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.
135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Example of using OpenAI Realtime voice LLM service with Vonage Video Connector transport."""
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import asyncio
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import os
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import sys
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from collections.abc import Callable
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from typing import Any
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
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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.worker import PipelineParams, PipelineWorker
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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.runner.vonage import configure
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from pipecat.services.openai.realtime.events import (
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AudioConfiguration,
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AudioInput,
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InputAudioNoiseReduction,
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InputAudioTranscription,
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SemanticTurnDetection,
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SessionProperties,
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)
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from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
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from pipecat.transports.vonage.video_connector import (
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VonageVideoConnectorTransport,
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VonageVideoConnectorTransportParams,
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)
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load_dotenv(override=True)
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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async def main() -> None:
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"""Main entry point for the OpenAI Realtime vonage video connector example."""
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(application_id, session_id, token) = await configure()
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transport = VonageVideoConnectorTransport(
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application_id,
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session_id,
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token,
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VonageVideoConnectorTransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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publisher_name="Bot",
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),
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)
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llm = OpenAIRealtimeLLMService(
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api_key=os.environ["OPENAI_API_KEY"],
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settings=OpenAIRealtimeLLMService.Settings(
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system_instruction="""You are a helpful and friendly AI.
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Act like a human, but remember that you aren't a human and that you can't do human
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things in the real world. Your voice and personality should be warm and engaging, with a lively and
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playful tone.
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If interacting in a non-English language, start by using the standard accent or dialect familiar to
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the user. Talk quickly.
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You are participating in a voice conversation. Keep your responses concise, short, and to the point
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unless specifically asked to elaborate on a topic.
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Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
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session_properties=SessionProperties(
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audio=AudioConfiguration(
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input=AudioInput(
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transcription=InputAudioTranscription(),
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turn_detection=SemanticTurnDetection(),
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noise_reduction=InputAudioNoiseReduction(type="near_field"),
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)
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),
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),
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),
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)
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context = LLMContext(
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[{"role": "developer", "content": "Say hello!"}],
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)
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user_aggregator, assistant_aggregator = 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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user_aggregator,
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llm,
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transport.output(),
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assistant_aggregator,
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]
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)
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worker = PipelineWorker(
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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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observers=[TranscriptionLogObserver()],
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)
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event_handler: Callable[[str], Callable[[Any], Any]] = transport.event_handler
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@event_handler("on_client_connected")
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async def on_client_connected(transport: VonageVideoConnectorTransport, client: object) -> None:
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logger.info("Client connected")
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await worker.queue_frames([LLMRunFrame()])
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runner = PipelineRunner()
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await runner.run(worker)
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if __name__ == "__main__":
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asyncio.run(main())
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