Quasara’s Post

Exciting new skills unlocked! 🤖 I've just finished an amazing course on LangGraph, a tool for building AI agents using GPT and Gemini. Over the past few weeks, I've been learning and building hands-on projects that you can now check out on my GitHub, github.com/Alex112525. The course highlighted the power of AI agents to automate complex tasks, improve decision-making, and customize interactions for specific needs. A big thank you to Lance Martin and Harrison Chase for delivering such insightful content! I can't wait to explore even more possibilities with AI agents.  #AI #GPT #AIAgents #Agents #LangGraph #ContinuousLearning

🚀 Announcing Vectorize: The easy path to accurate RAG applications! 🚀 We're excited to announce that Vectorize is now available to the public! Designed to tackle the biggest challenges in building LLM-powered applications, Vectorize optimizes your vector search indexes and streamlines your development process. 🔹 Key Features 🔹 🔬 Experiments: Automate testing of embedding models, chunking strategies, and retrieval configurations. 🏖 RAG Sandbox: Real-time testing with top LLMs like Llama 3, Gemma, GPT-3.5 and the brand new GPT-4o! 📊 Seamless Integration: Supports Pinecone Serverless and DataStax Astra. Struggling with inaccurate AI results? Vectorize makes your work more efficient and effective. 🔗 Sign up for a free account today at https://lnkd.in/eZRm9HKs and start optimizing your RAG applications with Vectorize! Read more about on our blog: https://lnkd.in/eJhsUcMn #AI #MachineLearning #GenerativeAI #RAG #TechInnovation #AIDevelopment #VectorizeAI #VectorDatabase

A Simple RAG (Retrieval-Augmented Generation) Pipeline! 🚀 📊 How it works: - Load Data Source: Bringing in raw data from various sources. 📥 - Embed: Transforming data into embeddings using advanced AI models. 💡 - Vector Store: Storing these embeddings in a high-speed vector database using ChromaDB and FAISS for efficient querying. ⚡ - Query & Retrieve: Finding the most similar data to any given query by leveraging vector similarity search. 🔍 Just a simple step before the RAG chatbot. #AI #MachineLearning #RAG #NaturalLanguageProcessing #DataScience #ChromaDB #FAISS #Innovation #LLMs #KnowledgeGraphs

Introducing "toy-o": A Journey into Step-by-Step AI Reasoning Ready to quick explore? Visit our live demos at https://lnkd.in/g_F7ZG38 and https://lnkd.in/g-2g974s. Happy to share our you project inspired by the influential STaR paper, OpenAI's o1, and various reflection techniques. We were particularly inspired by the SkunkworksAI/reasoning-0.01 dataset, https://lnkd.in/gFEdHWxt and the brilliant implementation from https://lnkd.in/gzcUBV_q. Standing on the shoulders of these giants, we've crafted two unique approaches: 1. "o1" leverages fixed reasoning based on the SkunkworksAI dataset, with an added twist of web search capabilities. 2. "o2" taps into the LLM's innate planning skills, adapting the innovative work from https://lnkd.in/gzcUBV_q We've built these demos using the Upstage Solar-Pro Preview LLM, but the beauty lies in its flexibility - you can easily swap in your preferred LLM using LangChain. We see this project as a collaborative effort to advance AI reasoning. Your thoughts, feedback, and contributions are not just welcome - they're essential. All prompts, source code are available at https://lnkd.in/gfZFnE58 #AI #Reasoning #OpenSource #MachineLearning #SolarProLLM

Dive into a vast sea of resources with our AI-driven Paper Search 2.0, simplifying your literature review process. 🔍 Get tailored AI insights: Enter your research question for precise summaries and actionable results. 📚 Integrate seamlessly: Search, import, and cite within ATLAS.ti Web. 💡 Enhanced by ATLAS.ti AI Lab. Experience the future of research today! https://lnkd.in/d3gHeZM7 #atlasti #atlasticommunity #atlastiai #datacoding #researchsolutions #smartdataanalysis #dataexploration #datavisualizationtools #businessintelligence #researchproductivity #datamining #datainterpretation

