Merge pull request #3074 from pipecat-ai/pk/tweak-moondream-example
Update Moondream example so that Moondream service output makes it in…
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@@ -15,14 +15,21 @@ 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, UserImageRequestFrame
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from pipecat.frames.frames import (
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Frame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMRunFrame,
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TextFrame,
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UserImageRequestFrame,
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)
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from pipecat.pipeline.parallel_pipeline import ParallelPipeline
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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.task import 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.processors.frame_processor import FrameDirection
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from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import (
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create_transport,
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@@ -66,6 +73,27 @@ async def fetch_user_image(params: FunctionCallParams):
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# await params.result_callback({"result": "Image is being captured."})
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class MoondreamTextFrameWrapper(FrameProcessor):
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"""Wraps Moondream-provided TextFrames with LLM response start/end frames.
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This processor detects TextFrames and automatically wraps them with
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LLMFullResponseStartFrame and LLMFullResponseEndFrame to provide proper
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response boundaries for downstream processors.
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"""
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async def process_frame(self, frame: Frame, direction: FrameDirection):
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await super().process_frame(frame, direction)
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# If we receive a TextFrame, wrap it with response start/end frames
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if isinstance(frame, TextFrame):
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await self.push_frame(LLMFullResponseStartFrame(), direction)
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await self.push_frame(frame, direction)
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await self.push_frame(LLMFullResponseEndFrame(), direction)
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else:
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# For all other frames, just pass them through
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await self.push_frame(frame, direction)
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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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@@ -130,6 +158,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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# If you run into weird description, try with use_cpu=True
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moondream = MoondreamService()
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# Wrap TextFrames with LLM response start/end frames, which makes Moondream
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# output be treated like LLM responses for the purpose of context
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# aggregation. Without this, the assistant context aggregator would ignore
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# Moondream output (if the TTS service is disabled).
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moondream_text_wrapper = MoondreamTextFrameWrapper()
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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@@ -137,7 +171,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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context_aggregator.user(), # User responses
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ParallelPipeline(
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[llm], # LLM
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[moondream],
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[moondream, moondream_text_wrapper],
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),
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tts, # TTS
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transport.output(), # Transport bot output
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