Libraries tagged by chase
rekalogika/file-association-entity
1122 Downloads
Utilities for handling files inside domain entities: EmbeddedMetadata for embedding metadata inside Doctrine entities, AbstractFile for creating one-to-many relations with files, and NullFile to handle cases in domain entities where a file must be present but is missing in the storage backend.
php-extended/php-http-client-redirecter
21018 Downloads
A psr-18 compliant client that handles special cases of redirections
oscarricardosan/mapper
4097 Downloads
Paquete PHP que mapea los DocComments de la clase y permite definir mutators y accessors
nnssn/str-rename
1200 Downloads
Converts the camel case and snake case each other.
networkteam/typo3-cachebase
1113 Downloads
It adds variables to the page cache identifier calculation for serving multiple versions of a webseite parallel. This is the case in container environments with rolling updates.
muffe/enum-constraint
3802 Downloads
A Symfony Validator constraint that validates if given strings are valid cases in a given PHP 8 Enum
mojitowp/pymexpress
379 Downloads
Clase de conexión para el nuevo Web Service de Correos de Costa Rica (Pymexpress).
matthv/laravel-dingo-transform
11448 Downloads
This repository lets your API users pass in and receive camelCased or snake_cased keys while your app receives and produces snake_cased ones.
macropage/laravel-scheduler-watcher
243 Downloads
logs artisan commands run via scheduler to mysql with plenty of infos, prevent running command again in case of error, allows full monitoing of artisan commands
lexide/k-switch
5105 Downloads
A PHP library to switch cases of a property or element name
leocello/sweet-enum
92 Downloads
Provides the ability to declare "properties" to enums using PHP Attributes, also to define classes for specific case functionalities and much more.
jossmp/datos-peru
1768 Downloads
Clase para realizar consultas a reniec, essalud, ministrio del trabajo con el numero de DNI
jf/tex
121 Downloads
Clases para simplificar la generación de archivos TeX
jf/base
389 Downloads
Repositorios con clases y traits de utilidad para realizar composición y/o herencia.
inda-hr/php_sdk
425 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.