Scrape and ingest web content into Supabase pgvector with Firecrawl Workflow Solution

Workflow overview

Why this workflow matters

Supports knowledge capture and document intelligence use cases.

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 Webhook receives a POST request with a url field Verify URL validates and normalizes the domain Supabase checks if the URL was already ingested (deduplication) If the URL already exists, ingestion is skipped; otherwise it continues Firecrawl fetches the page and converts it to clean markdown OpenAI generates vector embeddings from the scraped content Default Data Loader attaches the source URL as metadata Supabase Vector Store inserts the content and embeddings into pgvector Respond to Webhook confirms how many items were added Part 2: RAG Chat Agent Chat trigger receives a user question AI Agent (OpenRouter) queries the Supabase vector store filtered by URL Cohere Reranker improves retrieval quality before the agent responds 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 Create a Supabase project and run the following SQL in the SQL editor: -- 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 match_documents ( query_embedding extensions.vector(1536), match_count int default null, filter jsonb default '{}' ) returns table ( id bigint, content text, metadata jsonb, similarity float ) language plpgsql as $$ #variable_conflict use_column begin return query select id, content, metadata, 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding limit match_count; end; $$; Add your Firecrawl API key as a credential in AlekSystem Add your OpenAI API key as a credential (for embeddings) Add your OpenRouter API key as a credential (for the chat agent) Add your Cohere API key as a credential (for reranking) Activate the workflow How to use Send a POST request to the webhook URL: curl -X POST https://your-AlekSystem-instance/webhook/your-id \ -H "Content-Type: application/json" \ -d '{"url": "https://firecrawl.dev/docs"}' Then open the chat interface in AlekSystem to ask questions about the ingested content.

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