* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

        <groupId>org.codehaus.modello</groupId>
        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.2.0</version>
          <models>
            <model>../../api/maven-api-metadata/src/main/mdo/metadata.mdo</model>
          </models>
          <params>
            <param>forcedIOModelVersion=1.1.0</param>
            <param>packageModelV3=org.apache.maven.artifact.repository.metadata</param>

import org.apache.maven.api.annotations.Nonnull;
import org.apache.maven.api.di.Named;
import org.apache.maven.api.model.Model;

/**
 * Interface for model transformers that can modify Maven project models at different stages of processing.
 * <p>
 * Model transformers allow plugins and extensions to modify the POM model during the build process.
 * Transformations can be applied at three different stages:
 * <ol>

{* ../../docs_src/first_steps/tutorial001_py310.py hl[8] *}

You can return a `dict`, `list`, singular values as `str`, `int`, etc.

You can also return Pydantic models (you'll see more about that later).

There are many other objects and models that will be automatically converted to JSON (including ORMs, etc). Try using your favorite ones, it's highly probable that they are already supported.

We'll fix these things by adding a few **extra models**. Here's where SQLModel will shine. ✨

### Create Multiple Models { #create-multiple-models }

In **SQLModel**, any model class that has `table=True` is a **table model**.

And any model class that doesn't have `table=True` is a **data model**, these ones are actually just Pydantic models (with a couple of small extra features). 🤓

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs.

That same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Hey API { #hey-api }

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output { #pydantic-models-for-input-and-output }

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

        Doc(
            """
            Pydantic's `include` parameter, passed to Pydantic models to set the
            fields to include.
            """
        ),
    ] = None,
    exclude: Annotated[
        IncEx | None,
        Doc(
            """
            Pydantic's `exclude` parameter, passed to Pydantic models to set the
            fields to exclude.
            """
        ),
    ] = None,

    /**
     * Gets the level of validation to perform on processed models.
     *
     * @return The level of validation to perform on processed models.
     */
    int getValidationLevel();

    /**
     * Sets the level of validation to perform on processed models. For building of projects,

To distinguish from the models with all optional values for **updates** and models with required values for **creation**, you can use the ideas described in [Extra Models](extra-models.md).