PageIndex Offers a Vector-Free RAG Approach Using Hierarchical Document Trees

Retrieval-Augmented Generation (RAG) typically relies on chunking documents, generating embeddings, and storing them in vector databases for similarity-based retrieval — a process that grows costly and complex as data scales. An alternative approach called Vectorless RAG eliminates these preprocessing steps entirely by replacing semantic similarity search with LLM-driven reasoning. The open-source framework PageIndex organises documents into a hierarchical tree structure, allowing a language model to navigate content much like a reader consulting a book's index. When a query is received, the LLM reasons over the document tree to identify and retrieve relevant nodes before generating an answer. This method also addresses common RAG pitfalls such as hard chunking that fragments meaning and cross-references within documents that semantic matching often fails to resolve.