fix: FunctionFilter adds block_system_frame arg
This commit is contained in:
@@ -9,9 +9,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Changed
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- `FunctionFilter` now has a `block_system_frames` arg, which controls whether
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or not SystemFrames are filtered.
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- Upgraded `aws_sdk_bedrock_runtime` to v0.1.1 to resolve potential CPU issues
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when running `AWSNovaSonicLLMService`.
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### Fixed
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- Fixed an issue in `ServiceSwitcher` where the `STTService`s would result in
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all STT services producing `TranscriptionFrame`s.
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## [0.0.91] - 2025-10-21
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### Added
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138
examples/foundational/48-service-switcher.py
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138
examples/foundational/48-service-switcher.py
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#
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# Copyright (c) 2024–2025, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import asyncio
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import os
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from dotenv import load_dotenv
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from loguru import logger
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from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
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from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.frames.frames import LLMRunFrame, ManuallySwitchServiceFrame
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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.service_switcher import ServiceSwitcher, ServiceSwitcherStrategyManual
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from pipecat.pipeline.task import PipelineParams, 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 LLMContextAggregatorPair
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.stt import CartesiaSTTService
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.services.stt_service import STTService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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# We store functions so objects (e.g. SileroVADAnalyzer) don't get
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# instantiated. The function will be called when the desired transport gets
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# selected.
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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"twilio": lambda: FastAPIWebsocketParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
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turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
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),
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}
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt_cartesia = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
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stt_deepgram = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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stt_switcher = ServiceSwitcher(
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services=[stt_cartesia, stt_deepgram], strategy_type=ServiceSwitcherStrategyManual
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)
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
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},
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]
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context = LLMContext(messages)
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context_aggregator = LLMContextAggregatorPair(context)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt_switcher,
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context_aggregator.user(), # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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context_aggregator.assistant(), # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMRunFrame()])
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await asyncio.sleep(15)
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print(f"Switching to {stt_deepgram}")
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await task.queue_frames([ManuallySwitchServiceFrame(service=stt_deepgram)])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point compatible with Pipecat Cloud."""
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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@@ -138,13 +138,13 @@ class ServiceSwitcher(ParallelPipeline, Generic[StrategyType]):
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active_service: The currently active service.
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direction: The direction of frame flow to filter.
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"""
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self._wrapped_service = wrapped_service
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self._active_service = active_service
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async def filter(_: Frame) -> bool:
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return self._wrapped_service == self._active_service
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super().__init__(filter, direction)
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self._wrapped_service = wrapped_service
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self._active_service = active_service
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super().__init__(filter, direction, block_system_frames=True)
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async def process_frame(self, frame, direction):
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"""Process a frame through the filter, handling special internal filter-updating frames."""
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@@ -12,7 +12,7 @@ allowing for flexible frame filtering logic in processing pipelines.
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from typing import Awaitable, Callable
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from pipecat.frames.frames import EndFrame, Frame, SystemFrame
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from pipecat.frames.frames import CancelFrame, EndFrame, Frame, StartFrame, SystemFrame
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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@@ -28,6 +28,7 @@ class FunctionFilter(FrameProcessor):
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self,
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filter: Callable[[Frame], Awaitable[bool]],
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direction: FrameDirection = FrameDirection.DOWNSTREAM,
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block_system_frames: bool = False,
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):
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"""Initialize the function filter.
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@@ -36,10 +37,12 @@ class FunctionFilter(FrameProcessor):
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frame should pass through, False otherwise.
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direction: The direction to apply filtering. Only frames moving in
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this direction will be filtered. Defaults to DOWNSTREAM.
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block_system_frames: Whether to block system frames. Defaults to False.
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"""
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super().__init__()
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self._filter = filter
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self._direction = direction
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self._block_system_frames = block_system_frames
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#
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# Frame processor
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@@ -49,9 +52,19 @@ class FunctionFilter(FrameProcessor):
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# direction of this gate
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def _should_passthrough_frame(self, frame, direction):
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"""Check if a frame should pass through without filtering."""
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# Ignore system frames, end frames and frames that are not following the
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# direction of this gate
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return isinstance(frame, (SystemFrame, EndFrame)) or direction != self._direction
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# Always passthrough frames in the wrong direction
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if direction != self._direction:
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return True
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# Always passthrough lifecycle frames
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if isinstance(frame, (StartFrame, EndFrame, CancelFrame)):
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return True
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# If not blocking system frames, passthrough all other system frames
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if not self._block_system_frames and isinstance(frame, SystemFrame):
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return True
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return False
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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"""Process a frame through the filter.
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