How to Build a RAG Chatbot to Chat with Documents Using Sparse Data

Learn how to build a Retrieval-Augmented Generation (RAG) chatbot using the Semaphore documentation. This Python chatbot uses a language model to answer questions and provide document sources, even when data is sparse. Key challenges include preprocessing incomplete documents and optimizing prompt context within the fixed size limits. The tutorial guides you through setting up the code, including document summarization, embedding creation with FAISS, and building the retriever and question-answering components.