Libraries tagged by generated docs

MailSlurp is an API for sending and receiving emails from dynamically allocated email addresses. It's designed for developers and QA teams to test applications, process inbound emails, send templated notifications, attachments, and more. ## Resources - [Homepage](https://www.mailslurp.com) - Get an [API KEY](https://app.mailslurp.com/sign-up/) - Generated [SDK Clients](https://docs.mailslurp.com/) - [Examples](https://github.com/mailslurp/examples) repository

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Welcome to the Svix API documentation! Useful links: [Homepage](https://www.svix.com) | [Support email](mailto:[email protected]) | [Blog](https://www.svix.com/blog/) | [Slack Community](https://www.svix.com/slack/) # Introduction This is the reference documentation and schemas for the [Svix webhook service](https://www.svix.com) API. For tutorials and other documentation please refer to [the documentation](https://docs.svix.com). ## Main concepts In Svix you have four important entities you will be interacting with: - `messages`: these are the webhooks being sent. They can have contents and a few other properties. - `application`: this is where `messages` are sent to. Usually you want to create one application for each user on your platform. - `endpoint`: endpoints are the URLs messages will be sent to. Each application can have multiple `endpoints` and each message sent to that application will be sent to all of them (unless they are not subscribed to the sent event type). - `event-type`: event types are identifiers denoting the type of the message being sent. Event types are primarily used to decide which events are sent to which endpoint. ## Authentication Get your authentication token (`AUTH_TOKEN`) from the [Svix dashboard](https://dashboard.svix.com) and use it as part of the `Authorization` header as such: `Authorization: Bearer ${AUTH_TOKEN}`. For more information on authentication, please refer to the [authentication token docs](https://docs.svix.com/api-keys). ## Code samples The code samples assume you already have the respective libraries installed and you know how to use them. For the latest information on how to do that, please refer to [the documentation](https://docs.svix.com/). ## Idempotency Svix supports [idempotency](https://en.wikipedia.org/wiki/Idempotence) for safely retrying requests without accidentally performing the same operation twice. This is useful when an API call is disrupted in transit and you do not receive a response. To perform an idempotent request, pass the idempotency key in the `Idempotency-Key` header to the request. The idempotency key should be a unique value generated by the client. You can create the key in however way you like, though we suggest using UUID v4, or any other string with enough entropy to avoid collisions. Svix's idempotency works by saving the resulting status code and body of the first request made for any given idempotency key for any successful request. Subsequent requests with the same key return the same result. Please note that idempotency is only supported for `POST` requests. ## Cross-Origin Resource Sharing This API features Cross-Origin Resource Sharing (CORS) implemented in compliance with [W3C spec](https://www.w3.org/TR/cors/). And that allows cross-domain communication from the browser. All responses have a wildcard same-origin which makes them completely public and accessible to everyone, including any code on any site.

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

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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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Automatically generated documentation by parsing of Structured API endpoints.

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A simple index page for sami generated documentations

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Parse and generate API Blueprint markdown into Feature/Acceptance tests.

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Tranform a swagger (multifile or plainfile) into markdown doc file based on template files. The generated doc is more a sskeltton of doc than a full finished doc file (depends of the documentation into your swagger file)

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The Phpdoc addon provides Codex the means to parse the phpdoc generated xml file and integrate documenation in a user-friendly way.

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Generates documentation for your REST API from annotations and attributes

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Generate gorgeous recipes for your Laravel applications using MarkDown

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Automatically generate Laravel docs from request rules, controllers and routes

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Generate OpenAPI Specification for Laravel Applications

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Generate documents from existing Excel & Word templates | Export tables to Excel (Grids)

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Enlighten your APIs with auto-generated documentation

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