Self-RAG
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.
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.
What you'll learn
- 1Retrieval Decision: Determines if retrieval is necessary for a given query.
- 2Document Retrieval: Fetches potentially relevant documents from a vector store.
- 3Relevance Evaluation: Assesses the relevance of retrieved documents to the query.
- 4Response Generation: Generates responses based on relevant contexts.
- 5Support Assessment: Evaluates how well the generated response is supported by the context.
- 6Utility Evaluation: Rates the usefulness of the generated response.
About this tutorial
This hands-on Jupyter notebook is part of RAG Techniques, a free open-source repository by Nir Diamant covering rag techniques with runnable code examples and detailed explanations.
RAG Made Simple
The book that extends this repo: 22 RAG techniques with the intuition behind each, side-by-side comparisons of when each wins (and quietly fails), and original illustrations.
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