# coding: utf-8
# (C) Copyright IBM Corp. 2019, 2020.
#
# Licensed 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.
"""
The IBM Watson™ Assistant service combines machine learning, natural language
understanding, and an integrated dialog editor to create conversation flows between your
apps and your users.
The Assistant v2 API provides runtime methods your client application can use to send user
input to an assistant and receive a response.
"""
import json
from ibm_cloud_sdk_core.authenticators.authenticator import Authenticator
from .common import get_sdk_headers
from enum import Enum
from ibm_cloud_sdk_core import BaseService
from ibm_cloud_sdk_core import DetailedResponse
from ibm_cloud_sdk_core.get_authenticator import get_authenticator_from_environment
from typing import Dict
from typing import List
##############################################################################
# Service
##############################################################################
[docs]class AssistantV2(BaseService):
"""The Assistant V2 service."""
DEFAULT_SERVICE_URL = 'https://api.us-south.assistant.watson.cloud.ibm.com'
DEFAULT_SERVICE_NAME = 'assistant'
def __init__(
self,
version: str,
authenticator: Authenticator = None,
service_name: str = DEFAULT_SERVICE_NAME,
) -> None:
"""
Construct a new client for the Assistant service.
:param str version: The API version date to use with the service, in
"YYYY-MM-DD" format. Whenever the API is changed in a backwards
incompatible way, a new minor version of the API is released.
The service uses the API version for the date you specify, or
the most recent version before that date. Note that you should
not programmatically specify the current date at runtime, in
case the API has been updated since your application's release.
Instead, specify a version date that is compatible with your
application, and don't change it until your application is
ready for a later version.
:param Authenticator authenticator: The authenticator specifies the authentication mechanism.
Get up to date information from https://github.com/IBM/python-sdk-core/blob/master/README.md
about initializing the authenticator of your choice.
"""
if not authenticator:
authenticator = get_authenticator_from_environment(service_name)
BaseService.__init__(self,
service_url=self.DEFAULT_SERVICE_URL,
authenticator=authenticator,
disable_ssl_verification=False)
self.version = version
self.configure_service(service_name)
#########################
# Sessions
#########################
[docs] def create_session(self, assistant_id: str, **kwargs) -> 'DetailedResponse':
"""
Create a session.
Create a new session. A session is used to send user input to a skill and receive
responses. It also maintains the state of the conversation. A session persists
until it is deleted, or until it times out because of inactivity. (For more
information, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-settings).
:param str assistant_id: Unique identifier of the assistant. To find the
assistant ID in the Watson Assistant user interface, open the assistant
settings and click **API Details**. For information about creating
assistants, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-add#assistant-add-task).
**Note:** Currently, the v2 API does not support creating assistants.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if assistant_id is None:
raise ValueError('assistant_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME,
service_version='V2',
operation_id='create_session')
headers.update(sdk_headers)
params = {'version': self.version}
url = '/v2/assistants/{0}/sessions'.format(
*self._encode_path_vars(assistant_id))
request = self.prepare_request(method='POST',
url=url,
headers=headers,
params=params)
response = self.send(request)
return response
[docs] def delete_session(self, assistant_id: str, session_id: str,
**kwargs) -> 'DetailedResponse':
"""
Delete session.
Deletes a session explicitly before it times out. (For more information about the
session inactivity timeout, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-settings)).
:param str assistant_id: Unique identifier of the assistant. To find the
assistant ID in the Watson Assistant user interface, open the assistant
settings and click **API Details**. For information about creating
assistants, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-add#assistant-add-task).
**Note:** Currently, the v2 API does not support creating assistants.
:param str session_id: Unique identifier of the session.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if assistant_id is None:
raise ValueError('assistant_id must be provided')
if session_id is None:
raise ValueError('session_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME,
service_version='V2',
operation_id='delete_session')
headers.update(sdk_headers)
params = {'version': self.version}
url = '/v2/assistants/{0}/sessions/{1}'.format(
*self._encode_path_vars(assistant_id, session_id))
request = self.prepare_request(method='DELETE',
url=url,
headers=headers,
params=params)
response = self.send(request)
return response
#########################
# Message
#########################
[docs] def message(self,
assistant_id: str,
session_id: str,
*,
input: 'MessageInput' = None,
context: 'MessageContext' = None,
**kwargs) -> 'DetailedResponse':
"""
Send user input to assistant (stateful).
Send user input to an assistant and receive a response, with conversation state
(including context data) stored by Watson Assistant for the duration of the
session.
:param str assistant_id: Unique identifier of the assistant. To find the
assistant ID in the Watson Assistant user interface, open the assistant
settings and click **API Details**. For information about creating
assistants, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-add#assistant-add-task).
**Note:** Currently, the v2 API does not support creating assistants.
:param str session_id: Unique identifier of the session.
:param MessageInput input: (optional) An input object that includes the
input text.
:param MessageContext context: (optional) Context data for the
conversation. You can use this property to set or modify context variables,
which can also be accessed by dialog nodes. The context is stored by the
assistant on a per-session basis.
**Note:** The total size of the context data stored for a stateful session
cannot exceed 100KB.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if assistant_id is None:
raise ValueError('assistant_id must be provided')
if session_id is None:
raise ValueError('session_id must be provided')
if input is not None:
input = self._convert_model(input)
if context is not None:
context = self._convert_model(context)
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME,
service_version='V2',
operation_id='message')
headers.update(sdk_headers)
params = {'version': self.version}
data = {'input': input, 'context': context}
url = '/v2/assistants/{0}/sessions/{1}/message'.format(
*self._encode_path_vars(assistant_id, session_id))
request = self.prepare_request(method='POST',
url=url,
headers=headers,
params=params,
data=data)
response = self.send(request)
return response
[docs] def message_stateless(self,
assistant_id: str,
*,
input: 'MessageInputStateless' = None,
context: 'MessageContextStateless' = None,
**kwargs) -> 'DetailedResponse':
"""
Send user input to assistant (stateless).
Send user input to an assistant and receive a response, with conversation state
(including context data) managed by your application.
:param str assistant_id: Unique identifier of the assistant. To find the
assistant ID in the Watson Assistant user interface, open the assistant
settings and click **API Details**. For information about creating
assistants, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-add#assistant-add-task).
**Note:** Currently, the v2 API does not support creating assistants.
:param MessageInputStateless input: (optional) An input object that
includes the input text.
:param MessageContextStateless context: (optional) Context data for the
conversation. You can use this property to set or modify context variables,
which can also be accessed by dialog nodes. The context is not stored by
the assistant. To maintain session state, include the context from the
previous response.
**Note:** The total size of the context data for a stateless session cannot
exceed 250KB.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if assistant_id is None:
raise ValueError('assistant_id must be provided')
if input is not None:
input = self._convert_model(input)
if context is not None:
context = self._convert_model(context)
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME,
service_version='V2',
operation_id='message_stateless')
headers.update(sdk_headers)
params = {'version': self.version}
data = {'input': input, 'context': context}
url = '/v2/assistants/{0}/message'.format(
*self._encode_path_vars(assistant_id))
request = self.prepare_request(method='POST',
url=url,
headers=headers,
params=params,
data=data)
response = self.send(request)
return response
#########################
# Logs
#########################
[docs] def list_logs(self,
assistant_id: str,
*,
sort: str = None,
filter: str = None,
page_limit: int = None,
cursor: str = None,
**kwargs) -> 'DetailedResponse':
"""
List log events for an assistant.
List the events from the log of an assistant.
This method is available only with Premium plans.
:param str assistant_id: Unique identifier of the assistant. To find the
assistant ID in the Watson Assistant user interface, open the assistant
settings and click **API Details**. For information about creating
assistants, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-add#assistant-add-task).
**Note:** Currently, the v2 API does not support creating assistants.
:param str sort: (optional) How to sort the returned log events. You can
sort by **request_timestamp**. To reverse the sort order, prefix the
parameter value with a minus sign (`-`).
:param str filter: (optional) A cacheable parameter that limits the results
to those matching the specified filter. For more information, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-filter-reference#filter-reference).
:param int page_limit: (optional) The number of records to return in each
page of results.
:param str cursor: (optional) A token identifying the page of results to
retrieve.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if assistant_id is None:
raise ValueError('assistant_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME,
service_version='V2',
operation_id='list_logs')
headers.update(sdk_headers)
params = {
'version': self.version,
'sort': sort,
'filter': filter,
'page_limit': page_limit,
'cursor': cursor
}
url = '/v2/assistants/{0}/logs'.format(
*self._encode_path_vars(assistant_id))
request = self.prepare_request(method='GET',
url=url,
headers=headers,
params=params)
response = self.send(request)
return response
#########################
# User data
#########################
[docs] def delete_user_data(self, customer_id: str,
**kwargs) -> 'DetailedResponse':
"""
Delete labeled data.
Deletes all data associated with a specified customer ID. The method has no effect
if no data is associated with the customer ID.
You associate a customer ID with data by passing the `X-Watson-Metadata` header
with a request that passes data. For more information about personal data and
customer IDs, see [Information
security](https://cloud.ibm.com/docs/assistant?topic=assistant-information-security#information-security).
This operation is limited to 4 requests per minute. For more information, see
**Rate limiting**.
:param str customer_id: The customer ID for which all data is to be
deleted.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customer_id is None:
raise ValueError('customer_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME,
service_version='V2',
operation_id='delete_user_data')
headers.update(sdk_headers)
params = {'version': self.version, 'customer_id': customer_id}
url = '/v2/user_data'
request = self.prepare_request(method='DELETE',
url=url,
headers=headers,
params=params)
response = self.send(request)
return response
##############################################################################
# Models
##############################################################################
[docs]class CaptureGroup():
"""
CaptureGroup.
:attr str group: A recognized capture group for the entity.
:attr List[int] location: (optional) Zero-based character offsets that indicate
where the entity value begins and ends in the input text.
"""
def __init__(self, group: str, *, location: List[int] = None) -> None:
"""
Initialize a CaptureGroup object.
:param str group: A recognized capture group for the entity.
:param List[int] location: (optional) Zero-based character offsets that
indicate where the entity value begins and ends in the input text.
"""
self.group = group
self.location = location
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'CaptureGroup':
"""Initialize a CaptureGroup object from a json dictionary."""
args = {}
valid_keys = ['group', 'location']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class CaptureGroup: '
+ ', '.join(bad_keys))
if 'group' in _dict:
args['group'] = _dict.get('group')
else:
raise ValueError(
'Required property \'group\' not present in CaptureGroup JSON')
if 'location' in _dict:
args['location'] = _dict.get('location')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CaptureGroup object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'group') and self.group is not None:
_dict['group'] = self.group
if hasattr(self, 'location') and self.location is not None:
_dict['location'] = self.location
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this CaptureGroup object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'CaptureGroup') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'CaptureGroup') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogLogMessage():
"""
Dialog log message details.
:attr str level: The severity of the log message.
:attr str message: The text of the log message.
"""
def __init__(self, level: str, message: str) -> None:
"""
Initialize a DialogLogMessage object.
:param str level: The severity of the log message.
:param str message: The text of the log message.
"""
self.level = level
self.message = message
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'DialogLogMessage':
"""Initialize a DialogLogMessage object from a json dictionary."""
args = {}
valid_keys = ['level', 'message']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogLogMessage: '
+ ', '.join(bad_keys))
if 'level' in _dict:
args['level'] = _dict.get('level')
else:
raise ValueError(
'Required property \'level\' not present in DialogLogMessage JSON'
)
if 'message' in _dict:
args['message'] = _dict.get('message')
else:
raise ValueError(
'Required property \'message\' not present in DialogLogMessage JSON'
)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogLogMessage object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'level') and self.level is not None:
_dict['level'] = self.level
if hasattr(self, 'message') and self.message is not None:
_dict['message'] = self.message
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this DialogLogMessage object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'DialogLogMessage') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'DialogLogMessage') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class LevelEnum(Enum):
"""
The severity of the log message.
