Vector database
A vector database is a specialised database system storing information not as classic tables but as embeddings, that is, numeric vectors. It enables fast similarity search, in which related data is found by its closeness in the vector space. The vector database thus forms the technical basis for RAG systems and semantic search.
Also known as: vector database, vector store, embedding database
What is a vector database?
A vector database is a database system built to store high-dimensional vectors — so-called Embeddings — efficiently stored, indexed and searched. While conventional databases search for exact values or keys, a vector database works with the mathematical similarity between data points.
Every document, section of text or image is first turned by an AI model into a vector representing its meaning in numeric form. Content with similar meaning lies close together in the vector space. The vector database uses exactly that closeness to return the most relevant results to a query, even where the exact keywords do not match.
How does similarity search work?
On a query, the search term or question is likewise translated into a vector. The vector database compares that query vector with the stored embeddings and determines, through distance measures such as cosine similarity or Euclidean distance, which content is most similar.
Since a full comparison against every single vector would be too slow with large amounts of data, vector databases rely on specialised index methods such as HNSW (hierarchical navigable small world) or IVF. These methods enable a so-called approximate nearest neighbour search finding the closest vectors in fractions of a second, even with millions or billions of entries stored.
What role does the vector database play in RAG systems?
In retrieval-augmented generation (RAG) the vector database is the central knowledge storeelement. Company documents are split into sections, turned into embeddings and stored in the vector database. When a user asks a question, the system picks out the matching sections and passes them as context to a Language model.
A large language model can thus draw on current, company-specific knowledge without the model itself having to be retrained. The vector database therefore ensures the answers generated rest on the data actually present, a decisive factor in reducing hallucinations and raising AI applications' reliability.
Which vector databases are common?
Various solutions have become established. Pinecone is a fully managed cloud service distinguished by low operational effort. Weaviate and Milvus are powerful open-source systems suited to large production environments. Chroma is particularly popular for rapid development and prototypes.
Classic databases are extending their features too: extensions such as pgvector for PostgreSQL bring vector search into existing relational systems. Choosing the right solution depends on factors such as data volume, latency requirements, operating model and existing infrastructure.
What matters when choosing?
Several criteria matter when choosing a vector database: scalability for growing data volumes, search speed, the option to combine vector search with classic metadata filters (Hybrid search) as well as aspects of data security and data protection.
For companies in Germany and the EU in particular, hosting location, encryption and GDPR compliance are important criteria. At Elisabit we help you choose the vector database that fits your use case and integrate it securely into your AI architecture, from the first concept through to production.
How does the vector database fit into a modern AI architecture?
A vector database rarely stands alone but is part of a larger interplay. Upstream, a processing pipeline prepares documents, splits them into sections and turns them into vectors through an embedding model. Downstream, applications such as chatbots, search portals or recommendation systems draw on the search results.
For this interplay to work smoothly, the individual building blocks have to be carefully matched: the embedding model chosen has to fit the vector database, the index configuration the latency requirements, and the whole data flow has to meet the data protection requirements. A well-considered overall architecture decides whether a vector database can unfold its full value and whether the AI applications built on it work reliably and performantly.
Frequently asked questions
What is the difference between a vector database and a classic database?
A classic database searches for exact values, keys or conditions. A vector database, by contrast, searches by similarity of meaning, computing the closeness of embeddings in the vector space. It therefore finds matching content even when the search terms do not match literally.
What is a vector database used for?
Vector databases are used wherever similarity of meaning matters: in RAG systems, semantic search, recommendation systems, image and document search and AI chatbots. They are a central building block of modern AI applications.
Does RAG absolutely require a vector database?
For production RAG applications with larger data volumes a vector database is generally indispensable, since it enables efficient similarity search. For very small prototypes, embeddings can be held in memory, but that does not scale for professional use.
Which vector database is right for my company?
That depends on your requirements: data volume, latency, operating model and data protection rules. Cloud services such as Pinecone reduce operational effort, while open-source solutions such as Weaviate or Milvus offer more control. Sound advice helps make the right choice.
Related terms
An embedding is a numerical vector representation of meaning that places similar content close together.
RAG combines a language model with the retrieval of relevant information from external knowledge sources before answering.
Search by meaning rather than exact keywords — based on embeddings and vector search.
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