Transform Websites into a Conversational Knowledge Base with OpenAI RAG & Supabase

Overview

This advanced automation workflow enables deep web scraping combined with Retrieval-Augmented Generation (RAG) to transform websites into intelligent, queryable knowledge bases. The system recursively crawls target websites, extracts content, and indexes all data in a vector database for AI conversational access.

How the system works

Intelligent Web Scraping and RAG Pipeline

Recursive Web Scraper - Automatically crawls every accessible page of a target website Data Extraction - Collects text, metadata, emails, links, and PDF documents Supabase Integration - Stores content in PostgreSQL tables for scalability RAG Vectorization - Generates embeddings and stores them for semantic search AI Query Layer - Connects embeddings to an AI chat engine with citations Error Handling - Automatically retriggers failed queries

Setup Instructions

Estimated setup time: 30-45 minutes

Prerequisites

Self-hosted n8n instance (v0.200.0 or higher) Supabase account and project (PostgreSQL enabled) OpenAI/Gemini/Claude API key for embeddings and chat Optional: External vector database (Pinecone, Qdrant)

Detailed configuration steps

Step 1: Supabase configuration

Project creation**: New Supabase project with PostgreSQL enabled Generating credentials**: API keys (anon key and service_role key) and connection string Security configuration**: RLS policies according to your access requirements

Step 2: Connect Supabase to n8n

Configure Supabase node**: Add credentials to n8n Credentials Test connection**: Verify with a simple query Configure PostgreSQL**: Direct connection for advanced operations

Step 3: Preparing the database

Main tables**: pages: URLs, content, metadata, scraping statuses documents: Extracted and processed PDF files embeddings: Vectors for semantic search links: Link graph for navigation

Management functions**: Scripts to reactivate failed URLs and manage retries

Step 4: Configuring automation

Recursive scraper**: Starting URL, crawling depth, CSS selectors HTTP extraction**: User-Agent, headers, timeouts, and retry policies Supabase backup**: Batch insertion, data validation, duplicate management

Step 5: Error handling and re-executions

Failure monitoring**: Automatic detection of failed URLs Manual triggers**: Selective re-execution by domain or date Recovery sub-streams**: Retry logic with exponential backoff

Step 6: RAG processing

Embedding generation**: Text-embedding models with intelligent chunking Vector storage**: Supabase pgvector or external database Conversational engine**: Connection to chat models with source citations

Data structure

Main Supabase tables | Table | Content | Usage | |-------|---------|-------| | pages | URLs, HTML content, metadata | Main storage for scraped content | | documents | PDF files, extracted text | Downloaded and processed documents | | embeddings | Vectors, text chunks | Semantic search and RAG | | links | Link graph, navigation | Relationships between pages |

Use cases

Business and enterprise Competitive intelligence with conversational querying Market research from complex web domains Compliance monitoring and regulatory watch

Research and academia Literature extraction with semantic search Building datasets from fragmented sources

Legal and technical Scraping legal repositories with intelligent queries Technical documentation transformed into a conversational assistant

Key features

Advanced scraping Recursive crawling with automatic link discovery Multi-format extraction (HTML, PDF, emails) Intelligent error handling and retry

Intelligent RAG Contextual embeddings for semantic search Multi-document queries with citations Intuitive conversational interface

Performance and scalability Processing of thousands of pages per execution Embedding cache for fast responses Scalable architecture with Supabase Technical Architecture

Main flow: Target URL → Recursive scraping → Content extraction → Supabase storage → Vectorization → Conversational interface

Supported types: HTML pages, PDF documents, metadata, links, emails

Performance specifications

Capacity**: 10,000+ pages per run Response time**: < 5 seconds for RAG queries Accuracy**: >90% relevance for specific domains Scalability**: Distributed architecture via Supabase

Advanced configuration

Customization Crawling depth and scope controls Domain and content type filters Chunking settings to optimize RAG

Monitoring Real-time monitoring in Supabase Cost and performance metrics Detailed conversation logs