Ollama in Action

Run capable AI models locally with Ollama and connect them to Python. Manage models, build a stateful chatbot, process images with multimodal LLMs, and choose configurations that fit your hardware and privacy needs.Key FeaturesLocal-first coverage connects Ollama commands, Python integration, chat, and multimodal AIPractical context explains why hardware, privacy, speed, and model size shape local choicesComplete projects show how conversation memory and image analysis produce useful outputs wellBook DescriptionLocal AI can improve privacy and reduce reliance on cloud inference, but hardware and performance constraints require informed choices. The opening material explains these trade-offs and guides installation on macOS, Windows, or WSL, followed by the commands used to pull, run, list, switch, and remove models. The practical journey connects Ollama to Python and moves beyond simple prompts. Readers send images to a multimodal model, build a chatbot that retains conversation history, and create an application that scans image folders and records structured catalog details in CSV format. Model selection remains tied to available hardware and expected speed. By the end of this guide, readers can manage a local model library and build useful private AI applications. Complete project files make it easier to adapt the chatbot and multimodal workflow to personal experiments or new Python projects.What you will learnInstall Ollama on macOS, Windows, or WSLManage local models with Ollama commandsConnect Ollama to Python applicationsBuild a chatbot with conversation memoryProcess images with a multimodal modelSelect models for available hardwareWho this book is forPython users, developers, and technical learners who want greater privacy, lower ongoing inference costs, and hands-on experience with local AI. No prior AI application development is required, although basic Python familiarity will help with the chatbot and multimodal image cataloging projects.