Field Validation

Learn the fundamentals of field validation in FastAPI.

🎯 Core Concept

Pydantic's Field() function allows adding validation constraints and metadata to model fields. This enables fine-grained control over request data validation.

📖 What You'll Learn

In this section, you'll understand:

• Field constraints (min, max, length, pattern)

• Metadata and documentation

• Custom validators

• Error messages

• Real-world validation patterns

💡 Field Constraints Example

from fastapi import FastAPI
from pydantic import BaseModel, Field

app = FastAPI()

class Item(BaseModel):
    name: str = Field(..., min_length=1, max_length=100)
    price: float = Field(..., gt=0, le=1000000)
    quantity: int = Field(default=1, ge=0)
    email: str = Field(..., regex="^[\\w\\.-]+@[\\w\\.-]+\\.\\w+$")

@app.post("/items/")
async def create_item(item: Item):
    return item

🔍 Validation Constraints

• **String**: `min_length`, `max_length`, `regex`, `pattern`

• **Numbers**: `gt` (greater than), `ge` (greater or equal), `lt`, `le`

• **Metadata**: `title`, `description`, `example`

Real-World Usage

Field validation is essential for:

• **Data integrity**: Ensure correct value ranges

• **Security**: Validate email, URL, regex patterns

• **Documentation**: Automatically documents constraints

• **User feedback**: Clear error messages for invalid data

🔑 Key Takeaways

• ✅ Understand the purpose of field validation

• ✅ Know when to apply this pattern

• ✅ Recognize its benefits in real-world scenarios

• ✅ Be prepared to use it in your projects

Ready to explore more? Check out the advanced section for production patterns and edge cases.