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

The text outlines the development of a Retrieval-Augmented Generation (RAG) chatbot designed to assist users in navigating Semaphore's documentation, particularly useful when the documentation is incomplete. The chatbot, built using Python and the Langchain library, processes Semaphore's GitHub documentation by summarizing and augmenting document content to generate embeddings stored in a retriever. When users ask questions, the retriever finds relevant document summaries, and the chatbot, powered by a language model, provides answers based on these summaries while directing users to the source documents for further information. The tutorial discusses the challenges of working with sparse data and demonstrates a solution by analyzing document metadata to infer their purpose. It covers the setup and implementation steps, including document preprocessing, embedding generation, and building the question-answering component, using packages like langchain and FAISS for in-memory document retrieval. The project is shared in a public GitHub repository, allowing users to replicate the setup and explore the chatbot's functionality.