Libraries tagged by perimeter

Small script for parameters fetcher

Go to Download

The UtmCookieBundle is a Symfony Bundle to save utm parameters from url into cookie when exists. Than cookie (utm) can be used later without parsing google or any other cookies.

Go to Download

This packages allows you to add extra parameters to your Eloquent API resources and collections.

Go to Download

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.

Go to Download

Parser/builder for filters from API query parameters.

Go to Download

Provides objects for building request parameters for AWS low-level API.

Go to Download

Doctrine Dynamic Connection library allows you to modify database connection parameters at runtime

Go to Download

Uses Dingo/API Request Query Parameters to filter Laravel Models

Go to Download

This package adds some power to your api rest server by allowing you to specify joins and eager loaded associations using http query parameters

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

Laravel Fine tuner is a package designed for the Laravel framework that automates the fine-tuning of OpenAI models. It simplifies the process of adjusting model parameters to optimize performance, tailored specifically for Laravel applications. This tool is ideal for developers looking to enhance AI capabilities in their projects efficiently, with minimal manual intervention.

Go to Download

Automates the process of updating assets version in Symfony 5 projects still using parameters.yml

Go to Download

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.

Go to Download

Lightweight router library with named url parameters

Go to Download

Permalink routing system for Laravel. Advanced database driven routes. Handle your permalinks + SEO parameters directly from database.

Go to Download