Knowledge Graph Architecture

Chapter 3 told the story of building a knowledge graph from failure to function. These deep-dives explore the technical foundations that make it work.

The journey from scattered notes to searchable knowledge requires multiple systems working together: embeddings that capture meaning, indices that enable fast retrieval, link discovery that surfaces non-obvious connections, and evolution strategies that keep the graph valuable over time.

Each item in this cluster expands on a specific technical challenge from the chapter. Graph RAG Architecture explains how retrieval-augmented generation extends beyond flat document search. Semantic Search Implementation covers the embedding and indexing decisions that determine search quality. Link Discovery and PageRank addresses how connections form between documents. Incremental Indexing handles the continuous growth of knowledge. Query Patterns and Optimization ensures the system stays responsive. Graph Evolution Strategies addresses long-term maintenance and sustainability.

These aren't just theory. They're the actual patterns behind the knowledge graph that powers this site.