PageIndex

PageIndex is a tool that builds a hierarchical index of a document for vectorless, reasoning-based RAG pipelines, rather than relying on traditional embedding-based retrieval. Instead of chunking a document into vectors, which can lose structural information and provide limited visibility into why results were retrieved, PageIndex builds a table of contents index that an LLM traverses step-by-step to retrieve relevant content. This produces an explicit reasoning trace that explains why a particular section was selected, similar to how a human scans headings and drills down into specific sections. Some of our teams have found this approach works well for documents where meaning depends heavily on structure rather than semantics, such as financial reports with numerical data, legal documents with cross-referenced articles and complex clinical or scientific documents. However, this approach comes with trade-offs. For instance, because LLM inference is part of the retrieval process, it can introduce significant latency and cost, especially for large documents.