Building RAG over complex, real-world documents.

The biggest hurdle is document complexity. Real-world docs can be messy, with tables, images, and intricate flow charts. Traditional parsing and chunking methods struggle to handle these. So, what’s the solution? We need smart techniques that can intuitively chunk relevant content and understand what’s inside each chunk, whether it's text, images, or diagrams. In this video, I’ll walk you through a breakthrough technique for extracting structured information from complex documents. It's unlike any other technique you've seen before. It takes any unstructured (text, tables, images, flow-charts) input and parses it into a JSON format that LLMs can easily process. I used eyelevelai's GroundX platform for this – a powerful tool that allows you to build a RAG application in 3 steps. You can deploy it on-premise or through APIs for added flexibility. Take it for a spin: eyelevel.ai