Files
pipecat/src/dailyai/pipeline/pipeline.py
2024-03-15 11:04:22 -04:00

111 lines
4.6 KiB
Python

import asyncio
from typing import AsyncGenerator, AsyncIterable, Iterable, List
from dailyai.pipeline.frame_processor import FrameProcessor
from dailyai.pipeline.frames import EndPipeFrame, EndFrame, Frame
class Pipeline:
"""
This class manages a pipe of FrameProcessors, and runs them in sequence. The "source"
and "sink" queues are managed by the caller. You can use this class stand-alone to
perform specialized processing, or you can use the Transport's run_pipeline method to
instantiate and run a pipeline with the Transport's sink and source queues.
"""
def __init__(
self,
processors: List[FrameProcessor],
source: asyncio.Queue | None = None,
sink: asyncio.Queue[Frame] | None = None
):
"""Create a new pipeline. By default we create the sink and source queues
if they're not provided, but these can be overridden to point to other
queues. If this pipeline is run by a transport, its sink and source queues
will be overridden.
"""
self.processors: List[FrameProcessor] = processors
self.source: asyncio.Queue[Frame] = source or asyncio.Queue()
self.sink: asyncio.Queue[Frame] = sink or asyncio.Queue()
def set_source(self, source: asyncio.Queue[Frame]):
"""Set the source queue for this pipeline. Frames from this queue
will be processed by each frame_processor in the pipeline, or order
from first to last."""
self.source = source
def set_sink(self, sink: asyncio.Queue[Frame]):
"""Set the sink queue for this pipeline. After the last frame_processor
has processed a frame, its output will be placed on this queue."""
self.sink = sink
async def get_next_source_frame(self) -> AsyncGenerator[Frame, None]:
"""Convenience function to get the next frame from the source queue. This
lets us consistently have an AsyncGenerator yield frames, from either the
source queue or a frame_processor."""
yield await self.source.get()
async def queue_frames(
self,
frames: Iterable[Frame] | AsyncIterable[Frame],
) -> None:
"""Insert frames directly into a pipeline. This is typically used inside a transport
participant_joined callback to prompt a bot to start a conversation, for example."""
if isinstance(frames, AsyncIterable):
async for frame in frames:
await self.source.put(frame)
elif isinstance(frames, Iterable):
for frame in frames:
await self.source.put(frame)
else:
raise Exception("Frames must be an iterable or async iterable")
async def run_pipeline(self):
"""Run the pipeline. Take each frame from the source queue, pass it to
the first frame_processor, pass the output of that frame_processor to the
next in the list, etc. until the last frame_processor has processed the
resulting frames, then place those frames in the sink queue.
The source and sink queues must be set before calling this method.
This method will exit when an EndStreamQueueFrame is placed on the sink queue.
No more frames will be placed on the sink queue after an EndStreamQueueFrame, even
if it's not the last frame yielded by the last frame_processor in the pipeline..
"""
try:
while True:
initial_frame = await self.source.get()
async for frame in self._run_pipeline_recursively(
initial_frame, self.processors
):
await self.sink.put(frame)
if isinstance(initial_frame, EndFrame) or isinstance(
initial_frame, EndPipeFrame
):
break
except asyncio.CancelledError:
# this means there's been an interruption, do any cleanup necessary here.
for processor in self.processors:
await processor.interrupted()
pass
async def _run_pipeline_recursively(
self, initial_frame: Frame, processors: List[FrameProcessor]
) -> AsyncGenerator[Frame, None]:
"""Internal function to add frames to the pipeline as they're yielded
by each processor."""
if processors:
async for frame in processors[0].process_frame(initial_frame):
async for final_frame in self._run_pipeline_recursively(
frame, processors[1:]
):
yield final_frame
else:
yield initial_frame