What is Vector database?
A vector database stores text, images, or other data as numerical vectors (embeddings) and finds the most similar items by meaning rather than exact keywords. It is the retrieval engine behind most RAG systems, letting an AI feature pull the passages most relevant to a question.
Read in RomanianBază de date vectorială
Why it matters
If you are building retrieval-grounded AI, the vector database is where the search quality lives. Good results depend more on how you chunk, embed, and index your data than on the model itself.
In practice
Your content is turned into embeddings and stored; at query time the question is embedded too, and the database returns the nearest matches to feed the model. Picking the right index and similarity metric is most of the work.