LlamaIndex Webinar: ColPali - Efficient Document Retrieval with Vision Language Models

Enterprise RAG systems face a significant challenge when processing PDFs with complex layouts, tables, and figures. Conventional RAG pipelines typically convert these documents into text through parsing, OCR, or extraction before building lexical or semantic indexes. However, this approach often results in the loss of crucial visual information, potentially impacting retrieval performance.
​In this webinar, we hosted the authors of "ColPali: Efficient Document Retrieval with Vision Language Models" (Faysse et al.). The key idea is to bypass traditional conversion processes by directly embedding page screenshots with VLMs. By working with image representations, ColPali offers a streamlined, end-to-end optimizable system that achieves state-of-the-art results on a newly proposed benchmark.
​We’ve been working in this direction with LlamaParse, and we had a technical discussion with the authors about what a multimodal document RAG future looks like.
Paper: arxiv.org/abs/...