"""
INFO = "info"
ERROR = "error"
WARN = "warn"
[docs]class DialogNodeAction():
"""
DialogNodeAction.
:attr str name: The name of the action.
:attr str type: (optional) The type of action to invoke.
:attr dict parameters: (optional) A map of key/value pairs to be provided to the
action.
:attr str result_variable: The location in the dialog context where the result
of the action is stored.
:attr str credentials: (optional) The name of the context variable that the
client application will use to pass in credentials for the action.
"""
def __init__(self,
name: str,
result_variable: str,
*,
type: str = None,
parameters: dict = None,
credentials: str = None) -> None:
"""
Initialize a DialogNodeAction object.
:param str name: The name of the action.
:param str result_variable: The location in the dialog context where the
result of the action is stored.
:param str type: (optional) The type of action to invoke.
:param dict parameters: (optional) A map of key/value pairs to be provided
to the action.
:param str credentials: (optional) The name of the context variable that
the client application will use to pass in credentials for the action.
"""
self.name = name
self.type = type
self.parameters = parameters
self.result_variable = result_variable
self.credentials = credentials
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'DialogNodeAction':
"""Initialize a DialogNodeAction object from a json dictionary."""
args = {}
valid_keys = [
'name', 'type', 'parameters', 'result_variable', 'credentials'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodeAction: '
+ ', '.join(bad_keys))
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in DialogNodeAction JSON'
)
if 'type' in _dict:
args['type'] = _dict.get('type')
if 'parameters' in _dict:
args['parameters'] = _dict.get('parameters')
if 'result_variable' in _dict:
args['result_variable'] = _dict.get('result_variable')
else:
raise ValueError(
'Required property \'result_variable\' not present in DialogNodeAction JSON'
)
if 'credentials' in _dict:
args['credentials'] = _dict.get('credentials')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodeAction object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
if hasattr(self, 'parameters') and self.parameters is not None:
_dict['parameters'] = self.parameters
if hasattr(self,
'result_variable') and self.result_variable is not None:
_dict['result_variable'] = self.result_variable
if hasattr(self, 'credentials') and self.credentials is not None:
_dict['credentials'] = self.credentials
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this DialogNodeAction object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'DialogNodeAction') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'DialogNodeAction') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class TypeEnum(Enum):
"""
The type of action to invoke.
"""
CLIENT = "client"
SERVER = "server"
WEB_ACTION = "web-action"
CLOUD_FUNCTION = "cloud-function"
[docs]class DialogNodeOutputOptionsElement():
"""
DialogNodeOutputOptionsElement.
:attr str label: The user-facing label for the option.
:attr DialogNodeOutputOptionsElementValue value: An object defining the message
input to be sent to the assistant if the user selects the corresponding option.
"""
def __init__(self, label: str,
value: 'DialogNodeOutputOptionsElementValue') -> None:
"""
Initialize a DialogNodeOutputOptionsElement object.
:param str label: The user-facing label for the option.
:param DialogNodeOutputOptionsElementValue value: An object defining the
message input to be sent to the assistant if the user selects the
corresponding option.
"""
self.label = label
self.value = value
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'DialogNodeOutputOptionsElement':
"""Initialize a DialogNodeOutputOptionsElement object from a json dictionary."""
args = {}
valid_keys = ['label', 'value']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodeOutputOptionsElement: '
+ ', '.join(bad_keys))
if 'label' in _dict:
args['label'] = _dict.get('label')
else:
raise ValueError(
'Required property \'label\' not present in DialogNodeOutputOptionsElement JSON'
)
if 'value' in _dict:
args['value'] = DialogNodeOutputOptionsElementValue._from_dict(
_dict.get('value'))
else:
raise ValueError(
'Required property \'value\' not present in DialogNodeOutputOptionsElement JSON'
)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodeOutputOptionsElement object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'label') and self.label is not None:
_dict['label'] = self.label
if hasattr(self, 'value') and self.value is not None:
_dict['value'] = self.value._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this DialogNodeOutputOptionsElement object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'DialogNodeOutputOptionsElement') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'DialogNodeOutputOptionsElement') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogNodeOutputOptionsElementValue():
"""
An object defining the message input to be sent to the assistant if the user selects
the corresponding option.
:attr MessageInput input: (optional) An input object that includes the input
text.
"""
def __init__(self, *, input: 'MessageInput' = None) -> None:
"""
Initialize a DialogNodeOutputOptionsElementValue object.
:param MessageInput input: (optional) An input object that includes the
input text.
"""
self.input = input
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'DialogNodeOutputOptionsElementValue':
"""Initialize a DialogNodeOutputOptionsElementValue object from a json dictionary."""
args = {}
valid_keys = ['input']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodeOutputOptionsElementValue: '
+ ', '.join(bad_keys))
if 'input' in _dict:
args['input'] = MessageInput._from_dict(_dict.get('input'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodeOutputOptionsElementValue object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'input') and self.input is not None:
_dict['input'] = self.input._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this DialogNodeOutputOptionsElementValue object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'DialogNodeOutputOptionsElementValue') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'DialogNodeOutputOptionsElementValue') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogNodesVisited():
"""
DialogNodesVisited.
:attr str dialog_node: (optional) A dialog node that was triggered during
processing of the input message.
:attr str title: (optional) The title of the dialog node.
:attr str conditions: (optional) The conditions that trigger the dialog node.
"""
def __init__(self,
*,
dialog_node: str = None,
title: str = None,
conditions: str = None) -> None:
"""
Initialize a DialogNodesVisited object.
:param str dialog_node: (optional) A dialog node that was triggered during
processing of the input message.
:param str title: (optional) The title of the dialog node.
:param str conditions: (optional) The conditions that trigger the dialog
node.
"""
self.dialog_node = dialog_node
self.title = title
self.conditions = conditions
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'DialogNodesVisited':
"""Initialize a DialogNodesVisited object from a json dictionary."""
args = {}
valid_keys = ['dialog_node', 'title', 'conditions']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodesVisited: '
+ ', '.join(bad_keys))
if 'dialog_node' in _dict:
args['dialog_node'] = _dict.get('dialog_node')
if 'title' in _dict:
args['title'] = _dict.get('title')
if 'conditions' in _dict:
args['conditions'] = _dict.get('conditions')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodesVisited object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'dialog_node') and self.dialog_node is not None:
_dict['dialog_node'] = self.dialog_node
if hasattr(self, 'title') and self.title is not None:
_dict['title'] = self.title
if hasattr(self, 'conditions') and self.conditions is not None:
_dict['conditions'] = self.conditions
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this DialogNodesVisited object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'DialogNodesVisited') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'DialogNodesVisited') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogSuggestion():
"""
DialogSuggestion.
:attr str label: The user-facing label for the suggestion. This label is taken
from the **title** or **user_label** property of the corresponding dialog node,
depending on the disambiguation options.
:attr DialogSuggestionValue value: An object defining the message input to be
sent to the assistant if the user selects the corresponding disambiguation
option.
:attr dict output: (optional) The dialog output that will be returned from the
Watson Assistant service if the user selects the corresponding option.
"""
def __init__(self,
label: str,
value: 'DialogSuggestionValue',
*,
output: dict = None) -> None:
"""
Initialize a DialogSuggestion object.
:param str label: The user-facing label for the suggestion. This label is
taken from the **title** or **user_label** property of the corresponding
dialog node, depending on the disambiguation options.
:param DialogSuggestionValue value: An object defining the message input to
be sent to the assistant if the user selects the corresponding
disambiguation option.
:param dict output: (optional) The dialog output that will be returned from
the Watson Assistant service if the user selects the corresponding option.
"""
self.label = label
self.value = value
self.output = output
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'DialogSuggestion':
"""Initialize a DialogSuggestion object from a json dictionary."""
args = {}
valid_keys = ['label', 'value', 'output']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogSuggestion: '
+ ', '.join(bad_keys))
if 'label' in _dict:
args['label'] = _dict.get('label')
else:
raise ValueError(
'Required property \'label\' not present in DialogSuggestion JSON'
)
if 'value' in _dict:
args['value'] = DialogSuggestionValue._from_dict(_dict.get('value'))
else:
raise ValueError(
'Required property \'value\' not present in DialogSuggestion JSON'
)
if 'output' in _dict:
args['output'] = _dict.get('output')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogSuggestion object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'label') and self.label is not None:
_dict['label'] = self.label
if hasattr(self, 'value') and self.value is not None:
_dict['value'] = self.value._to_dict()
if hasattr(self, 'output') and self.output is not None:
_dict['output'] = self.output
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this DialogSuggestion object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'DialogSuggestion') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'DialogSuggestion') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogSuggestionValue():
"""
An object defining the message input to be sent to the assistant if the user selects
the corresponding disambiguation option.
:attr MessageInput input: (optional) An input object that includes the input
text.
"""
def __init__(self, *, input: 'MessageInput' = None) -> None:
"""
Initialize a DialogSuggestionValue object.
:param MessageInput input: (optional) An input object that includes the
input text.
"""
self.input = input
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'DialogSuggestionValue':
"""Initialize a DialogSuggestionValue object from a json dictionary."""
args = {}
valid_keys = ['input']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogSuggestionValue: '
+ ', '.join(bad_keys))
if 'input' in _dict:
args['input'] = MessageInput._from_dict(_dict.get('input'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogSuggestionValue object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'input') and self.input is not None:
_dict['input'] = self.input._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this DialogSuggestionValue object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'DialogSuggestionValue') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'DialogSuggestionValue') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Log():
"""
Log.
:attr str log_id: A unique identifier for the logged event.
:attr MessageRequest request: A stateful message request formatted for the
Watson Assistant service.
:attr MessageResponse response: A response from the Watson Assistant service.
:attr str assistant_id: Unique identifier of the assistant.
:attr str session_id: The ID of the session the message was part of.
:attr str skill_id: The unique identifier of the skill that responded to the
message.
:attr str snapshot: The name of the snapshot (dialog skill version) that
responded to the message (for example, `draft`).
:attr str request_timestamp: The timestamp for receipt of the message.
:attr str response_timestamp: The timestamp for the system response to the
message.
:attr str language: The language of the assistant to which the message request
was made.
:attr str customer_id: (optional) The customer ID specified for the message, if
any.
"""
def __init__(self,
log_id: str,
request: 'MessageRequest',
response: 'MessageResponse',
assistant_id: str,
session_id: str,
skill_id: str,
snapshot: str,
request_timestamp: str,
response_timestamp: str,
language: str,
*,
customer_id: str = None) -> None:
"""
Initialize a Log object.
:param str log_id: A unique identifier for the logged event.
:param MessageRequest request: A stateful message request formatted for the
Watson Assistant service.
:param MessageResponse response: A response from the Watson Assistant
service.
:param str assistant_id: Unique identifier of the assistant.
:param str session_id: The ID of the session the message was part of.
:param str skill_id: The unique identifier of the skill that responded to
the message.
:param str snapshot: The name of the snapshot (dialog skill version) that
responded to the message (for example, `draft`).
:param str request_timestamp: The timestamp for receipt of the message.
:param str response_timestamp: The timestamp for the system response to the
message.
:param str language: The language of the assistant to which the message
request was made.
:param str customer_id: (optional) The customer ID specified for the
message, if any.
