Libraries tagged by enricher

The "Symfony Standard Edition" distribution enriched with MopaBootstrapBundle and MopaBootstrapSandboxBundle to integrate twitter/bootstrap2

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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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Symfony bundle that enriches Sentry context from custom exceptions and Guzzle HTTP errors

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Polysource — drop-in package that enriches EasyAdmin v5 filters (ranges, multi-select, custom filter types, session persistence, chips) without forking EasyAdmin.

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Enriches OpenTelemetry SERVER spans with end-user identity (device id, session id, client address) for PHP apps using ext-opentelemetry auto-instrumentation.

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Browse the Mage-OS Extension Directory from the Magento admin panel, enriched with the shop's installed modules and Magento version.

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Advanced exception management for Laravel with enriched context, multiple logging channels, and user-friendly error messages

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An extension for EasyAdmin that enriches the TextEditorField (Trix) editor with advanced features: Tables, Colors, H2/H3 headings, Underlining, ...

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Laravel AI SDK Plus — A fork of the official Laravel AI SDK enriched with custom features.

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a group system which can be enriched with news, forum or calendar

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Extension for EXT:news to display additional content "around" a single news-article. In templates additional areas can be defined and each area can be filled with content-elements. Imagine news enriched with tables, diagrams, footnotes or any other kind of Content-Elements.

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Runtime-first Laravel package that enriches routes with Oxinfer static analysis

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A Collection class to manipulate the arrays with enriched routines and the ability of executing them in chain

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Sends the article text to an AI chat model on presave and writes the structured answer into real node fields: SEO meta description, reading time, sentiment and Tags terms. Triggered by node entity presave, guarded so that each article is enriched only once. Includes the library's Article tags recipe, which provides the Article content type with its body and Tags fields and the Tags vocabulary. Requires the ai_integration_eca and eca_tamper contributed modules. After applying the recipe, select a chat model that supports structured output in the "Ask AI for structured enrichment data" action.

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A lightweight Laravel package that enriches your logs with structured request, user, and application context.

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