Learn FastAPI the way AI engineers actually build with it.
Build a production-style AI Model Serving API — synchronous inference, token streaming, auth, rate limits, error handling, file uploads — all in one project. Every pattern is annotated and explained.
What you’ll build
Sync Inference
POST /predict runs text through a model with structured input & output validation.
Token Streaming
POST /stream streams tokens over Server-Sent Events, exactly like ChatGPT.
Auth & Logging
API-key auth via dependency injection + request logging with traceable X-Request-ID.
File Uploads
POST /vision/analyze — multipart image uploads with server-side validation.
Service Layer
Mocked async model service you can swap for OpenAI / a local GPU model in 20 lines.
Testing
13 passing tests pin down every behavior — your executable documentation.
The mental model
Pydantic validates at both ends of the Service; lifespan event wraps it all; exception handlers cover the failure path. Every AI inference API in production — OpenAI, Anthropic, Hugging Face — follows this exact flow.
Quick start
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
uvicorn app.main:app --reload
# → open http://localhost:8000/docsStarting point
Not sure where to begin? Follow the 13-step Learning Path — each step tells you the file to read, the command to run, what to observe, and what to learn. Or jump straight into the Core Concepts.