Beyond Vector Search: How PageIndex Reimagines Document Intelligence Through Hierarchical Reasoning

PageIndex challenges the dominant vector-based RAG paradigm by replacing embedding-based retrieval with a tree-structured document index that preserves hierarchical context. This approach directly addresses critical limitations of traditional RAG—fragmented context, loss of document structure, and retrieval noise—making it particularly valuable for complex documents like research papers and legal contracts. By enabling LLMs to reason over document structure rather than hunt through vector similarity scores, PageIndex reduces infrastructure complexity while improving retrieval accuracy.