Understanding RAG & Vector Databases with n8n 🤖 | Build Your First AI Knowledge Base

Ever wondered how AI can answer questions using your own documents and data? 🤔 In this video, I explain *RAG (Retrieval-Augmented Generation) and Vector Databases* using a practical n8n workflow. You'll see how documents can be processed, converted into embeddings, and stored in a vector database so an AI system can retrieve relevant information when answering questions. 🚀 *What you'll learn:* What RAG is and why AI applications use it What a vector database does How embeddings help AI find relevant information How to connect Google Drive with an n8n workflow How the Simple Vector Store, OpenAI embeddings, and Default Data Loader fit together How these components help build AI assistants that work with your own knowledge base ⚙️ *Tools used:* n8n Google Drive OpenAI Embeddings Simple Vector Store Default Data Loader 💡 *Why does this matter?* RAG can help AI assistants retrieve relevant information from business documents, FAQs, product catalogs, and internal knowledge bases instead of relying only on general model knowledge. If you're learning AI automation, n8n, chatbots, or AI agents, this is an important concept to understand. 👇 *Have a question about RAG or vector databases?* Drop it in the comments! 👍 Like the video, subscribe for more practical AI automation tutorials, and share it with someone learning n8n. #RAG #VectorDatabase #n8n #AIAutomation #AI #OpenAI #Embeddings #AIAgents #TechTutorial