Libraries tagged by wiring

Simple Auto-wiring, PSR-11 compliant Dependency injection library for PHP 8.

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A Zend factory implementation allowing for auto-wiring like in Symfony.

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Command for wiping your queues clear

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Smart Seeder adds the same methology to seeding that is currently used with migrations in order to let you seed in batches, seed to production databases or other environments, and to rerun seeds without wiping out your data.

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CryptoManana is a cryptography framework for boosting your project's security.

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Whether you call them doubles, stubs, mocks, partials, fakes, or something else there are times when a mocking library such as PHPUnit's mockBuilder, Mockery, Prophecy, etc. just doesn't do exactly what you need or want. Sometimes you just need a concrete class implementation to run through the unit test wringer or a full end to end functional unit test suite. MockMaker aims to simplify the process of generating concrete fake ORM entity objects. Flexible and extendable, the generated seed code can be altered to suit your particular project with relative ease. That means after the initial setup you can re-run MockMaker for any new entities that get added in or update existing entities that change with little to no fuss. What's more, once MockMaker has made your files it's done; you don't have to include it in your code base and can use the generated files like any other project class.

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A declaration library to extend PSR-11 for dynamic auto-wring based on interfaces

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Working with Telegram bots has never been easier

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Elasticsearch ORM like Eloquent

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Smart Seeder adds the same methology to seeding that is currently used with migrations in order to let you seed in batches, seed to production databases or other environments, and to rerun seeds without wiping out your data.

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This package extends Laravel's native Command Event class to allow for managing events fired on the same system with multiple servers, preventing an event from firing more than once

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Mechanism for firing events

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Insert, update or delete models in batch, while still firing model events.

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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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