Creating a RAG system using Gemini API from scratch. #RAGSystemCreation

Understanding RAG: Building a RAG system from scratch with Gemini API | by Saurabh Singh | Feb, 2024

The article discusses the concept of Retrieval Augmented Generation (RAG) and its application in question-answering chatbots. The RAG system is broken down into three major components: indexing, retrieval, and generation. The indexing component involves loading, splitting, embedding, and storing the data. The retrieval component retrieves relevant data from the indexed documents based on a user query. The generation component uses the relevant data to generate an answer for the user query.

The article provides a detailed explanation of each component and includes code snippets for implementing these steps. It also discusses the process of integrating the components to create a complete RAG system. The article also includes a practical example of using the RAG system to generate a response to a user query.

Overall, the article aims to provide a comprehensive understanding of the RAG system and its components, and how they work together to create a question-answering chatbot. It also provides practical examples and code snippets to demonstrate the implementation of the RAG system.

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