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.

examples/features/features-app-resources.py

@@ -4,27 +4,27 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-"""Example demonstrating ``PipelineTask(app_resources=...)``.
+"""Example demonstrating ``PipelineWorker(app_resources=...)``.
 
 ``app_resources`` is an application-defined bag of anything your
 application code may want to share across a session: database handles,
 HTTP clients, feature flags, per-user state, observability clients,
 in-memory caches — whatever fits your app. Pipecat passes it through
-untouched and exposes it as ``task.app_resources``, so any code with a
-handle on the task can read or mutate it.
+untouched and exposes it as ``worker.app_resources``, so any code with a
+handle on the worker can read or mutate it.
 
 Two of the convenience aliases exercised below:
 
 - Tool handlers read it from ``FunctionCallParams.app_resources``.
 - Custom ``FrameProcessor`` subclasses read it from
-  ``self.pipeline_task.app_resources``.
+  ``self.pipeline_worker.app_resources``.
 
 This example uses two small loggers as stand-ins for that "shared thing":
 ``ToolCallLogger`` (written from tool handlers) and
 ``TranscriptionLogger`` (written from a custom ``FrameProcessor`` that
 sits in the pipeline). A real app might just as easily pass a Postgres
 pool, a Redis client, a Stripe SDK instance, or any combination thereof.
-The mechanics shown here — construct once, hand to the task, read it
+The mechanics shown here — construct once, hand to the worker, read it
 from each site, inspect it after the session — are the same regardless
 of what you put in.
 
@@ -50,7 +50,7 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, LLMRunFrame, TranscriptionFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMContextAggregatorPair,
@@ -131,7 +131,7 @@ class AppResources:
     get autocomplete and refactor safety:
 
     - In tools: ``cast(AppResources, params.app_resources)``.
-    - In custom processors: ``cast(AppResources, self.pipeline_task.app_resources)``.
+    - In custom processors: ``cast(AppResources, self.pipeline_worker.app_resources)``.
     """
 
     tool_call_logger: ToolCallLogger
@@ -155,8 +155,8 @@ class TranscriptionLoggingProcessor(FrameProcessor):
 
     Demonstrates the second read site for ``app_resources``: any custom
     ``FrameProcessor`` can reach the same bag every tool handler sees by
-    going through ``self.pipeline_task.app_resources``. ``pipeline_task``
-    is ``None`` until the task sets the processor up, so we guard against
+    going through ``self.pipeline_worker.app_resources``. ``pipeline_worker``
+    is ``None`` until the worker sets the processor up, so we guard against
     that case.
     """
 
@@ -164,8 +164,8 @@ class TranscriptionLoggingProcessor(FrameProcessor):
         """Forward all frames; log final user transcriptions on the way through."""
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, TranscriptionFrame) and self.pipeline_task is not None:
-            resources = cast(AppResources, self.pipeline_task.app_resources)
+        if isinstance(frame, TranscriptionFrame) and self.pipeline_worker is not None:
+            resources = cast(AppResources, self.pipeline_worker.app_resources)
             resources.transcription_logger.log_transcription(frame.text)
 
         await self.push_frame(frame, direction)
@@ -282,7 +282,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         transcription_logger=transcription_logger,
     )
 
-    task = PipelineTask(
+    worker = PipelineWorker(
         pipeline,
         params=PipelineParams(
             enable_metrics=True,
@@ -299,16 +299,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         context.add_message(
             {"role": "developer", "content": "Please introduce yourself to the user."}
         )
-        await task.queue_frames([LLMRunFrame()])
+        await worker.queue_frames([LLMRunFrame()])
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):
         logger.info(f"Client disconnected")
-        await task.cancel()
+        await worker.cancel()
 
     runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
 
-    await runner.run(task)
+    await runner.run(worker)
 
     # The session has ended; read whatever state the handlers built up.
     logger.info(f"Tool calls logged during session:\n{tool_call_logger.dump()}")