"""
self.log_id = log_id
self.request = request
self.response = response
self.assistant_id = assistant_id
self.session_id = session_id
self.skill_id = skill_id
self.snapshot = snapshot
self.request_timestamp = request_timestamp
self.response_timestamp = response_timestamp
self.language = language
self.customer_id = customer_id
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'Log':
"""Initialize a Log object from a json dictionary."""
args = {}
valid_keys = [
'log_id', 'request', 'response', 'assistant_id', 'session_id',
'skill_id', 'snapshot', 'request_timestamp', 'response_timestamp',
'language', 'customer_id'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Log: ' +
', '.join(bad_keys))
if 'log_id' in _dict:
args['log_id'] = _dict.get('log_id')
else:
raise ValueError(
'Required property \'log_id\' not present in Log JSON')
if 'request' in _dict:
args['request'] = MessageRequest._from_dict(_dict.get('request'))
else:
raise ValueError(
'Required property \'request\' not present in Log JSON')
if 'response' in _dict:
args['response'] = MessageResponse._from_dict(_dict.get('response'))
else:
raise ValueError(
'Required property \'response\' not present in Log JSON')
if 'assistant_id' in _dict:
args['assistant_id'] = _dict.get('assistant_id')
else:
raise ValueError(
'Required property \'assistant_id\' not present in Log JSON')
if 'session_id' in _dict:
args['session_id'] = _dict.get('session_id')
else:
raise ValueError(
'Required property \'session_id\' not present in Log JSON')
if 'skill_id' in _dict:
args['skill_id'] = _dict.get('skill_id')
else:
raise ValueError(
'Required property \'skill_id\' not present in Log JSON')
if 'snapshot' in _dict:
args['snapshot'] = _dict.get('snapshot')
else:
raise ValueError(
'Required property \'snapshot\' not present in Log JSON')
if 'request_timestamp' in _dict:
args['request_timestamp'] = _dict.get('request_timestamp')
else:
raise ValueError(
'Required property \'request_timestamp\' not present in Log JSON'
)
if 'response_timestamp' in _dict:
args['response_timestamp'] = _dict.get('response_timestamp')
else:
raise ValueError(
'Required property \'response_timestamp\' not present in Log JSON'
)
if 'language' in _dict:
args['language'] = _dict.get('language')
else:
raise ValueError(
'Required property \'language\' not present in Log JSON')
if 'customer_id' in _dict:
args['customer_id'] = _dict.get('customer_id')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Log object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'log_id') and self.log_id is not None:
_dict['log_id'] = self.log_id
if hasattr(self, 'request') and self.request is not None:
_dict['request'] = self.request._to_dict()
if hasattr(self, 'response') and self.response is not None:
_dict['response'] = self.response._to_dict()
if hasattr(self, 'assistant_id') and self.assistant_id is not None:
_dict['assistant_id'] = self.assistant_id
if hasattr(self, 'session_id') and self.session_id is not None:
_dict['session_id'] = self.session_id
if hasattr(self, 'skill_id') and self.skill_id is not None:
_dict['skill_id'] = self.skill_id
if hasattr(self, 'snapshot') and self.snapshot is not None:
_dict['snapshot'] = self.snapshot
if hasattr(self,
'request_timestamp') and self.request_timestamp is not None:
_dict['request_timestamp'] = self.request_timestamp
if hasattr(
self,
'response_timestamp') and self.response_timestamp is not None:
_dict['response_timestamp'] = self.response_timestamp
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'customer_id') and self.customer_id is not None:
_dict['customer_id'] = self.customer_id
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this Log object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'Log') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'Log') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class LogCollection():
"""
LogCollection.
:attr List[Log] logs: An array of objects describing log events.
:attr LogPagination pagination: The pagination data for the returned objects.
"""
def __init__(self, logs: List['Log'], pagination: 'LogPagination') -> None:
"""
Initialize a LogCollection object.
:param List[Log] logs: An array of objects describing log events.
:param LogPagination pagination: The pagination data for the returned
objects.
"""
self.logs = logs
self.pagination = pagination
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'LogCollection':
"""Initialize a LogCollection object from a json dictionary."""
args = {}
valid_keys = ['logs', 'pagination']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class LogCollection: '
+ ', '.join(bad_keys))
if 'logs' in _dict:
args['logs'] = [Log._from_dict(x) for x in (_dict.get('logs'))]
else:
raise ValueError(
'Required property \'logs\' not present in LogCollection JSON')
if 'pagination' in _dict:
args['pagination'] = LogPagination._from_dict(
_dict.get('pagination'))
else:
raise ValueError(
'Required property \'pagination\' not present in LogCollection JSON'
)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a LogCollection object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'logs') and self.logs is not None:
_dict['logs'] = [x._to_dict() for x in self.logs]
if hasattr(self, 'pagination') and self.pagination is not None:
_dict['pagination'] = self.pagination._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this LogCollection object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'LogCollection') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'LogCollection') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContext():
"""
MessageContext.
:attr MessageContextGlobal global_: (optional) Session context data that is
shared by all skills used by the Assistant.
:attr MessageContextSkills skills: (optional) Information specific to particular
skills used by the assistant.
**Note:** Currently, only a single child property is supported, containing
variables that apply to the dialog skill used by the assistant.
"""
def __init__(self,
*,
global_: 'MessageContextGlobal' = None,
skills: 'MessageContextSkills' = None) -> None:
"""
Initialize a MessageContext object.
:param MessageContextGlobal global_: (optional) Session context data that
is shared by all skills used by the Assistant.
:param MessageContextSkills skills: (optional) Information specific to
particular skills used by the assistant.
**Note:** Currently, only a single child property is supported, containing
variables that apply to the dialog skill used by the assistant.
"""
self.global_ = global_
self.skills = skills
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContext':
"""Initialize a MessageContext object from a json dictionary."""
args = {}
valid_keys = ['global_', 'global', 'skills']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContext: '
+ ', '.join(bad_keys))
if 'global' in _dict:
args['global_'] = MessageContextGlobal._from_dict(
_dict.get('global'))
if 'skills' in _dict:
args['skills'] = MessageContextSkills._from_dict(
_dict.get('skills'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContext object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'global_') and self.global_ is not None:
_dict['global'] = self.global_._to_dict()
if hasattr(self, 'skills') and self.skills is not None:
_dict['skills'] = self.skills._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageContext object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContext') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContext') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextGlobal():
"""
Session context data that is shared by all skills used by the Assistant.
:attr MessageContextGlobalSystem system: (optional) Built-in system properties
that apply to all skills used by the assistant.
:attr str session_id: (optional) The session ID.
"""
def __init__(self,
*,
system: 'MessageContextGlobalSystem' = None,
session_id: str = None) -> None:
"""
Initialize a MessageContextGlobal object.
:param MessageContextGlobalSystem system: (optional) Built-in system
properties that apply to all skills used by the assistant.
:param str session_id: (optional) The session ID.
"""
self.system = system
self.session_id = session_id
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContextGlobal':
"""Initialize a MessageContextGlobal object from a json dictionary."""
args = {}
valid_keys = ['system', 'session_id']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextGlobal: '
+ ', '.join(bad_keys))
if 'system' in _dict:
args['system'] = MessageContextGlobalSystem._from_dict(
_dict.get('system'))
if 'session_id' in _dict:
args['session_id'] = _dict.get('session_id')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextGlobal object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'system') and self.system is not None:
_dict['system'] = self.system._to_dict()
if hasattr(self, 'session_id') and self.session_id is not None:
_dict['session_id'] = self.session_id
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageContextGlobal object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContextGlobal') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContextGlobal') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextGlobalStateless():
"""
Session context data that is shared by all skills used by the Assistant.
:attr MessageContextGlobalSystem system: (optional) Built-in system properties
that apply to all skills used by the assistant.
:attr str session_id: (optional) The unique identifier of the session.
"""
def __init__(self,
*,
system: 'MessageContextGlobalSystem' = None,
session_id: str = None) -> None:
"""
Initialize a MessageContextGlobalStateless object.
:param MessageContextGlobalSystem system: (optional) Built-in system
properties that apply to all skills used by the assistant.
:param str session_id: (optional) The unique identifier of the session.
"""
self.system = system
self.session_id = session_id
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContextGlobalStateless':
"""Initialize a MessageContextGlobalStateless object from a json dictionary."""
args = {}
valid_keys = ['system', 'session_id']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextGlobalStateless: '
+ ', '.join(bad_keys))
if 'system' in _dict:
args['system'] = MessageContextGlobalSystem._from_dict(
_dict.get('system'))
if 'session_id' in _dict:
args['session_id'] = _dict.get('session_id')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextGlobalStateless object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'system') and self.system is not None:
_dict['system'] = self.system._to_dict()
if hasattr(self, 'session_id') and self.session_id is not None:
_dict['session_id'] = self.session_id
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageContextGlobalStateless object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContextGlobalStateless') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContextGlobalStateless') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextGlobalSystem():
"""
Built-in system properties that apply to all skills used by the assistant.
:attr str timezone: (optional) The user time zone. The assistant uses the time
zone to correctly resolve relative time references.
:attr str user_id: (optional) A string value that identifies the user who is
interacting with the assistant. The client must provide a unique identifier for
each individual end user who accesses the application. For Plus and Premium
plans, this user ID is used to identify unique users for billing purposes. This
string cannot contain carriage return, newline, or tab characters.
:attr int turn_count: (optional) A counter that is automatically incremented
with each turn of the conversation. A value of 1 indicates that this is the the
first turn of a new conversation, which can affect the behavior of some skills
(for example, triggering the start node of a dialog).
:attr str locale: (optional) The language code for localization in the user
input. The specified locale overrides the default for the assistant, and is used
for interpreting entity values in user input such as date values. For example,
`04/03/2018` might be interpreted either as April 3 or March 4, depending on the
locale.
This property is included only if the new system entities are enabled for the
skill.
:attr str reference_time: (optional) The base time for interpreting any relative
time mentions in the user input. The specified time overrides the current server
time, and is used to calculate times mentioned in relative terms such as `now`
or `tomorrow`. This can be useful for simulating past or future times for
testing purposes, or when analyzing documents such as news articles.
This value must be a UTC time value formatted according to ISO 8601 (for
example, `2019-06-26T12:00:00Z` for noon on 26 June 2019.
This property is included only if the new system entities are enabled for the
skill.
"""
def __init__(self,
*,
timezone: str = None,
user_id: str = None,
turn_count: int = None,
locale: str = None,
reference_time: str = None) -> None:
"""
Initialize a MessageContextGlobalSystem object.
:param str timezone: (optional) The user time zone. The assistant uses the
time zone to correctly resolve relative time references.
:param str user_id: (optional) A string value that identifies the user who
is interacting with the assistant. The client must provide a unique
identifier for each individual end user who accesses the application. For
Plus and Premium plans, this user ID is used to identify unique users for
billing purposes. This string cannot contain carriage return, newline, or
tab characters.
:param int turn_count: (optional) A counter that is automatically
incremented with each turn of the conversation. A value of 1 indicates that
this is the the first turn of a new conversation, which can affect the
behavior of some skills (for example, triggering the start node of a
dialog).
:param str locale: (optional) The language code for localization in the
user input. The specified locale overrides the default for the assistant,
and is used for interpreting entity values in user input such as date
values. For example, `04/03/2018` might be interpreted either as April 3 or
March 4, depending on the locale.
This property is included only if the new system entities are enabled for
the skill.
:param str reference_time: (optional) The base time for interpreting any
relative time mentions in the user input. The specified time overrides the
current server time, and is used to calculate times mentioned in relative
terms such as `now` or `tomorrow`. This can be useful for simulating past
or future times for testing purposes, or when analyzing documents such as
news articles.
This value must be a UTC time value formatted according to ISO 8601 (for
example, `2019-06-26T12:00:00Z` for noon on 26 June 2019.
This property is included only if the new system entities are enabled for
the skill.
