The repository also discusses the importance of evaluating RAG applications to understand their effectiveness in combining information retrieval with generative models. It includes an end-to-end RAG implementation and evaluation part in Athina AI. The repository covers various RAG techniques like Naive RAG, Hybrid RAG, Hyde RAG, Parent Document Retriever, RAG fusion, Contextual RAG, Rewrite Retrieve Read, Corrective RAG, Self RAG, and Adaptive RAG. Each technique is explained with its tools, description, and notebooks for implementation.
Key takeaways:
- The repository provides a comprehensive collection of advanced Retrieval-Augmented Generation (RAG) techniques, which are methods that improve accuracy and relevance by finding the right information from reliable sources and transforming it into useful answers.
- RAG is a framework that uses external documents to improve the responses of Large Language Models (LLMs) through in-context learning, ensuring that the information provided is not only contextually relevant but also accurate and up-to-date.
- The repository covers various RAG techniques including Naive RAG, Hybrid RAG, Hyde RAG, Parent Document Retriever, RAG fusion, Contextual RAG, Rewrite Retrieve Read, Corrective RAG, Self RAG, and Adaptive RAG.
- Evaluating RAG applications is important for understanding how well these systems work, and this evaluation helps improve RAG applications in tasks like text summarization, chatbots, and question-answering.