Vector Database
A database built to store and search embeddings — numerical representations of text, images, or other content — by similarity, rather than exact match. The core infrastructure behind most RAG systems.
Vector databases index embeddings and support approximate-nearest-neighbor search, letting an application find semantically similar content ("find text like this") instead of only exact keyword matches. They're the retrieval layer behind most retrieval-augmented generation (RAG) systems: an LLM's prompt is grounded with the most relevant chunks pulled from a vector database rather than relying purely on the model's training data. The main buyer decision is self-hosted/open-source versus fully managed-only, followed by whether hybrid search (combining vector similarity with keyword and metadata filtering) is supported — pure vector search alone is often insufficient for production RAG.
Last verified: