Unstructured’s Preprocessing Pipelines Enable Enhanced RAG Performance

Unstructured has developed an innovative approach to enhancing Retrieval-Augmented Generation (RAG) systems by decomposing documents into discrete structural elements, such as titles and tables, instead of relying on traditional token-size chunking methods. This method leverages both computer vision and natural language processing to identify and categorize elements based on semantic relationships, improving the relevance and contextual richness of information for retrieval and generation tasks. Evaluations using the FinanceBench dataset demonstrated significant performance improvements in information retrieval and question-answering tasks, showcasing the superiority of element-based chunking over conventional strategies. The proprietary Chipper model, which identifies diverse document elements and transcribes tables into HTML, plays a crucial role in this process. The results highlight the potential for broader applicability and adaptability of Unstructured's approach across various document types, promising more accurate and efficient question-answering capabilities. Unstructured aims to extend the benefits of this method beyond financial reporting, enhancing RAG systems' interactions with unstructured data across different domains.