How to Build Graph Database from Scratch

I built a vector database from scratch last time. This time: a graph database that can answer questions vectors can't — by walking relationships instead of guessing similarity. This explainer video demonstrates the core algorithm for building a knowledge graph from scratch with coding techniques. What you'll learn: → What graphs actually are under the hood and how knowledge is stored as (subject, predicate, object) triples — the same atomic unit behind Google's Knowledge Graph, Wikidata, and every RDF store on Earth → How to build a working graph database in ~80 lines of Python with SQLite — supporting pattern matching, BFS traversal, shortest path, and multi-hop reasoning → Head-to-head benchmark from 10 to 10,000 queries: our GraphDB vs Neo4j — neighbor lookups and shortest path queries at scale What we build: 00:00 Introduction 00:59 Project Setup 03:04 Graph Concepts 06:02 Traversal Algorithms 15:55 Building GraphDB 25:03 Automated Extraction 33:27 Neo4j Integration 36:31 Conclusion The full notebook is on GitHub: https://github.com/iRahulPandey/under... Previous video — Vector Database from Scratch:    • Build Vector Database From Scratch   #graphdatabase #knowledgegraph #neo4j #ai #python #machinelearning #fromscratch #llm #graphs