Vector Database

A database built to store embeddings and retrieve the nearest ones to a query vector using approximate nearest-neighbor search.

term

Vector Database

category

knowledge-memory

short_def

A database built to store embeddings and retrieve the nearest ones to a query vector using approximate nearest-neighbor search.

long_def

A vector database (or vector store) indexes high-dimensional embeddings and finds the most similar ones with Approximate Nearest Neighbor (ANN) algorithms — commonly HNSW graphs or quantization — under metrics like cosine distance. It is the storage-and-retrieval backbone of RAG: documents go in as vectors, a query vector comes in, and the closest chunks come out as context.

see_also

embeddings rag agentic-rag

etymology_origin

A database category that emerged with the rise of embedding-based retrieval; popularized by systems such as FAISS, Pinecone, Weaviate, Milvus and pgvector, using ANN indexes like HNSW.

related_to

embeddings rag agentic-rag grounding

contrast_with

Unlike a relational database, which retrieves rows by exact field matches, a vector database retrieves items by approximate nearest-neighbor distance between embeddings — similarity ranking rather than exact lookup.

example

Vector databases such as Pinecone and Weaviate use HNSW-based Approximate Nearest Neighbor search over embeddings to return the most semantically similar documents to a query.

source

https://en.wikipedia.org/wiki/Vector_database

status

active

why_it_matters

The vector database is where your content lives once an AI system ingests it; content that chunks and embeds cleanly is retrieved more accurately into the answers agents generate.

sameAs

https://en.wikipedia.org/wiki/Vector_database

bridge_entity

models

last_verified

2026-06-15

md_twin

/glossary/vector-database.md

← all The Agentic Web Lexicon · .md · JSON