RAG VIII: Self RAG

This article is built on top of NirDiamant’s work in this GitHub repository.

Self-RAG is an advanced algorithm that combines the power of retrieval-based and generation-based approaches in natural language processing. It dynamically decides whether to use retrieved information and how to best utilize it in generating responses, aiming to produce more accurate, relevant, and useful outputs [1].

Traditional question-answering systems often struggle with balancing the use of retrieved information and the generation of new content. Some systems might rely too heavily on retrieved data, leading to responses that lack flexibility, while others might generate responses without sufficient grounding in factual information. Self-RAG addresses these issues by implementing a multi-step process that carefully evaluates the necessity and relevance of retrieved information and assesses the quality of generated responses [1].

Algorithm Chart (inspired by the source)

Key Components

1. Retrieval Decision: Determines if retrieval is necessary for a given query. The algorithm first decides if retrieval is necessary for the given query. This step prevents unnecessary retrieval for queries that can be answered directly.
2. Document Retrieval: Fetches potentially relevant documents from a vector store. If retrieval is deemed necessary, the algorithm fetches the top-k…