Bringing Vision-Language Intelligence to RAG with ColPali

Retrieval-Augmented Generation (RAG) systems often struggle to accurately process documents containing complex tables and images, as traditional parsing methods lose critical context. The ColPali model offers a solution by treating entire document pages as images, bypassing brittle parsing and OCR processes. Inspired by the ColBERT text embedding technique, ColPali divides document images into patches and generates granular, multi-vector embeddings for each one. This approach preserves contextual and spatial information, leading to more accurate retrieval for non-textual content, reduced development effort, and improved explainability.