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Data Object Model

The Data Object Model is a data structure specifically designed for full-code development mode, similar to DTO (Data Transfer Object), also known as Table-less Model. It is customized by developers according to specific business requirements and does not directly map or associate with any database tables. Data object models are mainly used in business logic such as service functions, event handling, and workflow orchestration for structured expression, transmission, and transformation of data. Through data object models, developers can flexibly define multi-layered nested and complex combined data structures, enabling standardized data interaction between different modules, and improving code maintainability, reusability, and business decoupling capabilities. Data object models are very suitable for handling complex business logic, multi-layer data interaction, temporary data encapsulation, and other scenarios.

Data Object Model Creation​

JitAi supports visual creation of object models, with fields and functions only supporting full-code editing.

Create Data Object Model

In the element tree of the development area, click the + button on the right side of Data Models, select Data Object Model from the popup list to open the New Data Object Model dialog.

Data Object Model Dialog

In the New Data Object Model dialog, fill in the model name (the system will automatically generate the English name), click Confirm to complete the creation, and automatically enter the full-code editing interface of the data object model.

Data Object Model Usage​

The usage of data object models is very flexible, suitable for business logic such as service functions and event handling, serving as data carriers for input parameters or output results, and enabling standardized data transmission between different modules. In actual development processes, data object models are commonly used to express and transform complex data structures, avoiding direct database table operations, thereby improving code maintainability and reusability. Additionally, data object models support multi-layered nesting and composition, meeting data organization requirements in complex business scenarios.

Data Object Model Full Code

In the full-code editing interface, developers can conveniently view and edit the specific implementation of data object models by clicking the model.py file in the left file list. Data object models need to inherit from the base class provided by the platform and can flexibly define properties (fields) and methods according to business requirements.

The following is a typical usage flow of a data object model:

Custom Fields​

In the model.py file of the object model, developers can customize the required fields according to business needs. The following figure shows the field definition interface of the object model:

Object Model Field Definition

After completing field definition, detailed field configuration methods can be referenced in Data Table Model.

Note

After configuring custom fields, check whether the datatypes package is imported.

Model Function Override​

To implement custom data query logic, it is usually necessary to override model CRUD methods. Here we take overriding the query method as an example. The following figure shows an interface example of object model method override:

Object Model Method Override

After completing method override, the query method can provide custom data query capabilities for table components and other elements.

Object Model Table Display

Finally, add a table component to the page and set the data object model as the data source to display the data content returned by the query method.

Besides overriding the query (data query) method, you can also override methods such as create (add data), updateByPK (update by primary key), deleteByPK (delete by primary key) according to business needs to implement custom data processing logic. For example, you can implement validation and processing of new data in the create method, handle business validation and transformation before and after data updates in the updateByPK method, and perform permission validation or cascading processing in the deleteByPK method. Additionally, you can extend custom methods for complex data transformation, aggregation, or validation business requirements. Through overriding and extending these methods, developers can flexibly control the behavior of data object models to meet diverse business scenarios.

Defining New Functions​

In the model.py file of the data object model, developers can not only customize fields but also flexibly add custom functions according to actual business needs. Custom functions can be used to implement complex data processing, business logic encapsulation, data validation, format conversion, and other operations, significantly improving model extensibility, flexibility, and code reusability.

Add New Function

When adding custom functions, it is recommended to reference the implementation methods of existing methods and write function code in the model.py file. To ensure that the platform can correctly identify and call newly defined methods, function declarations need to be made in the functionList of the e.json file. Only custom functions declared in functionList can be properly referenced and called in scenarios such as pages and service functions. Methods inherited from base classes and overridden do not need to be repeatedly declared in each object model. For specific function declaration configuration, please refer to Element Directory Specification.