109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Example of using AWS Nova Sonic LLM service with Vonage Video Connector transport."""
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import asyncio
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import os
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import sys
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from collections.abc import Callable
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from typing import Any
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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.frames.frames import LLMRunFrame
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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 PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.vonage import configure
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from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
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from pipecat.transports.vonage.video_connector import (
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VonageVideoConnectorTransport,
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VonageVideoConnectorTransportParams,
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)
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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() -> None:
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"""Main entry point for the nova sonic vonage video connector example."""
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(application_id, session_id, token) = await configure()
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system_instruction = (
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"You are a friendly assistant. The user and you will engage in a spoken dialog exchanging "
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"the transcripts of a natural real-time conversation. Keep your responses short, generally "
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"two or three sentences for chatty scenarios. "
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f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}"
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)
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transport = VonageVideoConnectorTransport(
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application_id,
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session_id,
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token,
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VonageVideoConnectorTransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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publisher_name="Bot",
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),
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)
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llm = AWSNovaSonicLLMService(
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secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY", ""),
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access_key_id=os.getenv("AWS_ACCESS_KEY_ID", ""),
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region=os.getenv("AWS_REGION", ""),
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session_token=os.getenv("AWS_SESSION_TOKEN", ""),
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voice_id="tiffany",
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)
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context = LLMContext(
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messages=[
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{"role": "system", "content": f"{system_instruction}"},
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{
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"role": "user",
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"content": "Tell me a fun fact!",
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},
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],
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)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context, user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer())
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)
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pipeline = Pipeline(
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[
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transport.input(),
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user_aggregator,
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llm,
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transport.output(),
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assistant_aggregator,
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]
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)
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task = PipelineTask(pipeline)
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# Handle client connection event
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event_handler: Callable[[str], Callable[[Any], Any]] = transport.event_handler
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@event_handler("on_client_connected")
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async def on_client_connected(transport: VonageVideoConnectorTransport, client: object) -> None:
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logger.info(f"Client connected")
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await task.queue_frames([LLMRunFrame()])
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runner = PipelineRunner()
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await asyncio.gather(runner.run(task))
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if __name__ == "__main__":
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asyncio.run(main())
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