rag - CybAI news

14 Nov

AI Agent Systems: Modular Engineering for Reliable Enterprise AI Applications

stp2y0 CommentsViral NewsAgent, GenAI, generative ai, LLM, rag

Monolithic to ModularThe proof of concept (POC) of any new technology often starts with large, monolithic units that are difficult to characterize. By definition, POCs are designed to show that a technology works without considering issues around extensibility, maintenance, and quality. However, once technologies achieve maturity and are deployed widely, these needs drive product development to be broken down into smaller, more manageable units. This is the fundamental concept behind systems thinking and why we are seeing AI implementation move from models to AI agent systems. The concept of modular design has been applied to:Cars: seats, tires, lights, and engines can…

10 Nov

The Rise and Fall of RAG-based Solutions

stp2y0 CommentsNewsagents, ai, autonomousagents, coding, community, development, engineering, inclusive, rag, software

Retrieval-Augmented Generation (RAG) has emerged as a pivotal advancement in the AI landscape, particularly in enhancing the capabilities of generative models. By integrating information retrieval mechanisms with generation models, RAG systems aim to overcome the limitations of traditional AI, especially in terms of accuracy and relevance. However, despite its promising start, RAG-based solutions have faced significant challenges, leading to a nuanced discussion about their sustainability and future growth. This article delves into the rise of RAG-based solutions, their strengths, the challenges they face, and the reasons behind their potential decline. What is RAG? Retrieval-Augmented Generation (RAG) is an architecture that…

23 Oct

How Ragie Outperformed the FinanceBench Test

stp2y0 CommentsNewsai, coding, community, development, engineering, inclusive, LLM, rag, showdev, software

In this article, we’ll walk you through how Ragie handled the ingestion of over 50,000+ pages in the FinanceBench dataset (360 PDF files, each roughly 150-250 pages long) in just 4 hours and outperformed the benchmarks in key areas like the Shared Store configuration, where we beat the benchmark by 42%. For those unfamiliar, the FinanceBench is a rigorous benchmark designed to evaluate RAG systems using real-world financial documents, such as 10-K filings and earnings reports from public companies. These documents are dense, often spanning hundreds of pages, and include a mixture of structured data like tables and charts with…

11 Oct

Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base

stp2y0 CommentsNewscoding, community, development, engineering, gpt3, inclusive, machinelearning, rag, software

選定理由 Paper: https://arxiv.org/abs/2408.00798 Code: N/A Blog: https://zenn.dev/knowledgesense/articles/90ac35eedf8b7c 内容詳細は上記ブログを参照。　概要【社会課題】あらゆる産業分野で社内外の大規模な知識データベースを効率的に活用することが求められているが、特定の業界用語や文脈を正確に解釈し、関連情報を迅速に取得できる検索・応答生成手法が必要である。【技術課題】従来の技術(RAG, self-RAG, CRAGなど)では業界特有の用語や文脈を正確に理解し、それに基づいて適切な情報を取得することが困難であった。これはその単語の意味をLLMが正確に把握できないことに起因している。このため、知識ベースから正確かつ効率的に情報を活用することができていなかった。【提案】質問の前処理段階で業界特有の用語や略語を認識し、事前に作成されたDBを参照することでその文脈に基づいて意味を明確にする。その後、明確化された質問に基づいて最も関連性の高い文書を取得するためのフレームワーク Golden-Retriever を提案した。【効果】 Golden-Retrieverは、業界特有のデータセットを用いた評価で、従来のLLMやRAGフレームワークと比較して優れたパフォーマンスを示した。 Source link lol

09 Sep

Unlocking the Power of Multimodal Data Analysis with LLMs and Python

stp2y0 CommentsNewsanalytics, coding, community, database, development, engineering, inclusive, python, rag, software

