Understanding Text Embeddings
Learn how text embeddings capture semantic meaning and their crucial role in enabling similarity search for RAG.
What are Text Embeddings?
Welcome to the world of text embeddings! These are a fundamental concept in modern AI, especially for tasks involving understanding and comparing text.
Simply put, text embeddings are numerical representations of text. They convert words, sentences, or even entire documents into lists of numbers, called vectors.
Meaning as Numbers (Vectors)
Imagine giving every word or phrase a unique coordinate in a vast, multi-dimensional space. Words with similar meanings would be located close to each other, while dissimilar words would be far apart.
These coordinates are what we call vectors. Each number in the vector represents a different 'feature' or 'dimension' of the text's meaning.
All lessons in this course
- Understanding Text Embeddings
- Introduction to Vector Databases
- Storing and Retrieving Embeddings
- Measuring Embedding Similarity