odata_type = "#Microsoft.Azure.Search.CustomAnalyzer"
self.tokenizer_name = kwargs["tokenizer_name"]
self.token_filters = kwargs.get("token_filters", None)
self.char_filters = kwargs.get("char_filters", None)
def _to_generated(self):
return _CustomAnalyzer(
name=self.name,
odata_type=self.odata_type,
tokenizer=self.tokenizer_name,
token_filters=self.token_filters,
char_filters=self.char_filters,
)
@classmethod
def _from_generated(cls, custom_analyzer):
if not custom_analyzer:
return None
return cls(
name=custom_analyzer.name,
odata_type=custom_analyzer.odata_type,
tokenizer_name=custom_analyzer.tokenizer,
token_filters=custom_analyzer.token_filters,
char_filters=custom_analyzer.char_filters,
)
[docs]class PatternAnalyzer(LexicalAnalyzer):
"""Flexibly separates text into terms via a regular expression.
This analyzer is implemented using Apache Lucene.
All required parameters must be populated in order to send to Azure.
:param name: Required. The name of the analyzer. It must only contain letters, digits, spaces,
dashes or underscores, can only start and end with alphanumeric characters, and is limited to
128 characters.
:type name: str
:param lower_case_terms: A value indicating whether terms should be lower-cased. Default is
true.
:type lower_case_terms: bool
:param pattern: A regular expression to match token separators. Default is an
expression that matches one or more white space characters.
:type pattern: str
:param flags: List of regular expression flags. Possible values of each flag include: 'CANON_EQ',
'CASE_INSENSITIVE', 'COMMENTS', 'DOTALL', 'LITERAL', 'MULTILINE', 'UNICODE_CASE', 'UNIX_LINES'.
:type flags: list[str] or list[~search_service_client.models.RegexFlags]
:param stopwords: A list of stopwords.
:type stopwords: list[str]
"""
_validation = {"odata_type": {"required": True}, "name": {"required": True}}
_attribute_map = {
"odata_type": {"key": "@odata\\.type", "type": "str"},
"name": {"key": "name", "type": "str"},
"lower_case_terms": {"key": "lowercase", "type": "bool"},
"pattern": {"key": "pattern", "type": "str"},
"flags": {"key": "flags", "type": "[str]"},
"stopwords": {"key": "stopwords", "type": "[str]"},
}
def __init__(self, **kwargs):
super(PatternAnalyzer, self).__init__(**kwargs)
self.odata_type = "#Microsoft.Azure.Search.PatternAnalyzer"
self.lower_case_terms = kwargs.get("lower_case_terms", True)
self.pattern = kwargs.get("pattern", r"\W+")
self.flags = kwargs.get("flags", None)
self.stopwords = kwargs.get("stopwords", None)
def _to_generated(self):
if not self.flags:
flags = None
else:
flags = DELIMITER.join(self.flags)
return _PatternAnalyzer(
name=self.name,
lower_case_terms=self.lower_case_terms,
pattern=self.pattern,
flags=flags,
stopwords=self.stopwords,
)
@classmethod
def _from_generated(cls, pattern_analyzer):
if not pattern_analyzer:
return None
if not pattern_analyzer.flags:
flags = None
else:
flags = pattern_analyzer.flags.split(DELIMITER)
return cls(
name=pattern_analyzer.name,
lower_case_terms=pattern_analyzer.lower_case_terms,
pattern=pattern_analyzer.pattern,
flags=flags,
stopwords=pattern_analyzer.stopwords,
)
[docs]class PatternTokenizer(LexicalTokenizer):
"""Tokenizer that uses regex pattern matching to construct distinct tokens.
This tokenizer is implemented using Apache Lucene.
All required parameters must be populated in order to send to Azure.
:param name: Required. The name of the tokenizer. It must only contain letters, digits, spaces,
dashes or underscores, can only start and end with alphanumeric characters, and is limited to
128 characters.
