🔍 Website content chatbot with Pinecone, Airtable & OpenAI for RAG applications

⚡ 1,104 views · 🔍 AI RAG & Knowledge Retrieval

Description

Use cases include:

Building a knowledge base from your website.
Creating a chatbot that answers customer queries using your own site content.
Powering RAG workflows for FAQs, support docs, or product knowledge.

Workflow starts with a manual trigger or chat message.
Website content is fetched via HTTP Request.
The HTML body is extracted and converted into clean Markdown.
Text is split into chunks (~500 chars with 50 overlap) using the Character Text Splitter.
OpenAI embeddings are generated for each chunk.
Content and embeddings are stored in Pinecone with namespace separation.
A Chat Agent (powered by OpenAI or OpenRouter) retrieves answers from Pinecone and Airtable.
Memory buffer allows multi-turn conversations.
A billing tool (Airtable) provides dynamic billing-related answers when needed.

Replace the sample website URL in the HTTP Request node with your own domain or content source.
Update Normalize code based on markdown content output to remove noise.
Adjust chunk size in the Text Splitter for your website markdown output.
- In this example, the Character Text Splitter with separator ###### worked really well.
- Always check the Markdown output to fine-tune your splitting logic.
Update Pinecone namespace to match your project.
Customize the Chat Agent system prompt to fit your brand voice and response rules.
Connect to your own Airtable schema if you want live billing/payment data access.

Ask in the n8n Forum!

Happy Automating! 🚀

HTTP Request, Markdown, AI Agent, Embeddings OpenAI, OpenAI Chat Model, Simple Memory

Download workflow.json and import into n8n: Workflow menu → Import from File