[ZephyrBlu]

Not a tutorial, but TLDR vector DBs are specialized DBs that store embeddings. Embeddings are vector representations of data (E.g. text or images), which means you can compare them in a quantifiable way.

This enables use cases like semantic search and Retrieval-Augmented Generation (RAG) as mentioned in the article.

Semantic search is: I search for "royal" and I get results that mention "king" or "queen" because they are semantically similar.

RAG is: I make a query asking, "tell me about the English royal family", semantically similar information is fetched using semantic search and provided as context to an LLM to generate an answer.