Progress on LLM failover support
This commit is contained in:
Paul Kompfner
@@ -17,6 +17,7 @@ from typing import List, Literal, Optional
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import BotStartedSpeakingFrame, BotStoppedSpeakingFrame, CancelFrame, EndFrame, InputAudioRawFrame, InterimTranscriptionFrame, LLMMessagesAppendFrame, LLMMessagesUpdateFrame, LLMSetToolChoiceFrame, LLMSetToolsFrame, SpeechControlParamsFrame, StartFrame, TranscriptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame
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from pipecat.processors.aggregators.llm_context import LLMContext, LLMContextFrame
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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@@ -140,7 +141,6 @@ class LLMContextAggregator(FrameProcessor):
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"""
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self._context.set_tools(tools)
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# TODO: should we be using LLMContextToolChoice here?
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def set_tool_choice(self, tool_choice: Literal["none", "auto", "required"] | dict):
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"""Set tool choice in the context.
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@@ -227,4 +227,103 @@ class LLMUserContextAggregator(LLMContextAggregator):
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"""
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self._context.add_message({"role": self.role, "content": aggregation})
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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"""Process frames for user speech aggregation and context management.
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Args:
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frame: The frame to process.
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direction: The direction of frame flow in the pipeline.
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"""
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await super().process_frame(frame, direction)
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if isinstance(frame, StartFrame):
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# Push StartFrame before start(), because we want StartFrame to be
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# processed by every processor before any other frame is processed.
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await self.push_frame(frame, direction)
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await self._start(frame)
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elif isinstance(frame, EndFrame):
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# Push EndFrame before stop(), because stop() waits on the task to
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# finish and the task finishes when EndFrame is processed.
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await self.push_frame(frame, direction)
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await self._stop(frame)
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elif isinstance(frame, CancelFrame):
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await self._cancel(frame)
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await self.push_frame(frame, direction)
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elif isinstance(frame, InputAudioRawFrame):
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await self._handle_input_audio(frame)
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await self.push_frame(frame, direction)
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elif isinstance(frame, UserStartedSpeakingFrame):
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await self._handle_user_started_speaking(frame)
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await self.push_frame(frame, direction)
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elif isinstance(frame, UserStoppedSpeakingFrame):
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await self._handle_user_stopped_speaking(frame)
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await self.push_frame(frame, direction)
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elif isinstance(frame, BotStartedSpeakingFrame):
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await self._handle_bot_started_speaking(frame)
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await self.push_frame(frame, direction)
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elif isinstance(frame, BotStoppedSpeakingFrame):
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await self._handle_bot_stopped_speaking(frame)
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await self.push_frame(frame, direction)
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elif isinstance(frame, TranscriptionFrame):
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await self._handle_transcription(frame)
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elif isinstance(frame, InterimTranscriptionFrame):
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await self._handle_interim_transcription(frame)
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elif isinstance(frame, LLMMessagesAppendFrame):
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await self._handle_llm_messages_append(frame)
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elif isinstance(frame, LLMMessagesUpdateFrame):
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await self._handle_llm_messages_update(frame)
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elif isinstance(frame, LLMSetToolsFrame):
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self.set_tools(frame.tools)
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elif isinstance(frame, LLMSetToolChoiceFrame):
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self.set_tool_choice(frame.tool_choice)
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elif isinstance(frame, SpeechControlParamsFrame):
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self._vad_params = frame.vad_params
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self._turn_params = frame.turn_params
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await self.push_frame(frame, direction)
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else:
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await self.push_frame(frame, direction)
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async def _process_aggregation(self):
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"""Process the current aggregation and push it downstream."""
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aggregation = self._aggregation
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await self.reset()
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await self.handle_aggregation(aggregation)
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frame = LLMContextFrame(self._context)
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await self.push_frame(frame)
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# TODO: you are here—there are errors to work out in the following method
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async def push_aggregation(self):
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"""Push the current aggregation based on interruption strategies and conditions."""
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if len(self._aggregation) > 0:
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if self.interruption_strategies and self._bot_speaking:
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should_interrupt = await self._should_interrupt_based_on_strategies()
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if should_interrupt:
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logger.debug(
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"Interruption conditions met - pushing BotInterruptionFrame and aggregation"
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)
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await self.push_frame(BotInterruptionFrame(), FrameDirection.UPSTREAM)
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await self._process_aggregation()
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else:
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logger.debug("Interruption conditions not met - not pushing aggregation")
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# Don't process aggregation, just reset it
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await self.reset()
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else:
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# No interruption config - normal behavior (always push aggregation)
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await self._process_aggregation()
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# Handles the case where both the user and the bot are not speaking,
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# and the bot was previously speaking before the user interruption.
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# Normally, when the user stops speaking, new text is expected,
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# which triggers the bot to respond. However, if no new text
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# is received, this safeguard ensures
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# the bot doesn't hang indefinitely while waiting to speak again.
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elif not self._seen_interim_results and self._was_bot_speaking and not self._bot_speaking:
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logger.warning("User stopped speaking but no new aggregation received.")
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# Resetting it so we don't trigger this twice
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self._was_bot_speaking = False
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# TODO: we are not enabling this for now, due to some STT services which can take as long as 2 seconds two return a transcription
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# So we need more tests and probably make this feature configurable, disabled it by default.
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# We are just pushing the same previous context to be processed again in this case
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# await self.push_frame(LLMContextFrame(self._context))
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# TODO: continue porting things over from LLMUserContextAggregator in backup file
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