Ollama Function Calling for Dummies

This is a simple, beginner-friendly example showing how to use function calling with Ollama.

What this example does

This project demonstrates the complete workflow for function calling with Ollama:

1. Create a custom function (get_stock_price) that fetches real stock prices
2. Register the function with Ollama so the LLM knows it exists
3. Handle tool calls when the LLM decides it needs to use your function
4. Feed results back to the LLM so it can generate a natural, coherent response
5. Avoid raw output – the LLM explains the results instead of just dumping numbers

Instead of getting a raw response like 175.42, you get a helpful answer like "Apple's current stock price is $175.42 per share."

Quick start

Prerequisites

• Python 3.8+
• Ollama installed and running
• The qwen3:4b-instruct model (or modify the code for your preferred model)

Installation

Clone repo

git clone https://github.com/kirillsaidov/ollama-function-calling.git
cd ollama-function-calling

Install dependencies

python3 -m venv venv
./venv/bin/pip install -r requirements.txt

Run the example

./venv/bin/python main.py

Try It Out

>> What's Apple's stock price?
Apple's current stock price is $252.13 per share.

>> How much is Google trading for?
Alphabet Inc. (GOOGL) is currently trading at 247.14 per share.

How It Works

Step 1: First LLM Call

• User asks a question
• LLM analyzes if it needs external data
• If yes, LLM returns a tool call instead of a final answer

Step 2: Execute Your Function

• Your code receives the tool call
• Executes your custom function (get_stock_price)
• Captures the real-world result

Step 3: Second LLM Call

• Tool result is added to the conversation history
• LLM gets the complete context (original question + tool result)
• LLM generates a natural, coherent response using the data

This ensures users get helpful explanations, not just raw data dumps!

This project structure

ollama-function-calling/
├── main.py             # Main example code
├── README.md           # This file
└── requirements.txt    # Dependencies

Customizing for your own functions

Want to add your own functions? Follow this procedure:

1. Create your function:

def get_weather(city: str) -> str:
    # Your implementation here
    return f"Sunny, 75°F in {city}"

2. Add it to the TOOLS dictionary:

TOOLS_LIST = {
    'get_stock_price': get_stock_price,
    'get_weather': get_weather, # Add your function
}

3. Define the tool schema:

TOOL_SCHEMA = [
    # ... existing stock price schema
    {
        'type': 'function',
        'function': {
            'name': 'get_weather',
            'description': 'Get current weather for a city',
            'parameters': {
                'type': 'object',
                'properties': {
                    'city': {'type': 'string', 'description': 'City name'}
                },
                'required': ['city'],
            },
        },
    }
]

Now you can test it by running the script.

License

Unlicense.