Libraries tagged by application base

phpblade/bladelib is an optimized Blade-based vendor package designed to enhance PHP execution and improve query performance directly within Blade views. It streamlines rendering logic, reduces redundant operations, and boosts overall runtime efficiency for Laravel applications.

Go to Download

Lightweight, enum-based definition, querying and mapping of permissions in PHP applications.

Go to Download

provides a nested object property based user interface for accessing this configuration data within application code (fork)

Go to Download

PSR-based Inertia helpers and middleware integration for PHP applications

Go to Download

File based lever out cache for laravel. This cache hook in before composer autoload and Laravel boostrapping. It will push your application into light speed.

Go to Download

# 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.

Go to Download

Flexible, strategy-based text chunking for Laravel with support for character, token, sentence, and markdown-aware splitting. Perfect for AI/LLM applications and RAG systems.

Go to Download

Web based applicant tracking system for managing jobs and applications

Go to Download

The assegai/common package is a collection of commonly used utility classes and functions for AssegaiPHP projects. This package provides a set of tools that are designed to make it easier for developers to perform common tasks such as working with arrays, strings, and paths. The package also includes classes for handling errors and logging, as well as a simple yet powerful configuration management system. The package is designed to be lightweight, with minimal dependencies, and is fully compatible with any AssegaiPHP based project. Whether you're building a small web application or a large enterprise system, assegai/common provides the essential building blocks you need to get started quickly and easily.

Go to Download

Driver-based form flow orchestration system with DirXML-style mapping for Laravel applications

Go to Download

Yii Framework Web Application Base

Go to Download

Yii Framework REST API Application Base

Go to Download

Base application template for Yii2

Go to Download

Base web application of the Ride framework

Go to Download

base component for traditional web application

Go to Download