115 lines
3.5 KiB
Python
115 lines
3.5 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 argparse
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import os
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from dotenv import load_dotenv
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from loguru import logger
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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.frames.frames import LLMMessagesAppendFrame
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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.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
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from pipecat.transports.base_transport import TransportParams
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from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
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from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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# Load environment variables
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load_dotenv(override=True)
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async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
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logger.info(f"Starting bot")
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# Initialize the SmallWebRTCTransport with the connection
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transport = SmallWebRTCTransport(
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webrtc_connection=webrtc_connection,
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params=TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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video_in_enabled=False,
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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.
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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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# Create the Gemini Multimodal Live LLM service
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system_instruction = f"""
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You are a helpful AI assistant.
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Your goal is to demonstrate your capabilities in a helpful and engaging 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.
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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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system_instruction=system_instruction,
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voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
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transcribe_user_audio=True,
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)
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# Build the pipeline
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pipeline = Pipeline(
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[
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transport.input(),
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llm,
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transport.output(),
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]
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)
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# Configure the pipeline task
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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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# Handle client connection event
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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await task.queue_frames(
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[
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LLMMessagesAppendFrame(
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messages=[
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{
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"role": "user",
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"content": f"Greet the user and introduce yourself.",
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}
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]
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)
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]
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)
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# Handle client disconnection events
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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@transport.event_handler("on_client_closed")
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async def on_client_closed(transport, client):
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logger.info(f"Client closed connection")
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await task.cancel()
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# Run the pipeline
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runner = PipelineRunner(handle_sigint=False)
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await runner.run(task)
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if __name__ == "__main__":
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from run import main
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main()
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