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Apipost Documentation

website·Indexed 31 days ago

https://wiki.apipost.cn

Comprehensive guide for Apipost, an API development and testing platform. Features include API design and documentation for HTTP, TCP, WebSocket, and Socket.IO; automated testing with custom scripts, assertions, and loop/transaction controllers; performance and load testing; and team collaboration tools with version history, project member management, and document archiving.

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What's inside Apipost

  1. Overview of Database Integration in Apipost

    Apipost allows developers to connect to databases to fetch input parameters or perform assertion validations during API debugging. Database operations can be configured in 'Api Debugging', 'Interface Test Cases', and the 'Pre-execution' or 'Post-execution' scripts of automated tests.

    Supported databases include:

    • MySQL
    • SQL Server
    • Oracle
    • Clickhouse
    • Dameng Database (达梦数据库)
    • PostgreSQL
    • Redis
    • MongoDB

  2. Overview of Apipost HTTP Load Testing

    Apipost provides a load testing feature for HTTP interfaces to verify performance during the development phase. It supports a maximum simulated concurrency of 10,000 users.

    Important Constraint: This feature is only supported in the Apipost desktop client; the web version does not support high concurrency or test data uploads.

  3. Overview of Apipost Performance Testing

    Starting from version 8.1.4, Apipost supports performance testing within automated tests to help developers and testers evaluate and optimize API performance. Note that this feature is exclusively available in the Apipost client application.

  4. Overview of AI-generated assertions in ApiPost

    The AI生成断言 (AI Generate Assertion) feature automatically analyzes the structure of an API response body to generate JavaScript assertion scripts. This allows developers to quickly verify API responses without writing all test scripts manually. Key capabilities include intelligent analysis of field types, batch generation of multiple assertions, the ability to add custom assertions via natural language descriptions, and direct integration into test cases.

  5. Overview of Apipost WebHooks

    Apipost supports third-party Webhook capabilities to push message notifications based on events to external platforms. This enables event-driven collaboration for critical project operations, CI/CD flows, and automated testing results.

    Supported Channels:

    • Feishu
    • Enterprise WeChat
    • DingTalk
    • Generic Webhook

    Permissions: Only users with Project Administrator privileges or higher can create and manage notification configurations.

  6. Overview of AI Complete Schema feature

    The AI Complete Schema feature automatically populates missing field descriptions, example values, and default values by analyzing JSON data structures and field name semantics. It is primarily used for API interface documentation and data model design to reduce manual data entry by over 70%.

  7. Overview of the Error Code Dictionary

    The Error Code Dictionary is a centralized module in ApiPost used to manage and maintain all error codes within API documentation. It ensures a standardized error code system across teams and automatically synchronizes changes to the shared API documentation.

  8. Overview of Apipost API Lifecycle Modules

    Apipost provides five core modules to manage the full API development lifecycle:

    1. API Design: Pre-define request parameters and response expectations (Mock) before development. Since v7, it supports visual Json-schema for generating Mock data structures.
    2. API Debugging: Validate interfaces during or after development. Supports HTTP, WebSocket, gRPC (v7+), and TCP/UDP (v7.2.2+). Pre/post-execution scripts and assertions are 100% compatible with Postman.
    3. Automated Testing: Perform complex scenario testing. Since v7, it includes controllers similar to Jmeter (Condition, Loop, Wait, Global Assertion, and Script controllers).
    4. API Documentation: Generate standardized documentation in HTML, Markdown, and Word formats. v7 supports custom documentation logos.
    5. IDE Plugin: Allows debugging interfaces and scanning code to upload interfaces and generate documentation directly within the IDE.

  9. Overview of AI Function Generation

    The AI Function Generation feature allows developers to generate standalone, reusable JavaScript functions by providing natural language descriptions of the required logic. It is specifically designed for cases where built-in system functions are insufficient, such as removing whitespace from parameters, handling illegal characters, converting date formats to timestamps, or implementing custom encryption/decryption.

  10. Overview of Apipost database connection capabilities

    Apipost allows developers to connect to databases to retrieve input parameters or perform assertion validations during API debugging. Database operations are supported in the following areas: API Debugging, Interface Test Cases, Automation Test Pre-execution, and Post-execution scripts.

    Supported databases include:

    • MySQL
    • SQL Server
    • Oracle
    • Clickhouse
    • Dameng (达梦数据库)
    • PostgreSQL
    • Redis
    • MongoDB

  11. Overview of AI Document Polishing

    The AI Document Polishing feature uses AI to intelligently optimize user-inputted text, making it more professional, fluent, and appropriate. It is designed for fine-tuning various types of documentation, specifically focusing on improving expression, adapting style to different audiences, correcting grammar, and enhancing professional tone.

  12. Overview of Pre-execution and Post-execution scripts

    ApiPost allows developers to run custom scripts at two different stages of an API request:

    • Pre-execution scripts: Executed before the request is sent. Used for setting input parameters, calculating complex values, and printing debug data.
    • Post-execution scripts: Executed after the request is sent. Used for setting variables based on responses, performing assertions (testing), and extracting data.