​
Understanding Function Calling

Function calling (also known as tool calling) allows LLMs to request information from external services and APIs. This enables your bot to access real-time data and perform actions that aren’t part of its training data.

For example, you could give your bot the ability to:

  • Check current weather conditions
  • Look up stock prices
  • Query a database
  • Control smart home devices
  • Schedule appointments

Here’s how it works:

  1. You define functions the LLM can use
  2. When needed, the LLM requests a function call
  3. Your application executes the function
  4. The result is sent back to the LLM
  5. The LLM uses this information in its response

​
Implementation

​
1. Define Functions

Functions are defined differently depending on your LLM provider. Here are examples of a weather function for supported providers:

​
2. Register Function Handlers

Register handlers for your functions using the LLM service’s register_function method:

llm = OpenAILLMService(api_key="your-api-key", model="gpt-4")

# Optional start callback - called when function execution begins
async def start_fetch_weather(function_name, llm, context):
    logger.debug(f"Starting weather fetch: {function_name}")

# Main function handler - called to execute the function
async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
    # Fetch weather data from your API
    weather_data = {"conditions": "sunny", "temperature": "75"}
    await result_callback(weather_data)

# Register the function
llm.register_function(
    "get_current_weather",
    fetch_weather_from_api,
    start_callback=start_fetch_weather
)

Alternatively, register a handle for all function calls:

# Or register a handler for all functions
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)

​
3. Create the Pipeline

Include your LLM service in your pipeline with the registered functions:

# Initiliaze the LLM context, including messges and tool calls
context = OpenAILLMContext(messages, tools)

# Create the context aggregator, to collec the user and assistnat context
context_aggregator = llm.create_context_aggregator(context)

# Create the pipeline
# 1. Input from the transport
# 2. User context aggregation
# 3. LLM processing
# 4. TTS processing
# 5. Output to the transport
# 6. Assistant context aggregation
pipeline = Pipeline([
    transport.input(),
    context_aggregator.user(),
    llm,
    tts,
    transport.output(),
    context_aggregator.assistant(),
])

​
Function Handler Details

​
Handler Parameters

  • function_name: Name of the called function
  • tool_call_id: Unique identifier for the function call
  • args: Arguments passed by the LLM
  • llm: Reference to the LLM service
  • context: Current conversation context
  • result_callback: Async function to return results

​
Return Values

  • Return data through the result_callback
  • Return None to ignore the function call
  • Errors should be handled within your function

​
Next steps

  • Check out the function calling examples to see a complete example for specific LLM providers.
  • Refer to your LLM providers documentation to learn more about their funciton calling capabilities.
GuidesGemini Multimodal Live