:type name: str
:param pattern: A regular expression to match token separators. Default is an
expression that matches one or more white space characters.
:type pattern: str
:param flags: List of regular expression flags. Possible values of each flag include: 'CANON_EQ',
'CASE_INSENSITIVE', 'COMMENTS', 'DOTALL', 'LITERAL', 'MULTILINE', 'UNICODE_CASE', 'UNIX_LINES'.
:type flags: list[str] or list[~search_service_client.models.RegexFlags]
:param group: The zero-based ordinal of the matching group in the regular expression to
extract into tokens. Use -1 if you want to use the entire pattern to split the input into
tokens, irrespective of matching groups. Default is -1.
:type group: int
"""
_validation = {"odata_type": {"required": True}, "name": {"required": True}}
_attribute_map = {
"odata_type": {"key": "@odata\\.type", "type": "str"},
"name": {"key": "name", "type": "str"},
"pattern": {"key": "pattern", "type": "str"},
"flags": {"key": "flags", "type": "[str]"},
"group": {"key": "group", "type": "int"},
}
def __init__(self, **kwargs):
super(PatternTokenizer, self).__init__(**kwargs)
self.odata_type = "#Microsoft.Azure.Search.PatternTokenizer"
self.pattern = kwargs.get("pattern", r"\W+")
self.flags = kwargs.get("flags", None)
self.group = kwargs.get("group", -1)
def _to_generated(self):
if not self.flags:
flags = None
else:
flags = DELIMITER.join(self.flags)
return _PatternTokenizer(
name=self.name,
pattern=self.pattern,
flags=flags,
group=self.group,
)
@classmethod
def _from_generated(cls, pattern_tokenizer):
if not pattern_tokenizer:
return None
if not pattern_tokenizer.flags:
flags = None
else:
flags = pattern_tokenizer.flags.split(DELIMITER)
return cls(
name=pattern_tokenizer.name,
pattern=pattern_tokenizer.pattern,
flags=flags,
group=pattern_tokenizer.group,
)
[docs]class SearchResourceEncryptionKey(msrest.serialization.Model):
"""A customer-managed encryption key in Azure Key Vault. Keys that you create and manage can be
used to encrypt or decrypt data-at-rest in Azure Cognitive Search, such as indexes and synonym maps.
All required parameters must be populated in order to send to Azure.
:param key_name: Required. The name of your Azure Key Vault key to be used to encrypt your data
at rest.
:type key_name: str
:param key_version: Required. The version of your Azure Key Vault key to be used to encrypt
your data at rest.
:type key_version: str
:param vault_uri: Required. The URI of your Azure Key Vault, also referred to as DNS name, that
contains the key to be used to encrypt your data at rest. An example URI might be https://my-
keyvault-name.vault.azure.net.
:type vault_uri: str
:param application_id: Required. An AAD Application ID that was granted the required access
permissions to the Azure Key Vault that is to be used when encrypting your data at rest. The
Application ID should not be confused with the Object ID for your AAD Application.
:type application_id: str
:param application_secret: The authentication key of the specified AAD application.
