Libraries tagged by response data

# SendX REST API Documentation ## 🚀 Introduction The SendX API is organized around REST principles. Our API has predictable resource-oriented URLs, accepts JSON-encoded request bodies, returns JSON-encoded responses, and uses standard HTTP response codes, authentication, and verbs. **Key Features:** - 🔒 **Security**: Team-based authentication with optional member-level access - 🎯 **Resource-Oriented**: RESTful design with clear resource boundaries - 📊 **Rich Data Models**: Three-layer model system (Input/Output/Internal) - 🔗 **Relationships**: Automatic prefix handling for resource relationships - 📈 **Scalable**: Built for high-volume email marketing operations ## 🏗️ Architecture Overview SendX uses a three-layer model architecture: 1. **Input Models** (`RestE*`): For API requests 2. **Output Models** (`RestR*`): For API responses with prefixed IDs 3. **Internal Models**: Core business logic (not exposed in API) ## 🔐 Security & Authentication SendX uses API key authentication: ### Team API Key ```http X-Team-ApiKey: YOUR_TEAM_API_KEY ``` - **Required for all requests** - Team-level access to resources - Available in SendX Settings → Team API Key ## 🆔 Encrypted ID System SendX uses encrypted IDs for security and better developer experience: - **Internal IDs**: Sequential integers (not exposed) - **Encrypted IDs**: 22-character alphanumeric strings - **Prefixed IDs**: Resource-type prefixes in API responses (`contact_`) ### ID Format **All resource IDs follow this pattern:** ``` _ ``` **Example:** ```json { "id": "contact_BnKjkbBBS500CoBCP0oChQ", "lists": ["list_OcuxJHdiAvujmwQVJfd3ss", "list_0tOFLp5RgV7s3LNiHrjGYs"], "tags": ["tag_UhsDkjL772Qbj5lWtT62VK", "tag_fL7t9lsnZ9swvx2HrtQ9wM"] } ``` ## 📚 Resource Prefixes | Resource | Prefix | Example | |----------|--------|---------| | Contact | `contact_` | `contact_BnKjkbBBS500CoBCP0oChQ` | | Campaign | `campaign_` | `campaign_LUE9BTxmksSmqHWbh96zsn` | | List | `list_` | `list_OcuxJHdiAvujmwQVJfd3ss` | | Tag | `tag_` | `tag_UhsDkjL772Qbj5lWtT62VK` | | Sender | `sender_` | `sender_4vK3WFhMgvOwUNyaL4QxCD` | | Template | `template_` | `template_f3lJvTEhSjKGVb5Lwc5SWS` | | Custom Field | `field_` | `field_MnuqBAG2NPLm7PZMWbjQxt` | | Webhook | `webhook_` | `webhook_9l154iiXlZoPo7vngmamee` | | Post | `post_` | `post_XyZ123aBc456DeF789GhI` | | Post Category | `post_category_` | `post_category_YzS1wOU20yw87UUHKxMzwn` | | Post Tag | `post_tag_` | `post_tag_123XyZ456AbC` | | Member | `member_` | `member_JkL012MnO345PqR678` | ## 🎯 Best Practices ### Error Handling - **Always check status codes**: 2xx = success, 4xx = client error, 5xx = server error - **Read error messages**: Descriptive messages help debug issues - **Handle rate limits**: Respect API rate limits for optimal performance ### Data Validation - **Email format**: Must be valid email addresses - **Required fields**: Check documentation for mandatory fields - **Field lengths**: Respect maximum length constraints ### Performance - **Pagination**: Use offset/limit for large datasets - **Batch operations**: Process multiple items when supported - **Caching**: Cache responses when appropriate ## 🛠️ SDKs & Integration Official SDKs available for: - [Golang](https://github.com/sendx/sendx-go-sdk) - [Python](https://github.com/sendx/sendx-python-sdk) - [Ruby](https://github.com/sendx/sendx-ruby-sdk) - [Java](https://github.com/sendx/sendx-java-sdk) - [PHP](https://github.com/sendx/sendx-php-sdk) - [JavaScript](https://github.com/sendx/sendx-javascript-sdk) ## 📞 Support Need help? Contact us: - 💬 **Website Chat**: Available on sendx.io - 📧 **Email**: [email protected] - 📚 **Documentation**: Full guides at help.sendx.io --- **API Endpoint:** `https://api.sendx.io/api/v1/rest` [](https://god.gw.postman.com/run-collection/33476323-44b198b0-5219-4619-a01f-cfc24d573885?action=collection%2Ffork&source=rip_markdown&collection-url=entityId%3D33476323-44b198b0-5219-4619-a01f-cfc24d573885%26entityType%3Dcollection%26workspaceId%3D6b1e4f65-96a9-4136-9512-6266c852517e)

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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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Generate beautiful API documentation from Laravel Data classes with attributes for query parameters, hidden fields, and response data

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Response building for Jardis request-response use cases: failure forms, response objects, response envelope data and the door steps

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Return original response alongside with the DataTransferObject

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JSON:API Response based on Custom Data

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PHP representation of Datatables' request and response.

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A lightweight, reflection-based PHP 8.4+ data mapping library for converting associative arrays into typed PHP objects (POPOs), and back. Ideal for OpenAPI-generated DTOs, form handling, and request/response serialization in modern API architectures.

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A Laravel package designed to simplify the creation of structured and formatted API responses (JSON, XML) with custom status codes, messages, and data. XML support is currently under construction.

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Laravel server-side datatable query builder for Eloquent and query builder responses.

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The Response is a package to response api-rest .

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Response easy the dates from views

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Laravel integration for the OneTrace.pro customer data platform: events after the response or via queues, tracker Blade directive, automatic identify, Eloquent product sync and a test fake.

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Http Galaxy: abstraction for HTTP request, response, and csp protection. Provides careful data sanitization and utility for URL and cookies manipulation.

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A simple data transformer for your API responses

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