The text discusses the use of Retrieval Augmented Generation (RAG) combined with Large Language Models (LLMs) in stream processing and data analytics. It explains how a chatbot can be built to answer user queries using RAG and LLMs, and how this technology can be used in enterprise applications such as customer relationship management (CRM) or healthcare. The text also introduces the FEDERATED_SEARCH() function in Confluent Cloud for Apache Flink, which enables searching through external vector databases like Elasticsearch, Pinecone, and MongoDB Atlas. It demonstrates how to use FEDERATED_SEARCH() with ML_PREDICT() to orchestrate the RAG workflow, converting user queries into vector embeddings and searching them against a knowledgebase stored in a vector database. The text concludes that these features enable developers to connect real-time data to external models through remote inference, paving the way for building complex agentic workflows for enterprise use cases.