#Vector vs. #Graph: The Battle for the Future of AI! Let’s talk about how RAG (Retrieval-Augmented Generation) is changing the game in AI applications. There are two main approaches you can use—vectors or knowledge graphs—and each has its own unique power. With vector databases, the process is all about turning your queries into numbers (embeddings) and finding relevant info based on their semantic similarity. It’s super-efficient for massive unstructured data and works great when we don’t need to explicitly define relationships between the data points. On the other hand, knowledge graphs use structured data and relationships between entities to retrieve the right information. It’s perfect when understanding the connections between data points is crucial, especially in fields that thrive on relationships. The beauty of RAG? You don’t need specialized databases to use either! Whether you go with vectors or graphs, you can unlock next-level AI-powered responses. So, which side are you on—Team Vector or Team Graph? #AI #RAG #VectorSearch #KnowledgeGraph #AIApplications #MachineLearning #DataScience #SemanticSearch #LLM #AIRevolution #TechTrends #UnstructuredData #DataRelationships #NextGenAI

🔍 Revolutionize Your Research with NVivo 15! For a limited time, get NVivo 15 at the price of NVivo 14 and experience a new level of efficiency in qualitative data analysis! With Lumivero AI Assistant, you can: - Summarize large text documents - Get AI-suggested codes - Unpack complex jargon and save time! Whether you’re a seasoned researcher or just starting out, NVivo 15 provides cutting-edge tools that allow you to stay organized, deliver deeper insights, and boost productivity. 🚀 Why choose NVivo? - Collaborate in real-time - Keep your data secure - Stay in complete control of your research Don’t miss out on this exclusive offer! Enhance your research with AI-powered assistance and achieve more in less time. 👉 Click to learn more: https://rb.gy/bbzk4h #QualitativeResearch #DataAnalysis #AI #NVivo15 #Lumivero #NumericalAnalytics #ResearchInnovation #AIInResearch

A Graph Too Far: Graph RAG Doesn’t Require Every Graph Tool When RAG developers decide to try graph RAG — that is, to build a knowledge graph and integrate it into their RAG (retrieval-augmented generation) system — they have a lot of options and choices to make. Brian Godsey, PhD argues that GenAI use cases are fundamentally different from traditional graph use cases, and requires a different approach, even if some tools can be shared between the two. Therefore, he claims, you don't necessarily need a graph DB to do graph RAG. Keeping the tech stack simple by focusing on the essentials enhances efficiency and lets you leverage the power of graph RAG without the bloat. What do you think? Read the article - link in comments - and decide for yourself. At #CDL24, we have a well-rounded program that explores different aspects of #KnowledgeGraphs #GraphDB Graph #AI #DataScience #MachineLearning #SemTech #EmergingTech #GenAI #LLM #RAG. We even have a dedicated Gen AI and RAG Stage - check it out! https://lnkd.in/d4qDQzV9

Say goodbye to survey design headaches and hello to instant insights with Prodege’s Pollfish AI Survey Builder. Watch our Lightning Demo to learn more! #Pollfish #LightningDemo #Prodege #PollfishAI #marketresearch #surveyresearch #surveydesign #ai

📢 Exciting news for AI enthusiasts! The latest dimensionality reduction techniques such as UMAP now allow you to visualize RAG data and see the relationships between questions, answers, and sources. The complex, high-dimensional data is transformed into a clear, interactive two-dimensional map that can be used to debug and improve the performance of RAG models. Color-coded by their relevance to the question "Who built the Nürburgring?" - it's a great example of the power of this visualization technique. 😎 Check out the link below for the tutorial: https://lnkd.in/gnVnvTAZ   #AI #RAG #LinkLayer