We will walk through a concrete RAG example - a pipeline over a corporate annual report - and build the testing layer that most teams skip entirely. The code is real and runnable. The failures are not hypothetical.
Your Python tools need persistence. Here's how to add it without spinning up a database server, using TinyDB, a zero-dependency document store that lives in a single JSON file.
If you've written a decorator before, you know the syntax. But let's slow down and look at what Python is actually doing, because that mental model is the key to understanding parametrized decorators later.
You push your code. Your teammate pulls it. Nothing works. GitHub Actions fixes that with a single YAML file - automatic tests, linting, and type checks on every push, no extra tools required. Here's how to set it up for your Python project.
