Add distributed-handoff examples (redis and pgmq)
Two transports of the same shape: a main task that hosts the
voice pipeline plus a network-backed `TaskBus` (`RedisBus` or
`PgmqBus`), and a standalone `llm.py` worker process for the
greeter / support LLM. Workers connect to the same bus channel,
register on the shared `TaskRegistry`, and the main task waits
on `runner.registry.watch("greeter", ...)` before sending the
welcome activation so it doesn't fire before the worker is up.
This commit is contained in:
Aleix Conchillo Flaqué
183
examples/multi-task/distributed-handoff/pgmq-handoff/llm.py
Normal file
183
examples/multi-task/distributed-handoff/pgmq-handoff/llm.py
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@@ -0,0 +1,183 @@
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#
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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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"""LLM worker task — run on Machine B (or locally alongside ``main.py``).
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A standalone process that runs one LLM task (greeter or support)
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attached to the same PGMQ-backed `TaskBus` as the main task.
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Multiple instances can run on different machines as long as they
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share a Postgres database with the PGMQ extension enabled.
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Usage::
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python llm.py greeter --database-url postgresql://...
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python llm.py support --database-url postgresql://...
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Requirements:
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- OPENAI_API_KEY
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- DATABASE_URL (or ``--database-url``)
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"""
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import argparse
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import asyncio
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import os
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from urllib.parse import unquote, urlparse
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from dotenv import load_dotenv
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from loguru import logger
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from pgmq.async_queue import PGMQueue
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from pipecat.bus.network.pgmq import PgmqBus
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from pipecat.pipeline.runner import PipelineRunner
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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 LLMTask, LLMTaskActivationArgs, tool
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load_dotenv(override=True)
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WORKER_CONFIG = {
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"greeter": {
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"system_instruction": (
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"You are a friendly greeter for Acme Corp. The available products "
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"are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
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"Tornado Kit. Ask which one they'd like to learn more about. "
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"When the user picks a product or asks a question about one, "
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"immediately call the transfer_to_agent tool with agent 'support'. "
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"Do not answer product questions yourself. If the user says goodbye, "
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"call the end_conversation tool. Do not mention transferring — just do it "
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"seamlessly. Keep responses brief — this is a voice conversation."
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),
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"watch": ["support"],
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},
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"support": {
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"system_instruction": (
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"You are a support agent for Acme Corp. You know about three "
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"products: Acme Rocket Boots (jet-powered boots, $299, run up "
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"to 60 mph), Acme Invisible Paint (makes anything invisible for "
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"24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
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"generator, $199, batteries included). Answer the user's questions "
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"about these products. If the user wants to browse other products "
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"or start over, call the transfer_to_agent tool with agent "
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"'greeter'. If the user says goodbye, call the end_conversation "
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"tool. Do not mention transferring — just do it seamlessly. "
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"Keep responses brief — this is a voice conversation."
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),
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"watch": ["greeter"],
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},
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}
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def pgmq_from_url(database_url: str, *, pool_size: int = 4) -> PGMQueue:
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"""Build a `PGMQueue` from a Postgres DSN string."""
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parsed = urlparse(database_url)
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if parsed.scheme not in ("postgres", "postgresql"):
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raise ValueError(f"Unsupported scheme '{parsed.scheme}' for database URL")
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return PGMQueue(
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host=parsed.hostname or "localhost",
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port=str(parsed.port or 5432),
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database=(parsed.path or "/postgres").lstrip("/") or "postgres",
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username=unquote(parsed.username or "postgres"),
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password=unquote(parsed.password or ""),
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pool_size=pool_size,
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)
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class AcmeLLMTask(LLMTask):
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"""LLM task for Acme Corp with transfer and end tools."""
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def __init__(self, name: str, *, system_instruction: str, watch: list[str]):
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"""Initialize the AcmeLLMTask.
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Args:
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name: Unique task name (``"greeter"`` or ``"support"``).
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system_instruction: System prompt for this LLM role.
