Document Index for Vectorless RAG Based on Reasoning

PageIndex is an open-source RAG framework that eliminates vector databases and chunking from the pipeline when searching for documents.

Most RAG systems rely on semantic similarity: they cut a document into pieces, build embeddings, and then retrieve fragments that "resemble the query".

But similarity does not equal relevance.

In professional documents such as financial reports, legal documents, and technical manuals, multi-step parsing and domain-specific logic are often needed. Vector-based search easily gets stuck when almost every section uses the same terminology.

PageIndex does it differently.

It builds a hierarchical tree from the document, similar to a table of contents, but tailored for LLMs. Then, it uses reasoning-based tree search to "navigate" the structure like a human expert would.

The two-step process:

1. Generate a tree-based index of the document structure
2. Retrieve the needed information through reasoning-based tree search

The LLM can "think" about the document structure. Instead of matching embeddings, it reasons like: "Trends in debt are usually in the financial summary or Appendix G. Let's look there."

Key features:

* No vector database or embedding pipeline
* No artificial chunking that breaks context at boundaries
* Traceable retrieval with precise references down to the page level
* Navigation based on reasoning, mirroring human document analysis

PageIndex is used in Mafin 2.5 and claims 98.7% accuracy on FinanceBench for financial document analysis.

And yes, it's completely open source.

👉 @DataScienceT