HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation
BlackRock & NVIDIA researchers propose HybridRAG — combining Knowledge Graph RAG (GraphRAG) with Vector RAG for financial document Q&A. The hybrid approach outperforms either method alone on earnings call transcripts, achieving higher answer relevancy and faithfulness because graph structure captures entity relationships that vector search misses.
If you're building RAG systems over complex documents (contracts, financial reports, research papers), this is a practical architecture showing how to combine graph and vector retrieval for meaningfully better accuracy.
Key points
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Problem: financial docs (earnings calls, 10-Ks) have complex entity relationships that pure vector search misses
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VectorRAG: good at semantic similarity but misses structured relationships between entities
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GraphRAG: builds KG of entities/relationships — great for structured queries, weaker on contextual prose
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HybridRAG: combines both approaches — KG for entity relationships + vectors for contextual similarity
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Evaluated on faithfulness, answer relevancy, context precision/recall — outperforms both standalone methods