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watch: Sibling task names this task will watch via the
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registry so it knows when they become available for
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handoff.
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"""
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llm = OpenAILLMService(
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name=f"{name}::OpenAILLMService",
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAILLMService.Settings(system_instruction=system_instruction),
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)
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super().__init__(name, llm=llm, bridged=())
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self._watch = watch
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async def start(self) -> None:
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"""Register watches for sibling tasks once ready."""
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await super().start()
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for task_name in self._watch:
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await self.watch_task(task_name)
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@tool(cancel_on_interruption=False)
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async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
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"""Transfer the user to another agent.
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Args:
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agent (str): The agent to transfer to (e.g. 'greeter', 'support').
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reason (str): Why the user is being transferred.
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"""
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logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
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await self.handoff_to(
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agent,
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activation_args=LLMTaskActivationArgs(
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messages=[{"role": "developer", "content": reason}]
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),
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result_callback=params.result_callback,
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)
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@tool
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async def end_conversation(self, params: FunctionCallParams, reason: str):
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"""End the conversation when the user says goodbye.
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Args:
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reason (str): Why the conversation is ending.
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"""
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logger.info(f"Task '{self.name}': ending conversation ({reason})")
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await self.end(
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reason=reason,
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messages=[{"role": "developer", "content": reason}],
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result_callback=params.result_callback,
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)
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async def main_async() -> None:
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parser = argparse.ArgumentParser(description="LLM worker task (greeter or support)")
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parser.add_argument("worker", choices=list(WORKER_CONFIG), help="Which worker to run")
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parser.add_argument(
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"--database-url",
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default=os.getenv("DATABASE_URL"),
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help="PostgreSQL DSN (or set DATABASE_URL env var)",
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)
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parser.add_argument(
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"--channel",
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default=os.getenv("PGMQ_CHANNEL", "pipecat_acme"),
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help="PGMQ channel prefix",
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)
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args = parser.parse_args()
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if not args.database_url:
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parser.error("--database-url is required (or set DATABASE_URL env var)")
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pgmq = pgmq_from_url(args.database_url)
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await pgmq.init()
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bus = PgmqBus(pgmq=pgmq, channel=args.channel)
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config = WORKER_CONFIG[args.worker]
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worker = AcmeLLMTask(
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args.worker,
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system_instruction=config["system_instruction"],
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watch=config["watch"],
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)
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runner = PipelineRunner(bus=bus, handle_sigint=True)
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logger.info(f"Starting {args.worker} worker, waiting for activation...")
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await runner.run(worker)
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if __name__ == "__main__":
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asyncio.run(main_async())
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196
examples/multi-task/distributed-handoff/pgmq-handoff/main.py
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196
examples/multi-task/distributed-handoff/pgmq-handoff/main.py
Normal file
@@ -0,0 +1,196 @@
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#
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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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"""Main transport task — run on Machine A.
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Handles audio I/O (STT, TTS) and bridges frames to the bus. The LLM
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worker tasks run as separate processes (possibly on different
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machines) connected via PGMQ on a shared Postgres database
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(e.g. Supabase).
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Usage::
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python main.py --database-url postgresql://...
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Requirements:
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- DEEPGRAM_API_KEY
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- CARTESIA_API_KEY
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- DATABASE_URL (or ``--database-url``)
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"""
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import argparse
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import os
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from urllib.parse import unquote, urlparse
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from dotenv import load_dotenv
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from loguru import logger
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from pgmq.async_queue import PGMQueue
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.bus import BusBridgeProcessor
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from pipecat.bus.network.pgmq import PgmqBus
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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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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.registry.types import TaskReadyData
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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.tasks.llm import LLMTaskActivationArgs
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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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MAIN_NAME = "acme"
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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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def pgmq_from_url(database_url: str, *, pool_size: int = 4) -> PGMQueue:
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"""Build a `PGMQueue` from a Postgres DSN string."""
