Github repo: https://github.com/VectifyAI/PageIndex

Would love feedback and suggestions.

No-Vector RAG with Reasoning and Expert Rules

Current vector-based RAG systems rely on semantic similarity to retrieve content — but similarity ≠ relevance.

In domains like finance or law, the answer isn't just in the paragraphs that look similar to the query — it's in the sections that human experts would look at first. Embedding models don't know that, and fine-tuning them to encode this domain logic is expensive and inflexible.

We built PageIndex to solve this.

It turns long documents into a tree-based index — like a searchable, LLM-friendly table of contents. Instead of splitting documents into flat chunks, it gives LLMs a way to reason and retrieve through the content like a human — navigating by structure and guided by expert rules.

Example: If someone asks "why did revenue go down last year?", experienced analysts would go directly to the "Management's Discussion and Analysis" section in a company's annual financial report, where changes in performance are explained.

These rules can be injected as prompts into LLM to guide PageIndex traversal. No model retraining needed.

It's reasoning-based RAG — not similarity search, but navigation guided by structured reasoning and domain logic.

Would love feedback, especially thoughts on reasoning-based RAG or other potential applications of PageIndex.

Github repo: https://github.com/VectifyAI/PageIndex

Would love feedback and suggestions!

How to align with user preference in a RAG system?

Current embedding-based RAG systems primarily rely on semantic similarity. Given a document and a query, the system usually retrieves multiple sections that appear semantically relevant. However, in domain-specific applications, such as financial analysis or legal research, users often have domain-specific preferences for which parts of a document to consult first. These preferences are typically driven by experience about where answers are typically found or which sections are considered more trustworthy sources of information.

For example:

- When querying about financial performance metrics (e.g., earnings adjustments), experienced analysts typically look first at the Management’s Discussion and Analysis (MD&A) section or related financial statement footnotes.

- For questions about company risks, they usually prioritize the Risk Factors section before turning to broader disclosures.

These expert-driven navigation patterns are difficult to capture using embedding-based RAG alone. Fine-tuning embedding models to reflect such preferences is possible, but it tends to be costly and resource-intensive.

An alternative approach is to incorporate reasoning-based retrieval, which mimics how humans find information. For example, when reading a long document, a human typically starts by reviewing the table of contents to determine which sections to read first, based on the context of the query and preference. Similarly, one can build an LLM agent that analyzes the "table of contents" and then navigates through the document according to expert preferences. This can be achieved by using few-shot prompting, where the system learns from sample user preference examples provided in the prompt, allowing it to prioritize sections based on the user’s needs.

To support this paradigm, we developed an open-sourced tool called PageIndex. It can transform any long documents into an LLM-friendly "table-of-contents" tree index, which is ready for the LLM agents to navigate. With PageIndex, you can easily build RAG agents that align with user preferences and domain logic.

Would love any feedback, particularly thoughts on reasoning-based RAG or other potential applications of PageIndex.

Show HN: RAG, No Vectors

We built PageIndex, a document indexing system that turns documents into hierarchical search trees to support reasoning-based RAG.

Traditional vector-based RAG often struggles with retrieval accuracy because it optimizes for similarity, not relevance. But what we really need in retrieval is relevance — which requires reasoning. When working with professional documents that demand 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 use tree search to perform structured document retrieval.

We open-sourced one of the key components: PageIndex. PageIndex is a hierarchical indexing system that builds search tree structures 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!