What if AI could supercharge the way we generate scientific insights and innovations? A critical challenge to realizing this vision is the efficient search and processing of vast amounts of heterogeneous scientific data and knowledge. With Activeloop, we implemented a multi-modal retrieval-augmented generation (RAG) framework that significantly improved data search accuracy by up to 18%, while also reducing the time required for data preprocessing across the entire corpus of biological research. We will discuss the development and integration process, technical hurdles encountered, and how this method enriches our current AI models and paves the way for future advancements.