Outline and code a simple RAG prototype in Python that answers questions from [documents]. Cover loading and chunking documents, creating embeddings, storing them in [vector store], retrieving relevant chunks, building the prompt, and citing sources in answers.
Coding & Development
Retrieval-augmented generation (RAG) prototype
Answers questions from your own documents using embeddings.