"content": "hello"
}
```

## Pydantic `bytes` for Output Data { #pydantic-bytes-for-output-data }

You can also use `bytes` fields with `ser_json_bytes` in the model config for output data, and Pydantic will *serialize* the bytes as base64 when generating the JSON response.

{* ../../docs_src/json_base64_bytes/tutorial001_py310.py ln[1:2,12:16,29,38:41] hl[16] *}

    category=FastAPIDeprecationWarning,
    stacklevel=2,
)
class UJSONResponse(JSONResponse):
    """JSON response using the ujson library to serialize data to JSON.

    **Deprecated**: `UJSONResponse` is deprecated. FastAPI now serializes data
    directly to JSON bytes via Pydantic when a return type or response model is
    set, which is faster and doesn't need a custom response class.

    Read more in the

With a response model, FastAPI will use Pydantic to serialize the data to JSON, without using intermediate steps, like converting it with `jsonable_encoder`, which would happen in any other case.

And under the hood, Pydantic uses the same underlying Rust mechanisms as `orjson` to serialize to JSON, so you will already get the best performance with a response model.

When you create a **FastAPI** *path operation* you can normally return any data from it: a `dict`, a `list`, a Pydantic model, a database model, etc.

If you declare a [Response Model](../tutorial/response-model.md) FastAPI will use it to serialize the data to JSON, using Pydantic.

If you don't declare a response model, FastAPI will use the `jsonable_encoder` explained in [JSON Compatible Encoder](../tutorial/encoder.md) and put it in a `JSONResponse`.

        return ObjectInputFilter.Status.REJECTED;
    };

    /**
     * Tests if the object can be serialized.
     *
     * @param obj the object to be serialized (must not be {@literal null})
     * @return the deserialized object
     */
    public static Object serialize(final Object obj) {
        assertArgumentNotNull("obj", obj);

        final byte[] binary = fromObjectToBinary(obj);

If you declare the return type as `AsyncIterable[Item]`, FastAPI will use it to **validate**, **document**, and **serialize** the data using Pydantic.

{* ../../docs_src/server_sent_events/tutorial001_py310.py ln[1:25] hl[10:12,23] *}

/// tip

As Pydantic will serialize it in the **Rust** side, you will get much higher **performance** than if you don't declare a return type.

///

If you declare the return type, FastAPI will use it to **validate** the data, **document** it in OpenAPI, **filter** it, and **serialize** it using Pydantic.

/// tip

As Pydantic will serialize it in the **Rust** side, you will get much higher **performance** than if you don't declare a return type.

///

@app.post("/items/")
async def create_item(item: Item, project_id: Annotated[int, Query(...)]): ...
```

## Return Type or Response Model

When possible, include a return type. It will be used to validate, filter, document, and serialize the response.

```python
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None

* Existing usages of Serializer outside of this infrastructure should be migrated to use it.
* Existing usages of Java serialization should be migrated to use it.
* It is ok for Serializer to be used as a replacement for Java serialization as a migration step.

However, they are now deprecated as you will now get better performance by using a [Response Model - Return Type](https://fastapi.tiangolo.com/tutorial/response-model/).

That way, Pydantic will serialize the data into JSON bytes on the Rust side, which will achieve better performance than these custom JSON responses.