Naive RAG: The Simplest Retrieval-Generative Integration
Explore the naive RAG method that combines document retrieval with large language models for improved question answering. Learn how to implement indexing, similarity-based retrieval, prompt construction, and response generation using LangChain and OpenAI tools. Understand the limitations of this basic RAG approach to prepare for more advanced techniques.
We’ve explored the fundamentals of RAG and how it addresses the limitations of LLMs. Let’s dive deeper into RAG systems’ different architectures or paradigms one by one, starting by examining naive RAG in detail.
Overview
Naive RAG is a simplified approach to using LLMs in conjunction with document retrieval for improved information access and response generation. Naive RAG works in three basic steps:
Indexing: Data from formats like PDF or HTML is cleaned up and converted into plain text. This text is then divided into smaller parts (chunks) and turned into vector representations by passing the chunks into the embedding model to make it easier to find later.
Retrieval: When someone asks a question, the RAG system turns that question into vector embedding using the same method used in indexing. Then, it compares this vector to the vectors of the indexed text parts to find the
most similar chunks. These ...