Update OpenAIRealtimeLLMService to work with LLMContext and LLMContextAggregatorPair

This commit is contained in:
Paul Kompfner
2025-09-30 10:52:28 -04:00
parent 236ac93ac6
commit ae22673595

View File

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