Why Vector Databases Fail at Complex Reasoning: GraphRAG Explained
Vector search ranks chunks, not relationships—limiting multi-hop answers. See GraphRAG-Bench gaps in complex reasoning and summarization, and explore GraphRAG SDK.
Vector databases do not perform reasoning.
Top-k semantic similarity search ranks text fragments individually with respect to the query. This is fine for single-step fact retrieval, but it breaks down where the answer requires linking information from multiple fragments.
This is evident in the FalkorDB GraphRAG-Bench results. The biggest gap is in the Complex Reasoning (83.61) and Contextual Summarization (85.08) tasks—query types where search should proceed via relationships between entities rather than evaluating chunks in isolation.
It’s worth taking a closer look if your workload involves long texts and multi-part answers.
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