Rajesh, Author at Nomidl

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…

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…

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.…

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…