def __call__(self, utterances: list, batch_history: list, *responses: list) -> list:
"""Selects for each utterance response with highest confidence and wraps them to RichControl objects.
Args:
utterances_batch: Not used.
history_batch: Not used.
responses: Each response positional argument corresponds to
response of one of Agent skills and is represented by
batch (list) of (response, confidence) tuple structures.
Returns:
result: A batch of responses corresponding to the utterance
batch received by agent.
"""
responses, confidences = zip(*[zip(*r) for r in responses])
indexes = [c.index(max(c)) for c in zip(*confidences)]
result = []
for i, *responses in zip(indexes, *responses):
rich_message = RichMessage()
plain_text = PlainText(str(responses[i]))
rich_message.add_control(plain_text)
result.append(rich_message)
return result
def __call__(self, utterances: list, batch_history: list,
*responses: list) -> list:
"""Selects for each utterance response with highest confidence and wraps them to RichControl objects.
Args:
utterances_batch: Not used.
history_batch: Not used.
responses: Each response positional argument corresponds to
response of one of Agent skills and is represented by
batch (list) of (response, confidence) tuple structures.
Returns:
result: A batch of responses corresponding to the utterance
batch received by agent.
"""
responses, confidences = zip(*[zip(*r) for r in responses])
indexes = [c.index(max(c)) for c in zip(*confidences)]
result = []
for i, *responses in zip(indexes, *responses):
rich_message = RichMessage()
plain_text = PlainText(str(responses[i]))
rich_message.add_control(plain_text)
result.append(rich_message)
return result
def _call(self,
utterances_batch: list,
utterances_ids: list = None) -> list:
"""Processes batch of utterances and returns corresponding responses batch.
Args:
utterances: Batch of incoming utterances.
ids: Batch of dialog IDs corresponding to incoming utterances.
Returns:
responses: A batch of responses corresponding to the
utterance batch received by agent.
"""
rich_message = RichMessage()
for utt, id_ in zip(utterances_batch, utterances_ids):
log.debug(f'Utterance: {utt}')
if utt == "/start":
welcome = "I am a new e-commerce bot. I will help you to find products that you are looking for. Please type your request in plain text."
rich_message.add_control(PlainText(welcome))
continue
if utt[0] == "@":
command, *parts = utt.split(":")
log.debug(f'Actions: {parts}')
if command == "@details":
rich_message.add_control(
PlainText(
show_details(self.history[id_][int(
parts[0])][0][int(parts[1])])))
continue
if command == "@entropy":
state = self.history[id_][int(parts[0])]
state[parts[1]] = parts[2]
state["start"] = 0
state["stop"] = 5
utt = state['query']
self.states[id_] = state
if command == "@next":
state = self.history[id_][int(parts[0])]
state['start'] = state['stop']
state['stop'] = state['stop'] + 5
utt = state['query']
self.states[id_] = state
if command == "@previous":
state = self.history[id_][int(parts[0])]
state['stop'] = state['start']
state['start'] = state['start'] - 5
utt = state['query']
self.states[id_] = state
else:
self.states[id_] = {"start": 0, "stop": 5}
responses, confidences, state = self.skills[0]([utt],
self.history[id_],
[self.states[id_]])
# update `self.states` with retrieved results
self.states[id_] = state
self.states[id_]["query"] = utt
items, entropy, total = responses
self.history[id_].append(responses)
self.history[id_].append(self.states[id_])
for idx, item in enumerate(items):
title = item['Title']
if 'ListPrice' in item:
title += " - **$" + item['ListPrice'].split('$')[1] + "**"
buttons_frame = ButtonsFrame(text=title)
buttons_frame.add_button(
Button(
'Show details', "@details:" +
str(len(self.history[id_]) - 2) + ":" + str(idx)))
rich_message.add_control(buttons_frame)
buttons_frame = ButtonsFrame(text="")
if self.states[id_]["start"] > 0:
buttons_frame.add_button(
Button('Previous',
"@previous:" + str(len(self.history[id_]) - 1)))
buttons_frame.add_button(
Button('Next', "@next:" + str(len(self.history[id_]) - 1)))
rich_message.add_control(buttons_frame)
if entropy:
buttons_frame = ButtonsFrame(text="Please specify a " +
entropy[0][1])
for ent_value in entropy[0][2][:3]:
button_a = Button(
ent_value[0],
f'@entropy:{len(self.history[id_])-1}:{entropy[0][1]}:{ent_value[0]}'
)
buttons_frame.add_button(button_a)
rich_message.add_control(buttons_frame)
return [rich_message]
def _call(self, utterances_batch: List[str], utterances_ids: List[int] = None) -> List[RichMessage]:
"""Processes batch of utterances and returns corresponding responses batch.
Args:
utterances_batch: Batch of incoming utterances.
utterances_ids: Batch of dialog IDs corresponding to incoming utterances.
