Learn how to build a scalable, open-source Retrieval-Augmented Generation (RAG) pipeline using Dify (a no-code LLM app framework) and Milvus (a high-performance vector database). This session will guide developers and AI practitioners through integrating Dify's intuitive interface for prompt engineering and workflow orchestration with Milvus's lightning-fast semantic search capabilities. The speaker will use an example of RAG from Zilliz's online guide. He'll demonstrate how to ingest, index, and retrieve unstructured data efficiently, enabling context-aware AI applications like chatbots, knowledge bases, and analytical tools. Attendees will gain actionable insights into optimizing accuracy, latency, and cost in RAG systems while leveraging open-source tools. A live demo will showcase end-to-end implementation, from dataset preparation to deployment, with reference to best practices outlined in Zilliz's guide. Ideal for developers seeking to harness generative AI without vendor lock-in, this talk bridges the gap between cutting-edge research and real-world deployment, emphasizing modularity, transparency, and community-driven innovation.