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Are you frustrated with vector database retrieval accuracy for long professional documents? Traditional vector-based RAG relies on semantic *similarity* rather than true *relevance*. But **similarity ≠ relevance** — what we truly need in retrieval is **relevance**, and that requires **reasoning**. When working with professional documents that demand domain expertise and multi-step reasoning, similarity search often falls short.
Inspired by AlphaGo, we propose [PageIndex](https://vectify.ai/pageindex), a **reasoning-based RAG** system that simulates how **human experts** navigate and extract knowledge from long documents through **tree search**, enabling LLMs to *think* and *reason* their way to the most relevant document sections. It performs retrieval in two steps:
Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)**, a **reasoning-based RAG** system that simulates how **human experts** navigate and extract knowledge from long documents through **tree search**, enabling LLMs to *think* and *reason* their way to the most relevant document sections. It performs retrieval in two steps:
1. Generate a "Table-of-Contents" **tree structure index** of documents
2. Perform reasoning-based retrieval through **tree search**
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Leave a star if you like our project. Thank you!
<p align="center">
<img src="https://github.com/user-attachments/assets/eae4ff38-48ae-4a7c-b19f-eab81201d794" width="75%">
<p>
<img src="https://github.com/user-attachments/assets/eae4ff38-48ae-4a7c-b19f-eab81201d794" width="60%">
</p>
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