# coding: utf-8
# Copyright 2018 IBM All Rights Reserved.
#
# 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.
"""
from __future__ import absolute_import
import json
from .common import get_sdk_headers
from ibm_cloud_sdk_core import BaseService
##############################################################################
# Service
##############################################################################
[docs]class AssistantV2(BaseService):
"""The Assistant V2 service."""
default_url = 'https://gateway.watsonplatform.net/assistant/api'
def __init__(
self,
version,
url=default_url,
username=None,
password=None,
iam_apikey=None,
iam_access_token=None,
iam_url=None,
iam_client_id=None,
iam_client_secret=None,
icp4d_access_token=None,
icp4d_url=None,
authentication_type=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 str url: The base url to use when contacting the service (e.g.
"https://gateway.watsonplatform.net/assistant/api/assistant/api").
The base url may differ between IBM Cloud regions.
:param str username: The username used to authenticate with the service.
Username and password credentials are only required to run your
application locally or outside of IBM Cloud. When running on
IBM Cloud, the credentials will be automatically loaded from the
`VCAP_SERVICES` environment variable.
:param str password: The password used to authenticate with the service.
Username and password credentials are only required to run your
application locally or outside of IBM Cloud. When running on
IBM Cloud, the credentials will be automatically loaded from the
`VCAP_SERVICES` environment variable.
:param str iam_apikey: An API key that can be used to request IAM tokens. If
this API key is provided, the SDK will manage the token and handle the
refreshing.
:param str iam_access_token: An IAM access token is fully managed by the application.
Responsibility falls on the application to refresh the token, either before
it expires or reactively upon receiving a 401 from the service as any requests
made with an expired token will fail.
:param str iam_url: An optional URL for the IAM service API. Defaults to
'https://iam.cloud.ibm.com/identity/token'.
:param str iam_client_id: An optional client_id value to use when interacting with the IAM service.
:param str iam_client_secret: An optional client_secret value to use when interacting with the IAM service.
:param str icp4d_access_token: A ICP4D(IBM Cloud Pak for Data) access token is
fully managed by the application. Responsibility falls on the application to
refresh the token, either before it expires or reactively upon receiving a 401
from the service as any requests made with an expired token will fail.
:param str icp4d_url: In order to use an SDK-managed token with ICP4D authentication, this
URL must be passed in.
:param str authentication_type: Specifies the authentication pattern to use. Values that it
takes are basic, iam or icp4d.
"""
BaseService.__init__(
self,
vcap_services_name='conversation',
url=url,
username=username,
password=password,
iam_apikey=iam_apikey,
iam_access_token=iam_access_token,
iam_url=iam_url,
iam_client_id=iam_client_id,
iam_client_secret=iam_client_secret,
use_vcap_services=True,
display_name='Assistant',
icp4d_access_token=icp4d_access_token,
icp4d_url=icp4d_url,
authentication_type=authentication_type)
self.version = version
#########################
# Sessions
#########################
[docs] def create_session(self, assistant_id, **kwargs):
"""
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.
: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/services/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('conversation', 'V2', 'create_session')
headers.update(sdk_headers)
params = {'version': self.version}
url = '/v2/assistants/{0}/sessions'.format(
*self._encode_path_vars(assistant_id))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
[docs] def delete_session(self, assistant_id, session_id, **kwargs):
"""
Delete session.
Deletes a session explicitly before it times out.
: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/services/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('conversation', 'V2', '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))
response = self.request(
method='DELETE',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
#########################
# Message
#########################
[docs] def message(self,
assistant_id,
session_id,
input=None,
context=None,
**kwargs):
"""
Send user input to assistant.
Send user input to an assistant and receive a response.
There is no rate limit for this operation.
: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/services/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: An input object that includes the input text.
:param MessageContext context: State information for the conversation. The context
is stored by the assistant on a per-session basis. You can use this property to
set or modify context variables, which can also be accessed by dialog nodes.
