RAG ChatBot for Website
This workflow allows users to convert any website into an intelligent RAG (Retrieval-Augmented Generation) chatbot. It extracts website content, generates vector embeddings, stores them in Supabase, and retrieves relevant answers using OpenAI when a user submits a question. Perfect for building custom site-specific chat experiences.
Main Use Cases:
- Turn any website into a question, answering chatbot.
- Allow users to query a specific site and receive relevant, contextual answers.
- Automate website data parsing and embedding with Supabase & n8n.
- Store and retrieve vector, based knowledge for dynamic, site, specific responses.
How It Works:
This workflow builds a Retrieval-Augmented Generation (RAG) chatbot by automating content extraction, embedding generation, and answer retrieval, all using Supabase.
1. Website URL Submission
- A user submits a website URL through a form trigger.
- The submitted URL triggers the workflow.
2. Website Data Extraction
- n8n fetches the HTML content from the provided URL.
- The content is parsed, cleaned, and chunked into manageable sections.
3. Generate Embeddings & Store in Supabase
- The text chunks are sent to an embedding model (e.g., OpenAI, Cohere).
- Resulting vectors are stored in Supabase’s vector database.
- Metadata like URL, content and section titles are saved with each vector.
4. User-Initiated Question
- A user submits a question via chat.
5. Vector-Based Retrieval & Answer Generation
- A similarity search is performed in Supabase to find relevant chunks.
- Top matching content is passed to a language model for answer generation.
- The user receives an accurate, context-based response.
Build Smart Site-Specific Chatbots in Minutes:
This solution makes it easy to deploy intelligent, RAG-based chatbots that reference live website data. Perfect for documentation sites, help centers, or internal tools.
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