This repository contains a python application, that provides a local conversational interface to an Ollama model (gemma3:4b) with built-in tool-calling capabilities.

Gemma 3:4b, is a lightweight, open model built from the same research and technology that powers Gemini 2.0 models. These are Google's most advanced, portable and responsibly developed open models yet. They are designed to run fast, directly on devices — from phones and laptops to workstations.

Gemma3 uses pythonic function calling. https://www.philschmid.de/gemma-function-calling

This is quite different approach from the OpenAI-style function calling with json schema.

You can read about pythonic function calling here: https://huggingface.co/blog/andthattoo/dria-agent-a

The script:

• Starts an interactive chat loop.
• Sends user prompts to the local Ollama API.
• Detects and executes embedded tool_code blocks in the model's responses.
• Returns tool execution output back into the conversation.

/  
├── main.py    # Chat client with tool-calling logic
└── README.md  # Project documentation
└── requirements.txt  # Python requirements

• Python 3.11+
• Create an .env file that contains the URL where to get exchange rates EXCHANGE_RATE_SITE=....oanda.com
• Download and run Ollama locally.
• Download Gemma3 Model. Run ollama run gemma3:4b after installing ollama.

1. Clone this repository:

   git clone https://github.com/davidmuraya/gemma3.git
   cd gemma3

2. (Optional) Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # on Windows use `venv\\Scripts\\activate`

3. Install dependencies:

   pip install -r requirements.txt

Run the chat client:

python main.py

You will see a prompt:

Starting chat with local model. Type 'quit' to exit.
You:

• Type your question and hit Enter.
• If the model responds with a tool_code block (e.g., to call get_exchange_rate(currency='TZS', new_currency='USD')), the script will execute the code and feed the output back to the model.
• Type quit, exit, or q to end the session.

1. Conversation Loop: The script collects messages in a messages list and streams responses from the Ollama API.

2. Function Injection: An instruction_prompt tells the model when and how to wrap code calls in a tool_code block.

3. Tool Extraction: The extract_tool_call function:

   • Searches for a tool_code fenced block.
   • Eval()s approved helper functions (e.g., get_exchange_rate(currency='TZS', new_currency='USD')`).
   • Captures and returns execution output as a tool_output block.

4. Second Pass: If a tool call occurred, the script appends the output to the conversation and streams a final model response.

   Output

   

Contributions are welcome! Feel free to open issues or submit pull requests.

1. Fork the repo.
2. Create a feature branch: git checkout -b feature/your-feature
3. Commit your changes and push: git push origin feature/your-feature
4. Open a pull request.

This project is licensed under the MIT License. See the LICENSE file for details.