Embedding

Embeddings convert unstructured data such as words, sentences or images into numerical vectors with hundreds or thousands of dimensions, placing semantically similar items close together. Analogies such as a relationship between the vector differences for “king” and “queen” and those for “man” and “woman” illustrate how language relationships can be approximated mathematically. Embeddings are fundamental to semantic search and RAG. Choosing a suitable model, from general-purpose options such as OpenAI text-embedding models and Google’s Gecko to medical or legal domain-specific models, influences retrieval accuracy.

Business applications and impact#

The technical foundation for semantic search, vector-similarity matching and recommendation algorithms.

Implementation guidance and considerations#

Select a high-quality embedding model suited to the domain and combine it with an appropriate chunk size.