Entities Agent
This agent allows the generation and extension of entities based on a conversational request, ensuring that they are correctly defined and adapted to business needs while complying with the modeling standards of Deyel .
The result includes the entity with its fields, grid, and useful filters. In addition, the agent uses the application context to determine relations with other entities, value lists, and rules, applying standards and best practices that facilitate its maintenance and ensure the integrity of the application.
Main Functionalities
•Interaction through a conversation in natural language with the agent, who guides the modeler user step by step to gather the necessary information.
During the dialogue, the agent can request details about the purpose, name, or application where the entity should be included, adapting their questions according to the context. If the modeler user is already working within an application, the new entity is automatically added to it.
Besides, the modeler user can attach files with requests or additional information to help define the entity's structure more accurately.
•Intelligent assistance in modeling , as the agent uses contextual knowledge and best data modeling practices to suggest field names, data types, relations, and structures consistent with the rest of the application. This ensures consistency and reuse of common elements, optimizing the quality of the model.
•Entity generation based on the descriptions of the modeler user, considering the context of the application and ensuring compliance with modeling standards.
The resulting entity includes all defined fields and their associated grid, with filters useful for its administration and display.
•Extending entities by adding fields in entities, according to the needs of the modeler user, either from a request or suggestions from the agent themselves.
This functionality is performed within the entities modeler, maintaining the coherence and traceability of the design.
•Generation of entity instances, to populate them with sample or test data, ensuring that they respect the relations established with other entities, value lists or rules... This allows validating the model and displaying the behavior with business information.
•Entities available in the Deyel modeler, they can be modified, extended or related to other objects through the platform's visual tools.
Step-by-Step Guide for Using the Entity Agent
1.Start interaction with Diana
If the modeler user is working in the modeler, they can start the conversation directly with Diana to define a new entity. Diana recognizes the context and automatically activates the entity agent.
2.Define the entity through a guided interaction
The modeler user begins the definition by specifying which entity they wish to create.
Based on this initial description, the entity agent leads the conversation to gather necessary information, such as purpose, name, and the application in which it will be included. If the user is already working within an application, the new entity is automatically added to it.
The agent builds the entity's data model, suggesting the fields that make it up, the relations with other entities, value lists, and any advanced rules. Through progressive interaction, it allows the entity to be developed collaboratively and accurately, adapting to the business needs. The modeler user can review, modify, or extend the structure until the desired outcome is achieved.
Additionally, files with requests or any supplementary information can be attached to enrich the definition.
3.Generation of the entity
The agent automatically generates the entity in the entities modeler, including its grid, filters, and relations configured as indicated during the conversation.
4.Generation of instances (optional)
The modeler user can ask Diana to create data instances to populate the entity with business examples, making it easier to validate and test the model.
5.Final review and adjustments
The generated entity can be displayed, modified, or extended within the modeler, maintaining its coherence with the rest of the application. The modeler user can continue the conversation with Diana to extend the entity and relate it to other objects, or to do so from the graphic modeling area available on the platform.
Features of the Generated Fields
The agent allows defining and configuring the properties of each field that makes up the entity, ensuring its correct description and operation within the data model.
The available properties are as follows:
•Name
•Description
•Data type
•Size
•Visibility in the entity's grid
•Availability in the search filters
•Group of fields to which it belongs: It allows grouping them into containers, for example, “Personal data” can contain “First name”, “Last name” and “Marital status”.
•Relation with other objects, being able to link to the data of another entity, an advanced rule or a value list.
Breakpoints
The generated entities are fully adaptable and display correctly at all resolutions and breakpoints, ensuring a consistent experience in both web and mobile environments.
Example
The modeler user initiates the conversation with the entity agent and makes the following request:
I would like to create an entity that represents the courses of a faculty.
The agent suggests the corresponding fields within the conversation:
Once the field structure is confirmed, the agent then generates the entity in a tab of the entities modeler
The modeler user can request the extension of the entity and the agent suggests the new fields in the side panel. After confirmation, the additional fields are automatically added to the end of the original entity.
Generation of Instances
The generation of instances with the assistance of Diana allows for the quick population of an entity with contextualized data tailored to user requirements.
This functionality streamlines development by facilitating testing, demonstrations, and configuration of working environments with coherent and functional data sets.
The modeler user can request the agent to generate a specific number of instances, indicating the desired data features in the conversation.
Example
The modeler user requests the following from the entity agent:
Generate 10 instances for the entity “Faculty Courses” with the following data:
•University courses in different areas (engineering, science, art, humanities).
•Number of hours: between 20 and 120.
•Maximum capacity: between 15 and 40 students.
•Dates: start between March 1 and September 1 of the current year; end within the following 3 months.
•Responsible professor: plausible academic names.
The agent generates instances of the "Faculty Courses" entity with data consistent with the established conditions, ready to be displayed and used in the entity grid.
