Vector Database
A specialized type of database that stores data as mathematical vectors, enabling search by meaning rather than exact word matches.
What is a vector database?
A vector database is a specialized database type that stores information as numerical representations (vectors) of text, images, or other data. Unlike traditional databases that store data as rows and columns, a vector database makes it possible to search based on semantic similarity. This means you search by meaning rather than exact words, resulting in much more relevant search results.
How does a vector database work?
A vector database works by converting text and other data into mathematical vectors using an embedding model. These vectors are stored in an optimized index structure that can quickly find similar vectors. When a search query comes in, it is also converted to a vector and compared against all stored vectors based on cosine similarity or a comparable metric. The system then returns the best matching results, ranked by relevance.
Example
A mid-sized company has Wabber build an AI chatbot for internal knowledge sharing. All company documents, manuals, and procedures are converted to embeddings via the RAG pipeline and stored in the vector database on Wabber's private cluster. When an employee asks "How do I submit a leave request?", the system does not search for the exact words but understands the meaning and finds the relevant HR document, even if terms like "vacation request" or "days off" are used in it.
Why is a vector database important?
A vector database is indispensable for modern AI applications such as chatbots and knowledge systems. Its power lies in the speed and accuracy with which relevant information is found, even in enormous datasets. For businesses, this means employees have access to exactly the knowledge they need within seconds. Wabber hosts these databases on its own hardware in the Netherlands, so your data remains fully under your control and no data is sent to external servers.
Related solutions
Frequently asked questions
What is the difference between a vector database and a regular database?
A regular (relational) database stores structured data in rows and columns and searches for exact matches. A vector database stores data as mathematical vectors and searches for semantic similarity, meaning by intent. This allows a vector database to find relevant results even when the words used do not exactly match the search query.
How is a vector database used in a RAG pipeline?
In a RAG pipeline, documents are first split into text chunks and converted into embeddings (vectors). These are stored in the vector database. When a user asks a question, the question is also converted to a vector and compared against the stored embeddings to retrieve the most relevant passages. These passages serve as context for the language model to generate an accurate answer.
Is my data safe in a vector database at Wabber?
Yes, Wabber hosts all vector databases on its own hardware in the Netherlands. No data leaves the private cluster, guaranteeing complete data sovereignty and privacy. This is a fundamental difference from cloud-based solutions where data is sent to external servers abroad.
How fast can a vector database search through large amounts of data?
Modern vector databases use optimized index structures such as HNSW (Hierarchical Navigable Small World) that can search through millions of vectors in milliseconds. On Wabber's cluster, search results are typically returned within 50 to 200 milliseconds, regardless of dataset size.
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