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parsed = urlparse(database_url)
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if parsed.scheme not in ("postgres", "postgresql"):
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raise ValueError(f"Unsupported scheme '{parsed.scheme}' for database URL")
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return PGMQueue(
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host=parsed.hostname or "localhost",
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port=str(parsed.port or 5432),
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database=(parsed.path or "/postgres").lstrip("/") or "postgres",
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username=unquote(parsed.username or "postgres"),
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password=unquote(parsed.password or ""),
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pool_size=pool_size,
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)
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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pgmq = pgmq_from_url(runner_args.cli_args.database_url)
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await pgmq.init()
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bus = PgmqBus(pgmq=pgmq, channel=runner_args.cli_args.channel)
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runner = PipelineRunner(bus=bus, 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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context = LLMContext()
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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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bridge = BusBridgeProcessor(
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bus=runner.bus,
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task_name=MAIN_NAME,
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name=f"{MAIN_NAME}::BusBridge",
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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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bridge,
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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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name=MAIN_NAME,
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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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# The remote LLM workers may take a moment to register on the bus.
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# We only activate ``greeter`` once *both* the client is connected
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# and the worker has been observed via the registry.
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state = {"client_connected": False, "greeter_ready": False}
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async def maybe_activate():
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if not (state["client_connected"] and state["greeter_ready"]):
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return
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await task.activate_task(
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"greeter",
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args=LLMTaskActivationArgs(
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messages=[
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{
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"role": "developer",
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"content": (
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"Welcome the user to Acme Corp, mention the available "
|
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"products and ask how you can help."
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),
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},
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],
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),
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)
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async def on_greeter_ready(_data: TaskReadyData) -> None:
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state["greeter_ready"] = True
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await maybe_activate()
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await runner.registry.watch("greeter", on_greeter_ready)
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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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state["client_connected"] = True
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await maybe_activate()
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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 task.cancel()
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await runner.run(task)
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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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parser = argparse.ArgumentParser(description="Main transport task (PGMQ bus)")
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parser.add_argument(
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"--database-url",
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default=os.getenv("DATABASE_URL"),
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help="PostgreSQL DSN (or set DATABASE_URL env var)",
|
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)
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parser.add_argument(
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"--channel",
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default=os.getenv("PGMQ_CHANNEL", "pipecat_acme"),
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help="PGMQ channel prefix",
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)
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main(parser)
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153
examples/multi-task/distributed-handoff/redis-handoff/llm.py
Normal file
153
examples/multi-task/distributed-handoff/redis-handoff/llm.py
Normal file
@@ -0,0 +1,153 @@
|
||||
#
|
||||
# Copyright (c) 2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""LLM worker task — run on Machine B (or locally alongside ``main.py``).
|
||||
|
||||
A standalone process that runs one LLM task (greeter or support)
|
||||
attached to the same Redis-backed `TaskBus` as the main task.
|
||||
Multiple instances can run on different machines.
|
||||
|
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Usage::
|
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|
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python llm.py greeter --redis-url redis://localhost:6379
|
||||
python llm.py support --redis-url redis://localhost:6379
|
||||
|
||||
Requirements:
|
||||
|
||||
- OPENAI_API_KEY
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import asyncio
|
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import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from redis.asyncio import Redis
|
||||
|
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from pipecat.bus.network.redis import RedisBus
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.tasks.llm import LLMTask, LLMTaskActivationArgs, tool
|
||||
|
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load_dotenv(override=True)
|
||||
|
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WORKER_CONFIG = {
|
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"greeter": {
|
||||
"system_instruction": (
|
||||
"You are a friendly greeter for Acme Corp. The available products "
|
||||
"are: the Acme Rocket Boots, the Acme Invisible Paint, and the Acme "
|
||||
"Tornado Kit. Ask which one they'd like to learn more about. "
|
||||
"When the user picks a product or asks a question about one, "
|
||||
"immediately call the transfer_to_agent tool with agent 'support'. "
|
||||
"Do not answer product questions yourself. If the user says goodbye, "
|
||||
"call the end_conversation tool. Do not mention transferring — just do it "
|
||||
"seamlessly. Keep responses brief — this is a voice conversation."