Introduction In today’s data-driven world, we no longer rely on a single type of data. From text and images to videos and audio, we are surrounded by multimodal data. This is where the magic of multimodal data analysis comes into play. By combining large language models (LLMs) with Python, you can unlock powerful insights hidden across different data types. Whether you’re analyzing social media posts, medical images, or financial records, LLMs, powered by Python, can revolutionize how you approach data integration. In this guide, we will take a deep dive into how you can master multimodal data analysis using LLMs…

20 Aug

Building a RAG Chatbot with LlamaIndex and eBay API Integration

stp2y0 CommentsNewsai, coding, community, development, engineering, inclusive, rag, software, typescript, webdev

RAG (Retrieval-Augmented Generation) is all the rage. And there's a good reason why. Like so many others, I instinctively felt an air of excitement at the beginning of the internet. The Browser Wars, Java vs Mocha. And then again in 2007 when the iPhone led a paradigm shift to how, where, and when we consume media. Just as I do now, In the rapidly advancing field of AI, Retrieval-Augmented Generation (RAG) has become a crucial technique, enhancing the capabilities of large language models by integrating external knowledge sources. By leveraging RAG, you can build chatbots that generate responses informed by…

18 Jul

Multimodal RAG locally with CLIP and Llama3

stp2y0 CommentsNewsai, coding, community, development, engineering, inclusive, openai, rag, software, tutorial

With the recent release of GPT-4o and Gemini, multimodal has been a hot topic lately. Another one that has been on top of the lighting spot is Retrieval Augmented Generation (RAG) for the past year, but it was mostly focused on text. This tutorial will show you how to build a Multimodal RAG System. By using Multimodal RAG, you don’t have to use text only; you can use different types of data such as images, audio, videos, and text, of course. It’s also possible to return different kinds of data; just because you use text as input for your RAG…

15 Jul

Use Guardrails to prevent hallucinations in generative AI applications

stp2y0 CommentsNewsai, aws, coding, community, development, engineering, inclusive, python, rag, software

With Contextual grounding check, you can prevent hallucinations by detecting irrelevant and ungrounded LLM responses. Guardrails for Amazon Bedrock enables you to implement safeguards for your generative AI applications based on your use cases and responsible AI policies. You can create multiple guardrails tailored to different use cases and apply them across multiple foundation models (FM), providing a consistent user experience and standardizing safety and privacy controls across generative AI applications. Until now, Guardrails supported four policies - denied topics, content filters, sensitive information filters, and word filters. The Contextual grounding check policy (the latest one added at the time…

13 Jun

Let’s Build Small AI Buzz, Offer ‘Claim Processing’ to Mid/Big Companies

stp2y0 CommentsNewscoding, community, development, engineering, GenAI, inclusive, LLM, machinelearning, rag, software

Discover How AI Can Transform Businesses, Every Details Spelled Out. Full Article Artificial Intelligence (AI) is rapidly reshaping business landscapes, promising unprecedented efficiency and accuracy across industries. In this article, we delve into how Aniket Insurance Inc. (Imaginary) leverages AI to revolutionize its claim processing operations, offering insights into the transformative power of AI in modern business environments. ➡️ What’s This Article About? The article explores how Aniket Insurance Inc. uses AI to transform its claim processing. It details the three main workflows: User claim submission, Admin + AI claim processing, and Executive + AI claim analysis. ➡️ Why Read…

30 May

How Retrieval Augmented Generation (RAG) Work

stp2y0 CommentsNewscoding, community, development, engineering, gpt3, inclusive, LLM, machinelearning, rag, software

Retrieval Augmented Generation (RAG, pronounced 'rag') works by fetching selective data from a custom knowledge base and integrating it with the output of a language model to provide accurate and up-to-date responses.RAG can be defined as a ChatGPT-like interface that can use your pdfs, documents or databases to answer questions from you. You can use it as a study assistant to understand documents by asking questions about those documents. In this article, we discuss the benefits of using a RAG system and explain its key components. We also detailhow RAG works to enhance the capabilities of large language models by…