To let an AI system look things up, text and images are turned into lists of numbers that capture their meaning, called embeddings. A vector database stores these and finds the closest matches to a query, so a search for a concept returns related material even when the exact words differ.
This is the engine behind assistants that answer from a company's own documents: the system retrieves the most relevant passages and feeds them to the model. The approach keeps answers grounded in real sources and easy to update, though results are only as good as the stored material and the quality of the embeddings used to index it.