|
||||
),
|
||||
"watch": ["support"],
|
||||
},
|
||||
"support": {
|
||||
"system_instruction": (
|
||||
"You are a support agent for Acme Corp. You know about three "
|
||||
"products: Acme Rocket Boots (jet-powered boots, $299, run up "
|
||||
"to 60 mph), Acme Invisible Paint (makes anything invisible for "
|
||||
"24 hours, $49 per can), and Acme Tornado Kit (portable tornado "
|
||||
"generator, $199, batteries included). Answer the user's questions "
|
||||
"about these products. If the user wants to browse other products "
|
||||
"or start over, call the transfer_to_agent tool with agent "
|
||||
"'greeter'. If the user says goodbye, call the end_conversation "
|
||||
"tool. Do not mention transferring — just do it seamlessly. "
|
||||
"Keep responses brief — this is a voice conversation."
|
||||
),
|
||||
"watch": ["greeter"],
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class AcmeLLMTask(LLMTask):
|
||||
"""LLM task for Acme Corp with transfer and end tools."""
|
||||
|
||||
def __init__(self, name: str, *, system_instruction: str, watch: list[str]):
|
||||
"""Initialize the AcmeLLMTask.
|
||||
|
||||
Args:
|
||||
name: Unique task name (``"greeter"`` or ``"support"``).
|
||||
system_instruction: System prompt for this LLM role.
|
||||
watch: Sibling task names this task will watch via the
|
||||
registry so it knows when they become available for
|
||||
handoff.
|
||||
"""
|
||||
llm = OpenAILLMService(
|
||||
name=f"{name}::OpenAILLMService",
|
||||
api_key=os.environ["OPENAI_API_KEY"],
|
||||
settings=OpenAILLMService.Settings(system_instruction=system_instruction),
|
||||
)
|
||||
super().__init__(name, llm=llm, bridged=())
|
||||
self._watch = watch
|
||||
|
||||
async def start(self) -> None:
|
||||
"""Register watches for sibling tasks once ready."""
|
||||
await super().start()
|
||||
for task_name in self._watch:
|
||||
await self.watch_task(task_name)
|
||||
|
||||
@tool(cancel_on_interruption=False)
|
||||
async def transfer_to_agent(self, params: FunctionCallParams, agent: str, reason: str):
|
||||
"""Transfer the user to another agent.
|
||||
|
||||
Args:
|
||||
agent (str): The agent to transfer to (e.g. 'greeter', 'support').
|
||||
reason (str): Why the user is being transferred.
|
||||
"""
|
||||
logger.info(f"Task '{self.name}': transferring to '{agent}' ({reason})")
|
||||
await self.handoff_to(
|
||||
agent,
|
||||
activation_args=LLMTaskActivationArgs(
|
||||
messages=[{"role": "developer", "content": reason}]
|
||||
),
|
||||
result_callback=params.result_callback,
|
||||
)
|
||||
|
||||
@tool
|
||||
async def end_conversation(self, params: FunctionCallParams, reason: str):
|
||||
"""End the conversation when the user says goodbye.
|
||||
|
||||
Args:
|
||||
reason (str): Why the conversation is ending.
|
||||
"""
|
||||
logger.info(f"Task '{self.name}': ending conversation ({reason})")
|
||||
await self.end(
|
||||
reason=reason,
|
||||
messages=[{"role": "developer", "content": reason}],
|
||||
result_callback=params.result_callback,
|
||||
)
|
||||
|
||||
|
||||
async def main_async() -> None:
|
||||
parser = argparse.ArgumentParser(description="LLM worker task (greeter or support)")
|
||||
parser.add_argument("worker", choices=list(WORKER_CONFIG), help="Which worker to run")
|
||||
parser.add_argument("--redis-url", default="redis://localhost:6379", help="Redis URL")
|
||||
parser.add_argument("--channel", default="pipecat:acme", help="Redis pub/sub channel")
|
||||
args = parser.parse_args()
|
||||
|
||||
redis = Redis.from_url(args.redis_url)
|
||||
bus = RedisBus(redis=redis, channel=args.channel)
|
||||
|
||||
config = WORKER_CONFIG[args.worker]
|
||||
worker = AcmeLLMTask(
|
||||
args.worker,
|
||||
system_instruction=config["system_instruction"],
|
||||
watch=config["watch"],
|
||||
)
|
||||
|
||||
runner = PipelineRunner(bus=bus, handle_sigint=True)
|
||||
logger.info(f"Starting {args.worker} worker, waiting for activation...")
