Insights

We explore the rise of agentic retrieval over vector indexing and how PageIndex can be used to build agentic RAG systems.

We argue that context blindness — the inability of vector-based retrieval to condition on full conversational and reasoning context — is the most fundamental limitation of vector RAG, and outline a paradigm shift from semantic similarity to context-dependent relevance classification.

We examine the inherent limitations of OCR from an information-theoretic perspective and show why a direct, vision-based approach with PageIndex is more effective.