d3021b4590
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.
139 lines
4.2 KiB
Python
139 lines
4.2 KiB
Python
#
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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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