|
||||
await runner.run(worker)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main_async())
|
||||
169
examples/multi-task/distributed-handoff/redis-handoff/main.py
Normal file
169
examples/multi-task/distributed-handoff/redis-handoff/main.py
Normal file
@@ -0,0 +1,169 @@
|
||||
#
|
||||
# Copyright (c) 2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Main transport task — run on Machine A.
|
||||
|
||||
Handles audio I/O (STT, TTS) and bridges frames to the bus. The LLM
|
||||
worker tasks run as separate processes (possibly on different
|
||||
machines) and connect to the same Redis-backed `TaskBus`.
|
||||
|
||||
Usage::
|
||||
|
||||
python main.py --redis-url redis://localhost:6379
|
||||
|
||||
Requirements:
|
||||
|
||||
- DEEPGRAM_API_KEY
|
||||
- CARTESIA_API_KEY
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from redis.asyncio import Redis
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.bus import BusBridgeProcessor
|
||||
from pipecat.bus.network.redis import RedisBus
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContextAggregatorPair,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.registry.types import TaskReadyData
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.tasks.llm import LLMTaskActivationArgs
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
MAIN_NAME = "acme"
|
||||
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
redis = Redis.from_url(runner_args.cli_args.redis_url)
|
||||
bus = RedisBus(redis=redis, channel=runner_args.cli_args.channel)
|
||||
runner = PipelineRunner(bus=bus, handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.environ["CARTESIA_API_KEY"],
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc", # Jacqueline
|
||||
),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
aggregators = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
|
||||
bridge = BusBridgeProcessor(
|
||||
bus=runner.bus,
|
||||
task_name=MAIN_NAME,
|
||||
name=f"{MAIN_NAME}::BusBridge",
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
aggregators.user(),
|
||||
bridge,
|
||||
tts,
|
||||
transport.output(),
|
||||
aggregators.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
name=MAIN_NAME,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# The remote LLM workers may take a moment to register on the bus.
|
||||
# We only activate ``greeter`` once *both* the client is connected
|
||||
# and the worker has been observed via the registry.
|
||||
state = {"client_connected": False, "greeter_ready": False}
|
||||
|
||||
async def maybe_activate():
|
||||
if not (state["client_connected"] and state["greeter_ready"]):
|
||||
return
|
||||
await task.activate_task(
|
||||
"greeter",
|
||||
args=LLMTaskActivationArgs(
|
||||
messages=[
|
||||
{
|
||||
"role": "developer",
|
||||
"content": (
|
||||
"Welcome the user to Acme Corp, mention the available "
|
||||
"products and ask how you can help."
|
||||
),
|
||||
},
|
||||
],
|
||||
),
|
||||
)
|
||||
|
||||
async def on_greeter_ready(_data: TaskReadyData) -> None:
|
||||
state["greeter_ready"] = True
|
||||
await maybe_activate()
|
||||
|
||||
await runner.registry.watch("greeter", on_greeter_ready)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected")
|
||||
state["client_connected"] = True
|
||||
await maybe_activate()
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info("Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
parser = argparse.ArgumentParser(description="Main transport task (Redis bus)")
|
||||
parser.add_argument("--redis-url", default="redis://localhost:6379", help="Redis URL")
|
||||
parser.add_argument("--channel", default="pipecat:acme", help="Redis pub/sub channel")
|
||||
|
||||
main(parser)
|
||||
Reference in New Issue
Block a user