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Vector Database

Vector Database

A vector database stores embeddings so a system can quickly find items with similar meaning.

Explained simply

A vector database stores embeddings so a system can quickly find items with similar meaning.

1Embeddings
→
2Store vectors
→
3Similarity query
→
4Closest matches

At a glance

Category
Data
Difficulty
Intermediate
Introduced
2010s

Real example

A support assistant stores document embeddings in a vector database and retrieves the closest matches.

Why it matters

It gives you a clear way to understand where this idea fits in AI and how it affects the tools you use.

Timeline

Origins

The ideas behind Vector Database begin developing.

2010s

Vector Database becomes a recognised term or technique.

Wider use

Research and practical applications increase.

Modern AI

Vector Database becomes connected to newer AI systems and products.

Today

Vector Database remains relevant in the data area of AI.

Dates for broad concepts may describe the period when the idea emerged or became widely used, rather than one exact invention date.

Learn next

Continue with these connected terms:

EmbeddingAn embedding turns meaning into a list of numbers, allowing an AI system to compare how similar things are.→Vector SearchVector search finds items whose embeddings are close together, which usually means their meaning is similar.→RAGRAG lets an AI search trusted information before it writes an answer. RAG stands for Retrieval-Augmented Generation.→

Simple infographic

A quick visual way to understand Vector Database.

1Embeddings
→
2Store vectors
→
3Similarity query
→
4Closest matches
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