115 lines
4.2 KiB
Python
115 lines
4.2 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.services.cartesia import CartesiaTTSService
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from pipecat.services.anthropic import AnthropicLLMService
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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(room_url: str, token):
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async with aiohttp.ClientSession() as 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_name=sys.argv[1] if len(sys.argv) > 1 else "British Lady"
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)
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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model="claude-3-5-sonnet-20240620",
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temperature=1.0
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)
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# todo: think more about how to handle system prompts in a more general way. OpenAI,
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# Google, and Anthropic all have slightly different approaches to providing a system
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# prompt.
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messages = [
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{
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"role": "system",
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"content": (
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"You are participating in a friendly competition to invent creative "
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"new ice cream flavors. Say the craziest flavor you can think of "
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"then wait for your opponent to come up with a different crazy flavor. "
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"then respond with another flavor idea. Repeat forever. Say only the "
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"ice cream flavors and nothing else. End each ice cream flavor statement "
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"with an exclamation mark! Go ..."
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)
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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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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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tma_trim
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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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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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(url, token) = configure()
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asyncio.run(main(url, token))
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# '{"action":"app-message","data":{"metrics":{"ttfb":[{"name":"AnthropicLLMService#0","time":0.5975627899169922}]},"type":"pipecat-metrics"},"fromId":"592d3489-90ba-401d-a760-c1a863d64a4a","callFrameId":"17189290998160.035120590426112264"}'
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# [Durian and Limburger Cheese Charcoal Activated Tar Twist!]
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# [Fermented Fish Sauce and Ghost Pepper Bubblegum Cotton Candy Nightmare!]
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# [Spoiled Yogurt and Ghost Pepper Gummy Bear Blizzard!]
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