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
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