remove LLMResponseStartFrame and LLMResponseEndFrame

This was added in the past to properly handle interruptions for the
LLMAssistantContextAggregator. But this is not necessary anymore since we can
handle interruptions by just processing the StartInterruptionFrame, so there's
no need for these extra frames.

CHANGELOG.md

@@ -5,6 +5,17 @@ All notable changes to **pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Removed
+
+- We remove the `LLMResponseStartFrame` and `LLMResponseEndFrame` frames. These
+  were added in the past to properly handle interruptions for the
+  `LLMAssistantContextAggregator`. But the `LLMContextAggregator` is now based
+  on `LLMResponseAggregator` which handles interruptions properly by just
+  processing the `StartInterruptionFrame`, so there's no need for these extra
+  frames any more.
+
 ## [0.0.36] - 2024-07-02
 
 ### Added

src/pipecat/frames/frames.py

@@ -282,27 +282,13 @@ class EndFrame(ControlFrame):
 
 @dataclass
 class LLMFullResponseStartFrame(ControlFrame):
-    """Used to indicate the beginning of a full LLM response. Following
-    LLMResponseStartFrame, TextFrame and LLMResponseEndFrame for each sentence
-    until a LLMFullResponseEndFrame."""
+    """Used to indicate the beginning of an LLM response. Following by one or
+    more TextFrame and a final LLMFullResponseEndFrame."""
     pass
 
 
 @dataclass
 class LLMFullResponseEndFrame(ControlFrame):
-    """Indicates the end of a full LLM response."""
-    pass
-
-
-@dataclass
-class LLMResponseStartFrame(ControlFrame):
-    """Used to indicate the beginning of an LLM response. Following TextFrames
-    are part of the LLM response until an LLMResponseEndFrame"""
-    pass
-
-
-@dataclass
-class LLMResponseEndFrame(ControlFrame):
     """Indicates the end of an LLM response."""
     pass
 

src/pipecat/processors/aggregators/llm_response.py

@@ -14,8 +14,6 @@ from pipecat.frames.frames import (
     InterimTranscriptionFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
     LLMMessagesFrame,
     StartInterruptionFrame,
     TranscriptionFrame,
@@ -173,7 +171,7 @@ class LLMUserResponseAggregator(LLMResponseAggregator):
 
 class LLMFullResponseAggregator(FrameProcessor):
     """This class aggregates Text frames until it receives a
-    LLMResponseEndFrame, then emits the concatenated text as
+    LLMFullResponseEndFrame, then emits the concatenated text as
     a single text frame.
 
     given the following frames:
@@ -182,12 +180,12 @@ class LLMFullResponseAggregator(FrameProcessor):
         TextFrame(" world.")
         TextFrame(" I am")
         TextFrame(" an LLM.")
-        LLMResponseEndFrame()]
+        LLMFullResponseEndFrame()]
 
     this processor will yield nothing for the first 4 frames, then
 
         TextFrame("Hello, world. I am an LLM.")
-        LLMResponseEndFrame()
+        LLMFullResponseEndFrame()
 
     when passed the last frame.
 
@@ -203,9 +201,9 @@ class LLMFullResponseAggregator(FrameProcessor):
     >>> asyncio.run(print_frames(aggregator, TextFrame(" world.")))
     >>> asyncio.run(print_frames(aggregator, TextFrame(" I am")))
     >>> asyncio.run(print_frames(aggregator, TextFrame(" an LLM.")))
-    >>> asyncio.run(print_frames(aggregator, LLMResponseEndFrame()))
+    >>> asyncio.run(print_frames(aggregator, LLMFullResponseEndFrame()))
     Hello, world. I am an LLM.
-    LLMResponseEndFrame
+    LLMFullResponseEndFrame
     """
 
     def __init__(self):
@@ -234,6 +232,11 @@ class LLMContextAggregator(LLMResponseAggregator):
     async def _push_aggregation(self):
         if len(self._aggregation) > 0:
             self._context.add_message({"role": self._role, "content": self._aggregation})
+
+            # Reset the aggregation. Reset it before pushing it down, otherwise
+            # if the tasks gets cancelled we won't be able to clear things up.
+            self._aggregation = ""
+
             frame = OpenAILLMContextFrame(self._context)
             await self.push_frame(frame)
 
@@ -247,9 +250,10 @@ class LLMAssistantContextAggregator(LLMContextAggregator):
             messages=[],
             context=context,
             role="assistant",
-            start_frame=LLMResponseStartFrame,
-            end_frame=LLMResponseEndFrame,
-            accumulator_frame=TextFrame
+            start_frame=LLMFullResponseStartFrame,
+            end_frame=LLMFullResponseEndFrame,
+            accumulator_frame=TextFrame,
+            handle_interruptions=True
         )
 
