Image created with gemini-2.5-flash-image with claude-sonnet-4-5. Image prompt: Create a 16:9 cinematic split-screen poster. LEFT SIDE (40% width): – A board or table with printed document snippets connected by colored string and small numbered tags, illustrating how information is retrieved from many sources to answer a question. – The background is a turquoise / teal abstract field made of stylized blue rods or data fibers, hinting at embedding spaces and retrieval graphs. – Use natural, soft lighting. No glowing lines or neon. RIGHT SIDE (60% width): – A green-toned abstract aerial forest canopy texture, symbolizing a knowledge forest. – Two clean rounded rectangles stacked vertically near the center-right. – The TOP rectangle contains the text: “RAG”. – The BOTTOM rectangle contains the text: “2025/10/10”. – Clean sans-serif font with dark green or charcoal. OVERALL STYLE: – Organized, investigative, and precise. – No extra slogans. – Keep the turquoise/forest split-screen.
From Claude Code to Agentic RAG https://vectifyai.notion.site/agentic-retrieval
📚 Awesome LangGraph.js A curated collection of real-world LangGraph.js implementations, featuring chat applications, RAG systems, educational content, and full-stack templates from both the official team and community contributors. Explore the collection here 🔍 https://x.com/LangChainAI/status/1974504790953734144
RAG can’t keep up with real-time data. Airweave builds live, bi-temporal knowledge bases so that your Agents always reason on the freshest facts. Supports fully agentic retrieval with semantic and keyword search, query expansion, and more across 30+ sources. 100% open-source. https://x.com/_avichawla/status/1973636885907120580
📢VideoRAG: Redefining Long-Context Video Comprehension In this week’s deep dive, we explore another interesting approach for performing RAG on videos. VideoRAG, a groundbreaking framework that brings RAG to the world of extremely long videos. Unlike traditional LVLMs that https://x.com/LearnOpenCV/status/1975593558523715921
