如何用Ollama调用工具

Ollama也一直更新Tools Calling的功能，包括现在也支持MCP协议了。工具调用使得LLMs不仅能输出文本，而且能够调用Python等函数，从而能够执行编程语言能完成的任何操作。官方文档举了一个例子：

import ollama

# Define the python function
def add_two_numbers(a: int, b: int) -> int:
  """
  Add two numbers

  Args:
    a (set): The first number as an int
    b (set): The second number as an int

  Returns:
    int: The sum of the two numbers
  """
  return a + b
  
from ollama import chat, ChatResponse 
messages = [{'role': 'user', 'content': 'what is three minus one?'}]

response: ChatResponse = chat(
  model='qwen3',
  messages=messages,
  tools=[add_two_numbers], # Python SDK supports passing tools as functions
  stream=True
)

for chunk in response:
	# Print model content
  print(chunk.message.content, end='', flush=True)
  # Print the tool call
  if chunk.message.tool_calls:
    print(chunk.message.tool_calls)

我们来分解下以上的步骤：

定义工具

In Ollama, a tool refers to an external function or capability that a language model can call dynamically during a conversation or task execution. 

定义工具其实就是定义一个函数。函数def之后会有一个docstring，功能就是描述这个函数。Docstrings are typically enclosed in triple quotes (""" or ''') and can span multiple lines. They are accessible via the function's __doc__ attribute or tools like help(). 如下这个函数：

def add_numbers(a, b):
    """Adds two numbers and returns their sum.

    Args:
        a (int or float): The first number.
        b (int or float): The second number.

    Returns:
        int or float: The sum of a and b.

    Example:
        >>> add_numbers(2, 3)
        5
    """
    return a + b

docstring的作用如下：

• Purpose: What the function does (e.g., "Adds two numbers and returns their sum").
• Parameters: The inputs, their types, and purpose (e.g., a and b as numbers).
• Return value: What the function returns and its type (e.g., sum as int or float).
• Examples (optional): Usage examples to clarify behavior.
• Additional info (optional): Exceptions, side effects, or notes.

In the context of Ollama's tool calling (also known as function calling), a Python function's docstring plays a critical role in defining how the language model (e.g., Llama 3.1) understands and interacts with the function as a tool. When a function is passed as a tool to Ollama’s chat API, the library parses the docstring to generate the tool’s JSON representation, which the model uses to decide when and how to call the function.

例如上面一个函数，Ollama的API在调用函数的时候会根据docstring的描述生成如下的JSON格式：

{
  "function_name": "add_numbers",
  "description": "Adds two numbers and returns their sum.",
  "parameters": [
    {
      "name": "a",
      "type": "int or float",
      "description": "The first number."
    },
    {
      "name": "b",
      "type": "int or float",
      "description": "The second number."
    }
  ],
  "returns": {
    "type": "int or float",
    "description": "The sum of a and b."
  },
  "example": {
    "input": "add_numbers(2, 3)",
    "output": 5
  }
}