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examples(foundational): use system_instruction in all examples

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This commit is contained in:
Aleix Conchillo Flaqué
2026-03-04 15:36:48 -08:00
parent 01f0caf252
commit 0004a116d8
192 changed files with 1118 additions and 1916 deletions
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34
examples/foundational/14l-function-calling-deepseek.py
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@@ -67,7 +67,21 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
)
llm = DeepSeekLLMService(api_key=os.getenv("DEEPSEEK_API_KEY"), model="deepseek-chat")
llm = DeepSeekLLMService(
api_key=os.getenv("DEEPSEEK_API_KEY"),
model="deepseek-chat",
system_instruction="""You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
You have one functions available:
1. get_current_weather is used to get current weather information.
Infer whether to use Fahrenheit or Celsius automatically based on the location, unless the user specifies a preference.
Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
Respond to what the user said in a creative and helpful way.""",
)
# You can also register a function_name of None to get all functions
# sent to the same callback with an additional function_name parameter.
llm.register_function("get_current_weather", fetch_weather_from_api)
@@ -93,24 +107,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
required=["location", "format"],
)
tools = ToolsSchema(standard_tools=[weather_function])
messages = [
{
"role": "system",
"content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
You have one functions available:
1. get_current_weather is used to get current weather information.
Infer whether to use Fahrenheit or Celsius automatically based on the location, unless the user specifies a preference.
Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
Respond to what the user said in a creative and helpful way.""",
},
]
context = LLMContext(messages, tools)
context = LLMContext(tools=tools)
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),