Extended Abstract: Simplifying Accessibility to NASA's Planetary Data System Using LLMs and Retrieval-Augmented Generation Techniques

NASA's Planetary Data System (PDS) is a crucial repository for planetary mission data, yet its complex interface challenges users, especially non-experts. This research develops a framework that leverages OpenAI's GPT-4o-mini-API and a Retrieval-Augmented Generation (RAG) with the aim of simplifying user interaction with PDS. Using semantic embeddings, BM25 retrieval, and GPT-4o-mini, the system translates natural language queries into structured outputs, such as PDS compatible URLs. Our pilot implementation testing with simple queries achieved 95.24% accuracy in generating correct URLs, outperforming traditional TF-IDF with cosine similarity approach, which gained 90.91% accuracy. This work highlights the transformative potential of large language models to improve usability for scientific data repositories.