Embeddings: Turning Meaning Into Numbers

Short summary

Embeddings transform words into numerical vectors where semantic relationships are preserved—similar items cluster together, and operations like king − man + woman ≈ queen show how meaning is encoded as directions. Combined with cosine similarity and vector databases, embeddings power semantic search, RAG systems, recommendations, and clustering. An interactive demo lets you explore these relationships and build the core mechanism yourself.

• •Embeddings encode semantic meaning as numerical vectors where similar concepts cluster together
• •Vector operations preserve relationships (king − man + woman ≈ queen) allowing mathematical manipulation of concepts
• •Cosine similarity and vector databases enable semantic search, RAG, recommendations, and clustering

Generated with AI, which can make mistakes.