examples(foundational): use system_instruction in all examples
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@@ -73,7 +73,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
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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 assistant who can check the weather. Always check the weather when a location is mentioned. Respond concisely and naturally. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.",
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)
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llm.register_function("get_current_weather", fetch_weather_from_api)
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weather_function = FunctionSchema(
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@@ -94,14 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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)
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tools = ToolsSchema(standard_tools=[weather_function])
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant who can check the weather. Always check the weather when a location is mentioned. Respond concisely and naturally. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.",
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},
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]
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context = LLMContext(messages, tools)
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context = LLMContext(tools=tools)
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(
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@@ -138,7 +134,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation with a weather-related prompt
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messages.append(
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context.add_message(
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{
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"role": "system",
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"content": "Ask the user what city they'd like to know the weather for.",
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