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