Embeddings and Vector Search

Embeddings turn words, pictures, or other data into lists of numbers called vectors. Each vector keeps the meaning of the original item. Things with similar meaning get vectors that sit close together in this number space. Vector search scans a large set of vectors and finds the ones nearest to a query vector, even if the exact words differ. This lets AI agents match questions with answers, suggest related items, and link ideas quickly.

Visit the following resources to learn more:

• @official@OpenAI Embeddings API Documentation
• @article@Understanding Embeddings and Vector Search (Pinecone Blog)