Deep Web Scraper + RAG
This Process automation workflow acts as a deep web scraper and knowledge engine. The automation recursively downloads each page of a target website and extracts links, emails, text, and PDF documents using scalable Workflow Systems and modern Integration Tools. It serves as a practical example of process automation solutions, workflow systems examples, and integration with other tools for large-scale content collection.
After extraction, all data is sent into a RAG layer, where it can later be queried through chat or another interface powered by an AI-powered chatbot. This makes the setup useful for teams building ai virtual assistants, internal knowledge tools, or virtual assistants that rely on structured retrieval. It is also a strong ai powered chatbot project for anyone exploring workflow automation systems, workflow management systems, and advanced integration software tools.
Steps to follow
- Create a Supabase account and project.
- Connect Supabase to n8n using reliable integration data tools and system integration tools.
- Connect PostgreSQL from Supabase to n8n.
- Create Supabase tables and functions to support the scraper and RAG logic.
- Run the automation.
- If the automation times out, re-run it with a click-to-start workflow node connected to the Check Supabase node.
- Sometimes an HTTP request fails and causes the automation to mark a URL as failed. After the automation is finished, you can reactivate those URLs with another sub-flow and then re-run the main deep web scraper automation.
This setup is ideal for users who want process automation tools, workflow systems software, automated workflow systems, and an AI-powered chatbot interface to extract and query website content at scale.
