The rise of generative AI has increased the demand for more context-aware applications, particularly in Retrieval-Augmented Generation (RAG) for chatbot development. Vector embeddings are essential for enabling efficient search and retrieval of unstructured data. We’ll explore how a vector store can boost RAG and how developers can harness them using SQL, Python, and Java through frameworks like LangChain or Spring AI. We’ll highlight the importance of vector embeddings in improving chatbot responses and optimizing knowledge retrieval, with practical code examples and a low-code platform.

Accepted @ HrOUG 2025