KohakuRAG: A simple RAG framework with hierarchical document indexing
Retrieval-augmented generation (RAG) systems that answer questions from document collections face compounding difficulties when high-precision citations are required: flat chunking strategies sacrifice document structure, single-query formulations miss relevant passages through vocabulary mismatch, and single-pass inference produces stochastic answers that vary in both content and citation selection. We present KohakuRAG, a hierarchical RAG framework that preserves document structure through a four-level tree representation (document $\rightarrow$ section $\rightarrow$ paragraph $\rightarrow$ sentence) with bottom-up embedding aggregation, improves retrieval coverage through an LLM-powere...
| Authors | Shih-Ying Yeh, Yueh-Feng Ku, Ko-Wei Huang et al. |
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| Primary category | cs.CL |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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