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Update OpenAIRealtimeLLMService to work with LLMContext and LLMContextAggregatorPair (cont'd).

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Update `AzureRealtimeLLMService` example (19a) to use new `LLMContext` pattern.
This commit is contained in:
Paul Kompfner
2025-10-23 14:36:22 -04:00
parent 8c03df1463
commit 917ea27352
1 changed files with 5 additions and 4 deletions
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9
examples/foundational/19a-azure-realtime.py
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@@ -18,7 +18,8 @@ from pipecat.frames.frames import LLMRunFrame
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.openai_llm_context import OpenAILLMContext from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.runner.types import RunnerArguments from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport from pipecat.runner.utils import create_transport
from pipecat.services.azure.realtime.llm import AzureRealtimeLLMService from pipecat.services.azure.realtime.llm import AzureRealtimeLLMService
@@ -155,10 +156,10 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
llm.register_function("get_current_weather", fetch_weather_from_api) llm.register_function("get_current_weather", fetch_weather_from_api)
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation) llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
# Create a standard OpenAI LLM context object using the normal messages format. The # Create a standard LLM context object using the normal messages format. The
# OpenAIRealtimeBetaLLMService will convert this internally to messages that the # OpenAIRealtimeBetaLLMService 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!"}],
# [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}], # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
# [ # [
@@ -173,7 +174,7 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
tools, tools,
) )
context_aggregator = llm.create_context_aggregator(context) context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline( pipeline = Pipeline(
[ [