Libraries tagged by OpenAi Sdk

Alibaba Cloud SDK for PHP

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Runtime library to be used with other SDK generated from OpenAPI docs

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optimize your website with SEO

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Email API and SMTP relay to not just send and measure email sending, but also alert and optimise. We provide you with tools, expertise and support needed to reliably deliver emails to your customers inboxes on time, every time.

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Complete MetaFab API references and guides can be found at: https://trymetafab.com

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Use the Taxonomy API to discover the most appropriate eBay categories under which sellers can offer inventory items for sale, and the most likely categories under which buyers can browse or search for items to purchase. In addition, the Taxonomy API provides metadata about the required and recommended category aspects to include in listings, and also has two operations to retrieve parts compatibility information.

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Use the Fulfillment API to complete the process of packaging, addressing, handling, and shipping each order on behalf of the seller, in accordance with the payment method and timing specified at checkout.

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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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The Account API gives sellers the ability to configure their eBay seller accounts, including the seller's policies (the Fulfillment Policy, Payment Policy, and Return Policy), opt in and out of eBay seller programs, configure sales tax tables, and get account information. For details on the restrictions and availability of the methods in this API, see API restrictions.

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API client generated from OpenAPI definition from Logistics-X.com

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API client generated from OpenAPI definition from Logistics-X.com

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This document describes the Plugin API for Lilt's Connector platform. The Plugin API is intented to be used by developers who wish to build integrations into their desired systems. The purpose of this API is to enable content transfer and status monitoring for localization projects. - Read more about the concepts and workflows in the [user guide](/docs/api/v1.0). - Test the API interactively via [Swagger UI](/api/v1.0/ui).

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An unofficial SDK for the official sevDesk API. Always up to date, powered by j-mastr/sevdesk-api and the OpenAPI Generator.

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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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