Adaptive RAG-Architecture for Intelligent Search in the Corpus of Educational Institutions Documents
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Abstract
The problem of improving the quality of intelligent search in a corpus of educational institution documents, including curricula, course syllabus, and regulatory documents, was solved. Classic architectures of the Retrieval-Augmented Generation (RAG) approach, based on a single plaintext search module, demonstrate low accuracy when used with documents containing tables, logical relationships between entities, and strict regulatory document wording. An adaptive RAG architecture consisting of four layers is proposed, each taking into account the specifics of data storage in such documents. The results show that considering document structure and adaptive query routing significantly improve the actual accuracy of responses. The proposed architecture can be used in the design of intelligent assistants for administrative and educational services at higher education institutions.
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References
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