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Vector Database

Vector Database

A vector database is a database built to store embeddings, the numeric vectors that represent meaning, and to find the vectors most similar to a query very quickly. It is the storage and search layer behind most semantic search and RAG systems.

A normal database is good at exact lookups: find the order with this ID, or every product in this category. A vector database answers a different question: which stored items are closest in meaning to this one. It does that across millions of vectors in milliseconds using approximate nearest neighbour search.

How Does a Vector Database Work?

  • Your content is split into chunks and each chunk is turned into an embedding.
  • The database stores each vector with metadata, such as the source URL, date or language.
  • A query is embedded with the same model, and the database returns the closest vectors, often filtered by metadata.

Vector Database and RAG

In a RAG system, the vector database is the retrieval step. When a user asks a question, the system finds the most relevant chunks in the vector database and places them in the model's context window, so the answer is grounded in your own content rather than the model's memory.

Common Vector Database Options

Dedicated vector databases include Pinecone, Weaviate, Qdrant, Milvus and Chroma. Many teams instead add vector search to a database they already run, such as PostgreSQL with the pgvector extension. For a marketing team building an internal knowledge assistant, the existing database route is often enough to start.

What to Watch Out For

Retrieval quality depends on how content is chunked, which embedding model is used, and whether metadata filters are set up well. A vector database will always return the closest match, even when nothing truly relevant exists, so good systems set a relevance threshold and say so when they cannot find an answer.

Frequently asked questions

What is a vector database?+

A vector database stores embeddings, numeric representations of meaning, and quickly finds the ones most similar to a query. It powers semantic search and RAG.

Do I need a vector database for RAG?+

You need some form of vector search. That can be a dedicated vector database or an existing database with vector support, such as PostgreSQL with pgvector.

What is the difference between a vector database and a regular database?+

A regular database finds exact matches on fields like IDs or categories. A vector database finds items that are similar in meaning, even when they share no exact words.

Related terms

Embedding

Vector Embedding

An embedding is a list of numbers, called a vector, that represents the meaning of a piece of data such as a word, sentence, document or image. Items with similar meaning get vectors that sit close together, which lets software compare meaning mathematically instead of matching exact words.

RAG

Retrieval-Augmented Generation

RAG (Retrieval-Augmented Generation) is a technique that lets a large language model retrieve relevant information from an external source before generating an answer, rather than relying only on what it learned during training. It grounds the model's response in specific, up-to-date documents.

Neural Search

Neural Search

Neural search is a search method that uses AI models to match results by meaning rather than by exact keywords. It converts text into vector embeddings, numerical representations of meaning, and returns results whose meaning is closest to the query, even when they share no words with it.