Reorganize examples into topic-based subfolders
Move 304 examples from a flat numbered directory into 14 descriptive subfolders: getting-started, services (speech + function-calling), transcription, vision, realtime, persistent-context, context-summarization, update-settings (stt/tts/llm), turn-management, thinking-and-mcp, transports, video-avatar, video-processing, and features. Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py becomes services/speech/deepgram.py) since the folder context makes them redundant. Keep numbered prefixes only in getting-started/ where ordering matters. Update eval script paths and README to match the new structure.
This commit is contained in:
Mark Backman
109
examples/transports/daily.py
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109
examples/transports/daily.py
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#
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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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import asyncio
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import os
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import sys
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import aiohttp
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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.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.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.daily import configure
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.daily.transport import DailyParams, DailyTransport
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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():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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transcription_enabled=True,
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),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.Settings(
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system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
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),
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)
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context = LLMContext()
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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(), # Transport user input
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user_aggregator, # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses
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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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)
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Kick off the conversation.
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context.add_message(
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{"role": "developer", "content": "Please introduce yourself to the user."}
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)
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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138
examples/transports/livekit.py
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138
examples/transports/livekit.py
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#
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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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import asyncio
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import json
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import os
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import sys
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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 (
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InterruptionFrame,
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TranscriptionFrame,
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TTSSpeakFrame,
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UserStartedSpeakingFrame,
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UserStoppedSpeakingFrame,
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)
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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.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.livekit import configure
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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.livekit.transport import LiveKitParams, LiveKitTransport
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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():
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(url, token, room_name) = await configure()
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transport = LiveKitTransport(
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url=url,
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token=token,
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room_name=room_name,
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params=LiveKitParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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)
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.Settings(
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system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
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),
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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)
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context = LLMContext()
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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(), # Transport user input
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stt,
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user_aggregator, # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses
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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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)
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# Register an event handler so we can play the audio when the
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# participant joins.
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant_id):
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await asyncio.sleep(1)
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await task.queue_frame(
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TTSSpeakFrame(
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"Hello there! How are you doing today? Would you like to talk about the weather?"
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)
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)
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# Register an event handler to receive data from the participant via text chat
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# in the LiveKit room. This will be used to as transcription frames and
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# interrupt the bot and pass it to llm for processing and
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# then pass back to the participant as audio output.
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@transport.event_handler("on_data_received")
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async def on_data_received(transport, data, participant_id):
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logger.info(f"Received data from participant {participant_id}: {data}")
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# convert data from bytes to string
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json_data = json.loads(data)
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await task.queue_frames(
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[
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InterruptionFrame(),
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UserStartedSpeakingFrame(),
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TranscriptionFrame(
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user_id=participant_id,
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timestamp=json_data["timestamp"],
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text=json_data["message"],
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),
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UserStoppedSpeakingFrame(),
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],
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)
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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181
examples/transports/small-webrtc.py
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181
examples/transports/small-webrtc.py
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@@ -0,0 +1,181 @@
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#
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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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import argparse
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import asyncio
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import os
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from contextlib import asynccontextmanager
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from typing import Dict
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import uvicorn
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from dotenv import load_dotenv
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from fastapi import BackgroundTasks, FastAPI
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from fastapi.responses import RedirectResponse
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from loguru import logger
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from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI
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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.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.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.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 TransportParams
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from pipecat.transports.smallwebrtc.connection import IceServer, SmallWebRTCConnection
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from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport
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load_dotenv(override=True)
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app = FastAPI()
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# Store connections by pc_id
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pcs_map: Dict[str, SmallWebRTCConnection] = {}
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ice_servers = [
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IceServer(
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urls="stun:stun.l.google.com:19302",
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)
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]
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# Mount the frontend at /
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app.mount("/client", SmallWebRTCPrebuiltUI)
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async def run_example(webrtc_connection: SmallWebRTCConnection):
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logger.info(f"Starting bot")
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# Create a transport using the WebRTC connection
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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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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),
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)
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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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settings=CartesiaTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.Settings(
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system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
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),
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)
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context = LLMContext()
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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(), # Transport user input
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stt,
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user_aggregator, # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses
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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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)
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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(f"Client connected")
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# Kick off the conversation.
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context.add_message(
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{"role": "developer", "content": "Please introduce yourself to the user."}
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)
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await task.queue_frames([LLMRunFrame()])
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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(f"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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@app.get("/", include_in_schema=False)
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async def root_redirect():
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return RedirectResponse(url="/client/")
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@app.post("/api/offer")
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async def offer(request: dict, background_tasks: BackgroundTasks):
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pc_id = request.get("pc_id")
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if pc_id and pc_id in pcs_map:
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pipecat_connection = pcs_map[pc_id]
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logger.info(f"Reusing existing connection for pc_id: {pc_id}")
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await pipecat_connection.renegotiate(
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sdp=request["sdp"],
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type=request["type"],
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restart_pc=request.get("restart_pc", False),
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)
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else:
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pipecat_connection = SmallWebRTCConnection(ice_servers)
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await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])
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@pipecat_connection.event_handler("closed")
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async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
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logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
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pcs_map.pop(webrtc_connection.pc_id, None)
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# Run example function with SmallWebRTC transport arguments.
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background_tasks.add_task(run_example, pipecat_connection)
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answer = pipecat_connection.get_answer()
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# Updating the peer connection inside the map
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pcs_map[answer["pc_id"]] = pipecat_connection
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return answer
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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yield # Run app
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coros = [pc.disconnect() for pc in pcs_map.values()]
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await asyncio.gather(*coros)
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pcs_map.clear()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
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parser.add_argument(
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"--host", default="localhost", help="Host for HTTP server (default: localhost)"
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)
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parser.add_argument(
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"--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
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)
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args = parser.parse_args()
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uvicorn.run(app, host=args.host, port=args.port)
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