Exposing It as an Endpoint
Explore how to create a FastAPI endpoint that accepts feedback texts via HTTP, applying two layers of validation—first on the incoming request and then on AI-generated responses. Understand how to expose AI-powered features safely and reliably, ensuring clear separation of validation boundaries to handle failures precisely and maintain service integrity.
Every backend feature eventually needs to be reachable from outside a single script, by a web frontend, a mobile app, or another internal service. This lesson builds exactly that: one complete file, runnable on its own, that takes a piece of feedback text over HTTP and returns a validated result. All the actual intelligence behind that result, the prompt, the parsing, the validation, already exists as code carried over from the previous chapter. What this lesson actually teaches is the thin layer around it: the endpoint itself, and the two separate checks it quietly enforces.
Already configured setup code
The block below is previous chapter's work, unchanged in spirit: the schema a valid result must match, the prompt builder, the code fence stripper, a call_llm function that runs with no API key using a mock response by default, and the pipeline that ties all of it together. None of this is new. It exists here purely so ...