Multimodal RAG with ColPali & Gemini

Extracting accurate information from complex documents, like industrial reports full of text, tables, charts, and images, can be a big challenge for traditional AI methods. Standard text-based retrieval-augmented generation (RAG) often struggles to handle all that visual information. In this session, we’re excited to explore a powerful alternative: creating a multimodal RAG system using ColPali (ColQwen2.5) and Google’s newly released Gemini models. We’ll show you how treating entire PDF pages as images can lead to smart visual retrieval and accurate, context-aware answers, all without the hassle of complicated parsing processes.

Key Takeaways:

• Discover the limitations of text-only RAG when dealing with visually complex documents.
• Learn how ColPali looks at entire pages as images for efficient multimodal retrieval.
• See how Gemini generates answers using context that’s been visually retrieved.
• Get a hands-on approach to building an AI-powered document analyzer for PDF files.

Sitam Meur is an AI Engineer at Daily Dose of Data Science, where he translates innovative AI and machine learning (ML) ideas into practical, impactful solutions. As an AI/ML Studio Community Publisher at Lightning AI, he develops open-source AI templates. His key technical experience includes creating advanced ML-integrated web applications during Google Summer of Code for RUXAILAB. You can reach him on LinkedIn.