  Large language models (LLMs) have emerged as powerful machine-learning
systems capable of handling a myriad of tasks. Tuned versions of these systems
have been turned into chatbots that can respond to user queries on a vast
diversity of topics, providing informative and creative replies. However, their
application to physical science research remains limited owing to their
incomplete knowledge in these areas, contrasted with the needs of rigor and
sourcing in science domains. Here, we demonstrate how existing methods and
software tools can be easily combined to yield a domain-specific chatbot. The
system ingests scientific documents in existing formats, and uses text
embedding lookup to provide the LLM with domain-specific contextual information
when composing its reply. We similarly demonstrate that existing image
embedding methods can be used for search and retrieval across publication
figures. These results confirm that LLMs are already suitable for use by
physical scientists in accelerating their research efforts.
