PageIndex is a reasoning-based retrieval engine designed to navigate long professional documents by building hierarchical tree structures similar to a table of contents. Unlike traditional vector-based systems that rely on semantic similarity through artificial text chunking and vector databases, this method enables Large Language Models to perform context-aware searches via agentic tree traversal. This approach mimics human expertise in document navigation, providing traceable and explainable results grounded in specific page and section references.
* Replaces approximate vector similarity with reasoning-driven retrieval
* Eliminates the need for artificial text chunking or vector databases
* Provides high traceability through explicit document structure grounding
* Achieved 98.7% accuracy on the FinanceBench benchmark
* Replaces approximate vector similarity with reasoning-driven retrieval
* Eliminates the need for artificial text chunking or vector databases
* Provides high traceability through explicit document structure grounding
* Achieved 98.7% accuracy on the FinanceBench benchmark
