RAG Without Vectors – Reasoning-Based RAG using PageIndex

Traditional vector-based RAG often struggles with retrieval accuracy because it optimizes for similarity, not relevance. But what we truly need in retrieval is relevance, which requires reasoning. When working with professional documents that require domain expertise and multi-step reasoning, vector-based RAG and similarity search often fall short.

So we started exploring a more reasoning-driven approach to RAG. Reasoning-based RAG enables LLMs to think and reason their way to the most relevant document sections. Inspired by AlphaGo, we propose to use tree search to perform structured document retrieval.

We open-sourced one of the key components: PageIndex. PageIndex is a hierarchical Document Indexing system that builds search trees from long documents (like financial reports, regulatory documents, or textbooks), making them ready for reasoning-based RAG.

Some highlights:

- Hierarchical Structure: Organizes lengthy PDFs into LLM-friendly trees — like a smart table of contents.

- Precise Referencing: Each node includes a summary and exact physical page numbers.

- Natural Segmentation: Nodes align with document sections, preserving context — no arbitrary chunking.

We've used PageIndex for financial document analysis with reasoning-based RAG and saw significant improvements in retrieval accuracy compared to vector-based systems.

Would love any feedback — especially thoughts on reasoning-based RAG, or ideas for where PageIndex could be applied!