337f048864
Pipecat has a pipeline-based architecture. The pipeline consists of frame processors linked to each other. The elements travelling across the pipeline are called frames. To have a deterministic behavior the frames travelling through the pipeline should always be ordered, except system frames which are out-of-band frames. To achieve that, each frame processor should only output frames from a single task. There are synchronous and asynchronous frame processors. The synchronous processors push output frames from the same task that they receive input frames, and therefore only pushing frames from one task. Asynchrnous frame processors can have internal tasks to perform things asynchrnously (e.g. receiving data from a websocket) but they also have a single task where they push frames from.
108 lines
3.6 KiB
Python
108 lines
3.6 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, LLMUserResponseAggregator)
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from pipecat.processors.user_idle_processor import UserIdleProcessor
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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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 = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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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 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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async def user_idle_callback(user_idle: UserIdleProcessor):
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messages.append(
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{"role": "system", "content": "Ask the user if they are still there and try to prompt for some input, but be short."})
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await user_idle.push_frame(LLMMessagesFrame(messages))
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user_idle = UserIdleProcessor(callback=user_idle_callback, timeout=5.0)
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pipeline = Pipeline([
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transport.input(), # Transport user input
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user_idle, # Idle user check-in
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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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task = PipelineTask(pipeline, PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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report_only_initial_ttfb=True,
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))
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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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# Kick off the conversation.
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messages.append(
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{"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMMessagesFrame(messages)])
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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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