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Python Automation Cookbook

Python Automation Cookbook - Third Edition

By : Jaime Buelta
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Python Automation Cookbook

Python Automation Cookbook

By: Jaime Buelta

Overview of this book

Automating repetitive tasks and integrating systems efficiently becomes increasingly complex as workflows scale. This book helps you solve that problem with practical Python recipes that guide you from foundational automation to advanced, AI-powered workflows. You start by building a strong base in Python automation, exploring tested solutions for file handling, web scraping, APIs, testing, and system operations, and learning how to design reliable automation workflows. The cookbook approach enables you to quickly apply solutions to real problems while building a deeper understanding through hands-on practice. This third edition expands the scope of automation by introducing AI-powered capabilities. You learn how to call AI models within your scripts, use and implement the Model Context Protocol (MCP) for system integration, and design intelligent agents that automate decision-making processes. New chapters provide real-world examples of AI agents in business automation, helping you move beyond scripts to adaptive systems. This book combines practical knowledge with modern techniques to ensure you stay current with evolving automation practices. By the end of this book, you will be able to design, build, and extend Python automation workflows, including AI-driven solutions, to handle complex real-world tasks with confidence.
Table of Contents (20 chapters)
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18
Other Books You May Enjoy
19
Index

Using a local model

Calling a commercial AI model is not the only option when using GenAI. In this recipe, we will see how to use a local model installed on our computer, instead of calling an external API.

Unless you have specialized hardware, local models are much smaller and less capable than commercial ones. But you can use them without paying extra, and they can provide good results for your use case.

For that, we will use Ollama, which will allow us to download and run open-weight sources on our computer.

Getting ready

You need to install Ollama locally (https://ollama.com/). Go to its page and follow the instructions at https://ollama.com/download.

Once installed, you should be able to run it locally and pull models. For this example, we will use a small model that should run on any modern machine with enough RAM, qwen‑3:8b.

Download and install this model with this command.

$ ollama pull qwen3:8b
pulling manifest
pulling a3de86cd1c13: 100% ▕███...
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Python Automation Cookbook
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