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Overview This guide will walk you through creating a simple Retrieval Augmentation Generation (RAG) system using pure Python. We will use an embedding model and a large language model (LLM) to retrieve relevant documents and generate responses based on user queries. https://github.com/adithya-s-k/A...

Introduction Retrieval-enhanced generation (RAG) is a powerful technique that combines the benefits of large language models with the ability to retrieve relevant information from a knowledge base. This approach improves the quality and accuracy of generated responses by basing them on specific retrieved information.a This notebook aims ...

INTRODUCTION BM25 Retrieval Augmented Generation (BM25 RAG) is an advanced technique that combines the BM25 (Best Matching 25) algorithm for information retrieval with a large language model for text generation. By using a validated probabilistic retrieval model, this method improves the accuracy and relevance of the generated responses....

INTRODUCTION Data chunking is a key step in Retrieval Augmented Generation (RAG) systems. It breaks large documents into smaller, manageable pieces for efficient indexing, retrieval, and processing. This README provides an overview of the various chunking methods available in the RAG pipeline. https://github.com/adithya-...

One of the biggest breakthroughs in the field of AI this year should be in the field of programming, AI programming tools like Cursor and v0 dev have not only drastically lowered the threshold of programming for the average person, but also allowed professional programmers to dramatically increase their development efficiency. But all the news we hear is about high school students who can't program,...

Introduction Evaluation is a key component in the development and optimization of Retrieval Augmentation Generation (RAG) systems. Evaluation involves measuring the performance, accuracy, and quality of all aspects of the RAG process, from retrieval effectiveness to the relevance and authenticity of generated responses. Importance of RAG Evaluation An effective RAG system...

Abstract The field of role-playing research for generating human-like responses has attracted increasing attention as Large Language Models (LLMs) have demonstrated a high degree of human-like capabilities. This has facilitated the exploration of role-playing agents in a variety of applications, such as chatbots that can engage in natural conversations with users, and those that can provide personalized...

The reordering model will improve the results of semantic ranking by reordering the list of candidate documents based on their semantic match to the user's question. Commonly used bge-reranker-v2-m3 or cohere

Education has long been considered one of the industries that will be changed the most by LLM. education makes up a large portion of ChatGPT's usage scenarios, and its usage often fluctuates with the start of the school year and the regularity of vacations. Andrej Karpathy has chosen education as the direction of his venture. People are expecting to have all-round AI Tutor,...

Sentence-Window-Based Retriever RAG Approach Introduction The Sentence-Window-Based Retriever RAG (Retrieval-Augmented Generation) approach is a high-level implementation of the RAG framework designed to enhance the context-awareness and coherence of AI-generated responses. The approach combines a large-scale language model with a high ...