tiny bit more cleanup
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@@ -18,7 +18,7 @@ from pipecat.frames.frames import (
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Frame,
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LLMModelUpdateFrame,
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TextFrame,
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UserImageRawFrame,
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UserImageRequestFrame,
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LLMMessagesFrame,
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LLMFullResponseStartFrame,
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LLMFullResponseEndFrame,
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@@ -249,17 +249,9 @@ class TogetherUserContextAggregator(LLMUserContextAggregator):
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if isinstance(context, OpenAILLMContext):
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self._context = TogetherLLMContext.from_openai_context(context)
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def get_messages_frame(self):
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return OpenAILLMContextFrame(self._context)
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async def push_messages_frame(self):
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await self.push_frame(self.get_messages_frame())
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def append_image_description_tool_message(self, description):
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self._context.add_message({
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"role": "tool",
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"content": json.dumps({"image_description": description})
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})
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frame = OpenAILLMContextFrame(self._context)
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await self.push_frame(frame)
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async def process_frame(self, frame, direction):
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await super().process_frame(frame, direction)
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@@ -268,10 +260,20 @@ class TogetherUserContextAggregator(LLMUserContextAggregator):
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# to talk through (tagging @aleix). At some point we might need to refactor these
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# context aggregators.
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try:
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if isinstance(frame, UserImageRawFrame):
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if frame.description:
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self.append_image_description_tool_message(frame.description)
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await self.push_messages_frame()
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if isinstance(frame, UserImageRequestFrame):
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# The LLM sends a UserImageRequestFrame upstream. Cache any context provided with
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# that frame so we can use it when we assemble the image message in the assistant
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# context aggregator.
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if (frame.context):
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if isinstance(frame.context, str):
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self._context._user_image_request_context[frame.user_id] = frame.context
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else:
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logger.error(
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f"Unexpected UserImageRequestFrame context type: {type(frame.context)}")
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del self._context._user_image_request_context[frame.user_id]
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else:
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if frame.user_id in self._context._user_image_request_context:
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del self._context._user_image_request_context[frame.user_id]
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except Exception as e:
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logger.error(f"Error processing frame: {e}")
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