: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, MessageInput)
if context is not None:
context = self._convert_model(context, MessageContext)
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('conversation', 'V2', '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))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
json=data,
accept_json=True)
return response
##############################################################################
# Models
##############################################################################
[docs]class CaptureGroup(object):
"""
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, location=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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CaptureGroup object from a json dictionary."""
args = {}
validKeys = ['group', 'location']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class CaptureGroup: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this CaptureGroup object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogLogMessage(object):
"""
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, message):
"""
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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogLogMessage object from a json dictionary."""
args = {}
validKeys = ['level', 'message']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogLogMessage: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this DialogLogMessage object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogNodeAction(object):
"""
DialogNodeAction.
:attr str name: The name of the action.
:attr str action_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,
result_variable,
action_type=None,
parameters=None,
credentials=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 action_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.action_type = action_type
self.parameters = parameters
self.result_variable = result_variable
self.credentials = credentials
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodeAction object from a json dictionary."""
args = {}
validKeys = [
'name', 'action_type', 'type', 'parameters', 'result_variable',
'credentials'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodeAction: '
+ ', '.join(badKeys))
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in DialogNodeAction JSON'
)
if 'type' in _dict or 'action_type' in _dict:
args['action_type'] = _dict.get('type') or _dict.get('action_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)
def _to_dict(self):
"""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, 'action_type') and self.action_type is not None:
_dict['type'] = self.action_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 __str__(self):
"""Return a `str` version of this DialogNodeAction object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogNodeOutputOptionsElement(object):
"""
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, value):
"""
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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodeOutputOptionsElement object from a json dictionary."""
args = {}
validKeys = ['label', 'value']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodeOutputOptionsElement: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this DialogNodeOutputOptionsElement object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogNodeOutputOptionsElementValue(object):
"""
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=None):
"""
Initialize a DialogNodeOutputOptionsElementValue object.
:param MessageInput input: (optional) An input object that includes the input
text.
"""
self.input = input
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodeOutputOptionsElementValue object from a json dictionary."""
args = {}
validKeys = ['input']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodeOutputOptionsElementValue: '
+ ', '.join(badKeys))
if 'input' in _dict:
args['input'] = MessageInput._from_dict(_dict.get('input'))
return cls(**args)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this DialogNodeOutputOptionsElementValue object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogNodesVisited(object):
"""
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=None, title=None, conditions=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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogNodesVisited object from a json dictionary."""
args = {}
validKeys = ['dialog_node', 'title', 'conditions']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogNodesVisited: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this DialogNodesVisited object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogRuntimeResponseGeneric(object):
"""
DialogRuntimeResponseGeneric.
: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.
**Note:** The **suggestion** response type is part of the disambiguation feature,
which is only available for Premium users.
: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.
**Note:** The **suggestions** property is part of the disambiguation feature, which is
only available for Premium users.
: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,
text=None,
time=None,
typing=None,
source=None,
title=None,
description=None,
preference=None,
options=None,
message_to_human_agent=None,
topic=None,
suggestions=None,
header=None,
results=None):
"""
Initialize a DialogRuntimeResponseGeneric 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.
**Note:** The **suggestion** response type is part of the disambiguation feature,
which is only available for Premium users.
: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.
**Note:** The **suggestions** property is part of the disambiguation feature,
which is only available for Premium users.
: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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogRuntimeResponseGeneric object from a json dictionary."""
args = {}
validKeys = [
'response_type', 'text', 'time', 'typing', 'source', 'title',
'description', 'preference', 'options', 'message_to_human_agent',
'topic', 'suggestions', 'header', 'results'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogRuntimeResponseGeneric: '
+ ', '.join(badKeys))
if 'response_type' in _dict:
args['response_type'] = _dict.get('response_type')
else:
raise ValueError(
'Required property \'response_type\' not present in DialogRuntimeResponseGeneric 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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this DialogRuntimeResponseGeneric object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogSuggestion(object):
"""
DialogSuggestion.
:attr str label: The user-facing label for the disambiguation option. This label is
taken from the **user_label** property of the corresponding dialog node.
: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, value, output=None):
"""
Initialize a DialogSuggestion object.
:param str label: The user-facing label for the disambiguation option. This label
is taken from the **user_label** property of the corresponding dialog node.
