add new LLMFullResponseAggregator

src/pipecat/processors/aggregators/llm_response.py

@@ -22,6 +22,7 @@ from pipecat.frames.frames import (
     LLMMessagesFrame,
     LLMMessagesUpdateFrame,
     LLMSetToolsFrame,
+    LLMTextFrame,
     StartFrame,
     StartInterruptionFrame,
     TextFrame,
@@ -36,6 +37,59 @@ from pipecat.processors.aggregators.openai_llm_context import (
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
+class LLMFullResponseAggregator(FrameProcessor):
+    """This is an LLM aggregator that aggregates a full LLM completion. It
+    aggregates LLM text frames (tokens) received between
+    `LLMFullResponseStartFrame` and `LLMFullResponseEndFrame`. Every full
+    completion is returned via the "on_completion" event handler:
+
+       @aggregator.event_handler("on_completion")
+       async def on_completion(
+           aggregator: LLMFullResponseAggregator,
+           completion: str,
+           completed: bool,
+       )
+
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self._aggregation = ""
+        self._started = False
+
+        self._register_event_handler("on_completion")
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, StartInterruptionFrame):
+            await self._call_event_handler("on_completion", self._aggregation, False)
+            self._aggregation = ""
+            self._started = False
+        elif isinstance(frame, LLMFullResponseStartFrame):
+            await self._handle_llm_start(frame)
+        elif isinstance(frame, LLMFullResponseEndFrame):
+            await self._handle_llm_end(frame)
+        elif isinstance(frame, LLMTextFrame):
+            await self._handle_llm_text(frame)
+
+        await self.push_frame(frame, direction)
+
+    async def _handle_llm_start(self, _: LLMFullResponseStartFrame):
+        self._started = True
+
+    async def _handle_llm_end(self, _: LLMFullResponseEndFrame):
+        await self._call_event_handler("on_completion", self._aggregation, True)
+        self._started = False
+        self._aggregation = ""
+
+    async def _handle_llm_text(self, frame: TextFrame):
+        if not self._started:
+            return
+        self._aggregation += frame.text
+
+
 class BaseLLMResponseAggregator(FrameProcessor):
     """This is the base class for all LLM response aggregators. These
     aggregators process incoming frames and aggregate content until they are