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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents
TL;DR
VikingRAG is a novel retrieval-augmented generation system that significantly reduces token costs while maintaining high accuracy in processing structured documents. It achieves this through efficient multi-round retrieval strategies and adaptive escalation techniques.
✦ Why It Matters
Implement VikingRAG to enhance your document retrieval systems while significantly cutting token usage.
Key Takeaways
- Adopt VikingRAG for efficient document retrieval with lower token costs.
- Utilize experience edges to avoid redundant multi-round retrieval.
- Implement adaptive escalation to optimize retrieval strategies.
- Test VikingRAG on your datasets to evaluate performance improvements.
How It Works
VikingRAG combines semantic and structural access to streamline evidence gathering. It materializes retrieval traces as experience edges, allowing the system to reuse past retrieval efforts for similar queries.
This reduces the need for repeated multi-round explorations, thus lowering token costs. The adaptive escalation strategy intelligently decides when to use one-round retrieval based on the sufficiency of available evidence, invoking multi-round retrieval only when necessary.