An expert AI chatbot specialized in the EU AI Act, powered by PageIndex and LangChain. This agent demonstrates how to build a high-performance RAG (Retrieval-Augmented Generation) application with minimal setup.
PageIndex is a vectorless, reasoning-based RAG (retrieval) framework that simulates how human experts navigate and extract knowledge from long, complex documents.
Instead of relying on vector similarity search, it transforms documents into a tree-structured index and enables LLMs to perform agentic reasoning over that structure for context-aware retrieval.
| Feature | Conventional Vector DB (Pinecone, Weaviate, etc.) | PageIndex |
|---|---|---|
| Data Preparation | Requires manual chunking, overlapping, and cleaning. | No chunking required. Just upload the document. |
| Embedding | Must choose an embedding model and manage vector conversions. | Vectorless. Uses a tree-structured index for reasoning-based retrieval. |
| Retrieval Method | K-Nearest Neighbors (KNN) based on mathematical similarity. | Agentic Reasoning. Simulates human expert navigation. |
| Traceability | Often a "black box"; hard to explain why specific chunks matched. | Interpretable & Traceable. Retrieval logic is clear and reasoned. |
| Infrastructure | Requires managing a vector database and index pipeline. | Zero infra. Direct API-driven insight without the overhead. |
| Context Awareness | Often loses document structure and hierarchy. | Full Context. Understands the document's original structure. |
