examples(foundational): use system_instruction in all examples
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@@ -90,6 +90,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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include_thoughts=True,
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)
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),
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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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llm.register_direct_function(check_flight_status)
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@@ -97,14 +98,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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tools = ToolsSchema(standard_tools=[check_flight_status, book_taxi])
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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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@@ -135,16 +129,11 @@ 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(
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{
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"role": "user",
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"content": "Say hello briefly.",
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}
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)
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context.add_message({"role": "user", "content": "Say hello briefly."})
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# Replace the above with one of these example prompts to demonstrate
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# thinking and function calling.
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# This example comes from Gemini docs.
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# messages.append(
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# context.add_message(
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# {
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# "role": "user",
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# "content": "Check the status of flight AA100 and, if it's delayed, book me a taxi 2 hours before its departure time.",
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