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.

examples/foundational/12-describe-video.py

@@ -11,12 +11,19 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, TextFrame, TTSSpeakFrame, UserImageRequestFrame
+from pipecat.frames.frames import (
+    Frame,
+    LLMContextFrame,
+    TextFrame,
+    TTSSpeakFrame,
+    UserImageRawFrame,
+    UserImageRequestFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
-from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import (
@@ -34,6 +41,8 @@ load_dotenv(override=True)
 
 
 class UserImageRequester(FrameProcessor):
+    """Converts incoming text into requests for user images."""
+
     def __init__(self, participant_id: Optional[str] = None):
         super().__init__()
         self._participant_id = participant_id
@@ -46,9 +55,32 @@ class UserImageRequester(FrameProcessor):
 
         if self._participant_id and isinstance(frame, TextFrame):
             await self.push_frame(
-                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+                UserImageRequestFrame(self._participant_id, context=frame.text),
+                FrameDirection.UPSTREAM,
             )
-        await self.push_frame(frame, direction)
+        else:
+            await self.push_frame(frame, direction)
+
+
+class UserImageProcessor(FrameProcessor):
+    """Converts incoming user images into context frames."""
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserImageRawFrame):
+            if frame.request and frame.request.context:
+                context = LLMContext()
+                context.add_image_frame_message(
+                    image=frame.image,
+                    text=frame.request.context,
+                    size=frame.size,
+                    format=frame.format,
+                )
+                frame = LLMContextFrame(context)
+                await self.push_frame(frame)
+        else:
+            await self.push_frame(frame, direction)
 
 
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
@@ -78,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     # Initialize the image requester without setting the participant ID yet
     image_requester = UserImageRequester()
 
-    vision_aggregator = VisionImageFrameAggregator()
+    image_processor = UserImageProcessor()
 
     # If you run into weird description, try with use_cpu=True
     moondream = MoondreamService()
@@ -96,7 +128,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             stt,
             user_response,
             image_requester,
-            vision_aggregator,
+            image_processor,
             moondream,
             tts,
             transport.output(),
@@ -119,7 +151,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         image_requester.set_participant_id(client_id)
 
         # Welcome message
-        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me what I see."))
+        await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):