Beyond Single Query: Why Top RAG Systems Search Multiple Times

Single-query vector search suffers from incomplete semantic coverage—a single embedding lands in only one neighborhood of vector space, missing relevant context from other conceptual clusters. Leading RAG frameworks like LangChain and LlamaIndex solve this by decomposing user questions into multiple search queries and fusing results, capturing diverse perspectives and delivering more comprehensive retrieval outcomes.