diff --git a/README.md b/README.md index 0d34612..4d7f198 100644 --- a/README.md +++ b/README.md @@ -8,14 +8,18 @@ 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. -🧠 **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 use *tree search* to perform structured document retrieval. +🧠 **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 use *tree search* to perform structured document retrieval. -**[PageIndex](https://vectify.ai/pageindex)** is a *document indexing system* that builds *search tree structures* from long documents, making them ready for reasoning-based RAG. +**[PageIndex](https://vectify.ai/pageindex)** is a *document indexing system* that builds *search tree structures* from long documents, making them ready for reasoning-based RAG. It has been used to develop a RAG system that achieved 98.7% accuracy on [FinanceBench](https://vectify.ai/blog/Mafin2.5), demonstrating state-of-the-art performance in document analysis. + +
+ + PageIndex + +
Self-host it with this open-source repo, or try our ☁️ [Cloud service](https://pageindex.vectify.ai/) — no setup required, with advanced features like OCR for complex and scanned PDFs. -PageIndex powers a reasoning-based RAG system that achieved 98.7% accuracy on [FinanceBench](https://vectify.ai/blog/Mafin2.5), demonstrating state-of-the-art performance in financial document analysis. - Built by Vectify AI. ---