Specialized chatbot using rag (retrieval augmented generation) Part I

In the previous episode, we successfully built an interactive chatbot that can respond to user questions and secure the API key itself. Now, we want to enhance it by implementing Retrieval-Augmented Generation (RAG). RAG allows us to specialize our chatbot by enabling it to use our own documents as a knowledge source. These documents can belong to specific domains such as finance, law, science, mathematics, or any other specialized field. Instead of relying only on the model’s pre-trained knowledge, we provide it with domain-specific information so it can generate more accurate and relevant responses. The first step in implementing RAG is preparing the source data. The source consists of the documents we want the chatbot to learn from, such as PDFs, text files, reports, or databases. However, a language model cannot efficiently process large raw documents directly every time a user asks a question. Large documents may exceed the model’s context limit and would be inefficient to pass in