129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
#
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# Copyright (c) 2024–2025, 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.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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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.google.llm import GoogleLLMService
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from pipecat.transports.tavus.transport import TavusParams, TavusTransport
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from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
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from pipecat.turns.user_turn_strategies import UserTurnStrategies
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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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transport = TavusTransport(
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bot_name="Pipecat bot",
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api_key=os.getenv("TAVUS_API_KEY"),
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replica_id=os.getenv("TAVUS_REPLICA_ID"),
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session=session,
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params=TavusParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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microphone_out_enabled=False,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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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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voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
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)
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llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(
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user_turn_strategies=UserTurnStrategies(
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stop=[
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TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())
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]
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),
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),
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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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context_aggregator.user(), # 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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context_aggregator.assistant(), # 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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audio_in_sample_rate=16000,
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audio_out_sample_rate=24000,
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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, participant):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append(
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{
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"role": "system",
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"content": "Start by greeting the user and ask how you can help.",
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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, participant):
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logger.info(f"Client disconnected")
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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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