Text Embedding Models

Text embedding models are powerful tools that transform human language into numerical representations called embeddings.

These embeddings are vectors (lists of numbers) that capture the semantic meaning of words, sentences, or even entire documents.

They are crucial for tasks like semantic search, recommendation systems, and understanding text similarity in AI applications.

Imagine a map where words with similar meanings are located close to each other. That's essentially what an embedding model creates!

It takes text input and outputs a vector where the 'distance' between vectors reflects the 'relatedness' of their original text.

• Similar words have vectors close together.
• Different words have vectors far apart.