f22350ce2f
Switch every example to ``await runner.spawn(task)`` followed by ``await runner.run()`` (no task argument), and ``await runner.cancel()`` on client-disconnected instead of ``await task.cancel()``. This makes the main pipeline task look the same as the worker / proxy tasks spawned alongside it, and lets ``runner.cancel()`` drive a uniform shutdown across every root task on the bus.
237 lines
8.0 KiB
Python
237 lines
8.0 KiB
Python
#
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# Copyright (c) 2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Parallel debate using job groups.
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A voice bot receives a topic from the user and fans out to three
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worker tasks in parallel via ``task.job_group(...)``. Each worker
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runs its own LLM context, so it remembers previous topics across
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debate rounds. The bot collects all three perspectives and the
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main-task LLM synthesizes a balanced answer.
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Architecture::
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Main task (transport + LLM + ``debate`` tool)
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└── job_group(advocate, critic, analyst)
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└── DebateWorker (LLMContextTask, one per role)
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Requirements:
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- OPENAI_API_KEY
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- DEEPGRAM_API_KEY
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- CARTESIA_API_KEY
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- DAILY_API_KEY (for Daily transport)
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"""
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.bus import BusJobRequestMessage
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from pipecat.frames.frames import LLMMessagesAppendFrame, LLMRunFrame
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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AssistantTurnStoppedMessage,
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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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.deepgram.stt import DeepgramSTTService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.tasks.llm import LLMContextTask
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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load_dotenv(override=True)
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ROLE_PROMPTS = {
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"advocate": (
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"You argue IN FAVOR of the topic. Present the strongest case for why "
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"this is a good idea, with concrete benefits. Be persuasive but honest. "
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"Be concise, just 2-3 sentences."
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),
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"critic": (
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"You argue AGAINST the topic. Present the strongest concerns, risks, "
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"and downsides. Be critical but fair. Be concise, just 2-3 sentences."
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),
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"analyst": (
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"You provide a BALANCED, NEUTRAL analysis. Weigh both sides objectively "
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"and highlight the key trade-offs. Be concise, just 2-3 sentences."
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),
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}
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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class DebateWorker(LLMContextTask):
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"""Worker that generates a perspective using its own LLM context.
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Each worker keeps its own ``LLMContext`` so it remembers previous
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topics across multiple debate rounds. Job requests append the new
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topic and trigger the LLM; the assistant-aggregator captures the
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full reply and sends it back as the job response.
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"""
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def __init__(self, role: str):
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"""Initialize the DebateWorker.
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Args:
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role: One of ``"advocate"``, ``"critic"``, ``"analyst"`` —
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used as the task name and selects the system prompt.
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"""
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(system_instruction=ROLE_PROMPTS[role]),
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)
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super().__init__(role, llm=llm)
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self._role = role
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self._current_job_id: str | None = None
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@self.assistant_aggregator.event_handler("on_assistant_turn_stopped")
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async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage):
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text = message.content
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logger.info(f"Worker '{self.name}': completed ({len(text)} chars)")
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if self._current_job_id:
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job_id = self._current_job_id
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self._current_job_id = None
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await self.send_job_response(job_id, {"role": self._role, "text": text})
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async def on_job_request(self, message: BusJobRequestMessage) -> None:
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"""Inject the topic and run the LLM."""
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await super().on_job_request(message)
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self._current_job_id = message.job_id
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await self.queue_frame(
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LLMMessagesAppendFrame(
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messages=[{"role": "developer", "content": f"Topic: {message.payload['topic']}"}],
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run_llm=True,
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)
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)
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async def debate(params: FunctionCallParams, topic: str):
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"""Analyze a topic from multiple perspectives (advocate, critic, analyst).
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Args:
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topic (str): The topic or question to debate.
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"""
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logger.info(f"Starting debate on '{topic}'")
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async with params.pipeline_task.job_group(
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*ROLE_PROMPTS, payload={"topic": topic}, timeout=30
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) as tg:
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pass
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result = "\n\n".join(f"{r['role'].upper()}: {r['text']}" for r in tg.responses.values())
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logger.info("Debate complete, synthesizing")
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await params.result_callback(result)
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info("Starting parallel-debate bot")
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
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tts = CartesiaTTSService(
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api_key=os.environ["CARTESIA_API_KEY"],
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settings=CartesiaTTSService.Settings(
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voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc", # Jacqueline
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),
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)
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llm = OpenAILLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(
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system_instruction=(
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"You are a debate moderator in a voice conversation. When the user "
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"gives you a topic, call the debate tool to gather perspectives from "
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"three viewpoints (advocate, critic, analyst). Then synthesize the "
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"results into a clear, balanced summary for the user. Keep your "
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"responses concise and natural for speaking."
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),
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),
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)
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llm.register_direct_function(debate, cancel_on_interruption=False, timeout_secs=60)
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context = LLMContext(tools=ToolsSchema(standard_tools=[debate]))
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aggregators = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(),
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stt,
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aggregators.user(),
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llm,
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tts,
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transport.output(),
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aggregators.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info("Client connected")
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context.add_message(
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{
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"role": "developer",
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"content": (
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"Greet the user and tell them you can moderate a debate on any "
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"topic. Ask what they'd like to explore."
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),
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}
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)
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await task.queue_frame(LLMRunFrame())
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info("Client disconnected")
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await runner.cancel()
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for role in ROLE_PROMPTS:
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await runner.spawn(DebateWorker(role))
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await runner.spawn(task)
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await runner.run()
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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