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Merge pull request #2309 from pipecat-ai/mb/remove-runner-examples

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Remove examples/runner-examples
This commit is contained in:
Mark Backman
2025-07-31 09:18:22 -07:00
committed by GitHub
parent e78d9c2c95 e80281c3c4
commit b5edbbc0ca
9 changed files with 0 additions and 674 deletions
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218
examples/runner-examples/01-all-transport-bot.py
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#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Pipecat Cloud-compatible bot example.
Transports are:
- Daily
- SmallWebRTC
- Twilio
- Telnyx
- Plivo
"""
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.runner.types import (
DailyRunnerArguments,
RunnerArguments,
SmallWebRTCRunnerArguments,
WebSocketRunnerArguments,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport
load_dotenv(override=True)
async def run_bot(transport: BaseTransport):
"""Main bot logic that works with any transport."""
logger.info(f"Starting bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
pipeline = Pipeline(
[
transport.input(),
rtvi,
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
observers=[RTVIObserver(rtvi)],
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Client connected")
messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info("Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
async def bot(runner_args: RunnerArguments):
"""Main bot entry point compatible with Pipecat Cloud."""
transport = None
if isinstance(runner_args, DailyRunnerArguments):
from pipecat.transports.services.daily import DailyParams, DailyTransport
if os.environ.get("ENV") != "local":
from pipecat.audio.filters.krisp_filter import KrispFilter
krisp_filter = KrispFilter()
else:
krisp_filter = None
transport = DailyTransport(
runner_args.room_url,
runner_args.token,
"Pipecat Bot",
params=DailyParams(
audio_in_enabled=True,
audio_in_filter=krisp_filter,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
elif isinstance(runner_args, SmallWebRTCRunnerArguments):
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
transport = SmallWebRTCTransport(
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
webrtc_connection=runner_args.webrtc_connection,
)
elif isinstance(runner_args, WebSocketRunnerArguments):
# Use the utility to parse WebSocket data
from pipecat.runner.utils import parse_telephony_websocket
transport_type, call_data = await parse_telephony_websocket(runner_args.websocket)
logger.info(f"Auto-detected transport: {transport_type}")
# Create transport based on detected type
if transport_type == "twilio":
from pipecat.serializers.twilio import TwilioFrameSerializer
serializer = TwilioFrameSerializer(
stream_sid=call_data["stream_id"],
call_sid=call_data["call_id"],
account_sid=os.getenv("TWILIO_ACCOUNT_SID", ""),
auth_token=os.getenv("TWILIO_AUTH_TOKEN", ""),
)
elif transport_type == "telnyx":
from pipecat.serializers.telnyx import TelnyxFrameSerializer
serializer = TelnyxFrameSerializer(
stream_id=call_data["stream_id"],
call_control_id=call_data["call_control_id"],
outbound_encoding=call_data["outbound_encoding"],
inbound_encoding="PCMU", # Set manually
api_key=os.getenv("TELNYX_API_KEY", ""),
)
elif transport_type == "plivo":
from pipecat.serializers.plivo import PlivoFrameSerializer
serializer = PlivoFrameSerializer(
stream_id=call_data["stream_id"],
call_id=call_data["call_id"],
auth_id=os.getenv("PLIVO_AUTH_ID", ""),
auth_token=os.getenv("PLIVO_AUTH_TOKEN", ""),
)
else:
# Generic fallback
serializer = None
# Create the transport
from pipecat.transports.network.fastapi_websocket import (
FastAPIWebsocketParams,
FastAPIWebsocketTransport,
)
transport = FastAPIWebsocketTransport(
websocket=runner_args.websocket,
params=FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
add_wav_header=False,
vad_analyzer=SileroVADAnalyzer(),
serializer=serializer,
),
)
else:
logger.error(f"Unsupported runner arguments type: {type(runner_args)}")
return
if transport is None:
logger.error("Failed to create transport")
return
await run_bot(transport)
if __name__ == "__main__":
from pipecat.runner.run import main
main()

144
examples/runner-examples/01-all-transport-factory-bot.py
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#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Pipecat Cloud-compatible bot example.
Transports are:
- Daily
- SmallWebRTC
- Twilio
- Telnyx
- Plivo
"""
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
from pipecat.transports.services.daily import DailyParams
load_dotenv(override=True)
# Define transport configurations using factory functions
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
# add_wav_header and serializer will be set automatically
),
"telnyx": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
# add_wav_header and serializer will be set automatically
),
"plivo": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
# add_wav_header and serializer will be set automatically
),
}
async def run_bot(transport: BaseTransport):
"""Main bot logic that works with any transport."""
logger.info("Starting bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
pipeline = Pipeline(
[
transport.input(),
rtvi,
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
observers=[RTVIObserver(rtvi)],
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Client connected")
messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info("Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
async def bot(runner_args: RunnerArguments):
"""Main bot entry point compatible with Pipecat Cloud."""
transport = await create_transport(runner_args, transport_params)
await run_bot(transport)
if __name__ == "__main__":
from pipecat.runner.run import main
main()

