Scrape and ingest web content into Supabase pgvector with Firecrawl

What this does Receives a URL via webhook, uses Firecrawl to scrape the page into clean markdown, and stores it as vector embeddings in Supabase pgvector. A visual, self-hosted ingestion pipeline for RAG knowledge bases. Adding a new source is as simple as sending a URL. The second part of the workflow exposes a chat interface where an AI Agent queries the stored knowledge base to answer questions, with Cohere reranking for better retrieval quality. How it works Part 1: Ingestion Pipeline 1. Webhook receives a POST request with a url field 2. Verify URL validates and normalizes the domain 3. Supabase checks if the URL was already ingested (deduplication) 4. If the URL already exists, ingestion is skipped; otherwise it continues 5. Firecrawl fetches the page and converts it to clean markdown 6. OpenAI generates vector embeddings from the scraped content 7. Default Data Loader attaches the source URL as metadata 8. Supabase Vector Store inserts the content and embeddings into pgvector 9. Respond to Webhook confirms how many items were added Part 2: RAG Chat Agent 1. Chat trigger receives a user question 2. AI Agent (OpenRouter) queries the Supabase vector store filtered by URL 3. Cohere Reranker improves retrieval quality before the agent responds 4. Agent answers based solely on the ingested knowledge base Requirements - Firecrawl API key - OpenAI API key (for embeddings) - OpenRouter API key (for the chat agent) - Cohere API key (for reranking) - Supabase project with pgvector enabled Setup 1. Create a Supabase project and run the following SQL in the SQL editor: sql -- Enable the pgvector extension create extension vector with schema extensions; -- Create a table to store documents create table documents ( id bigserial primary key, content text, metadata jsonb, embedding extensions.vector(1536) ); -- Create a function to search for documents create function matchdocuments ( queryembedding extensions.vector(1536), matchcount int default null, filter jsonb default '{}' ) returns table ( id bigint, content text, metadata jsonb, similarity float ) language plpgsql as $$ variableconflict usecolumn begin return query select id, content, metadata, 1 - (documents.embedding <=> queryembedding) as similarity from documents where metadata @> filter order by documents.embedding <=> queryembedding limit matchcount; end; $$; 2. Add your Firecrawl API key as a credential in n8n 3. Add your OpenAI API key as a credential (for embeddings) 4. Add your OpenRouter API key as a credential (for the chat agent) 5. Add your Cohere API key as a credential (for reranking) 6. Activate the workflow How to use Send a POST request to the webhook URL: bash curl -X POST https://your-n8n-instance/webhook/your-id \ -H "Content-Type: application/json" \ -d '{"url": "https://firecrawl.dev/docs"}' Then open the chat interface in n8n to ask questions about the ingested content.