133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
#
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# Copyright (c) 2024–2025, 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 os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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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.openai_llm_context import OpenAILLMContext
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from pipecat.services.cartesia import CartesiaTTSService
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from pipecat.services.gemini_multimodal_live.gemini import (
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GeminiMultimodalLiveLLMService,
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GeminiMultimodalModalities,
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InputParams,
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)
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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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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SYSTEM_INSTRUCTION = f"""
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"You are Gemini Chatbot, a friendly, helpful robot.
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Your goal is to demonstrate your capabilities in a succinct way.
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Your output will be converted to audio so don't include special characters in your answers.
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Respond to what the user said in a creative and helpful way. Keep your responses brief. One or two sentences at most.
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"""
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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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vad_enabled=True,
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vad_audio_passthrough=True,
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# set stop_secs to something roughly similar to the internal setting
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# of the Multimodal Live api, just to align events. This doesn't really
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# matter because we can only use the Multimodal Live API's phrase
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# endpointing, for now.
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
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),
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)
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llm = GeminiMultimodalLiveLLMService(
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api_key=os.getenv("GOOGLE_API_KEY"),
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transcribe_user_audio=True,
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transcribe_model_audio=True,
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system_instruction=SYSTEM_INSTRUCTION,
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tools=[{"google_search": {}}, {"code_execution": {}}],
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params=InputParams(modalities=GeminiMultimodalModalities.TEXT),
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)
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# Optionally, you can set the response modalities via a function
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# llm.set_model_modalities(
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# GeminiMultimodalModalities.TEXT
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# )
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
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)
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messages = [
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{
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"role": "user",
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"content": 'Start by saying "Hello, I\'m Gemini".',
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},
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]
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# Set up conversation context and management
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# The context_aggregator will automatically collect conversation context
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context = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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pipeline = Pipeline(
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[
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transport.input(),
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context_aggregator.user(),
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llm,
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tts,
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transport.output(),
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context_aggregator.assistant(),
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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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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await transport.capture_participant_transcription(participant["id"])
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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print(f"Participant left: {participant}")
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await task.cancel()
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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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