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Scrape and ingest web content into Supabase pgvector with Firecrawl

n8nn8n18 modulesv1.0
OpenAI

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. Web

At a glance

Scrape and ingest web content into Supabase pgvector with Firecrawl is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects OpenAI. It's free to download. Follow the 5-step import below to go live in minutes.

Platform
n8n
Connects
OpenAI
Modules
18
Price
Free
Version
v1.0
Scrape and ingest web content into Supabase pgvector with Firecrawl workflow diagram

About this workflow

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: 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: Then open the chat interface in n8n to ask questions about the ingested content.

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How to import this n8n workflow

  1. 1

    Download the workflow JSON file after purchase.

  2. 2

    Open n8n → click the menu → Import from File.

  3. 3

    Select the downloaded JSON and import.

  4. 4

    Set up credentials for each node that requires them.

  5. 5

    Click Execute Workflow to test, then activate.

Setup guide

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