Rajeswaran V (PhD)’s Post

I've posted before about the evolution of RAG to include Knowledge Graphs as well as Vectors to source the most relevant context for your LLM queries. Here's a great follow up article by Ben Cambers on how DataStax recently introduced content-centric knowledge graphs as a better fit for GenAI and graph retrieval augmented generation (RAG). ✨ Read about all of these latest advancements in this new article 👇🏼 https://ow.ly/xqRg50SR5Mc #GenAI #GenerativeAI #DataStax

Excited to share iText2KG! 🚀 This GitHub library takes Knowledge Graph construction to the next level using Large Language Models, enabling incremental updates with resolved entities and relationships. At Pivot-al, we’re leveraging this innovative technology to build more intelligent and adaptive AI-driven solutions. Check out the visual below to see how iText2KG processes unstructured text into high-quality Knowledge Graphs, perfect for applications in data integration and analysis. https://lnkd.in/eN-9-kPC #AI #KnowledgeGraph #MachineLearning #DataScience #Neo4j #LLM

RAG (Retrieval-Augmented Generation) is a framework designed to improve the performance of #GenAI #models by incorporating external information retrieval mechanisms. RAG combines the strengths of retrieval-based and generative approaches to deliver superior performance in information-intensive tasks. #RetrievalAugmentedGeneration #niceread ZDNEThttps://lnkd.in/gAADtAGj

💎✨ Transforming Diamond Valuation with AI! ✨💎 I’m thrilled to unveil my latest project: Gem Value – an AI-powered solution for accurate diamond price prediction. Whether you're a first-time buyer or a gem enthusiast, this tool simplifies pricing decisions like never before! 🔍 Key Features: 🌟 Predicts diamond prices with high accuracy using Machine Learning. 🌐 Built on a robust Django backend for seamless user interaction. 💡 Designed with a user-friendly interface to cater to those with little or no knowledge of diamonds. The goal? Making diamond shopping transparent, informed, and stress-free! This project bridges the gap between data science and real-world challenges – and I’m proud of the journey so far. 💬 Curious about how it works or have feedback? Let’s connect! Github Repo Link :- https://lnkd.in/gvAQ3f4U #AI #MachineLearning #Django #Innovation #DiamondPricePrediction #TechForGood

🤝 From Day 0, We Support Llama 3.1 405B! 🤝 ScrapegraphAI supports Llama 3.1 405B, the largest and most capable open-source model, thanks to our seamless integration with Groq, Bedrock, and Hugging Face connectors. 😱 This powerful model surpasses GPT-4o 😱 , ensuring enhanced quality and performance in our scraping tasks. Let's take our data collection to new heights with this incredible open-source model! Join us in leveraging this cutting-edge technology to make the most out of your web scraping endeavors. Go to the first comment for the repo link 👇 #AI #WebScraping #MachineLearning #DataScience #ScrapegraphAI #Llama3.1 #OpenSource #TechInnovation

Generative AI Summarization API.. A Generative AI Flask application for summarizing text. It uses llama3.1 but can be quickly adapted to work with any other model. Ollama as a Docker image service is used to make the deployment efficient and scalable

Deep dive into Advanced RAG, complete with code examples! 🧑💻 If you've been following me, you know how passionate I am about RAG. It's one of the hottest use cases across industries right now and the perfect partner for LLMs. I've shared a lot about RAG, and today I bring you this repo—a hub for cutting-edge techniques designed to boost the accuracy, efficiency, and contextual depth of RAG systems 👇 🔗 Link to the repo: https://lnkd.in/d4JYupDA 🌱 Foundational RAG 🔍 Query Enhancement 📚 Context and Content Enrichment 🚀 Advanced Retrieval Methods 🔁 Iterative and Adaptive Techniques 📊 Evaluation 🔬 Explainability and Transparency 🏗️ Advanced Architectures 🤖 Agents #AI #RAG #GenAI

Very nice visual of a LLM+KG system ! The combination of LLM based knowledge and knowledge graph is the right path imo The complexity and the value come from ontology and the way the KG is structured, as well as how the LLM knowledge is defined Making synergy between both approach allow to reduce the cons from both and trying to maximize the pros of both

New to GenAI and want to learn the basics of Retrieval Augmented Generation (RAG)? Head of GenAI Ecosystem Alex Leventer wraps up the main highlights from our recent livestream, 'GenAI 101: What is RAG?' in this new DataStax blog 👇🏼 https://ow.ly/t6oL50Te42j #DataStax

When I was a developer, I would write 'Switch' statements or multiple 'If Then Else' blocks (or even use locator patterns) to help navigate the flow of logic through my code depending on user input. Google Gemini has built in "function calling" which utilises it's 'user intent' parsing capabilities to navigate the path through code blocks, through calls to LLMs and calls to datastores in real-time instead, so the flow is much more dynamic and not hard coded as a series of function calls. I wish I had this when I was a developer!! This is a really smart way of using Generative AI https://lnkd.in/dTs6vuMZ #genai #llm #functions