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FastRAG: Retrieval Augmented Generation for Semi-structured Data

Created by
  • Haebom

Author

Amar Abane, Anis Bekri, Abdella Battou, Saddek Bensalem

Outline

This paper presents a method to efficiently process and interpret network data for increasingly complex network operations. Existing Retrieval-Augmented Generation (RAG) methods, VectorRAG and GraphRAG, have problems in time, cost, and search efficiency due to the complexity and implicit nature of semi-structured technical data. In this paper, we propose FastRAG, a new RAG approach for semi-structured data. FastRAG extracts and structures data through schema learning and script learning without submitting the entire data source to LLM, and accurately retrieves rich information by integrating text search and knowledge graph (KG) queries. The evaluation results show that FastRAG provides accurate question-answering while improving time by up to 90% and cost by up to 85% compared to GraphRAG.

Takeaways, Limitations

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Takeaways:
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We present FastRAG, an efficient RAG approach for processing semi-structured network data.
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Reduce the load on LLM and improve efficiency through schema learning and script learning.
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Improve accuracy by integrating text search and KG queries.
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Confirmed time and cost savings compared to existing RAG methods (up to 90% time reduction, 85% cost reduction).
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Limitations:
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FastRAG's performance may depend on the quality of the schema and scripts used.
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Need to verify generalization performance for various types of semi-structured data.
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Further research is needed into configuration parameters optimized for specific network environments.
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Scalability and stability evaluation in actual large-scale network environments is required.
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