Dynamic RAG Chatbot for Live Notion Knowledge Bases
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This n8n workflow empowers you to create a dynamic Retrieval Augmented Generation (RAG) chatbot that answers questions based on your live Notion knowledge base. It automatically keeps your AI up-to-date with your latest information, ensuring intelligent and accurate responses.
About This Workflow
This robust n8n workflow establishes a powerful RAG system, continuously synchronizing your Notion knowledge base with a Supabase vector store. It intelligently processes and embeds your Notion content using OpenAI, ensuring your AI assistant always has access to the most current information. When a chat message is received, the system retrieves relevant data semantically, then leverages the advanced GPT-4o model to generate precise and contextually rich answers. This setup is ideal for internal knowledge management, customer support bots, or any application requiring an AI that learns and adapts from evolving data sources.
Key Features
- Automated Notion Sync: Periodically updates your AI's knowledge base from Notion pages, ensuring your information is always current.
- Dynamic RAG System: Continuously ingests, chunks, embeds (using OpenAI), and stores (in Supabase) your 'living data' for real-time relevance.
- Advanced LLM Integration: Utilizes OpenAI's powerful GPT-4o model for highly accurate, contextualized, and human-like answers.
- Interactive Chat Interface: Responds to user queries in real-time via a dedicated chat message trigger, creating a seamless user experience.
- Intelligent Text Chunking: Optimizes document processing by splitting content into manageable chunks, improving retrieval accuracy and context handling for the LLM.
How To Use
- Set up Notion Integration: Configure the
Get updated pages(or optionally, theNotion Trigger) node with your Notion API credentials and specify the database ID of your knowledge base. - Configure Supabase Vector Store: Connect the
Delete old embeddings if existnode (and implicitly, your embedding storage) to your Supabase instance, ensuring your vector database is properly initialized. - Provide OpenAI Credentials: Enter your OpenAI API key for both the
Embeddings OpenAIandOpenAI Chat Modelnodes to enable embedding generation and LLM responses. - Customize Data Processing: Adjust the
Token Splitterchunk size andDefault Data Loadermetadata as needed to optimize document ingestion for your specific data. - Activate Chat Trigger: The
When chat message receivednode serves as your chatbot's entry point; ensure it's enabled for external interactions to start receiving queries.
Apps Used
Workflow JSON
{
"id": "90c9285c-6364-4eea-8746-4e359df051da",
"name": "Dynamic RAG Chatbot for Live Notion Knowledge Bases",
"nodes": 21,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 90c9285c-6364...
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