AI Memory Systems: Production RAG Pipelines with n8n

Overview

What if your AI could search your own documents and respond with context it actually understands? That is exactly what you will build here. This course teaches you to build AI memory systems using RAG, the retrieval architecture behind intelligent document search, enterprise knowledge bases, and AI-driven support tools. Here is what you will mainly build: AI Memory & Retrieval Foundations: Understand why AI loses context, how RAG retrieves the right information before responding, & how vector embeddings enable semantic search. You will also compare traditional RAG with agent-based RAG. A Working RAG Knowledge Base: Ingest PDFs, store OpenAI embeddings in Supabase with pgvector, & build a live query pipeline using GPT-4o mini with a fallback handler for unanswerable queries. Multimodal RAG with Gemini Vision: Use OCR and Gemini Vision to extract context from images, store image embeddings, and query visual content through semantic search. Built for professionals across any function who want AI that works with real organizational data. Over 200,000 professionals across 160+ LearnKartS courses have already built these skills. Start building yours now.

Syllabus

RAG Systems, Embeddings & Vector Databases

Learn how RAG systems solve AI memory limitations using embeddings and vector databases. You will understand similarity search, retrieval pipelines, and common failure modes in AI memory systems.

Supabase, Vector Databases & End-to-End RAG Systems

Build a complete RAG pipeline using Supabase, PostgreSQL, and vector extensions. You will design ingestion workflows, embeddings pipelines, and query systems for real-world AI applications.

Multimodal RAG with OCR, Vision Models & Vector Search

Extend RAG systems beyond text using OCR and vision models for image understanding. You will build multimodal pipelines with image embeddings and semantic search across visual data.