Rajesh, Author at Nomidl

Rajesh Yerremshetty
February 8, 2025November 8, 2025

A Comprehensive Guide on Attention-Based RAG

The advanced model Attention-Based RAG (Retrieval-Augmented Generation) extends RAG principles through attention mechanisms to improve both retrieval precision and document synthesis. This method utilizes self-attention techniques together with cross-attention approaches to improve document selection and content synthesis thus generating more contextually appropriate responses. Extensive research is presented in this article through a deep analysis of…

Rajesh Yerremshetty
February 8, 2025November 9, 2025

The Ultimate Guide to Memo RAG: Everything You Need to Know

Memo RAG is an innovative adaptation of the RAG (Retrieval-Augmented Generation) technique, designed to focus on memory-efficient and context-aware implementations. Memo RAG achieves scalability while maintaining performance and relevance through the utilization of compact retrievers and dynamic memory modules. This article provides in-depth analysis of the Memo RAG method, including operating mechanisms, available applications, the…

Rajesh Yerremshetty
February 1, 2025November 9, 2025

Replug RAG: The Ultimate Guide to Retrieval-Augmented Generation

Replug RAG is a refined variant of RAG technique designed to improve flexibility, accuracy, and performance by separating the retriever and generator stages into modular and reusable components. This article delves into the Replug RAG technique, explaining how it works, when and where it can be applied, its advantages and disadvantages, and its step-by-step implementation,…

Rajesh Yerremshetty
January 25, 2025November 9, 2025

Building an AI Agents with Lang Graph

Retrieval-augmented generation (RAG) systems are now widely used in artificial intelligence (AI) to process basic inquiries and produce contextually relevant answers. However, solutions that expand beyond these retrieval capabilities are required as the need for increasingly complex AI applications increases. AI agents are self-governing entities capable of performing intricate, multi-step activities, maintaining state throughout interactions,…

Rajesh Yerremshetty
October 5, 2024March 31, 2025

Building a Production-Ready RAG Application with Milvus and LangChain

A modern approach that merges information retrieval and natural language generation to deliver precise and contextually fitting answers. Advanced techniques like query rewriting and reranking will be integrated to elevate the application’s efficiency. Furthermore, practical coding instances with sample documents will be presented to demonstrate the implementation Introduction to RAG RAG is a novel method…

Rajesh Yerremshetty
June 11, 2024March 31, 2025

Build Robust RAG System with Qdrant Vector: Advanced Techniques

Introduction Recent advances in AI & ML have significantly transformed information retrieval and data processing. Another important feature is the RAG model, which combines standardized retrieval techniques with powerful generative models to produce more accurate and contextually relevant responses. When paired with a robust vector database like Qdrant, RAG can be further optimized to handle…

Rajesh Yerremshetty
May 25, 2024June 3, 2024

Revolutionizing Query Rewrite and Extension: RAG Advanced Approach with HyDE

Introduction In the present-day times of database management and information retrieval, enhancement of query processing is very important. Techniques containing query rewrite and extension play an important role in optimizing search operations. In this article, we delve into the innovative approach of RAG (Recursive Aggregation Graph) Advanced, coupled with HyDE, exploring its principles, applications, advantages…

Rajesh Yerremshetty
April 30, 2024April 30, 2024

Leveraging RAG Rerank Technique for Prompt Compression and Retrieving Correct Responses

Introduction: The utilization of Large Language Models has increased across various domains of natural language processing. As these models develop, their increased size and complexity present important challenges concerning efficiency, prompt interaction, and response accuracy. Addressing these challenges, the RAG rerank technique emerges as a crucial solution, combining the strengths of retrieval and generation models.…

Rajesh Yerremshetty
April 15, 2024April 30, 2024

Introduction to Retrieval Augmented Generation (RAG)

In today’s field of artificial intelligence, where language models are highly valued, one of the most critical requirements is to ensure that the answers generated can be reliably accurate. Retrieval Augmented Generation (RAG) is an innovative artificial intelligence system that aims to improve the quality of responses produced by LLM using additional data sources. But…