dsRAG is a search engine optimized for unstructured data, especially good at dealing with the complex query needs of financial reports, legal documents, academic papers and other intensive texts. Its core architecture realizes breakthroughs through three innovative technologies: firstly, it adopts thesemantic segmentation technique, which utilizes a large language model to intelligently slice and dice documents; followed by the application of theAutomatic context generationmechanism to dynamically create blocks of text containing multiple levels of context; and finally implement theRelevant Segment Extraction AlgorithmThe system supports the free combination of modularized components to intelligently combine related texts during querying. The synergy of these three technologies enables it to achieve an accuracy of 96.61 TP3T in the FinanceBench benchmark test, which is more than 2001 TP3T compared with the traditional RAG solution.The system supports free combination of the retrieval process through modularized components, including the flexible configuration of mainstream vector databases, embedded models, and reorderers, which provides a complete solution for the professional knowledge retrieval scenarios. The system supports flexible configuration of modular components, including mainstream vector databases and embedded models and reorderers, providing a complete solution for professional knowledge retrieval scenarios.
This answer comes from the articledsRAG: A Retrieval Engine for Unstructured Data and Complex QueriesThe
