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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. 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](https://pageindex.ai)** 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](https://pageindex.ai)** 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, which simulates how **human experts** navigate and extract knowledge from long documents.
**[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](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.
Try [Reasoning-based RAG with PageIndex](https://pageindex.ai). Say goodbye to "vibe retrieval" 👋
- No *Vector DB* required
- No *Chunking* required
- No *Top-K selection* required
#### 🚀 Deployment Options #### 🚀 Deployment Options
- 🛠️ Self-host: run it yourself from this open-source repo - 🛠️ Self-host: run it yourself from this open-source repo
- **[☁️ Cloud service](https://dash.pageindex.ai/)**: try instantly with our [🖥️ Dashboard](https://dash.pageindex.ai/) or [🔌 API](https://docs.vectify.ai/quickstart) *(no setup required)* - **[☁️ Cloud service](https://dash.pageindex.ai/)**: try instantly with our [🖥️ Dashboard](https://dash.pageindex.ai/) or [🔌 API](https://docs.vectify.ai/quickstart) *(no setup required)*