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docs #35: use business objects as mappings
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Closes #35
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Guillaume Bernard committed Nov 4, 2024
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- Advanced User Guide:
- Parameters: parameters.md
- Rule sets: rule_sets.md
- Use your business objects: business_objects.md
- Custom conditions: custom_conditions.md
- API Reference: api_reference.md
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99 changes: 99 additions & 0 deletions docs/pages/business_objects.md
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You can use business objects (the instances of the classes from your business model) directly with the rule engine, without converting them to dictionaries.

ARTA processes mappings in the rules and actions to inject data into functions, as you saw in the [simple example](a_simple_example.md) of this documentation.

## The problem with `dict` serialisation

Let’s consider the following example, representing a `Car` with its `Engine` which itself has attributes.

```python
class Engine:
power: int
consumption: int

class Car:
engine: Engine
```

When following the regular usage of Arta, one would convert an instance of a `Car` to a dictionary with a sort of serializer. A candidate code for this might be:

```python
from typing import Any

def serialize_car_to_dict(car: Car) -> dict[str, Any]:
return {
"engine": {
"power": car.engine.power,
"consumption": car.engine.consumption
}
}
```

!!! tip "When using Pydantic"

If you use Pydantic, you might directly use the `model_dump` function in order to represent your object as a dictionnary object.

This way, you can write you conditions as follows:

```yaml
conditions:
HAS_LOWER_CONSUMPTION:
description: "Whether an engine as a low consumption".
validation_function: is_value_below_threshold
condition_parameters:
value: input.engine.power
threshold: 10
```
This is where the mapping is important: to go through the `engine` data and access the `power` attribute. We serialised the object in a custom code but there might be a better solution with less code…

## Transform any business object to a mapping

The solution to this issue is to use any business object as a mapping. In Python, any object may behave as such by subclassing [`Mapping`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping) and implementing the abstract methods.

As an example, we provide the following mixin:

```python
from collections.abc import Mapping, Iterator
from typing import Any
class ObjectToMappingMixin(Mapping):
def __iter__(self) -> Iterator[Any]:
return iter(vars(self))
def __len__(self) -> int:
return len(vars(self))
def __getitem__(self, key: str, /) -> Any:
return getattr(self, key)
```

Now, we can make our business objects subclass this mixin:

```python
class Engine(ObjectToMappingMixin):
power: int
consumption: int
class Car(ObjectToMappingMixin):
engine: Engine
```

`Engine` and `Car` now behave as mapping and it’s possible to access the attributes of `Engine` from car using the dict’s `getitem` strategy, such as:

```python
engine = Engine(power=1, consumption= 12)
car = Car(engine=engine)
assert car["engine"]["power"] == 1
assert car["engine"]["consumption"] == 12
```

Finaly, when using the `RulesEngine.apply_rules` method, there is not longer need to convert your business objects to dictionaries, you can directly use them like:

```python
from arta import RulesEngine
eng = RulesEngine(config_path="/to/my/config/dir")
result = eng.apply_rules(input_data={"car": car})
```

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