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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 ...

  Introduction The Sentence Window-based Retrieval-Augmented Generation (RAG) method is a high-level implementation of the RAG framework that aims to enhance the context-awareness and coherence of AI-generated responses. The method combines the power of large language modeling with efficient information ...

Introduction The Automated Merge Retriever is a high-level implementation of the Enhanced Retrieval Generation (RAG) framework. It aims to enhance the context-awareness and coherence of AI-generated responses by merging potentially fragmented and smaller contexts into larger and more comprehensive ones. https://github.com/adith...

In 2022 OpenAI released ChatGPT, which became the world's fastest APP to break through the hundreds of millions of users, and at that time people thought that we were closer to true artificial intelligence. But people soon realized that ChatGPT could talk and chat, and even write poems and articles, but it still wasn't quite as good at simple logic...

TOML is a clean and simple configuration file format 📄 designed to be more human readable and writable ✨. ✅ Easier to write: Configurations are represented as key-value pairs without complex indentation and syntax rules, reducing the error rate. ✅ Clearer: Support grouping and nesting structure, clear hierarchy, configuration logic at a glance...

Introduction The Query Transformations User Manual demonstrates a variety of techniques for transforming and disambiguating user queries before they are executed in a Retrieval-Augmented Generation (RAG) query engine, intelligences, or other processes. These transformations can improve the quality and relevance of responses in AI applications. https://github.com/adithya-s-k/AI-...

Since yesterday's release of Anthropic's open-source Model Context Protocol: Model Context Protocol (MCP), which according to Anthropic, Block, and Apollo has been integrated into their systems, Replit, Codeium, and Sourcegraph...

It's like being a smart kid who doesn't understand coding best practices. You need to tell the AI exactly what you want: is it a web application? What functionality is needed? What is the structure? And so on. Here's how to make AI your full-stack developer: Context is critical! You need to...

Introduction Thomas joined Vespa in April 2024 as a Senior Software Engineer. In one of his last previous assignments as an AI consultant, he actually built a RAG application based on Vespa's massive PDF collections. PDFs are ubiquitous in the corporate world, and searching and retrieving from them...