Update OpenAIRealtimeLLMService to work with LLMContext and LLMContextAggregatorPair

examples/foundational/19-openai-realtime.py

@@ -19,6 +19,8 @@ from pipecat.observers.loggers.transcription_log_observer import TranscriptionLo
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments
@@ -162,12 +164,12 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
     # Create a standard OpenAI LLM context object using the normal messages format. The
     # OpenAIRealtimeLLMService will convert this internally to messages that the
     # openai WebSocket API can understand.
-    context = OpenAILLMContext(
+    context = LLMContext(
         [{"role": "user", "content": "Say hello!"}],
         tools,
     )
 
-    context_aggregator = llm.create_context_aggregator(context)
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [