In a previous article, we explored how to use Ollama and DeepSeek-R1 with SingleStore for a simple example. In this article, we’ll build on that example by working with a PDF document from the internet. We’ll store the document and its vector embeddings in SingleStore, then use DeepSeek-R1 to identify blockchain investment opportunities. The notebook … Read more
Retrieval-augmented generation (RAG) is revolutionizing artificial intelligence by combining powerful generative AI models with sophisticated information retrieval systems. This comprehensive guide explores foundational concepts essential for understanding RAG, including information retrieval, generative AI models, embeddings, and vector databases, followed by a detailed, practical step-by-step implementation using LangChain. Understanding these fundamentals and their practical application through … Read more
Large language models (LLMs) are reshaping the landscape of artificial intelligence, yet they face an ongoing challenge — retrieving and utilizing information beyond their training data. Two competing methods have emerged as solutions to this problem: InfiniRetri, an approach that exploits the LLM’s own attention mechanism to retrieve relevant context from within long inputs, and … Read more
Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Generative AI: The Democratization of Intelligent Systems. Enterprise AI is rapidly evolving and transforming. With the recent hype around large language models (LLMs), which promise intelligent automation and seamless workflows, we are moving beyond mere data synthesis toward a more … Read more
Scenario Customer support tickets with screenshots, technical documentation with diagrams, and a mountain of legacy PDFs — all containing valuable information, but impossible to query efficiently. “There has to be a better way,” I thought. That’s when I dove headfirst into the world of multimodal retrieval-augmented generation (RAG). The Multimodal Revelation Like many developers, I … Read more
In this post, we’ll explore the concept of Agentic RAG, its architecture, and why this powerful combination is reshaping the future of AI systems. Plus, we’ll walk through implementing a basic version of an Agentic RAG system from scratch! What Is RAG and Agentic RAG? To start, let’s clarify what RAG is. Retrieval-augmented generation (RAG) is … Read more
The importance of integrating artificial intelligence in finance stems from its ability to process vast amounts of data at unprecedented speeds, enabling financial institutions to make more informed decisions and improve operational efficiencies. To understand what AI brings to the table, let’s first delve into the basics of AI as per recent trends. Let’s start … Read more
Observability Integration Observability is the cornerstone of reliability and trust in any production-grade retrieval-augmented generation (RAG) pipeline. As these systems become more complex — handling sensitive data, supporting real-time queries, and interfacing with multiple services — being able to trace and measure each step of the data flow and inference process becomes critical. From retrieving … Read more
Artificial intelligence is transforming how people interact with information, and retrieval-augmented generation (RAG) is at the forefront of this innovation. RAG enhances large language models by enabling access to external knowledge bases, providing highly accurate and context-aware answers. In this tutorial, I’ll guide you through building an AI-powered assistant using RAG to create a smarter, … Read more
Introduction to RAG and Quarkus Retrieval-augmented generation (RAG) is a technique that enhances AI-generated responses by retrieving relevant information from a knowledge source. In this tutorial, we’ll build a simple RAG-powered application using Java and Quarkus (a Kubernetes-native Java framework). Perfect for Java beginners! Why Quarkus? Quarkus provides multiple LangChain4j extensions to simplify AI application … Read more