Libraries tagged by resultSet

Create simple survey. View results in the BE module

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An extension of PHP's DOMDocument library. This library implements many common jQuery functions, adds support for HTML5, CSS selectors, method chaining and performing operations on sets of matched results.

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The Browse API has the following resources:item_summary:Allows shoppers to search for specific items by keyword, GTIN, category, charity, product, image, or item aspects and refine the results by using filters, such as aspects, compatibility, and fields values, or UI parameters.item:Allows shoppers to retrieve the details of a specific item or all items in an item group, which is an item with variations such as color and size and check if a product is compatible with the specified item, such as if a specific car is compatible with a specific part.This resource also provides a bridge between the eBay legacy APIs, such as the Finding, and the RESTful APIs, which use different formats for the item IDs.The item_summary, search_by_image, and item resource calls require an Application access token.

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Advanced News Search for TYPO3 - Enhance your TYPO3 News Extension with the Advanced TYPO3 News Extension. Expand your news search capabilities with detailed, precise results and the ability to filter content by specific fields using the Advanced Extension. This extension incorporates specially crafted search algorithms to ensure both speed and relevance in your searches.

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Cross-domain search orchestrator for the Nawasara command palette (⌘K). Discovers per-package search providers and aggregates results behind one permission-aware endpoint.

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Adding cache on your Laravel Eloquent queries' results is now a breeze.

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Generate interactive HTML reports of Pest test results

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The Browse API has the following resources: item_summary: Lets shoppers search for specific items by keyword, GTIN, category, charity, product, or item aspects and refine the results by using filters.  (Experimental) search_by_image: Lets shoppers search for specific items by image. You can refine the results by using URI parameters and filters. item: Lets you retrieve the details of a specific item or all the items in an item group, which is an item with variations such as color and size. This resource also provides a bridge between the eBay legacy APIs, such as Trading and Finding, and the RESTful APIs, such as Browse, which use different formats for the item IDs. You can use the Browse API to retrieve the basic details of the item and the RESTful item ID using a legacy item ID.  (Experimental) shopping_cart: Provides the ability for eBay members to see the contents of their eBay cart, and add, remove, and change the quantity of items in their eBay cart.  *Note:* This resource is not available in the eBay API Explorer. The item_summary, search_by_image, and item resource calls require an Application access token. The shopping_cart resource calls require a User access token.

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Assign one or multiple tags to your products and improve the navigation for your customers, who can narrow the search results by tags in the Product Filters or, vice versa, see all products with the tags

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kazist Whois - library for querying whois services and parsing results. Based on phpwhois.org

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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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Google search results crawler, get google search results that you need.

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Search address by postcode in multiple Api's and optionally get geocoding in Google Maps with the results.

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Server-side code highlighting with cached results.

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Highlight search results in HTML post content and partial Meilisearch hits.

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