: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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogSuggestion object from a json dictionary."""
args = {}
validKeys = ['label', 'value', 'output']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogSuggestion: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this DialogSuggestion object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class DialogSuggestionValue(object):
"""
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=None):
"""
Initialize a DialogSuggestionValue object.
:param MessageInput input: (optional) An input object that includes the input
text.
"""
self.input = input
@classmethod
def _from_dict(cls, _dict):
"""Initialize a DialogSuggestionValue object from a json dictionary."""
args = {}
validKeys = ['input']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class DialogSuggestionValue: '
+ ', '.join(badKeys))
if 'input' in _dict:
args['input'] = MessageInput._from_dict(_dict.get('input'))
return cls(**args)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this DialogSuggestionValue object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContext(object):
"""
MessageContext.
:attr MessageContextGlobal global_: (optional) Information 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 property named `main skill` is supported. This
object contains variables that apply to the dialog skill used by the assistant.
"""
def __init__(self, global_=None, skills=None):
"""
Initialize a MessageContext object.
:param MessageContextGlobal global_: (optional) Information 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 property named `main skill` is supported. This
object contains variables that apply to the dialog skill used by the assistant.
"""
self.global_ = global_
self.skills = skills
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContext object from a json dictionary."""
args = {}
validKeys = ['global_', 'global', 'skills']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContext: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this MessageContext object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextGlobal(object):
"""
Information 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.
"""
def __init__(self, system=None):
"""
Initialize a MessageContextGlobal object.
:param MessageContextGlobalSystem system: (optional) Built-in system properties
that apply to all skills used by the assistant.
"""
self.system = system
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextGlobal object from a json dictionary."""
args = {}
validKeys = ['system']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextGlobal: '
+ ', '.join(badKeys))
if 'system' in _dict:
args['system'] = MessageContextGlobalSystem._from_dict(
_dict.get('system'))
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'system') and self.system is not None:
_dict['system'] = self.system._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this MessageContextGlobal object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextGlobalSystem(object):
"""
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).
"""
def __init__(self, timezone=None, user_id=None, turn_count=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).
"""
self.timezone = timezone
self.user_id = user_id
self.turn_count = turn_count
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextGlobalSystem object from a json dictionary."""
args = {}
validKeys = ['timezone', 'user_id', 'turn_count']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextGlobalSystem: '
+ ', '.join(badKeys))
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')
return cls(**args)
def _to_dict(self):
"""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
return _dict
def __str__(self):
"""Return a `str` version of this MessageContextGlobalSystem object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextSkill(object):
"""
Contains information specific to a particular skill used by the Assistant.
:attr dict user_defined: (optional) Arbitrary variables that can be read and written
by a particular skill.
"""
def __init__(self, user_defined=None):
"""
Initialize a MessageContextSkill object.
:param dict user_defined: (optional) Arbitrary variables that can be read and
written by a particular skill.
"""
self.user_defined = user_defined
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextSkill object from a json dictionary."""
args = {}
validKeys = ['user_defined']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageContextSkill: '
+ ', '.join(badKeys))
if 'user_defined' in _dict:
args['user_defined'] = _dict.get('user_defined')
return cls(**args)
def _to_dict(self):
"""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
return _dict
def __str__(self):
"""Return a `str` version of this MessageContextSkill object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageContextSkills(object):
"""
Information specific to particular skills used by the Assistant.
**Note:** Currently, only a single property named `main skill` is supported. This
object contains variables that apply to the dialog skill used by the assistant.
"""
def __init__(self, **kwargs):
"""
Initialize a MessageContextSkills object.
:param **kwargs: (optional) Any additional properties.
"""
for _key, _value in kwargs.items():
setattr(self, _key, _value)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageContextSkills object from a json dictionary."""
args = {}
xtra = _dict.copy()
args.update(xtra)
return cls(**args)
def _to_dict(self):
"""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 __setattr__(self, name, value):
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):
"""Return a `str` version of this MessageContextSkills object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageOutput(object):
"""
Assistant output to be rendered or processed by the client.
:attr list[DialogRuntimeResponseGeneric] 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.
