Hierarchical Re-ranker Retriever (HRR)

Retrieving the right level of context for a given query is a perennial challenge in information retrieval - too large a chunk dilutes semantic specificity, while chunks that are too small lack broader context. This paper introduces the Hierarchical Re-ranker Retriever (HRR), a framework designed to achieve both fine-grained and high-level context retrieval for large language model (LLM) applications. In HRR, documents are split into sentence-level and intermediate-level (512 tokens) chunks to maximize vector-search quality for both short and broad queries. We then employ a reranker that operates on these 512-token chunks, ensuring an optimal balance neither too coarse nor too fine for robust relevance scoring. Finally, top-ranked intermediate chunks are mapped to parent chunks (2048 tokens) to provide an LLM with sufficiently large context.