examples(foundational): use system_instruction in all examples
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@@ -74,6 +74,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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aws_region="us-west-2",
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model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
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params=AWSBedrockLLMService.InputParams(temperature=0.8),
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system_instruction="You are a helpful LLM in a WebRTC call. 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. Respond to what the user said in a creative and helpful way.",
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)
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# You can also register a function_name of None to get all functions
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@@ -110,14 +111,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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)
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tools = ToolsSchema(standard_tools=[weather_function, restaurant_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 LLM in a WebRTC call. 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. Respond to what the user said in a creative and helpful way.",
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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(vad_analyzer=SileroVADAnalyzer()),
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@@ -148,7 +142,7 @@ 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.
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messages.append({"role": "user", "content": "Please introduce yourself to the user."})
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context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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