"""
def __init__(self,
generic=None,
intents=None,
entities=None,
actions=None,
debug=None,
user_defined=None):
"""
Initialize a MessageOutput object.
:param list[DialogRuntimeResponseGeneric] 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.
"""
self.generic = generic
self.intents = intents
self.entities = entities
self.actions = actions
self.debug = debug
self.user_defined = user_defined
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageOutput object from a json dictionary."""
args = {}
validKeys = [
'generic', 'intents', 'entities', 'actions', 'debug', 'user_defined'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageOutput: '
+ ', '.join(badKeys))
if 'generic' in _dict:
args['generic'] = [
DialogRuntimeResponseGeneric._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')
return cls(**args)
def _to_dict(self):
"""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
return _dict
def __str__(self):
"""Return a `str` version of this MessageOutput object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageOutputDebug(object):
"""
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=None,
log_messages=None,
branch_exited=None,
branch_exited_reason=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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageOutputDebug object from a json dictionary."""
args = {}
validKeys = [
'nodes_visited', 'log_messages', 'branch_exited',
'branch_exited_reason'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageOutputDebug: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this MessageOutputDebug object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class MessageResponse(object):
"""
A response from the Watson Assistant service.
:attr MessageOutput output: Assistant output to be rendered or processed by the
client.
:attr MessageContext context: (optional) State information for the conversation. The
context is stored by the assistant on a per-session basis. You can use this property
to access context variables.
**Note:** The context is included in message responses only if
**return_context**=`true` in the message request.
"""
def __init__(self, output, context=None):
"""
Initialize a MessageResponse object.
:param MessageOutput output: Assistant output to be rendered or processed by the
client.
:param MessageContext context: (optional) State information for the conversation.
The context is stored by the assistant on a per-session basis. You can use this
property to access context variables.
**Note:** The context is included in message responses only if
**return_context**=`true` in the message request.
"""
self.output = output
self.context = context
@classmethod
def _from_dict(cls, _dict):
"""Initialize a MessageResponse object from a json dictionary."""
args = {}
validKeys = ['output', 'context']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class MessageResponse: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this MessageResponse object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RuntimeEntity(object):
"""
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.
"""
def __init__(self,
entity,
location,
value,
confidence=None,
metadata=None,
groups=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.
"""
self.entity = entity
self.location = location
self.value = value
self.confidence = confidence
self.metadata = metadata
self.groups = groups
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeEntity object from a json dictionary."""
args = {}
validKeys = [
'entity', 'location', 'value', 'confidence', 'metadata', 'groups'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeEntity: '
+ ', '.join(badKeys))
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'))
]
return cls(**args)
def _to_dict(self):
"""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]
return _dict
def __str__(self):
"""Return a `str` version of this RuntimeEntity object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RuntimeIntent(object):
"""
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, confidence):
"""
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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RuntimeIntent object from a json dictionary."""
args = {}
validKeys = ['intent', 'confidence']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RuntimeIntent: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this RuntimeIntent object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SearchResult(object):
"""
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 a beta feature
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,
result_metadata,
body=None,
title=None,
url=None,
highlight=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 a beta
feature 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
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SearchResult object from a json dictionary."""
args = {}
validKeys = [
'id', 'result_metadata', 'body', 'title', 'url', 'highlight'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SearchResult: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this SearchResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SearchResultHighlight(object):
"""
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=None, title=None, url=None, **kwargs):
"""
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)
@classmethod
def _from_dict(cls, _dict):
"""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)
def _to_dict(self):
"""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 __setattr__(self, name, value):
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):
"""Return a `str` version of this SearchResultHighlight object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SessionResponse(object):
"""
SessionResponse.
:attr str session_id: The session ID.
"""
def __init__(self, session_id):
"""
Initialize a SessionResponse object.
:param str session_id: The session ID.
"""
self.session_id = session_id
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SessionResponse object from a json dictionary."""
args = {}
validKeys = ['session_id']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SessionResponse: '
+ ', '.join(badKeys))
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)
def _to_dict(self):
"""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 __str__(self):
"""Return a `str` version of this SessionResponse object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""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):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other