Automated Web Scraping with AI: Make.com and OpenAI

Added:

Setup & Prep
Build Workflow
Parse & Extract
Format & Update
Validate & Run
Pros & Cons
Pro Tips
Wrap & Start

Setup & Prep

0:00
Playing Section
  • 1

    Create Google Sheets with 'link' column for website URLs.

  • 2

    Add data columns for targeted information like product details.

Basic HTML and CSS Selectors: Understanding how web pages are structured to target specific elements for extraction.
Introduction to Cloud Automation: Familiarity with workflow-based automation platforms, including triggers, actions, and data mapping.
API Fundamentals and JSON: Knowledge of how web services communicate via HTTP requests and how structured data is formatted.
Basic Prompt Engineering: Understanding how to instruct Large Language Models (LLMs) to output consistent, structured formats.
Handling Dynamic Content: Learning to scrape JavaScript-heavy or single-page applications using headless browsers (e.g., Playwright, Puppeteer).
Advanced Error Handling in Make.com: Designing robust workflows that can handle rate limits, API timeouts, and website structural changes.
Data Warehousing and Scalability: Moving from Google Sheets to relational databases (e.g., PostgreSQL) or data warehouses (e.g., BigQuery) for handling large-scale scraped datasets.
Retrieval-Augmented Generation (RAG): Storing scraped structured data into vector databases to build domain-specific AI chatbots or search engines.
20.7K views609likes14:47@Jake.DawsonOriginal Release: 2024-11-19

This video demonstrates how to build an automated web scraping system using Make.com, Google Sheets, and OpenAI. The workflow involves: (1) creating a Google Sheet with URLs in the 'Link' column and columns for desired data fields, (2) using Make.com's Google Sheets module to search for rows containing URLs, (3) making HTTP GET requests to fetch HTML content from each URL, (4) using the Text Parser module to clean HTML into plain text, (5) leveraging OpenAI to extract specific information like product names, descriptions, and prices from the cleaned text, (6) parsing the JSON output, and (7) updating the Google Sheet with the extracted data. This automation can be scheduled to run regularly, ensuring fresh data without manual effort.