:type application_secret: str
"""
_validation = {
"key_name": {"required": True},
"key_version": {"required": True},
"vault_uri": {"required": True},
}
_attribute_map = {
"key_name": {"key": "keyVaultKeyName", "type": "str"},
"key_version": {"key": "keyVaultKeyVersion", "type": "str"},
"vault_uri": {"key": "keyVaultUri", "type": "str"},
"application_id": {"key": "applicationId", "type": "str"},
"application_secret": {"key": "applicationSecret", "type": "str"},
}
def __init__(self, **kwargs):
super(SearchResourceEncryptionKey, self).__init__(**kwargs)
self.key_name = kwargs["key_name"]
self.key_version = kwargs["key_version"]
self.vault_uri = kwargs["vault_uri"]
self.application_id = kwargs.get("application_id", None)
self.application_secret = kwargs.get("application_secret", None)
def _to_generated(self):
if self.application_id and self.application_secret:
access_credentials = AzureActiveDirectoryApplicationCredentials(
application_id=self.application_id,
application_secret=self.application_secret,
)
else:
access_credentials = None
return _SearchResourceEncryptionKey(
key_name=self.key_name,
key_version=self.key_version,
vault_uri=self.vault_uri,
access_credentials=access_credentials,
)
@classmethod
def _from_generated(cls, search_resource_encryption_key):
if not search_resource_encryption_key:
return None
if search_resource_encryption_key.access_credentials:
application_id = (
search_resource_encryption_key.access_credentials.application_id
)
application_secret = (
search_resource_encryption_key.access_credentials.application_secret
)
else:
application_id = None
application_secret = None
return cls(
key_name=search_resource_encryption_key.key_name,
key_version=search_resource_encryption_key.key_version,
vault_uri=search_resource_encryption_key.vault_uri,
application_id=application_id,
application_secret=application_secret,
)
[docs]class SynonymMap(msrest.serialization.Model):
"""Represents a synonym map definition.
Variables are only populated by the server, and will be ignored when sending a request.
All required parameters must be populated in order to send to Azure.
:param name: Required. The name of the synonym map.
:type name: str
:ivar format: Required. The format of the synonym map. Only the 'solr' format is currently
supported. Default value: "solr".
:vartype format: str
:param synonyms: Required. A series of synonym rules in the specified synonym map format. The
rules must be separated by newlines.
:type synonyms: list[str]
:param encryption_key: A description of an encryption key that you create in Azure Key Vault.
This key is used to provide an additional level of encryption-at-rest for your data when you
want full assurance that no one, not even Microsoft, can decrypt your data in Azure Cognitive
Search. Once you have encrypted your data, it will always remain encrypted. Azure Cognitive
Search will ignore attempts to set this property to null. You can change this property as
needed if you want to rotate your encryption key; Your data will be unaffected. Encryption with
customer-managed keys is not available for free search services, and is only available for paid
services created on or after January 1, 2019.
:type encryption_key: ~azure.search.documents.indexes.models.SearchResourceEncryptionKey
:param e_tag: The ETag of the synonym map.
:type e_tag: str
"""
_validation = {
"name": {"required": True},
"format": {"required": True, "constant": True},
"synonyms": {"required": True},
}
_attribute_map = {
"name": {"key": "name", "type": "str"},
"format": {"key": "format", "type": "str"},
"synonyms": {"key": "synonyms", "type": "[str]"},
"encryption_key": {
"key": "encryptionKey",
"type": "SearchResourceEncryptionKey",
},
"e_tag": {"key": "@odata\\.etag", "type": "str"},
}
format = "solr"
def __init__(self, **kwargs):
super(SynonymMap, self).__init__(**kwargs)
self.name = kwargs["name"]
self.synonyms = kwargs["synonyms"]
self.encryption_key = kwargs.get("encryption_key", None)
self.e_tag = kwargs.get("e_tag", None)
def _to_generated(self):
return _SynonymMap(
name=self.name,
synonyms="\n".join(self.synonyms),
encryption_key=self.encryption_key._to_generated() # pylint:disable=protected-access
if self.encryption_key
else None,
e_tag=self.e_tag,
)
@classmethod
def _from_generated(cls, synonym_map):
if not synonym_map:
return None
return cls(
name=synonym_map.name,
synonyms=synonym_map.synonyms.split("\n"),
# pylint:disable=protected-access
encryption_key=SearchResourceEncryptionKey._from_generated(
synonym_map.encryption_key
),
e_tag=synonym_map.e_tag,
)
[docs]class SearchIndexerDataSourceConnection(msrest.serialization.Model):
"""Represents a datasource connection definition, which can be used to configure an indexer.
All required parameters must be populated in order to send to Azure.
:param name: Required. The name of the datasource connection.
:type name: str
:param description: The description of the datasource connection.
