Quickstart Guide — Ollama Toolkit 0.1.9 documentation

# Quickstart Guide

This guide will help you get started with Ollama Toolkit in minutes—following Eidosian principles of efficiency and elegance.

## 🚀 Installation

Install from PyPI with a single command:

    pip install ollama-toolkit

## 🔍 Verify Installation

Ensure everything is working correctly:

    from ollama_toolkit import OllamaClient, __version__

    # Print version
    print(f"Ollama Toolkit version: {__version__}")

    # Initialize client
    client = OllamaClient()

    # Check Ollama connection
    try:
        version = client.get_version()
        print(f"Connected to Ollama {version['version']}!")
    except Exception as e:
        print(f"Connection failed: {e}")

## 💬 Generate Text

Here’s a minimal example to generate text:

    from ollama_toolkit import OllamaClient

    # Initialize client
    client = OllamaClient()

    # Generate text with the default model
    response = client.generate(
        model="llama2",
        prompt="Explain what APIs are in simple terms.",
        stream=False
    )

    print(response["response"])

## 🔄 Complete Example

This example showcases automatic installation, model listing, and text generation:

    from ollama_toolkit import OllamaClient
    from ollama_toolkit.utils.common import ensure_ollama_running

    # Ensure Ollama is running (will attempt to install if not found)
    is_running, message = ensure_ollama_running()
    if not is_running:
        print(f"Error: {message}")
        exit(1)

    print(f"Ollama status: {message}")

    # Initialize client
    client = OllamaClient()

    # List models
    models = client.list_models()
    model_names = [model["name"] for model in models.get("models", [])]

    if not model_names:
        print("No models available. Pulling a small model...")
        client.pull_model("llama2")
        models = client.list_models()
        model_names = [model["name"] for model in models.get("models", [])]

    # Generate text
    if model_names:
        model = model_names[0]
        print(f"Using model: {model}")

        response = client.generate(
            model=model,
            prompt="What are three key principles of good API design?",
            stream=False
        )

        print(f"\nResponse: {response['response']}")

## 🔗 Next Steps

- Dive into detailed examples

- Learn about the API reference

- Explore chat completion and embeddings