In this lecture, Ashwani provides an overview of vector retrieval augmented generation (RAG) and introduce knowledge graphs, highlighting their evolution and significance in enhancing search capabilities. He discuss the limitations of vector RAG, particularly in handling deep relationship queries and fragmented context, and how knowledge graphs can address these challenges. He also explains the process of constructing knowledge graphs from structured and unstructured data, emphasizing the importance of defining schemas for effective graph creation. Lastly, he outlines the retrieval patterns and strategies for leveraging knowledge graphs.