152 lines
5.1 KiB
Python
152 lines
5.1 KiB
Python
#
|
|
# Copyright (c) 2024-2026, Daily
|
|
#
|
|
# SPDX-License-Identifier: BSD 2-Clause License
|
|
#
|
|
|
|
|
|
import os
|
|
|
|
from dotenv import load_dotenv
|
|
from loguru import logger
|
|
|
|
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
|
from pipecat.frames.frames import LLMRunFrame
|
|
from pipecat.pipeline.pipeline import Pipeline
|
|
from pipecat.pipeline.runner import PipelineRunner
|
|
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
|
from pipecat.processors.aggregators.llm_context import LLMContext
|
|
from pipecat.processors.aggregators.llm_response_universal import (
|
|
LLMContextAggregatorPair,
|
|
LLMUserAggregatorParams,
|
|
)
|
|
from pipecat.runner.types import RunnerArguments
|
|
from pipecat.runner.utils import create_transport
|
|
from pipecat.services.aws.llm import AWSBedrockLLMService, AWSBedrockLLMSettings
|
|
from pipecat.services.deepgram.flux.sagemaker.stt import DeepgramFluxSageMakerSTTService
|
|
from pipecat.services.deepgram.sagemaker.tts import DeepgramSageMakerTTSService
|
|
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
|
from pipecat.transports.daily.transport import DailyParams
|
|
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
|
from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies
|
|
|
|
load_dotenv(override=True)
|
|
|
|
|
|
# We use lambdas to defer transport parameter creation until the transport
|
|
# type is selected at runtime.
|
|
transport_params = {
|
|
"daily": lambda: DailyParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
),
|
|
"twilio": lambda: FastAPIWebsocketParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
),
|
|
"webrtc": lambda: TransportParams(
|
|
audio_in_enabled=True,
|
|
audio_out_enabled=True,
|
|
),
|
|
}
|
|
|
|
|
|
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
|
logger.info(f"Starting bot")
|
|
|
|
# Initialize Deepgram Flux SageMaker STT Service
|
|
# This requires:
|
|
# - AWS credentials configured (via environment variables or AWS CLI)
|
|
# - A deployed SageMaker endpoint with Deepgram Flux model
|
|
stt = DeepgramFluxSageMakerSTTService(
|
|
endpoint_name=os.getenv("SAGEMAKER_STT_ENDPOINT_NAME"),
|
|
region=os.getenv("AWS_REGION"),
|
|
settings=DeepgramFluxSageMakerSTTService.Settings(
|
|
min_confidence=0.3,
|
|
),
|
|
)
|
|
|
|
# Initialize Deepgram SageMaker TTS Service
|
|
# This requires:
|
|
# - AWS credentials configured (via environment variables or AWS CLI)
|
|
# - A deployed SageMaker endpoint with Deepgram TTS model
|
|
tts = DeepgramSageMakerTTSService(
|
|
endpoint_name=os.getenv("SAGEMAKER_TTS_ENDPOINT_NAME"),
|
|
region=os.getenv("AWS_REGION"),
|
|
settings=DeepgramSageMakerTTSService.Settings(
|
|
voice="aura-2-andromeda-en",
|
|
),
|
|
)
|
|
|
|
llm = AWSBedrockLLMService(
|
|
aws_region=os.getenv("AWS_REGION"),
|
|
settings=AWSBedrockLLMSettings(
|
|
model="us.amazon.nova-pro-v1:0",
|
|
temperature=0.8,
|
|
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
|
),
|
|
)
|
|
|
|
context = LLMContext()
|
|
# Use ExternalUserTurnStrategies since Flux handles turn detection natively
|
|
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
|
context,
|
|
user_params=LLMUserAggregatorParams(
|
|
user_turn_strategies=ExternalUserTurnStrategies(),
|
|
vad_analyzer=SileroVADAnalyzer(),
|
|
),
|
|
)
|
|
|
|
pipeline = Pipeline(
|
|
[
|
|
transport.input(), # Transport user input
|
|
stt, # STT
|
|
user_aggregator, # User responses
|
|
llm, # LLM
|
|
tts, # TTS
|
|
transport.output(), # Transport bot output
|
|
assistant_aggregator, # Assistant spoken responses
|
|
]
|
|
)
|
|
|
|
task = PipelineTask(
|
|
pipeline,
|
|
params=PipelineParams(
|
|
enable_metrics=True,
|
|
enable_usage_metrics=True,
|
|
),
|
|
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
|
)
|
|
|
|
@transport.event_handler("on_client_connected")
|
|
async def on_client_connected(transport, client):
|
|
logger.info(f"Client connected")
|
|
# Kick off the conversation.
|
|
context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
|
|
await task.queue_frames([LLMRunFrame()])
|
|
|
|
@transport.event_handler("on_client_disconnected")
|
|
async def on_client_disconnected(transport, client):
|
|
logger.info(f"Client disconnected")
|
|
await task.cancel()
|
|
|
|
@stt.event_handler("on_update")
|
|
async def on_deepgram_flux_update(stt, transcript):
|
|
logger.debug(f"On deepgram flux update: {transcript}")
|
|
|
|
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
|
|
|
await runner.run(task)
|
|
|
|
|
|
async def bot(runner_args: RunnerArguments):
|
|
"""Main bot entry point compatible with Pipecat Cloud."""
|
|
transport = await create_transport(runner_args, transport_params)
|
|
await run_bot(transport, runner_args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pipecat.runner.run import main
|
|
|
|
main()
|