Libraries tagged by ai data analysis

Framework-agnostic PHP database library with unified API for MySQL, MariaDB, PostgreSQL, SQLite, MSSQL, and Oracle. Query Builder, caching, sharding, window functions, CTEs, JSON, migrations, ActiveRecord, CLI tools, AI-powered analysis. Zero external dependencies.

Go to Download

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, grammar and spelling correction, keywords and keyphrases extraction, chatbot, product description and ad generation, intent classification, text generation, image generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, speech synthesis, embeddings, and dependency parsing. It is ready for production, served through a REST API. This is the PHP client for the API. More details here: https://nlpcloud.com. Documentation: https://docs.nlpcloud.com. Github: https://github.com/nlpcloud/nlpcloud-php

Go to Download

Opinionated PHP code-quality analyzer with SARIF output, baselines, and a local dashboard.

Go to Download

Yoast-style SEO suite for Twill CMS: content analysis with traffic lights, meta tags, XML sitemaps and schema.org structured data

Go to Download

# 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.

Go to Download

AI-powered topic analysis and structured data generation for Drupal. Automatically analyzes content to identify relevant topics and generates schema.org markup for improved SEO.

Go to Download

Using AI to analyze data and receive insights to improve specific areas of your business

Go to Download

A robust and well-structured library providing seamless technical integration for AI-driven kissing detection and analysis. Facilitates the development of applications requiring sophisticated understanding of kissing events in video or image data.

Go to Download

Official PHP SDK for QiuXiaoCe (球小策) Football Big Data & AI Analysis API.

Go to Download

A Laravel package for seamless integration with DeepSeek AI services, enabling efficient and scalable AI-powered functionalities such as natural language processing, data analysis, and automation through a simple and intuitive API wrapper.

Go to Download

ScorpionFuzzy To Analysis Your Data

Go to Download

Connect Google Search Console to your Magento SEO suite with OAuth. The GSC Connector syncs real search performance data — queries, impressions, clicks, and position — directly into Search Intelligence Core for AI-assisted analysis and change-set proposals.

Go to Download

A high-level machine learning and deep learning library for the PHP language.

Go to Download

Experimental features for the Rubix ML library.

Go to Download

Google Analytics 4 MCP tools for Magebit_Mcp — wraps the GA Data and Admin APIs as MCP tools so AI clients can query property metadata, run reports, run realtime reports, and run funnel reports against your store's analytics property.

Go to Download