Returns:
responses: A batch of responses corresponding to the
utterance batch received by agent.
"""
rich_message = RichMessage()
for utt_id, utt in enumerate(utterances_batch):
if utterances_ids:
id_ = utterances_ids[utt_id]
log.debug(f'Utterance: {utt}')
if utt == "/start":
welcome = "I am a new e-commerce bot. I will help you to find products that you are looking for. Please type your request in plain text."
rich_message.add_control(PlainText(welcome))
continue
if utt[0] == "@":
command, *parts = utt.split(":")
log.debug(f'Actions: {parts}')
if command == "@details":
batch_index = int(parts[0]) # batch index in history list
item_index = int(parts[1]) # index in batch
rich_message.add_control(PlainText(show_details(
self.history[id_][batch_index][item_index])))
continue
if command == "@entropy":
state = self.history[id_][int(parts[0])]
state[parts[1]] = parts[2]
state["start"] = 0
state["stop"] = 5
utt = state['query']
self.states[id_] = state
if command == "@next":
state = self.history[id_][int(parts[0])]
state['start'] = state['stop']
state['stop'] = state['stop'] + 5
utt = state['query']
self.states[id_] = state
else:
if id_ not in self.states:
self.states[id_] = {}
self.states[id_]["start"] = 0
self.states[id_]["stop"] = 5
responses_batch, confidences_batch, state_batch = self.skills[0](
[utt], self.history[id_], [self.states[id_]])
# update `self.states` with retrieved results
self.states[id_] = state_batch[0]
self.states[id_]["query"] = utt
items_batch, entropy_batch = responses_batch
for batch_idx, items in enumerate(items_batch):
self.history[id_].append(items)
self.history[id_].append(self.states[id_])
for idx, item in enumerate(items):
rich_message.add_control(_draw_item(item, idx, self.history[id_]))
if len(items) == self.states[id_]['stop'] - self.states[id_]['start']:
buttons_frame = _draw_tail(entropy_batch[batch_idx], self.history[id_])
rich_message.add_control(buttons_frame)
return [rich_message]
def _send_infer_results(self, response: RichMessage, in_activity: dict):
ms_bf_response = response.ms_bot_framework()
for out_activity in ms_bf_response:
if out_activity:
self.out_gateway.send_activity(out_activity, in_activity)
def _send_infer_results(self, response: RichMessage, in_activity: dict):
ms_bf_response = response.ms_bot_framework()
for out_activity in ms_bf_response:
if out_activity:
self.out_gateway.send_activity(out_activity, in_activity)
def _call(self, utterances_batch: List[str], utterances_ids: List[int] = None) -> List[RichMessage]:
"""Processes batch of utterances and returns corresponding responses batch.
Args:
utterances_batch: Batch of incoming utterances.
utterances_ids: Batch of dialog IDs corresponding to incoming utterances.
Returns:
responses: A batch of responses corresponding to the
utterance batch received by agent.
"""
rich_message = RichMessage()
for utt_id, utt in enumerate(utterances_batch):
if utterances_ids:
id_ = utterances_ids[utt_id]
log.debug(f'Utterance: {utt}')
if utt == "/start":
welcome = "I am a new e-commerce bot. I will help you to find products that you are looking for. Please type your request in plain text."
rich_message.add_control(PlainText(welcome))
continue
if utt[0] == "@":
command, *parts = utt.split(":")
log.debug(f'Actions: {parts}')
if command == "@details":
batch_index = int(parts[0]) # batch index in history list
item_index = int(parts[1]) # index in batch
rich_message.add_control(PlainText(show_details(
self.history[id_][batch_index][item_index])))
continue
if command == "@entropy":
state = self.history[id_][int(parts[0])]
state[parts[1]] = parts[2]
state["start"] = 0
state["stop"] = 5
utt = state['query']
self.states[id_] = state
if command == "@next":
state = self.history[id_][int(parts[0])]
state['start'] = state['stop']
state['stop'] = state['stop'] + 5
utt = state['query']
self.states[id_] = state
else:
if id_ not in self.states:
self.states[id_] = {}
self.states[id_]["start"] = 0
self.states[id_]["stop"] = 5
responses_batch, confidences_batch, state_batch = self.skills[0](
[utt], self.history[id_], [self.states[id_]])
# update `self.states` with retrieved results
self.states[id_] = state_batch[0]
self.states[id_]["query"] = utt
items_batch, entropy_batch = responses_batch
for batch_idx, items in enumerate(items_batch):
self.history[id_].append(items)
self.history[id_].append(self.states[id_])
for idx, item in enumerate(items):
rich_message.add_control(_draw_item(item, idx, self.history[id_]))
if len(items) == self.states[id_]['stop'] - self.states[id_]['start']:
buttons_frame = _draw_tail(entropy_batch[batch_idx], self.history[id_])
rich_message.add_control(buttons_frame)
return [rich_message]