Libraries tagged by model data

Laravel models for Norwegian zip codes, municipalities and counties. Includes an artisan command to update data from the official authority.

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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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Laravel package for generating realistic factory data using AI (OpenAI, Gemini, DeepSeek) with support for manual and hardcoded fields, model events, and bulk inserts.

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This is yet another laravel livewire table and come as is. You can filter, you can sort, you can bulk, toggle columns, the basics. The data input is a collection/array, we cant handle models.

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DbArchiver archive and restore old data from DB to file by Yii2 ActiveRecord models. For big tables allow direct file export from MySQL database(fast archiving, but file save only on MySQL server drive)

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With Laravel Query Adviser you can get more insights into the SQL queries created by Eloquent models used in your app. It logs the queries used by your application and provides a helpful overview of the SQL queries. You can rerun the SQL query to analyze the database impact or copy the SQL query to your clipboard. The handy card view allows you to quickly point out the pain points of your application's SQL queries.

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Firestore Eloquent, an object-relational mapper (ORM) that makes it enjoyable to interact with your database. When using Eloquent, each firestore collection has a corresponding \"Model\"/\"Class\" that is used to interact with that collection. In addition to retrieving records from the firestore collection, Eloquent models/classes allow you to insert, update, and delete records from the collection as well.

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WPORM is a lightweight, Eloquent-inspired ORM for WordPress plugins and themes. It provides expressive, fluent query building, model relationships, schema management, attribute casting, and event hooks—while fully supporting the WordPress database API and table prefixing. WPORM makes it easy to build modern, maintainable database code in any WordPress project.

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Integrate artificial intelligence into FilamentPHP, leveraging your Eloquent Model data as knowledge.

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Cut/Copy & Paste Laravel Eloquent model data into another table

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Automatically convert an Eloquent model's dates to and from the current user's timezone

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Add virtual columns in any model of laravel

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Provides model data and state change tracking for Yii2

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Active record-thingy models, database Schemas, automagically doing stuffs, in need of optimisation

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Data Models and Factories for the OMDB API

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