"""
self.timezone = timezone
self.user_id = user_id
self.turn_count = turn_count
self.locale = locale
self.reference_time = reference_time
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContextGlobalSystem':
"""Initialize a MessageContextGlobalSystem object from a json dictionary."""
args = {}
valid_keys = [
'timezone', 'user_id', 'turn_count', 'locale', 'reference_time'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextGlobalSystem: '
+ ', '.join(bad_keys))
if 'timezone' in _dict:
args['timezone'] = _dict.get('timezone')
if 'user_id' in _dict:
args['user_id'] = _dict.get('user_id')
if 'turn_count' in _dict:
args['turn_count'] = _dict.get('turn_count')
if 'locale' in _dict:
args['locale'] = _dict.get('locale')
if 'reference_time' in _dict:
args['reference_time'] = _dict.get('reference_time')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextGlobalSystem object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'timezone') and self.timezone is not None:
_dict['timezone'] = self.timezone
if hasattr(self, 'user_id') and self.user_id is not None:
_dict['user_id'] = self.user_id
if hasattr(self, 'turn_count') and self.turn_count is not None:
_dict['turn_count'] = self.turn_count
if hasattr(self, 'locale') and self.locale is not None:
_dict['locale'] = self.locale
if hasattr(self, 'reference_time') and self.reference_time is not None:
_dict['reference_time'] = self.reference_time
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageContextGlobalSystem object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContextGlobalSystem') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContextGlobalSystem') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class LocaleEnum(Enum):
"""
The language code for localization in the user input. The specified locale
overrides the default for the assistant, and is used for interpreting entity
values in user input such as date values. For example, `04/03/2018` might be
interpreted either as April 3 or March 4, depending on the locale.
This property is included only if the new system entities are enabled for the
skill.
"""
EN_US = "en-us"
EN_CA = "en-ca"
EN_GB = "en-gb"
AR_AR = "ar-ar"
CS_CZ = "cs-cz"
DE_DE = "de-de"
ES_ES = "es-es"
FR_FR = "fr-fr"
IT_IT = "it-it"
JA_JP = "ja-jp"
KO_KR = "ko-kr"
NL_NL = "nl-nl"
PT_BR = "pt-br"
ZH_CN = "zh-cn"
ZH_TW = "zh-tw"
[docs]class MessageContextSkill():
"""
Contains information specific to a particular skill used by the Assistant. The
property name must be the same as the name of the skill (for example, `main skill`).
:attr dict user_defined: (optional) Arbitrary variables that can be read and
written by a particular skill.
:attr dict system: (optional) System context data used by the skill.
"""
def __init__(self,
*,
user_defined: dict = None,
system: dict = None) -> None:
"""
Initialize a MessageContextSkill object.
:param dict user_defined: (optional) Arbitrary variables that can be read
and written by a particular skill.
:param dict system: (optional) System context data used by the skill.
"""
self.user_defined = user_defined
self.system = system
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContextSkill':
"""Initialize a MessageContextSkill object from a json dictionary."""
args = {}
valid_keys = ['user_defined', 'system']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextSkill: '
+ ', '.join(bad_keys))
if 'user_defined' in _dict:
args['user_defined'] = _dict.get('user_defined')
if 'system' in _dict:
args['system'] = _dict.get('system')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextSkill object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'user_defined') and self.user_defined is not None:
_dict['user_defined'] = self.user_defined
if hasattr(self, 'system') and self.system is not None:
_dict['system'] = self.system
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageContextSkill object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContextSkill') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContextSkill') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextSkillSystem():
"""
System context data used by the skill.
:attr str state: (optional) An encoded string representing the current
conversation state. By saving this value and then sending it in the context of a
subsequent message request, you can restore the conversation to the same state.
This can be useful if you need to return to an earlier point in the
conversation. If you are using stateful sessions, you can also use a stored
state value to restore a paused conversation whose session has expired.
"""
def __init__(self, *, state: str = None, **kwargs) -> None:
"""
Initialize a MessageContextSkillSystem object.
:param str state: (optional) An encoded string representing the current
conversation state. By saving this value and then sending it in the context
of a subsequent message request, you can restore the conversation to the
same state. This can be useful if you need to return to an earlier point in
the conversation. If you are using stateful sessions, you can also use a
stored state value to restore a paused conversation whose session has
expired.
:param **kwargs: (optional) Any additional properties.
"""
self.state = state
for _key, _value in kwargs.items():
setattr(self, _key, _value)
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContextSkillSystem':
"""Initialize a MessageContextSkillSystem object from a json dictionary."""
args = {}
xtra = _dict.copy()
if 'state' in _dict:
args['state'] = _dict.get('state')
del xtra['state']
args.update(xtra)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextSkillSystem object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'state') and self.state is not None:
_dict['state'] = self.state
if hasattr(self, '_additionalProperties'):
for _key in self._additionalProperties:
_value = getattr(self, _key, None)
if _value is not None:
_dict[_key] = _value
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __setattr__(self, name: str, value: object) -> None:
properties = {'state'}
if not hasattr(self, '_additionalProperties'):
super(MessageContextSkillSystem,
self).__setattr__('_additionalProperties', set())
if name not in properties:
self._additionalProperties.add(name)
super(MessageContextSkillSystem, self).__setattr__(name, value)
def __str__(self) -> str:
"""Return a `str` version of this MessageContextSkillSystem object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContextSkillSystem') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContextSkillSystem') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextSkills():
"""
Information specific to particular skills used by the assistant.
**Note:** Currently, only a single child property is supported, containing variables
that apply to the dialog skill used by the assistant.
"""
def __init__(self, **kwargs) -> None:
"""
Initialize a MessageContextSkills object.
:param **kwargs: (optional) Any additional properties.
"""
for _key, _value in kwargs.items():
setattr(self, _key, _value)
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContextSkills':
"""Initialize a MessageContextSkills object from a json dictionary."""
args = {}
xtra = _dict.copy()
args.update(xtra)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextSkills object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, '_additionalProperties'):
for _key in self._additionalProperties:
_value = getattr(self, _key, None)
if _value is not None:
_dict[_key] = _value
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __setattr__(self, name: str, value: object) -> None:
properties = {}
if not hasattr(self, '_additionalProperties'):
super(MessageContextSkills,
self).__setattr__('_additionalProperties', set())
if name not in properties:
self._additionalProperties.add(name)
super(MessageContextSkills, self).__setattr__(name, value)
def __str__(self) -> str:
"""Return a `str` version of this MessageContextSkills object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContextSkills') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContextSkills') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextStateless():
"""
MessageContextStateless.
:attr MessageContextGlobalStateless global_: (optional) Session context data
that is shared by all skills used by the Assistant.
:attr MessageContextSkills skills: (optional) Information specific to particular
skills used by the assistant.
**Note:** Currently, only a single child property is supported, containing
variables that apply to the dialog skill used by the assistant.
"""
def __init__(self,
*,
global_: 'MessageContextGlobalStateless' = None,
skills: 'MessageContextSkills' = None) -> None:
"""
Initialize a MessageContextStateless object.
:param MessageContextGlobalStateless global_: (optional) Session context
data that is shared by all skills used by the Assistant.
:param MessageContextSkills skills: (optional) Information specific to
particular skills used by the assistant.
**Note:** Currently, only a single child property is supported, containing
variables that apply to the dialog skill used by the assistant.
"""
self.global_ = global_
self.skills = skills
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageContextStateless':
"""Initialize a MessageContextStateless object from a json dictionary."""
args = {}
valid_keys = ['global_', 'global', 'skills']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextStateless: '
+ ', '.join(bad_keys))
if 'global' in _dict:
args['global_'] = MessageContextGlobalStateless._from_dict(
_dict.get('global'))
if 'skills' in _dict:
args['skills'] = MessageContextSkills._from_dict(
_dict.get('skills'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextStateless object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'global_') and self.global_ is not None:
_dict['global'] = self.global_._to_dict()
if hasattr(self, 'skills') and self.skills is not None:
_dict['skills'] = self.skills._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageContextStateless object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageContextStateless') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageContextStateless') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageOutput():
"""
Assistant output to be rendered or processed by the client.
:attr List[RuntimeResponseGeneric] generic: (optional) Output intended for any
channel. It is the responsibility of the client application to implement the
supported response types.
:attr List[RuntimeIntent] intents: (optional) An array of intents recognized in
the user input, sorted in descending order of confidence.
:attr List[RuntimeEntity] entities: (optional) An array of entities identified
in the user input.
:attr List[DialogNodeAction] actions: (optional) An array of objects describing
any actions requested by the dialog node.
:attr MessageOutputDebug debug: (optional) Additional detailed information about
a message response and how it was generated.
:attr dict user_defined: (optional) An object containing any custom properties
included in the response. This object includes any arbitrary properties defined
in the dialog JSON editor as part of the dialog node output.
:attr MessageOutputSpelling spelling: (optional) Properties describing any
spelling corrections in the user input that was received.
"""
def __init__(self,
*,
generic: List['RuntimeResponseGeneric'] = None,
intents: List['RuntimeIntent'] = None,
entities: List['RuntimeEntity'] = None,
actions: List['DialogNodeAction'] = None,
debug: 'MessageOutputDebug' = None,
user_defined: dict = None,
spelling: 'MessageOutputSpelling' = None) -> None:
"""
Initialize a MessageOutput object.
:param List[RuntimeResponseGeneric] generic: (optional) Output intended for
any channel. It is the responsibility of the client application to
implement the supported response types.
:param List[RuntimeIntent] intents: (optional) An array of intents
recognized in the user input, sorted in descending order of confidence.
:param List[RuntimeEntity] entities: (optional) An array of entities
identified in the user input.
:param List[DialogNodeAction] actions: (optional) An array of objects
describing any actions requested by the dialog node.
:param MessageOutputDebug debug: (optional) Additional detailed information
about a message response and how it was generated.
:param dict user_defined: (optional) An object containing any custom
properties included in the response. This object includes any arbitrary
properties defined in the dialog JSON editor as part of the dialog node
output.
:param MessageOutputSpelling spelling: (optional) Properties describing any
spelling corrections in the user input that was received.
"""
self.generic = generic
self.intents = intents
self.entities = entities
self.actions = actions
self.debug = debug
self.user_defined = user_defined
self.spelling = spelling
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageOutput':
"""Initialize a MessageOutput object from a json dictionary."""
args = {}
valid_keys = [
'generic', 'intents', 'entities', 'actions', 'debug',
'user_defined', 'spelling'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageOutput: '
+ ', '.join(bad_keys))
if 'generic' in _dict:
args['generic'] = [
RuntimeResponseGeneric._from_dict(x)
for x in (_dict.get('generic'))
]
if 'intents' in _dict:
args['intents'] = [
RuntimeIntent._from_dict(x) for x in (_dict.get('intents'))
]
if 'entities' in _dict:
args['entities'] = [
RuntimeEntity._from_dict(x) for x in (_dict.get('entities'))
]
if 'actions' in _dict:
args['actions'] = [
DialogNodeAction._from_dict(x) for x in (_dict.get('actions'))
]
if 'debug' in _dict:
args['debug'] = MessageOutputDebug._from_dict(_dict.get('debug'))
if 'user_defined' in _dict:
args['user_defined'] = _dict.get('user_defined')
if 'spelling' in _dict:
args['spelling'] = MessageOutputSpelling._from_dict(
_dict.get('spelling'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageOutput object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'generic') and self.generic is not None:
_dict['generic'] = [x._to_dict() for x in self.generic]
if hasattr(self, 'intents') and self.intents is not None:
_dict['intents'] = [x._to_dict() for x in self.intents]
if hasattr(self, 'entities') and self.entities is not None:
_dict['entities'] = [x._to_dict() for x in self.entities]
if hasattr(self, 'actions') and self.actions is not None:
_dict['actions'] = [x._to_dict() for x in self.actions]
if hasattr(self, 'debug') and self.debug is not None:
_dict['debug'] = self.debug._to_dict()
if hasattr(self, 'user_defined') and self.user_defined is not None:
_dict['user_defined'] = self.user_defined
if hasattr(self, 'spelling') and self.spelling is not None:
_dict['spelling'] = self.spelling._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageOutput object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageOutput') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageOutput') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageOutputDebug():
"""
Additional detailed information about a message response and how it was generated.
:attr List[DialogNodesVisited] nodes_visited: (optional) An array of objects
containing detailed diagnostic information about the nodes that were triggered
during processing of the input message.
