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Module · 5 lessons

Request Bodies and Pydantic

Accept and validate JSON data with Pydantic models — request bodies, field constraints, nested models, and response models that shape exactly what you send back.

Start module Back to FastAPI: Build Production APIs in Python

At a glance

Level
Beginner to Intermediate
Lessons
5 lessons
Time to complete
1 week
Cost
Free forever · no sign-up

Welcome to Request Bodies and Pydantic, the second module of the course. In Module 1 your API could only read — clients asked for data and you sent it back. Now your API will accept data: clients will send JSON, and you’ll receive it as validated Python objects. The tool that makes this safe and effortless is Pydantic, the data-validation library FastAPI is built on.

You’ll declare the shape of incoming data with a Pydantic model and watch FastAPI parse and validate every request against it. You’ll add field constraints (lengths, ranges) and custom validators for rules of your own, handle nested models and complex types like dates and lists, and use response models to control exactly what your API sends back — including keeping private fields out of responses. The module ends with a guided project: a validated Tasks API that finally lets clients create tasks, with bad input rejected automatically.

Every example is real, runnable code verified end to end. Start with Lesson 1, where you’ll accept your first request body.

Lessons in this module

1 Request Bodies with Pydantic Let clients send JSON to your API: declare a Pydantic model, receive validated data as a typed Python object, and reject bad input automatically. 2 Field Validation and Constraints Add rules to your data: string lengths, numeric ranges, and custom validators with Field and field_validator, so only sensible values get through. 3 Nested and Complex Models Model richer data: lists, models inside models, and types like datetime — all validated to any depth, with clear errors that point to the exact bad field. 4 Response Models Control exactly what your API returns: declare a response_model to shape output, filter out private fields, and keep input and output models cleanly separate. 5 Guided Project: Validated Tasks API Grow the Task Manager into one that creates tasks with full validation: input and output models, field constraints, a custom validator, and clean responses.
Achievement

Complete all 5 lessons to finish the Request Bodies and Pydantic module.

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