pipecat/examples/turn-management/turn-management-filter-incomplete-turns-function-calling.py

#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

"""Example 22: Filter Incomplete Turns

Demonstrates LLM-based turn completion detection to suppress bot responses when
the user was cut off mid-thought. The LLM outputs one of three markers:
- ✓ (complete): User finished their thought, respond normally
- ○ (incomplete short): User was cut off, wait ~5s then prompt
- ◐ (incomplete long): User needs time to think, wait ~10s then prompt

When incomplete is detected, the bot's response is suppressed. After the timeout
expires, the LLM is automatically prompted to re-engage the user.
"""

import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.adapters.schemas.tools_schema import ToolsSchema
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.worker import PipelineParams, PipelineWorker
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    AssistantTurnStoppedMessage,
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
    UserTurnStoppedMessage,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.openai.llm import OpenAILLMService
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 FilterIncompleteUserTurnStrategies

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 get_weather(params: FunctionCallParams, location: str):
    """Return the current weather for a location.

    A stub that always reports the same conditions — replace with a real
    weather API in production.

    Args:
        location (str): The city and state or country, e.g. "Paris, France".
    """
    await params.result_callback(
        {
            "location": location,
            "temperature_celsius": 22,
            "conditions": "partly cloudy",
        }
    )


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot")

    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])

    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        settings=OpenAILLMService.Settings(
            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. "
                "If the user asks about the weather, call the get_weather "
                "tool and speak the result back naturally."
            ),
        ),
    )
    llm.register_direct_function(get_weather)

    tts = CartesiaTTSService(
        api_key=os.environ["CARTESIA_API_KEY"],
        settings=CartesiaTTSService.Settings(
            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        ),
    )

    context = LLMContext(tools=ToolsSchema(standard_tools=[get_weather]))
    # `FilterIncompleteUserTurnStrategies` pairs the default detector
    # chain with `LLMTurnCompletionUserTurnStopStrategy`: detectors
    # trigger LLM inference but the public `on_user_turn_stopped` event
    # fires only when the LLM confirms ✓. The LLM marks each response
    # with one of:
    # ✓ = complete (respond normally)
    # ○ = incomplete short (wait 5s, then prompt)
    # ◐ = incomplete long (wait 10s, then prompt)
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(
            vad_analyzer=SileroVADAnalyzer(),
            user_turn_strategies=FilterIncompleteUserTurnStrategies(
                # Optional: customize turn completion behavior
                # config=UserTurnCompletionConfig(
                #     incomplete_short_timeout=5.0,
                #     incomplete_long_timeout=10.0,
                #     incomplete_short_prompt="Custom prompt...",
                #     incomplete_long_prompt="Custom prompt...",
                #     instructions="Custom turn completion instructions...",
                # ),
            ),
        ),
    )

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt,
            user_aggregator,  # User responses
            llm,  # LLM
            tts,  # TTS
            transport.output(),  # Transport bot output
            assistant_aggregator,  # Assistant spoken responses
        ]
    )

    worker = PipelineWorker(
        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": "developer", "content": "Please introduce yourself to the user."}
        )
        await worker.queue_frames([LLMRunFrame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")
        await worker.cancel()

    @user_aggregator.event_handler("on_user_turn_stopped")
    async def on_user_turn_stopped(aggregator, strategy, message: UserTurnStoppedMessage):
        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}")

    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

    await runner.run(worker)


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()