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
165
examples/features/gpu-container-local-bot.py
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165
examples/features/gpu-container-local-bot.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 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.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.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.deepgram.tts import DeepgramTTSService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import (
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DailyOutputTransportMessageFrame,
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DailyOutputTransportMessageUrgentFrame,
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DailyParams,
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)
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: 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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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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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 = DeepgramTTSService(
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api_key=os.getenv("DEEPGRAM_API_KEY"),
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settings=DeepgramTTSService.Settings(
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voice="aura-asteria-en",
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),
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base_url="http://0.0.0.0:8080",
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)
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llm = OpenAILLMService(
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# To use OpenAI
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# api_key=os.getenv("OPENAI_API_KEY"),
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# Or, to use a local vLLM (or similar) api server
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settings=OpenAILLMService.Settings(
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model="meta-llama/Meta-Llama-3-8B-Instruct",
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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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base_url="http://0.0.0.0:8000/v1",
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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, # STT
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user_aggregator,
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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,
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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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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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# When the first participant joins, the bot should introduce itself.
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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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# Handle "latency-ping" messages. The client will send app messages that look like
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# this:
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# { "latency-ping": { ts: <client-side timestamp> }}
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#
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# We want to send an immediate pong back to the client from this handler function.
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# Also, we will push a frame into the top of the pipeline and send it after the
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#
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@transport.event_handler("on_app_message")
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async def on_app_message(transport, message, sender):
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try:
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if "latency-ping" in message:
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logger.debug(f"Received latency ping app message: {message}")
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ts = message["latency-ping"]["ts"]
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# Send immediately
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await task.queue_frame(
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DailyOutputTransportMessageUrgentFrame(
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message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
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)
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)
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# And push to the pipeline for the Daily transport.output to send
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await task.queue_frame(
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DailyOutputTransportMessageFrame(
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message={"latency-pong-pipeline-delivery": {"ts": ts}},
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participant_id=sender,
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)
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)
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except Exception as e:
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logger.debug(f"message handling error: {e} - {message}")
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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=runner_args.handle_sigint)
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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 = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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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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