examples(foundational): update 22 series with simple main pipelines
This commit is contained in:
Aleix Conchillo Flaqué
@@ -47,11 +47,13 @@ from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.google.llm import GoogleLLMContext, GoogleLLMService
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from pipecat.services.llm_service import LLMService
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from pipecat.sync.base_notifier import BaseNotifier
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from pipecat.sync.event_notifier import EventNotifier
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
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from pipecat.transports.services.daily import DailyParams
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from pipecat.utils.time import time_now_iso8601
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load_dotenv(override=True)
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@@ -607,23 +609,90 @@ class OutputGate(FrameProcessor):
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self._gate_task = None
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async def _gate_task_handler(self):
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while True:
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try:
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await self._notifier.wait()
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await self._notifier.wait()
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transcription = await self._transcription_buffer.wait_for_transcription() or "-"
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self._context.add_message(Content(role="user", parts=[Part(text=transcription)]))
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transcription = await self._transcription_buffer.wait_for_transcription() or "-"
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self._context.add_message(Content(role="user", parts=[Part(text=transcription)]))
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self.open_gate()
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for frame, direction in self._frames_buffer:
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await self.push_frame(frame, direction)
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self._frames_buffer = []
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.error(f"OutputGate error: {e}")
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raise e
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break
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self.open_gate()
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for frame, direction in self._frames_buffer:
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await self.push_frame(frame, direction)
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self._frames_buffer = []
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class TurnDetectionLLM(Pipeline):
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def __init__(self, llm: LLMService, context: OpenAILLMContext):
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# This is the LLM that will transcribe user speech.
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tx_llm = GoogleLLMService(
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name="Transcriber",
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model=TRANSCRIBER_MODEL,
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api_key=os.getenv("GOOGLE_API_KEY"),
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temperature=0.0,
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system_instruction=transcriber_system_instruction,
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)
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# This is the LLM that will classify user speech as complete or incomplete.
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classifier_llm = GoogleLLMService(
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name="Classifier",
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model=CLASSIFIER_MODEL,
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api_key=os.getenv("GOOGLE_API_KEY"),
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temperature=0.0,
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system_instruction=classifier_system_instruction,
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)
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# This is a notifier that we use to synchronize the two LLMs.
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notifier = EventNotifier()
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# This turns the LLM context into an inference request to classify the user's speech
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# as complete or incomplete.
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# statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
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audio_accumulater = AudioAccumulator()
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# This sends a UserStoppedSpeakingFrame and triggers the notifier event
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completeness_check = CompletenessCheck(
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notifier=notifier, audio_accumulator=audio_accumulater
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)
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async def block_user_stopped_speaking(frame):
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return not isinstance(frame, UserStoppedSpeakingFrame)
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conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
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llm_aggregator_buffer = LLMAggregatorBuffer()
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bot_output_gate = OutputGate(
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notifier=notifier, context=context, llm_transcription_buffer=llm_aggregator_buffer
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)
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super().__init__(
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[
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audio_accumulater,
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ParallelPipeline(
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[
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# Pass everything except UserStoppedSpeaking to the elements after
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# this ParallelPipeline
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FunctionFilter(filter=block_user_stopped_speaking),
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],
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[
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ParallelPipeline(
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[
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classifier_llm,
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completeness_check,
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],
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[
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tx_llm,
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llm_aggregator_buffer,
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],
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)
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],
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[
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conversation_audio_context_assembler,
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llm,
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bot_output_gate, # buffer output until notified, then flush frames and update context
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],
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),
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]
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)
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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@@ -656,24 +725,6 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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# This is the LLM that will transcribe user speech.
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tx_llm = GoogleLLMService(
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name="Transcriber",
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model=TRANSCRIBER_MODEL,
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api_key=os.getenv("GOOGLE_API_KEY"),
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temperature=0.0,
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system_instruction=transcriber_system_instruction,
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)
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# This is the LLM that will classify user speech as complete or incomplete.
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classifier_llm = GoogleLLMService(
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name="Classifier",
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model=CLASSIFIER_MODEL,
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api_key=os.getenv("GOOGLE_API_KEY"),
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temperature=0.0,
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system_instruction=classifier_system_instruction,
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)
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# This is the regular LLM that responds conversationally.
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conversation_llm = GoogleLLMService(
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name="Conversation",
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@@ -685,57 +736,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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context = OpenAILLMContext()
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context_aggregator = conversation_llm.create_context_aggregator(context)
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# This is a notifier that we use to synchronize the two LLMs.
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notifier = EventNotifier()
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# This turns the LLM context into an inference request to classify the user's speech
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# as complete or incomplete.
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# statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
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audio_accumulater = AudioAccumulator()
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# This sends a UserStoppedSpeakingFrame and triggers the notifier event
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completeness_check = CompletenessCheck(notifier=notifier, audio_accumulator=audio_accumulater)
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async def block_user_stopped_speaking(frame):
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return not isinstance(frame, UserStoppedSpeakingFrame)
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conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
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llm_aggregator_buffer = LLMAggregatorBuffer()
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bot_output_gate = OutputGate(
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notifier=notifier, context=context, llm_transcription_buffer=llm_aggregator_buffer
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)
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llm = TurnDetectionLLM(conversation_llm, context)
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pipeline = Pipeline(
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[
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transport.input(),
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audio_accumulater,
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ParallelPipeline(
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[
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# Pass everything except UserStoppedSpeaking to the elements after
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# this ParallelPipeline
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FunctionFilter(filter=block_user_stopped_speaking),
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],
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[
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ParallelPipeline(
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[
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classifier_llm,
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completeness_check,
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],
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[
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tx_llm,
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llm_aggregator_buffer,
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],
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)
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],
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[
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conversation_audio_context_assembler,
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conversation_llm,
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bot_output_gate, # buffer output until notified, then flush frames and update context
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# TempPrinter(),
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],
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),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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@@ -766,7 +772,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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await task.queue_frames(
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[
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UserStartedSpeakingFrame(),
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TranscriptionFrame(user_id="", timestamp=time.time(), text=message["message"]),
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TranscriptionFrame(
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user_id="", timestamp=time_now_iso8601(), text=message["message"]
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),
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UserStoppedSpeakingFrame(),
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]
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)
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