Summary
In this chapter, we built a fully functional local AI assistant using Mistral models and the Ollama runtime. We started by installing Ollama and running models such as mistral-small and mistral-nemo, confirming our setup and observing system performance through basic prompts and terminal interaction.
From there, we introduced system prompts, template customization, and runtime parameters such as temperature and context length to shape how the model behaves and responds. Using a Modelfile, we created inherited model variants, each configured with unique instructions for tone, role, or behavior.
We integrated these custom models with two popular user interfaces. First, we configured Obsidian using the BMO Chatbot plugin, enabling AI-assisted note-taking directly in our knowledge base. Then, we deployed WebUI in a Docker container, creating a browser-based chatbot complete with multi-user support, theming, and audio features.
To ensure appropriate and safe interactions...
