examples(foundational): use system_instruction in all examples
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@@ -63,16 +63,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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)
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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system_instruction="You are a helpful LLM in a WebRTC call demonstrating dynamic keyterms updates. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Try saying difficult names like 'Xiomara', 'Saoirse', or 'Krzystof' to test transcription accuracy.",
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)
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messages = [
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{
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"role": "system",
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"content": "You are a helpful LLM in a WebRTC call demonstrating dynamic keyterms updates. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Try saying difficult names like 'Xiomara', 'Saoirse', or 'Krzystof' to test transcription accuracy.",
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},
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]
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context = LLMContext(messages)
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context = LLMContext()
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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@@ -105,7 +101,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(
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"Phase 1: No keyterms boosting - try saying 'Xiomara', 'Saoirse', or 'Krzystof'"
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)
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messages.append({"role": "system", "content": "Please introduce yourself to the user."})
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context.add_message({"role": "system", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMRunFrame()])
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await asyncio.sleep(15)
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