Infinidat: A Perfect Fit for Retrieval Augmented Generation (RAG): Making AI models more accurate

Generative AI learning models are powerful, modern tools for automating interactive delivery of knowledge and information. In this blog, we cover some of the important points about RAG (Retrieval Augmented Generation) and why RAG is important. We believe that there is still some lack of clarity in the industry about the important role that the storage infrastructure plays in this. We will cut through some of the fog to clarify the role of storage.

Infinidat storage, be it our all-flash SSA or Hybrid, has a vital role in RAG implementations. Why? Because we're not just discussing it, many analysts and industry experts are saying the same thing: Enterprises deploying RAG will be able to do so on their existing storage infrastructure. In the case of Infinidat, we believe that is especially true because of the performance capabilities that InfuzeOS™ enables in our InfiniBox® platforms and our ability to deliver outstanding performance for many diverse workloads. Did I forget to mention that we scale pretty well, too, with over 17PB of enterprise-class capacity in a single rack? While there may be corner cases where specialized storage needs come into play, enterprises deploying RAG don’t need to run out and spend big dollars for new storage infrastructure.

What do we do in our InfiniBox family that is different to support an AI RAG solution?….. Nothing!

Ironically, that might be a strange answer. But the truth is that we don’t have to do anything new because of the powerful capabilities we already deliver, enabled by our proven platform technology ─ the power of InfuzeOS, our patented Neural Cache technology, and our industry-leading low latency performance. We already deliver performance and low latency at scale. These benefits extend to vector databases, a critical component in GenAI. We are already recognized for how well we perform with mission-critical databases, like Oracle, which already supports embedding vector databases. Current versions of commonly used database engines support storing and retrieving vector data, e.g. Oracle, Postgres, MongoDB, and Datastax Enterprise, making them RAG-ready! But that is a story for a later time.

Back to why RAG is important. Without iterative updating and fine-tuning of these models, which are static or only tend to leverage publicly available data, the return of a query will often deliver incorrect or misleading results referred to as “AI hallucinations.” AI hallucinations appear as factually inaccurate content, false attributions, or citation of nonexistent information. RAG workflow has emerged as a key tool to bridge this gap and provide continued refinement of data queries. RAG combines the power of generative AI models with enterprises’ active private data to produce continuously updated, correctly informed responses to live queries, like ChatGPT.

In today’s data-driven world, organizations accumulate vast repositories of data. Despite this, extracting actionable insights is a persistent challenge. The technology of Large Language Models (LLMs) has improved tremendously in recent years but is very resource intensive and requires substantial amounts of computational resources and energy (electricity and cooling to run the computational resources). To combat this, more compact versions of these models, called Small Language Models (SLMs), have emerged and are increasingly popular. Most enterprises do not have the budget or resources to deploy AI systems at the scale required to create Large Language Models. Regardless of the model, training workloads should not be confused with RAG; they are different, and training is well known to be resource intensive. Hyper-scalers have bulked up tremendously in this area to provide those types of training services, but once trained and operating within an enterprise, RAG is hugely important to produce accurate results.

Let’s highlight a RAG workflow and how it can be a game-changer for your organization.

Basic Components

The graphic above is a simplistic view of a RAG architecture. The graphic below shows that the development of a RAG pipeline is an inherently iterative process. By continuously refining a RAG pipeline with new data, organizations can significantly enhance the accuracy and practicality of AI-model driven insights, maximizing the benefits and promise of Generative AI technology.

We are not going to dive into all those areas in this blog, because we have a solution brief that goes into the details on many of these components that get deployed to support a GenAI architecture that leverages RAG to enhance and produce reliable results. You can see the solution brief here.

Infinidat’s RAG Workflow Deployment Architecture

We used a Kubernetes cluster as the foundation for running a RAG pipeline, which results in making it portable, scalable, resource efficient, and highly available. We used Terraform to significantly simplify the process of setting up a RAG system enabling just one command to run the entire automation. Within 10 minutes, a fully functioning RAG system, hosted in the cloud, was ready to work with the data replicated from on-premises to InfuzeOS Cloud Edition (AWS and Azure). You can see that video here.

Conclusion

Enterprises should leverage RAG using existing storage that is in-place data. In most cases, they do not need to invest in a lot of specialized resources like GPUs and storage. Many will tell you that you do, and they are expensive. We all know the answer is somewhere in between. To host and run the Large Language Model, yes, you likely may need specialized resources if hyper-scalers don’t fit. But for RAG, it is a very different story, and we are well aligned to support an enterprise and can provide flexibility that can leverage the cloud as a fast and convenient deployment solution as well. Infinidat’s solution can encompass any number of InfiniBox platforms and enables extensibility to third-party storage solutions via file-based protocols such as NFS. Our RAG solution makes it easy to leverage your most important data to produce the best possible and accurate results from your GenAI.

For more information, check out our Press Release and Solution Brief:

Press Release ─ “Infinidat Introduces Retrieval-Augmented Generation (RAG) Workflow Deployment Architecture to Make AI more Accurate for Enterprises”

Solution Brief ─ “AI Workloads”

Andrew Wang contributed to this blog.