This RAG AI Agent with n8n + Supabase is the Real Deal - YouTube

for the first month

Transform how you read and learn

Briefy turns all kinds of lengthy content into structured summaries in just 1 click. Save, review, find, and share knowledge effortlessly.

Offer expires in

23:59:59

Overview

This video demonstrates how to build a robust and production-ready RAG AI agent using n8n and Supabase, without writing any code. The agent leverages Supabase's vector database capabilities for efficient knowledge retrieval and utilizes n8n's workflow automation to create a seamless user experience. The video highlights the importance of using a persistent chat memory and vector database for scalability and reliability, contrasting it with the limitations of in-memory solutions often seen in other tutorials. The tutorial provides a step-by-step guide to setting up the workflow, including creating a Supabase account, configuring the n8n nodes, and integrating with Google Drive for document management.

Introduction to RAG AI Agent
- 💬
  The video introduces the concept of RAG (Retrieval Augmented Generation) AI agents, which use a knowledge base to answer user queries.
- 🚀
  The tutorial focuses on building a production-ready RAG agent using n8n and Supabase, addressing the limitations of simpler solutions often found in other tutorials.
- 💡
  The video emphasizes the importance of using a persistent chat memory and vector database for scalability and reliability, contrasting it with the limitations of in-memory solutions.
Demonstration of RAG AI Agent
- 🤖
  The video showcases a live demonstration of the RAG AI agent, highlighting its ability to answer questions based on a knowledge base of documents.
- 📚
  The agent is initially empty, demonstrating its ability to handle a knowledge base with no documents.
- 🔍
  The agent is then tested with a question it cannot answer, showcasing its ability to learn and adapt as new documents are added to the knowledge base.
Setting Up Supabase
- ☁️
  The video guides viewers through setting up a free Supabase account, which will be used for both chat memory and vector database.
- 🔑
  The video highlights the importance of the free tier for getting started with Supabase, emphasizing its affordability and scalability.
- ⚙️
  The video explains how to access the necessary credentials for connecting to Supabase from n8n, including the PostgreSQL connection details and the API connection URL.
Workflow Execution in n8n
- 🔌
  The video walks through the n8n workflow step-by-step, explaining the purpose and functionality of each node.
- 💬
  The video demonstrates how to use the chat trigger node to receive user input and test the AI agent within the n8n workflow.
- 🧠
  The video explains the role of the AI agent node, which uses GPT-3 as the chat model and connects to the Supabase chat memory and vector database.
Managing Document Updates
- 📂
  The video emphasizes the importance of deleting old vectors in the Supabase database when a document is updated to prevent duplicates.
- 🔄
  The video explains how the workflow handles document updates by deleting the old vector and inserting a new one with the updated content.
- 🔍
  The video demonstrates how the workflow ensures that the vector database is cleared and ready for the new record, preventing duplicate knowledge.
Finalizing the RAG AI Agent
- 🚀
  The video concludes by showcasing the fully functional RAG AI agent, demonstrating its ability to answer questions based on the updated knowledge base.
- 💡
  The video encourages viewers to explore further enhancements to the agent, such as implementing semantic search or keyword search.
- 🤝
  The video invites viewers to share feedback and questions in the comments, encouraging further discussion and collaboration.