:type description: str
:param type: Required. The type of the datasource connection. Possible values include: "azuresql",
"cosmosdb", "azureblob", "azuretable", "mysql", "adlsgen2".
:type type: str or ~azure.search.documents.indexes.models.SearchIndexerDataSourceType
:param connection_string: The connection string for the datasource connection.
:type connection_string: str
:param container: Required. The data container for the datasource connection.
:type container: ~azure.search.documents.indexes.models.SearchIndexerDataContainer
:param data_change_detection_policy: The data change detection policy for the datasource connection.
:type data_change_detection_policy: ~azure.search.documents.models.DataChangeDetectionPolicy
:param data_deletion_detection_policy: The data deletion detection policy for the datasource connection.
:type data_deletion_detection_policy:
~azure.search.documents.models.DataDeletionDetectionPolicy
:param e_tag: The ETag of the data source.
:type e_tag: str
"""
_validation = {
"name": {"required": True},
"type": {"required": True},
"connection_string": {"required": True},
"container": {"required": True},
}
_attribute_map = {
"name": {"key": "name", "type": "str"},
"description": {"key": "description", "type": "str"},
"type": {"key": "type", "type": "str"},
"connection_string": {"key": "connectionString", "type": "str"},
"container": {"key": "container", "type": "SearchIndexerDataContainer"},
"data_change_detection_policy": {
"key": "dataChangeDetectionPolicy",
"type": "DataChangeDetectionPolicy",
},
"data_deletion_detection_policy": {
"key": "dataDeletionDetectionPolicy",
"type": "DataDeletionDetectionPolicy",
},
"e_tag": {"key": "@odata\\.etag", "type": "str"},
}
def __init__(self, **kwargs):
super(SearchIndexerDataSourceConnection, self).__init__(**kwargs)
self.name = kwargs["name"]
self.description = kwargs.get("description", None)
self.type = kwargs["type"]
self.connection_string = kwargs["connection_string"]
self.container = kwargs["container"]
self.data_change_detection_policy = kwargs.get(
"data_change_detection_policy", None
)
self.data_deletion_detection_policy = kwargs.get(
"data_deletion_detection_policy", None
)
self.e_tag = kwargs.get("e_tag", None)
def _to_generated(self):
if self.connection_string is None or self.connection_string == "":
connection_string = "<unchanged>"
else:
connection_string = self.connection_string
credentials = DataSourceCredentials(connection_string=connection_string)
return _SearchIndexerDataSource(
name=self.name,
description=self.description,
type=self.type,
credentials=credentials,
container=self.container,
data_change_detection_policy=self.data_change_detection_policy,
data_deletion_detection_policy=self.data_deletion_detection_policy,
e_tag=self.e_tag,
)
@classmethod
def _from_generated(cls, search_indexer_data_source):
if not search_indexer_data_source:
return None
connection_string = (
search_indexer_data_source.credentials.connection_string
if search_indexer_data_source.credentials
else None
)
return cls(
name=search_indexer_data_source.name,
description=search_indexer_data_source.description,
type=search_indexer_data_source.type,
connection_string=connection_string,
container=search_indexer_data_source.container,
data_change_detection_policy=search_indexer_data_source.data_change_detection_policy,
data_deletion_detection_policy=search_indexer_data_source.data_deletion_detection_policy,
e_tag=search_indexer_data_source.e_tag,
)
def pack_analyzer(analyzer):
if not analyzer:
return None
if isinstance(analyzer, (PatternAnalyzer, CustomAnalyzer)):
return analyzer._to_generated() # pylint:disable=protected-access
return analyzer
def unpack_analyzer(analyzer):
if not analyzer:
return None
if isinstance(analyzer, _PatternAnalyzer):
return PatternAnalyzer._from_generated( # pylint:disable=protected-access
analyzer
)
if isinstance(analyzer, _CustomAnalyzer):
return CustomAnalyzer._from_generated( # pylint:disable=protected-access
analyzer
)
return analyzer