Knowledge Graph or Vector Database… Which is Better?

In the evolving landscape of AI and information retrieval, knowledge graphs have emerged as a powerful way to represent complex, interconnected information. But how do they work, and are they better than traditional RAG setups? Resources: Notebook Repo - https://github.com/ALucek/GraphRAG-Breakdown GraphRAG - https://microsoft.github.io/graphrag/ GraphRAG Paper - https://arxiv.org/pdf/2404.16130 GraphRAG Case Study from LinkedIN - https://arxiv.org/pdf/2404.17723 Unifying Large Language Models and Knowledge Graphs - https://arxiv.org/pdf/2306.08302 The Ultimate Guide to Fine-Tuning LLMs - https://arxiv.org/pdf/2408.13296 Google’s Knowledge Graph Introduction - https://blog.google/products/search/introducing-knowledge-graph-things-not/ Leiden Algorithm - https://en.wikipedia.org/wiki/Leiden_algorithm Chapters: 00:00 - Why RAG Fails 01:54 - What is a Knowledge Graph? 03:35 - Knowledge Graphs & LLMs 05:39 - Introducing GraphRAG 06:17 - Main Components of Knowledge Graphs 07:39 - Setting up GraphRAG 11:10 - Data Flow: Overview 12:13 - Data Flow: Entity & Relationship Extraction 16:40 - Data Flow: Community Clustering 18:28 - Data Flow: Community Report Generation 20:11 - Observing Final Knowledge Graph 22:12 - RAG Setup 23:58 - RAG: Local Search 27:10 - RAG: Global Search 30:23 - RAG: DRIFT Search 35:06 - Comparing GraphRAG vs Regular RAG 36:59 - Comparison Discussion #ai #datascience #programming