From 5fa56df01409ae47ff3a34d5b63ad0d4da0dc305 Mon Sep 17 00:00:00 2001 From: Paul Kompfner Date: Mon, 20 Oct 2025 15:51:00 -0400 Subject: [PATCH] Update `OpenAIRealtimeLLMService` to work with `LLMContext` and `LLMContextAggregatorPair` (cont'd). Update 19b example with new pattern. --- examples/foundational/19b-openai-realtime-text.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/foundational/19b-openai-realtime-text.py b/examples/foundational/19b-openai-realtime-text.py index bb63a4814..fab029bb6 100644 --- a/examples/foundational/19b-openai-realtime-text.py +++ b/examples/foundational/19b-openai-realtime-text.py @@ -18,6 +18,8 @@ from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage 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 @@ -169,20 +171,20 @@ 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( [ transport.input(), # Transport user input context_aggregator.user(), + transcript.user(), # LLM pushes TranscriptionFrames upstream llm, # LLM tts, # TTS - transcript.user(), # Placed after the LLM, as LLM pushes TranscriptionFrames downstream transport.output(), # Transport bot output transcript.assistant(), # After the transcript output, to time with the audio output context_aggregator.assistant(),