Request Validation, Models, and Error Handling

Real APIs fail gracefully. They reject bad input clearly, return meaningful error messages, and never expose internal errors to callers. Build these habits from day one.

~1 hour Hands-on Precision AI Academy

Today's Objective

A fully validated API with Pydantic models for every endpoint, custom error responses with clear messages, and input sanitization that prevents the most common API failures.

code

from pydantic import BaseModel, Field, validator
from typing import Literal

class TextRequest(BaseModel):
    text: str = Field(
        min_length=1,
        max_length=10000,
        description="The text to process"
    )
    tone: Literal["formal", "casual", "technical"] = "formal"

    @validator("text")
    def strip_whitespace(cls, v):
        stripped = v.strip()
        if not stripped:
            raise ValueError("text cannot be only whitespace")
        return stripped

class APIResponse(BaseModel):
    result: str
    model: str
    tokens_used: int

HTTP Status Codes and Error Responses

Every HTTP response has a status code. 200 means success. 400 means the caller made a mistake. 500 means something broke on your end. Using the right codes makes your API usable by other developers.

main.py

python

from fastapi import FastAPI, HTTPException, status
import anthropic

app = FastAPI()
client = anthropic.Anthropic()

@app.post("/analyze", status_code=status.HTTP_200_OK)
def analyze(req: TextRequest):
    try:
        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            max_tokens=1024,
            messages=[{"role": "user", "content": req.text}]
        )
        return {"result": response.content[0].text}

    except anthropic.AuthenticationError:
        raise HTTPException(
            status_code=503,
            detail="AI service configuration error"
        )
    except anthropic.RateLimitError:
        raise HTTPException(
            status_code=429,
            detail="Rate limit exceeded. Try again in a moment."
        )

API Keys and Authentication

Your API needs its own authentication — callers need to prove who they are before using it. The simplest approach: API key in the request header.

auth.py

python

from fastapi import Header, HTTPException
import os

VALID_API_KEYS = {os.getenv("APP_API_KEY")}

def verify_api_key(x_api_key: str = Header(...)):
    if x_api_key not in VALID_API_KEYS:
        raise HTTPException(
            status_code=401,
            detail="Invalid API key"
        )

# Use as dependency on protected routes:
# @app.post("/analyze", dependencies=[Depends(verify_api_key)])

Want live instruction and hands-on projects? Join the AI bootcamp — 3 days, 5 cities.

Day 2 Checkpoint

Before moving on, confirm understanding of these key concepts:

What is the core concept introduced in this lesson?
How does the main technique or tool work in practice?
What common mistakes should be avoided?
How would this apply in a real-world project?
What is the next logical step to build on this knowledge?

Request Validation, Models, and Error Handling

Today's Objective

HTTP Status Codes and Error Responses

API Keys and Authentication

Supporting References & Reading

Go deeper with these external resources.

Day 2 Checkpoint