Context-Aware Document Retrieval Method
TokenSaver
designed to enhance document intelligence across large document collections. The system enables scalable AI applications with improved efficiency and resource utilization.
Key Features & Advantages
Retains important document context to support accurate information access
Handles complex document formats for streamlined knowledge extraction
Accelerates retrieval of relevant information from large document collections
Balances efficiency and retrieval performance
TokenSaver improves the efficiency of document-based question answering by reducing unnecessary processing and helping AI systems make better use of relevant information from large document collections. By enhancing Retrieval-Augmented Generation (RAG), a technique that supplements large language models with information retrieved from external documents, the system enables scalable document intelligence and achieves up to 68% reduction in token usage across real-world PDF datasets.
On a benchmark evaluation of 45 real-world OCR-processed documents, TokenSaver significantly outperformed pure RAG method. TokenSaver achieved 100% retrieval accuracy (versus 91% for standard RAG) while reducing the amount of text passed to the AI from 2,563 tokens down to just 747 tokens — an over 70% reduction in data usage without any loss of critical information.
