# # Copyright (c) 2024-2026, Daily # # SPDX-License-Identifier: BSD 2-Clause License # import os import random from datetime import datetime from dotenv import load_dotenv from loguru import logger from pipecat.evals.transport import EvalTransportParams from pipecat.frames.frames import LLMRunFrame from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.worker import PipelineParams, PipelineWorker, ProcessorUnusablePolicy from pipecat.processors.aggregators.llm_context import LLMContext from pipecat.processors.aggregators.llm_response_universal import ( AssistantTurnStoppedMessage, LLMContextAggregatorPair, UserTurnMessageAddedMessage, ) from pipecat.runner.types import RunnerArguments from pipecat.runner.utils import create_transport from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService from pipecat.services.aws.nova_sonic.session_continuation import SessionContinuationParams from pipecat.services.llm_service import FunctionCallParams from pipecat.transports.base_transport import BaseTransport, TransportParams from pipecat.transports.daily.transport import DailyParams from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams from pipecat.workers.runner import WorkerRunner # Load environment variables load_dotenv(override=True) async def get_current_weather(params: FunctionCallParams, location: str, format: str): """Get the current weather. Args: location: The city and state, e.g. "San Francisco, CA". format: The temperature unit to use. Must be either "celsius" or "fahrenheit". Infer this from the user's location. """ temperature = random.randint(60, 85) if format == "fahrenheit" else random.randint(15, 30) await params.result_callback( { "conditions": "nice", "temperature": temperature, "location": location, "format": format, "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), } ) # We use lambdas to defer transport parameter creation until the transport # type is selected at runtime. transport_params = { "eval": lambda: EvalTransportParams( audio_in_enabled=True, audio_out_enabled=True, ), "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("Starting bot") # Specify initial system instruction. system_instruction = ( "You are a friendly assistant. The user and you will engage in a spoken dialog exchanging " "the transcripts of a natural real-time conversation. Keep your responses short, generally " "two or three sentences for chatty scenarios." # HACK: if using the older Nova Sonic (pre-2) model, note that you need to inject a special # bit of text into this instruction to allow the first assistant response to be # programmatically triggered (which happens in the on_client_connected handler) # f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}" ) # Create the AWS Nova Sonic LLM service llm = AWSNovaSonicLLMService( secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"], access_key_id=os.environ["AWS_ACCESS_KEY_ID"], # as of 2025-12-09, these are the supported regions: # - Nova 2 Sonic (the default model): # - us-east-1 # - us-west-2 # - ap-northeast-1 # - Nova Sonic (the older model): # - us-east-1 # - ap-northeast-1 region=os.environ["AWS_REGION"], session_token=os.getenv("AWS_SESSION_TOKEN"), settings=AWSNovaSonicLLMService.Settings( voice="tiffany", system_instruction=system_instruction, ), # Session continuation is enabled by default, allowing seamless # conversations longer than the AWS ~8-minute session limit. # The service rotates sessions in the background with no # user-perceptible interruption. You can tune the threshold or # disable it with: session_continuation=SessionContinuationParams(enabled=False) session_continuation=SessionContinuationParams( # When to start preparing the next session (default: 360 = 6 min). # Lower this (e.g. 20) to see a handoff happen quickly during testing. transition_threshold_seconds=360, ), # you could choose to pass tools here rather than via context # tools=[get_current_weather] ) # AWS Nova Sonic drives the conversation server-side. # # It does not, however, emit turn frames (UserStartedSpeakingFrame, # UserStoppedSpeakingFrame). Context aggregation works without those # frames, but you can add supplemental local turn frames for consumption # by other pipeline processors that expect them (like RTVI), or to trigger # on_user_turn_* events. WARNING: you should consider supplemental local # turn frames approximate, as they may not always align with server turns. # # To enable supplemental local turn frames, uncomment the SileroVADAnalyzer # and related imports below and the `user_params=` argument further down. # Doing so enables the on_user_turn_stopped event, which you could then # also uncomment. # # from pipecat.audio.vad.silero import SileroVADAnalyzer # from pipecat.processors.aggregators.llm_response_universal import ( # LLMUserAggregatorParams, # UserTurnStoppedMessage, # ) # from pipecat.turns.user_stop import BaseUserTurnStopStrategy context = LLMContext(tools=[get_current_weather]) user_aggregator, assistant_aggregator = LLMContextAggregatorPair( context, # user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()), ) # Build the pipeline pipeline = Pipeline( [ transport.input(), user_aggregator, llm, transport.output(), assistant_aggregator, ] ) # Configure the pipeline worker worker = PipelineWorker( pipeline, params=PipelineParams( enable_metrics=True, enable_usage_metrics=True, ), idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, processor_unusable_policy=ProcessorUnusablePolicy.END, ) runner = WorkerRunner(handle_sigint=runner_args.handle_sigint) await runner.add_workers(worker) # Handle client connection event @transport.event_handler("on_client_connected") async def on_client_connected(transport, client): logger.info("Client connected") # Kick off the conversation. context.add_message( {"role": "developer", "content": "Please introduce yourself to the user."} ) await worker.queue_frames([LLMRunFrame()]) # HACK: if using the older Nova Sonic (pre-2) model, you need this special way of # triggering the first assistant response. Note that this trigger requires a special # corresponding bit of text in the system instruction. # await llm.trigger_assistant_response() # Handle client disconnection events @transport.event_handler("on_client_disconnected") async def on_client_disconnected(transport, client): logger.info("Client disconnected") await runner.cancel() # See comment above the user_aggregator for details on why this is # commented out and instructions for enabling it. # @user_aggregator.event_handler("on_user_turn_stopped") # async def on_user_turn_stopped( # aggregator, # strategy: BaseUserTurnStopStrategy, # message: UserTurnStoppedMessage, # ): # logger.info(f"User turn stopped at {message.timestamp}") @user_aggregator.event_handler("on_user_turn_message_added") async def on_user_turn_message_added(aggregator, message: UserTurnMessageAddedMessage): timestamp = f"[{message.timestamp}] " if message.timestamp else "" line = f"{timestamp}user: {message.content}" logger.info(f"Transcript: {line}") @assistant_aggregator.event_handler("on_assistant_turn_stopped") async def on_assistant_turn_stopped(aggregator, message: AssistantTurnStoppedMessage): timestamp = f"[{message.timestamp}] " if message.timestamp else "" line = f"{timestamp}assistant: {message.content}" logger.info(f"Transcript: {line}") await runner.run() 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()