MinIO’s Post

In the age of #AI, data is your competitive moat. That requires building a production-grade Retrieval Augmented Generation (RAG) application with a robust data infrastructure for storing, versioning, processing, evaluating and querying your proprietary data. Our default infrastructure recommendation is to set up a modern #datalake and a vector database. These two units will serve as your center of gravity for all other elements of your #RAG application. Check out this post that uses MinIO to build a RAG-based chat application using commodity, off-the-shelf hardware. https://hubs.li/Q02D-7Zy0

In the age of #AI, data is your competitive moat. That requires building a production-grade Retrieval Augmented Generation (RAG) application with a robust data infrastructure for storing, versioning, processing, evaluating and querying your proprietary data. Our default infrastructure recommendation is to set up a modern #datalake and a vector database. These two units will serve as your center of gravity for all other elements of your #RAG application. Check out this post that uses MinIO to build a RAG-based chat application using commodity, off-the-shelf hardware. https://hubs.ly/Q02LtsZb0

In the age of #AI, data is your competitive moat. That requires building a production-grade Retrieval Augmented Generation (RAG) application with a robust data infrastructure for storing, versioning, processing, evaluating and querying your proprietary data. Our default infrastructure recommendation is to set up a modern #datalake and a vector database. These two units will serve as your center of gravity for all other elements of your #RAG application. Check out this post that uses MinIO to build a RAG-based chat application using commodity, off-the-shelf hardware. https://hubs.li/Q02FbmNB0

Tech Blog Series with code: In this third edition of our state-of-the-art retrieval augmented generation (#SOTARAG) blog series, we dive into the main content, starting with the indexer. The indexer breaks down input elements into core components and stores them in various data stores. It also manages dataset versions, updates metadata, and ensures no duplicate data exists in any data sources....... Full blog in the first comment below 👇 If you like this content please give it a like 👍 and share 🔄 so we know to create more! #cloudflare #genai #rag

💻 🚀 ENGINEERING CORNER 🚀💻 Red pandas are naturally curious animals 🐾 . Similarly, when our #engineering pandas aren't building a world-class #streamingdata platform, they love to explore new use cases with cool #tech. Their latest experiment? Converting live data to embeddings using local #LLMs, then streaming it into #Elasticsearch — all while keeping the data safe for #AI processing with Sovereign AI 👀 Check out the demo👇 https://lnkd.in/ejBjiWMW

In-database ML represents a strategic shift to leverage data more effectively. By enabling MLOPs directly within database environments, even orgs outside of the “magnificent seven” can make real-world applications that are more efficient, effective and reactive to real-time data changes. How? Read on in our blog: http://dlvr.it/T851Vv

Cortex Search is now out in Public Preview!!! What is it? ✳ Cortex Search is a powerful search assistant powered by LLM, designed to simplify data analysis. ✳ It ensures robust data governance and seamlessly integrates into your existing Snowflake workflow. ✳ You can ask open-ended questions about your data structure, send follow-up inquiries, or even use it to refine and improve your own SQL queries. Preview limitations: ✳ Optimized for documents and queries in English. ✳ Use base tables no larger than 10M rows in size to maintain optimal serving performance. https://lnkd.in/gbSc8GMi #snowflake #infostrux #datasuperhero #snowflake_advocate #genai #llm

OpenAI's recently-released structured output functionality is going to be hugely beneficial for anybody tackling problems in the unstructured data + LLM space. One of the biggest issues (up until this was released, anyway) with tool use was the inability to consistently convert the LLM output into an interpretable form for downstream APIs. I expect many more features to come out like this as these models get rolled out in more complicated enterprise processes. https://lnkd.in/g7PV6T_t

https://lnkd.in/g4dXMy2S Datadog today made generally available an ability to observe LLMs that IT teams can use to monitor latency, exposure of data and more. #datadog #itteams #llms #observability

Longer context windows in LLMs? My thoughts on implications and potential developments in response to the expansion of context windows to 1 million tokens - https://lnkd.in/dzq39bAW. The blog also mentions that the tests were successful on 10x more than that - 10 million tokens. What is a context window? The increase in context windows allows AI models to "see" and "understand" more data at once, without the need to segment it into separate parts. For example, one can upload a whole book to Gemini (40,000 - 120,000 words) which can handle over 700,000 words and start conversing with it on the book topics. So how does it impact today’s popular LLM-based architectures such as RAG? And what about security? 1) Expect context windows to increase even more. 10x is already possible, so exponential growth to 1000x and more is not that far into the future anymore. 2) RAG won’t go away (not yet at least), but it will be significantly simplified. It still doesn’t make sense to upload the majority of the data in a single request even if the context window allows it. 3) RAGs will move closer to LLMs. OpenAI and similar LLM providers will ensure that the data can be asynchronously uploaded into “buckets”, or integrated on top of existing data lake solutions to avoid data duplication and save on storage costs. There will be less need to build your own RAG at all. 4) LLMs will move closer to RAGs - running your own LLMs will be so easy it will be integrated into existing solutions. Think of “search indexes” as a potential analogy. In both cases of p.3 and p.4, the barriers will become thinner. I think of RAGs as a necessary outcome between “your LLM” and “my data”. The more LLMs become ubiquitous, the less “your LLM” or “my data” is there. 5) Security-wise the landscape becomes even more complex - the more data is uploaded on a single request, the more complexity is there to restrict/monitor/detect accesses and reveal cracks in the system. Right now, the simplest thinking is that “my RAG is my data, I only need data to be properly permissioned”, leaving out the fact that LLMs are: * data themselves, and they operate on the latest and greatest training data. Where that data comes from will make LLMs more or less useful. The separation of data and LLM is an artificial inefficiency. * has access to your data (with all of its complexities in dealing with caching your data and retention of it). * needs to have access to more data to be better. Did I miss anything? If you agree or disagree, happy to engage in comments below. #llmcontextwindow #genai #llm #gemini