Architecture
Vector Database
A store that does not search for exact values but for proximity: give me the ten entries most similar to this one. That makes company knowledge searchable for AI by meaning instead of by keyword. In practice, quality is decided less by the database than by what you put into it — and how you split it up.
An example
A machine builder stores 12,000 pages of maintenance documentation as embeddings. A technician asks “What to do about error code E-114 on the series 3?”, the system finds the four most fitting sections and passes them to the language model. The answer is there in seconds and names the page number. Without this intermediate step, the model would never have seen the error code and would still have answered fluently — just made up.
A closer look
It stores embeddings and finds the nearest neighbors for a query, usually through approximate methods, because an exact comparison would be too slow with millions of entries. Filters on metadata such as time period, department or permissions run along with it.
Why it matters
The vector database is the point at which an AI project turns from a model topic into a data topic, and the point at which most fail. Not at the search method, but at upkeep: Who updates the documents when something changes? How are access rights mapped, so that an employee does not get to see through search documents they would not be allowed to open in the file system? And how is data deleted when someone files an access or erasure request? These three questions decide operation, not the choice of product.
Commonly misunderstood
The database is rarely the problem. The effort lies in splitting up the documents, in updating and in access rights. That is where the errors arise that later appear as model errors.