Scripting Local Language Models with Ollama and the Vercel AI SDK

Build Powerful, Local AI-Driven Command-Line Scripts

Go beyond simple AI chats and unlock the true potential of local language models by learning to script them. This course will guide you step-by-step through the process of building a flexible and intelligent command-line tool that can understand natural language, interact with your file system, and automate complex tasks—all running entirely on your local machine.

Starting with a blank TypeScript file, you'll learn how to combine the power of Ollama, the Vercel AI SDK, and Zod to create a script that's not just functional, but smart. We'll begin by teaching the AI to parse a simple command from a sentence. From there, we'll progressively add layers of sophistication: processing dynamic user input, making the script aware of your project's files, and generating meaningful text-based output.

You'll also learn critical patterns for building robust and extensible tools. We'll cover how to handle invalid commands and unexpected AI responses gracefully. Then, you'll refactor the script to use a modular, file-based system for its commands, allowing you to easily add new functionality without touching the core logic. Finally, we'll add a layer of intelligence that allows your script to infer exactly what you mean, right down to the type of file you want it to process.

By the end of this course, you will have built a powerful, personalized AI assistant and gained the foundational skills to create sophisticated local automations for any task you can imagine.

What You'll Learn:

• How to script local language models using Ollama and the Vercel AI SDK.
• To parse and process dynamic user input from the command line.
• To use Zod schemas, including enums, to validate and structure AI model output.
• Techniques for providing file-system context to your scripts with globby.
• How to generate context-aware text based on file content.
• Robust error handling for invalid user commands and unexpected AI responses.
• To build a modular and extensible tool by defining commands in separate files.
• To intelligently infer user intent, such as dynamically determining file types from a prompt.