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examples/runner-examples/02-two-transport-bot.py
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#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Pipecat Cloud-compatible bot example.
Transports are Daily or SmallWebRTC.
Run it with:
- WebRTC transport::
python 02-two-transport-bot.py
- Daily transport::
python 02-two-transport-bot.py --transport daily
"""
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.runner.types import DailyRunnerArguments, RunnerArguments, SmallWebRTCRunnerArguments
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport
load_dotenv(override=True)
async def run_bot(transport: BaseTransport):
"""Main bot logic that works with any transport."""
logger.info(f"Starting bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
pipeline = Pipeline(
[
transport.input(),
rtvi,
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
observers=[RTVIObserver(rtvi)],
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Client connected")
messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info("Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
async def bot(runner_args: RunnerArguments):
"""Main bot entry point compatible with Pipecat Cloud."""
transport = None
if isinstance(runner_args, DailyRunnerArguments):
from pipecat.transports.services.daily import DailyParams, DailyTransport
if os.environ.get("ENV") != "local":
from pipecat.audio.filters.krisp_filter import KrispFilter
krisp_filter = KrispFilter()
else:
krisp_filter = None
transport = DailyTransport(
runner_args.room_url,
runner_args.token,
"Pipecat Bot",
params=DailyParams(
audio_in_enabled=True,
audio_in_filter=krisp_filter,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
)
elif isinstance(runner_args, SmallWebRTCRunnerArguments):
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
transport = SmallWebRTCTransport(
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
webrtc_connection=runner_args.webrtc_connection,
)
else:
logger.error(f"Unsupported runner arguments type: {type(runner_args)}")
return
if transport is None:
logger.error("Failed to create transport")
return
await run_bot(transport)
if __name__ == "__main__":
from pipecat.runner.run import main
main()

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examples/runner-examples/03-single-transport-bot.py
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#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Pipecat development runner example.
This example has a single transport—SmallWebRTCTransport.
Run it with::
python 03-single-transport-bot.py
"""
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
load_dotenv(override=True)
async def run_bot(transport: BaseTransport):
"""Main bot logic that works with any transport."""
logger.info(f"Starting bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
pipeline = Pipeline(
[
transport.input(),
rtvi,
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
observers=[RTVIObserver(rtvi)],
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Client connected")
messages.append({"role": "system", "content": "Say hello and briefly introduce yourself."})
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info("Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=False)
await runner.run(task)
async def bot(runner_args: RunnerArguments):
"""Main bot entry point compatible with Pipecat Cloud."""
transport = SmallWebRTCTransport(
params=TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
),
webrtc_connection=runner_args.webrtc_connection,
)
await run_bot(transport)
if __name__ == "__main__":
from pipecat.runner.run import main
main()

7
examples/runner-examples/Dockerfile
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FROM dailyco/pipecat-base:latest
COPY ./requirements.txt requirements.txt
RUN pip install --no-cache-dir --upgrade -r requirements.txt
COPY ./02-two-transport-bot.py bot.py

19
examples/runner-examples/build.sh
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#!/bin/bash
set -e
VERSION="0.1"
DOCKER_USERNAME="your_docker_username"
AGENT_NAME="two-transport-bot"
# Build the Docker image with the correct context
echo "Building Docker image..."
docker build --platform=linux/arm64 -t "$DOCKER_USERNAME/$AGENT_NAME:$VERSION" -t "$DOCKER_USERNAME/$AGENT_NAME:latest" .
# Push the Docker images
echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:$VERSION..."
docker push "$DOCKER_USERNAME/$AGENT_NAME:$VERSION"
echo "Pushing Docker image $DOCKER_USERNAME/$AGENT_NAME:latest..."
docker push "$DOCKER_USERNAME/$AGENT_NAME:latest"
echo "Successfully built and pushed $DOCKER_USERNAME/$AGENT_NAME:$VERSION and $DOCKER_USERNAME/$AGENT_NAME:latest"

3
examples/runner-examples/env.example
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DEEPGRAM_API_KEY=your_deepgram_api_key
OPENAI_API_KEY=your_openai_api_key
CARTESIA_API_KEY=your_cartesia_api_key

8
examples/runner-examples/pcc-deploy.toml
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agent_name = "two-transport-bot"
image = "your_dockerhub_username/two-transport-bot:0.1"
image_credentials = "dockerhub-access"
secret_set = "two-transport-bot-secrets"
enable_krisp = true
[scaling]
min_agents = 0

1
examples/runner-examples/requirements.txt
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@@ -1 +0,0 @@
pipecat-ai[openai,daily,deepgram,cartesia,silero,webrtc,websocket,runner]