Vector Databases Explained: The Memory Layer Behind Modern AI

Modern AI apps need more than a language model. They need a way to retrieve the right knowledge at the right moment.

In this video, we break down vector databases visually: how embeddings turn meaning into vectors, how documents become chunks, how metadata filters results, how similarity search finds nearest neighbors, and how indexing plus approximate nearest neighbor search makes retrieval fast at scale.

Then we connect everything to RAG: Retrieval-Augmented Generation.

You’ll see how an AI app takes a user question, embeds it, searches a vector database, retrieves relevant chunks, and gives that context to an LLM before generating an answer.

▬▬▬▬▬▬ ⏳ T I M E S T A M P S ▬▬▬▬▬▬
02:18 Embeddings
03:34 What a vector database stores
06:13 Similarity search and nearest neighbors
07:37 Indexing and (ANN)
08:48 How this powers RAG

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Видео Vector Databases Explained: The Memory Layer Behind Modern AI канала Microlearn