GraphRAG

Graph RAG in Production: Comparing Microsoft GraphRAG, LightRAG, Fast GraphRAG, and HippoRAG Architecture, Indexing Overhead, and Multi-Hop Retrieval Economics

Standard vector retrieval-augmented generation (RAG) relies on dense semantic embeddings to map text chunks into a shared vector space. While effective for localized semantic lookups, dense retrieval degrades on two distinct query classes: corpus-wide global summarization (such as identifying overarching themes across an entire document store) and complex multi-hop associative queries (connecting entity A to entity D through multiple intermediary relationships across disparate documents). Graph

August 25, 20261 min

GraphRAG Frameworks and Architectures in Production: Comparing Microsoft GraphRAG, LightRAG, Neo4j GenAI, and Kùzu

Graph RAG in Production: Comparing Microsoft GraphRAG, LightRAG, Fast GraphRAG, and HippoRAG Architecture, Indexing Overhead, and Multi-Hop Retrieval Economics