The Language of Vectors: Embeddings and Similarity

Explain how text embeddings represent semantic meaning as numerical vectors in a high-dimensional space.

Generate text embeddings for text snippets using a pre-trained model via an API.

Calculate semantic similarity between embeddings using metrics like cosine similarity and dot product.

Differentiate between semantic search and traditional keyword-based search.