b03247f360
Replaces every "task" identifier that referred to the BaseTask abstraction with "worker". Asyncio task plumbing (asyncio.Task, BaseTaskManager, TaskManager, create_task, cancel_task, etc.) stays untouched. Highlights: - Classes: BaseTask → BaseWorker, PipelineTask → PipelineWorker, LLMTask → LLMWorker, LLMContextTask → LLMContextWorker, TaskBus → WorkerBus, TaskRegistry → WorkerRegistry, TaskActivationArgs → WorkerActivationArgs, TaskReadyData → WorkerReadyData, TaskRegistryEntry → WorkerRegistryEntry, TaskObserver → WorkerObserver, all Bus*TaskMessage → Bus*WorkerMessage, BusAddTaskMessage.task field → worker, BusWorkerRegistryMessage.tasks field → workers. - Methods/decorators: activate_task → activate_worker, deactivate_task → deactivate_worker, add_task → add_worker, watch_task → watch_worker, @task_ready → @worker_ready, setup_pipeline_task hook → setup_pipeline_worker. - Params/fields: FrameProcessorSetup.pipeline_task and FunctionCallParams.pipeline_task → pipeline_worker. Parameter names like task_name → worker_name; spawn/run accept worker:. - Files: pipeline/base_task.py → base_worker.py, pipeline/task.py → worker.py (plus a re-export shim at pipeline/task.py), task_observer.py → worker_observer.py, task_ready_decorator.py → worker_ready_decorator.py, pipecat.tasks → pipecat.workers, llm_task.py → llm_worker.py, llm_context_task.py → llm_context_worker.py, examples/multi-task → examples/multi-worker. Back-compat: - PipelineTask kept as a deprecated subclass of PipelineWorker that warns on construction. - pipecat.pipeline.task re-exports PipelineWorker/PipelineTask/etc. so existing user imports keep working. - FrameProcessor.pipeline_task kept as a deprecated property that forwards to pipeline_worker. Local variables in examples that hold a worker (task = PipelineTask(...)) are renamed to worker = PipelineWorker(...). Asyncio-task locals (runner_task, etc.) are preserved.
121 lines
3.8 KiB
Python
121 lines
3.8 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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"""Remote assistant LLM server.
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Runs a FastAPI server that accepts WebSocket connections from a
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``main.py``-style client. Each connection spins up a
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`WebSocketProxyServerTask` bridging the socket to a local
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`PipelineRunner` and an `LLMWorker` that handles the conversation.
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Usage::
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python assistant.py
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python assistant.py --port 9000
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Requirements:
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- OPENAI_API_KEY
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"""
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import argparse
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import os
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import uvicorn
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from dotenv import load_dotenv
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from fastapi import FastAPI, WebSocket
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from loguru import logger
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from pipecat.bus import BusFrameMessage
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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.workers.llm import LLMWorker, tool
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from pipecat.workers.proxy.websocket import WebSocketProxyServerTask
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load_dotenv(override=True)
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app = FastAPI()
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class AcmeAssistant(LLMWorker):
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"""Handles greetings, product questions, and conversation end."""
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def __init__(self):
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"""Initialize the AcmeAssistant LLM worker."""
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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 friendly assistant for Acme Corp. You know about three "
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"products: Acme Rocket Boots (jet-powered boots, $299, run up to "
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"60 mph), Acme Invisible Paint (makes anything invisible for 24 hours, "
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"$49 per can), and Acme Tornado Kit (portable tornado generator, $199, "
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"batteries included). Greet the user, help them with product questions, "
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"and call end_conversation when the user says goodbye. "
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"Keep responses brief, this is a voice conversation."
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),
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),
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)
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super().__init__("assistant", llm=llm, bridged=())
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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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@app.websocket("/ws")
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async def websocket_endpoint(websocket: WebSocket):
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"""Handle a WebSocket connection from the main bot's proxy."""
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await websocket.accept()
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runner = PipelineRunner(handle_sigint=False)
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proxy = WebSocketProxyServerTask(
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"gateway",
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websocket=websocket,
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worker_name="assistant",
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remote_worker_name="acme",
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forward_messages=(BusFrameMessage,),
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)
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@proxy.event_handler("on_client_connected")
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async def on_client_connected(proxy, client):
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logger.info("WebSocket client connected")
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@proxy.event_handler("on_client_disconnected")
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async def on_client_disconnected(proxy, client):
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logger.info("WebSocket client disconnected")
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await runner.cancel()
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assistant = AcmeAssistant()
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await runner.add_worker(proxy)
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await runner.add_worker(assistant)
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logger.info("Assistant server ready, waiting for activation")
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await runner.run()
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logger.info("Assistant server session ended")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Remote assistant LLM server")
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parser.add_argument("--host", default="0.0.0.0", help="Host to bind to")
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parser.add_argument("--port", type=int, default=8765, help="Port to listen on")
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args = parser.parse_args()
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uvicorn.run(app, host=args.host, port=args.port)
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