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Dictionary

Vector Database

A database optimized for similarity search over embeddings.

1 min read

/ quick answer

A Vector Database stores high-dimensional embeddings and retrieves the nearest neighbors of a query vector, enabling semantic search, recommendation, and the retrieval step of RAG. A database optimized for similarity search over embeddings.

A database optimized for similarity search over embeddings. A Vector Database stores high-dimensional embeddings and retrieves the nearest neighbors of a query vector, enabling semantic search, recommendation, and the retrieval step of RAG. In practice: Embed every paragraph of your docs, then at query time embed the user question and pull the top 5 nearest paragraphs as grounding. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
A Vector Database stores high-dimensional embeddings and retrieves the nearest neighbors of a query vector, enabling semantic search, recommendation, and the retrieval step of RAG.
Example
Embed every paragraph of your docs, then at query time embed the user question and pull the top 5 nearest paragraphs as grounding.
Related Workflows
Related Tool Stacks
Related Prompts
/ frequently asked

What is Vector Database?

A Vector Database stores high-dimensional embeddings and retrieves the nearest neighbors of a query vector, enabling semantic search, recommendation, and the retrieval step of RAG.

What is an example of Vector Database?

Embed every paragraph of your docs, then at query time embed the user question and pull the top 5 nearest paragraphs as grounding.

Why does Vector Database matter for AI and automation?

A database optimized for similarity search over embeddings. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.

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