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#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
from typing import List
from pipecat.services.openai import OpenAILLMContextFrame, OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.frames.frames import (
Frame,
InterimTranscriptionFrame,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
LLMResponseEndFrame,
LLMResponseStartFrame,
LLMMessagesFrame,
StartInterruptionFrame,
TranscriptionFrame,
TextFrame,
UserStartedSpeakingFrame,
UserStoppedSpeakingFrame)
class LLMResponseAggregator(FrameProcessor):
def __init__(
self,
*,
messages: List[dict],
role: str,
start_frame,
end_frame,
accumulator_frame: TextFrame,
interim_accumulator_frame: TextFrame | None = None,
handle_interruptions: bool = False
):
super().__init__()
self._messages = messages
self._role = role
self._start_frame = start_frame
self._end_frame = end_frame
self._accumulator_frame = accumulator_frame
self._interim_accumulator_frame = interim_accumulator_frame
self._handle_interruptions = handle_interruptions
# Reset our accumulator state.
self._reset()
@property
def messages(self):
return self._messages
@property
def role(self):
return self._role
#
# Frame processor
#
# Use cases implemented:
#
# S: Start, E: End, T: Transcription, I: Interim, X: Text
#
# S E -> None
# S T E -> X
# S I T E -> X
# S I E T -> X
# S I E I T -> X
# S E T -> X
# S E I T -> X
#
# The following case would not be supported:
#
# S I E T1 I T2 -> X
#
# and T2 would be dropped.
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
send_aggregation = False
if isinstance(frame, self._start_frame):
self._aggregation = ""
self._aggregating = True
self._seen_start_frame = True
self._seen_end_frame = False
self._seen_interim_results = False
await self.push_frame(frame, direction)
elif isinstance(frame, self._end_frame):
self._seen_end_frame = True
self._seen_start_frame = False
# We might have received the end frame but we might still be
# aggregating (i.e. we have seen interim results but not the final
# text).
self._aggregating = self._seen_interim_results or len(self._aggregation) == 0
# Send the aggregation if we are not aggregating anymore (i.e. no
# more interim results received).
send_aggregation = not self._aggregating
await self.push_frame(frame, direction)
elif isinstance(frame, self._accumulator_frame):
if self._aggregating:
self._aggregation += f" {frame.text}"
# We have recevied a complete sentence, so if we have seen the
# end frame and we were still aggregating, it means we should
# send the aggregation.
send_aggregation = self._seen_end_frame
# We just got our final result, so let's reset interim results.
self._seen_interim_results = False
elif self._interim_accumulator_frame and isinstance(frame, self._interim_accumulator_frame):
self._seen_interim_results = True
elif self._handle_interruptions and isinstance(frame, StartInterruptionFrame):
await self._push_aggregation()
# Reset anyways
self._reset()
await self.push_frame(frame, direction)
else:
await self.push_frame(frame, direction)
if send_aggregation:
await self._push_aggregation()
async def _push_aggregation(self):
if len(self._aggregation) > 0:
self._messages.append({"role": self._role, "content": self._aggregation})
# Reset the aggregation. Reset it before pushing it down, otherwise
# if the tasks gets cancelled we won't be able to clear things up.
self._aggregation = ""
frame = LLMMessagesFrame(self._messages)
await self.push_frame(frame)
def _reset(self):
self._aggregation = ""
self._aggregating = False
self._seen_start_frame = False
self._seen_end_frame = False
self._seen_interim_results = False
class LLMAssistantResponseAggregator(LLMResponseAggregator):
def __init__(self, messages: List[dict] = []):
super().__init__(
messages=messages,
role="assistant",
start_frame=LLMFullResponseStartFrame,
end_frame=LLMFullResponseEndFrame,
accumulator_frame=TextFrame,
handle_interruptions=True
)
class LLMUserResponseAggregator(LLMResponseAggregator):
def __init__(self, messages: List[dict] = []):
super().__init__(
messages=messages,
role="user",
start_frame=UserStartedSpeakingFrame,
end_frame=UserStoppedSpeakingFrame,
accumulator_frame=TranscriptionFrame,
interim_accumulator_frame=InterimTranscriptionFrame
)
class LLMFullResponseAggregator(FrameProcessor):
"""This class aggregates Text frames until it receives a
LLMResponseEndFrame, then emits the concatenated text as
a single text frame.
given the following frames:
TextFrame("Hello,")
TextFrame(" world.")
TextFrame(" I am")
TextFrame(" an LLM.")
LLMResponseEndFrame()]
this processor will yield nothing for the first 4 frames, then
TextFrame("Hello, world. I am an LLM.")
LLMResponseEndFrame()
when passed the last frame.
>>> async def print_frames(aggregator, frame):
... async for frame in aggregator.process_frame(frame):
... if isinstance(frame, TextFrame):
... print(frame.text)
... else:
... print(frame.__class__.__name__)
>>> aggregator = LLMFullResponseAggregator()
>>> asyncio.run(print_frames(aggregator, TextFrame("Hello,")))
>>> asyncio.run(print_frames(aggregator, TextFrame(" world.")))
>>> asyncio.run(print_frames(aggregator, TextFrame(" I am")))
>>> asyncio.run(print_frames(aggregator, TextFrame(" an LLM.")))
>>> asyncio.run(print_frames(aggregator, LLMResponseEndFrame()))
Hello, world. I am an LLM.
LLMResponseEndFrame
"""
def __init__(self):
super().__init__()
self._aggregation = ""
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame):
self._aggregation += frame.text
elif isinstance(frame, LLMFullResponseEndFrame):
await self.push_frame(TextFrame(self._aggregation))
await self.push_frame(frame)
self._aggregation = ""
else:
await self.push_frame(frame, direction)
class LLMContextAggregator(LLMResponseAggregator):
def __init__(self, *, context: OpenAILLMContext, **kwargs):
self._context = context
super().__init__(**kwargs)
async def _push_aggregation(self):
if len(self._aggregation) > 0:
self._context.add_message({"role": self._role, "content": self._aggregation})
frame = OpenAILLMContextFrame(self._context)
await self.push_frame(frame)
# Reset our accumulator state.
self._reset()
class LLMAssistantContextAggregator(LLMContextAggregator):
def __init__(self, context: OpenAILLMContext):
super().__init__(
messages=[],
context=context,
role="assistant",
start_frame=LLMResponseStartFrame,
end_frame=LLMResponseEndFrame,
accumulator_frame=TextFrame
)
class LLMUserContextAggregator(LLMContextAggregator):
def __init__(self, context: OpenAILLMContext):
super().__init__(
messages=[],
context=context,
role="user",
start_frame=UserStartedSpeakingFrame,
end_frame=UserStoppedSpeakingFrame,
accumulator_frame=TranscriptionFrame,
interim_accumulator_frame=InterimTranscriptionFrame
)
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