PHP download

Libraries tagged by QTech

researchgate/libris

2 Favers
2958 Downloads

An RIS parser. RIS is a format for reference metadata. A fork of technosophos/LibRIS.

Go to Download

polygontech/common-helpers

1 Favers
1435 Downloads

Common helper classes, functions, DTOs... (used in Polygon Technology)

Go to Download

php-extended/php-blocklist-interface

0 Favers
23900 Downloads

A library to define how domain blocking technology be presented

Go to Download

JObfuscator is a source code obfuscator for the Java programming language. Obfuscate and protect your Java source code and algorithms from hacking, cracking, reverse engineering, decompilation, and technology theft. JObfuscator provides advanced Java source code parsing based on AST trees, multiple advanced obfuscation strategies are available.

Go to Download

paulschoeman/filament-impersonate

0 Favers
802 Downloads

A temporary fork of stechstudio's Filament package to impersonate your users. Dependencies updated to be Laravel 11 compatible.

Go to Download

padosoft/laravel-super-cache-invalidate

1 Favers
2349 Downloads

A powerful package that provides an efficient and scalable cache invalidation system for Laravel applications. It is designed to handle high-throughput cache invalidation scenarios, such as those found in e-commerce platforms, by implementing advanced techniques like event queuing, coalescing, debouncing, sharding, and partitioning.

Go to Download

kenangundogan/flexiblegrid

156 Favers
12 Downloads

For users who can embrace the newest of technology, Flexiblegrid comes with an flex based grid. It’s the same grid you know and love, but with even better source ordering and alignment options.

Go to Download

inda-hr/php_sdk

6 Favers
829 Downloads

# 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