:attr List[DialogLogMessage] log_messages: (optional) An array of up to 50
messages logged with the request.
:attr bool branch_exited: (optional) Assistant sets this to true when this
message response concludes or interrupts a dialog.
:attr str branch_exited_reason: (optional) When `branch_exited` is set to `true`
by the Assistant, the `branch_exited_reason` specifies whether the dialog
completed by itself or got interrupted.
"""
def __init__(self,
*,
nodes_visited: List['DialogNodesVisited'] = None,
log_messages: List['DialogLogMessage'] = None,
branch_exited: bool = None,
branch_exited_reason: str = None) -> None:
"""
Initialize a MessageOutputDebug object.
:param List[DialogNodesVisited] nodes_visited: (optional) An array of
objects containing detailed diagnostic information about the nodes that
were triggered during processing of the input message.
:param List[DialogLogMessage] log_messages: (optional) An array of up to 50
messages logged with the request.
:param bool branch_exited: (optional) Assistant sets this to true when this
message response concludes or interrupts a dialog.
:param str branch_exited_reason: (optional) When `branch_exited` is set to
`true` by the Assistant, the `branch_exited_reason` specifies whether the
dialog completed by itself or got interrupted.
"""
self.nodes_visited = nodes_visited
self.log_messages = log_messages
self.branch_exited = branch_exited
self.branch_exited_reason = branch_exited_reason
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageOutputDebug':
"""Initialize a MessageOutputDebug object from a json dictionary."""
args = {}
valid_keys = [
'nodes_visited', 'log_messages', 'branch_exited',
'branch_exited_reason'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageOutputDebug: '
+ ', '.join(bad_keys))
if 'nodes_visited' in _dict:
args['nodes_visited'] = [
DialogNodesVisited._from_dict(x)
for x in (_dict.get('nodes_visited'))
]
if 'log_messages' in _dict:
args['log_messages'] = [
DialogLogMessage._from_dict(x)
for x in (_dict.get('log_messages'))
]
if 'branch_exited' in _dict:
args['branch_exited'] = _dict.get('branch_exited')
if 'branch_exited_reason' in _dict:
args['branch_exited_reason'] = _dict.get('branch_exited_reason')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageOutputDebug object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'nodes_visited') and self.nodes_visited is not None:
_dict['nodes_visited'] = [x._to_dict() for x in self.nodes_visited]
if hasattr(self, 'log_messages') and self.log_messages is not None:
_dict['log_messages'] = [x._to_dict() for x in self.log_messages]
if hasattr(self, 'branch_exited') and self.branch_exited is not None:
_dict['branch_exited'] = self.branch_exited
if hasattr(self, 'branch_exited_reason'
) and self.branch_exited_reason is not None:
_dict['branch_exited_reason'] = self.branch_exited_reason
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageOutputDebug object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageOutputDebug') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageOutputDebug') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class BranchExitedReasonEnum(Enum):
"""
When `branch_exited` is set to `true` by the Assistant, the `branch_exited_reason`
specifies whether the dialog completed by itself or got interrupted.
"""
COMPLETED = "completed"
FALLBACK = "fallback"
[docs]class MessageOutputSpelling():
"""
Properties describing any spelling corrections in the user input that was received.
:attr str text: (optional) The user input text that was used to generate the
response. If spelling autocorrection is enabled, this text reflects any spelling
corrections that were applied.
:attr str original_text: (optional) The original user input text. This property
is returned only if autocorrection is enabled and the user input was corrected.
:attr str suggested_text: (optional) Any suggested corrections of the input
text. This property is returned only if spelling correction is enabled and
autocorrection is disabled.
"""
def __init__(self,
*,
text: str = None,
original_text: str = None,
suggested_text: str = None) -> None:
"""
Initialize a MessageOutputSpelling object.
:param str text: (optional) The user input text that was used to generate
the response. If spelling autocorrection is enabled, this text reflects any
spelling corrections that were applied.
:param str original_text: (optional) The original user input text. This
property is returned only if autocorrection is enabled and the user input
was corrected.
:param str suggested_text: (optional) Any suggested corrections of the
input text. This property is returned only if spelling correction is
enabled and autocorrection is disabled.
"""
self.text = text
self.original_text = original_text
self.suggested_text = suggested_text
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageOutputSpelling':
"""Initialize a MessageOutputSpelling object from a json dictionary."""
args = {}
valid_keys = ['text', 'original_text', 'suggested_text']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageOutputSpelling: '
+ ', '.join(bad_keys))
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'original_text' in _dict:
args['original_text'] = _dict.get('original_text')
if 'suggested_text' in _dict:
args['suggested_text'] = _dict.get('suggested_text')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageOutputSpelling object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'original_text') and self.original_text is not None:
_dict['original_text'] = self.original_text
if hasattr(self, 'suggested_text') and self.suggested_text is not None:
_dict['suggested_text'] = self.suggested_text
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageOutputSpelling object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageOutputSpelling') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageOutputSpelling') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageRequest():
"""
A stateful message request formatted for the Watson Assistant service.
:attr MessageInput input: (optional) An input object that includes the input
text.
:attr MessageContext context: (optional) Context data for the conversation. You
can use this property to set or modify context variables, which can also be
accessed by dialog nodes. The context is stored by the assistant on a
per-session basis.
**Note:** The total size of the context data stored for a stateful session
cannot exceed 100KB.
"""
def __init__(self,
*,
input: 'MessageInput' = None,
context: 'MessageContext' = None) -> None:
"""
Initialize a MessageRequest object.
:param MessageInput input: (optional) An input object that includes the
input text.
:param MessageContext context: (optional) Context data for the
conversation. You can use this property to set or modify context variables,
which can also be accessed by dialog nodes. The context is stored by the
assistant on a per-session basis.
**Note:** The total size of the context data stored for a stateful session
cannot exceed 100KB.
"""
self.input = input
self.context = context
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageRequest':
"""Initialize a MessageRequest object from a json dictionary."""
args = {}
valid_keys = ['input', 'context']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageRequest: '
+ ', '.join(bad_keys))
if 'input' in _dict:
args['input'] = MessageInput._from_dict(_dict.get('input'))
if 'context' in _dict:
args['context'] = MessageContext._from_dict(_dict.get('context'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageRequest object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'input') and self.input is not None:
_dict['input'] = self.input._to_dict()
if hasattr(self, 'context') and self.context is not None:
_dict['context'] = self.context._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageRequest object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageRequest') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageRequest') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageResponse():
"""
A response from the Watson Assistant service.
:attr MessageOutput output: Assistant output to be rendered or processed by the
client.
:attr MessageContext context: (optional) Context data for the conversation. You
can use this property to access context variables. The context is stored by the
assistant on a per-session basis.
**Note:** The context is included in message responses only if
**return_context**=`true` in the message request. Full context is always
included in logs.
"""
def __init__(self,
output: 'MessageOutput',
*,
context: 'MessageContext' = None) -> None:
"""
Initialize a MessageResponse object.
:param MessageOutput output: Assistant output to be rendered or processed
by the client.
:param MessageContext context: (optional) Context data for the
conversation. You can use this property to access context variables. The
context is stored by the assistant on a per-session basis.
**Note:** The context is included in message responses only if
**return_context**=`true` in the message request. Full context is always
included in logs.
"""
self.output = output
self.context = context
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageResponse':
"""Initialize a MessageResponse object from a json dictionary."""
args = {}
valid_keys = ['output', 'context']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageResponse: '
+ ', '.join(bad_keys))
if 'output' in _dict:
args['output'] = MessageOutput._from_dict(_dict.get('output'))
else:
raise ValueError(
'Required property \'output\' not present in MessageResponse JSON'
)
if 'context' in _dict:
args['context'] = MessageContext._from_dict(_dict.get('context'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageResponse object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'output') and self.output is not None:
_dict['output'] = self.output._to_dict()
if hasattr(self, 'context') and self.context is not None:
_dict['context'] = self.context._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageResponse object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageResponse') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageResponse') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageResponseStateless():
"""
A stateless response from the Watson Assistant service.
:attr MessageOutput output: Assistant output to be rendered or processed by the
client.
:attr MessageContextStateless context: Context data for the conversation. You
can use this property to access context variables. The context is not stored by
the assistant; to maintain session state, include the context from the response
in the next message.
"""
def __init__(self, output: 'MessageOutput',
context: 'MessageContextStateless') -> None:
"""
Initialize a MessageResponseStateless object.
:param MessageOutput output: Assistant output to be rendered or processed
by the client.
:param MessageContextStateless context: Context data for the conversation.
You can use this property to access context variables. The context is not
stored by the assistant; to maintain session state, include the context
from the response in the next message.
"""
self.output = output
self.context = context
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'MessageResponseStateless':
"""Initialize a MessageResponseStateless object from a json dictionary."""
args = {}
valid_keys = ['output', 'context']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageResponseStateless: '
+ ', '.join(bad_keys))
if 'output' in _dict:
args['output'] = MessageOutput._from_dict(_dict.get('output'))
else:
raise ValueError(
'Required property \'output\' not present in MessageResponseStateless JSON'
)
if 'context' in _dict:
args['context'] = MessageContextStateless._from_dict(
_dict.get('context'))
else:
raise ValueError(
'Required property \'context\' not present in MessageResponseStateless JSON'
)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageResponseStateless object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'output') and self.output is not None:
_dict['output'] = self.output._to_dict()
if hasattr(self, 'context') and self.context is not None:
_dict['context'] = self.context._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MessageResponseStateless object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'MessageResponseStateless') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MessageResponseStateless') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RuntimeEntity():
"""
The entity value that was recognized in the user input.
:attr str entity: An entity detected in the input.
:attr List[int] location: An array of zero-based character offsets that indicate
where the detected entity values begin and end in the input text.
:attr str value: The term in the input text that was recognized as an entity
value.
:attr float confidence: (optional) A decimal percentage that represents Watson's
confidence in the recognized entity.
:attr dict metadata: (optional) Any metadata for the entity.
:attr List[CaptureGroup] groups: (optional) The recognized capture groups for
the entity, as defined by the entity pattern.
:attr RuntimeEntityInterpretation interpretation: (optional) An object
containing detailed information about the entity recognized in the user input.
This property is included only if the new system entities are enabled for the
skill.
For more information about how the new system entities are interpreted, see the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-beta-system-entities).
:attr List[RuntimeEntityAlternative] alternatives: (optional) An array of
possible alternative values that the user might have intended instead of the
value returned in the **value** property. This property is returned only for
`@sys-time` and `@sys-date` entities when the user's input is ambiguous.
This property is included only if the new system entities are enabled for the
skill.
:attr RuntimeEntityRole role: (optional) An object describing the role played by
a system entity that is specifies the beginning or end of a range recognized in
the user input. This property is included only if the new system entities are
enabled for the skill.
"""
def __init__(self,
entity: str,
location: List[int],
value: str,
*,
confidence: float = None,
metadata: dict = None,
groups: List['CaptureGroup'] = None,
interpretation: 'RuntimeEntityInterpretation' = None,
alternatives: List['RuntimeEntityAlternative'] = None,
role: 'RuntimeEntityRole' = None) -> None:
"""
Initialize a RuntimeEntity object.
:param str entity: An entity detected in the input.
:param List[int] location: An array of zero-based character offsets that
indicate where the detected entity values begin and end in the input text.
:param str value: The term in the input text that was recognized as an
entity value.
:param float confidence: (optional) A decimal percentage that represents
Watson's confidence in the recognized entity.
:param dict metadata: (optional) Any metadata for the entity.
:param List[CaptureGroup] groups: (optional) The recognized capture groups
for the entity, as defined by the entity pattern.
:param RuntimeEntityInterpretation interpretation: (optional) An object
containing detailed information about the entity recognized in the user
input. This property is included only if the new system entities are
enabled for the skill.
