Libraries tagged by model support

An laravel package that enables custom field support for any Laravel models

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A simple, extensible validation library for PHP with support for filtering and validating any input array along with generating client side validation code.

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This extension provide application configuration support stored in database.

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A fork of rinvex/laravel-categories - A polymorphic Laravel package for category management with Nested Sets, Sluggable, and Translatable support.

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Project installation support extension for the Yii2 framework

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Eloquent Settings allows you to bind key-value pairs to any Laravel Eloquent model. It supports even casting for boolean, float or integer types.

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The Ahrmerd/TestGenerator package is a Laravel command-line tool that automatically generates API and web tests for your Eloquent models. With just a few simple commands, you can quickly generate comprehensive test files that cover CRUD operations, validation, and other common use cases for your models. The package provides options to generate tests for all models in your application or for specific models, and supports overwriting existing test files with the --force option. Additionally, it automatically generates test content based on your model's form request classes, making it easy to ensure your tests reflect your application's validation rules. Speed up your Laravel testing workflow with the Ahrmerd/TestGenerator package

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Easy HashIds for Laravel Eloquent models with Livewire Support

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An authorization library that supports access control models like ACL, RBAC, ABAC in Laravel.

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An authorization library that supports access control models like ACL, RBAC, ABAC in Laravel.

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Adds support for creating Route Model bindings to an authenticated user in Laravel

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A simple drop-in solution for UUID support in your Eloquent models.

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Taggable Trait for using tag inside Laravel Eloquent models, with Baum's Nested Set pattern support.

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This package provides domain-driven design (DDD) support for Laravel applications by enhancing artisan make commands with --domain argument. When used, generated files (like models, casts, events, etc.) are placed into a specific domain folder within your app.

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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