Rename BaseTask → BaseWorker and reserve "task" for asyncio
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.
This commit is contained in:
Aleix Conchillo Flaqué
186
examples/multi-worker/sensor-controller/sensor.py
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186
examples/multi-worker/sensor-controller/sensor.py
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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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"""Temperature sensor processors for the sensor-controller example.
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Two custom :class:`FrameProcessor` subclasses that give the worker
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pipeline real autonomous frame flow:
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- :class:`SensorReader` simulates a thermometer. It runs an async tick
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loop that advances ``current`` toward ``target`` with a first-order
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lag plus Gaussian noise, and pushes a :class:`SensorReadingFrame` on
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every tick. ``target`` and ``response_rate`` are mutable so the
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worker's LLM can adjust them via tool calls.
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- :class:`SensorStats` consumes the readings, maintains a rolling
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window, and exposes ``current`` / ``min`` / ``max`` / ``avg`` /
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``trend`` as properties. The worker LLM reads these directly when
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answering the user.
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"""
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import random
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import time
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from collections import deque
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from dataclasses import dataclass
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from pipecat.frames.frames import DataFrame, Frame, StartFrame
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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@dataclass
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class SensorReadingFrame(DataFrame):
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"""A single temperature reading emitted by :class:`SensorReader`.
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Parameters:
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temperature: The reading in degrees Celsius.
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timestamp: Unix timestamp when the reading was taken.
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"""
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temperature: float = 0.0
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timestamp: float = 0.0
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class SensorReader(FrameProcessor):
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"""Simulated temperature sensor with adjustable target and response rate.
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Each tick, ``current`` is updated as::
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current += (target - current) * response_rate + gauss(0, noise_sigma)
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This is a first-order lag toward ``target``. With ``response_rate=0.05``
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and a 1s tick, the current reading reaches ~halfway to target in ~14s;
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with ``response_rate=0.2`` it converges in ~5–10s.
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"""
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def __init__(
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self,
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*,
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start_temp: float = 22.0,
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sample_period_s: float = 1.0,
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response_rate: float = 0.05,
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noise_sigma: float = 0.1,
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):
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"""Initialize the sensor.
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Args:
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start_temp: Initial temperature and initial target (°C).
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sample_period_s: Seconds between successive readings.
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response_rate: Fraction of the gap toward target closed each tick
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(clamped to ``[0.0, 1.0]``).
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noise_sigma: Standard deviation of the Gaussian noise added to
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each reading.
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"""
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super().__init__()
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self._current = start_temp
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self._target = start_temp
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self._response_rate = max(0.0, min(1.0, response_rate))
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self._noise_sigma = noise_sigma
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self._sample_period_s = sample_period_s
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self._tick_task = None
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@property
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def current(self) -> float:
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"""The most recent temperature reading (°C)."""
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return self._current
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@property
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def target(self) -> float:
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"""The temperature the sensor is drifting toward (°C)."""
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return self._target
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@property
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def response_rate(self) -> float:
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"""Fraction of the target-current gap closed per tick."""
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return self._response_rate
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def set_target(self, value: float) -> None:
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"""Set a new target temperature (°C)."""
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self._target = value
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def set_response_rate(self, rate: float) -> None:
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"""Set how aggressively the sensor approaches the target.
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Args:
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rate: Fraction in ``[0.0, 1.0]``. Clamped to that range.
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"""
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self._response_rate = max(0.0, min(1.0, rate))
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, StartFrame) and self._tick_task is None:
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self._tick_task = self.create_task(self._tick_loop(), "ticker")
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await self.push_frame(frame, direction)
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async def cleanup(self) -> None:
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if self._tick_task is not None:
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await self.cancel_task(self._tick_task)
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self._tick_task = None
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await super().cleanup()
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async def _tick_loop(self) -> None:
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import asyncio
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while True:
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await asyncio.sleep(self._sample_period_s)
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gap = self._target - self._current
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self._current += gap * self._response_rate + random.gauss(0, self._noise_sigma)
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await self.push_frame(
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SensorReadingFrame(temperature=self._current, timestamp=time.time()),
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FrameDirection.DOWNSTREAM,
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)
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class SensorStats(FrameProcessor):
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"""Rolling-window statistics over :class:`SensorReadingFrame`s.
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Consumes readings as they flow downstream and exposes rolling
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``min`` / ``max`` / ``avg`` / ``trend`` as properties — the worker
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LLM reads them directly when responding to the user.
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"""
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def __init__(self, window: int = 30):
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"""Initialize the stats aggregator.
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Args:
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window: Number of recent readings to retain.
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"""
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super().__init__()
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self._readings: deque[float] = deque(maxlen=window)
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@property
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def current(self) -> float:
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"""The most recent reading, or 0.0 if none have been seen."""
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return self._readings[-1] if self._readings else 0.0
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@property
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def min(self) -> float:
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return min(self._readings) if self._readings else 0.0
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@property
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def max(self) -> float:
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return max(self._readings) if self._readings else 0.0
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@property
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def avg(self) -> float:
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return sum(self._readings) / len(self._readings) if self._readings else 0.0
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@property
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def trend(self) -> str:
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"""``"rising"`` / ``"falling"`` / ``"stable"`` based on first vs. last half of the window."""
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if len(self._readings) < 4:
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return "stable"
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half = len(self._readings) // 2
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old_avg = sum(list(self._readings)[:half]) / half
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new_avg = sum(list(self._readings)[half:]) / (len(self._readings) - half)
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diff = new_avg - old_avg
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if abs(diff) < 0.25:
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return "stable"
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return "rising" if diff > 0 else "falling"
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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if isinstance(frame, SensorReadingFrame):
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self._readings.append(frame.temperature)
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await self.push_frame(frame, direction)
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