9bbb824248
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 are awlays ordered, except system frames which are out-of-band frames. To achieve ordering each frame processor only outputs frames from a single task. This single task is internally created in each FrameProcessor and the developer doesn't need to know much about it, except conceptually. Having a single output task avoids problems such as audio overlapping.
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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