Operations & Systems
Multi-Step Parallel API Schema Researcher
This n8n workflow automates the discovery, extraction, and generation of API schemas. It searches the web for API documentation, scrapes content, uses AI to identify and extract API operations, and then structures this information into a custom JSON schema which is then uploaded…
Built · ~5 hours saved per week
This workflow is for anyone needing to find, extract, and organize API documentation for various services. This includes developers, data analysts, and technical researchers.
This workflow automates the process of finding API documentation, extracting API operations, and generating a structured JSON schema. It uses web scraping, search engines, and AI to achieve this. It processes requests in three stages: research, extraction, and generation, updating a Google Sheet with progress and results.
You must connect to Google Sheets, Apify, Google Gemini, and Qdrant. Set up a Google Sheet to track the services and their processing stages. The workflow triggers manually or by an external event in n8n.
This workflow requires credentials for Google Sheets, Apify (for web scraping and search), Google Gemini (for AI tasks), and Qdrant (for vector storage). You also need a Google Drive folder for output.
You can customize search queries, AI prompts, and the output JSON schema. You can also change the services being tracked in the Google Sheet and adjust web scraping parameters.
Key Technologies: n8n (workflow automation),Google Sheets (database),Apify (web scraping, search API),Google Gemini (Large Language Model - LLM),Qdrant (vector database),Google Drive (file storage)
Automation Value: Accelerates API discovery: Quickly finds relevant API documentation across many services. Automates data extraction: Automatically pulls API operations, saving manual effort. Generates structured data: Creates consistent JSON schemas for API integration or analysis. Reduces manual labor: Eliminates the need for manual searching, scraping, and data entry. Enhances data quality: Uses AI to extract specific, structured API information, reducing errors. Improves efficiency: Processes multiple services in parallel, speeding up the overall research process.
Best Practices: Modular Design: The workflow divides complex tasks (research, extraction, generation) into sub-workflows, improving readability and maintainability.
Tags: Leadership Automations, Reports & Analytics, RAG, System (Parent/Child), QDRANT, AI Automation