143 lines
5.0 KiB
Python
143 lines
5.0 KiB
Python
#
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# Copyright (c) 2024, 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 aiohttp
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import os
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import sys
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from pipecat.frames.frames import LLMMessagesFrame
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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.services.deepgram import DeepgramTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import (
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DailyParams,
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DailyTransport,
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DailyTransportMessageFrame,
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)
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from pipecat.vad.silero import SileroVADAnalyzer
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from runner import configure
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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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async def main():
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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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transport = DailyTransport(
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room_url,
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token,
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"Respond bot",
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DailyParams(
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audio_out_enabled=True,
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transcription_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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),
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)
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tts = DeepgramTTSService(
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aiohttp_session=session,
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api_key=os.getenv("DEEPGRAM_API_KEY"),
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voice="aura-asteria-en",
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base_url="http://0.0.0.0:8080/v1/speak",
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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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# model="gpt-4o"
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# Or, to use a local vLLM (or similar) api server
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model="meta-llama/Meta-Llama-3-8B-Instruct",
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base_url="http://0.0.0.0:8000/v1",
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)
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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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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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(), # Transport user input
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tma_in, # 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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tma_out, # Assistant spoken responses
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]
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)
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
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# When a participant joins, start transcription for that participant so the
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# bot can "hear" and respond to them.
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@transport.event_handler("on_participant_joined")
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async def on_participant_joined(transport, participant):
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transport.capture_participant_transcription(participant["id"])
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# When the first participant joins, the bot should introduce itself.
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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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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMMessagesFrame(messages)])
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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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transport.output().send_message(
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DailyTransportMessageFrame(
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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 tma_in.push_frame(
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DailyTransportMessageFrame(
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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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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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