For more information about how the new system entities are interpreted, see
the
[documentation](https://cloud.ibm.com/docs/assistant?topic=assistant-beta-system-entities).
:param List[RuntimeEntityAlternative] alternatives: (optional) An array of
possible alternative values that the user might have intended instead of
the value returned in the **value** property. This property is returned
only for `@sys-time` and `@sys-date` entities when the user's input is
ambiguous.
This property is included only if the new system entities are enabled for
the skill.
:param RuntimeEntityRole role: (optional) An object describing the role
played by a system entity that is specifies the beginning or end of a range
recognized in the user input. This property is included only if the new
system entities are enabled for the skill.
"""
self.entity = entity
self.location = location
self.value = value
self.confidence = confidence
self.metadata = metadata
self.groups = groups
self.interpretation = interpretation
self.alternatives = alternatives
self.role = role
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'RuntimeEntity':
"""Initialize a RuntimeEntity object from a json dictionary."""
args = {}
valid_keys = [
'entity', 'location', 'value', 'confidence', 'metadata', 'groups',
'interpretation', 'alternatives', 'role'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeEntity: '
+ ', '.join(bad_keys))
if 'entity' in _dict:
args['entity'] = _dict.get('entity')
else:
raise ValueError(
'Required property \'entity\' not present in RuntimeEntity JSON'
)
if 'location' in _dict:
args['location'] = _dict.get('location')
else:
raise ValueError(
'Required property \'location\' not present in RuntimeEntity JSON'
)
if 'value' in _dict:
args['value'] = _dict.get('value')
else:
raise ValueError(
'Required property \'value\' not present in RuntimeEntity JSON')
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
if 'metadata' in _dict:
args['metadata'] = _dict.get('metadata')
if 'groups' in _dict:
args['groups'] = [
CaptureGroup._from_dict(x) for x in (_dict.get('groups'))
]
if 'interpretation' in _dict:
args['interpretation'] = RuntimeEntityInterpretation._from_dict(
_dict.get('interpretation'))
if 'alternatives' in _dict:
args['alternatives'] = [
RuntimeEntityAlternative._from_dict(x)
for x in (_dict.get('alternatives'))
]
if 'role' in _dict:
args['role'] = RuntimeEntityRole._from_dict(_dict.get('role'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeEntity object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'entity') and self.entity is not None:
_dict['entity'] = self.entity
if hasattr(self, 'location') and self.location is not None:
_dict['location'] = self.location
if hasattr(self, 'value') and self.value is not None:
_dict['value'] = self.value
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
if hasattr(self, 'metadata') and self.metadata is not None:
_dict['metadata'] = self.metadata
if hasattr(self, 'groups') and self.groups is not None:
_dict['groups'] = [x._to_dict() for x in self.groups]
if hasattr(self, 'interpretation') and self.interpretation is not None:
_dict['interpretation'] = self.interpretation._to_dict()
if hasattr(self, 'alternatives') and self.alternatives is not None:
_dict['alternatives'] = [x._to_dict() for x in self.alternatives]
if hasattr(self, 'role') and self.role is not None:
_dict['role'] = self.role._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this RuntimeEntity object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'RuntimeEntity') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'RuntimeEntity') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RuntimeEntityAlternative():
"""
An alternative value for the recognized entity.
:attr str value: (optional) The entity value that was recognized in the user
input.
:attr float confidence: (optional) A decimal percentage that represents Watson's
confidence in the recognized entity.
"""
def __init__(self, *, value: str = None, confidence: float = None) -> None:
"""
Initialize a RuntimeEntityAlternative object.
:param str value: (optional) The entity value that was recognized in the
user input.
:param float confidence: (optional) A decimal percentage that represents
Watson's confidence in the recognized entity.
"""
self.value = value
self.confidence = confidence
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'RuntimeEntityAlternative':
"""Initialize a RuntimeEntityAlternative object from a json dictionary."""
args = {}
valid_keys = ['value', 'confidence']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeEntityAlternative: '
+ ', '.join(bad_keys))
if 'value' in _dict:
args['value'] = _dict.get('value')
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeEntityAlternative object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'value') and self.value is not None:
_dict['value'] = self.value
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this RuntimeEntityAlternative object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'RuntimeEntityAlternative') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'RuntimeEntityAlternative') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RuntimeEntityInterpretation():
"""
RuntimeEntityInterpretation.
:attr str calendar_type: (optional) The calendar used to represent a recognized
date (for example, `Gregorian`).
:attr str datetime_link: (optional) A unique identifier used to associate a
recognized time and date. If the user input contains a date and time that are
mentioned together (for example, `Today at 5`, the same **datetime_link** value
is returned for both the `@sys-date` and `@sys-time` entities).
:attr str festival: (optional) A locale-specific holiday name (such as
`thanksgiving` or `christmas`). This property is included when a `@sys-date`
entity is recognized based on a holiday name in the user input.
:attr str granularity: (optional) The precision or duration of a time range
specified by a recognized `@sys-time` or `@sys-date` entity.
:attr str range_link: (optional) A unique identifier used to associate multiple
recognized `@sys-date`, `@sys-time`, or `@sys-number` entities that are
recognized as a range of values in the user's input (for example, `from July 4
until July 14` or `from 20 to 25`).
:attr str range_modifier: (optional) The word in the user input that indicates
that a `sys-date` or `sys-time` entity is part of an implied range where only
one date or time is specified (for example, `since` or `until`).
:attr float relative_day: (optional) A recognized mention of a relative day,
represented numerically as an offset from the current date (for example, `-1`
for `yesterday` or `10` for `in ten days`).
:attr float relative_month: (optional) A recognized mention of a relative month,
represented numerically as an offset from the current month (for example, `1`
for `next month` or `-3` for `three months ago`).
:attr float relative_week: (optional) A recognized mention of a relative week,
represented numerically as an offset from the current week (for example, `2` for
`in two weeks` or `-1` for `last week).
:attr float relative_weekend: (optional) A recognized mention of a relative date
range for a weekend, represented numerically as an offset from the current
weekend (for example, `0` for `this weekend` or `-1` for `last weekend`).
:attr float relative_year: (optional) A recognized mention of a relative year,
represented numerically as an offset from the current year (for example, `1` for
`next year` or `-5` for `five years ago`).
:attr float specific_day: (optional) A recognized mention of a specific date,
represented numerically as the date within the month (for example, `30` for
`June 30`.).
:attr str specific_day_of_week: (optional) A recognized mention of a specific
day of the week as a lowercase string (for example, `monday`).
:attr float specific_month: (optional) A recognized mention of a specific month,
represented numerically (for example, `7` for `July`).
:attr float specific_quarter: (optional) A recognized mention of a specific
quarter, represented numerically (for example, `3` for `the third quarter`).
:attr float specific_year: (optional) A recognized mention of a specific year
(for example, `2016`).
:attr float numeric_value: (optional) A recognized numeric value, represented as
an integer or double.
:attr str subtype: (optional) The type of numeric value recognized in the user
input (`integer` or `rational`).
:attr str part_of_day: (optional) A recognized term for a time that was
mentioned as a part of the day in the user's input (for example, `morning` or
`afternoon`).
:attr float relative_hour: (optional) A recognized mention of a relative hour,
represented numerically as an offset from the current hour (for example, `3` for
`in three hours` or `-1` for `an hour ago`).
:attr float relative_minute: (optional) A recognized mention of a relative time,
represented numerically as an offset in minutes from the current time (for
example, `5` for `in five minutes` or `-15` for `fifteen minutes ago`).
:attr float relative_second: (optional) A recognized mention of a relative time,
represented numerically as an offset in seconds from the current time (for
example, `10` for `in ten seconds` or `-30` for `thirty seconds ago`).
:attr float specific_hour: (optional) A recognized specific hour mentioned as
part of a time value (for example, `10` for `10:15 AM`.).
:attr float specific_minute: (optional) A recognized specific minute mentioned
as part of a time value (for example, `15` for `10:15 AM`.).
:attr float specific_second: (optional) A recognized specific second mentioned
as part of a time value (for example, `30` for `10:15:30 AM`.).
:attr str timezone: (optional) A recognized time zone mentioned as part of a
time value (for example, `EST`).
"""
def __init__(self,
*,
calendar_type: str = None,
datetime_link: str = None,
festival: str = None,
granularity: str = None,
range_link: str = None,
range_modifier: str = None,
relative_day: float = None,
relative_month: float = None,
relative_week: float = None,
relative_weekend: float = None,
relative_year: float = None,
specific_day: float = None,
specific_day_of_week: str = None,
specific_month: float = None,
specific_quarter: float = None,
specific_year: float = None,
numeric_value: float = None,
subtype: str = None,
part_of_day: str = None,
relative_hour: float = None,
relative_minute: float = None,
relative_second: float = None,
specific_hour: float = None,
specific_minute: float = None,
specific_second: float = None,
timezone: str = None) -> None:
"""
Initialize a RuntimeEntityInterpretation object.
:param str calendar_type: (optional) The calendar used to represent a
recognized date (for example, `Gregorian`).
:param str datetime_link: (optional) A unique identifier used to associate
a recognized time and date. If the user input contains a date and time that
are mentioned together (for example, `Today at 5`, the same
**datetime_link** value is returned for both the `@sys-date` and
`@sys-time` entities).
:param str festival: (optional) A locale-specific holiday name (such as
`thanksgiving` or `christmas`). This property is included when a
`@sys-date` entity is recognized based on a holiday name in the user input.
:param str granularity: (optional) The precision or duration of a time
range specified by a recognized `@sys-time` or `@sys-date` entity.
:param str range_link: (optional) A unique identifier used to associate
multiple recognized `@sys-date`, `@sys-time`, or `@sys-number` entities
that are recognized as a range of values in the user's input (for example,
`from July 4 until July 14` or `from 20 to 25`).
:param str range_modifier: (optional) The word in the user input that
indicates that a `sys-date` or `sys-time` entity is part of an implied
range where only one date or time is specified (for example, `since` or
`until`).
:param float relative_day: (optional) A recognized mention of a relative
day, represented numerically as an offset from the current date (for
example, `-1` for `yesterday` or `10` for `in ten days`).
:param float relative_month: (optional) A recognized mention of a relative
month, represented numerically as an offset from the current month (for
example, `1` for `next month` or `-3` for `three months ago`).
:param float relative_week: (optional) A recognized mention of a relative
week, represented numerically as an offset from the current week (for
example, `2` for `in two weeks` or `-1` for `last week).
:param float relative_weekend: (optional) A recognized mention of a
relative date range for a weekend, represented numerically as an offset
from the current weekend (for example, `0` for `this weekend` or `-1` for
`last weekend`).
:param float relative_year: (optional) A recognized mention of a relative
year, represented numerically as an offset from the current year (for
example, `1` for `next year` or `-5` for `five years ago`).
:param float specific_day: (optional) A recognized mention of a specific
date, represented numerically as the date within the month (for example,
`30` for `June 30`.).
:param str specific_day_of_week: (optional) A recognized mention of a
specific day of the week as a lowercase string (for example, `monday`).
:param float specific_month: (optional) A recognized mention of a specific
month, represented numerically (for example, `7` for `July`).
:param float specific_quarter: (optional) A recognized mention of a
specific quarter, represented numerically (for example, `3` for `the third
quarter`).
:param float specific_year: (optional) A recognized mention of a specific
year (for example, `2016`).
:param float numeric_value: (optional) A recognized numeric value,
represented as an integer or double.
:param str subtype: (optional) The type of numeric value recognized in the
user input (`integer` or `rational`).
:param str part_of_day: (optional) A recognized term for a time that was
mentioned as a part of the day in the user's input (for example, `morning`
or `afternoon`).
:param float relative_hour: (optional) A recognized mention of a relative
hour, represented numerically as an offset from the current hour (for
example, `3` for `in three hours` or `-1` for `an hour ago`).
