# Validation
source: https://docs.chalk.ai/docs/validation

## Validate features and resolvers

### Feature Values

Chalk can enforce requirements on your feature values.
You can validate the values and length of many primitive types
through the keyword arguments min, max, min_length, and max_length.

To prevent 'invalid' features from being written
to the offline or online store, set strict=True.
In this case an error will be thrown when an invalid
feature is observed.
The validations keyword argument can be used when there are
multiple validations, only some of which are strict.

```
from chalk import Validation
from chalk.features import feature, features

@features
class Office:
    size_sqft: int = feature(min=0, max=100_000_000)
    street: str = feature(validations=[
        Validation(min_length=1, max_length=256, strict=True),
        Validation(min_length=20, max_length=100)
    ])
```

### Feature validation requirements

For some features, it is important to specify metadata
such as owner and description.
Chalk allows you to enforce metadata requirement easily.
In addition, you can specify for each feature the severity
at which to raise a missing metadata issue.

In chalk.yml, the validation > feature > metadata
section specifies settings for metadata validation.
In the following example, users will be blocked from
deploying features (chalk apply) with
an unspecified owner. They are allowed to deploy
features with missing description and tags.

```
project: Predict Q2 Spending

validation:
  feature:
    metadata:
      - name: owner
        missing: error
      - name: description
        missing: warning
      - name: tags
        missing: info

environments:
  default:
    runtimes: 'python310'
    requirements: requirements.txt
```