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Stop doing RAG the old way! 🛑 Tired of 'vibe retrieval' and poor accuracy with long documents? 😤 It is time to move beyond the limitations of traditional vector databases. Introducing *PageIndex**, the revolutionary **vectorless, reasoning-based RAG* system that is changing the game for professional document analysis! 🚀🧠 Traditional RAG relies on semantic *similarity**, but as the experts say: **similarity ≠ relevance**! 💡 When you are dealing with complex financial reports or legal manuals, you need **reasoning**, not just a 'close enough' match. PageIndex is inspired by AlphaGo and uses a **hierarchical tree index* to let LLMs navigate documents just like a human expert would. 🌲📖 *Why PageIndex is a total game-changer:* ✅ *No Vector DB Required:* Skip the complex database setups. ✅ *No Artificial Chunking:* Documents are organised into natural sections, not random snippets. ✅ *Human-like Retrieval:* It simulates how experts navigate knowledge via tree search. ✅ *Explainable & Traceable:* Get clear page and section references instead of opaque vector results. ✅ *SOTA Performance:* Achieved a massive *98.7% accuracy* on the FinanceBench benchmark! 📈🔥. Whether you are building for **Healthcare, Finance, or Government**, PageIndex provides the agentic, context-aware retrieval you have been waiting for. 🤖✨ It is perfect for any document that exceeds LLM context limits, from technical manuals to SEC filings. *Get Started with PageIndex:* 💻 *Open Source:* Run it locally or explore the code on GitHub! 🌐 *Cloud Service:* Try the ChatGPT-style platform for instant document analysis. 🔌 *Integrate:* Use the MCP or API to power your own intelligent apps. Don't forget to *star the repo* if you find this tool helpful! 🌟 *Source Attribution:* Information provided from the official GitHub repository: **VectifyAI/PageIndex: 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG**. #AI #RAG #VectifyAI #LLM #MachineLearning #Python #GitHub #VectorlessRAG #TechTrends #SoftwareDevelopment #PageIndex