In this complete RAG tutorial, I'll show you how to build a smarter AI assistant that can remember your personal data using Retrieval Augmented Generation (RAG). You'll learn how to implement a vector database with Supabase and create an n8n workflow that allows your LLM to access custom knowledge. Using a Japan trip itinerary as an example, I demonstrate the entire process from initial setup to final implementation.
RAG (Retrieval Augmented Generation) is the key technique that allows large language models to access and remember information beyond their training data. This step-by-step guide walks you through setting up Supabase as your vector database, implementing memory functionality, and embedding your personal data for seamless retrieval. Whether you're working with business documents or personal notes, this tutorial provides everything you need to enhance your AI with custom knowledge.
Learn the fundamentals of vector databases, embedding techniques, and how to structure your data for optimal retrieval. This beginner-friendly tutorial requires no prior experience with RAG or vector databases - I'll guide you through each step of the process to create your own custom AI solution.
🔗 Links Mentioned in Video 🔗
👉 n8n workflow platform: https://bit.ly/lakshit_n8n
👉 Workflow Template: https://bit.ly/n8n_template
⏱️ Timestamps ⏱️
00:00:00 - Introduction & Problem Statement
00:00:23 - Showcasing the RAG Solution
00:01:17 - Basic Setup Overview
00:01:43 - RAG Implementation Plan
00:01:55 - Supabase Setup
00:03:13 - Memory Configuration
00:05:29 - Understanding RAG Technique
00:06:24 - Creating the RAG Model in n8n
00:08:48 - Embedding Explanation
00:09:53 - Supabase Vector Database Setup
00:12:14 - Tool Configuration
00:12:57 - Testing the RAG Implementation
00:14:11 - Conclusion
✨ Connect With Me ✨
🔹 LinkedIn: / lakshit-ukani
#️⃣ Hashtags #️⃣
#RAGTutorial #VectorDatabase #AICustomization
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