# SPDX-License-Identifier: Apache-2.0
#
# The OpenSearch Contributors require contributions made to
# this file be licensed under the Apache-2.0 license or a
# compatible open source license.
#
# Modifications Copyright OpenSearch Contributors. See
# GitHub history for details.
#
# Licensed to Elasticsearch B.V. under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you under
# the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from typing import Any, Optional
import six
from opensearchpy.connection.connections import get_connection
from .utils import AttrDict, DslBase, merge
class AnalysisBase(object):
@classmethod
def _type_shortcut(
cls: Any, name_or_instance: Any, type: Any = None, **kwargs: Any
) -> Any:
if isinstance(name_or_instance, cls):
if type or kwargs:
raise ValueError("%s() cannot accept parameters." % cls.__name__)
return name_or_instance
if not (type or kwargs):
return cls.get_dsl_class("builtin")(name_or_instance)
return cls.get_dsl_class(type, "custom")(
name_or_instance, type or "custom", **kwargs
)
class CustomAnalysis(object):
name: Optional[str] = "custom"
def __init__(
self, filter_name: str, builtin_type: str = "custom", **kwargs: Any
) -> None:
self._builtin_type = builtin_type
self._name = filter_name
super(CustomAnalysis, self).__init__(**kwargs)
def to_dict(self) -> Any:
# only name to present in lists
return self._name
def get_definition(self) -> Any:
d = super(CustomAnalysis, self).to_dict() # type: ignore
d = d.pop(self.name)
d["type"] = self._builtin_type
return d
class CustomAnalysisDefinition(CustomAnalysis):
def get_analysis_definition(self: Any) -> Any:
out = {self._type_name: {self._name: self.get_definition()}}
t: Any = getattr(self, "tokenizer", None)
if "tokenizer" in self._param_defs and hasattr(t, "get_definition"):
out["tokenizer"] = {t._name: t.get_definition()}
filters = {
f._name: f.get_definition()
for f in self.filter
if hasattr(f, "get_definition")
}
if filters:
out["filter"] = filters
# any sub filter definitions like multiplexers etc?
for f in self.filter:
if hasattr(f, "get_analysis_definition"):
d = f.get_analysis_definition()
if d:
merge(out, d, True)
char_filters = {
f._name: f.get_definition()
for f in self.char_filter
if hasattr(f, "get_definition")
}
if char_filters:
out["char_filter"] = char_filters
return out
class BuiltinAnalysis(object):
name: Optional[str] = "builtin"
def __init__(self, name: Any) -> None:
self._name = name
super(BuiltinAnalysis, self).__init__()
def to_dict(self) -> Any:
# only name to present in lists
return self._name
[docs]class Analyzer(AnalysisBase, DslBase):
_type_name: str = "analyzer"
name: Optional[str] = None
class BuiltinAnalyzer(BuiltinAnalysis, Analyzer):
def get_analysis_definition(self) -> Any:
return {}
class CustomAnalyzer(CustomAnalysisDefinition, Analyzer):
_param_defs = {
"filter": {"type": "token_filter", "multi": True},
"char_filter": {"type": "char_filter", "multi": True},
"tokenizer": {"type": "tokenizer"},
}
def simulate(
self,
text: Any,
using: str = "default",
explain: bool = False,
attributes: Any = None,
) -> Any:
"""
Use the Analyze API of opensearch to test the outcome of this analyzer.
:arg text: Text to be analyzed
:arg using: connection alias to use, defaults to ``'default'``
:arg explain: will output all token attributes for each token. You can
filter token attributes you want to output by setting ``attributes``
option.
:arg attributes: if ``explain`` is specified, filter the token
attributes to return.
"""
opensearch = get_connection(using)
body = {"text": text, "explain": explain}
if attributes:
body["attributes"] = attributes
definition = self.get_analysis_definition()
analyzer_def = self.get_definition()
for section in ("tokenizer", "char_filter", "filter"):
if section not in analyzer_def:
continue
sec_def = definition.get(section, {})
sec_names = analyzer_def[section]
if isinstance(sec_names, six.string_types):
body[section] = sec_def.get(sec_names, sec_names)
else:
body[section] = [
sec_def.get(sec_name, sec_name) for sec_name in sec_names
]
if self._builtin_type != "custom":
body["analyzer"] = self._builtin_type
return AttrDict(opensearch.indices.analyze(body=body))
class Normalizer(AnalysisBase, DslBase):
_type_name: str = "normalizer"
name: Optional[str] = None
class BuiltinNormalizer(BuiltinAnalysis, Normalizer):
def get_analysis_definition(self) -> Any:
return {}
class CustomNormalizer(CustomAnalysisDefinition, Normalizer):
_param_defs = {
"filter": {"type": "token_filter", "multi": True},
"char_filter": {"type": "char_filter", "multi": True},
}
class Tokenizer(AnalysisBase, DslBase):
_type_name: str = "tokenizer"
name: Optional[str] = None
class BuiltinTokenizer(BuiltinAnalysis, Tokenizer):
pass
class CustomTokenizer(CustomAnalysis, Tokenizer):
pass
class TokenFilter(AnalysisBase, DslBase):
_type_name: str = "token_filter"
name: Optional[str] = None
class BuiltinTokenFilter(BuiltinAnalysis, TokenFilter):
pass
class CustomTokenFilter(CustomAnalysis, TokenFilter):
pass
class MultiplexerTokenFilter(CustomTokenFilter):
name = "multiplexer"
def get_definition(self) -> Any:
d = super(CustomTokenFilter, self).get_definition()
if "filters" in d:
d["filters"] = [
# comma delimited string given by user
(
fs
if isinstance(fs, six.string_types)
else
# list of strings or TokenFilter objects
", ".join(f.to_dict() if hasattr(f, "to_dict") else f for f in fs)
)
for fs in self.filters
]
return d
def get_analysis_definition(self) -> Any:
if not hasattr(self, "filters"):
return {}
fs: Any = {}
d = {"filter": fs}
for filters in self.filters:
if isinstance(filters, six.string_types):
continue
fs.update(
{
f._name: f.get_definition()
for f in filters
if hasattr(f, "get_definition")
}
)
return d
class ConditionalTokenFilter(CustomTokenFilter):
name = "condition"
def get_definition(self) -> Any:
d = super(CustomTokenFilter, self).get_definition()
if "filter" in d:
d["filter"] = [
f.to_dict() if hasattr(f, "to_dict") else f for f in self.filter
]
return d
def get_analysis_definition(self) -> Any:
if not hasattr(self, "filter"):
return {}
return {
"filter": {
f._name: f.get_definition()
for f in self.filter
if hasattr(f, "get_definition")
}
}
class CharFilter(AnalysisBase, DslBase):
_type_name: str = "char_filter"
name: Optional[str] = None
class BuiltinCharFilter(BuiltinAnalysis, CharFilter):
pass
class CustomCharFilter(CustomAnalysis, CharFilter):
pass
# shortcuts for direct use
analyzer = Analyzer._type_shortcut
tokenizer = Tokenizer._type_shortcut
token_filter = TokenFilter._type_shortcut
char_filter = CharFilter._type_shortcut
normalizer = Normalizer._type_shortcut
__all__ = ["tokenizer", "analyzer", "char_filter", "token_filter", "normalizer"]