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API Schema Crawler & Extractor

This workflow implements automated research, content retrieval, and operation extraction for API documentation. It combines web search, web crawling, and natural language processing technologies to support the generation of custom API architectures. Through intelligent analysis and multi-stage task management, it efficiently filters out irrelevant information, reduces manual parsing work, and stores API operations in a structured manner, thereby enhancing the efficiency of API integration and documentation maintenance. It is suitable for developers, product managers, and technical teams, significantly accelerating project progress and improving the accuracy of information collection.

Workflow Diagram
API Schema Crawler & Extractor Workflow diagram

Workflow Name

API Schema Crawler & Extractor

Key Features and Highlights

This workflow automates the research, content crawling, API operation extraction, and custom API schema generation from API documentation. It integrates multiple advanced technologies, including web search engine APIs, web scraping, natural language processing (NLP) models (Google Gemini), vector databases (Qdrant), and data storage and management via Google Sheets and Google Drive, achieving end-to-end automated collection and organization of API information.

Core Problems Addressed

  • Automatically retrieves API documentation of target services from the internet, eliminating the tediousness and inefficiency of manual searching across dispersed documents.
  • Filters and analyzes web content automatically to avoid interference from irrelevant or low-quality search results.
  • Utilizes large language models to intelligently extract API operations (GET, POST, PATCH, DELETE, etc.), reducing manual parsing efforts.
  • Structures extracted API operations and generates unified custom API schema files for easy subsequent integration and invocation.
  • Implements multi-stage task management and status tracking to ensure stable execution and error handling throughout the process.

Application Scenarios

  • API Integration Platform Development: Automate the collection of third-party service API documentation and rapidly generate usage specifications.
  • Developer Tools: Assist developers in quickly understanding and utilizing target service APIs.
  • Product Research and Competitor Analysis: Automatically gather API information of competing services.
  • Documentation Management and Automated Generation: Periodically update API catalogs and operation lists to improve documentation maintenance efficiency.

Main Workflow Steps

  1. Research Phase

    • Retrieve the list of services to research from Google Sheets.
    • Use Google Search API to perform customized searches for relevant API documentation pages.
    • Employ Apify Web Scraper to crawl webpage content and filter out irrelevant pages.
    • Store webpage content and metadata in the Qdrant vector database to facilitate subsequent similarity searches.
    • Update research status and results back to Google Sheets.

  2. Extraction Phase

    • Obtain the list of services pending extraction from Google Sheets.
    • Query the Qdrant database to retrieve related products, solutions, and API documentation for each service.
    • Use the Google Gemini large language model to intelligently identify and extract API operations.
    • Deduplicate and filter the extraction results.
    • Write the extracted API operations into Google Sheets and update extraction status.

  3. Generation Phase

    • Query Google Sheets to get the list of services and corresponding API operations pending schema generation.
    • Use code nodes to group and structure API operations, generating API schema JSON files conforming to custom formats.
    • Upload the generated schema files to Google Drive.
    • Update generation status and output file information in Google Sheets.

Involved Systems and Services

  • Google Sheets: Database for storing service lists, task statuses, and API operation data.
  • Google Drive: Storage for generated API schema files.
  • Google Search API (via Apify): Customized web search capabilities.
  • Apify Web Scraper: Crawling and retrieving API documentation webpage content.
  • Qdrant Vector Database: Stores vectorized webpage content for semantic search.
  • Google Gemini Large Language Model (Google Gemini Chat Model, Embeddings): Text understanding, API operation extraction, and text embedding generation.
  • n8n Workflow Automation Platform: Overall workflow orchestration and node scheduling.

Target Users and Value

  • API Developers and Integration Engineers: Quickly obtain detailed API information of target services, reducing manual search and parsing workload.
  • Product Managers and Technical Researchers: Efficiently research third-party API capabilities and product features.
  • Automation Operations and Data Engineers: Build automated API documentation management and update systems.
  • Technical Teams: Enhance the accuracy and automation level of API information collection, accelerating project timelines.

By leveraging multi-stage distributed task execution and intelligent analysis, this workflow significantly improves the efficiency and quality of API documentation crawling and structuring, making it a vital tool for modern API management and integration.