New runner module (#2269)
* Adds pipecat.runner.run - FastAPI-based development server with automatic bot discovery * Adds new RunnerArguments types for different transports * Adds new runner utils for creating transports and parsing data * Adds new Daily and LiveKit utils for setup
This commit is contained in:
Mark Backman
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examples/runner-examples/01-all-transport-bot.py
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examples/runner-examples/01-all-transport-bot.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Pipecat Cloud-compatible bot example.
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Transports are:
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- Daily
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- SmallWebRTC
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- Twilio
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- Telnyx
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- Plivo
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"""
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import os
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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.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.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
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from pipecat.runner.types import (
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DailyRunnerArguments,
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RunnerArguments,
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SmallWebRTCRunnerArguments,
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WebSocketRunnerArguments,
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)
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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.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport
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load_dotenv(override=True)
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async def run_bot(transport: BaseTransport):
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"""Main bot logic that works with any transport."""
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logger.info(f"Starting bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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messages = [
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{
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"role": "system",
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"content": "You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
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},
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]
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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pipeline = Pipeline(
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[
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transport.input(),
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rtvi,
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stt,
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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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=[RTVIObserver(rtvi)],
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)
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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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messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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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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runner = PipelineRunner(handle_sigint=False)
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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 = None
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if isinstance(runner_args, DailyRunnerArguments):
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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if os.environ.get("ENV") != "local":
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from pipecat.audio.filters.krisp_filter import KrispFilter
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krisp_filter = KrispFilter()
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else:
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krisp_filter = None
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transport = DailyTransport(
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runner_args.room_url,
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runner_args.token,
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"Pipecat Bot",
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params=DailyParams(
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audio_in_enabled=True,
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audio_in_filter=krisp_filter,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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elif isinstance(runner_args, SmallWebRTCRunnerArguments):
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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transport = SmallWebRTCTransport(
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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webrtc_connection=runner_args.webrtc_connection,
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)
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elif isinstance(runner_args, WebSocketRunnerArguments):
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# Use the utility to parse WebSocket data
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from pipecat.runner.utils import parse_telephony_websocket
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transport_type, call_data = await parse_telephony_websocket(runner_args.websocket)
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logger.info(f"Auto-detected transport: {transport_type}")
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# Create transport based on detected type
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if transport_type == "twilio":
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from pipecat.serializers.twilio import TwilioFrameSerializer
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serializer = TwilioFrameSerializer(
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stream_sid=call_data["stream_id"],
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call_sid=call_data["call_id"],
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account_sid=os.getenv("TWILIO_ACCOUNT_SID", ""),
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auth_token=os.getenv("TWILIO_AUTH_TOKEN", ""),
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)
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elif transport_type == "telnyx":
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from pipecat.serializers.telnyx import TelnyxFrameSerializer
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serializer = TelnyxFrameSerializer(
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stream_id=call_data["stream_id"],
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call_control_id=call_data["call_control_id"],
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outbound_encoding=call_data["outbound_encoding"],
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inbound_encoding="PCMU", # Set manually
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api_key=os.getenv("TELNYX_API_KEY", ""),
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)
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elif transport_type == "plivo":
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from pipecat.serializers.plivo import PlivoFrameSerializer
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serializer = PlivoFrameSerializer(
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stream_id=call_data["stream_id"],
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call_id=call_data["call_id"],
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auth_id=os.getenv("PLIVO_AUTH_ID", ""),
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auth_token=os.getenv("PLIVO_AUTH_TOKEN", ""),
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)
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else:
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# Generic fallback
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serializer = None
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# Create the transport
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from pipecat.transports.network.fastapi_websocket import (
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FastAPIWebsocketParams,
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FastAPIWebsocketTransport,
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)
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transport = FastAPIWebsocketTransport(
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websocket=runner_args.websocket,
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params=FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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add_wav_header=False,
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vad_analyzer=SileroVADAnalyzer(),
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serializer=serializer,
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),
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)
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else:
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logger.error(f"Unsupported runner arguments type: {type(runner_args)}")
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return
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if transport is None:
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logger.error("Failed to create transport")
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return
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await run_bot(transport)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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