Analytics Siksha

Integrating a Retrieval-Augmented Generation (RAG) system with a live database allows the model to generate contextually relevant responses using the most up-to-date and accurate information. This setup is especially valuable for dynamic applications like customer support, real-time analytics, or news summarization. Below is a step-by-step guide to achieving this integration.   1. Understand the Workflow

Retrieval-Augmented Generation (RAG) represents a significant evolution in how question-answering (QA) systems are designed, combining the strengths of retrieval-based systems and generative models. Here’s a breakdown of the key differences between RAG and traditional QA systems:   1. Static Knowledge vs. Dynamic Knowledge Traditional QA Systems: Often rely on pre-trained models that use static knowledge.

  DPR stands for Dense Passage Retrieval, a cutting-edge method for retrieving relevant documents based on their semantic content. It is a deep learning-based retrieval model introduced by Facebook AI and designed to improve the accuracy and efficiency of information retrieval tasks.   How Does DPR Work? DPR employs a dual-encoder architecture with two main