Libraries tagged by ai api

# 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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OpenAI GPT-3 Api Client in PHP

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XWMS.nl provides secure, sophisticated, and custom-built login and authentication APIs for businesses. Our solutions ensure robust security, seamless integration with existing systems, and compliance with the highest industry standards. These are not simple APIs, but comprehensive identity management systems designed to deliver reliability and protection at enterprise scale.

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A Laravel package that scans public BoardDocs sites for agendas and attachments, exposes a fluent unofficial API, and exports self-contained meeting PDFs with remote links rewritten as in-document anchors.

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PHP/Laravel SDK for Yandex Search API integration. Supports web search, generative search, image search, and keyword statistics (Wordstat).

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Universal field/property metadata attributes for search, grid, and AI — #[Field], Widget enum, and FieldReader.

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A simple MCP (Model Context Protocol) server package for Laravel - Easy to extend with custom tools and JSON-RPC 2.0 compliant

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PHP agent SDK for Anthropic, OpenAI Responses, and OpenAI Chat Completions APIs.

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Coze PHP SDK for seamless AI integration into your applications

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A production-ready Laravel package to integrate with the Google Gemini API. Supports text, image, video, audio, long-context, structured output, files, caching, function-calling and understanding capabilities.

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RunAPI core Composer package for PHP applications

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Deploy your Rubix ML models to production with scalable stand-alone inference servers.

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AI-powered helper for PHP errors, warnings and exceptions: practical, context-aware explanations and suggestions via local or API LLMs.

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Web UI + API to execute Symfony console commands from the browser or any AI agent. Auto-discovers commands, dynamic forms, NDJSON streaming terminal.

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Official PHP SDK for Navi AI Agent Platform - Build AI-powered conversations with ease

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