Retrieval-Augmented Generation (RAG) is a technique for improving an LLM’s response by including contextual information from external sources. In other terms, it helps a large language model answer a question by providing facts and information for the prompt. For the purposes of this tutorial, we will implement RAG by leveraging a Chroma DB as a vector store with the FDIC Failed Bank List dataset. Langchain with CSV data in a vector store A vector store leverages a vector database, like Chr...
