diff --git a/changelog/4501.fixed.md b/changelog/4501.fixed.md new file mode 100644 index 000000000..9da88a9e1 --- /dev/null +++ b/changelog/4501.fixed.md @@ -0,0 +1 @@ +- Fixed bot hangs when `filter_incomplete_user_turns` was enabled and the LLM responded by calling a tool. The user turn never finalized, so the assistant aggregator gated the tool-result context push and the LLM continuation never ran. Tool calls now finalize the turn the moment they start, before the function dispatches. diff --git a/examples/function-calling/function-calling-openai.py b/examples/function-calling/function-calling-openai.py index 38c9d6385..20777eaca 100644 --- a/examples/function-calling/function-calling-openai.py +++ b/examples/function-calling/function-calling-openai.py @@ -68,9 +68,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): tts = OpenAITTSService( api_key=os.environ["OPENAI_API_KEY"], settings=OpenAITTSService.Settings( + instructions="Please speak clearly and at a moderate pace.", voice="ballad", ), - instructions="Please speak clearly and at a moderate pace.", ) llm = OpenAILLMService( diff --git a/examples/turn-management/turn-management-filter-incomplete-turns-function-calling.py b/examples/turn-management/turn-management-filter-incomplete-turns-function-calling.py new file mode 100644 index 000000000..3cdd0110b --- /dev/null +++ b/examples/turn-management/turn-management-filter-incomplete-turns-function-calling.py @@ -0,0 +1,201 @@ +# +# 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.task import PipelineParams, PipelineTask +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 + ] + ) + + 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": "developer", "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() + + @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(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() diff --git a/scripts/evals/run-release-evals.py b/scripts/evals/run-release-evals.py index 230b174d6..c881d4432 100644 --- a/scripts/evals/run-release-evals.py +++ b/scripts/evals/run-release-evals.py @@ -242,6 +242,7 @@ TESTS_VIDEO_AVATAR = [ TESTS_TURN_MANAGEMENT = [ ("turn-management/turn-management-filter-incomplete-turns.py", EVAL_COMPLETE_TURN), + ("turn-management/turn-management-filter-incomplete-turns-function-calling.py", EVAL_WEATHER), ] TESTS_THINKING = [ diff --git a/src/pipecat/turns/user_turn_completion_mixin.py b/src/pipecat/turns/user_turn_completion_mixin.py index c423e16bf..15fbd78ec 100644 --- a/src/pipecat/turns/user_turn_completion_mixin.py +++ b/src/pipecat/turns/user_turn_completion_mixin.py @@ -20,6 +20,7 @@ from loguru import logger from pipecat.frames.frames import ( Frame, + FunctionCallsStartedFrame, InterruptionFrame, LLMFullResponseEndFrame, LLMMarkerFrame, @@ -222,6 +223,14 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor): # ensures graceful degradation if the LLM disobeys and outputs additional text. self._turn_suppressed = False self._turn_complete_found = False # True when ✓ (COMPLETE) is detected + # Set when the LLM made a tool call during this turn. Informational + # only — broadcasting is idempotency-gated by + # ``_turn_completion_broadcasted``. + self._turn_had_function_call = False + # True once ``UserTurnInferenceCompletedFrame`` has been broadcast + # for this turn. Prevents double-broadcast when ✓ and a tool call + # both occur in the same turn. + self._turn_completion_broadcasted = False # Timeout handling self._user_turn_completion_config = UserTurnCompletionConfig() @@ -236,6 +245,27 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor): """ self._user_turn_completion_config = config + async def _broadcast_turn_completion(self): + """Broadcast ``UserTurnInferenceCompletedFrame`` at most once per turn. + + Called from the two places we know the LLM has committed to a + response for the current user turn: + + - the ``✓`` marker is detected in the text stream + - a ``FunctionCallsStartedFrame`` is emitted — the LLM committed + to a tool call before producing (or instead of) a marker. + + Broadcasting on the tool-call path matters for races: the + downstream ``UserStoppedSpeakingFrame`` needs to propagate + before the function actually executes and a + ``FunctionCallResultFrame`` flows back to the assistant + aggregator. + """ + if self._turn_completion_broadcasted: + return + self._turn_completion_broadcasted = True + await self.broadcast_frame(UserTurnInferenceCompletedFrame) + async def _start_incomplete_timeout(self, incomplete_type: Literal["short", "long"]): """Start a timeout task for incomplete turn handling. @@ -325,6 +355,8 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor): self._turn_text_buffer = "" self._turn_suppressed = False self._turn_complete_found = False + self._turn_had_function_call = False + self._turn_completion_broadcasted = False async def process_frame(self, frame: Frame, direction: FrameDirection): """Process frames, handling turn completion state resets. @@ -351,7 +383,14 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor): frame: The frame to push downstream. direction: The direction of frame flow. Defaults to downstream. """ - if isinstance(frame, LLMFullResponseEndFrame): + if isinstance(frame, FunctionCallsStartedFrame): + self._turn_had_function_call = True + # Broadcast turn completion now, before the function dispatches + # — gives ``UserStoppedSpeakingFrame`` maximum time to propagate + # so the assistant aggregator's ``_user_speaking`` is False by + # the time a ``FunctionCallResultFrame`` arrives. + await self._broadcast_turn_completion() + elif isinstance(frame, LLMFullResponseEndFrame): await self._turn_reset() await super().push_frame(frame, direction) @@ -427,7 +466,9 @@ class UserTurnCompletionLLMServiceMixin(FrameProcessor): # LLMTurnCompletionUserTurnStopStrategy) can fire # `on_user_turn_stopped`. Must fire before the marker so # downstream consumers see the signal before the response. - await self.broadcast_frame(UserTurnInferenceCompletedFrame) + # Idempotent: a tool call earlier in the turn may have + # already broadcast. + await self._broadcast_turn_completion() # Push the marker as a sideband signal that the assistant # aggregator will prepend to the upcoming aggregated text,