Libraries tagged by generated

ElasticSearch PHP Thrift transport client. Generated from elasticsearch-transport-thrift plugin thrift file

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Filters to control where the size of images are generated.

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A trait you can apply to Eloquent models to have slugs automatically generated on save.

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Panth LLMs.txt — AI Indexing Engine for Magento 2. Serves structured /llms.txt, /llms-full.txt and /llms.json with a dynamic multi-URL sitemap engine, weighted ranking, auto-generated summaries, product-type and use-case grouping, and cron-based cache warm-up. Built for ChatGPT, Claude, Perplexity, Gemini and future LLM crawlers.

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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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Add support for vite manifest inside wp so you can load assets from generated vite manifest and have HMR enabled.

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No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)

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Load the graphql schema data from a generated file instead of cache to improve performance

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GTFS Realtime protobuf bindings for PHP, generated from the official specification.

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A fully-typed Linear API SDK for PHP and Laravel, generated from Linear's GraphQL schema.

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An always-current Lucide blade-icons set and PHP enum for Laravel and Filament, generated from the official Lucide release.

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Fusio generated models

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If your website has static caching on, and you drive traffic to it from social media, Google Ads and other sources that add query string parameters to the URL, there is a chance that each time new user visits a page, it will not be served from cache, but will be generated from scratch. This is because the URL with query string parameters is treated as a different URL from the one without query string parameters.

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Auto-generated, interactive API documentation for Laravel with a modern Vue 3 UI. Zero config, real-time testing, code snippets, OpenAPI export.

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A high-performance PHP framework featuring automatic autoloading, smart routing, and auto-generated CRUDs, seamlessly integrated with NLP capabilities and Large Language Model (LLM) orchestration.

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