Rename example files to prepend parent folder name, preventing package shadowing
Example files like openai.py shadow installed packages when Python adds the script directory to sys.path. Prepend the parent folder name to each example file (e.g. openai.py -> function-calling-openai.py). Also split thinking-and-mcp/ into separate mcp/ and thinking/ directories.
This commit is contained in:
Mark Backman
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examples/features/features-switch-voices.py
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examples/features/features-switch-voices.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.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import Frame, LLMRunFrame
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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.processors.filters.function_filter import FunctionFilter
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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.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.llm_service import FunctionCallParams
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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 DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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class SwitchVoices(ParallelPipeline):
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def __init__(self):
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self._current_voice = "News Lady"
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news_lady = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="bf991597-6c13-47e4-8411-91ec2de5c466", # Newslady
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),
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)
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british_lady = 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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barbershop_man = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="a0e99841-438c-4a64-b679-ae501e7d6091", # Barbershop Man
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),
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)
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super().__init__(
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# News Lady voice
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[FunctionFilter(self.news_lady_filter), news_lady],
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# British Reading Lady voice
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[FunctionFilter(self.british_lady_filter), british_lady],
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# Barbershop Man voice
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[FunctionFilter(self.barbershop_man_filter), barbershop_man],
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)
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@property
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def current_voice(self):
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return self._current_voice
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async def switch_voice(self, params: FunctionCallParams):
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self._current_voice = params.arguments["voice"]
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await params.result_callback(
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{
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"voice": f"You are now using your {self.current_voice} voice. Your responses should now be as if you were a {self.current_voice}."
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}
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)
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async def news_lady_filter(self, _: Frame) -> bool:
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return self.current_voice == "News Lady"
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async def british_lady_filter(self, _: Frame) -> bool:
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return self.current_voice == "British Lady"
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async def barbershop_man_filter(self, _: Frame) -> bool:
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return self.current_voice == "Barbershop Man"
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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 = SwitchVoices()
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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 and helpful way. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
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),
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)
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llm.register_function("switch_voice", tts.switch_voice)
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switch_voice_function = FunctionSchema(
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name="switch_voice",
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description="Switch your voice only when the user asks you to",
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properties={
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"voice": {
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"type": "string",
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"description": "The voice the user wants you to use",
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},
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},
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required=["voice"],
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)
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tools = ToolsSchema(standard_tools=[switch_voice_function])
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context = LLMContext(tools=tools)
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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 with switch voice functionality
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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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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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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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{
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"role": "developer",
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"content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {tts.current_voice}.",
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}
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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=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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