Embeddings

Embeddings are dense vector representations that encode the semantic meaning of data (text, images, audio, etc.) into a fixed-dimensional numerical format. These vectors are designed so that semantically similar items are placed close together in the vector space, enabling mathematical operations on meaning.

Text embeddings are generated by specialized models (like OpenAI's text-embedding-ada-002 or open-source alternatives like Sentence-BERT) that process input text and output a vector of floating-point numbers. The dimensionality typically ranges from 384 to 3072 dimensions.

Embeddings are foundational to many AI applications: they power semantic search (finding conceptually similar content), RAG systems (matching queries to relevant documents), clustering (grouping similar items), classification (categorizing content), and recommendation systems. Understanding embeddings is crucial for AI engineers building intelligent search and retrieval systems.