Embedding Models: Choosing and Using Them

Quick Summary

An embedding model converts text into vectors so semantically related content can be compared. Model choice must fit language, domain, input length, privacy, and measured retrieval quality.

What you will learn

·Explain embeddings in plain language
·Choose evaluation criteria for an embedding model
·Plan a safe model or version change