107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
import asyncio
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import os
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import sys
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import argparse
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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_response import (
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LLMAssistantResponseAggregator,
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LLMUserResponseAggregator,
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)
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from pipecat.frames.frames import LLMMessagesFrame, EndFrame
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyDialinSettings
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from pipecat.vad.silero import SileroVADAnalyzer
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from loguru import logger
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from dotenv import load_dotenv
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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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daily_api_key = os.getenv("DAILY_API_KEY", "")
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daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
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async def main(room_url: str, token: str, callId: str, callDomain: str):
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# diallin_settings are only needed if Daily's SIP URI is used
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# If you are handling this via Twilio, Telnyx, set this to None
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# and handle call-forwarding when on_dialin_ready fires.
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diallin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain)
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transport = DailyTransport(
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room_url,
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token,
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"Chatbot",
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DailyParams(
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api_url=daily_api_url,
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api_key=daily_api_key,
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dialin_settings=diallin_settings,
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audio_in_enabled=True,
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audio_out_enabled=True,
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camera_out_enabled=False,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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transcription_enabled=True,
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),
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)
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY", ""),
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voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
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messages = [
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{
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"role": "system",
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"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Oh, hello! Who dares dial me at this hour?!'.",
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},
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]
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tma_in = LLMUserResponseAggregator(messages)
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tma_out = LLMAssistantResponseAggregator(messages)
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pipeline = Pipeline(
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[
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transport.input(),
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tma_in,
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llm,
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tts,
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transport.output(),
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tma_out,
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
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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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transport.capture_participant_transcription(participant["id"])
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await task.queue_frames([LLMMessagesFrame(messages)])
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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.queue_frame(EndFrame())
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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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parser = argparse.ArgumentParser(description="Pipecat Simple ChatBot")
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parser.add_argument("-u", type=str, help="Room URL")
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parser.add_argument("-t", type=str, help="Token")
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parser.add_argument("-i", type=str, help="Call ID")
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parser.add_argument("-d", type=str, help="Call Domain")
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config = parser.parse_args()
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asyncio.run(main(config.u, config.t, config.i, config.d))
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