API Schema Crawler & Extractor
The API architecture crawling and extraction workflow is an intelligent automation tool that efficiently searches, crawls, and extracts API documentation for specified services. By integrating search engines, web crawlers, and large language models, this workflow not only accurately identifies API operations but also structures the information for storage in Google Sheets. Additionally, it generates customized API architecture JSON files for centralized management and sharing, significantly enhancing development and integration efficiency, and helping users quickly obtain and organize API information.
Tags
Workflow Name
API Schema Crawler & Extractor
Key Features and Highlights
This workflow automates the intelligent search, content crawling, information extraction, and custom API schema generation for specified service APIs. Core highlights include:
- Automatically retrieving web links related to target service APIs via Google Search
- Using the Apify platform for web content crawling, filtering out irrelevant resources to ensure data accuracy
- Leveraging Google Gemini large language model (LLM) for intelligent processing such as content classification, API operation extraction, and product identification
- Structuring extracted API operations and storing them in Google Sheets for easy management and review
- Generating customized API schema JSON files and uploading them to Google Drive for centralized document management
- Multi-stage workflow design (Research, Extraction, Generation) supporting asynchronous batch processing and status tracking
Core Problems Addressed
- Manual API documentation search is cumbersome and prone to missing critical information
- API documentation formats vary widely and lack uniform structure, making it difficult to quickly extract effective API operation data
- Need for unified management and standardized API schema document generation to improve development and integration efficiency
Application Scenarios
- Software development teams requiring rapid understanding of third-party service APIs
- Automated API documentation collection and maintenance systems
- Product managers or technical analysts conducting API service research and comparative analysis
- Automated testing or integration platforms needing dynamic API interface information retrieval
- Data-driven API catalogs or knowledge base construction
Main Workflow Steps
- Research Phase:
- Retrieve the list of services to research from Google Sheets
- Use Google Search to find API-related documentation links
- Crawl web content via Apify, filtering out irrelevant files
- Store crawled content as vector embeddings in Qdrant for subsequent retrieval
- Extraction Phase:
- Extract pending items from Google Sheets based on research results
- Query the vector database to locate relevant products and documentation content
- Use Google Gemini model to extract REST API operations (GET, POST, PATCH, DELETE, etc.)
- Write the extracted API operation information back into Google Sheets
- Generation Phase:
- Aggregate all extracted API operation data
- Use code nodes to consolidate and generate customized JSON-format API schema documents
- Upload the generated documents to Google Drive for sharing and archiving
Involved Systems and Services
- Google Sheets: Serves as the database storing service lists, intermediate crawl and extraction data, and final results
- Apify: Used for web content crawling and batch crawl management
- Google Gemini Model (LLM): Performs text classification, information extraction, and semantic search
- Qdrant Vector Database: Stores vector representations of web content for efficient semantic retrieval
- Google Drive: Stores the generated API schema document files
- n8n Automation Platform: Integrates the above services to realize workflow automation
Target Users and Value
- API developers, architects, and technical analysts can quickly and automatically obtain and organize API information, boosting work efficiency
- Product managers and business analysts gain better understanding of service functionalities and API capabilities to support decision-making and planning
- Automation testing and integration teams achieve dynamic API documentation updates and management
- Enterprises or teams needing bulk research and maintenance of multi-service API documentation
In summary, the API Schema Crawler & Extractor workflow is a highly automated and intelligent solution for API documentation collection and processing. By combining search engines, web crawlers, large language models, and vector databases, it enables precise identification and structured management of API operations, significantly simplifying the API research and generation process while greatly enhancing user productivity and data utilization value.
Create AI-Ready Vector Datasets for LLMs with Bright Data, Gemini & Pinecone
This workflow automates the process of web data scraping, extracting and formatting content, generating high-quality text vector embeddings, and storing them in a vector database, forming a complete data processing loop. By combining efficient data crawling, intelligent content extraction, and vector retrieval technologies, users can quickly build vector datasets suitable for training large language models, enhancing data quality and processing efficiency, and making it applicable to various scenarios such as machine learning, intelligent search, and knowledge management.
AI Document Assistant via Telegram + Supabase
This workflow transforms a Telegram bot into an intelligent document assistant. Users can upload PDF documents via Telegram, and the system automatically parses them to generate semantic vectors, which are stored in a Supabase database for easy intelligent retrieval and Q&A. The bot utilizes a powerful language model to answer complex questions in real-time, supporting rich HTML format output and automatically splitting long replies to ensure clear information presentation. Additionally, it integrates a weather query feature to enhance user experience, making it suitable for personal knowledge management, corporate assistance, educational tutoring, and customer support scenarios.
Automated Document Note Generation and Export Workflow
This workflow automatically extracts new documents, generates intelligent summaries, stores vectors, and produces various formats of documents such as study notes, briefings, and timelines by monitoring a local folder. It supports multiple file formats including PDF, DOCX, and plain text. By integrating advanced AI language models and vector databases, it enhances content understanding and retrieval capabilities, significantly reducing the time required for traditional document organization. This workflow is suitable for scenarios such as academic research, training, content creation, and corporate knowledge management, greatly improving the efficiency of information extraction and utilization.
Intelligent Document Q&A – Vector Retrieval Chat System Based on Google Drive and Pinecone
This workflow primarily implements the automatic downloading of documents from Google Drive, utilizing OpenAI for text processing and vector generation, which are then stored in the Pinecone vector database. Users can quickly ask questions in natural language through a chat interface, and the system will return relevant answers based on vector retrieval. This solution effectively addresses the inefficiencies and inaccuracies of traditional document retrieval, making it widely applicable in scenarios such as corporate knowledge bases, legal, research, and customer service, thereby enhancing the convenience and accuracy of information retrieval.
Easily Compare LLMs Using OpenAI and Google Sheets
This workflow is designed to automate the comparison of different large language models by real-time invoking independent responses from multiple models based on user chat input. It records the results and contextual information into Google Sheets for easy subsequent evaluation and comparison. It supports memory isolation management to ensure accurate context transmission while providing user-friendly templates to facilitate the participation of non-technical personnel in model performance evaluation, thereby enhancing the team's decision-making efficiency and testing accuracy.
AI Agent to Chat with Your Search Console Data Using OpenAI and Postgres
This workflow builds an intelligent AI chat agent that allows users to converse with it in natural language to query and analyze website data from Google Search Console in real time. Leveraging OpenAI's intelligent conversational understanding and the historical memory storage of a Postgres database, users can easily obtain accurate data reports without needing to understand API details. Additionally, the agent can proactively guide users, optimizing the data querying process and enhancing user experience, while supporting multi-turn conversations to simplify data analysis and decision-making processes.