 

src/pipecat/processors/frameworks/langchain.py

@@ -11,8 +11,6 @@ from pipecat.frames.frames import (
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
     TextFrame)
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
@@ -69,9 +67,7 @@ class LangchainProcessor(FrameProcessor):
                 {self._transcript_key: text},
                 config={"configurable": {"session_id": self._participant_id}},
             ):
-                await self.push_frame(LLMResponseStartFrame())
                 await self.push_frame(TextFrame(self.__get_token_value(token)))
-                await self.push_frame(LLMResponseEndFrame())
         except GeneratorExit:
             logger.warning(f"{self} generator was closed prematurely")
         except Exception as e:

src/pipecat/services/anthropic.py

@@ -12,8 +12,6 @@ from pipecat.frames.frames import (
     VisionImageRawFrame,
     LLMMessagesFrame,
     LLMFullResponseStartFrame,
-    LLMResponseStartFrame,
-    LLMResponseEndFrame,
     LLMFullResponseEndFrame
 )
 from pipecat.processors.frame_processor import FrameDirection
@@ -118,9 +116,7 @@ class AnthropicLLMService(LLMService):
             async for event in response:
                 # logger.debug(f"Anthropic LLM event: {event}")
                 if (event.type == "content_block_delta"):
-                    await self.push_frame(LLMResponseStartFrame())
                     await self.push_frame(TextFrame(event.delta.text))
-                    await self.push_frame(LLMResponseEndFrame())
 
         except Exception as e:
             logger.exception(f"{self} exception: {e}")

src/pipecat/services/google.py

@@ -14,8 +14,6 @@ from pipecat.frames.frames import (
     VisionImageRawFrame,
     LLMMessagesFrame,
     LLMFullResponseStartFrame,
-    LLMResponseStartFrame,
-    LLMResponseEndFrame,
     LLMFullResponseEndFrame
 )
 from pipecat.processors.frame_processor import FrameDirection
@@ -95,9 +93,7 @@ class GoogleLLMService(LLMService):
             async for chunk in self._async_generator_wrapper(response):
                 try:
                     text = chunk.text
-                    await self.push_frame(LLMResponseStartFrame())
                     await self.push_frame(TextFrame(text))
-                    await self.push_frame(LLMResponseEndFrame())
                 except Exception as e:
                     # Google LLMs seem to flag safety issues a lot!
                     if chunk.candidates[0].finish_reason == 3:

src/pipecat/services/openai.py

@@ -21,8 +21,6 @@ from pipecat.frames.frames import (
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
     TextFrame,
     URLImageRawFrame,
     VisionImageRawFrame
@@ -151,9 +149,7 @@ class BaseOpenAILLMService(LLMService):
                     # Keep iterating through the response to collect all the argument fragments
                     arguments += tool_call.function.arguments
             elif chunk.choices[0].delta.content:
-                await self.push_frame(LLMResponseStartFrame())
                 await self.push_frame(TextFrame(chunk.choices[0].delta.content))
-                await self.push_frame(LLMResponseEndFrame())
 
         # if we got a function name and arguments, check to see if it's a function with
         # a registered handler. If so, run the registered callback, save the result to

tests/integration/integration_openai_llm.py

@@ -8,8 +8,6 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.frames.frames import (
     LLMFullResponseStartFrame,
     LLMFullResponseEndFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
     TextFrame
 )
 from pipecat.utils.test_frame_processor import TestFrameProcessor
@@ -64,7 +62,7 @@ if __name__ == "__main__":
         llm.register_function("get_current_weather", get_weather_from_api)
         t = TestFrameProcessor([
             LLMFullResponseStartFrame,
-            [LLMResponseStartFrame, TextFrame, LLMResponseEndFrame],
+            TextFrame,
             LLMFullResponseEndFrame
         ])
         llm.link(t)
@@ -98,7 +96,7 @@ if __name__ == "__main__":
         llm.register_function("get_current_weather", get_weather_from_api)
         t = TestFrameProcessor([
             LLMFullResponseStartFrame,
-            [LLMResponseStartFrame, TextFrame, LLMResponseEndFrame],
+            TextFrame,
             LLMFullResponseEndFrame
         ])
         llm.link(t)
@@ -121,7 +119,7 @@ if __name__ == "__main__":
         api_key = os.getenv("OPENAI_API_KEY")
         t = TestFrameProcessor([
             LLMFullResponseStartFrame,
-            [LLMResponseStartFrame, TextFrame, LLMResponseEndFrame],
+            TextFrame,
             LLMFullResponseEndFrame
         ])
         llm = OpenAILLMService(