Image created with Flux Pro v1.1 Ultra. Image prompt: RAG, research notebook with banana-shaped index tabs retrieving color-coded slips, layered evidence metaphor, photorealistic, editorial, minimal, high detail, 3:2 landscape
Meet Google’s new best small embedding model, EmbeddingGemma It’s a 300M embedding model made for retrieval augmented generation (RAG) use cases. ollama pull embeddinggemma 🧵 https://x.com/ollama/status/1963667967184617703
Introducing SemTools – add blazing-fast semantic search to your entire filesystem without a vector database ⚡️ Coding agents like Claude Code/Cursor have full access to the CLI like grep, cat, and pipe operations for search. But they lack ‘proper` semantic search that’s actually https://x.com/jerryjliu0/status/1961488443663597857
this repo is wild. 100+ production-ready AI Agents, RAG, Multi-Agent teams, Voice Agents, MCP, and LLM apps with step-by-step tutorials. 100% free and open source by @Saboo_Shubham_ 👏 link in next post 👀 https://x.com/MakerThrive/status/1962661273335742780
Introducing Command A Translate, our state-of-the-art model designed for high-quality translation tasks. https://x.com/cohere/status/1961081779789447519
RAG is not dead! However, we are in an interesting phase of exploring unique ways to index and retrieve information. This vectorless RAG framework uses a tree structure index in place of vectors. Reasoning models will enable methods that mimic human-like search. Early days! https://x.com/omarsar0/status/1961446862012960840
