GraphRAG Explained Line by Line | RAG for ML #20

Chunk-based RAG treats every document as an isolated island. GraphRAG connects the islands. By extracting entities and relationships from your documents and storing them in a knowledge graph, you can answer questions that require following connections across multiple documents. In this episode we cover: Why chunk-based retrieval misses relational questions What a knowledge graph is and how it stores relationships Extracting entities and relationships with spaCy Building a graph with NetworkX Traversing the graph to build richer context Combining graph traversal with vector retrieval Next up: Multimodal RAG