Scrape and ingest web pages into a Pinecone RAG stack with Firecrawl and OpenAI

Description

What this does

Receives a URL via webhook, uses Firecrawl to scrape the page into clean markdown, and stores it as vector embeddings in Pinecone. 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, returning a 422 error if invalid
Firecrawl /scrape fetches the page and converts it to clean markdown
Embeddings OpenAI generates 1536-dimensional vector embeddings from the scraped content
Default Data Loader attaches the source URL as metadata
Pinecone Vector Store inserts the content and embeddings into the index
Respond to Webhook confirms how many items were added

Part 2: RAG Chat Agent
Chat trigger receives a user question
AI Agent (OpenRouter / Claude Sonnet) queries the Pinecone vector store
Cohere Reranker improves retrieval quality before the agent responds
Agent answers based solely on the ingested knowledge base

🔥 Firecrawl
🌲 Pinecone
🧠 OpenAI Embeddings
🤖 OpenRouter (Claude Sonnet)
🎯 Cohere Reranker

Webhook usage

Send a POST request to the webhook URL:

curl -X POST https://your-n8n-instance/webhook/your-id \
-H "Content-Type: application/json" \
-d '{"url": "firecrawl.dev"}'

Pinecone setup

Your Pinecone index must be configured with 1536 dimensions to match the OpenAI text-embedding-3-small model output. See the sticky note inside the workflow for the exact index settings.

Requirements
Firecrawl API key
OpenAI API key (for embeddings)
OpenRouter API key (for the chat agent)
Cohere API key (for reranking)
Pinecone account with a properly configured index