(Without Embeddings & a Vector Database)
The construction of a Vectorless, Reasoning-Based RAG system using hierarchical page indexing.
➡️ What's inside?
The document is transformed into a tree, and the LLM traverses this tree to find an answer. Without embeddings. Without searching for similarity.
This is very similar to how we search for information in real life. When we need to find something in a textbook, we don't read all the pages in a row. First, we open the table of contents, find the needed chapter, then browse the sections and go directly to the required one.
PageIndex works similarly. You pass it a document, and it builds a tree where each branch is a section, and each leaf is the text itself. When you ask a question, the LLM traverses the tree level by level to find the correct answer.
Full code: GitHub
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