Implementierung eines spezifizierten Frage-Antwort-Large Language Models (LLM) für das Robot Operating System (ROS) mit Nutzung von Retrieval Augmented Generation (RAG)

A Retrieval‑Augmented Generation (RAG) system was built to extend a large language model with external ROS‑specific knowledge, using Python, LangChain, a Chroma vector database, Ollama for local model execution, and a Streamlit web interface. The system’s answers were compared with those of the plain language model, and a scoring‑based evaluation showed that RAG can produce noticeably better responses for certain question types and when sufficient domain data are available.