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Merge pull request #484 from pipecat-ai/mb/llm-input-params
Add input params for OpenAI, Anthropic, Together AI LLMs
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# | ||
# Copyright (c) 2024, Daily | ||
# | ||
# SPDX-License-Identifier: BSD 2-Clause License | ||
# | ||
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import asyncio | ||
import aiohttp | ||
import os | ||
import sys | ||
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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.llm_response import ( | ||
LLMAssistantResponseAggregator, LLMUserResponseAggregator) | ||
from pipecat.services.cartesia import CartesiaTTSService | ||
from pipecat.services.together import TogetherLLMService | ||
from pipecat.transports.services.daily import DailyParams, DailyTransport | ||
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 | ||
load_dotenv(override=True) | ||
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logger.remove(0) | ||
logger.add(sys.stderr, level="DEBUG") | ||
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async def main(): | ||
async with aiohttp.ClientSession() as session: | ||
(room_url, token) = await configure(session) | ||
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transport = DailyTransport( | ||
room_url, | ||
token, | ||
"Respond bot", | ||
DailyParams( | ||
audio_out_enabled=True, | ||
transcription_enabled=True, | ||
vad_enabled=True, | ||
vad_analyzer=SileroVADAnalyzer() | ||
) | ||
) | ||
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tts = CartesiaTTSService( | ||
api_key=os.getenv("CARTESIA_API_KEY"), | ||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady | ||
) | ||
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llm = TogetherLLMService( | ||
api_key=os.getenv("TOGETHER_API_KEY"), | ||
model=os.getenv("TOGETHER_MODEL"), | ||
params=TogetherLLMService.InputParams( | ||
temperature=1.0, | ||
frequency_penalty=2.0, | ||
presence_penalty=0.0, | ||
top_p=0.9, | ||
top_k=40 | ||
) | ||
) | ||
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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.", | ||
}, | ||
] | ||
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tma_in = LLMUserResponseAggregator(messages) | ||
tma_out = LLMAssistantResponseAggregator(messages) | ||
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pipeline = Pipeline([ | ||
transport.input(), # Transport user input | ||
tma_in, # User responses | ||
llm, # LLM | ||
tts, # TTS | ||
transport.output(), # Transport bot output | ||
tma_out # Assistant spoken responses | ||
]) | ||
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task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True)) | ||
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@transport.event_handler("on_first_participant_joined") | ||
async def on_first_participant_joined(transport, participant): | ||
transport.capture_participant_transcription(participant["id"]) | ||
# Kick off the conversation. | ||
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__": | ||
asyncio.run(main()) |
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