:param float relative_minute: (optional) A recognized mention of a relative
time, represented numerically as an offset in minutes from the current time
(for example, `5` for `in five minutes` or `-15` for `fifteen minutes
ago`).
:param float relative_second: (optional) A recognized mention of a relative
time, represented numerically as an offset in seconds from the current time
(for example, `10` for `in ten seconds` or `-30` for `thirty seconds ago`).
:param float specific_hour: (optional) A recognized specific hour mentioned
as part of a time value (for example, `10` for `10:15 AM`.).
:param float specific_minute: (optional) A recognized specific minute
mentioned as part of a time value (for example, `15` for `10:15 AM`.).
:param float specific_second: (optional) A recognized specific second
mentioned as part of a time value (for example, `30` for `10:15:30 AM`.).
:param str timezone: (optional) A recognized time zone mentioned as part of
a time value (for example, `EST`).
"""
self.calendar_type = calendar_type
self.datetime_link = datetime_link
self.festival = festival
self.granularity = granularity
self.range_link = range_link
self.range_modifier = range_modifier
self.relative_day = relative_day
self.relative_month = relative_month
self.relative_week = relative_week
self.relative_weekend = relative_weekend
self.relative_year = relative_year
self.specific_day = specific_day
self.specific_day_of_week = specific_day_of_week
self.specific_month = specific_month
self.specific_quarter = specific_quarter
self.specific_year = specific_year
self.numeric_value = numeric_value
self.subtype = subtype
self.part_of_day = part_of_day
self.relative_hour = relative_hour
self.relative_minute = relative_minute
self.relative_second = relative_second
self.specific_hour = specific_hour
self.specific_minute = specific_minute
self.specific_second = specific_second
self.timezone = timezone
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'RuntimeEntityInterpretation':
"""Initialize a RuntimeEntityInterpretation object from a json dictionary."""
args = {}
valid_keys = [
'calendar_type', 'datetime_link', 'festival', 'granularity',
'range_link', 'range_modifier', 'relative_day', 'relative_month',
'relative_week', 'relative_weekend', 'relative_year',
'specific_day', 'specific_day_of_week', 'specific_month',
'specific_quarter', 'specific_year', 'numeric_value', 'subtype',
'part_of_day', 'relative_hour', 'relative_minute',
'relative_second', 'specific_hour', 'specific_minute',
'specific_second', 'timezone'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeEntityInterpretation: '
+ ', '.join(bad_keys))
if 'calendar_type' in _dict:
args['calendar_type'] = _dict.get('calendar_type')
if 'datetime_link' in _dict:
args['datetime_link'] = _dict.get('datetime_link')
if 'festival' in _dict:
args['festival'] = _dict.get('festival')
if 'granularity' in _dict:
args['granularity'] = _dict.get('granularity')
if 'range_link' in _dict:
args['range_link'] = _dict.get('range_link')
if 'range_modifier' in _dict:
args['range_modifier'] = _dict.get('range_modifier')
if 'relative_day' in _dict:
args['relative_day'] = _dict.get('relative_day')
if 'relative_month' in _dict:
args['relative_month'] = _dict.get('relative_month')
if 'relative_week' in _dict:
args['relative_week'] = _dict.get('relative_week')
if 'relative_weekend' in _dict:
args['relative_weekend'] = _dict.get('relative_weekend')
if 'relative_year' in _dict:
args['relative_year'] = _dict.get('relative_year')
if 'specific_day' in _dict:
args['specific_day'] = _dict.get('specific_day')
if 'specific_day_of_week' in _dict:
args['specific_day_of_week'] = _dict.get('specific_day_of_week')
if 'specific_month' in _dict:
args['specific_month'] = _dict.get('specific_month')
if 'specific_quarter' in _dict:
args['specific_quarter'] = _dict.get('specific_quarter')
if 'specific_year' in _dict:
args['specific_year'] = _dict.get('specific_year')
if 'numeric_value' in _dict:
args['numeric_value'] = _dict.get('numeric_value')
if 'subtype' in _dict:
args['subtype'] = _dict.get('subtype')
if 'part_of_day' in _dict:
args['part_of_day'] = _dict.get('part_of_day')
if 'relative_hour' in _dict:
args['relative_hour'] = _dict.get('relative_hour')
if 'relative_minute' in _dict:
args['relative_minute'] = _dict.get('relative_minute')
if 'relative_second' in _dict:
args['relative_second'] = _dict.get('relative_second')
if 'specific_hour' in _dict:
args['specific_hour'] = _dict.get('specific_hour')
if 'specific_minute' in _dict:
args['specific_minute'] = _dict.get('specific_minute')
if 'specific_second' in _dict:
args['specific_second'] = _dict.get('specific_second')
if 'timezone' in _dict:
args['timezone'] = _dict.get('timezone')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeEntityInterpretation object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'calendar_type') and self.calendar_type is not None:
_dict['calendar_type'] = self.calendar_type
if hasattr(self, 'datetime_link') and self.datetime_link is not None:
_dict['datetime_link'] = self.datetime_link
if hasattr(self, 'festival') and self.festival is not None:
_dict['festival'] = self.festival
if hasattr(self, 'granularity') and self.granularity is not None:
_dict['granularity'] = self.granularity
if hasattr(self, 'range_link') and self.range_link is not None:
_dict['range_link'] = self.range_link
if hasattr(self, 'range_modifier') and self.range_modifier is not None:
_dict['range_modifier'] = self.range_modifier
if hasattr(self, 'relative_day') and self.relative_day is not None:
_dict['relative_day'] = self.relative_day
if hasattr(self, 'relative_month') and self.relative_month is not None:
_dict['relative_month'] = self.relative_month
if hasattr(self, 'relative_week') and self.relative_week is not None:
_dict['relative_week'] = self.relative_week
if hasattr(self,
'relative_weekend') and self.relative_weekend is not None:
_dict['relative_weekend'] = self.relative_weekend
if hasattr(self, 'relative_year') and self.relative_year is not None:
_dict['relative_year'] = self.relative_year
if hasattr(self, 'specific_day') and self.specific_day is not None:
_dict['specific_day'] = self.specific_day
if hasattr(self, 'specific_day_of_week'
) and self.specific_day_of_week is not None:
_dict['specific_day_of_week'] = self.specific_day_of_week
if hasattr(self, 'specific_month') and self.specific_month is not None:
_dict['specific_month'] = self.specific_month
if hasattr(self,
'specific_quarter') and self.specific_quarter is not None:
_dict['specific_quarter'] = self.specific_quarter
if hasattr(self, 'specific_year') and self.specific_year is not None:
_dict['specific_year'] = self.specific_year
if hasattr(self, 'numeric_value') and self.numeric_value is not None:
_dict['numeric_value'] = self.numeric_value
if hasattr(self, 'subtype') and self.subtype is not None:
_dict['subtype'] = self.subtype
if hasattr(self, 'part_of_day') and self.part_of_day is not None:
_dict['part_of_day'] = self.part_of_day
if hasattr(self, 'relative_hour') and self.relative_hour is not None:
_dict['relative_hour'] = self.relative_hour
if hasattr(self,
'relative_minute') and self.relative_minute is not None:
_dict['relative_minute'] = self.relative_minute
if hasattr(self,
'relative_second') and self.relative_second is not None:
_dict['relative_second'] = self.relative_second
if hasattr(self, 'specific_hour') and self.specific_hour is not None:
_dict['specific_hour'] = self.specific_hour
if hasattr(self,
'specific_minute') and self.specific_minute is not None:
_dict['specific_minute'] = self.specific_minute
if hasattr(self,
'specific_second') and self.specific_second is not None:
_dict['specific_second'] = self.specific_second
if hasattr(self, 'timezone') and self.timezone is not None:
_dict['timezone'] = self.timezone
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this RuntimeEntityInterpretation object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'RuntimeEntityInterpretation') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'RuntimeEntityInterpretation') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class GranularityEnum(Enum):
"""
The precision or duration of a time range specified by a recognized `@sys-time` or
`@sys-date` entity.
"""
DAY = "day"
FORTNIGHT = "fortnight"
HOUR = "hour"
INSTANT = "instant"
MINUTE = "minute"
MONTH = "month"
QUARTER = "quarter"
SECOND = "second"
WEEK = "week"
WEEKEND = "weekend"
YEAR = "year"
[docs]class RuntimeEntityRole():
"""
An object describing the role played by a system entity that is specifies the
beginning or end of a range recognized in the user input. This property is included
only if the new system entities are enabled for the skill.
:attr str type: (optional) The relationship of the entity to the range.
"""
def __init__(self, *, type: str = None) -> None:
"""
Initialize a RuntimeEntityRole object.
:param str type: (optional) The relationship of the entity to the range.
"""
self.type = type
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'RuntimeEntityRole':
"""Initialize a RuntimeEntityRole object from a json dictionary."""
args = {}
valid_keys = ['type']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeEntityRole: '
+ ', '.join(bad_keys))
if 'type' in _dict:
args['type'] = _dict.get('type')
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeEntityRole object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this RuntimeEntityRole object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'RuntimeEntityRole') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'RuntimeEntityRole') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class TypeEnum(Enum):
"""
The relationship of the entity to the range.
"""
DATE_FROM = "date_from"
DATE_TO = "date_to"
NUMBER_FROM = "number_from"
NUMBER_TO = "number_to"
TIME_FROM = "time_from"
TIME_TO = "time_to"
[docs]class RuntimeIntent():
"""
An intent identified in the user input.
:attr str intent: The name of the recognized intent.
:attr float confidence: A decimal percentage that represents Watson's confidence
in the intent.
"""
def __init__(self, intent: str, confidence: float) -> None:
"""
Initialize a RuntimeIntent object.
:param str intent: The name of the recognized intent.
:param float confidence: A decimal percentage that represents Watson's
confidence in the intent.
"""
self.intent = intent
self.confidence = confidence
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'RuntimeIntent':
"""Initialize a RuntimeIntent object from a json dictionary."""
args = {}
valid_keys = ['intent', 'confidence']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeIntent: '
+ ', '.join(bad_keys))
if 'intent' in _dict:
args['intent'] = _dict.get('intent')
else:
raise ValueError(
'Required property \'intent\' not present in RuntimeIntent JSON'
)
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
else:
raise ValueError(
'Required property \'confidence\' not present in RuntimeIntent JSON'
)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeIntent object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'intent') and self.intent is not None:
_dict['intent'] = self.intent
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this RuntimeIntent object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'RuntimeIntent') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'RuntimeIntent') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RuntimeResponseGeneric():
"""
RuntimeResponseGeneric.
:attr str response_type: The type of response returned by the dialog node. The
specified response type must be supported by the client application or channel.
:attr str text: (optional) The text of the response.
:attr int time: (optional) How long to pause, in milliseconds.
:attr bool typing: (optional) Whether to send a "user is typing" event during
the pause.
:attr str source: (optional) The URL of the image.
:attr str title: (optional) The title or introductory text to show before the
response.
:attr str description: (optional) The description to show with the the response.
:attr str preference: (optional) The preferred type of control to display.
:attr List[DialogNodeOutputOptionsElement] options: (optional) An array of
objects describing the options from which the user can choose.
:attr str message_to_human_agent: (optional) A message to be sent to the human
agent who will be taking over the conversation.
:attr str topic: (optional) A label identifying the topic of the conversation,
derived from the **user_label** property of the relevant node.
:attr List[DialogSuggestion] suggestions: (optional) An array of objects
describing the possible matching dialog nodes from which the user can choose.
:attr str header: (optional) The title or introductory text to show before the
response. This text is defined in the search skill configuration.
:attr List[SearchResult] results: (optional) An array of objects containing
search results.
"""
def __init__(self,
response_type: str,
*,
text: str = None,
time: int = None,
typing: bool = None,
source: str = None,
title: str = None,
description: str = None,
preference: str = None,
options: List['DialogNodeOutputOptionsElement'] = None,
message_to_human_agent: str = None,
topic: str = None,
suggestions: List['DialogSuggestion'] = None,
header: str = None,
results: List['SearchResult'] = None) -> None:
"""
Initialize a RuntimeResponseGeneric object.
