GFM-RAG Explained | Graph-Based RAG That Thinks Like Humans

Description

In this video, I explain GFM-RAG-8M, a next-generation Graph-based Retrieval Augmented Generation (RAG) model that goes beyond simple keyword search and performs real reasoning over knowledge graphs. Traditional RAG systems often fail when questions require multi-document understanding or logical connections. GFM-RAG solves this by converting documents into a Knowledge Graph and using a Graph Neural Network (Graph Foundation Model) to retrieve the most relevant information. I break down: What problem GFM-RAG is designed to solve Why graph-based reasoning is better than vector search How Knowledge Graph Indexing works What makes GFM-RAG different from normal RAG and GraphRAG Real-world use cases like research, medical, and AI agents This explanation is beginner-friendly, practical, and perfect for anyone working with LLMs, RAG systems, AI agents, or knowledge graphs. If you’re building advanced AI applications or want to understand the future of RAG systems, this video is for you. 👍 Like | 🔔 Subscribe | 💬 Comment if you want a FastAPI + Neo4j + GFM-RAG demo #ArtificialIntelligence #MachineLearning #LLM #GenerativeAI #AIEngineering