When a user searches for "comfortable work from home chair" on an e-commerce site, they are not looking for those exact words.

They want results like "ergonomic office seating" or "lumbar support desk chair," even though these share no keywords with the query.

Traditional databases with keyword matching cannot solve this problem. They find exact matches, not semantic matches.

This is where vector databases come in. They store data as high-dimensional vectors called embeddings, which capture the meaning of text, images, or other data. Similar concepts end up close together in this vector space, even if they use different words or representations.

A vector database can find the most similar vectors to a query vector in milliseconds, enabling semantic search, recommendations, and AI-powered applications.

The rise of large language models (LLMs) like GPT has accelerated the importance of vector databases.

Understanding Embeddings