Agentic RAG with SingleStore

Retrieval Augmented Generation (RAG) is a pivotal technique that enhances Large Language Models by integrating external knowledge sources, allowing for more accurate and contextually relevant responses. Agentic RAG introduces intelligent agents capable of dynamic decision-making and tool utilization to refine information retrieval and generation processes, enabling LLMs to handle intricate tasks effectively. By combining agentic RAG with SingleStore's unified querying capabilities, developers can create sophisticated AI applications that simplify development workflows, enhance performance, and enable comprehensive data analysis. This integration enables intelligent systems capable of delivering accurate and contextually relevant information, thereby improving user experience and satisfaction.