A pharmaceutical company is building a research assistant on Amazon Bedrock Knowledge Bases. Analysts frequently ask questions that require connecting related entities across many documents — for example, 'Which compounds share a mechanism of action with the drug studied in trial X, and what were their adverse events?' With standard vector-based RAG, the assistant retrieves individually relevant chunks but consistently fails to synthesize answers that depend on multi-hop relationships between entities scattered across the corpus. Which approach should the team adopt to best improve answer quality for these relationship-heavy queries?