examples(foundational): use system_instruction in all examples
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@@ -103,7 +103,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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tts = SwitchLanguage()
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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. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can speak the following languages: 'English' and 'Spanish'.",
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)
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llm.register_function("switch_language", tts.switch_language)
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switch_language_function = FunctionSchema(
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@@ -118,14 +121,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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required=["language"],
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)
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tools = ToolsSchema(standard_tools=[switch_language_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. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can speak the following languages: 'English' and 'Spanish'.",
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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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@@ -156,7 +152,7 @@ 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(
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context.add_message(
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{
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"role": "system",
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"content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {tts.current_language}.",
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