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Field Validators

The --validators option allows you to add custom field validators to generated Pydantic v2 models. This enables you to inject validation logic into generated code without manually editing it.

Basic Usage

datamodel-codegen --input schema.json --output model.py \
  --validators validators.json \
  --output-model-type pydantic_v2.BaseModel

Validators File Format

The validators file is a JSON file that maps model names to their validator definitions.

Structure

{
  "ModelName": {
    "validators": [
      {
        "field": "field_name",
        "function": "module.path.to.validator_function",
        "mode": "after"
      }
    ]
  }
}

Fields

Field Description Required
field Single field name to validate One of field or fields
fields List of field names (for multi-field validators) One of field or fields
function Fully qualified path to the validator function Yes
mode Validator mode: before, after, wrap, or plain No (default: after)

Validator Modes

Pydantic v2 supports different validator modes, each with its own signature:

before / after Mode

Standard validators that run before or after Pydantic's own validation:

def validate_name(v: Any, info: ValidationInfo) -> Any:
    if not v:
        raise ValueError("Name cannot be empty")
    return v.strip()

wrap Mode

Wrap validators receive a handler to call the next validator in the chain:

from pydantic import ValidationInfo, ValidatorFunctionWrapHandler

def wrap_validate_name(
    v: Any,
    handler: ValidatorFunctionWrapHandler,
    info: ValidationInfo
) -> Any:
    # Pre-processing
    v = v.strip() if isinstance(v, str) else v
    # Call next validator
    result = handler(v)
    # Post-processing
    return result.upper()

plain Mode

Plain validators replace Pydantic's validation entirely:

def plain_validate_name(v: Any) -> str:
    if not isinstance(v, str):
        raise TypeError("Expected string")
    return v

Example

Input Schema

{
  "type": "object",
  "title": "User",
  "properties": {
    "name": {"type": "string"},
    "email": {"type": "string", "format": "email"},
    "age": {"type": "integer", "minimum": 0}
  },
  "required": ["name", "email"]
}

Validators File

{
  "User": {
    "validators": [
      {
        "field": "name",
        "function": "myapp.validators.validate_name",
        "mode": "before"
      },
      {
        "field": "email",
        "function": "myapp.validators.validate_email",
        "mode": "after"
      },
      {
        "fields": ["name", "email"],
        "function": "myapp.validators.validate_contact_info",
        "mode": "after"
      }
    ]
  }
}

Validator Functions (myapp/validators.py)

from typing import Any
from pydantic import ValidationInfo

def validate_name(v: Any, info: ValidationInfo) -> Any:
    if isinstance(v, str):
        return v.strip()
    return v

def validate_email(v: Any, info: ValidationInfo) -> Any:
    if isinstance(v, str) and not v.endswith("@example.com"):
        # Custom email domain validation
        pass
    return v

def validate_contact_info(v: Any, info: ValidationInfo) -> Any:
    # This runs for both name and email fields
    return v

Generated Output

from __future__ import annotations

from typing import Any

from myapp.validators import validate_contact_info, validate_email, validate_name
from pydantic import BaseModel, EmailStr, ValidationInfo, conint, field_validator


class User(BaseModel):
    name: str
    email: EmailStr
    age: conint(ge=0) | None = None

    @field_validator('name', mode='before')
    @classmethod
    def validate_name_validator(cls, v: Any, info: ValidationInfo) -> Any:
        return validate_name(v, info)

    @field_validator('email', mode='after')
    @classmethod
    def validate_email_validator(cls, v: Any, info: ValidationInfo) -> Any:
        return validate_email(v, info)

    @field_validator('name', 'email', mode='after')
    @classmethod
    def validate_contact_info_validator(cls, v: Any, info: ValidationInfo) -> Any:
        return validate_contact_info(v, info)

Notes

  • This feature only supports Pydantic v2 (--output-model-type pydantic_v2.BaseModel)
  • The ModelName in the validators file must match the generated Python class name
  • Validator functions are imported automatically based on the function path
  • When the same validator function is used multiple times, an incrementing suffix (_1, _2, etc.) is added to ensure method name uniqueness

See Also