What is Vector Database?
A vector database is a specialized database designed to store and query high-dimensional vector embeddings, enabling fast similarity searches instead of traditional exact-match queries.
It stores data points as numerical vectors generated by embedding models. These vectors capture semantic meaning, allowing the database to find items that are conceptually similar based on distance metrics like cosine similarity or Euclidean distance.
Key techniques include approximate nearest neighbor (ANN) algorithms such as HNSW or IVF for efficient scaling to millions or billions of vectors, along with indexing structures that trade a small amount of accuracy for major speed gains.
Vector databases often integrate with AI pipelines to support operations like inserting new embeddings, updating metadata, and performing real-time similarity queries.
Example
A company stores product descriptions as vectors in a vector database; when a user searches for 'comfortable running shoes,' the system quickly returns the most semantically similar products even if the exact words don't match.
Why it matters
Vector databases power semantic search and retrieval-augmented generation (RAG) for large language models, making AI systems more accurate and context-aware without retraining.
Frequently asked questions
Traditional databases excel at exact matches on structured rows and columns, while vector databases specialize in finding similar items using vector distance in high-dimensional space.
Related terms
An embedding (or vector embedding) is a way to represent words, sentences, or other data as dense numerical vectors in a high-dimensional space so that similar items end up close together.
Retrieval-Augmented Generation (RAG) is a technique that improves large language models by retrieving relevant external information before generating a response.
Batch size is the number of training examples processed together in a single forward and backward pass during model training.
Data augmentation is a technique that artificially increases the size and diversity of a training dataset by creating modified versions of existing data samples.
A dataset is a structured collection of data points used to train, validate, or test machine learning models.
An epoch is one complete pass of a machine learning model through the entire training dataset during training.