Memories are a powerful way to provide broader context to your prompts. Semantic memory uses embeddings to represent words or data as vectors which enables semantic memory to perform meaningful comparisons and operations on text data, which is crucial for AI comprehension and processing. In this video, I show you how Semantic Memory works, and how we can use it with the Semantic Kernel SDK. 0:00 Memories in Semantic Kernel 1:17 How semantic memory works 2:02 Why embeddings are important 3:24 How are embeddings used? 3:50 Where do Vector Databases come in? 5:19 Available connectors to vector databases 5:34 Adding Azure AI Search Memory Store 8:34 Manually adding memories 10:50 Adding memories to kernel arguments 15:52 Adding documents to your memory 19:09 Wrap up Connect with me! Twitter: https://twitter.com/willvelida GitHub: https://github.com/willvelida Bluesky: https://bsky.app/profile/willvelida.bsky.social