Libraries tagged by model_to_table

meta library for store your data in table like options,objects,strings and etc, that can related to another models

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Model validate package, uses the laravel validation class, rules must be passed as as a variable to and every table should be passed as a model in the app

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The Doplio ORM provides a beautiful, PHP DataMapper implementation for working with your database and objects using Eloquent for a access to the raw table data. It completely unties your model from the database.

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A simple package to neatly keep models into sub-directories without breaking the table naming.

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A Yii2 extension that includes database tables, models and an admin module to store locations.

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Package to generate the models using the structure of the database tables

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💻 This Bolt extension works with the User-Agent to detect devices (desktop, tablet, mobile, etc.), clients, operating systems, brands and models.

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Utility to read data of a table without having a model created

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A laravel package to generate CRUD by giving Model and database table column

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LaravelFilter is a package designed to simplify the process of filtering table fields in a Laravel project. It provides a straightforward way to implement custom query filters for your models.

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This package is developed for easing up the creation, validation & maintenance of web forms. The HTML forms can be generated directly from Mysql Tables, Eloquent ORM Models, using PHP code as well as from predefined XML format, if required the Loader can also be extended to support more type of objects. The package can output forms in HTML table layout as well as Bootstrap and you can further customize them using templates or by extending the Renderer.

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This package allows you to create unique slugs. It keeps all the slugs you define in the slugs table. It also satisfies all requests and directs it to the controller you define in your model.

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Schemator is an advanced Laravel package designed to streamline development workflows by automatically generating Eloquent models and optional Filament resources. It offers features like selective table generation, skipping default Laravel tables, and enhanced model generation with Laravel Sanctum support for the User model.

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Cookie Consent Panel (Optin) with DSGVO/GDPR compliant use of cookies. Preconfigured modules for Google Analytics, Facebook and other frequently used services. Arbitrary expandability with tracking scripts that generate cookies on your website. Support for Google Tag Manager incl. Google Consent Mode and Google Consent Mode v2. Easy export for Google Tag Manager. Third-party cookies and scripts are only loaded when active consent is given. Website visitors can edit their privacy settings at any time. Automatic update of cookie information when new cookies/scripts are inserted with secure consent procedure. Cookies can be automatically added to the privacy policy via a plugin. Multilingual and full support for desktop, tablet and mobile. Four standard modes for displaying the content solution. Based on Klaro!.

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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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