Function (Tool) Calling with NVIDIA NIM for LLMs#

You can connect NIM to external tools and services using function calling (also known as tool calling). By providing a list of available functions, NIM can choose to output function arguments for the relevant function(s) which you can execute to augment the prompt with relevant external information.

Function calling is controlled using the tool_choice and tools parameters.

Prerequisites#

Tool calling is supported and automatically enabled for the following models: - Llama 3.1 models - Llama 3.2 models - Llama 3.3 models - Mistral models

Tool calling is supported for the following models with detailed thinking off: - Llama Nemotron Nano models - Llama Nemotron Super models - Llama Nemotron Ultra models

Parameters#

To use function calling, modify the tool_choice and tools parameters.

Parameter

Description

tool_choice

How the model should choose tools. One of : "none", "auto", or a named tool choice. Requires that tools is also set.

tools

The list of tool objects that define the functions the model can call. Requires that tool_choice is also set.

Note

tool_choice can only be set when tools is also set, and vice versa. These parameters work together to define and control the use of tools in the model’s responses. For further information on these parameters and their usage, see the OpenAI API documentation.

tool_choice options#

  • "none": Disables the use of tools.

  • "auto": Enables the model to decide whether to use tools and which ones to use.

  • Named tool choice: Forces the model to use a specific tool. It must be in the following format:

    {
  "type": "function",
  "function": {
    "name": "name of the tool goes here"
  }
}

    Note

    Note: The type field is optional and defaults to function if not specified.

Example Workflows#

These examples showcase various ways to use function calling with NIM:

  1. Basic Function Calling: Demonstrates how to use a single function with automatic tool choice.

  2. Multiple Tools: Shows how to provide multiple tools, including one without parameters.

  3. Forced Tool Usage: Illustrates how to force the model to use a specific tool.

1. Basic Function Calling#

This example shows how to use a single function with automatic tool choice.

from openai import OpenAI

client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="not-used")
MODEL_NAME = "meta/llama-3.1-70b-instruct"

# Define available function
weather_tool = {
    "type": "function",
    "function": {
        "name": "get_current_weather",
        "description": "Get the current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                },
                "format": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "The temperature unit to use. Infer this from the user's location."
                }
            },
            "required": ["location", "format"]
        }
    }
}

messages = [
    {"role": "user", "content": "Is it hot in Pittsburgh, PA right now?"}
]

chat_response = client.chat.completions.create(
    model=MODEL_NAME,
    messages=messages,
    tools=[weather_tool],
    tool_choice="auto",
    stream=False
)

assistant_message = chat_response.choices[0].message
messages.append(assistant_message)

print(assistant_message)
# Example output:
# ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_abc123', function=Function(arguments='{"location": "Pittsburgh, PA", "format": "fahrenheit"}', name='get_current_weather'), type='function')])

# Simulate external function call
tool_call_result = 88
tool_call_id = assistant_message.tool_calls[0].id
tool_function_name = assistant_message.tool_calls[0].function.name
messages.append({"role": "tool", "content": str(tool_call_result), "tool_call_id": tool_call_id, "name": tool_function_name})

chat_response = client.chat.completions.create(
    model=MODEL_NAME,
    messages=messages,
    tools=[weather_tool],
    tool_choice="auto",
    stream=False
)

assistant_message = chat_response.choices[0].message
print(assistant_message)
# Example output:
# ChatCompletionMessage(content='Based on the current temperature of 88°F (31°C) in Pittsburgh, PA, it is indeed quite hot right now. This temperature is generally considered warm to hot, especially if accompanied by high humidity, which is common in Pittsburgh during summer months.', role='assistant', function_call=None, tool_calls=None)

2. Multiple Tools#

You can also define more than one tool for tools, including tools with no parameters, like the time_tool below.

weather_tool = {
    # ... (same as in the previous example)
}

time_tool = {
    "type": "function",
    "function": {
        "name": "get_current_time_nyc",
        "description": "Get the current time in NYC.",
        "parameters": {}
    }
}

messages = [
    {"role": "user", "content": "What's the current time in New York?"}
]

chat_response = client.chat.completions.create(
    model="meta/llama-3.1-70b-instruct",
    messages=messages,
    tools=[weather_tool, time_tool],
    tool_choice="auto",
    stream=False
)

assistant_message = chat_response.choices[0].message
print(assistant_message)
# Example output:
# ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[
#     ChatCompletionMessageToolCall(id='call_ghi789', function=Function(arguments='{}', name='get_current_time_nyc'), type='function')
# ])

# Process tool calls and generate final response as in the previous example

3. Named Tool Usage#

This example forces the model to use a specific tool.

chat_response = client.chat.completions.create(
    model="meta/llama-3.1-70b-instruct",
    messages=[{"role": "user", "content": "What's the weather in New York City like?"}],
    tools=[weather_tool],
    tool_choice={
        "type": "function",
        "function": {
            "name": "get_current_weather"
        }
    },
    stream=False
)

assistant_message = chat_response.choices[0].message
print(assistant_message)
# Example output:
# ChatCompletionMessage(content=None, role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='call_jkl012', function=Function(arguments='{"location": "New York, NY", "format": "fahrenheit"}', name='get_current_weather'), type='function')])

# Process tool call and generate final response as in the previous examples
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