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# PageIndex
### **Document Index System for Reasoning-Based RAG**
Frustrated with vector database retrieval accuracy for long professional documents? You need a reasoning-based native index for your RAG system.
Are you frustrated with vector database retrieval accuracy for long professional documents? You need a reasoning-based native index for your RAG system.
Traditional vector-based retrieval relies heavily on semantic similarity. But when working with professional documents that require domain expertise and multi-step reasoning, similarity search often falls short.
Traditional vector-based retrieval relies heavily on semantic similarity. However, when working with professional documents that require domain expertise and multi-step reasoning, similarity search often falls short.
**Reasoning-Based RAG** offers a better alternative: enabling LLMs to *think* and *reason* their way to the most relevant document sections. Inspired by **AlphaGo**, we leverage **tree search** to perform structured document retrieval.
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Enables LLMs to traverse documents logically—like an intelligent, LLM-optimized table of contents.
- **Precise Page Referencing**
Every node contains its own summary and start/end page physical index, allowing pinpoint retrieval.
Every node contains its summary and start/end page physical index, allowing pinpoint retrieval.
- **Chunk-Free Segmentation**
No arbitrary chunking. Nodes follow the natural structure of the document.
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:loudspeaker: [Join our Discord](https://discord.com/invite/nnyyEdT2RG)
:envelope: [Leave us a Message](https://ii2abc2jejf.typeform.com/to/meB40zV0)
:envelope: [Leave us a message](https://ii2abc2jejf.typeform.com/to/meB40zV0)