Implementing Retrieval-Augmented Generation (RAG) with Keras NLP | Step-by-Step Tutorial

Описание: In this tutorial, we walk through how to implement Retrieval-Augmented Generation (RAG) using only Keras NLP package, without relying on third-party tools for storing vector embeddings or document retrieval. I’ll show you how to set up the environment, generate embeddings, retrieve relevant documents, and create answers using two different methods. This method might not give you the best text generation for your context raw data, but it's a good way to start understanding both RAG and the Keras-NLP package.
I didn’t receive answers that aligned with the context of my query. If desired, you can explore other LLM models from the Keras-NLP package, including potentially larger ones. Using a different LLM model will require a different setup than the code provided in the video. Additionally, consider presenting the corpus in various ways; instead of listing sentences individually, you can provide the text as paragraphs. You might also explore alternative vector stores. Furthermore, employing prompt engineering can help to guide the LLM to focus more on the retrieved documents, resulting in more relevant answers.

If you’re unfamiliar with RAG, check out the detailed explanation in the video available at    • Understanding Retrieval-Augmented Generati...  .

The script used in the video is available at:
https://gitlab.com/Nayan.1989/youtube...

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