Vector Embeddings Explained: The Core of modern Search | Glossary

Numerical representations of text or images that allow AI models to perform semantic searches based on meaning rather than keyword matching.

Turning Language into Math: Embeddings

Vector Embeddings are the foundational technology behind modern AI features like 'Smart Search' and RAG. They convert a string of text into a high-dimensional vector (a list of numbers) where 'closeness' in number-space equals 'closeness' in meaning.

How it Works

If you embed the word 'Cat' and the word 'Kitten', the resulting vectors will be very close to each other. The word 'Submarine' will be very far away. This allows an AI to understand context even if the exact words don't match.

Dev Benefits

Semantic Search: Build search bars that understand intent.
Clustering: Group 100,000 documents by topic automatically.
Deduplication: Find similar content even if it's phrased differently.

Check the token footprint of your embeddings before storage using our **AI Token Counter** to optimize your vector database costs.

Numerical representations of text or images that allow AI models to perform semantic searches based on meaning rather than keyword matching.

Turning Language into Math: Embeddings

How it Works

Dev Benefits

Semantic Search: Build search bars that understand intent.
Clustering: Group 100,000 documents by topic automatically.
Deduplication: Find similar content even if it's phrased differently.

Check the token footprint of your embeddings before storage using our **AI Token Counter** to optimize your vector database costs.