Add append_to_context boolean field to TextFrames

This allows any given TextFrame to be marked in a way such that it does not get
added to the context.

Specifically, this fixes a problem with the new AggregatedTextFrames where we
need to send LLM text both in an aggregated form as well as word-by-word but
avoid duplicating the text in the context.

src/pipecat/frames/frames.py

@@ -337,11 +337,14 @@ class TextFrame(DataFrame):
     # mandatory fields of theirs to have defaults to preserve
     # non-default-before-default argument order)
     includes_inter_frame_spaces: bool = field(init=False)
+    # Whether this text frame should be appended to the LLM context.
+    append_to_context: bool = field(init=False)
 
     def __post_init__(self):
         super().__post_init__()
         self.skip_tts = False
         self.includes_inter_frame_spaces = False
+        self.append_to_context = True
 
     def __str__(self):
         pts = format_pts(self.pts)

src/pipecat/processors/aggregators/llm_response.py

@@ -22,6 +22,7 @@ from pipecat.audio.interruptions.base_interruption_strategy import BaseInterrupt
 from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
+    AggregatedLLMTextFrame,
     BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
@@ -1001,7 +1002,7 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator):
         await self.push_aggregation()
 
     async def _handle_text(self, frame: TextFrame):
-        if not self._started:
+        if not self._started or not frame.append_to_context:
             return
 
         if self._params.expect_stripped_words:

src/pipecat/processors/aggregators/llm_response_universal.py

@@ -814,7 +814,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
         await self.push_aggregation()
 
     async def _handle_text(self, frame: TextFrame):
-        if not self._started:
+        if not self._started or not frame.append_to_context:
             return
 
         # Make sure we really have text (spaces count, too!)

src/pipecat/services/tts_service.py

@@ -523,7 +523,9 @@ class TTSService(AIService):
                     # is being spoken. Here, we assume this flag is used when the TTS
                     # provider supports word timestamps and the TTSTextFrames will be
                     # generated in the word_task_handler.
-                    await self.push_frame(AggregatedLLMTextFrame(text, aggregated_by=aggregated_by))
+                    frame = AggregatedLLMTextFrame(text, aggregated_by=aggregated_by)
+                    frame.append_to_context = False
+                    await self.push_frame(frame)
                 await self.process_generator(self.run_tts(text))
 
             await self.stop_processing_metrics()