Vector Database
A vector database stores data as mathematical vectors (numerical representations of meaning) and enables similarity search — finding content that is semantically similar to a query, not just keyword-matching. It is the storage layer that powers RAG systems.
What is a vector database?
Traditional databases search for exact matches: find all rows where the column equals a specific value. Vector databases work differently. They store data as high-dimensional numerical vectors — called embeddings — where similar concepts are represented by vectors that are close together in mathematical space.
When you ask a question, your question is also converted into a vector. The database finds the stored vectors that are mathematically closest to your question vector, and returns those items. This is called semantic search or similarity search: it finds content that means the same thing, even if the exact words are different.
Why vector databases are essential for enterprise AI
RAG systems need a fast, accurate way to find the most relevant documents from a large corpus when a user asks a question. A vector database solves this. Your documents are chunked, converted to embeddings (via an embedding model), and stored. At query time, the question is embedded, the nearest document chunks are retrieved, and passed to the LLM as context.
Without a vector database, you would have to send every document to the LLM for every query — which is impossibly slow and expensive at enterprise scale. The vector database makes retrieval instant even across millions of document chunks.
Popular vector databases for enterprise use
Managed cloud options (Pinecone, Weaviate Cloud, Qdrant Cloud) are easy to set up but involve sending your data to a third-party service. For private enterprise deployments with data sovereignty requirements, self-hosted options are standard: pgvector (extends PostgreSQL), Qdrant (self-hosted), Chroma, or Weaviate self-hosted. Wonka AI's private deployment uses a self-hosted vector database within your infrastructure.
Frequently asked questions
What is an embedding?
An embedding is a numerical representation of a piece of text as a high-dimensional vector (typically 768 to 3,072 numbers). Texts with similar meaning produce vectors that are close together in this high-dimensional space. Embeddings are created by embedding models — specialized neural networks trained to capture semantic meaning.
How many documents can a vector database handle?
Modern vector databases scale to hundreds of millions of vectors. For enterprise document corpora (SharePoint libraries, email archives, CRM data), typical sizes are 1–50 million chunks, which vector databases handle without performance degradation. The engineering challenge is usually document ingestion and update pipelines, not the database itself.
Do vector databases replace traditional databases?
No. Vector databases are specialized for similarity search. They work alongside your existing databases — SQL for structured data, document stores for unstructured data, vector databases for semantic retrieval. In a typical enterprise AI architecture, you have all three.
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