π Is Vector RAG Dead? Why We Built FastMemory to Beat PageIndex If you've built a RAG pipeline for complex financial documents, you already know the painful truth: Standard vector search fails when things get complicated.
While tools like PageIndex and Mafin 2.5 provide great out-of-the-box PDF chat experiences, they hit structural bottlenecks the second you push them past basic queries.
We just published a comprehensive benchmark study comparing FastMemory against PageIndex across 5 advanced datasets. The results fundamentally change how we should think about document ingestion.
I am working on a new benchmark to establish human language dexterity. My hypothesis is that certain language allow for more accurate dexterous behaviour - Pointed, unambigous, and confusion-free references of parts of speech in small and large contexts. There are certain languages with high degree of accurate grammar like Sanskrit, Esperanto, and Turkish. I am native Sanskrit speaker. I have plans to establish this benchmark and test this hypothesis across 100 langauges. I have created 25 task prompts for text, image, video and robotics manipulation. We can test langauges across multiple popular models. Here is the github link: https://github.com/ParamTatva-org/Linguistic-Dexterity-Benchmark