GraphRAG and Vectorless RAG Fix Vector RAG's Silent Failures
Vector RAG structurally fails by confidently hallucinating on semantically similar but incorrect chunks with no errors logged. GraphRAG maps entity relationships via graphs; Vectorless RAG skips vectors for LLM reasoning over document structure—each excels where the other can't.
Vector RAG's Structural Blind Spot
Traditional vector RAG retrieves semantically similar document chunks, but these are often close yet wrong, leading the LLM to generate confident, plausible answers that mislead users. No errors trigger, no logs flag issues—failures surface only via delayed user complaints weeks later. Tweaking parameters like embedding models or top-k doesn't fix this; it's baked into relying solely on vector similarity, which ignores entity relationships and document structure.
GraphRAG: Leverage Knowledge Graphs for Relational Context
GraphRAG overlays a knowledge graph on your data to explicitly map relationships between entities (e.g., people, places, concepts). Retrieval pulls connected subgraphs, not just isolated chunks, enabling the LLM to reason over interconnected facts. Use this when your domain has complex entities—like legal docs or enterprise knowledge bases—where proximity alone fails but relational paths reveal truth. Trade-off: Higher upfront indexing cost for graph construction, but gains precision on global queries.
Vectorless RAG: LLM-Driven Reasoning Over Raw Structure
Vectorless RAG ditches vector databases entirely, feeding the LLM hierarchical document outlines or tree structures (e.g., via markdown headers, XML tags). The model navigates and summarizes sections dynamically without embeddings. Ideal for hierarchical content like reports or codebases, where structure guides relevance better than semantics. Trade-off: Slower at scale without vector speedups, but avoids embedding drift and shines on precise, local queries.
Neither replaces vector RAG as a drop-in—pick GraphRAG for entity-heavy data, Vectorless for structured docs. Both eliminate the 'smoothly wrong' failure by addressing what vectors miss: relationships or hierarchy.