Retrieval-Augmented Generation (RAG) is becoming a cornerstone of enterprise AI, enabling models to access domain-specific knowledge without retraining. This guide focuses on implementing RAG in Java, a language widely used in enterprise environments, using Milvus as the vector database. The article walks through the essential components: generating embeddings from documents, storing them efficiently in Milvus, and implementing retrieval logic that feeds relevant context to the LLM. Key considerations include choosing the right embedding model, optimizing vector indexes for low latency, and handling data updates gracefully. For Java developers, this fills a gap in the ecosystem, as most RAG tutorials target Python. The patterns described are transferable to other vector databases, making this a valuable reference for architects designing AI-powered search and Q&A systems.
Learn how to build a Java-based RAG system with Milvus, covering embeddings, storage, and retrieval for production-ready AI applications.