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Overview

BasePipeline is the abstract base class that provides core functionality for all pipeline implementations. It extends FrameProcessor to enable pipelines to be used as components within other pipelines.

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Class Definition

class BasePipeline(FrameProcessor):
    def __init__(self):
        super().__init__()

    @abstractmethod
    def processors_with_metrics(self) -> List[FrameProcessor]:
        """
        Returns list of processors that can generate metrics.
        Must be implemented by subclasses.
        """
        pass

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Inheritance

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Required Methods

processors_with_metrics
List[FrameProcessor]
required

Must be implemented by subclasses to return a list of processors capable of generating metrics. Used for metrics collection and monitoring.

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Frame Processing

Inherits frame processing capabilities from FrameProcessor:

  • Frame direction control
  • Single push task rule
  • System frame handling
  • Error propagation

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Usage Example

# Creating a custom pipeline implementation
class CustomPipeline(BasePipeline):
    def __init__(self, processors: List[FrameProcessor]):
        super().__init__()
        self._processors = processors

    def processors_with_metrics(self) -> List[FrameProcessor]:
        return [p for p in self._processors if p.can_generate_metrics()]

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        # Custom frame processing logic

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Notes

  • All pipeline implementations must inherit from BasePipeline
  • Provides foundation for metrics collection
  • Enables pipeline composition
  • Maintains frame processing guarantees
  • Supports both synchronous and asynchronous processing
STTMuteFilterPipeline