RAG demos are easy. Production systems are not. What matters is how data is stored, indexed, and retrieved at scale. This walkthrough shows how to build a local RAG lab with Milvus and AIStor—combining vector search with high-performance object storage in a local environment that reflects real-world systems. Storage is what makes AI work. https://bit.ly/3O0QVsG
Building a Scalable RAG Lab with Milvus and AIStor
This title was summarized by AI from the post below.
More Relevant Posts
-
𝗜𝗻𝗻𝗼𝘃𝗮𝗶𝗰 𝗝𝗼𝗶𝗻𝘀 𝘁𝗵𝗲 𝗟𝗮𝘂𝗻𝗰𝗵 𝗼𝗳 𝘁𝗵𝗲 𝗗𝘀𝗰𝗼𝗼𝗽 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗖𝗹𝘂𝗯 𝗯𝘆 𝗛𝗣 What started as a packed, high-energy AI Automation Hack-A-Thon at Dscoop quickly became something more - a signal that the print industry is ready to move beyond theory and into real, measurable automation. Now, that momentum is becoming a community. Learn more from Innovaic - https://lnkd.in/e3QK-_Cy
-
-
Coming later this week on RoboPapers: Geeking out with Fanqi Lin and Jose Barreiros on A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation https://lnkd.in/emTpyFtH
-
SHIFT removes diffusion watermarks by using stochastic resampling to deflect generative trajectories. It achieves 95% to 100% success rates across nine watermarking methods without retraining models. https://lnkd.in/dD-JPcRG
-
-
One myth in computer vision is that you need massive datasets before meaningful models can emerge. In this case study the baseline model was trained from 315 labeled images drawn directly from deployment footage. The real advantage came from structured iteration and validation, not dataset scale. #ArtificialIntelligence #ComputerVision
-
Want to build your first #TensorFlow model, but not sure where to start? In this tutorial, you’ll: → Load and explore a dataset → Build and train your model → See what actually improves accuracy ▶️ Watch the full video by Iulia Feroli: https://lnkd.in/eyNJQJ7W
-
Second keynote on the first day of SWAT4LS 2026 #Amsterdam presented by Hannah Bast, wonderful set of interactive demos showing how clever #Qlever RDF triple store is and even more clever it will become with new features to come. We will try it out for our KnowledgeGraph projects NFDI4DS #MLentory, metadata for AI models, and #ConnOSS, metadata for research software.
-
What feature of Opus 4.7 are you most excited about? 👇 3x visual resolution. A new xhigh reasoning level. Task Budgets for smarter compute control. At Afnexis, we don't wait for the dust to settle — we're already integrating it into client builds. If you're building AI products and want a team that ships with the latest models in production (not demos), let's talk.
-
[OpenClaw] Unlocking Extreme Science: How Ultra-Robust Machine Learning Models Revolutionize High-Temperature Molecular Simulations Discover how new ultra-robust machine learning models are enabling stable, high-fidelity molecular simulations at extreme temperatures, overcoming traditional limitations. Read more: https://lnkd.in/g-Aitx4e
-
Built something interesting recently — ElectroMate ⚡ An AI-powered voice assistant designed for electronics stores that can answer product-related queries using your own data. 🔧 What it does: • Takes user queries via API • Retrieves relevant info from a Supabase vector database • Generates accurate answers using an LLM • Converts responses into natural voice using ElevenLabs 🧠 Tech behind it: n8n · Supabase · ElevenLabs · OpenRouter · Gemini (embeddings) The goal was to simulate a real in-store assistant — something that can guide customers, answer questions, and enhance the buying experience. This project helped me understand how to combine automation + RAG + voice AI into a practical system. 🔗 GitHub: https://lnkd.in/d5X53YiG Would love to hear your thoughts or suggestions for improving it.
-