:param str response_type: The type of response returned by the dialog node.
The specified response type must be supported by the client application or
channel.
:param str text: (optional) The text of the response.
:param int time: (optional) How long to pause, in milliseconds.
:param bool typing: (optional) Whether to send a "user is typing" event
during the pause.
:param str source: (optional) The URL of the image.
:param str title: (optional) The title or introductory text to show before
the response.
:param str description: (optional) The description to show with the the
response.
:param str preference: (optional) The preferred type of control to display.
:param List[DialogNodeOutputOptionsElement] options: (optional) An array of
objects describing the options from which the user can choose.
:param str message_to_human_agent: (optional) A message to be sent to the
human agent who will be taking over the conversation.
:param str topic: (optional) A label identifying the topic of the
conversation, derived from the **user_label** property of the relevant
node.
:param List[DialogSuggestion] suggestions: (optional) An array of objects
describing the possible matching dialog nodes from which the user can
choose.
:param str header: (optional) The title or introductory text to show before
the response. This text is defined in the search skill configuration.
:param List[SearchResult] results: (optional) An array of objects
containing search results.
"""
self.response_type = response_type
self.text = text
self.time = time
self.typing = typing
self.source = source
self.title = title
self.description = description
self.preference = preference
self.options = options
self.message_to_human_agent = message_to_human_agent
self.topic = topic
self.suggestions = suggestions
self.header = header
self.results = results
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'RuntimeResponseGeneric':
"""Initialize a RuntimeResponseGeneric object from a json dictionary."""
args = {}
valid_keys = [
'response_type', 'text', 'time', 'typing', 'source', 'title',
'description', 'preference', 'options', 'message_to_human_agent',
'topic', 'suggestions', 'header', 'results'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeResponseGeneric: '
+ ', '.join(bad_keys))
if 'response_type' in _dict:
args['response_type'] = _dict.get('response_type')
else:
raise ValueError(
'Required property \'response_type\' not present in RuntimeResponseGeneric JSON'
)
if 'text' in _dict:
args['text'] = _dict.get('text')
if 'time' in _dict:
args['time'] = _dict.get('time')
if 'typing' in _dict:
args['typing'] = _dict.get('typing')
if 'source' in _dict:
args['source'] = _dict.get('source')
if 'title' in _dict:
args['title'] = _dict.get('title')
if 'description' in _dict:
args['description'] = _dict.get('description')
if 'preference' in _dict:
args['preference'] = _dict.get('preference')
if 'options' in _dict:
args['options'] = [
DialogNodeOutputOptionsElement._from_dict(x)
for x in (_dict.get('options'))
]
if 'message_to_human_agent' in _dict:
args['message_to_human_agent'] = _dict.get('message_to_human_agent')
if 'topic' in _dict:
args['topic'] = _dict.get('topic')
if 'suggestions' in _dict:
args['suggestions'] = [
DialogSuggestion._from_dict(x)
for x in (_dict.get('suggestions'))
]
if 'header' in _dict:
args['header'] = _dict.get('header')
if 'results' in _dict:
args['results'] = [
SearchResult._from_dict(x) for x in (_dict.get('results'))
]
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeResponseGeneric object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'response_type') and self.response_type is not None:
_dict['response_type'] = self.response_type
if hasattr(self, 'text') and self.text is not None:
_dict['text'] = self.text
if hasattr(self, 'time') and self.time is not None:
_dict['time'] = self.time
if hasattr(self, 'typing') and self.typing is not None:
_dict['typing'] = self.typing
if hasattr(self, 'source') and self.source is not None:
_dict['source'] = self.source
if hasattr(self, 'title') and self.title is not None:
_dict['title'] = self.title
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
if hasattr(self, 'preference') and self.preference is not None:
_dict['preference'] = self.preference
if hasattr(self, 'options') and self.options is not None:
_dict['options'] = [x._to_dict() for x in self.options]
if hasattr(self, 'message_to_human_agent'
) and self.message_to_human_agent is not None:
_dict['message_to_human_agent'] = self.message_to_human_agent
if hasattr(self, 'topic') and self.topic is not None:
_dict['topic'] = self.topic
if hasattr(self, 'suggestions') and self.suggestions is not None:
_dict['suggestions'] = [x._to_dict() for x in self.suggestions]
if hasattr(self, 'header') and self.header is not None:
_dict['header'] = self.header
if hasattr(self, 'results') and self.results is not None:
_dict['results'] = [x._to_dict() for x in self.results]
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this RuntimeResponseGeneric object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'RuntimeResponseGeneric') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'RuntimeResponseGeneric') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs] class ResponseTypeEnum(Enum):
"""
The type of response returned by the dialog node. The specified response type must
be supported by the client application or channel.
"""
TEXT = "text"
PAUSE = "pause"
IMAGE = "image"
OPTION = "option"
CONNECT_TO_AGENT = "connect_to_agent"
SUGGESTION = "suggestion"
SEARCH = "search"
[docs] class PreferenceEnum(Enum):
"""
The preferred type of control to display.
"""
DROPDOWN = "dropdown"
BUTTON = "button"
[docs]class SearchResult():
"""
SearchResult.
:attr str id: The unique identifier of the document in the Discovery service
collection.
This property is included in responses from search skills, which are available
only to Plus or Premium plan users.
:attr SearchResultMetadata result_metadata: An object containing search result
metadata from the Discovery service.
:attr str body: (optional) A description of the search result. This is taken
from an abstract, summary, or highlight field in the Discovery service response,
as specified in the search skill configuration.
:attr str title: (optional) The title of the search result. This is taken from a
title or name field in the Discovery service response, as specified in the
search skill configuration.
:attr str url: (optional) The URL of the original data object in its native data
source.
:attr SearchResultHighlight highlight: (optional) An object containing segments
of text from search results with query-matching text highlighted using HTML
`<em>` tags.
"""
def __init__(self,
id: str,
result_metadata: 'SearchResultMetadata',
*,
body: str = None,
title: str = None,
url: str = None,
highlight: 'SearchResultHighlight' = None) -> None:
"""
Initialize a SearchResult object.
:param str id: The unique identifier of the document in the Discovery
service collection.
This property is included in responses from search skills, which are
available only to Plus or Premium plan users.
:param SearchResultMetadata result_metadata: An object containing search
result metadata from the Discovery service.
:param str body: (optional) A description of the search result. This is
taken from an abstract, summary, or highlight field in the Discovery
service response, as specified in the search skill configuration.
:param str title: (optional) The title of the search result. This is taken
from a title or name field in the Discovery service response, as specified
in the search skill configuration.
:param str url: (optional) The URL of the original data object in its
native data source.
:param SearchResultHighlight highlight: (optional) An object containing
segments of text from search results with query-matching text highlighted
using HTML `<em>` tags.
"""
self.id = id
self.result_metadata = result_metadata
self.body = body
self.title = title
self.url = url
self.highlight = highlight
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'SearchResult':
"""Initialize a SearchResult object from a json dictionary."""
args = {}
valid_keys = [
'id', 'result_metadata', 'body', 'title', 'url', 'highlight'
]
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SearchResult: '
+ ', '.join(bad_keys))
if 'id' in _dict:
args['id'] = _dict.get('id')
else:
raise ValueError(
'Required property \'id\' not present in SearchResult JSON')
if 'result_metadata' in _dict:
args['result_metadata'] = SearchResultMetadata._from_dict(
_dict.get('result_metadata'))
else:
raise ValueError(
'Required property \'result_metadata\' not present in SearchResult JSON'
)
if 'body' in _dict:
args['body'] = _dict.get('body')
if 'title' in _dict:
args['title'] = _dict.get('title')
if 'url' in _dict:
args['url'] = _dict.get('url')
if 'highlight' in _dict:
args['highlight'] = SearchResultHighlight._from_dict(
_dict.get('highlight'))
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SearchResult object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'id') and self.id is not None:
_dict['id'] = self.id
if hasattr(self,
'result_metadata') and self.result_metadata is not None:
_dict['result_metadata'] = self.result_metadata._to_dict()
if hasattr(self, 'body') and self.body is not None:
_dict['body'] = self.body
if hasattr(self, 'title') and self.title is not None:
_dict['title'] = self.title
if hasattr(self, 'url') and self.url is not None:
_dict['url'] = self.url
if hasattr(self, 'highlight') and self.highlight is not None:
_dict['highlight'] = self.highlight._to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this SearchResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'SearchResult') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'SearchResult') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SearchResultHighlight():
"""
An object containing segments of text from search results with query-matching text
highlighted using HTML `<em>` tags.
:attr List[str] body: (optional) An array of strings containing segments taken
from body text in the search results, with query-matching substrings
highlighted.
:attr List[str] title: (optional) An array of strings containing segments taken
from title text in the search results, with query-matching substrings
highlighted.
:attr List[str] url: (optional) An array of strings containing segments taken
from URLs in the search results, with query-matching substrings highlighted.
"""
def __init__(self,
*,
body: List[str] = None,
title: List[str] = None,
url: List[str] = None,
**kwargs) -> None:
"""
Initialize a SearchResultHighlight object.
:param List[str] body: (optional) An array of strings containing segments
taken from body text in the search results, with query-matching substrings
highlighted.
:param List[str] title: (optional) An array of strings containing segments
taken from title text in the search results, with query-matching substrings
highlighted.
:param List[str] url: (optional) An array of strings containing segments
taken from URLs in the search results, with query-matching substrings
highlighted.
:param **kwargs: (optional) Any additional properties.
"""
self.body = body
self.title = title
self.url = url
for _key, _value in kwargs.items():
setattr(self, _key, _value)
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'SearchResultHighlight':
"""Initialize a SearchResultHighlight object from a json dictionary."""
args = {}
xtra = _dict.copy()
if 'body' in _dict:
args['body'] = _dict.get('body')
del xtra['body']
if 'title' in _dict:
args['title'] = _dict.get('title')
del xtra['title']
if 'url' in _dict:
args['url'] = _dict.get('url')
del xtra['url']
args.update(xtra)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SearchResultHighlight object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'body') and self.body is not None:
_dict['body'] = self.body
if hasattr(self, 'title') and self.title is not None:
_dict['title'] = self.title
if hasattr(self, 'url') and self.url is not None:
_dict['url'] = self.url
if hasattr(self, '_additionalProperties'):
for _key in self._additionalProperties:
_value = getattr(self, _key, None)
if _value is not None:
_dict[_key] = _value
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __setattr__(self, name: str, value: object) -> None:
properties = {'body', 'title', 'url'}
if not hasattr(self, '_additionalProperties'):
super(SearchResultHighlight,
self).__setattr__('_additionalProperties', set())
if name not in properties:
self._additionalProperties.add(name)
super(SearchResultHighlight, self).__setattr__(name, value)
def __str__(self) -> str:
"""Return a `str` version of this SearchResultHighlight object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'SearchResultHighlight') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'SearchResultHighlight') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SessionResponse():
"""
SessionResponse.
:attr str session_id: The session ID.
"""
def __init__(self, session_id: str) -> None:
"""
Initialize a SessionResponse object.
:param str session_id: The session ID.
"""
self.session_id = session_id
[docs] @classmethod
def from_dict(cls, _dict: Dict) -> 'SessionResponse':
"""Initialize a SessionResponse object from a json dictionary."""
args = {}
valid_keys = ['session_id']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SessionResponse: '
+ ', '.join(bad_keys))
if 'session_id' in _dict:
args['session_id'] = _dict.get('session_id')
else:
raise ValueError(
'Required property \'session_id\' not present in SessionResponse JSON'
)
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SessionResponse object from a json dictionary."""
return cls.from_dict(_dict)
[docs] def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'session_id') and self.session_id is not None:
_dict['session_id'] = self.session_id
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this SessionResponse object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other: 'SessionResponse') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'SessionResponse') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other