Libraries tagged by cases

Simple flexible and multifunctional Admin Panel CRUD in which all the necessary for 95% of the cases are developed.

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Allows the admin to force users to reset their password, useful in case of breaches.

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A developer-experience focused HTTP client, optimized for most common use cases.

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a laravel package to deal with specific case of micro-services connection. So, it doesn't suit all needs or handle all connection cases

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Manage your customer service via social media in a modern way. Every Tweet or Facebook post becomes a 'case' so you or your team can handle customer service in a clear way with easey follow-ups.

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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.

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A psr-18 compliant client that handles special cases of redirections

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SilverStripe module to allow MySQLi SSL connection with CA certificate only

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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.

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A Symfony Validator constraint that validates if given strings are valid cases in a given PHP 8 Enum

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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

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manager for mysql queries to improve database code flexibility, security and automation of simpler database use cases

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This repository offers a PHP-based solution for declension of nouns by grammatical cases along with numerals. It ensures correct noun forms based on case (nominative, genitive, etc.) and the numeral used, handling the specific rules of declension in supported languages.

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Provides the ability to declare "properties" to enums using PHP Attributes, also to define classes for specific case functionalities and much more.

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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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