Create RAG-ready knowledge bases from websites using Apify, Gemini & Supabase

Convert any website into a searchable vector database for AI chatbots. Submit a URL, choose scraping scope, and this workflow handles everything: scraping, cleaning, chunking, embedding, and storing in Supabase.

What it does - Scrapes websites using Apify (3 modes: full site unlimited, full site limited, single URL) - Cleans content (removes navigation, footer, ads, cookie banners, etc) - Chunks text (800 chars, markdown-aware) - Generates embeddings (Google Gemini, 768 dimensions) - Stores in Supabase vector database

Requirements - Apify account + API token - Supabase database with pgvector extension - Google Gemini API key

Setup 1. Create Supabase documents table with embedding column (vector 768). [Run this SQL query](https://docs.langchain.com/oss/javascript/integrations/vectorstores/supabase) in your Supabase project to enable the vector store setup 2. Add your Apify API token to all three "Run Apify Scraper" nodes 3. Add Supabase and Gemini credentials 4. Test with small site (5-10 pages) or single page/URL first

Next steps Connect your vector store to an AI chatbot for RAG-powered Q&A, or build semantic search features into your apps.

Tip: Start with page limits to test content quality before full-site scraping. Review chunks in Supabase and adjust Apify filters if needed for better vector embeddings.

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Sample Outputs