In Pydantic, BaseModel is the core class used for data validation and settings management. It provides an easy way to define data models with automatic type validation, serialization, and parsing.

📦 Basic Example

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from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

# Valid data
user = User(id=1, name="Abhishek", email="abhi@example.com")
print(user)

# Invalid data: will raise ValidationError
invalid_user = User(id="not_an_int", name=123, email=True)
🔍 Features of BaseModel

Automatic Validation:
Ensures that the data matches the types declared.
Throws ValidationError if data is invalid.
Auto Conversion (when possible):
Will try to coerce types if reasonable.
user = User(id='1', name='Abhi', email='abhi@example.com')
# id is str, but Pydantic will convert to int

3.Dict Support:
Convert model to dictionary easily:
user_dict = user.dict()
4.JSON Support:
user_json = user.json()
5.Field Aliases, Defaults, Optional Fields:
from typing import Optional

class User(BaseModel):
    id: int
    name: str = "Guest"
    email: Optional[str] = None
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📌 Real-World Use Cases

FastAPI uses Pydantic models as request and response schemas.
Helps enforce data contracts in APIs.
Useful for configuration management using BaseSettings.

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🚫 Error Handling Example
from pydantic import ValidationError
try:
    user = User(id="not-an-int", name=123, email="wrong")
except ValidationError as e:
    print(e.json())




Feature
Description




Data validation
Ensures fields match specified types


Auto type coercion
Converts compatible types (e.g., str → int)


Built-in serialization
Easily convert to dict or JSON


Error reporting
Clean and structured error messages


Default values
Use default values and optional fields