whitespace fix

examples/foundational/07l-interruptible-together.py

@@ -5,29 +5,24 @@
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
+from pipecat.services.ai_services import OpenAILLMContext
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.together import TogetherLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -76,17 +71,19 @@ async def main():
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+        user_aggregator = context_aggregator.user()
+        assistant_aggregator = context_aggregator.assistant()
 
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
-                tma_in,  # User responses
+                user_aggregator,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                tma_out,  # Assistant spoken responses
+                assistant_aggregator,  # Assistant spoken responses
             ]
         )
 

src/pipecat/processors/aggregators/llm_response.py

@@ -6,12 +6,6 @@
 
 from typing import List, Type
 
-from pipecat.processors.aggregators.openai_llm_context import (
-    OpenAILLMContextFrame,
-    OpenAILLMContext,
-)
-
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.frames.frames import (
     Frame,
     InterimTranscriptionFrame,
@@ -22,11 +16,16 @@
     LLMMessagesUpdateFrame,
     LLMSetToolsFrame,
     StartInterruptionFrame,
-    TranscriptionFrame,
     TextFrame,
+    TranscriptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+    OpenAILLMContextFrame,
+)
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 
 class LLMResponseAggregator(FrameProcessor):
@@ -111,7 +110,7 @@ async def process_frame(self, frame: Frame, direction: FrameDirection):
             await self.push_frame(frame, direction)
         elif isinstance(frame, self._accumulator_frame):
             if self._aggregating:
-                self._aggregation += f" {frame.text}" if self._aggregation else frame.text
+                self._aggregation += frame.text if self._aggregation else frame.text
                 # We have recevied a complete sentence, so if we have seen the
                 # end frame and we were still aggregating, it means we should
                 # send the aggregation.