Structured Retrieval for Production RAG

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  • VikingRAG: Fewer Retrieval Rounds, Less Context Waste for Structured-Document RAG

    Advanced
    Structured Retrieval for Production RAG: Part 1 , Note: Sep 15, 2026 , Paper: 2026 , Information Retrieval

    A deep reading of VikingRAG: hierarchy-preserving URI-addressable storage, Search/List/Grep/Read tools, reusable experience edges, and adaptive escalation for reducing repeated retrieval tokens and latency.

    Understand it in 90 seconds
    Problem
    Enterprise manuals, syllabi, papers, contracts, and financial reports are not bags of unrelated chunks. An answer may require finding the right document, then following chapter, section, or subsection structure to collect facts that are far apart. Serializing every directory into the prompt makes structural context expensive; doing only one flat top-k retrieval can miss cross-section dependencies on the first pass.
    Core insight
    Move the hierarchy from the prompt into queryable external semantic storage. Every directory node, chunk, and multi-level abstract receives a URI whose prefix preserves ancestor–descendant relations. A vector result is therefore not only text; it is a navigation handle that can drive scoped List, Grep, and Read operations (Sections 2.2 and 3.1).
    Strongest evidence
    The evaluation covers six structured-document datasets and eight baselines, with default settings K=10, L=1,000, and B=15. End-to-end accuracy, latency, LLM tokens, ingestion, and deletion are measured. Figure 3 and Table 3 report 11.6%–51.9% token ratios for VikingRAG and 5.1%–32.5% for VikingRAG-E+; Figure 7 repeats the central comparison with GPT-5.5, Seed-2.0, and GLM-4.7 on VersionQA.
    Main boundary
    Accuracy is an LLM-as-a-judge semantic-consistency proxy with expert verification, not a direct retrieval-recall or independently replicated correctness proof. Experience edges are warmed with 1,000 synthetic historical questions generated from the same document corpus; false-no-escalation is still 14.4% on QASPER and 6.7% on FinanceBench (Table 7).
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