AI Radar: Google, Parallel, Moonshot AI, GigaML, OpenAI updates

Gemini File Search capability is an impressive leap, it abstracts the RAG (Retrieval-Augmented Generation) pattern into a seamless, built-in experience. It acts like an in-built RAG, letting the model search, retrieve, and reason from your files automatically, reducing the need to build complex retrieval pipelines. This kind of RAG abstraction is not yet seen in open source LLMs as a native function though it’s only a matter of time before similar capabilities arrive to automate parts of the RAG process. RAG is not going away it is just becoming smarter and more effortless, letting us focus more on outcomes. Reference: https://lnkd.in/ddvHFca4 #AI #RAG

Google just dropped something huge for AI builders — the new File Search Gemini API. It’s an end-to-end RAG framework baked right into Gemini: embeddings, chunking, vector search, citations — all handled natively. You bring your files; Gemini handles the retrieval intelligence. This could seriously reduce the friction in building AI products that reason over your own data — no custom pipelines, no maintenance overhead, just focused innovation. Excited to see how this changes the landscape for AI product teams. #AI #RAG #GeminiAPI #Google #ProductManagement #Innovation https://lnkd.in/gupHyWA2

Google’s new File Search could simplify how we build RAG systems Google just launched File Search, a fully managed Retrieval-Augmented Generation (RAG) capability built into the Gemini API — and I think it’s a meaningful step forward. What stands out to me is how it removes so much of the operational friction. No vector databases to maintain, no chunking logic, no retriever pipelines. You just upload files and start querying — Gemini handles the rest. Embeddings and storage are free, and the system takes care of chunking, semantic retrieval, and even built-in citations for transparency. It’s still early days, but I see this as a move toward accessible, context-rich AI, where developers can focus less on infrastructure and more on crafting real intelligence and value for users. #AI #AgenticAI #RAG #Innovation #FutureOfAI P.S.: my personal analysis and views. https://lnkd.in/ezXdV2uP

Exciting news for developers and AI builders Google has rolled out the File Search tool as part of the Gemini API, making it easier than ever to implement Retrieval-Augmented Generation (RAG) without starting from scratch. No more wrestling with custom chunking, embeddings, or indexing—Google handles it all. Upload your files (PDFs, docs, up to 100MB each), and the system automatically processes them into a ready-to-query RAG setup. Then, integrate it seamlessly with Gemini models for semantic searches that deliver precise, context-aware responses. Key perks: • 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆: Auto-embedding with models like gemini-embedding-001. • 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Supports metadata filtering, multiple stores, and citations in outputs. • 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Free tier starts at 1GB storage; scales up for bigger needs. • 𝗖𝗼𝘀𝘁-𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲: Embeddings at $0.15/million tokens, with query-time magic included. This is a game-changer for building smarter apps, chatbots, or knowledge bases. If you're in AI/ML, check out the docs and give it a spin, it's competing head-on with tools like OpenAI's Assistants API. https://lnkd.in/gUcgvmdY

Big news for devs: Google's just dropped File Search in the #Gemini API—a fully managed #RAG tool that supercharges your apps with semantic search over your docs! Upload files, get cited responses, and say goodbye to manual indexing hassles. Build smarter bots & assistants in minutes. Dive in: https://lnkd.in/gvia_Pry #GeminiAPI #AI #DevTools

I’ve been studying how to build a RAG demo using LangChain…handling every step manually: loading files, chunking documents, generating embeddings, managing a vector database, and retrieval search. Then… boom 🚀 Google releases the File Search tool that does all of that automatically. Now, you can focus only on building amazing apps without the backend weight. This tool truly transforms how we integrate generative AI with knowledge. Learn more: https://lnkd.in/dQ3eW3j3 #AI #RAG #LangChain #GoogleAI #GenAI

I was exploring RAG, and the very next day, Google redefines it. 🤯 It’s fascinating to witness technology evolve at the exact moment you’re trying to understand it. While exploring Generative AI (GenAI), I began to understand how Retrieval-Augmented Generation (RAG) systems work: the manual plumbing, chunking, embedding generation, and vector database setup that keeps LLMs grounded and accurate. And then the very next day I came across Google’s Gemini File Search Tool, a complete reimagining of RAG. Earlier, developers had to: 🔹 Connect multiple services manually to build a retrieval pipeline 🔹 Host and manage a vector DB like Pinecone or Chroma 🔹 Write code for file loading, chunking, and embedding generation 🔹 Handle all retrieval logic themselves Now, the Gemini API handles it in a single managed call. It automatically manages file storage, chunking, embeddings, and context injection without requiring additional infrastructure or MLOps expertise. This is essentially Automatic RAG, a system that dramatically lowers the barrier to entry for anyone building grounded and verifiable AI applications. Storage and query-time embedding generation are free, with only a small fixed cost for initial file indexing. Watching this space evolve in real time shows how rapidly the foundation of GenAI is transforming, making sophisticated AI systems more accessible than ever. If you are interested you can go and check out this research paper on RAG: https://lnkd.in/dNruSzyU Here's the google blog regarding the File Search Tool: https://lnkd.in/dFhQxKCq #GenAI #RAG #GoogleAI #GeminiAPI #LLMs #Developers #MachineLearning

🚀 Exciting news for developers! Google has just unveiled the File Search Tool for the Gemini API, revolutionizing the way you interact with your documents. 🔍 The File Search Tool is a fully managed RAG system that streamlines the process of connecting Gemini to your files, taking care of all the intricate details seamlessly. Key benefits include: - **Simple setup:** Automates file storage, chunking, and embeddings effortlessly. - **Cost-effective:** Pay only for indexing your files, not for queries. - **Built-in citations:** Responses display their sources directly from your documents. - **Smart search:** Utilizes vector search for meaning-based information retrieval, going beyond traditional keyword searches. ℹ️ This advancement accelerates the development of robust AI applications grounded in your data, making the process faster and more efficient. 📢 Check out the full announcement [here](https://lnkd.in/eFsMrMGB). #Google #Gemini #API #AI #Developers #RAG #GenAI

Large Language Models face a fundamental limitation. Since they are trained with static datasets, LLMs cannot retrieve real-time information for their output. In this article, Tamar Stern shows how Function Calling and AI Agents revolutionize this limitation, turning passive LLMs into active, intelligent systems that can search, calculate, and interact with live data sources. 🌐⚙️ Learn how to connect your AI apps to the web, build reasoning agents, and supercharge your workflows with real-time insights. 👉 Read the full article now on JavaScript Magazine and start building smarter AI-powered apps today! 🔗 https://lnkd.in/dURJ_Xuv

"How hard is it to add AI vision to a webapp?"  We shipped a working example in 2 hours to find out.  CMS Media Intelligence features:  • Smart image search by description  • Auto-tagging on upload  • Bulk alt text generation  • Real-time analysis progress  Total Mix SDK integration code: ~30 lines.  Getting started takes 3 commands:  bun create mix-tstart@latest  cd my-app  bun dev  The SDK gives you:  → ReadMedia for vision (analyze images)  → Search for web research  → PythonExecution for data processing  → Multi-model routing (Claude/Gemini/OpenAI)  All with streaming, session management, and tool execution built-in.  We're not saying "this is the future of AI integration."  We're saying "this is how simple it should be TODAY."  Full working code: https://lnkd.in/gTxfHZQE   Try it. Break it. Ship it.  #BuildInPublic #AI #TypeScript #OpenSource