Google launches File Search Gemini API for AI builders

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

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

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

AI Radar - 0110010 🚀 Latest in AI - Google launches the File Search tool in Gemini API—a new, fully managed system that allows developers to ask Gemini questions about their own files (PDFs, docs, and more) and instantly get grounded answers with citations, making AI integration much easier and more accurate for apps and agents. Source: https://lnkd.in/dNQeUj-j - Parallel debuts its Search API, a new web search engine built from scratch for AI agents. Unlike traditional search, Parallel’s API fetches the most information-rich pieces of content—called “high-signal tokens”—and delivers precise answers directly to LLMs with better speed, accuracy, and cost than before. Source: https://lnkd.in/dHqJYipA - Kimi K2 Thinking by Moonshot AI is an open-source “thinking agent” model built for deep reasoning and long tool use. It sets new records on open benchmarks (like SWE-Bench Verified and BrowseComp), can perform 300+ step tool chains, and runs efficiently thanks to native INT4 quantization and a 256k context window. Source: https://lnkd.in/dzWhxr2j - GigaML officially launches, empowering enterprises to deploy large language models on their own infrastructure, keeping data secure and boosting performance by providing 2.3x faster inference and full customization for each business use case. Source: https://lnkd.in/dH6z2CTj - OpenAI enhances GPT-5 with “context injection,” improving the model’s ability to remember and use relevant information from previous conversations and uploaded data, leading to smarter and more personalized interactions. Source: https://lnkd.in/d8bGZBd7 #AIRadar #GeminiAPI #ParallelSearch #KimiK2 #GigaML #GPT5 #AI #TechNews

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

🚀 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

Google’s new File Search Tool in the Gemini API is a pivotal shift in how Gen-AI products will be built. Earlier, grounding AI in your own data meant dealing with chunking, embeddings, vector DBs, and retrieval pipelines - a lot of engineering before any real product work. Now, that entire layer is abstracted. So, you just upload your files and Gemini handles retrieval, context, and citations. This changes the game: Infrastructure is now commodity. Differentiation moves to problem definition, domain depth, workflows, and UX. The real question now isn’t that “Can you build RAG?” instead it is “Can you build something useful, trusted, and domain-smart on top of it?” That’s where the next wave of AI products will be won. Read for more details: https://lnkd.in/gh4tHTFN #Products #GenAI #Google #Gemini

Google Gemini just realised their File Search Tool and it's fantastic! https://lnkd.in/d72JKgTT It's hard to not take the opportunity to do a little comparison post since Subworkflow AI is after the same problem space! In no particular order, here are the key differences... * Subworkflow supports files larger than 100mb (up to 3gb) * Subworkflow has higher limit of file stores (we call them datasets) * Subworkflow generates image embeddings by page image rather than text chunks which gives you grounding without the extra step. * Subworkflow doesn't tie you into Gemini or any LLM! We just handle the retrieval part, you bring the AI/LLM! * Subworkflow is an API and not an AI tool. Use in any application or platform. * Subworkflow handles less file types (but only because we focus on ones which make sense!) In our own experience, a key blocker for developers using the File Search Tool is likely the lock-in and direct dependency to Gemini LLM. With Subworkflow AI, it was one of our initial goals to be LLM-agnostic as we believe the magic of LLMs lies with developers and domain experts. Of course, still early days for File Search Tool and we look forward to seeing how it evolves. --- Ps. Sign up for the Beta @ https://lnkd.in/eeYVeVH6