Libraries tagged by Request Models

MvcCore is PHP MVC framework to develop and pack projects (partialy or completely) into super fast single file apps and tools.

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This is package to use query filter for each model. This package is made entirely for personal use. But if you are eager to use this, you can easily use this package by using readme. This is very simple and amateur :).

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# Introduction Welcome to the Passbase Verifications API docs. This documentation will help you understand our models and the Verification API with its endpoints. Based on this you can build your own system (i.e. verification) and hook it up to Passbase. In case of feedback or questions you can reach us under this email address: [[email protected]](mailto:[email protected]). A User submits a video selfie and valid identifying __Resources__ during a __Verification__ guided by the Passbase client-side integration. Once all the necessary __Resources__ are submitted, __Data points__ are extracted, digitized, and authenticated. These Data points then becomes part of the User's __Identity__. The User then consents to share __Resources__ and/or __Data points__ from their Identity with you. This information is passed to you and can be used to make decisions about a User (e.g. activate account). This table below explains our terminology further. | Term | Description | |-----------------------------------------|-------------| | [Identity](#tag/identity_model) | A set of Data points and Resources related to and owned by one single User. This data can be accessed by you through a Verification. | | Data points | Any data about a User extracted from a Resource (E.g. Passport Number, or Age). | | [Resource](#tag/resource_model) | A source document used to generate the Data points for a User (E.g. Passport). | | [User](#tag/user_model) | The owner of an email address associated with an Identity. | | Verification | A transaction through which a User consents to share Data points with you. If the Data points you request are not already available in the User's Identity, the Passbase client will ask the User to submit the necessary Resource required to extract them. | | Re-authentication (login) | A transaction through which a User can certify the ownership of Personal data previously shared through an Authentication. | # Authentication There are two forms of authentication for the API: • API Key • Bearer JWT Token

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Simple Request Model Validation

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InfyOm Laravel Generator Laravel 11

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Laravel SDK for searching data in DB using requests

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Forked from rinvex/laravel-statistics, Rinvex Statistics is a lightweight, yet detailed package for tracking and recording user visits across your Laravel application. With only one simple query per request, important data is being stored, and later a cronjob crush numbers to extract meaningful stories from within the haystack.

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Track and store usage information including page requests, database field changes and system errors.

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Rinvex Statistics is a lightweight, yet detailed package for tracking and recording user visits across your Laravel application. With only one simple query per request, important data is being stored, and later a cronjob crush numbers to extract meaningful stories from within the haystack.

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Create Migration, Seed, Request, Controller, Model and Views for your resource

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Automatically generate PHP Swagger comments from Laravel's Controller, Model, and form requests.

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Easily filter eloquent model queries from HTTP requests

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Uses Dingo/API Request Query Parameters to filter Laravel Models

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A Laravel Code Generator to help you get started with a lot of staff including generating Migrations, Models, Controllers, Factories, Routes, Views, Database Seeders and Request Validation Rules.

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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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