Remove VisionImageRawFrame, which was previously being handled directly by the LLM services, and deprecate the associated VisionImageFrameAggregator.

Removing `VisionImageRawFrame` lets us simplify LLM services' logic, getting us closer to the idealized architecture where all they care about is handling context frames.

This change is in service of getting us closer to ready to deprecate usage of `OpenAILLMContext` and subclasses in favor of the universal `LLMContext`, at least for the traditional text-to-text LLMs.

Why remove `VisionImageRawFrame` rather than deprecate? It's "internal"—only created by `VisionImageFrameAggregator`—and never intended to be used directly by users (it would be difficult to use directly anyway).

Move the logic that was once in `VisionImageFrameAggregator` directly into the examples. Reasoning:
- If `UserImageRequester` is defined in the examples, it makes sense for `UserImageProcessor` to be too, as it’s the flip side of the same coin, so to speak
- The logic is now pretty trivial
- This kind of one-shot, history-less image-describing pipeline shouldn't be common at all; it's ok for it to live in examples rather than as a dedicated class
- In the short term, this enables us to create `LLMContext`s for services that support it and `OpenAILLMContext`s for services that don't yet (AWS)

This commit also adds missing translation from OpenAI-format image context messages to AWS format. Note that this isn't a wasted effort in the face of the upcoming migration to universal `LLMContext`—this work will be reused as it has to be implemented there too.

src/pipecat/frames/frames.py

@@ -1253,23 +1253,6 @@ class UserImageRawFrame(InputImageRawFrame):
         return f"{self.name}(pts: {pts}, user: {self.user_id}, source: {self.transport_source}, size: {self.size}, format: {self.format}, request: {self.request})"
 
 
-@dataclass
-class VisionImageRawFrame(InputImageRawFrame):
-    """Image frame for vision/image analysis with associated text prompt.
-
-    An image with an associated text to ask for a description of it.
-
-    Parameters:
-        text: Optional text prompt describing what to analyze in the image.
-    """
-
-    text: Optional[str] = None
-
-    def __str__(self):
-        pts = format_pts(self.pts)
-        return f"{self.name}(pts: {pts}, text: [{self.text}], size: {self.size}, format: {self.format})"
-
-
 @dataclass
 class InputDTMFFrame(DTMFFrame, SystemFrame):
     """DTMF keypress input frame from transport."""