138 lines
5.0 KiB
Python
138 lines
5.0 KiB
Python
#
|
|
# Copyright (c) 2024, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
import asyncio
|
|
import os
|
|
import sys
|
|
|
|
import aiohttp
|
|
from dotenv import load_dotenv
|
|
from loguru import logger
|
|
from runner import configure
|
|
|
|
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
|
from pipecat.frames.frames import LLMMessagesFrame
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
|
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
|
from pipecat.services.deepgram import DeepgramTTSService
|
|
from pipecat.services.openai import OpenAILLMService
|
|
from pipecat.transports.services.daily import (
|
|
DailyParams,
|
|
DailyTransport,
|
|
DailyTransportMessageFrame,
|
|
)
|
|
|
|
load_dotenv(override=True)
|
|
|
|
logger.remove(0)
|
|
logger.add(sys.stderr, level="DEBUG")
|
|
|
|
|
|
async def main():
|
|
async with aiohttp.ClientSession() as session:
|
|
(room_url, token) = await configure(session)
|
|
|
|
transport = DailyTransport(
|
|
room_url,
|
|
token,
|
|
"Respond bot",
|
|
DailyParams(
|
|
audio_out_enabled=True,
|
|
transcription_enabled=True,
|
|
vad_enabled=True,
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
),
|
|
)
|
|
|
|
tts = DeepgramTTSService(
|
|
aiohttp_session=session,
|
|
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
|
voice="aura-asteria-en",
|
|
base_url="http://0.0.0.0:8080/v1/speak",
|
|
)
|
|
|
|
llm = OpenAILLMService(
|
|
# To use OpenAI
|
|
# api_key=os.getenv("OPENAI_API_KEY"),
|
|
# model="gpt-4o"
|
|
# Or, to use a local vLLM (or similar) api server
|
|
model="meta-llama/Meta-Llama-3-8B-Instruct",
|
|
base_url="http://0.0.0.0:8000/v1",
|
|
)
|
|
|
|
messages = [
|
|
{
|
|
"role": "system",
|
|
"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.",
|
|
},
|
|
]
|
|
|
|
context = OpenAILLMContext(messages)
|
|
context_aggregator = llm.create_context_aggregator(context)
|
|
|
|
pipeline = Pipeline(
|
|
[
|
|
transport.input(), # Transport user input
|
|
context_aggregator.user(),
|
|
llm, # LLM
|
|
tts, # TTS
|
|
transport.output(), # Transport bot output
|
|
context_aggregator.assistant(),
|
|
]
|
|
)
|
|
|
|
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
|
|
|
|
# When a participant joins, start transcription for that participant so the
|
|
# bot can "hear" and respond to them.
|
|
@transport.event_handler("on_participant_joined")
|
|
async def on_participant_joined(transport, participant):
|
|
await transport.capture_participant_transcription(participant["id"])
|
|
|
|
# When the first participant joins, the bot should introduce itself.
|
|
@transport.event_handler("on_first_participant_joined")
|
|
async def on_first_participant_joined(transport, participant):
|
|
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
|
await task.queue_frames([LLMMessagesFrame(messages)])
|
|
|
|
# Handle "latency-ping" messages. The client will send app messages that look like
|
|
# this:
|
|
# { "latency-ping": { ts: <client-side timestamp> }}
|
|
#
|
|
# We want to send an immediate pong back to the client from this handler function.
|
|
# Also, we will push a frame into the top of the pipeline and send it after the
|
|
#
|
|
@transport.event_handler("on_app_message")
|
|
async def on_app_message(transport, message, sender):
|
|
try:
|
|
if "latency-ping" in message:
|
|
logger.debug(f"Received latency ping app message: {message}")
|
|
ts = message["latency-ping"]["ts"]
|
|
# Send immediately
|
|
transport.output().send_message(
|
|
DailyTransportMessageFrame(
|
|
message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
|
|
)
|
|
)
|
|
# And push to the pipeline for the Daily transport.output to send
|
|
await task.queue_frame(
|
|
DailyTransportMessageFrame(
|
|
message={"latency-pong-pipeline-delivery": {"ts": ts}},
|
|
participant_id=sender,
|
|
)
|
|
)
|
|
except Exception as e:
|
|
logger.debug(f"message handling error: {e} - {message}")
|
|
|
|
runner = PipelineRunner()
|
|
await runner.run(task)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|